+
+
+## Quick start
+
+Install the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) with the standalone installer:
+
+```bash
+# On macOS and Linux.
+curl -LsSf https://hf.co/cli/install.sh | bash
+```
+
+```powershell
+# On Windows.
+powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"
+```
+
+Log in, then start working with the Hub:
+
+```bash
+# Log in (use --token $HF_TOKEN in non-interactive environments)
+hf auth login
+
+# Find models served by Inference Providers
+hf models ls --warm
+
+# Download a model
+hf download Qwen/Qwen3-0.6B
+
+# Upload files to your own repo
+hf upload username/my-cool-model ./model.safetensors
+
+# Sync a local folder to a storage bucket
+hf buckets sync ./checkpoints hf://buckets/username/my-bucket
+
+# Run a job on Hugging Face infrastructure
+hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
+
+# Discover everything else
+hf --help
+```
+
+The Hub uses tokens to authenticate applications (see [docs](https://huggingface.co/docs/hub/security-tokens)). Check out the [CLI guide](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for a tour of the main features.
+
+## What is `huggingface_hub`?
+
+The `huggingface_hub` library allows you to interact with the [Hugging Face Hub](https://huggingface.co/), a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects, play with the thousands of machine learning apps hosted on the Hub, or create and share your own models, datasets and demos with the community. Everything ships in one package with two interfaces: the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for your terminal and the `huggingface_hub` library for Python — both designed to work well for humans and AI agents. Use them to:
+
+- [Download files](https://huggingface.co/docs/huggingface_hub/en/guides/download) from the Hub.
+- [Upload files](https://huggingface.co/docs/huggingface_hub/en/guides/upload) to the Hub.
+- [Manage your repositories](https://huggingface.co/docs/huggingface_hub/en/guides/repository).
+- [Run Inference](https://huggingface.co/docs/huggingface_hub/en/guides/inference) on deployed models.
+- [Run Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) on Hugging Face infrastructure.
+- [Search](https://huggingface.co/docs/huggingface_hub/en/guides/search) for models, datasets and Spaces.
+- [Share Model Cards](https://huggingface.co/docs/huggingface_hub/en/guides/model-cards) to document your models.
+- [Engage with the community](https://huggingface.co/docs/huggingface_hub/en/guides/community) through PRs and comments.
+- Do all of the above from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli).
+
+## Built for humans and AI agents
+
+The `hf` CLI is designed for people and coding agents alike: the same commands adapt their output when run by an agent. If you use Claude Code, Codex, Cursor, or another coding agent, install the `hf` CLI Skill — a command reference generated from your installed CLI:
+
+```bash
+# works with Claude Code, Codex, Cursor, OpenCode, Pi and any agent that loads skills from `.agents/skills`
+hf skills add
+```
+
+Learn more in the [Hugging Face CLI for AI agents guide](https://huggingface.co/docs/hub/agents-cli) and the [announcement blog post](https://huggingface.co/blog/hf-cli-for-agents).
+
+## Use the Python library
+
+Install the `huggingface_hub` package with [pip](https://pypi.org/project/huggingface-hub/) (this also installs the `hf` CLI):
+
+```bash
+pip install huggingface_hub
+```
+
+We recommend using [`uv`](https://docs.astral.sh/uv/) for a fast and reliable install:
+
+```bash
+uv pip install huggingface_hub
+```
+
+In order to keep the package minimal by default, `huggingface_hub` comes with optional dependencies useful for some use cases. For example, if you want to use the MCP module, run:
+
+```bash
+pip install "huggingface_hub[mcp]"
+```
+
+To learn more about installation and optional dependencies, check out the [installation guide](https://huggingface.co/docs/huggingface_hub/en/installation).
+
+### Download files
+
+Download a single file
+
+```py
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="zai-org/GLM-5.2", filename="config.json")
+```
+
+Or an entire repository
+
+```py
+from huggingface_hub import snapshot_download
+
+snapshot_download("sentence-transformers/all-MiniLM-L6-v2")
+```
+
+Files will be downloaded in a local cache folder. More details in [this guide](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache).
+
+### Create a repository
+
+```py
+from huggingface_hub import create_repo
+
+create_repo(repo_id="super-cool-model")
+```
+
+### Upload files
+
+Upload a single file
+
+```py
+from huggingface_hub import upload_file
+
+upload_file(
+ path_or_fileobj="/home/lysandre/dummy-test/README.md",
+ path_in_repo="README.md",
+ repo_id="lysandre/test-model",
+)
+```
+
+Or an entire folder
+
+```py
+from huggingface_hub import upload_folder
+
+upload_folder(
+ folder_path="/path/to/local/space",
+ repo_id="username/my-cool-space",
+ repo_type="space",
+)
+```
+
+More details in the [upload guide](https://huggingface.co/docs/huggingface_hub/en/guides/upload).
+
+## Integrating with the Hub.
+
+We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations [here](https://huggingface.co/docs/hub/libraries).
+
+The advantages are:
+
+- Free model or dataset hosting for libraries and their users.
+- Built-in file versioning, even with very large files, made possible by [Xet](https://huggingface.co/docs/hub/xet/index), the Hub's chunk-deduplicated storage backend.
+- In-browser widgets to play with the uploaded models.
+- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
+- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
+- Usage stats and more features to come.
+
+If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a [step-by-step guide](https://huggingface.co/docs/hub/adding-a-library) with ❤️ showing how to do this integration.
+
+## Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community.
+Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
+We wrote a [contribution guide](https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md) to summarize
+how to get started to contribute to this repository.
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/RECORD b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..764aa5cee02d2804e4a4e35869ac1e1c66efea4e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/RECORD
@@ -0,0 +1,375 @@
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/REQUESTED b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/REQUESTED
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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/WHEEL b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..13013d7b49be5a84c3ee5931b06285b9553cf5d0
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (84.0.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/entry_points.txt b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ab4e46d125b35cd248ddf8f289d96a8452cb1333
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/entry_points.txt
@@ -0,0 +1,6 @@
+[console_scripts]
+hf = huggingface_hub.cli.hf:main
+tiny-agents = huggingface_hub.inference._mcp.cli:app
+
+[fsspec.specs]
+hf = huggingface_hub.HfFileSystem
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/licenses/LICENSE b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/licenses/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/top_level.txt b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub-2.2.0.dist-info/top_level.txt
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_city_game.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_city_game.py
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+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_city_game.py
@@ -0,0 +1,661 @@
+"""Interactive isometric city explorer — easter egg for `hf repos ls --explore`."""
+
+import dataclasses
+import math
+import os
+import random
+import re
+import select
+import shutil
+import sys
+import time
+
+from huggingface_hub.hf_api import RepoStorageInfo
+
+from ._file_listing import format_size
+
+
+Color = tuple[int, int, int]
+
+# (top_face, left_face, right_face) — lighter to darker for 3D effect
+_TYPE_COLORS: dict[str, tuple[Color, Color, Color]] = {
+ "model": ((175, 148, 240), (138, 112, 208), (105, 80, 180)),
+ "dataset": ((245, 128, 128), (222, 92, 92), (190, 60, 60)),
+ "space": ((245, 175, 85), (218, 140, 55), (185, 110, 30)),
+ "bucket": ((112, 185, 242), (70, 150, 220), (40, 118, 192)),
+}
+_EXTRA_COLORS: tuple[Color, Color, Color] = ((168, 176, 188), (128, 136, 148), (90, 98, 110))
+_GRID_COLOR: Color = (178, 182, 190)
+
+_DX = 4 # isometric half-width (pixels)
+_DY = 2 # isometric half-height (pixels)
+_MAX_H = 16 # tallest tile (pixels)
+_MIN_H = 1
+_COLS = 6
+_EXT = 1 # grid extension beyond tiles
+_MAX_TILES = 30
+
+# Cursor sprite — pixel art arrow pointer
+_OUTLINE: Color = (30, 30, 30)
+_FILL: Color = (255, 255, 255)
+
+_CURSOR_GRID = [
+ " X ",
+ " XWX ",
+ "XWWWX",
+ " XWX ",
+ " X ",
+]
+_CURSOR_PALETTE: dict[str, Color] = {
+ "X": _OUTLINE,
+ "W": _FILL,
+}
+_CURSOR_H = len(_CURSOR_GRID)
+
+_MOVE_FRAMES = 8
+_MOVE_DELAY = 0.03
+_CURSOR_PAD = _CURSOR_H + 16
+_GAP = 3
+_MIN_TERM_W = 100
+_MIN_TERM_H = 24
+_SUMMARY_W = 24
+
+
+# ---------------------------------------------------------------------------
+# Data structures
+# ---------------------------------------------------------------------------
+
+
+@dataclasses.dataclass
+class TileInfo:
+ grid_row: int
+ grid_col: int
+ height: int
+ top: Color
+ left: Color
+ right: Color
+ repo: RepoStorageInfo | None
+
+
+@dataclasses.dataclass
+class CityData:
+ tiles: list[TileInfo]
+ rows: int
+ cols: int
+ x_off: int
+ y_off: int
+ buf_w: int
+ buf_h: int
+ total_storage: int
+ extra_count: int
+ extra_storage: int
+ all_repos: list[RepoStorageInfo]
+
+
+# ---------------------------------------------------------------------------
+# City layout
+# ---------------------------------------------------------------------------
+
+
+def _prepare_city_data(repos: list[RepoStorageInfo]) -> CityData:
+ sorted_repos = sorted(repos, key=lambda r: r.storage, reverse=True)
+ display = sorted_repos[:_MAX_TILES]
+ extra_count = max(0, len(sorted_repos) - _MAX_TILES)
+ extra_storage = sum(r.storage for r in sorted_repos[_MAX_TILES:])
+ total_storage = sum(r.storage for r in repos)
+ max_storage = max(1, display[0].storage)
+
+ n = len(display) + (1 if extra_count > 0 else 0)
+ cols = min(n, _COLS)
+ rows = math.ceil(n / cols) if cols > 0 else 1
+
+ tiles: list[TileInfo] = []
+ for i, repo in enumerate(display):
+ r, c = divmod(i, cols)
+ h = max(_MIN_H, round(math.sqrt(repo.storage / max_storage) * _MAX_H))
+ top, left, right = _TYPE_COLORS.get(repo.type, _EXTRA_COLORS)
+ tiles.append(TileInfo(r, c, h, top, left, right, repo))
+ if extra_count > 0:
+ r, c = divmod(len(display), cols)
+ h = max(_MIN_H, round(math.sqrt(extra_storage / max_storage) * _MAX_H))
+ tiles.append(TileInfo(r, c, h, *_EXTRA_COLORS, None))
+
+ r_lo, r_hi = -_EXT, rows - 1 + _EXT
+ c_lo, c_hi = -_EXT, cols - 1 + _EXT
+
+ xs: list[int] = []
+ ys: list[int] = []
+ for rr in range(r_lo, r_hi + 1):
+ for cc in range(c_lo, c_hi + 1):
+ cx, cy = (cc - rr) * _DX, (cc + rr) * _DY
+ xs.extend([cx - _DX, cx + _DX])
+ ys.extend([cy, cy + 2 * _DY])
+ for tile in tiles:
+ ys.append((tile.grid_col + tile.grid_row) * _DY - tile.height)
+
+ x_off = -min(xs)
+ y_off = -min(ys)
+ buf_w = max(xs) - min(xs) + 1
+ buf_h = max(ys) - min(ys) + 1
+ if buf_h % 2:
+ buf_h += 1
+
+ return CityData(
+ tiles=tiles,
+ rows=rows,
+ cols=cols,
+ x_off=x_off,
+ y_off=y_off,
+ buf_w=buf_w,
+ buf_h=buf_h,
+ total_storage=total_storage,
+ extra_count=extra_count,
+ extra_storage=extra_storage,
+ all_repos=repos,
+ )
+
+
+# ---------------------------------------------------------------------------
+# Drawing primitives
+# ---------------------------------------------------------------------------
+
+
+def _draw_diamond_outline(buf: list[list[Color | None]], cx: int, cy: int) -> None:
+ t = (cx, cy)
+ r = (cx + _DX, cy + _DY)
+ b = (cx, cy + 2 * _DY)
+ ll = (cx - _DX, cy + _DY)
+ _draw_line(buf, *t, *r, _GRID_COLOR)
+ _draw_line(buf, *r, *b, _GRID_COLOR)
+ _draw_line(buf, *b, *ll, _GRID_COLOR)
+ _draw_line(buf, *ll, *t, _GRID_COLOR)
+
+
+def _draw_block(
+ buf: list[list[Color | None]],
+ cx: int,
+ cy: int,
+ h: int,
+ top: Color,
+ left: Color,
+ right: Color,
+) -> None:
+ _fill_poly(
+ buf,
+ [(cx - _DX, cy + _DY - h), (cx, cy + 2 * _DY - h), (cx, cy + 2 * _DY), (cx - _DX, cy + _DY)],
+ left,
+ )
+ _fill_poly(
+ buf,
+ [(cx, cy + 2 * _DY - h), (cx + _DX, cy + _DY - h), (cx + _DX, cy + _DY), (cx, cy + 2 * _DY)],
+ right,
+ )
+ _fill_poly(
+ buf,
+ [(cx, cy - h), (cx + _DX, cy + _DY - h), (cx, cy + 2 * _DY - h), (cx - _DX, cy + _DY - h)],
+ top,
+ )
+
+
+def _fill_poly(buf: list[list[Color | None]], verts: list[tuple[int, int]], color: Color) -> None:
+ bh = len(buf)
+ bw = len(buf[0]) if buf else 0
+ all_y = [v[1] for v in verts]
+ y0 = max(0, min(all_y))
+ y1 = min(bh - 1, max(all_y))
+ n = len(verts)
+ for y in range(y0, y1 + 1):
+ xl: float = float("inf")
+ xr: float = float("-inf")
+ for i in range(n):
+ ax, ay = verts[i]
+ bx, by = verts[(i + 1) % n]
+ if ay == by:
+ if y == ay:
+ xl = min(xl, float(min(ax, bx)))
+ xr = max(xr, float(max(ax, bx)))
+ continue
+ if not (min(ay, by) <= y <= max(ay, by)):
+ continue
+ t = (y - ay) / (by - ay)
+ ix = ax + t * (bx - ax)
+ xl = min(xl, ix)
+ xr = max(xr, ix)
+ if xl <= xr:
+ for x in range(max(0, round(xl)), min(bw, round(xr) + 1)):
+ buf[y][x] = color
+
+
+def _draw_line(buf: list[list[Color | None]], x0: int, y0: int, x1: int, y1: int, color: Color) -> None:
+ bh = len(buf)
+ bw = len(buf[0]) if buf else 0
+ dx = abs(x1 - x0)
+ dy = abs(y1 - y0)
+ steps = max(dx, dy)
+ if steps == 0:
+ if 0 <= y0 < bh and 0 <= x0 < bw:
+ buf[y0][x0] = color
+ return
+ xi = (x1 - x0) / steps
+ yi = (y1 - y0) / steps
+ fx, fy = float(x0), float(y0)
+ for _ in range(steps + 1):
+ px, py = round(fx), round(fy)
+ if 0 <= py < bh and 0 <= px < bw:
+ buf[py][px] = color
+ fx += xi
+ fy += yi
+
+
+# ---------------------------------------------------------------------------
+# Pixel buffer → terminal
+# ---------------------------------------------------------------------------
+
+_ANSI_RE = re.compile(r"\033\[[0-9;]*m")
+
+
+def _strip_ansi(s: str) -> str:
+ return _ANSI_RE.sub("", s)
+
+
+def _visible_len(s: str) -> int:
+ return len(_strip_ansi(s))
+
+
+def _pixels_to_lines(buf: list[list[Color | None]]) -> list[str]:
+ height = len(buf)
+ width = len(buf[0]) if buf else 0
+ lines: list[str] = []
+ for row in range(0, height, 2):
+ last = -1
+ for col in range(width - 1, -1, -1):
+ top = buf[row][col]
+ bot = buf[row + 1][col] if row + 1 < height else None
+ if top or bot:
+ last = col
+ break
+ if last < 0:
+ lines.append("")
+ continue
+
+ parts: list[str] = []
+ cfg: Color | None = None
+ cbg: Color | None = None
+
+ for col in range(last + 1):
+ top = buf[row][col]
+ bot = buf[row + 1][col] if row + 1 < height else None
+
+ if not top and not bot:
+ if cfg is not None or cbg is not None:
+ parts.append("\033[0m")
+ cfg = cbg = None
+ parts.append(" ")
+ continue
+
+ if top and bot and top == bot:
+ nfg, nbg, ch = top, None, "█"
+ elif top and bot:
+ nfg, nbg, ch = bot, top, "▄"
+ elif top:
+ nfg, nbg, ch = top, None, "▀"
+ else:
+ nfg, nbg, ch = bot, None, "▄" # type: ignore[assignment]
+
+ esc = ""
+ if nfg != cfg:
+ esc += f"\033[38;2;{nfg[0]};{nfg[1]};{nfg[2]}m"
+ cfg = nfg
+ if nbg != cbg:
+ esc += "\033[49m" if nbg is None else f"\033[48;2;{nbg[0]};{nbg[1]};{nbg[2]}m"
+ cbg = nbg
+ parts.append(esc + ch)
+
+ if cfg is not None or cbg is not None:
+ parts.append("\033[0m")
+ lines.append("".join(parts))
+ return lines
+
+
+# ---------------------------------------------------------------------------
+# Rendering
+# ---------------------------------------------------------------------------
+
+
+def _render_base_buffer(city: CityData) -> list[list[Color | None]]:
+ buf: list[list[Color | None]] = [[None] * city.buf_w for _ in range(city.buf_h)]
+
+ for tile in city.tiles:
+ cx = city.x_off + (tile.grid_col - tile.grid_row) * _DX
+ cy = city.y_off + (tile.grid_col + tile.grid_row) * _DY
+ _draw_diamond_outline(buf, cx, cy)
+
+ sorted_tiles = sorted(city.tiles, key=lambda t: (t.grid_row + t.grid_col, t.grid_col))
+ for tile in sorted_tiles:
+ cx = city.x_off + (tile.grid_col - tile.grid_row) * _DX
+ cy = city.y_off + (tile.grid_col + tile.grid_row) * _DY
+ _draw_block(buf, cx, cy, tile.height, tile.top, tile.left, tile.right)
+
+ return buf
+
+
+# ---------------------------------------------------------------------------
+# Summary panel
+# ---------------------------------------------------------------------------
+
+
+def _colored_square(color: Color) -> str:
+ return f"\033[38;2;{color[0]};{color[1]};{color[2]}m■\033[0m"
+
+
+def _build_summary(
+ repos: list[RepoStorageInfo],
+ total_storage: int,
+ extra_count: int,
+) -> list[str]:
+ lines: list[str] = [""]
+ lines.append(" Storage Overview")
+ lines.append(" " + "─" * 16)
+ lines.append(f" {format_size(total_storage, human_readable=True)} total")
+ lines.append("")
+
+ order = ["model", "dataset", "space", "bucket"]
+ labels = {"model": "Models", "dataset": "Datasets", "space": "Spaces", "bucket": "Buckets"}
+ for rtype in order:
+ group = [r for r in repos if r.type == rtype]
+ if not group:
+ continue
+ storage = sum(r.storage for r in group)
+ sq = _colored_square(_TYPE_COLORS[rtype][0])
+ lines.append(f" {sq} {labels[rtype]}")
+ lines.append(f" {len(group)} repos · {format_size(storage, human_readable=True)}")
+ lines.append("")
+
+ if extra_count > 0:
+ sq = _colored_square(_EXTRA_COLORS[0])
+ lines.append(f" {sq} +{extra_count} more repos")
+
+ return lines
+
+
+# ---------------------------------------------------------------------------
+# Cursor
+# ---------------------------------------------------------------------------
+
+
+def _build_cursor() -> list[tuple[int, int, Color]]:
+ pixels: list[tuple[int, int, Color]] = []
+ for ri, row in enumerate(_CURSOR_GRID):
+ for ci, ch in enumerate(row):
+ if ch in _CURSOR_PALETTE:
+ pixels.append((ci - len(row) // 2, ri - _CURSOR_H + 1, _CURSOR_PALETTE[ch]))
+ return pixels
+
+
+_CURSOR_PIXELS = _build_cursor()
+
+
+# ---------------------------------------------------------------------------
+# Interactive game
+# ---------------------------------------------------------------------------
+
+
+def run_city_game(repos: list[RepoStorageInfo]) -> None:
+ """Launch the interactive city explorer."""
+ if not repos:
+ print("No repositories found.")
+ return
+
+ try:
+ import termios
+ import tty
+ except ImportError:
+ print("Interactive mode requires a Unix-like terminal (Linux/macOS).")
+ return
+
+ if not sys.stdin.isatty() or not sys.stdout.isatty():
+ print("Interactive mode requires a terminal.")
+ return
+
+ term = shutil.get_terminal_size()
+ if term.columns < _MIN_TERM_W or term.lines < _MIN_TERM_H:
+ print(f"Your terminal is {term.columns}×{term.lines} characters.")
+ print(f"Please resize to at least {_MIN_TERM_W}×{_MIN_TERM_H} to explore the city!")
+ return
+
+ city = _prepare_city_data(repos)
+
+ tiles_with_repos = [t for t in city.tiles if t.repo is not None]
+ start_tile = random.choice(tiles_with_repos) if tiles_with_repos else city.tiles[0]
+
+ fd = sys.stdin.fileno()
+ old_settings = termios.tcgetattr(fd)
+ try:
+ tty.setraw(fd)
+ sys.stdout.write("\033[?1049h\033[?25l\033[2J")
+ sys.stdout.flush()
+ _game_loop(city, start_tile.grid_row, start_tile.grid_col)
+ finally:
+ sys.stdout.write("\033[?25h\033[?1049l")
+ sys.stdout.flush()
+ termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+
+
+def _game_loop(city: CityData, cur_row: int, cur_col: int) -> None:
+ tile_map: dict[tuple[int, int], TileInfo] = {(t.grid_row, t.grid_col): t for t in city.tiles}
+ city = dataclasses.replace(city, buf_h=city.buf_h + _CURSOR_PAD, y_off=city.y_off + _CURSOR_PAD)
+ base_buf = _render_base_buffer(city)
+
+ summary = _build_summary(city.all_repos, city.total_storage, city.extra_count)
+
+ # Intro: cursor drops onto starting tile
+ tx, ty = _tile_top_center(city, cur_row, cur_col, tile_map)
+ for i in range(1, _MOVE_FRAMES + 1):
+ t = i / _MOVE_FRAMES
+ t = t * t * (3 - 2 * t)
+ drop_y = ty - 16 * (1 - t)
+ frame = _copy_buf(base_buf)
+ _highlight_tile(frame, city, tile_map[(cur_row, cur_col)])
+ _draw_cursor(frame, tx, round(drop_y))
+ _present(city, frame, tile_map.get((cur_row, cur_col)), summary)
+ time.sleep(_MOVE_DELAY)
+
+ while True:
+ cx, cy = _tile_top_center(city, cur_row, cur_col, tile_map)
+ frame = _copy_buf(base_buf)
+ _highlight_tile(frame, city, tile_map[(cur_row, cur_col)])
+ _draw_cursor(frame, cx, cy)
+ _present(city, frame, tile_map.get((cur_row, cur_col)), summary)
+
+ key = _read_key()
+ if key in ("q", "Q", "esc", "\x03"):
+ return
+
+ dr, dc = _key_to_direction(key)
+ if dr == 0 and dc == 0:
+ continue
+
+ nr, nc = cur_row + dr, cur_col + dc
+ if (nr, nc) not in tile_map:
+ continue
+
+ ex, ey = _tile_top_center(city, nr, nc, tile_map)
+ for i in range(1, _MOVE_FRAMES + 1):
+ t = i / _MOVE_FRAMES
+ t = t * t * (3 - 2 * t)
+ bx = cx + (ex - cx) * t
+ by = cy + (ey - cy) * t
+ frame = _copy_buf(base_buf)
+ _highlight_tile(frame, city, tile_map[(nr, nc)])
+ _draw_cursor(frame, round(bx), round(by))
+ _present(city, frame, tile_map.get((nr, nc)), summary)
+ time.sleep(_MOVE_DELAY)
+
+ cur_row, cur_col = nr, nc
+
+
+def _tile_top_center(city: CityData, row: int, col: int, tile_map: dict[tuple[int, int], TileInfo]) -> tuple[int, int]:
+ tile = tile_map.get((row, col))
+ h = tile.height if tile else 1
+ cx = city.x_off + (col - row) * _DX
+ cy = city.y_off + (col + row) * _DY
+ return cx, cy + _DY - h
+
+
+def _key_to_direction(key: str) -> tuple[int, int]:
+ match key:
+ case "w" | "W" | "\x1b[A":
+ return -1, 0
+ case "s" | "S" | "\x1b[B":
+ return 1, 0
+ case "a" | "A" | "\x1b[D":
+ return 0, -1
+ case "d" | "D" | "\x1b[C":
+ return 0, 1
+ case _:
+ return 0, 0
+
+
+def _draw_cursor(buf: list[list[Color | None]], cx: int, cy: int) -> None:
+ bh = len(buf)
+ bw = len(buf[0]) if buf else 0
+ for dx, dy, color in _CURSOR_PIXELS:
+ px, py = cx + dx, cy + dy
+ if 0 <= py < bh and 0 <= px < bw:
+ buf[py][px] = color
+
+
+def _highlight_tile(buf: list[list[Color | None]], city: CityData, tile: TileInfo) -> None:
+ cx = city.x_off + (tile.grid_col - tile.grid_row) * _DX
+ cy = city.y_off + (tile.grid_col + tile.grid_row) * _DY
+ h = tile.height
+ _fill_poly(
+ buf,
+ [(cx, cy - h), (cx + _DX, cy + _DY - h), (cx, cy + 2 * _DY - h), (cx - _DX, cy + _DY - h)],
+ _brighten(tile.top, 35),
+ )
+
+
+def _brighten(color: Color, amount: int) -> Color:
+ return (min(255, color[0] + amount), min(255, color[1] + amount), min(255, color[2] + amount))
+
+
+def _present(
+ city: CityData,
+ buf: list[list[Color | None]],
+ tile: TileInfo | None,
+ summary: list[str],
+) -> None:
+ city_lines = _pixels_to_lines(buf)
+ while city_lines and not _strip_ansi(city_lines[0]).strip():
+ city_lines.pop(0)
+ while city_lines and not _strip_ansi(city_lines[-1]).strip():
+ city_lines.pop()
+
+ city_w = max((_visible_len(line) for line in city_lines), default=0)
+ term = shutil.get_terminal_size()
+ panel_max_w = max(20, term.columns - city_w - _SUMMARY_W - 2 * _GAP)
+
+ info = _build_info_panel(tile, city, panel_max_w)
+
+ n = max(len(summary), len(city_lines), len(info))
+ summary_lo = max(0, (n - len(summary)) // 2)
+ info_lo = max(0, (n - len(info)) // 2)
+
+ lines: list[str] = []
+ for i in range(n):
+ si = i - summary_lo
+ lt = summary[si] if 0 <= si < len(summary) else ""
+ lpad = max(0, _SUMMARY_W - _visible_len(lt))
+
+ ct = city_lines[i] if i < len(city_lines) else ""
+ cpad = max(0, city_w - _visible_len(ct))
+
+ ri = i - info_lo
+ rt = info[ri] if 0 <= ri < len(info) else ""
+
+ lines.append(lt + " " * lpad + " " * _GAP + ct + " " * cpad + " " * _GAP + rt)
+
+ lines.append("")
+ lines.append(" \033[90mWASD/Arrows: move · Q/ESC: quit\033[0m")
+
+ while len(lines) < term.lines - 1:
+ lines.append("")
+
+ output = "\033[H"
+ for line in lines[: term.lines - 1]:
+ output += line + "\033[K\r\n"
+ sys.stdout.write(output)
+ sys.stdout.flush()
+
+
+def _build_info_panel(tile: TileInfo | None, city: CityData, max_w: int) -> list[str]:
+ reset = "\033[0m"
+ gray = "\033[90m"
+ bold = "\033[1m"
+ indent = " "
+ content_w = max_w - len(indent)
+
+ lines: list[str] = [""]
+ lines.append(f"{indent}{bold}City Explorer{reset}")
+ lines.append(indent + "─" * min(22, content_w))
+ lines.append("")
+
+ if tile is None:
+ lines.append(f"{indent}{gray}Move to a tile")
+ lines.append(f"{indent}to see details.{reset}")
+ return lines
+
+ if tile.repo is None:
+ lines.append(f"{indent}{gray}+{city.extra_count} more repos{reset}")
+ lines.append(f"{indent}{gray}{format_size(city.extra_storage, human_readable=True)} combined{reset}")
+ return lines
+
+ repo = tile.repo
+ name = repo.id
+ if len(name) > content_w:
+ name = name[: content_w - 3] + "..."
+ lines.append(f"{indent}{bold}{name}{reset}")
+ lines.append("")
+
+ type_ansi = {
+ "model": "\033[38;2;175;148;240m",
+ "dataset": "\033[38;2;245;128;128m",
+ "space": "\033[38;2;245;175;85m",
+ "bucket": "\033[38;2;112;185;242m",
+ }
+ tc = type_ansi.get(repo.type, "")
+
+ lines.append(f"{indent}Type {tc}{repo.type}{reset}")
+ lines.append(f"{indent}Visibility {repo.visibility}")
+ lines.append(f"{indent}Storage {format_size(repo.storage, human_readable=True)}")
+ lines.append(f"{indent}Usage {repo.storage_percent:.1f}%")
+ lines.append("")
+
+ bar_w = min(18, content_w)
+ filled = max(0, min(bar_w, round(repo.storage_percent / 100 * bar_w)))
+ lines.append(f"{indent}{tc}{'█' * filled}{gray}{'░' * (bar_w - filled)}{reset}")
+
+ return lines
+
+
+def _copy_buf(buf: list[list[Color | None]]) -> list[list[Color | None]]:
+ return [row[:] for row in buf]
+
+
+def _read_key() -> str:
+ fd = sys.stdin.fileno()
+ ch = os.read(fd, 1)
+ if ch == b"\x1b":
+ if _has_input(fd, 0.05):
+ ch2 = os.read(fd, 1)
+ if ch2 == b"[" and _has_input(fd, 0.05):
+ ch3 = os.read(fd, 1)
+ return f"\x1b[{ch3.decode()}"
+ return "esc"
+ return ch.decode("utf-8", errors="replace")
+
+
+def _has_input(fd: int, timeout: float) -> bool:
+ r, _, _ = select.select([fd], [], [], timeout)
+ return bool(r)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cli_utils.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cli_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..443ead3d450e2d31302748264286195375682326
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cli_utils.py
@@ -0,0 +1,1253 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains CLI utilities (styling, helpers)."""
+
+import difflib
+import importlib.metadata
+import logging
+import os
+import re
+import shlex
+import subprocess
+import sys
+import time
+from collections.abc import Callable, Sequence
+from enum import Enum
+from pathlib import Path
+from typing import TYPE_CHECKING, Annotated, Any, Literal, TypeVar, cast
+
+import click
+
+from huggingface_hub import __version__, constants
+from huggingface_hub.errors import CLIError
+from huggingface_hub.utils._dotenv import load_dotenv
+
+from ._framework import Argument, HfCommand, HfGroup, Option, build_command
+from ._help_formatter import StyledContext
+from ._output import OutputFormat, out
+
+
+logger = logging.getLogger(__name__)
+
+# Arbitrary default limit for models/datasets/spaces list commands.
+REPO_LIST_DEFAULT_LIMIT = 30
+
+if TYPE_CHECKING:
+ from huggingface_hub import Volume
+ from huggingface_hub.hf_api import HfApi
+
+
+def installation_method() -> str:
+ from huggingface_hub.utils import installation_method as _installation_method
+
+ return _installation_method()
+
+
+def get_hf_api(token: str | None = None) -> "HfApi":
+ # Import here to avoid circular import
+ from huggingface_hub.hf_api import HfApi
+
+ return HfApi(token=token, library_name="huggingface-cli", library_version=__version__)
+
+
+#### TYPER UTILS
+
+CLI_REFERENCE_URL = "https://huggingface.co/docs/huggingface_hub/en/guides/cli"
+
+
+def generate_epilog(examples: list[str], docs_anchor: str | None = None) -> str:
+ """Generate an epilog with examples and a Learn More section.
+
+ Args:
+ examples: List of example commands (without the `$ ` prefix).
+ docs_anchor: Optional anchor for the docs URL (e.g., "#hf-download").
+
+ Returns:
+ Formatted epilog string.
+ """
+ docs_url = f"{CLI_REFERENCE_URL}{docs_anchor}" if docs_anchor else CLI_REFERENCE_URL
+ examples_str = "\n".join(f" $ {ex}" for ex in examples)
+ return f"""\
+Examples
+{examples_str}
+
+Learn more
+ Use `hf --help` for more information about a command.
+ Read the documentation at {docs_url}
+"""
+
+
+TOPIC_T = Literal["main", "help"] | str
+FallbackHandlerT = Callable[[list[str], set[str]], int | None]
+ExpandPropertyT = TypeVar("ExpandPropertyT", bound=str)
+
+
+def _format_epilog_no_indent(epilog: str | None, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ """Write the epilog without indentation."""
+ if epilog:
+ formatter.write_paragraph()
+ for line in epilog.split("\n"):
+ formatter.write_text(line)
+
+
+_ALIAS_SPLIT = re.compile(r"\s*\|\s*")
+
+
+class HFCliTyperGroup(HfGroup):
+ """
+ CLI Group that:
+ - lists commands alphabetically within sections.
+ - separates commands by topic (main, help, etc.).
+ - formats epilog without extra indentation.
+ - supports aliases via pipe-separated names (e.g. ``name="list | ls"``).
+ - consumes the global formatting flags (``--format``, ``--json``, ``-q`` / ``--quiet``, ``--no-truncate``)
+ anywhere in the args of a leaf command and applies them to ``out``, so leaf
+ commands don't need to declare these options themselves.
+ - rewrites ``spaces/user/repo`` to ``user/repo --type space`` for commands that accept ``--type``.
+ - enriches "No such option" / "No such command" errors with available options or commands.
+ """
+
+ context_class = StyledContext
+
+ def invoke(self, ctx: click.Context) -> None:
+ """Enrich unknown-option errors with available options or subcommands.
+
+ Catches `NoSuchOption` raised during subcommand `make_context()`
+ (option parsing). For leaf commands (e.g. `hf repos create --test`)
+ we list the command's options; for groups (e.g. `hf cache --test`)
+ we list subcommands since groups have no user-facing options.
+ """
+ try:
+ return super().invoke(ctx)
+ except click.NoSuchOption as e:
+ if e.ctx is not None and e.ctx.command is not None:
+ cmd = e.ctx.command
+ if isinstance(cmd, click.Group):
+ # Group has no user-facing options -> show subcommands instead
+ items = [
+ (name, sub.get_short_help_str(limit=80))
+ for name in cmd.list_commands(e.ctx)
+ if (sub := cmd.get_command(e.ctx, name)) is not None and not sub.hidden
+ ]
+ _enrich_usage_error(e, "commands", items)
+ else:
+ # Leaf command -> show its options using Click's rich formatting
+ items = [
+ record
+ for p in cmd.get_params(e.ctx)
+ if isinstance(p, click.Option) and not p.hidden and (record := p.get_help_record(e.ctx))
+ ]
+ _enrich_usage_error(e, "options", items)
+ raise
+
+ def resolve_command(self, ctx: click.Context, args: list[str]) -> tuple:
+ cmd_name = args[0] if args and not args[0].startswith("-") else None
+ cmd = self.get_command(ctx, cmd_name) if cmd_name else None
+
+ if isinstance(cmd, (LazyHfCommand, LazyHfGroup)):
+ loaded_cmd = cmd.materialize()
+ for registered_name, registered_cmd in self.commands.items():
+ if registered_cmd is cmd:
+ self.commands[registered_name] = loaded_cmd
+ cmd = loaded_cmd
+
+ if cmd is not None:
+ self._rewrite_repo_type_prefix(cmd, args)
+
+ try:
+ name, resolved_cmd, sub_args = super().resolve_command(ctx, args)
+ except click.UsageError as e:
+ # Unknown subcommand -> add fuzzy suggestions and list available commands.
+ if cmd is None and cmd_name is not None:
+ # Expand aliases ("list | ls" → ["list", "ls"]) for accurate fuzzy matching.
+ visible_names = [
+ alias
+ for key, registered in self.commands.items()
+ if not registered.hidden
+ for alias in _ALIAS_SPLIT.split(key)
+ ]
+ matches = difflib.get_close_matches(cmd_name, visible_names)
+ if matches:
+ suggestions = ", ".join(f"'{m}'" for m in matches)
+ setattr(e, "message", f"{e.message.rstrip('.')}. Did you mean {suggestions}?")
+ items = [
+ (name, sub.get_short_help_str(limit=80))
+ for name in self.list_commands(ctx)
+ if (sub := self.get_command(ctx, name)) is not None and not sub.hidden
+ ]
+ _enrich_usage_error(e, "commands", items)
+ raise
+
+ # If we just resolved a leaf command, eagerly consume any global formatting
+ # flags (--format / --json / -q / --quiet / --no-truncate) from its args before click parses
+ # them. Group resolution is recursive — leaves (and only leaves) need this.
+ if resolved_cmd is not None and not isinstance(resolved_cmd, click.Group):
+ _consume_format_flags_for_leaf(resolved_cmd, sub_args)
+
+ return name, resolved_cmd, sub_args
+
+ @staticmethod
+ def _rewrite_repo_type_prefix(cmd: click.Command, args: list[str]) -> None:
+ """Rewrite prefixed repo IDs (e.g. ``spaces/user/repo``) to ``user/repo --type space``.
+
+ Only applies to commands that have a ``--type`` / ``--repo-type`` option and
+ at least one repo-ID positional argument (any ``click.Argument`` whose name
+ ends with ``_id``, e.g. ``repo_id``, ``from_id``, ``to_id``). When the
+ token that maps to such an argument matches ``{prefix}/org/repo`` (where
+ *prefix* is one of ``spaces``, ``datasets``, or ``models``), the prefix is
+ stripped and an implicit ``--type {type}`` is appended. An error is raised
+ if ``--type`` is also provided explicitly or if multiple prefixed arguments
+ disagree on the repo type.
+
+ Only repo-ID positional slots are inspected so that other positional
+ arguments (filenames, local paths, patterns …) are never misinterpreted as
+ prefixed repo IDs.
+ """
+ has_type_option = any(isinstance(param, click.Option) and "--type" in param.opts for param in cmd.params)
+ if not has_type_option:
+ return
+
+ # Locate all repo-ID positional arguments and their indices among Arguments.
+ repo_id_positions: set[int] = set()
+ arg_idx = 0
+ for param in cmd.params:
+ if isinstance(param, click.Argument):
+ if param.name in ("repo_id", "from_id", "to_id"):
+ repo_id_positions.add(arg_idx)
+ arg_idx += 1
+
+ if not repo_id_positions:
+ return
+
+ # Build a set of option names that consume a following value token.
+ value_options: set[str] = set()
+ for param in cmd.params:
+ if isinstance(param, click.Option) and not param.is_flag:
+ for opt in (*param.opts, *param.secondary_opts):
+ value_options.add(opt)
+
+ # Walk through args (skipping args[0] = command name) to map positional
+ # slots to their indices in `args`.
+ positional_count = 0
+ repo_id_arg_indices: list[int] = []
+ i = 1
+ while i < len(args):
+ arg = args[i]
+ if arg == "--":
+ break # everything after -- is positional literal; stop rewriting
+ if arg.startswith("-"):
+ if "=" in arg or arg not in value_options:
+ i += 1 # flag or --opt=val — single token
+ else:
+ i += 2 # value-taking option — skip the value too
+ else:
+ if positional_count in repo_id_positions:
+ repo_id_arg_indices.append(i)
+ positional_count += 1
+ i += 1
+
+ if not repo_id_arg_indices:
+ return
+
+ # Check each repo-ID arg for a type prefix and collect rewrites.
+ inferred_type: str | None = None
+ first_prefix: str | None = None
+ rewrites: list[tuple[int, str]] = [] # (args index, new value without prefix)
+
+ for arg_index in repo_id_arg_indices:
+ parts = args[arg_index].split("/", 2)
+ if len(parts) != 3 or parts[0] not in constants.REPO_TYPES_MAPPING:
+ continue
+ prefix = parts[0]
+ mapped_type = constants.REPO_TYPES_MAPPING[prefix]
+ if inferred_type is not None and mapped_type != inferred_type:
+ raise click.UsageError(f"Conflicting repo type prefixes: '{first_prefix}/' and '{prefix}/'.")
+ inferred_type = mapped_type
+ first_prefix = prefix
+ rewrites.append((arg_index, f"{parts[1]}/{parts[2]}"))
+
+ if not rewrites:
+ return
+
+ # Error if --type / --repo-type was also provided explicitly.
+ if any(
+ arg == "--type" or arg.startswith("--type=") or arg == "--repo-type" or arg.startswith("--repo-type=")
+ for arg in args
+ ):
+ raise click.UsageError(
+ f"Ambiguous repo type: got prefix '{first_prefix}/' in repo ID and explicit --type. Use one or the other."
+ )
+
+ # Apply all rewrites and append --type once.
+ for arg_index, new_value in rewrites:
+ args[arg_index] = new_value
+ args.extend(["--type", inferred_type]) # type: ignore
+
+ def get_command(self, ctx: click.Context, cmd_name: str) -> click.Command | None:
+ # Try exact match first
+ cmd = super().get_command(ctx, cmd_name)
+ if cmd is not None:
+ return cmd
+ # Fall back to alias lookup: check if cmd_name matches any alias
+ # taken from https://github.com/fastapi/typer/issues/132#issuecomment-2417492805
+ for registered_name, registered_cmd in self.commands.items():
+ aliases = _ALIAS_SPLIT.split(registered_name)
+ if cmd_name in aliases:
+ return registered_cmd
+ return None
+
+ def _alias_map(self) -> dict[str, list[str]]:
+ """Build a mapping from primary command name to its aliases (if any)."""
+ result: dict[str, list[str]] = {}
+ for registered_name in self.commands:
+ parts = _ALIAS_SPLIT.split(registered_name)
+ primary = parts[0]
+ result[primary] = parts[1:]
+ return result
+
+ def format_commands(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ topics: dict[str, list] = {}
+ alias_map = self._alias_map()
+
+ for name in self.list_commands(ctx):
+ cmd = self.get_command(ctx, name)
+ if cmd is None or cmd.hidden:
+ continue
+ help_text = cmd.get_short_help_str(limit=formatter.width)
+ aliases = alias_map.get(name, [])
+ if aliases:
+ help_text = f"{help_text} [alias: {', '.join(aliases)}]"
+ topic = getattr(cmd, "topic", "main")
+ topics.setdefault(topic, []).append((name, help_text))
+
+ with formatter.section("Main commands"):
+ formatter.write_dl(topics["main"])
+ for topic in sorted(topics.keys()):
+ if topic == "main":
+ continue
+ with formatter.section(f"{topic.capitalize()} commands"):
+ formatter.write_dl(topics[topic])
+
+ def format_epilog(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ # Collect only the first example from each command (to keep group help concise)
+ # Full examples are shown in individual subcommand help (e.g. `hf buckets sync --help`)
+ all_examples: list[str] = []
+ for name in self.list_commands(ctx):
+ cmd = self.get_command(ctx, name)
+ if cmd is None or cmd.hidden:
+ continue
+ cmd_examples = getattr(cmd, "examples", [])
+ if cmd_examples:
+ all_examples.append(cmd_examples[0])
+
+ if all_examples:
+ epilog = generate_epilog(all_examples)
+ _format_epilog_no_indent(epilog, ctx, formatter)
+ elif self.epilog:
+ _format_epilog_no_indent(self.epilog, ctx, formatter)
+
+ def list_commands(self, ctx: click.Context) -> list[str]: # type: ignore[name-defined]
+ # For aliased commands ("list | ls"), use the primary name (first entry).
+ primary_names: list[str] = []
+ for name in self.commands:
+ primary = _ALIAS_SPLIT.split(name)[0]
+ primary_names.append(primary)
+ return sorted(primary_names)
+
+ def command( # type: ignore # adds topic/examples on top of HfGroup.command
+ self,
+ name: str | None = None,
+ *,
+ topic: TOPIC_T = "main",
+ examples: list[str] | None = None,
+ epilog: str | None = None,
+ **kwargs: Any,
+ ) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+ # Generate the epilog from examples when not provided explicitly, then build the
+ # command with a topic/examples-aware command class.
+ if epilog is None and examples:
+ epilog = generate_epilog(examples)
+ return super().command(name, cls=HFCliCommand(topic, examples), epilog=epilog, **kwargs)
+
+
+_FORMAT_HELP = "Output format. Defaults to 'auto' which picks 'agent' or 'human' based on the terminal."
+_JSON_HELP = "JSON output. Equivalent to '--format json'."
+_QUIET_HELP = "Quiet output (one ID per line). Equivalent to '--format quiet'."
+
+_FORMATTING_OPTIONS_HELP_RECORDS: list[tuple[str, str]] = [
+ ("--format [auto|human|agent|json|quiet]", _FORMAT_HELP),
+ ("--json", _JSON_HELP),
+ ("-q, --quiet", _QUIET_HELP),
+ ("--no-truncate", "Do not truncate scalar values in human tables (list/dict columns stay shortened)."),
+]
+
+
+def _format_formatting_options_section(formatter: click.HelpFormatter) -> None:
+ with formatter.section("Formatting options"):
+ formatter.write_dl(_FORMATTING_OPTIONS_HELP_RECORDS)
+
+
+# Formatting options for pass-through commands (`ignore_unknown_options=True`, e.g. `hf jobs run`), which
+# `_consume_format_flags_for_leaf` leaves untouched. Such commands must declare the flags explicitly (so click
+# parses them instead of forwarding them to the container command) and apply them with `set_output_format()`.
+FormatOpt = Annotated[OutputFormat | None, Option("--format", help=_FORMAT_HELP)]
+JsonOpt = Annotated[bool, Option("--json", help=_JSON_HELP)]
+QuietOpt = Annotated[bool, Option("-q", "--quiet", help=_QUIET_HELP)]
+
+
+def set_output_format(format: OutputFormat | None, json: bool, quiet: bool) -> None:
+ """Apply the formatting flags declared by a pass-through command to the `out` singleton."""
+ flags = [flag for flag, given in (("--format", format is not None), ("--json", json), ("-q", quiet)) if given]
+ if len(flags) > 1:
+ raise click.UsageError(f"'{flags[0]}' and '{flags[1]}' are mutually exclusive.")
+ if format is not None:
+ out.set_mode(format)
+ elif json:
+ out.set_mode(OutputFormat.json)
+ elif quiet:
+ out.set_mode(OutputFormat.quiet)
+
+
+def _has_local_formatting_option(cmd: click.Command) -> bool:
+ """Return True if the command defines its own --format, --json or --quiet / -q.
+
+ Used to skip the global formatting flag pre-processor and the duplicated "Formatting options" help section for
+ legacy commands like 'hf jobs ls' that have their own format/quiet options.
+ """
+ for param in cmd.params:
+ if not isinstance(param, click.Option):
+ continue
+ opts = (*param.opts, *param.secondary_opts)
+ if "--format" in opts or "--json" in opts or "--quiet" in opts or "-q" in opts:
+ return True
+ return False
+
+
+def _consume_format_flags_for_leaf(cmd: click.Command, args: list[str]) -> None:
+ """Apply global formatting flags from 'args' to a leaf command.
+
+ Two modes, depending on the command:
+
+ * **Pass-through commands** (ignore_unknown_options=True, e.g. 'hf extensions exec'):
+ args are forwarded verbatim to an external binary; we don't touch them.
+
+ * **Legacy commands with a local --format option** (e.g. 'hf jobs ls' whose '--format' accepts Go templates):
+ the global flags are rewritten in-place to the legacy form ('--json' → '--format json', '--quiet'/'-q' → '--format quiet'
+ when the cmd has no own '--quiet') so click can parse them locally. This preserves backwards compatibility with the previous shorthand behavior.
+
+ * **Modern commands** (no local format/quiet/json options): the flags '--format ' / '--json' / '--quiet' / '-q' are stripped from 'args' and applied to the singleton 'out'.
+
+ '--no-truncate' is stripped for all non-pass-through commands; when present, human table cells are not truncated.
+
+ Raises click.UsageError if multiple conflicting flags are supplied (e.g. '--json' together with '--format table').
+ """
+ if cmd.context_settings.get("ignore_unknown_options"):
+ return
+
+ no_truncate = _consume_no_truncate_flags(args)
+ out.set_no_truncate(no_truncate)
+
+ has_local_format = False
+ has_local_quiet = False
+ has_local_json = False
+ for param in cmd.params:
+ if not isinstance(param, click.Option):
+ continue
+ opts = (*param.opts, *param.secondary_opts)
+ if "--format" in opts:
+ has_local_format = True
+ if "--quiet" in opts or "-q" in opts:
+ has_local_quiet = True
+ if "--json" in opts:
+ has_local_json = True
+
+ if has_local_format:
+ _rewrite_legacy_shorthands(args, rewrite_json=not has_local_json, rewrite_quiet=not has_local_quiet)
+ return
+
+ # Strip --format/--json/-q/--quiet from 'args' and apply to 'out'
+ chosen_mode: OutputFormat = OutputFormat.auto
+ chosen_flag: str | None = None
+
+ def _check_conflict(new_flag: str) -> None:
+ # Reject any second formatting flag before parsing values, so the user gets
+ # a "mutually exclusive" error rather than e.g. an "invalid value" error
+ # from the second flag's argument.
+ if chosen_flag is not None:
+ raise click.UsageError(f"'{chosen_flag}' and '{new_flag}' are mutually exclusive.")
+
+ i = 0
+ while i < len(args):
+ arg = args[i]
+ if arg == "--":
+ break # everything after '--' is a positional literal
+ if arg == "--format":
+ _check_conflict("--format")
+ if i + 1 >= len(args):
+ raise click.UsageError("Option '--format' requires a value.")
+ chosen_mode = _parse_format_value(args[i + 1])
+ chosen_flag = "--format"
+ del args[i : i + 2] # --format value => 2 args removed
+ continue
+ if arg.startswith("--format="):
+ _check_conflict("--format")
+ chosen_mode = _parse_format_value(arg[len("--format=") :])
+ chosen_flag = "--format"
+ del args[i : i + 1]
+ continue
+ if arg == "--json":
+ _check_conflict("--json")
+ chosen_mode = OutputFormat.json
+ chosen_flag = "--json"
+ del args[i : i + 1]
+ continue
+ if arg in ("-q", "--quiet"):
+ _check_conflict(arg)
+ chosen_mode = OutputFormat.quiet
+ chosen_flag = arg
+ del args[i : i + 1]
+ continue
+ i += 1
+
+ out.set_mode(chosen_mode)
+
+
+def _consume_no_truncate_flags(args: list[str]) -> bool:
+ """Strip all global --no-truncate flags from args and return whether any was provided."""
+ no_truncate = False
+ i = 0
+ while i < len(args):
+ arg = args[i]
+ if arg == "--":
+ break # everything after '--' is a positional literal
+ if arg == "--no-truncate":
+ no_truncate = True
+ del args[i : i + 1]
+ continue
+ if arg.startswith("--no-truncate="):
+ raise click.UsageError("Option '--no-truncate' does not take a value.")
+ i += 1
+ return no_truncate
+
+
+def _rewrite_legacy_shorthands(args: list[str], *, rewrite_json: bool, rewrite_quiet: bool) -> None:
+ """Rewrite --json / -q / --quiet to --format ... for legacy commands.
+
+ Used for commands like 'hf jobs ls' that still own their '--format' option.
+ The rewrite lets users keep using the global shorthand while click parses
+ '--format ' locally.
+ """
+ has_format_in_args = any(arg == "--format" or arg.startswith("--format=") for arg in args)
+
+ if rewrite_json and "--json" in args:
+ if has_format_in_args:
+ raise click.UsageError("'--json' and '--format' are mutually exclusive.")
+ idx = args.index("--json")
+ args[idx : idx + 1] = ["--format", "json"]
+ has_format_in_args = True
+
+ if rewrite_quiet:
+ flag = "-q" if "-q" in args else ("--quiet" if "--quiet" in args else None)
+ if flag is not None:
+ if has_format_in_args:
+ raise click.UsageError(f"'{flag}' and '--format' are mutually exclusive.")
+ idx = args.index(flag)
+ args[idx : idx + 1] = ["--format", "quiet"]
+
+
+def _parse_format_value(value: str) -> "OutputFormat":
+ try:
+ return OutputFormat(value)
+ except ValueError:
+ valid = ", ".join(m.value for m in OutputFormat)
+ raise click.UsageError(f"Invalid value for '--format': '{value}'. Valid values: {valid}.") from None
+
+
+def _enrich_usage_error(error: click.UsageError, label: str, items: list[tuple[str, str]]) -> None:
+ """Append a list of available options or commands to a usage error message."""
+ if not items or error.ctx is None or f"Available {label} for" in error.message:
+ return
+ cmd_path = error.ctx.command_path
+ lines = [f"\n\nAvailable {label} for '{cmd_path}':"]
+ for name, help_text in items:
+ lines.append(f" {name:30s} {help_text}")
+ lines.append(f"\nRun '{cmd_path} --help' for full details.")
+ if isinstance(error, click.NoSuchOption) and error.possibilities:
+ lines.append(f"\nDid you mean: {', '.join(sorted(error.possibilities))}?")
+ setattr(error, "possibilities", [])
+ setattr(error, "message", error.message + "\n".join(lines))
+
+
+def fallback_typer_group_factory(
+ fallback_handler: FallbackHandlerT,
+ extra_commands_provider: Callable[[], list[tuple[str, str]]] | None = None,
+) -> type[HFCliTyperGroup]:
+ """Return a Typer group class that runs a fallback handler before command resolution."""
+
+ class FallbackTyperGroup(HFCliTyperGroup):
+ def resolve_command(self, ctx: click.Context, args: list[str]) -> tuple:
+ fallback_exit_code = fallback_handler(args, set(self.commands.keys()))
+ if fallback_exit_code is not None:
+ raise SystemExit(fallback_exit_code)
+ return super().resolve_command(ctx, args)
+
+ def format_commands(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ super().format_commands(ctx, formatter)
+ if extra_commands_provider is not None:
+ entries = extra_commands_provider()
+ if entries:
+ with formatter.section("Extension commands"):
+ formatter.write_dl(entries)
+
+ return FallbackTyperGroup
+
+
+def HFCliCommand(topic: TOPIC_T, examples: list[str] | None = None) -> type[HfCommand]:
+ def format_epilog(self: click.Command, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ _format_epilog_no_indent(self.epilog, ctx, formatter)
+
+ def format_options(self: HfCommand, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ HfCommand.format_options(self, ctx, formatter)
+ # Skip the section for commands that define their own --format / --quiet / --json,
+ # or for pass-through commands that forward args to an external binary.
+ if _has_local_formatting_option(self):
+ return
+ if self.context_settings.get("ignore_unknown_options"):
+ return
+ _format_formatting_options_section(formatter)
+
+ def parse_args(self: click.Command, ctx: click.Context, args: list[str]) -> list[str]:
+ # Show help when a command with required arguments is invoked without any args
+ # (mirrors group behavior: `hf jobs` prints help, so `hf download` should too).
+ if not args and not ctx.resilient_parsing:
+ if any(isinstance(p, click.Argument) and p.required for p in self.params):
+ click.echo(ctx.get_help(), color=ctx.color)
+ ctx.exit()
+ return HfCommand.parse_args(self, ctx, args)
+
+ return type(
+ f"HfCommand{topic.capitalize()}",
+ (HfCommand,),
+ {
+ "context_class": StyledContext,
+ "topic": topic,
+ "examples": examples or [],
+ "format_epilog": format_epilog,
+ "format_options": format_options,
+ "parse_args": parse_args,
+ },
+ )
+
+
+class LazyHfCommand(click.Command):
+ """A lightweight command placeholder that imports its implementation when selected."""
+
+ def __init__(
+ self,
+ name: str,
+ *,
+ module: str,
+ attribute: str,
+ is_factory: bool = False,
+ topic: TOPIC_T = "main",
+ examples_attribute: str | None = None,
+ hidden: bool = False,
+ ) -> None:
+ super().__init__(name=name, hidden=hidden)
+ self.module = module
+ self.attribute = attribute
+ self.is_factory = is_factory
+ self.topic = topic
+ self.examples: list[str] = []
+ self.examples_attribute = examples_attribute
+ self._loaded: click.Command | None = None
+
+ def materialize(self) -> click.Command:
+ if self._loaded is None:
+ module = importlib.import_module(self.module)
+ callback = getattr(module, self.attribute)
+ examples = getattr(module, self.examples_attribute) if self.examples_attribute is not None else []
+ if self.is_factory:
+ callback = callback()
+ if isinstance(callback, click.Command):
+ command = callback
+ command.name = self.name
+ command.hidden = self.hidden
+ else:
+ epilog = generate_epilog(examples) if examples else None
+ command = build_command(
+ callback,
+ name=self.name,
+ cls=HFCliCommand(self.topic, examples),
+ epilog=epilog,
+ hidden=self.hidden,
+ )
+ self.examples = getattr(command, "examples", examples)
+ self._loaded = command
+ return self._loaded
+
+ def get_short_help_str(self, limit: int = 45) -> str:
+ return self.materialize().get_short_help_str(limit)
+
+
+class LazyHfGroup(click.Group):
+ """A lightweight group placeholder that imports its implementation when selected or inspected."""
+
+ def __init__(self, name: str, *, module: str, attribute: str, hidden: bool = False) -> None:
+ super().__init__(name=name, hidden=hidden)
+ self.module = module
+ self.attribute = attribute
+ self._loaded: click.Group | None = None
+
+ def materialize(self) -> click.Group:
+ if self._loaded is None:
+ group = getattr(importlib.import_module(self.module), self.attribute)
+ if not isinstance(group, click.Group):
+ raise TypeError(f"{self.module}.{self.attribute} is not a Click group")
+ group.name = self.name
+ group.hidden = self.hidden
+ self._loaded = group
+ return self._loaded
+
+ def list_commands(self, ctx: click.Context) -> list[str]:
+ return self.materialize().list_commands(ctx)
+
+ def get_command(self, ctx: click.Context, cmd_name: str) -> click.Command | None:
+ return self.materialize().get_command(ctx, cmd_name)
+
+ def get_short_help_str(self, limit: int = 45) -> str:
+ return self.materialize().get_short_help_str(limit)
+
+
+def typer_factory(help: str, epilog: str | None = None, cls: type[HFCliTyperGroup] | None = None) -> "HFCliTyperGroup":
+ """Create a CLI command group with consistent settings.
+
+ The returned group is the app: register commands with ``@group.command(...)``,
+ subgroups with ``group.add_group(sub, name=...)``, and a group-level callback
+ with ``@group.callback(...)``.
+
+ Args:
+ help: Help text for the group.
+ epilog: Optional epilog text (use `generate_epilog` to create one).
+ cls: Optional group class to use (defaults to `HFCliTyperGroup`).
+
+ Returns:
+ A configured `HFCliTyperGroup` instance.
+ """
+ if cls is None:
+ cls = HFCliTyperGroup
+ return cls(
+ help=help,
+ epilog=epilog,
+ no_args_is_help=True,
+ # Increase max content width for better readability
+ context_settings={
+ "max_content_width": 120,
+ "help_option_names": ["-h", "--help"],
+ },
+ )
+
+
+class SoftChoice(click.Choice):
+ """A click Choice that suggests choices for autocompletion/docs but accepts any string.
+
+ Unlike `click.Choice`, unknown values are passed through as-is instead of raising an error.
+ This makes CLI options future-compatible when new server-side values are added.
+
+ Accepts either a sequence of strings or an Enum class:
+ ```python
+ SoftChoice(SpaceHardware) # from an enum
+ SoftChoice(["a", "b", "c"]) # from a list
+ ```
+ """
+
+ def __init__(self, choices: Sequence[str] | type[Enum]) -> None:
+ values = (
+ [m.value for m in choices] if isinstance(choices, type) and issubclass(choices, Enum) else list(choices)
+ )
+ super().__init__(values, case_sensitive=True)
+
+ def convert(self, value: Any, param: click.Parameter | None, ctx: click.Context | None) -> str:
+ try:
+ return super().convert(value, param, ctx)
+ except click.exceptions.BadParameter:
+ return str(value)
+
+
+class RepoType(str, Enum):
+ model = "model"
+ dataset = "dataset"
+ space = "space"
+ kernel = "kernel"
+
+
+RepoIdArg = Annotated[
+ str,
+ Argument(
+ help="The ID of the repo (e.g. `username/repo-name` or `spaces/username/repo-name`).",
+ ),
+]
+
+
+RepoTypeOpt = Annotated[
+ RepoType,
+ Option(
+ "--type",
+ "--repo-type",
+ help="The type of repository (model, dataset, space, or kernel).",
+ ),
+]
+
+# Same as `RepoTypeOpt` but optional (defaults to `None` rather than `model`). Used by commands that
+# accept an `hf://` URI as repo id: a `None` default lets us tell apart "user did not pass --repo-type"
+# from "user explicitly passed --repo-type model", which is required to detect conflicts with the URI.
+RepoTypeOptionalOpt = Annotated[
+ RepoType | None,
+ Option(
+ "--type",
+ "--repo-type",
+ help="The type of repository (model, dataset, space, or kernel).",
+ show_default="model",
+ ),
+]
+
+# --- Secret hygiene: keep secret material out of argv ---
+#
+# Anything passed as a flag value lands in the shell history file and, on Linux, in
+# `/proc//cmdline`, which any process of the same user can read. The CLI already offers
+# argv-free alternatives (`hf auth login`, `HF_TOKEN`, bare `--secrets KEY`, `--secrets-file`);
+# these warnings point at them. They stay warnings on purpose: erroring out would break
+# existing automation.
+
+
+def _warn_secret_hygiene(message: str) -> None:
+ """Emit a secret-hygiene warning on stderr in every output mode."""
+ out.warning(message)
+
+
+def _warn_on_inline_token(token: str | None) -> str | None:
+ """Option callback for `TokenOpt`: warn when a token is passed as a flag value."""
+ ctx = click.get_current_context(silent=True)
+ # `hf auth login --token` is the non-interactive login flow this warning points users *to*,
+ # so it is exempt. Matched on a suffix because the root command name depends on how the CLI
+ # was invoked (`hf`, `huggingface-cli`, a test runner's prog name...).
+ if token is not None and not (ctx is not None and ctx.command_path.endswith("auth login")):
+ _warn_secret_hygiene(
+ "`--token ` leaves your token in your shell history and in process listings. Run "
+ "`hf auth login` once, or export `HF_TOKEN`, and drop the flag."
+ )
+ return token
+
+
+def _warn_on_inline_secrets(secrets: list[str] | None) -> list[str] | None:
+ """Option callback for `SecretsOpt`: warn once if any pair carries an inline value."""
+ if secrets and any("=" in secret for secret in secrets):
+ _warn_secret_hygiene(
+ "`--secrets KEY=value` leaves the value in your shell history and in process listings. Prefer "
+ "`--secrets KEY` (value read from your environment), `--secrets-file FILE` or `--secrets-file -`."
+ )
+ return secrets
+
+
+TokenOpt = Annotated[
+ str | None,
+ Option(
+ help="A User Access Token generated from https://huggingface.co/settings/tokens.",
+ callback=_warn_on_inline_token,
+ ),
+]
+
+PrivateOpt = Annotated[
+ bool | None,
+ Option(
+ help="Whether to create a private repo if repo doesn't exist on the Hub. Ignored if the repo already exists.",
+ ),
+]
+
+RevisionOpt = Annotated[
+ str | None,
+ Option(
+ help="Git revision id which can be a branch name, a tag, or a commit hash.",
+ ),
+]
+
+
+LimitOpt = Annotated[
+ int,
+ Option(help="Limit the number of results."),
+]
+
+AuthorOpt = Annotated[
+ str | None,
+ Option(help="Filter by author or organization."),
+]
+
+FilterOpt = Annotated[
+ list[str] | None,
+ Option(help="Filter by tags (e.g. 'text-classification'). Can be used multiple times."),
+]
+
+SearchOpt = Annotated[
+ str | None,
+ Option(help="Search query."),
+]
+
+
+# --- Env / Secrets shared options and parsing helpers (used by jobs, repos, etc.) ---
+
+EnvOpt = Annotated[
+ list[str] | None,
+ Option(
+ "-e",
+ "--env",
+ help="Set environment variables. E.g. --env ENV=value",
+ ),
+]
+
+SecretsOpt = Annotated[
+ list[str] | None,
+ Option(
+ "-s",
+ "--secrets",
+ help=(
+ "Set secret environment variables. Prefer `--secrets SECRET` to read the value from your"
+ " environment (e.g. `--secrets HF_TOKEN` to pass your Hugging Face token); `--secrets SECRET=value`"
+ " puts the value in your shell history."
+ ),
+ callback=_warn_on_inline_secrets,
+ ),
+]
+
+EnvFileOpt = Annotated[
+ str | None,
+ Option(
+ "--env-file",
+ help="Read in a file of environment variables. Use `-` to read them from stdin.",
+ ),
+]
+
+SecretsFileOpt = Annotated[
+ str | None,
+ Option(
+ help="Read in a file of secret environment variables. Use `-` to read them from stdin.",
+ ),
+]
+
+
+def _get_extended_environ() -> dict[str, str]:
+ """Return a copy of ``os.environ`` with the user's HF token injected (if available)."""
+ from huggingface_hub import get_token
+
+ extended_environ = os.environ.copy()
+ if (token := get_token()) is not None:
+ extended_environ["HF_TOKEN"] = token
+ return extended_environ
+
+
+def _read_env_file(env_file: str) -> str:
+ """Read an env/secrets file, or stdin when ``env_file`` is ``-``.
+
+ Piping through stdin is the only way to hand the CLI a secret value that touches neither
+ argv nor the disk.
+ """
+ if env_file == "-":
+ return sys.stdin.read()
+ path = Path(env_file)
+ # POSIX only: Windows reports a synthetic 0o666/0o444 mode, so this would always fire there.
+ if os.name == "posix" and (mode := path.stat().st_mode & 0o777) & 0o077:
+ _warn_secret_hygiene(
+ f"'{env_file}' is readable by other users (mode {mode:o}). Run `chmod 600 {env_file}` to restrict it."
+ )
+ # "utf-8-sig" drops the BOM that Windows tools (e.g. PowerShell 5.1) write at the start of the file
+ return path.read_text(encoding="utf-8-sig")
+
+
+def parse_env_map(
+ env: list[str] | None = None,
+ env_file: str | None = None,
+) -> dict[str, str | None]:
+ """Parse ``-e``/``--env``/``-s``/``--secrets`` and ``--env-file``/``--secrets-file`` CLI args into a dict.
+
+ Uses an extended environment that includes the user's HF token so that
+ bare ``--secrets HF_TOKEN`` resolves correctly.
+ """
+ env_map: dict[str, str | None] = {}
+ if env_file or env:
+ extended_environ = _get_extended_environ()
+ if env_file:
+ env_map.update(load_dotenv(_read_env_file(env_file), environ=extended_environ))
+ for env_value in env or []:
+ env_map.update(load_dotenv(env_value, environ=extended_environ))
+ return env_map
+
+
+def env_map_to_key_value_list(env_map: dict[str, str | None]) -> list[dict[str, str]] | None:
+ """Convert an env/secrets dict to the ``[{"key": ..., "value": ...}]`` format used by the Hub API."""
+ if not env_map:
+ return None
+ return [{"key": k, "value": v or ""} for k, v in env_map.items()]
+
+
+VolumesOpt = Annotated[
+ list[str] | None,
+ Option(
+ "-v",
+ "--volume",
+ help="Mount one or more volumes. Format: hf://[TYPE/]SOURCE:/MOUNT_PATH[:ro]. "
+ "TYPE is one of: models, datasets, spaces, buckets. "
+ "TYPE defaults to models if omitted. "
+ "models, datasets and spaces are always mounted read-only. buckets are read+write by default. "
+ "E.g. -v hf://org/m:/data or -v hf://datasets/org/ds:/data or -v hf://buckets/org/b:/mnt:ro",
+ ),
+]
+
+
+def parse_volumes(volumes: list[str] | None) -> "list[Volume] | None":
+ """Parse volume specs from CLI arguments.
+
+ Format: hf://[TYPE/]SOURCE[/PATH]:/MOUNT_PATH[:ro|:rw]
+ Where TYPE is one of: models, datasets, spaces, buckets (defaults to models if omitted).
+ SOURCE is the repo/bucket identifier (e.g. 'username/my-model').
+ PATH is an optional subfolder inside the repo/bucket.
+ MOUNT_PATH starts with '/'.
+ Optional ':ro' or ':rw' suffix for read-only or read-write.
+
+ Examples:
+ hf://my-org/my-model:/data (model, implicit type)
+ hf://models/my-org/my-model:/data (model, explicit type)
+ hf://datasets/my-org/my-dataset:/data:ro
+ hf://buckets/my-org/my-bucket:/mnt
+ hf://spaces/my-org/my-space:/app
+ hf://datasets/org/ds/train:/data (with path inside repo)
+ hf://buckets/org/b/sub/dir:/mnt (with path inside bucket)
+ """
+ if not volumes:
+ return None
+
+ from huggingface_hub import Volume
+ from huggingface_hub.utils import parse_hf_mount
+
+ result: list[Volume] = []
+ for raw_spec in volumes:
+ mount = parse_hf_mount(raw_spec)
+ result.append(
+ Volume(
+ type=mount.source.type,
+ source=mount.source.id,
+ mount_path=mount.mount_path,
+ read_only=mount.read_only,
+ path=mount.source.path_in_repo or None,
+ revision=mount.source.revision or None,
+ )
+ )
+ return result
+
+
+def make_expand_properties_parser(valid_properties: Sequence[ExpandPropertyT]):
+ """Create a callback to parse and validate comma-separated expand properties."""
+
+ def _parse_expand_properties(value: str | None) -> list[ExpandPropertyT] | None:
+ if value is None:
+ return None
+ properties = [p.strip() for p in value.split(",")]
+ for prop in properties:
+ if prop not in valid_properties:
+ raise click.BadParameter(
+ f"Invalid expand property: '{prop}'. Valid values are: {', '.join(valid_properties)}"
+ )
+ return [cast(ExpandPropertyT, prop) for prop in properties]
+
+ return _parse_expand_properties
+
+
+### SSH
+
+
+SshIdentityFileOpt = Annotated[
+ Path | None,
+ Option("-i", "--identity-file", help="Path to the SSH identity file (forwarded to `ssh -i`)."),
+]
+
+SshDryRunOpt = Annotated[
+ bool,
+ Option("--dry-run", help="Print the SSH command instead of running it."),
+]
+
+
+def exec_ssh(
+ destination: str, *, port: int | None = None, identity_file: Path | None = None, dry_run: bool = False
+) -> None:
+ """Run an interactive `ssh` command to `destination` (`user@host`) and exit with its return code.
+
+ With `dry_run`, print the command instead of running it.
+ """
+ cmd = ["ssh"]
+ if identity_file is not None:
+ cmd += ["-i", str(identity_file)]
+ if port is not None:
+ cmd += ["-p", str(port)]
+ cmd.append(destination)
+ if dry_run:
+ out.text(shlex.join(cmd))
+ return
+ out.text(f"Running `{shlex.join(cmd)}`")
+ result = subprocess.run(cmd)
+ raise click.exceptions.Exit(code=result.returncode)
+
+
+### PyPI VERSION CHECKER
+
+
+def check_cli_update(library: Literal["huggingface_hub", "transformers"]) -> None:
+ """
+ Check whether a newer version of a library is available on PyPI.
+
+ If a newer version is found, print a hint pointing at `hf update`.
+
+ If current version is a pre-release (e.g. `1.0.0.rc1`), or a dev version (e.g. `1.0.0.dev1`), no check is performed.
+ If `HF_HUB_DISABLE_UPDATE_CHECK` is set, the check is skipped entirely.
+
+ This function is called at the entry point of the CLI. It only performs the check once every 24 hours, and any error
+ during the check is caught and logged, to avoid breaking the CLI.
+
+ Args:
+ library: The library to check for updates. Currently supports "huggingface_hub" and "transformers".
+ """
+ try:
+ _check_cli_update(library)
+ except Exception:
+ # We don't want the CLI to fail on version checks, no matter the reason.
+ logger.debug("Error while checking for CLI update.", exc_info=True)
+
+
+def _check_cli_update(library: Literal["huggingface_hub", "transformers"]) -> None:
+ if constants.HF_HUB_DISABLE_UPDATE_CHECK:
+ return
+
+ current_version = importlib.metadata.version(library)
+
+ # Skip if current version is a pre-release or dev version
+ if any(tag in current_version for tag in ["rc", "dev"]):
+ return
+
+ # Skip if already checked in the last 24 hours
+ if os.path.exists(constants.CHECK_FOR_UPDATE_DONE_PATH):
+ mtime = os.path.getmtime(constants.CHECK_FOR_UPDATE_DONE_PATH)
+ if (time.time() - mtime) < 24 * 3600:
+ return
+
+ # Touch the file to mark that we did the check now
+ Path(constants.CHECK_FOR_UPDATE_DONE_PATH).parent.mkdir(parents=True, exist_ok=True)
+ Path(constants.CHECK_FOR_UPDATE_DONE_PATH).touch()
+
+ # Check latest version from the appropriate registry
+ if library == "huggingface_hub" and installation_method() == "brew":
+ latest_version = _fetch_latest_brew_version()
+ else:
+ latest_version = _fetch_latest_pypi_version(library)
+ if latest_version is None or current_version == latest_version:
+ return
+
+ if library == "huggingface_hub":
+ update_command = _get_huggingface_hub_update_command()
+ else:
+ update_command = _get_transformers_update_command()
+
+ message = f"A new version of {library} ({latest_version}) is available! You are using version {current_version}."
+ if update_command is not None:
+ match library:
+ case "huggingface_hub":
+ message += "\nTo update, run: hf update"
+ case _:
+ message += f"\nTo update, run: {' '.join(update_command)}"
+ out.hint(message)
+
+
+def _fetch_latest_pypi_version(library: str) -> str | None:
+ """Fetch the latest version of a library from PyPI. Returns None if the request fails."""
+ try:
+ from huggingface_hub.utils import get_session, hf_raise_for_status
+
+ response = get_session().get(f"https://pypi.org/pypi/{library}/json", timeout=2)
+ hf_raise_for_status(response)
+ return response.json()["info"]["version"]
+ except Exception:
+ logger.debug("Error while fetching latest version from PyPI.", exc_info=True)
+ return None
+
+
+def _fetch_latest_brew_version() -> str | None:
+ """Fetch the latest version of the `hf` formula from the Homebrew registry. Returns None if the request fails."""
+ try:
+ from huggingface_hub.utils import get_session, hf_raise_for_status
+
+ response = get_session().get("https://formulae.brew.sh/api/formula/hf.json", timeout=2)
+ hf_raise_for_status(response)
+ return response.json()["versions"]["stable"]
+ except Exception:
+ logger.debug("Error while fetching latest version from Homebrew.", exc_info=True)
+ return None
+
+
+def run_update(*, exclude_skill: bool = False) -> int:
+ """Run the install-method-appropriate update command for the `hf` CLI.
+
+ Set `exclude_skill` to tell the standalone installer not to (re)install the `hf-cli` skill.
+
+ Raises CLIError if the installation method can't be determined.
+ Returns the subprocess exit code on success/failure of the update itself.
+ """
+ cmd = _get_huggingface_hub_update_command(exclude_skill=exclude_skill)
+ if cmd is None:
+ raise CLIError(
+ "Cannot determine how to update huggingface_hub (unknown installation method). Please update manually."
+ )
+ return subprocess.call(cmd)
+
+
+def _get_huggingface_hub_update_command(*, exclude_skill: bool = False) -> list[str] | None:
+ """Return the command to update huggingface_hub as an argv list, or None if the installation method is unknown."""
+ match installation_method():
+ case "brew":
+ return ["brew", "upgrade", "hf"]
+ case "hf_installer" if os.name == "nt":
+ # `iwr ... | iex` cannot take parameters: create a scriptblock so flags reach the installer.
+ flags = " -ExcludeSkill" if exclude_skill else ""
+ script = f"& ([scriptblock]::Create((iwr -useb https://hf.co/cli/install.ps1))){flags}"
+ return ["powershell", "-NoProfile", "-Command", script]
+ case "hf_installer":
+ flags = " -s -- --exclude-skill" if exclude_skill else " -"
+ return ["bash", "-c", f"curl -LsSf https://hf.co/cli/install.sh | bash{flags}"]
+ case "pip":
+ return [sys.executable, "-m", "pip", "install", "-U", "huggingface_hub"]
+ case _:
+ return None
+
+
+def _get_transformers_update_command() -> list[str] | None:
+ """Return the command to update transformers as an argv list, or None if the installation method is unknown."""
+ match installation_method():
+ case "hf_installer" if os.name == "nt":
+ return [
+ "powershell",
+ "-NoProfile",
+ "-Command",
+ "& ([scriptblock]::Create((iwr -useb https://hf.co/cli/install.ps1))) -WithTransformers",
+ ]
+ case "hf_installer":
+ return ["bash", "-c", "curl -LsSf https://hf.co/cli/install.sh | bash -s -- --with-transformers"]
+ case "pip":
+ return [sys.executable, "-m", "pip", "install", "-U", "transformers"]
+ case _:
+ return None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_completion.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_completion.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbbb3d1c0ab2e0835eca7692f31644731094fe15
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_completion.py
@@ -0,0 +1,105 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shell completion for the ``hf`` CLI, built on Click's native completion.
+
+Click generates completion scripts activated by the ``_HF_COMPLETE`` env var (this
+works out of the box because ``hf`` is a Click command). This module exposes the two
+conveniences Typer used to provide — ``--install-completion`` / ``--show-completion``
+— as thin eager options over that machinery.
+"""
+
+import os
+from pathlib import Path
+from typing import Annotated
+
+import click
+from click.shell_completion import get_completion_class
+
+from ._framework import Option
+
+
+_COMPLETE_VAR = "_HF_COMPLETE"
+
+# Shells with a shared rc file: the line that activates completion (appended once).
+_RC_ACTIVATION: dict[str, tuple[Path, str]] = {
+ "bash": (Path.home() / ".bashrc", f'eval "$({_COMPLETE_VAR}=bash_source hf)"'),
+ "zsh": (Path.home() / ".zshrc", f'eval "$({_COMPLETE_VAR}=zsh_source hf)"'),
+}
+# Fish auto-loads per-command files from its completions directory. The file is dedicated
+# to `hf`, so the full script is written there directly (overwriting any stale version)
+# instead of re-generating it via `hf` on every shell startup.
+_FISH_COMPLETION_PATH = Path.home() / ".config" / "fish" / "completions" / "hf.fish"
+
+
+def _detect_shell() -> str:
+ return Path(os.environ.get("SHELL", "")).name or "bash"
+
+
+def _completion_script(shell: str) -> str:
+ # Imported lazily to avoid a circular import (hf.py imports the options below).
+ from .hf import app
+
+ completion_cls = get_completion_class(shell)
+ if completion_cls is None:
+ raise click.ClickException(f"Shell '{shell}' is not supported for completion.")
+ return completion_cls(app, {}, "hf", _COMPLETE_VAR).source()
+
+
+def _show_completion(value: bool) -> None:
+ if not value:
+ return
+ click.echo(_completion_script(_detect_shell()))
+ raise click.exceptions.Exit()
+
+
+def _install_completion(value: bool) -> None:
+ if not value:
+ return
+ shell = _detect_shell()
+ if shell == "fish":
+ path = _FISH_COMPLETION_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_text(_completion_script("fish"))
+ elif shell in _RC_ACTIVATION:
+ path, activation = _RC_ACTIVATION[shell]
+ path.parent.mkdir(parents=True, exist_ok=True)
+ existing = path.read_text() if path.exists() else ""
+ if activation not in existing:
+ with path.open("a") as file:
+ file.write(f"\n{activation}\n")
+ else:
+ raise click.ClickException(f"Shell '{shell}' is not supported for completion.")
+ click.echo(f"{shell} completion installed in {path}. Restart your shell for it to take effect.")
+ raise click.exceptions.Exit()
+
+
+InstallCompletionOpt = Annotated[
+ bool,
+ Option(
+ "--install-completion",
+ callback=_install_completion,
+ is_eager=True,
+ help="Install completion for the current shell.",
+ ),
+]
+
+ShowCompletionOpt = Annotated[
+ bool,
+ Option(
+ "--show-completion",
+ callback=_show_completion,
+ is_eager=True,
+ help="Show completion for the current shell, to copy it or customize the installation.",
+ ),
+]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cp.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cp.py
new file mode 100644
index 0000000000000000000000000000000000000000..e163cc6596188e052ae177bebaac1cf6786669fb
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_cp.py
@@ -0,0 +1,253 @@
+# Copyright 2026-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shared ``cp`` command to copy files between local paths, repositories and buckets.
+
+This single command backs three identical CLI entry points: ``hf cp`` (top-level),
+``hf repos cp`` and ``hf buckets cp``. It supports any source/destination combination
+of local file, repo/bucket ``hf://`` URI, and ``-`` (stdin/stdout), with two exceptions:
+- bucket-to-repo copies are not supported (server limitation), and
+- local-to-local copies (use a regular ``cp`` for that).
+"""
+
+import os
+import sys
+from dataclasses import replace
+from typing import Annotated, Literal
+
+import click
+
+from huggingface_hub import HfApi
+from huggingface_hub.errors import CLIError
+from huggingface_hub.utils import HfUri, SoftTemporaryDirectory, disable_progress_bars, is_hf_uri, parse_hf_uri
+
+from ._cli_utils import TokenOpt, get_hf_api
+from ._framework import Argument
+from ._output import out
+
+
+CP_EXAMPLES = [
+ # Download (repo or bucket -> local / stdout)
+ "hf cp hf://username/my-model/config.json",
+ "hf cp hf://username/my-model/config.json ./config.json",
+ "hf cp hf://datasets/username/my-dataset/data.csv ./data/",
+ "hf cp hf://buckets/username/my-bucket/config.json -",
+ # Upload (local / stdin -> repo or bucket)
+ "hf cp ./model.safetensors hf://username/my-model/model.safetensors",
+ "hf cp ./config.json hf://buckets/username/my-bucket/logs/",
+ "hf cp - hf://buckets/username/my-bucket/config.json",
+ # Remote to remote (repo/bucket -> repo/bucket, server-side when possible)
+ "hf cp hf://username/source-model/ hf://username/dest-model/",
+ "hf cp hf://datasets/username/my-dataset/processed/ hf://buckets/username/my-bucket/processed/",
+ "hf cp hf://buckets/username/my-bucket/logs/ hf://buckets/username/archive-bucket/ # copies contents only",
+]
+
+
+# Which alias registered the command, used to restrict the remote endpoint type (see `_enforce_context`).
+CpContext = Literal["repos", "buckets"]
+
+
+def make_cp(context: CpContext | None = None):
+ """Build the ``cp`` command function for a given alias.
+
+ The three entry points (`hf cp`, `hf repos cp`, `hf buckets cp`) share the exact same logic;
+ 'context' only adds a guardrail on the remote endpoint type (see `_enforce_context`).
+ """
+
+ def cp(
+ src: Annotated[
+ str,
+ Argument(help="Source: local file, hf:// URI (repo or bucket), or - for stdin."),
+ ],
+ dst: Annotated[
+ str | None,
+ Argument(help="Destination: local path, hf:// URI (repo or bucket), or - for stdout."),
+ ] = None,
+ token: TokenOpt = None,
+ ) -> None:
+ """Copy files between local paths, repositories, and buckets.
+
+ Handles uploads (local/stdin -> repo/bucket), downloads (repo/bucket -> local/stdout) and
+ remote-to-remote copies (repo/bucket -> repo/bucket). Bucket-to-repo and local-to-local
+ copies are not supported. For directories, use `hf upload`/`hf download` (repos) or
+ `hf buckets sync` (buckets). Remote-to-remote copies only work within the same storage
+ region (https://huggingface.co/docs/hub/storage-regions).
+ """
+ _enforce_context(context, src, dst)
+ _run_cp(src, dst, token)
+
+ return cp
+
+
+def _enforce_context(context: CpContext | None, src: str, dst: str | None) -> None:
+ """Guardrail for the `hf repos cp` / `hf buckets cp` aliases.
+
+ These aliases are exact duplicates of `hf cp`, so a bare `hf repos cp` could otherwise touch a
+ bucket (and vice versa). We validate the type of the remote side: the destination for uploads and
+ remote-to-remote copies, or the source when downloading to a local path / stdout. The top-level
+ `hf cp` (i.e. 'context' is None) accepts any combination.
+ """
+ if context is None:
+ return
+ # The remote endpoint is the destination when it is an hf:// URI, otherwise the source (download).
+ remote = dst if (dst is not None and is_hf_uri(dst)) else src
+ if not is_hf_uri(remote):
+ return
+ if context == "repos" and parse_hf_uri(remote).is_bucket:
+ raise CLIError("`hf repos cp` only works with repositories. Use `hf cp` or `hf buckets cp` for buckets.")
+ if context == "buckets" and not parse_hf_uri(remote).is_bucket:
+ raise CLIError("`hf buckets cp` only works with buckets. Use `hf cp` or `hf repos cp` for repositories.")
+
+
+def _run_cp(src: str, dst: str | None, token: str | None) -> None:
+ api = get_hf_api(token=token)
+
+ src_is_stdin = src == "-"
+ dst_is_stdout = dst == "-"
+ src_is_hf = is_hf_uri(src)
+ dst_is_hf = dst is not None and is_hf_uri(dst)
+
+ # --- Remote to remote: delegate to copy_files (repo/bucket -> repo/bucket) ---
+ if src_is_hf and dst_is_hf:
+ assert dst is not None # guaranteed by dst_is_hf
+ api.copy_files(src, dst)
+ out.result("Copied", src=src, dst=dst)
+ return
+
+ # --- At least one side must be a remote hf:// URI (rules out local->local, stdin->local, etc.) ---
+ if not src_is_hf and not dst_is_hf:
+ if dst is None:
+ raise click.BadParameter("Missing destination. Provide a repo or bucket hf:// URI as DST.")
+ raise click.BadParameter(
+ "One of SRC or DST must be a repo (hf://username/...) or bucket (hf://buckets/...) URI."
+ )
+
+ # --- Download: repo/bucket -> local file or stdout ---
+ if src_is_hf:
+ if dst_is_stdout:
+ _download_file_to_stdout(api, src)
+ return
+ _download_file_to_local(api, src, dst)
+ return
+
+ # --- Upload: local file or stdin -> repo/bucket ---
+ assert dst is not None # guaranteed: reaching here means dst_is_hf is True
+ _upload_file_to_remote(api, src, dst, src_is_stdin=src_is_stdin)
+
+
+def _download_file_to_stdout(api: HfApi, src: str) -> None:
+ uri = parse_hf_uri(src)
+ filename = _source_filename(uri, src)
+ # Suppress progress bars to avoid polluting the piped output.
+ with disable_progress_bars():
+ with SoftTemporaryDirectory() as tmp_dir:
+ tmp_path = os.path.join(tmp_dir, filename)
+ _download_single(api, uri, tmp_path)
+ with open(tmp_path, "rb") as f:
+ while chunk := f.read(32_000_000): # 32MB chunks
+ sys.stdout.buffer.write(chunk)
+
+
+def _download_file_to_local(api: HfApi, src: str, dst: str | None) -> None:
+ uri = parse_hf_uri(src)
+ filename = _source_filename(uri, src)
+
+ if dst is None:
+ local_path = filename
+ elif os.path.isdir(dst) or dst.endswith(os.sep) or dst.endswith("/"):
+ local_path = os.path.join(dst, filename)
+ else:
+ local_path = dst
+
+ parent_dir = os.path.dirname(local_path)
+ if parent_dir:
+ os.makedirs(parent_dir, exist_ok=True)
+
+ _download_single(api, uri, local_path)
+ out.result("Downloaded", src=src, dst=local_path)
+
+
+def _download_single(api: HfApi, uri: HfUri, local_path: str) -> None:
+ """Download a single file (repo or bucket) to ``local_path``.
+
+ Used by `_download_file_to_local` and `_download_file_to_stdout`.
+ """
+ if uri.is_bucket:
+ api.download_bucket_files(uri.id, [(uri.path_in_repo, local_path)], raise_on_missing_files=True)
+ else:
+ # Download into a temporary folder next to the destination (rather than the shared cache)
+ # so the final move stays on the same filesystem and is instant. The temp folder is
+ # cleaned up automatically once the move is complete.
+ parent_dir = os.path.dirname(local_path) or "."
+ with SoftTemporaryDirectory(prefix=".tmp", dir=parent_dir) as tmp_dir:
+ downloaded_path = api.hf_hub_download(
+ repo_id=uri.id,
+ repo_type=uri.type,
+ filename=uri.path_in_repo,
+ revision=uri.revision,
+ local_dir=tmp_dir,
+ )
+ os.replace(downloaded_path, local_path)
+
+
+def _source_filename(uri: HfUri, src: str) -> str:
+ if uri.path_in_repo == "" or src.endswith("/"):
+ raise click.BadParameter(
+ "Source path must include a file name, not just a repo/bucket or directory path."
+ " Use `hf download` or `hf buckets sync` to copy directories."
+ )
+ return uri.path_in_repo.rsplit("/", 1)[-1]
+
+
+def _upload_file_to_remote(api: HfApi, src: str, dst: str, *, src_is_stdin: bool) -> None:
+ uri = parse_hf_uri(dst)
+
+ if src_is_stdin:
+ if uri.path_in_repo == "" or dst.endswith("/"):
+ raise click.BadParameter("Stdin upload requires a full destination path including filename.")
+ data = sys.stdin.buffer.read()
+ _upload_single(api, uri, data, uri.path_in_repo)
+ out.result("Uploaded", src="stdin", dst=uri.to_uri())
+ return
+
+ if os.path.isdir(src):
+ raise click.BadParameter(
+ "Source must be a file, not a directory. Use `hf upload` or `hf buckets sync` for directories."
+ )
+ if not os.path.isfile(src):
+ raise click.BadParameter(f"Source file not found: {src}")
+
+ prefix = uri.path_in_repo
+ if prefix == "":
+ remote_path = os.path.basename(src)
+ elif dst.endswith("/"):
+ remote_path = prefix + "/" + os.path.basename(src)
+ else:
+ remote_path = prefix
+
+ _upload_single(api, uri, src, remote_path)
+ out.result("Uploaded", src=src, dst=replace(uri, path_in_repo=remote_path).to_uri())
+
+
+def _upload_single(api: HfApi, uri: HfUri, source: str | bytes, remote_path: str) -> None:
+ """Upload a single file or bytes (to a repo or bucket)."""
+ if uri.is_bucket:
+ api.batch_bucket_files(uri.id, add=[(source, remote_path)])
+ else:
+ api.upload_file(
+ path_or_fileobj=source,
+ path_in_repo=remote_path,
+ repo_id=uri.id,
+ repo_type=uri.type,
+ revision=uri.revision,
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_errors.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..45e98da0907f2b4b559c48fedc41b652eb438499
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_errors.py
@@ -0,0 +1,155 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""CLI error handling utilities."""
+
+import traceback
+from collections.abc import Callable
+
+from huggingface_hub.errors import (
+ BucketNotFoundError,
+ CLIError,
+ CLIExtensionInstallError,
+ DeviceCodeError,
+ EntryNotFoundError,
+ GatedRepoError,
+ HfHubHTTPError,
+ HfUriError,
+ IncompleteSnapshotError,
+ LocalEntryNotFoundError,
+ LocalTokenNotFoundError,
+ OfflineModeIsEnabled,
+ OIDCError,
+ RemoteEntryNotFoundError,
+ RepositoryNotFoundError,
+ RevisionNotFoundError,
+)
+
+
+def _format_repo_not_found(error: RepositoryNotFoundError) -> str:
+ label = error.repo_type.capitalize() if error.repo_type else "Repository"
+ if error.repo_id:
+ msg = f"{label} '{error.repo_id}' not found."
+ else:
+ msg = f"{label} not found."
+ msg += "\nIf the repo is private, make sure you are authenticated and your token has the required permissions."
+
+ msg += "\nIf the repo does not exist, create it with: "
+ if error.repo_id is not None:
+ type_flag = f" --type {error.repo_type}" if error.repo_type and error.repo_type != "model" else ""
+ msg += f"hf repos create {error.repo_id}{type_flag}"
+ else:
+ msg += "hf repos create "
+
+ return msg
+
+
+def _format_gated_repo(error: GatedRepoError) -> str:
+ label = error.repo_type if error.repo_type else "repository"
+ if error.repo_id:
+ return f"Access denied. {label.capitalize()} '{error.repo_id}' requires approval."
+ return f"Access denied. This {label} requires approval."
+
+
+def _format_bucket_not_found(error: BucketNotFoundError) -> str:
+ if error.bucket_id:
+ msg = f"Bucket '{error.bucket_id}' not found."
+ cmd = f"hf buckets create {error.bucket_id}"
+ else:
+ msg = "Bucket not found."
+ cmd = "hf buckets create "
+ msg += "\nIf the bucket is private, make sure you are authenticated and your token has the required permissions."
+ msg += f"\nIf the bucket does not exist, create it with: {cmd}"
+ return msg
+
+
+def _format_entry_not_found(error: RemoteEntryNotFoundError) -> str:
+ label = error.repo_type if error.repo_type else "repository"
+ url = str(error.response.url) if error.response else None
+ if error.repo_id:
+ msg = f"File not found in {label} '{error.repo_id}'."
+ else:
+ msg = f"File not found in {label}."
+ if url:
+ msg += f"\nURL: {url}"
+ return msg
+
+
+def _format_local_entry_not_found(error: LocalEntryNotFoundError) -> str:
+ cause = error.__cause__
+ if cause is not None:
+ return f"Local entry not found. {cause}"
+ return f"Local entry not found. {error}"
+
+
+def _format_incomplete_snapshot(error: IncompleteSnapshotError) -> str:
+ msg = _format_local_entry_not_found(error)
+ msg += f"\nIncomplete snapshot available at: {error.snapshot_path}"
+ return msg
+
+
+def _format_revision_not_found(error: RevisionNotFoundError) -> str:
+ label = error.repo_type if error.repo_type else "repository"
+ if error.repo_id:
+ return f"Revision not found in {label} '{error.repo_id}'."
+ return f"Revision not found in {label}. Check the revision parameter."
+
+
+def _format_cli_error(error: CLIError) -> str:
+ """No traceback, just the error message."""
+ return str(error)
+
+
+def _format_cli_extension_install_error(error: CLIExtensionInstallError) -> str:
+ """Format a CLI extension installation error.
+
+ The error is likely to be a tricky subprocess error to investigate. In this specific case we want to format the
+ traceback of the root cause while keeping the "nicely formatted" error message of the CLIExtensionInstallError
+ as a 1-line message.
+ """
+ cause_tb = (
+ "".join(traceback.format_exception(type(error.__cause__), error.__cause__, error.__cause__.__traceback__))
+ if error.__cause__ is not None
+ else ""
+ )
+ return f"{cause_tb}\n{error}"
+
+
+CLI_ERROR_MAPPINGS: dict[type[Exception], Callable[..., str]] = {
+ OfflineModeIsEnabled: lambda error: str(error),
+ # GatedRepoError must come before RepositoryNotFoundError (it's a subclass).
+ GatedRepoError: _format_gated_repo,
+ BucketNotFoundError: _format_bucket_not_found,
+ RepositoryNotFoundError: _format_repo_not_found,
+ RevisionNotFoundError: _format_revision_not_found,
+ LocalTokenNotFoundError: lambda _: "Not logged in. Run 'hf auth login' first.",
+ OIDCError: lambda error: f"OIDC Exchange failed. {error}",
+ DeviceCodeError: lambda error: f"Login failed: {error}",
+ RemoteEntryNotFoundError: _format_entry_not_found,
+ # IncompleteSnapshotError must come before LocalEntryNotFoundError (it's a subclass).
+ IncompleteSnapshotError: _format_incomplete_snapshot,
+ LocalEntryNotFoundError: _format_local_entry_not_found,
+ EntryNotFoundError: lambda error: str(error),
+ HfHubHTTPError: lambda error: str(error),
+ HfUriError: lambda error: f"Invalid HF URI: {error.uri}. {error.msg}",
+ ValueError: lambda error: f"Invalid value. {error}",
+ CLIExtensionInstallError: _format_cli_extension_install_error,
+ CLIError: _format_cli_error,
+}
+
+
+def format_known_exception(error: Exception) -> str | None:
+ for exc_type, formatter in CLI_ERROR_MAPPINGS.items():
+ if isinstance(error, exc_type):
+ return formatter(error)
+ return None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_file_listing.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_file_listing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebfae069e9906ea680c21288d5c4213454dff629
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_file_listing.py
@@ -0,0 +1,225 @@
+# Copyright 2026-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shared helpers for listing files in buckets and repos (tree view, flat view, formatting)."""
+
+import json
+from datetime import datetime
+from typing import Sequence
+
+import click
+
+from huggingface_hub._buckets import BucketFile, BucketFolder
+from huggingface_hub.hf_api import RepoFile, RepoFolder
+
+from ._cli_utils import get_hf_api
+from ._output import OutputFormat, _ascii_safe, _dataclass_to_dict, _escape_control_chars, out
+
+
+BucketItem = BucketFile | BucketFolder
+RepoItem = RepoFile | RepoFolder
+ListingItem = BucketItem | RepoItem
+
+
+def get_item_date(item: ListingItem) -> datetime | None:
+ """Extract date from an item, supporting both repo items (last_commit.date) and bucket items (mtime/uploaded_at)."""
+ match item:
+ case BucketFile(mtime=mtime) if mtime is not None:
+ return mtime
+ case BucketFile(uploaded_at=uploaded_at) | BucketFolder(uploaded_at=uploaded_at) if uploaded_at is not None:
+ return uploaded_at
+ case RepoFile(last_commit=last_commit) | RepoFolder(last_commit=last_commit) if last_commit is not None:
+ return last_commit.date
+ case _:
+ return None
+
+
+def format_size(size: int | float, human_readable: bool = False) -> str:
+ """Format a size in bytes."""
+ if not human_readable:
+ return str(size)
+
+ for unit in ["B", "KB", "MB", "GB", "TB"]:
+ if size < 1000:
+ if unit == "B":
+ return f"{size} {unit}"
+ return f"{size:.1f} {unit}"
+ size /= 1000
+ return f"{size:.1f} PB"
+
+
+def format_date(dt: datetime | None, human_readable: bool = False) -> str:
+ """Format a datetime to a readable date string."""
+ if dt is None:
+ return ""
+ if human_readable:
+ return dt.strftime("%b %d %H:%M")
+ return dt.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def build_tree(
+ items: Sequence[BucketItem] | Sequence[RepoItem],
+ human_readable: bool = False,
+ quiet: bool = False,
+) -> list[str]:
+ """Build a tree representation of files and directories.
+
+ Produces ASCII tree with size and date columns before the tree connector.
+ When quiet=True, only the tree structure is shown (no size/date).
+ """
+ tree: dict = {}
+
+ for item in items:
+ parts = item.path.split("/")
+ current = tree
+ for part in parts[:-1]:
+ if part not in current:
+ current[part] = {"__children__": {}}
+ current = current[part]["__children__"]
+
+ final_part = parts[-1]
+ if isinstance(item, BucketFolder | RepoFolder):
+ if final_part not in current:
+ current[final_part] = {"__children__": {}}
+ else:
+ current[final_part] = {"__item__": item}
+
+ prefix_width = 0
+ max_size_width = 0
+ max_date_width = 0
+ if not quiet:
+ for item in items:
+ if isinstance(item, BucketFile | RepoFile):
+ size_str = format_size(item.size, human_readable)
+ max_size_width = max(max_size_width, len(size_str))
+ date_str = format_date(get_item_date(item), human_readable)
+ max_date_width = max(max_date_width, len(date_str))
+ if max_size_width > 0:
+ prefix_width = max_size_width + 2 + max_date_width
+
+ lines: list[str] = []
+ _render_tree(
+ tree,
+ lines,
+ "",
+ prefix_width=prefix_width,
+ max_size_width=max_size_width,
+ human_readable=human_readable,
+ )
+ return lines
+
+
+def _render_tree(
+ node: dict,
+ lines: list[str],
+ indent: str,
+ prefix_width: int = 0,
+ max_size_width: int = 0,
+ human_readable: bool = False,
+) -> None:
+ """Recursively render a tree structure with size+date prefix."""
+ sorted_items = sorted(node.items())
+ for i, (name, value) in enumerate(sorted_items):
+ is_last = i == len(sorted_items) - 1
+ connector = _ascii_safe("└── ", "`-- ") if is_last else _ascii_safe("├── ", "|-- ")
+
+ is_dir = "__children__" in value
+ children = value.get("__children__", {})
+
+ if prefix_width > 0:
+ if is_dir:
+ prefix = " " * prefix_width
+ else:
+ item = value.get("__item__")
+ if item is not None:
+ size_str = format_size(item.size, human_readable)
+ date_str = format_date(get_item_date(item), human_readable)
+ prefix = f"{size_str:>{max_size_width}} {date_str}"
+ else:
+ prefix = " " * prefix_width
+ lines.append(f"{prefix} {indent}{connector}{_escape_control_chars(name)}{'/' if is_dir else ''}")
+ else:
+ lines.append(f"{indent}{connector}{_escape_control_chars(name)}{'/' if is_dir else ''}")
+
+ if children:
+ child_indent = indent + (" " if is_last else _ascii_safe("│ ", "| "))
+ _render_tree(
+ children,
+ lines,
+ child_indent,
+ prefix_width=prefix_width,
+ max_size_width=max_size_width,
+ human_readable=human_readable,
+ )
+
+
+def list_repo_files_cmd(
+ repo_id: str,
+ repo_type: str,
+ human_readable: bool,
+ as_tree: bool,
+ recursive: bool,
+ revision: str | None,
+ token: str | None,
+) -> None:
+ """List files in a repo on the Hub. Used by models/datasets/spaces ls commands."""
+ if as_tree and out.mode == OutputFormat.json:
+ raise click.BadParameter("Cannot use --tree with --format json.")
+
+ api = get_hf_api(token=token)
+ items = list(api.list_repo_tree(repo_id, recursive=recursive, revision=revision, repo_type=repo_type, expand=True))
+ print_file_listing(items, human_readable=human_readable, as_tree=as_tree, recursive=recursive)
+
+
+def print_file_listing(
+ items: Sequence[BucketItem] | Sequence[RepoItem],
+ *,
+ human_readable: bool = False,
+ as_tree: bool = False,
+ recursive: bool = False,
+) -> None:
+ """Print a file listing in the appropriate format based on the current output mode.
+
+ Supports tree, json, quiet, and flat human-readable views. Works with both
+ BucketFile/BucketFolder and RepoFile/RepoFolder items.
+ """
+ if not items:
+ out.text("(empty)")
+ return
+
+ has_directories = any(isinstance(item, BucketFolder | RepoFolder) for item in items)
+
+ if as_tree:
+ quiet = out.mode == OutputFormat.quiet
+ for line in build_tree(items, human_readable=human_readable, quiet=quiet):
+ print(line)
+ elif out.mode == OutputFormat.json:
+ print(json.dumps([_dataclass_to_dict(item) for item in items], indent=2))
+ elif out.mode == OutputFormat.quiet:
+ for item in items:
+ if isinstance(item, BucketFolder | RepoFolder):
+ print(f"{_escape_control_chars(item.path)}/")
+ else:
+ print(_escape_control_chars(item.path))
+ else:
+ for item in items:
+ if isinstance(item, BucketFolder | RepoFolder):
+ date_str = format_date(get_item_date(item), human_readable)
+ print(f"{'':>12} {date_str:>19} {_escape_control_chars(item.path)}/")
+ else:
+ size_str = format_size(item.size, human_readable)
+ date_str = format_date(get_item_date(item), human_readable)
+ print(f"{size_str:>12} {date_str:>19} {_escape_control_chars(item.path)}")
+
+ if not recursive and has_directories:
+ out.hint("Use -R to list files recursively.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_framework.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_framework.py
new file mode 100644
index 0000000000000000000000000000000000000000..c421c05fe93b1f1b10cb5b258711bc17297e25eb
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_framework.py
@@ -0,0 +1,657 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Minimal declaration layer over Click 8.x for the ``hf`` CLI.
+
+This module vendors *only* the slice of Typer the CLI actually uses: turning
+annotated function signatures (``Annotated[T, Option(...)]`` / ``Argument(...)``)
+into Click parameters, and rendering argument/option help the way Typer did.
+It exists so the CLI depends on Click's stable public API instead of Typer's
+now-internal vendored Click (``typer._click``), which Typer reserves the right
+to refactor at any release.
+
+Deliberately *not* implemented (add a case here only when a command needs it):
+rich rendering, shell-completion install commands, prompts/confirmation options,
+env-var options, ``File``/``UUID`` params, ``count`` options, ``default_factory``.
+Click's own machinery already handles exceptions, aborts, exits, and native
+``_HF_COMPLETE`` shell completion, so none of Typer's ``main()`` overrides are
+reproduced here.
+
+The interesting logic mirrors four Typer functions, kept faithful so ``--help``
+output stays byte-identical:
+- :func:`_get_click_type` <- ``typer.main.get_click_type``
+- :func:`_build_click_param` <- ``typer.main.get_click_param``
+- :func:`_make_handler` <- ``typer.main.get_callback``
+- :class:`HfArgument` / :class:`HfOption` help records <- ``typer.core.Typer*``
+"""
+
+import enum
+import functools
+import inspect
+from collections.abc import Callable, Sequence
+from datetime import datetime
+from pathlib import Path
+from types import UnionType
+from typing import Annotated, Any, Literal, Union, get_args, get_origin
+
+import click
+
+
+def get_command_name(name: str) -> str:
+ """Normalize a Python identifier to a CLI name (``force_download`` -> ``force-download``)."""
+ return name.lower().replace("_", "-")
+
+
+# ---------------------------------------------------------------------------
+# 1. Declaration markers — replace ``typer.Option`` / ``typer.Argument``.
+# Attached in ``Annotated[T, Option(...)]``; the ``= value`` in the function
+# signature carries the default (never the marker).
+# ---------------------------------------------------------------------------
+
+
+class ParameterInfo:
+ """Base marker holding the (tiny) set of Click knobs the CLI actually sets.
+
+ ``callback`` is a Typer-style value parser ``fn(value) -> parsed`` — it does NOT
+ receive Click's ``(ctx, param, value)``. It consumes the enum/list convertors, so
+ ``value`` is already converted when it runs.
+ """
+
+ def __init__(
+ self,
+ *param_decls: str,
+ help: str | None = None,
+ show_default: bool | str = True,
+ hidden: bool = False,
+ is_eager: bool = False,
+ callback: Callable[..., Any] | None = None,
+ min: int | None = None,
+ click_type: click.ParamType | None = None,
+ ) -> None:
+ self.param_decls = list(param_decls)
+ self.help = help
+ self.show_default = show_default
+ self.hidden = hidden
+ self.is_eager = is_eager
+ self.callback = callback
+ self.min = min
+ self.click_type = click_type
+
+
+class Option(ParameterInfo):
+ """Declare a CLI option, e.g. ``Annotated[str | None, Option("--revision")]``."""
+
+
+class Argument(ParameterInfo):
+ """Declare a positional CLI argument, e.g. ``Annotated[str, Argument()]``."""
+
+
+# ---------------------------------------------------------------------------
+# 2. Annotation -> Click type + value convertors.
+# ---------------------------------------------------------------------------
+
+
+def _get_click_type(annotation: Any, info: ParameterInfo) -> click.ParamType:
+ """Map a (already unwrapped) annotation to a Click ``ParamType``."""
+ if info.click_type is not None:
+ return info.click_type
+ if annotation is str:
+ return click.STRING
+ if annotation is bool:
+ return click.BOOL
+ if annotation is int:
+ return click.IntRange(min=info.min) if info.min is not None else click.INT
+ if annotation is float:
+ return click.FloatRange(min=info.min) if info.min is not None else click.FLOAT
+ if annotation is Path:
+ return click.Path(path_type=Path)
+ if annotation is datetime:
+ return click.DateTime()
+ if isinstance(annotation, type) and issubclass(annotation, enum.Enum):
+ values = [item.value for item in annotation]
+ if not all(isinstance(value, str) for value in values):
+ # A non-str member default would fail Choice validation at parse time with a
+ # cryptic error; fail at build time with a clear one instead.
+ raise TypeError(f"Unsupported enum {annotation.__name__!r}: only str-valued enums are supported.")
+ return click.Choice(values)
+ if get_origin(annotation) is Literal:
+ return click.Choice([str(value) for value in get_args(annotation)])
+ raise TypeError(f"Unsupported CLI annotation {annotation!r}. Add a case in `_framework._get_click_type()`.")
+
+
+def _enum_convertor(enum_type: type[enum.Enum]) -> Callable[[Any], Any]:
+ """Map a parsed choice string back to its ``Enum`` member (mirrors Typer)."""
+ value_map = {str(member.value): member for member in enum_type}
+
+ def convertor(value: Any) -> Any:
+ if value is not None:
+ return value_map.get(str(value))
+ return None
+
+ return convertor
+
+
+def _list_convertor(
+ inner: Callable[[Any], Any] | None, default_value: Any | None
+) -> Callable[[Sequence[Any] | None], list[Any] | None]:
+ """Turn Click's tuple (from ``multiple=True``) into a list, applying ``inner`` per item."""
+
+ def convertor(value: Sequence[Any] | None) -> list[Any] | None:
+ if value is None or (default_value is None and len(value) == 0):
+ return None
+ return [inner(item) if inner else item for item in value]
+
+ return convertor
+
+
+def _make_convertor(
+ annotation: Any, is_list: bool, default_value: Any | None, info: ParameterInfo
+) -> Callable[[Any], Any] | None:
+ """Build the value convertor for a param (``None`` when no coercion is needed).
+
+ An explicit ``click_type`` (e.g. ``SoftChoice``) owns its own conversion, so no
+ enum convertor is added on top of it.
+ """
+ inner = None
+ if info.click_type is None and isinstance(annotation, type) and issubclass(annotation, enum.Enum):
+ inner = _enum_convertor(annotation)
+ if is_list:
+ return _list_convertor(inner, default_value)
+ return inner
+
+
+def _unwrap_optional_and_list(annotation: Any) -> tuple[Any, bool]:
+ """Strip ``T | None`` / ``Optional[T]`` and ``list[T]``; return ``(base_type, is_list)``."""
+ origin = get_origin(annotation)
+ if origin in (Union, UnionType):
+ non_none = [arg for arg in get_args(annotation) if arg is not type(None)]
+ if len(non_none) != 1:
+ raise TypeError(f"Unsupported union annotation {annotation!r}; only ``T | None`` is supported.")
+ annotation = non_none[0]
+ origin = get_origin(annotation)
+ if origin in (list, Sequence):
+ return get_args(annotation)[0], True
+ return annotation, False
+
+
+def _split_annotated(annotation: Any) -> tuple[Any, ParameterInfo | None]:
+ """Split ``Annotated[T, Option(...)]`` into ``(T, marker)``; ``(annotation, None)`` otherwise."""
+ if get_origin(annotation) is Annotated:
+ args = get_args(annotation)
+ marker = next((meta for meta in args[1:] if isinstance(meta, ParameterInfo)), None)
+ if marker is None and (typer_marker := next((meta for meta in args[1:] if _is_typer_marker(meta)), None)):
+ marker = _from_typer_marker(typer_marker, from_annotated=True)
+ return args[0], marker
+ return annotation, None
+
+
+# ---------------------------------------------------------------------------
+# Typer-marker compatibility shim (transition helper).
+#
+# ``typer_factory`` is exposed publicly and downstream CLIs (e.g. `transformers`)
+# still register commands whose parameters carry ``typer.Option`` /
+# ``typer.Argument`` markers. Recognize those markers structurally (no typer
+# import required) and translate the fields this framework supports, so such
+# commands keep working while downstream migrates.
+# TODO: remove once transformers pins huggingface_hub>=1.22.0.
+# ---------------------------------------------------------------------------
+
+
+def _is_typer_marker(obj: Any) -> bool:
+ return type(obj).__module__.partition(".")[0] == "typer" and type(obj).__name__ in ("OptionInfo", "ArgumentInfo")
+
+
+def _from_typer_marker(meta: Any, *, from_annotated: bool) -> ParameterInfo:
+ """Translate a ``typer.models.OptionInfo`` / ``ArgumentInfo`` into our marker."""
+ param_decls = list(getattr(meta, "param_decls", None) or ())
+ if type(meta).__name__ == "OptionInfo":
+ # In ``Annotated[...]`` usage typer stores the first flag name in ``default``
+ # (``Option("--flag", "-f")`` -> ``default="--flag"``, ``param_decls=("-f",)``).
+ default = getattr(meta, "default", ...)
+ if from_annotated and isinstance(default, str):
+ param_decls = [default, *param_decls]
+ cls: type[ParameterInfo] = Option
+ else:
+ cls = Argument
+ return cls(
+ *param_decls,
+ help=getattr(meta, "help", None),
+ show_default=getattr(meta, "show_default", True),
+ hidden=getattr(meta, "hidden", False),
+ is_eager=getattr(meta, "is_eager", False),
+ callback=getattr(meta, "callback", None),
+ min=getattr(meta, "min", None),
+ click_type=getattr(meta, "click_type", None),
+ )
+
+
+# ---------------------------------------------------------------------------
+# 3. Signature -> Click parameters (mirrors ``typer.main.get_click_param``).
+# ---------------------------------------------------------------------------
+
+
+def _wrap_param_callback(
+ user_callback: Callable[[Any], Any], convertor: Callable[[Any], Any] | None
+) -> Callable[[click.Context, click.Parameter, Any], Any]:
+ """Adapt a Typer-style value parser ``fn(value) -> parsed`` to a Click param callback."""
+
+ def click_callback(ctx: click.Context, param: click.Parameter, value: Any) -> Any:
+ return user_callback(convertor(value) if convertor else value)
+
+ return click_callback
+
+
+def _build_click_param(
+ name: str, annotation: Any, info: ParameterInfo | None, signature_default: Any
+) -> tuple[click.Parameter, Callable[[Any], Any] | None]:
+ """Build a Click ``Parameter`` (and its post-parse value convertor) for one function param."""
+ required = signature_default is inspect.Parameter.empty
+ default_value = None if required else signature_default
+ if isinstance(default_value, enum.Enum):
+ default_value = default_value.value
+ if info is None:
+ # A bare annotation with no marker: required positional -> Argument, else Option.
+ info = Argument() if required else Option()
+
+ base_type, is_list = _unwrap_optional_and_list(annotation)
+ convertor = _make_convertor(base_type, is_list, default_value, info)
+
+ # A user ``callback`` consumes the value itself (and the convertor with it); otherwise the
+ # convertor runs later in the command handler wrapper. This keeps conversion single-pass.
+ if info.callback is not None:
+ param_callback: Callable[..., Any] | None = _wrap_param_callback(info.callback, convertor)
+ handler_convertor: Callable[[Any], Any] | None = None
+ else:
+ param_callback = None
+ handler_convertor = convertor
+
+ if isinstance(info, Argument):
+ argument = HfArgument(
+ [name],
+ type=_get_click_type(base_type, info),
+ required=required,
+ default=default_value,
+ nargs=-1 if is_list else 1,
+ is_eager=info.is_eager,
+ callback=param_callback,
+ help=info.help,
+ show_default=info.show_default,
+ hidden=info.hidden,
+ )
+ return argument, handler_convertor
+
+ # Option: prepend the Python name so ``click.Parameter.name`` matches the function kwarg,
+ # regardless of the user-facing flag names ("-v"/"--version", "--type"/"--repo-type", ...).
+ is_flag = base_type is bool
+ decls = [name]
+ if info.param_decls:
+ decls.extend(info.param_decls)
+ else:
+ kebab = get_command_name(name)
+ decls.append(f"--{kebab}/--no-{kebab}" if is_flag else f"--{kebab}")
+ option = HfOption(
+ decls,
+ # Click infers the flag type itself; passing a type alongside ``is_flag`` is rejected.
+ type=None if is_flag else _get_click_type(base_type, info),
+ required=required,
+ default=default_value,
+ is_flag=is_flag,
+ multiple=is_list,
+ is_eager=info.is_eager,
+ callback=param_callback,
+ help=info.help,
+ show_default=info.show_default,
+ hidden=info.hidden,
+ )
+ return option, handler_convertor
+
+
+def _build_params(
+ func: Callable[..., Any],
+) -> tuple[list[click.Parameter], dict[str, Callable[[Any], Any]], str | None]:
+ """Introspect ``func`` and return ``(params, convertors, context_param_name)``."""
+ params: list[click.Parameter] = []
+ convertors: dict[str, Callable[[Any], Any]] = {}
+ context_param_name: str | None = None
+ for name, sig_param in inspect.signature(func).parameters.items():
+ # Read raw signature annotations rather than ``typing.get_type_hints``: the CLI never uses
+ # ``from __future__ import annotations``, so annotations are already live objects, and
+ # get_type_hints mangles ``Annotated[X | None, ...]`` on Python 3.10 (drops the origin).
+ annotation = sig_param.annotation if sig_param.annotation is not inspect.Parameter.empty else str
+ base_type, info = _split_annotated(annotation)
+ if isinstance(base_type, type) and (
+ issubclass(base_type, click.Context)
+ # typer.Context subclasses typer's *vendored* click since typer 0.26, so an
+ # issubclass check against real click misses it. Match it structurally.
+ or (base_type.__module__.partition(".")[0] == "typer" and base_type.__name__ == "Context")
+ ):
+ context_param_name = name
+ continue
+ signature_default = sig_param.default
+ if _is_typer_marker(signature_default):
+ # Old typer style: the marker *is* the signature default and carries the value.
+ if info is None:
+ info = _from_typer_marker(signature_default, from_annotated=False)
+ marker_default = getattr(signature_default, "default", ...)
+ signature_default = inspect.Parameter.empty if marker_default is ... else marker_default
+ click_param, convertor = _build_click_param(name, base_type, info, signature_default)
+ if convertor is not None and click_param.name is not None:
+ convertors[click_param.name] = convertor
+ params.append(click_param)
+ return params, convertors, context_param_name
+
+
+def _make_handler(
+ func: Callable[..., Any],
+ convertors: dict[str, Callable[[Any], Any]],
+ context_param_name: str | None,
+) -> Callable[..., Any]:
+ """Wrap ``func`` so Click's kwargs map back to it, applying convertors and injecting the ctx."""
+
+ def handler(**kwargs: Any) -> Any:
+ call_kwargs = {key: (convertors[key](value) if key in convertors else value) for key, value in kwargs.items()}
+ if context_param_name is not None:
+ call_kwargs[context_param_name] = click.get_current_context()
+ return func(**call_kwargs)
+
+ functools.update_wrapper(handler, func)
+ return handler
+
+
+# ---------------------------------------------------------------------------
+# 4. Click parameter subclasses — port Typer's help records so arguments show up
+# in ``--help`` (plain ``click.Argument`` renders none) and defaults/metavars
+# read identically. Rich/env-var branches are dropped (never enabled here).
+# ---------------------------------------------------------------------------
+
+
+def _split_opt(opt: str) -> tuple[str, str]:
+ """Split an option string into ``(prefix, name)`` (Click's ``split_opt``, inlined)."""
+ first = opt[:1]
+ if first.isalnum():
+ return "", opt
+ if opt[1:2] == first:
+ return opt[:2], opt[2:]
+ return first, opt[1:]
+
+
+def _extract_default_help_str(param: click.Parameter, ctx: click.Context) -> Any:
+ # Resilient parsing avoids type casting failing while rendering the default.
+ resilient = ctx.resilient_parsing
+ ctx.resilient_parsing = True
+ try:
+ return param.get_default(ctx, call=False)
+ finally:
+ ctx.resilient_parsing = resilient
+
+
+def _default_string(
+ param: "HfArgument | HfOption", ctx: click.Context, show_default_is_str: bool, default_value: Any
+) -> str:
+ if show_default_is_str:
+ return f"({param.show_default})"
+ if isinstance(default_value, (list, tuple)):
+ return ", ".join(_default_string(param, ctx, show_default_is_str, item) for item in default_value)
+ if isinstance(default_value, enum.Enum):
+ return str(default_value.value)
+ if inspect.isfunction(default_value):
+ return "(dynamic)"
+ if isinstance(param, HfOption) and param.is_bool_flag and param.secondary_opts:
+ # Boolean toggle: show the opt name (without prefix) matching the current default.
+ if default_value:
+ return _split_opt(param.opts[0])[1] if param.opts else str(default_value)
+ return _split_opt(param.secondary_opts[0])[1]
+ if isinstance(param, HfOption) and param.is_bool_flag and not param.secondary_opts and not default_value:
+ return ""
+ return str(default_value)
+
+
+def _describe_number_range(param_type: "click.IntRange | click.FloatRange") -> str:
+ """Human-readable range hint like ``x>=1`` or ``1<=x<10``.
+
+ Reproduces click's private ``_NumberRangeBase._describe_range()`` using the public
+ ``IntRange``/``FloatRange`` attributes, so the framework stays on Click's public API.
+ """
+ if param_type.min is None:
+ return f"x{'<' if param_type.max_open else '<='}{param_type.max}"
+ if param_type.max is None:
+ return f"x{'>' if param_type.min_open else '>='}{param_type.min}"
+ left = "<" if param_type.min_open else "<="
+ right = "<" if param_type.max_open else "<="
+ return f"{param_type.min}{left}x{right}{param_type.max}"
+
+
+def _build_help_extra(param: "HfArgument | HfOption", ctx: click.Context, base_help: str) -> str:
+ """Append the ``[default: ...; required]`` suffix to a help string (shared by arg/option)."""
+ extra: list[str] = []
+ default_value = _extract_default_help_str(param, ctx)
+ show_default_is_str = isinstance(param.show_default, str)
+ if show_default_is_str or (default_value is not None and (param.show_default or ctx.show_default)):
+ default_string = _default_string(param, ctx, show_default_is_str, default_value)
+ if default_string:
+ extra.append(f"default: {default_string}")
+ # Numeric range hints are shown for options only (matches Typer; arguments omit them).
+ if isinstance(param, HfOption) and isinstance(param.type, (click.IntRange, click.FloatRange)):
+ range_str = _describe_number_range(param.type)
+ if range_str:
+ extra.append(range_str)
+ if param.required:
+ extra.append("required")
+ if extra:
+ suffix = f"[{'; '.join(extra)}]"
+ return f"{base_help} {suffix}" if base_help else suffix
+ return base_help
+
+
+class HfArgument(click.Argument):
+ """Positional argument that renders help text and a metavar (Click's does neither)."""
+
+ def __init__(
+ self,
+ param_decls: Sequence[str],
+ *,
+ help: str | None = None,
+ show_default: bool | str = True,
+ hidden: bool = False,
+ **attrs: Any,
+ ) -> None:
+ self.show_default = show_default
+ self.hidden = hidden
+ super().__init__(param_decls, **attrs)
+ # Set *after* ``super().__init__``: Click >= 8.5 gives ``Argument`` its own ``help``
+ # parameter and resets ``self.help`` to ``None`` in its constructor.
+ self.help = help
+
+ def make_metavar(self, ctx: click.Context) -> str:
+ if self.metavar is not None:
+ var = self.metavar
+ if not self.required and not var.startswith("["):
+ var = f"[{var}]"
+ return var
+ var = (self.name or "").upper()
+ if not self.required:
+ var = f"[{var}]"
+ type_var = self.type.get_metavar(self, ctx=ctx)
+ if type_var:
+ var += f":{type_var}"
+ if self.nargs != 1:
+ var += "..."
+ return var
+
+ def get_help_record(self, ctx: click.Context) -> tuple[str, str] | None:
+ if self.hidden:
+ return None
+ return self.make_metavar(ctx=ctx), _build_help_extra(self, ctx, self.help or "")
+
+
+class HfOption(click.Option):
+ """Option whose help record ports Typer's default/metavar rendering verbatim."""
+
+ show_default: bool | str
+
+ def get_help_record(self, ctx: click.Context) -> tuple[str, str] | None:
+ if self.hidden:
+ return None
+
+ any_prefix_is_slash = False
+
+ def _write_opts(opts: Sequence[str]) -> str:
+ nonlocal any_prefix_is_slash
+ rv, any_slashes = click.formatting.join_options(opts)
+ if any_slashes:
+ any_prefix_is_slash = True
+ if not self.is_flag and not self.count:
+ rv += f" {self.make_metavar(ctx=ctx)}"
+ return rv
+
+ rv = [_write_opts(self.opts)]
+ if self.secondary_opts:
+ rv.append(_write_opts(self.secondary_opts))
+
+ help_text = _build_help_extra(self, ctx, self.help or "")
+ return ("; " if any_prefix_is_slash else " / ").join(rv), help_text
+
+
+# ---------------------------------------------------------------------------
+# 5. Command / group base classes and the decorator API (replace ``typer.Typer``).
+# ---------------------------------------------------------------------------
+
+
+def _format_params(command: click.Command, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ """Render params split into "Arguments" and "Options" sections (mirrors Typer)."""
+ args: list[tuple[str, str]] = []
+ opts: list[tuple[str, str]] = []
+ for param in command.get_params(ctx):
+ record = param.get_help_record(ctx)
+ if record is None:
+ continue
+ if param.param_type_name == "argument":
+ args.append(record)
+ elif param.param_type_name == "option":
+ opts.append(record)
+ if args:
+ with formatter.section("Arguments"):
+ formatter.write_dl(args)
+ if opts:
+ with formatter.section("Options"):
+ formatter.write_dl(opts)
+
+
+class HfCommand(click.Command):
+ """Leaf command that renders arguments and options in separate help sections."""
+
+ def format_arguments(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ # Click >= 8.5 added a `format_arguments` step to `format_help` that prints its own
+ # "Positional arguments" section. `_format_params` already lists arguments (Typer-style),
+ # so this override avoids printing them twice. Never called on Click < 8.5.
+ return None
+
+ def format_options(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ _format_params(self, ctx, formatter)
+
+
+def build_command(
+ func: Callable[..., Any],
+ *,
+ name: str | None = None,
+ cls: type[click.Command] | None = None,
+ help: str | None = None,
+ epilog: str | None = None,
+ short_help: str | None = None,
+ options_metavar: str = "[OPTIONS]",
+ add_help_option: bool = True,
+ no_args_is_help: bool = False,
+ hidden: bool = False,
+ deprecated: bool = False,
+ context_settings: dict[str, Any] | None = None,
+) -> click.Command:
+ """Build a Click ``Command`` from a function with ``Annotated`` params.
+
+ Replaces ``typer.main.get_command_from_info``.
+ """
+ params, convertors, context_param_name = _build_params(func)
+ handler = _make_handler(func, convertors, context_param_name)
+ command_help = inspect.cleandoc(help) if help else inspect.getdoc(func)
+ return (cls or HfCommand)(
+ name=name if name is not None else get_command_name(func.__name__),
+ callback=handler,
+ params=params,
+ help=command_help,
+ epilog=epilog,
+ short_help=short_help,
+ options_metavar=options_metavar,
+ add_help_option=add_help_option,
+ no_args_is_help=no_args_is_help,
+ hidden=hidden,
+ deprecated=deprecated,
+ context_settings=context_settings or {},
+ )
+
+
+class HfGroup(click.Group):
+ """Command group with the decorator API the CLI relies on (``command``/``callback``/``add_group``).
+
+ Subclasses (see ``HFCliTyperGroup``) layer on styling, aliases, topics and error
+ enrichment; this base only wires functions to Click via :func:`build_command`.
+ """
+
+ #: Command class used by ``@group.command()`` unless overridden per call.
+ command_class: type[click.Command] = HfCommand
+
+ def format_arguments(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ # See `HfCommand.format_arguments`.
+ return None
+
+ def format_options(self, ctx: click.Context, formatter: click.HelpFormatter) -> None:
+ _format_params(self, ctx, formatter)
+ self.format_commands(ctx, formatter)
+
+ def list_commands(self, ctx: click.Context) -> list[str]:
+ # Preserve declaration order rather than Click's alphabetical default.
+ return list(self.commands)
+
+ def command( # type: ignore # deliberately narrows click.Group.command (builds from annotated signatures)
+ self, name: str | None = None, *, cls: type[click.Command] | None = None, **kwargs: Any
+ ) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+ def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+ command = build_command(func, name=name, cls=cls or self.command_class, **kwargs)
+ self.add_command(command, command.name)
+ return func
+
+ return decorator
+
+ def add_group(self, group: click.Group, *, name: str, hidden: bool = False) -> None:
+ """Register a subgroup under ``name`` (which may carry pipe aliases, e.g. ``"repos | repo"``)."""
+ group.name = name
+ group.hidden = hidden
+ self.add_command(group, name)
+
+ def group_callback(
+ self, *, invoke_without_command: bool = False
+ ) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
+ """Register the function invoked for this group (named ``group_callback`` because Click
+ already uses the ``callback`` attribute for the group's own handler)."""
+
+ def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+ params, convertors, context_param_name = _build_params(func)
+ self.callback = _make_handler(func, convertors, context_param_name)
+ self.params = [*self.params, *params]
+ self.invoke_without_command = invoke_without_command
+ if invoke_without_command:
+ # Match Typer: the subcommand is optional when the group runs without one.
+ self.subcommand_metavar = "[COMMAND] [ARGS]..."
+ if self.help is None and func.__doc__:
+ self.help = inspect.cleandoc(func.__doc__)
+ return func
+
+ return decorator
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_help_formatter.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_help_formatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..19c2909b7be9a151d912db851e6493f2b6e46bd3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_help_formatter.py
@@ -0,0 +1,34 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Pretty ANSI help formatter for the `hf` CLI."""
+
+from collections.abc import Iterable
+
+import click
+
+from huggingface_hub.utils import ANSI
+
+
+class StyledHelpFormatter(click.HelpFormatter):
+ def write_heading(self, heading: str) -> None:
+ styled = ANSI.underline(heading + ":")
+ self.write(f"{'':>{self.current_indent}}{styled}\n")
+
+ def write_dl(self, rows: Iterable[tuple[str, str]], col_max: int = 30, col_spacing: int = 2) -> None:
+ rows = [(ANSI.bold(first), second) for first, second in rows]
+ super().write_dl(rows, col_max=col_max, col_spacing=col_spacing)
+
+
+class StyledContext(click.Context):
+ formatter_class = StyledHelpFormatter
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_output.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_output.py
new file mode 100644
index 0000000000000000000000000000000000000000..c16e06816422d74f0af486576d34257c4ad5dece
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_output.py
@@ -0,0 +1,398 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Output framework for the `hf` CLI."""
+
+import dataclasses
+import datetime
+import json
+import re
+import shutil
+import sys
+from collections.abc import Sequence
+from enum import Enum
+from functools import cache
+from typing import Any, cast
+
+import click
+
+from huggingface_hub import constants
+from huggingface_hub.errors import ConfirmationError
+from huggingface_hub.repocard_data import CardData
+from huggingface_hub.utils import ANSI, StatusLine, disable_progress_bars, enable_progress_bars, is_agent, tabulate
+
+
+class OutputFormat(str, Enum):
+ """Output format for CLI commands with auto detection of agent/human mode."""
+
+ agent = "agent"
+ auto = "auto"
+ human = "human"
+ json = "json"
+ quiet = "quiet"
+
+
+def _print_flush(*values: Any, **kwargs: Any) -> None:
+ """Like `print`, but always flushed: some CLI flows block on user action right after
+ printing (e.g. the device-code login), so output must not stay buffered."""
+ print(*values, **kwargs, flush=True)
+
+
+class Output:
+ """Output sink for the `hf` CLI.
+
+ Mode is resolved once at init time based on `is_agent()` auto-detection
+ and can be overridden per-command via `set_mode()`.
+ """
+
+ mode: OutputFormat
+ no_truncate: bool
+
+ def __init__(self) -> None:
+ self.no_truncate = False
+ self.set_mode()
+
+ def set_mode(self, mode: OutputFormat = OutputFormat.auto) -> None:
+ """Override the output mode (called once at startup and again per '--format' flag)."""
+ if mode == OutputFormat.auto:
+ mode = OutputFormat.agent if is_agent() else OutputFormat.human
+ self.mode = mode
+ is_human = mode == OutputFormat.human
+ ANSI.set_enabled(is_human)
+ if constants.HF_HUB_DISABLE_PROGRESS_BARS is None: # env var has priority
+ if is_human:
+ enable_progress_bars()
+ else:
+ disable_progress_bars()
+
+ def set_no_truncate(self, no_truncate: bool) -> None:
+ """Toggle off cell truncation for human table output."""
+ self.no_truncate = no_truncate
+
+ def is_quiet(self) -> bool:
+ return self.mode == OutputFormat.quiet
+
+ def text(self, msg: str | None = None, *, human: str | None = None, agent: str | None = None) -> None:
+ """Print a free-form text message to stdout."""
+ if msg is not None:
+ if human is not None or agent is not None:
+ raise ValueError("Cannot mix 'msg' with 'human'/'agent'.")
+ human = msg
+ agent = _strip_ansi(msg)
+
+ match self.mode:
+ case OutputFormat.human:
+ if human is not None:
+ _print_flush(human)
+ case OutputFormat.agent:
+ if agent is not None:
+ _print_flush(agent)
+ # json/quiet: no-op
+
+ def table(
+ self,
+ items: Sequence[dict[str, Any]],
+ *,
+ headers: list[str] | None = None,
+ id_key: str | None = None,
+ alignments: dict[str, str] | None = None,
+ ) -> None:
+ """Print tabular data to stdout.
+
+ Args:
+ items: List of dicts. Headers are auto-detected from keys if not provided.
+ headers: Explicit column names. If None, derived from dict keys (all-None columns filtered).
+ id_key: Key to print in quiet mode. If None, uses the first header.
+ alignments: Optional mapping of header name to "left" or "right". Defaults to "left".
+ """
+ if not items:
+ match self.mode:
+ case OutputFormat.agent | OutputFormat.human:
+ _print_flush("No results found.")
+ case OutputFormat.json:
+ _print_flush("[]")
+ return
+
+ if headers is None:
+ all_columns = list(items[0].keys())
+ headers = [col for col in all_columns if any(item.get(col) is not None for item in items)]
+ rows = [[item.get(h) for h in headers] for item in items]
+
+ match self.mode:
+ case OutputFormat.human: # padded table, adaptive truncation, SCREAMING_SNAKE headers
+ screaming_headers = [_to_header(h) for h in headers]
+ formatted_rows: list[list[str]] = [[_format_table_value_human(v) for v in row] for row in rows]
+
+ is_truncated = _truncate_columns(screaming_headers, formatted_rows, no_truncate=self.no_truncate)
+
+ inferred = {**_infer_alignments(headers, rows), **(alignments or {})}
+ screaming_alignments = {_to_header(k): v for k, v in inferred.items()}
+ _print_flush(
+ tabulate(
+ cast("list[list[str | int]]", formatted_rows),
+ headers=screaming_headers,
+ alignments=screaming_alignments,
+ ),
+ )
+ if is_truncated:
+ self.hint("Use `--no-truncate` or `--format json` to display full values.")
+ case OutputFormat.agent: # TSV, no truncation, full timestamps
+ _print_flush("\t".join(headers))
+ for row in rows:
+ _print_flush("\t".join(_format_table_cell_agent(v) for v in row))
+ case OutputFormat.json: # compact JSON array
+ _print_flush(json.dumps(list(items), default=str))
+ case OutputFormat.quiet: # id_key column (or first column), one per line
+ quiet_key = id_key or headers[0]
+ for item in items:
+ _print_flush(_escape_control_chars(str(item.get(quiet_key, ""))))
+
+ def dict(self, data: Any, *, id_key: str | None = None) -> None:
+ """Print structured data as JSON in all modes (indented for human, compact otherwise).
+
+ Accepts a dict or a dataclass.
+ """
+ if dataclasses.is_dataclass(data) and not isinstance(data, type):
+ data = _dataclass_to_dict(data)
+ if self.mode == OutputFormat.quiet and id_key is not None:
+ _print_flush(data.get(id_key, ""))
+ return
+ indent = 2 if self.mode == OutputFormat.human else None
+ _print_flush(json.dumps(data, indent=indent, default=str))
+
+ def result(self, message: str, **data: Any) -> None:
+ """Print a success summary to stdout."""
+ match self.mode:
+ case OutputFormat.human: # ✓ message + key: value lines
+ parts = [ANSI.green(f"{_ascii_safe('✓', '[OK]')} {message}")]
+ for k, v in data.items():
+ if v is not None:
+ parts.append(f" {k}: {v}")
+ _print_flush("\n".join(parts))
+ case OutputFormat.agent: # key=val pairs, space-separated
+ parts = [f"{k}={v}" for k, v in data.items() if v is not None]
+ _print_flush(" ".join(parts) if parts else message)
+ case OutputFormat.json: # json.dumps(data), message ignored
+ _print_flush(json.dumps(data, default=str) if data else "")
+ case OutputFormat.quiet: # first value only
+ values = list(data.values())
+ if values:
+ _print_flush(values[0])
+
+ def confirm(self, message: str, *, default: bool = False, yes: bool = False, confirm_param: str = "--yes") -> None:
+ """
+ Ask for confirmation. Raises `ConfirmationError` in non-human modes.
+ """
+ if yes:
+ return
+ if self.mode != OutputFormat.human:
+ raise ConfirmationError(f"{message} Use {confirm_param} to skip confirmation.")
+ click.confirm(message, default=default, abort=True)
+
+ def status(self, message: str | None = None) -> StatusLine:
+ """Return a status line that emits only in human mode (no-op otherwise)."""
+ status = StatusLine(enabled=self.mode == OutputFormat.human)
+ if message is not None:
+ status.update(message)
+ return status
+
+ def warning(self, message: str) -> None:
+ """Print a non-fatal warning to stderr (all modes)."""
+ if self.mode == OutputFormat.human:
+ _print_flush(ANSI.yellow(f"Warning: {message}"), file=sys.stderr)
+ else:
+ _print_flush(f"Warning: {message}", file=sys.stderr)
+
+ def error(self, message: str) -> None:
+ """Print an error to stderr (all modes)."""
+ if self.mode == OutputFormat.human:
+ _print_flush(ANSI.red(f"Error: {message}"), file=sys.stderr)
+ else:
+ _print_flush(f"Error: {message}", file=sys.stderr)
+
+ def log(self, message: str) -> None:
+ """Print a text message to stderr (human: gray, json/agent: plain text).
+
+ Suppressed in quiet mode. Kept in json mode (like agent) since agents
+ commonly run with ``--format json`` and the message goes to stderr so
+ it never pollutes the parsed stdout.
+ """
+ if self.mode == OutputFormat.quiet:
+ return
+ if self.mode == OutputFormat.human:
+ _print_flush(ANSI.gray(message), file=sys.stderr)
+ else:
+ _print_flush(message, file=sys.stderr)
+
+ def hint(self, message: str) -> None:
+ """Print a helpful hint to stderr (human: gray, json/agent: plain text).
+
+ Suppressed in quiet mode. Kept in json mode (like agent) since agents
+ commonly run with ``--format json`` and the next-command hints are useful
+ there; hints go to stderr so they never pollute the parsed stdout.
+ """
+ self.log(f"Hint: {message}")
+
+
+# HELPERS
+
+
+@cache
+def _ascii_safe(char: str, fallback: str) -> str:
+ """Return `char`, or `fallback` if stdout cannot encode it.
+
+ On Windows, `sys.stdout` uses the ANSI code page (e.g. cp1252) instead of the console
+ encoding whenever it is not attached to a console (output redirected, piped, captured by
+ a CI step). Printing a decoration like "✓" then raises `UnicodeEncodeError` - a
+ `ValueError` subclass, so it surfaces as a confusing "Invalid value." error and aborts
+ the command. Cached since the stream encoding is fixed for the lifetime of the process.
+ """
+ encoding = getattr(sys.stdout, "encoding", None) or "utf-8"
+ try:
+ char.encode(encoding)
+ except (LookupError, UnicodeEncodeError):
+ return fallback
+ return char
+
+
+def _serialize_value(v: object) -> object:
+ """Recursively serialize a value to be JSON-compatible."""
+ if isinstance(v, datetime.datetime):
+ return v.isoformat()
+ elif isinstance(v, dict):
+ return {key: _serialize_value(val) for key, val in v.items() if val is not None}
+ elif isinstance(v, list):
+ return [_serialize_value(item) for item in v]
+ return v
+
+
+def _dataclass_to_dict(info: Any) -> dict[str, Any]:
+ """Convert a dataclass to a json-serializable dict."""
+ data = dataclasses.asdict(info)
+
+ for field in dataclasses.fields(info):
+ value = getattr(info, field.name)
+ if isinstance(value, CardData):
+ data[field.name] = value.to_dict()
+
+ return {k: _serialize_value(v) for k, v in data.items() if v is not None}
+
+
+_ANSI_RE = re.compile(r"\033\[[0-9;]*m")
+
+
+def _strip_ansi(text: str) -> str:
+ return _ANSI_RE.sub("", text)
+
+
+def _escape_control_chars(text: str) -> str:
+ """Escape non-printable characters (CR, LF, ESC, ...) so Hub-provided strings cannot alter the terminal output."""
+ return "".join(c if c.isprintable() else repr(c)[1:-1] for c in text)
+
+
+def _single_line(text: str) -> str:
+ return _escape_control_chars(" ".join(text.split()))
+
+
+def _to_header(name: str) -> str:
+ """Convert a camelCase or PascalCase string to SCREAMING_SNAKE_CASE."""
+ s = re.sub(r"([a-z])([A-Z])", r"\1_\2", name)
+ return s.upper()
+
+
+def _infer_alignments(headers: list[str], rows: list[list[Any]]) -> dict[str, str]:
+ """Return ``{"col": "right"}`` for columns where every non-None value is numeric."""
+ result: dict[str, str] = {}
+ for c, h in enumerate(headers):
+ if all(row[c] is None or (isinstance(row[c], (int, float)) and not isinstance(row[c], bool)) for row in rows):
+ result[h] = "right"
+ return result
+
+
+def _format_table_value_human(value: Any) -> str:
+ """Convert a value to string for terminal display."""
+ if value is None:
+ return ""
+ if isinstance(value, bool):
+ return _ascii_safe("✔", "yes") if value else ""
+ if isinstance(value, datetime.datetime):
+ return value.strftime("%Y-%m-%d")
+ if isinstance(value, str) and re.search(r"^\d{4}-\d{2}-\d{2}T", value):
+ return value[:10]
+ if isinstance(value, str):
+ return _single_line(value)
+ if isinstance(value, list):
+ return ", ".join(_format_table_value_human(v) for v in value)
+ elif isinstance(value, dict):
+ if "name" in value: # Likely to be a user or org => print name
+ return _single_line(str(value["name"]))
+ return _single_line(json.dumps(value))
+ return _single_line(str(value))
+
+
+def _truncate_columns(
+ headers: list[str],
+ rows: list[list[str]],
+ *,
+ no_truncate: bool,
+) -> bool:
+ """Truncate cells in-place to fit the current terminal width.
+
+ Returns `True` if any cell was truncated, so the caller can emit a hint.
+ `shutil.get_terminal_size` is cross-platform: it honors `$COLUMNS`, then
+ queries the OS-native API, then falls back to `(80, 24)`.
+ """
+ if no_truncate or not rows:
+ return False
+
+ n = len(headers)
+ # Per-column natural width: longest of header label and cell values.
+ natural = [max(len(headers[c]), *(len(rows[r][c]) for r in range(len(rows)))) for c in range(n)]
+
+ # `max(0, n - 1)` accounts for the single-space separator between columns.
+ budget = shutil.get_terminal_size().columns - max(0, n - 1)
+ if sum(natural) <= budget:
+ return False
+
+ # Shrink the widest column 1 char at a time. Floors keep the header label
+ # visible; the `4` is the smallest cap that still shows "x..." (one content
+ # char plus the "..." marker).
+ caps = natural.copy()
+ min_widths = [max(len(h), 4) for h in headers]
+ while sum(caps) > budget:
+ widest = max(
+ (i for i, w in enumerate(caps) if w > min_widths[i]),
+ key=lambda i: caps[i],
+ default=-1,
+ )
+ if widest < 0:
+ break # everything at floor — table wraps slightly
+ caps[widest] -= 1
+
+ truncated = False
+ for row in rows:
+ for c, cell in enumerate(row):
+ if len(cell) > caps[c]:
+ truncated = True
+ row[c] = cell[: caps[c] - 3] + "..."
+ return truncated
+
+
+def _format_table_cell_agent(value: Any) -> str:
+ """Format a cell value for agent TSV output (ISO timestamps, tabs escaped)."""
+ if isinstance(value, datetime.datetime):
+ return value.isoformat()
+ return _single_line(str(value))
+
+
+out = Output()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_skills.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_skills.py
new file mode 100644
index 0000000000000000000000000000000000000000..f47d3c16ec8eafd3c3ae52cddbd206a9dbdfe1bf
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_skills.py
@@ -0,0 +1,389 @@
+"""Internal helpers for Hugging Face marketplace skill installation and upgrades."""
+
+import json
+import re
+import shutil
+import tempfile
+import time
+from collections.abc import Callable
+from dataclasses import dataclass, replace
+from pathlib import Path, PurePosixPath
+from typing import Any, Literal
+
+from huggingface_hub import __version__, constants
+from huggingface_hub._buckets import BucketFile
+from huggingface_hub.errors import CLIError
+
+from ..utils import disable_progress_bars, logging
+from ._cli_utils import get_hf_api
+from ._output import out
+
+
+logger = logging.get_logger(__name__)
+
+DEFAULT_SKILL_ID = "hf-cli"
+DEFAULT_SKILLS_BUCKET_ID = "huggingface/skills"
+MARKETPLACE_PATH = "marketplace.json"
+# Empty marker file dropped into managed skill installs so `hf skills update` knows
+# to touch them and leave user-placed skill dirs alone. Filename is historical (used
+# to be a JSON manifest with a revision); we keep it for backward compat with installs
+# made by previous versions.
+MANAGED_MARKER_FILENAME = ".hf-skill-manifest.json"
+
+SkillUpdateStatus = Literal["up_to_date", "unmanaged", "source_unreachable"]
+
+
+@dataclass(frozen=True)
+class MarketplaceSkill:
+ name: str
+ repo_path: str
+ description: str | None = None
+
+
+@dataclass(frozen=True)
+class SkillUpdateInfo:
+ name: str
+ skill_dir: Path
+ status: SkillUpdateStatus
+ detail: str | None = None
+
+
+def add_skill(skill_name: str, destination_root: Path, force: bool = False) -> Path:
+ """Resolve a marketplace skill by name and install it."""
+ api = get_hf_api()
+ with disable_progress_bars():
+ marketplace_skills = _load_marketplace_skills(api)
+ skill = _select_marketplace_skill(marketplace_skills, skill_name)
+ if skill is None:
+ raise CLIError(
+ f"Skill '{skill_name}' not found in {DEFAULT_SKILLS_BUCKET_ID}. "
+ "Try `hf skills add` to install `hf-cli` or use a known skill name."
+ )
+ return _install_marketplace_skill(api, skill, destination_root, force=force)
+
+
+def install_generated_skill(content: str, destination_root: Path, force: bool = False) -> Path:
+ """Install the `hf-cli` skill from locally generated SKILL.md content (no bucket download)."""
+
+ def populate(install_dir: Path) -> None:
+ install_dir.mkdir(parents=True, exist_ok=True)
+ (install_dir / "SKILL.md").write_text(content, encoding="utf-8")
+ (install_dir / MANAGED_MARKER_FILENAME).touch()
+
+ return _install_skill(DEFAULT_SKILL_ID, destination_root, populate=populate, force=force)
+
+
+def update_skills(roots: list[Path], selector: str | None = None, *, hf_cli_content: str) -> list[SkillUpdateInfo]:
+ """Re-sync managed skill installs (`hf-cli` is rewritten from `hf_cli_content`, the rest from the bucket)."""
+ skill_dirs = _iter_unique_skill_dirs(roots)
+ if selector is not None:
+ selector_lower = selector.strip().lower()
+ skill_dirs = [d for d in skill_dirs if d.name.lower() == selector_lower]
+ if not skill_dirs:
+ raise CLIError(f"No installed skill matches '{selector}'. Install it with `hf skills add {selector}`.")
+
+ # `hf-cli` is regenerated locally, so only hit the marketplace when another managed skill needs it.
+ needs_marketplace = any(d.name != DEFAULT_SKILL_ID and (d / MANAGED_MARKER_FILENAME).exists() for d in skill_dirs)
+ api = None
+ marketplace_skills: dict[str, MarketplaceSkill] = {}
+ if needs_marketplace:
+ api = get_hf_api()
+ with disable_progress_bars():
+ marketplace_skills = {skill.name.lower(): skill for skill in _load_marketplace_skills(api)}
+
+ return [_apply_single_update(api, skill_dir, marketplace_skills, hf_cli_content) for skill_dir in skill_dirs]
+
+
+def _load_marketplace_skills(api) -> list[MarketplaceSkill]:
+ payload = _load_marketplace_payload(api)
+ plugins = payload.get("plugins")
+ if not isinstance(plugins, list):
+ raise CLIError("Invalid marketplace payload: expected a top-level 'plugins' list.")
+
+ skills: list[MarketplaceSkill] = []
+ for plugin in plugins:
+ if not isinstance(plugin, dict):
+ continue
+ name = plugin.get("name")
+ source = plugin.get("source")
+ if not isinstance(name, str) or not isinstance(source, str):
+ continue
+ description = plugin.get("description")
+ skills.append(
+ MarketplaceSkill(
+ name=name,
+ repo_path=_normalize_repo_path(source),
+ description=description if isinstance(description, str) else None,
+ )
+ )
+ return skills
+
+
+def _install_marketplace_skill(api, skill: MarketplaceSkill, destination_root: Path, force: bool = False) -> Path:
+ """Install a marketplace skill into a local skills directory."""
+
+ def populate(install_dir: Path) -> None:
+ install_dir.mkdir(parents=True, exist_ok=True)
+ bucket_files = _list_skill_files(api, skill)
+ _download_skill_files(api, skill, bucket_files, install_dir)
+ _validate_installed_skill_dir(install_dir)
+ (install_dir / MANAGED_MARKER_FILENAME).touch()
+
+ return _install_skill(skill.name, destination_root, populate=populate, force=force)
+
+
+_VALID_SKILL_NAME = re.compile(r"[A-Za-z0-9][A-Za-z0-9._-]*")
+
+
+def _install_skill(
+ name: str,
+ destination_root: Path,
+ populate: Callable[[Path], None],
+ force: bool = False,
+) -> Path:
+ """Install a skill into ``destination_root`` by calling ``populate(install_dir)`` to fill it.
+
+ Used by both the marketplace install (populate = download from bucket) and the
+ locally-generated install (populate = write content). When the install already
+ exists and ``force`` is set, the new content is staged in a sibling tempdir and
+ atomically swapped in, so the existing install stays intact if ``populate``
+ fails halfway through.
+ """
+ # `name` may come from the remote marketplace payload and the install dir is removed on
+ # reinstall: validate it as defense-in-depth against path traversal.
+ if not _VALID_SKILL_NAME.fullmatch(name):
+ raise CLIError(f"Invalid skill name '{name}'.")
+ destination_root = destination_root.expanduser().resolve()
+ destination_root.mkdir(parents=True, exist_ok=True)
+ install_dir = destination_root / name
+ already_exists = install_dir.exists()
+
+ if already_exists and not force:
+ raise FileExistsError(f"Skill already exists: {install_dir}")
+
+ if already_exists:
+ with tempfile.TemporaryDirectory(dir=destination_root, prefix=f".{install_dir.name}.install-") as tmp_dir_str:
+ staged_dir = Path(tmp_dir_str) / install_dir.name
+ populate(staged_dir)
+ _atomic_replace_directory(existing_dir=install_dir, staged_dir=staged_dir)
+ return install_dir
+
+ try:
+ populate(install_dir)
+ except Exception:
+ if install_dir.exists():
+ shutil.rmtree(install_dir)
+ raise
+ return install_dir
+
+
+def _load_marketplace_payload(api) -> dict[str, Any]:
+ with tempfile.TemporaryDirectory() as tmp_dir:
+ local_path = Path(tmp_dir) / "marketplace.json"
+ api.download_bucket_files(
+ DEFAULT_SKILLS_BUCKET_ID,
+ [(MARKETPLACE_PATH, local_path)],
+ raise_on_missing_files=True,
+ )
+ parsed = json.loads(local_path.read_text(encoding="utf-8"))
+
+ if not isinstance(parsed, dict):
+ raise CLIError("Invalid marketplace payload: expected a JSON object.")
+ return parsed
+
+
+def _select_marketplace_skill(skills: list[MarketplaceSkill], selector: str) -> MarketplaceSkill | None:
+ selector_lower = selector.strip().lower()
+ for skill in skills:
+ if skill.name.lower() == selector_lower:
+ return skill
+ return None
+
+
+def _normalize_repo_path(path: str) -> str:
+ normalized = path.strip()
+ while normalized.startswith("./"):
+ normalized = normalized[2:]
+ normalized = normalized.strip("/")
+ if not normalized:
+ raise CLIError("Invalid marketplace entry: empty source path.")
+ return normalized
+
+
+def _validate_installed_skill_dir(skill_dir: Path) -> None:
+ skill_file = skill_dir / "SKILL.md"
+ if not skill_file.is_file():
+ raise RuntimeError(f"Installed skill is missing SKILL.md: {skill_file}")
+
+
+def _list_skill_files(api, skill: MarketplaceSkill) -> list[BucketFile]:
+ """List all files under `skill.repo_path` in the marketplace bucket."""
+ prefix = skill.repo_path.rstrip("/")
+ files: list[BucketFile] = [
+ item
+ for item in api.list_bucket_tree(DEFAULT_SKILLS_BUCKET_ID, prefix=prefix, recursive=True)
+ if isinstance(item, BucketFile)
+ ]
+ if not files:
+ raise FileNotFoundError(f"Path '{prefix}' not found in bucket '{DEFAULT_SKILLS_BUCKET_ID}'.")
+ return files
+
+
+def _download_skill_files(api, skill: MarketplaceSkill, files: list[BucketFile], install_dir: Path) -> None:
+ """Download bucket files into `install_dir`."""
+ prefix = skill.repo_path.rstrip("/")
+ prefix_with_slash = f"{prefix}/"
+
+ # `list_bucket_tree(prefix=...)` matches as a raw string prefix, so e.g. asking for
+ # "skills/gradio" can also return "skills/gradio-tools/...". Filter on the trailing
+ # slash to keep only files actually inside the directory, then strip it so files land
+ # directly under `install_dir` preserving any nested structure.
+ download_specs: list[tuple[str | BucketFile, str | Path]] = []
+ for bucket_file in files:
+ if not bucket_file.path.startswith(prefix_with_slash):
+ continue
+ relative = bucket_file.path[len(prefix_with_slash) :]
+ local_file = install_dir.joinpath(*PurePosixPath(relative).parts)
+ local_file.parent.mkdir(parents=True, exist_ok=True)
+ download_specs.append((bucket_file, local_file))
+
+ if not download_specs:
+ raise FileNotFoundError(f"No files found under '{prefix}' in bucket '{DEFAULT_SKILLS_BUCKET_ID}'.")
+
+ api.download_bucket_files(DEFAULT_SKILLS_BUCKET_ID, download_specs)
+
+
+def _atomic_replace_directory(existing_dir: Path, staged_dir: Path) -> None:
+ backup_dir = staged_dir.parent / f"{existing_dir.name}.backup"
+ try:
+ existing_dir.rename(backup_dir)
+ staged_dir.rename(existing_dir)
+ shutil.rmtree(backup_dir)
+ except Exception:
+ if backup_dir.exists() and not existing_dir.exists():
+ backup_dir.rename(existing_dir)
+ raise
+
+
+def _iter_unique_skill_dirs(roots: list[Path]) -> list[Path]:
+ seen: set[Path] = set()
+ discovered: list[Path] = []
+ for root in roots:
+ root = root.expanduser().resolve()
+ if not root.is_dir():
+ continue
+ for child in sorted(root.iterdir()):
+ if child.name.startswith("."):
+ continue
+ if not child.is_dir() and not child.is_symlink():
+ continue
+ resolved = child.resolve()
+ if resolved in seen or not resolved.is_dir():
+ continue
+ seen.add(resolved)
+ discovered.append(resolved)
+ return discovered
+
+
+def _apply_single_update(
+ api, skill_dir: Path, marketplace_skills: dict[str, MarketplaceSkill], hf_cli_content: str
+) -> SkillUpdateInfo:
+ base = SkillUpdateInfo(name=skill_dir.name, skill_dir=skill_dir, status="unmanaged")
+
+ if not (skill_dir / MANAGED_MARKER_FILENAME).exists():
+ return base
+
+ if skill_dir.name == DEFAULT_SKILL_ID:
+ try:
+ install_generated_skill(hf_cli_content, skill_dir.parent, force=True)
+ except Exception as exc:
+ return replace(base, status="source_unreachable", detail=str(exc))
+ return replace(base, status="up_to_date")
+
+ skill = marketplace_skills.get(skill_dir.name.lower())
+ if skill is None:
+ return replace(
+ base,
+ status="source_unreachable",
+ detail=f"Skill '{skill_dir.name}' is no longer available in {DEFAULT_SKILLS_BUCKET_ID}.",
+ )
+
+ try:
+ _install_marketplace_skill(api, skill, skill_dir.parent, force=True)
+ except Exception as exc:
+ return replace(base, status="source_unreachable", detail=str(exc))
+
+ return replace(base, status="up_to_date")
+
+
+### `hf-cli` SKILL CHECKER
+
+
+# Version stamp written in the generated SKILL.md (see `build_skill_md`).
+_GENERATED_VERSION_RE = re.compile(r"Generated with `huggingface_hub v([^`]+)`")
+
+
+def check_skill_update() -> None:
+ """Hint at installing or updating the `hf-cli` skill, at most once every 24 hours.
+
+ Only prints a hint: installing or updating stays an explicit user action.
+
+ Skipped entirely if `HF_HUB_DISABLE_UPDATE_CHECK` is set. Called at the entry point of
+ the CLI, so any error is caught and logged to avoid breaking the CLI.
+ """
+ try:
+ _check_skill_update()
+ except Exception:
+ logger.debug("Error while checking for hf-cli skill update.", exc_info=True)
+
+
+def _check_skill_update() -> None:
+ if constants.HF_HUB_DISABLE_UPDATE_CHECK:
+ return
+
+ # Skip if already checked in the last 24 hours
+ stamp_path = Path(constants.CHECK_FOR_SKILL_UPDATE_DONE_PATH)
+ if stamp_path.exists() and (time.time() - stamp_path.stat().st_mtime) < 24 * 3600:
+ return
+ stamp_path.parent.mkdir(parents=True, exist_ok=True)
+ stamp_path.touch()
+
+ global_dirs = _installed_hf_cli_dirs(constants.AGENTS_SKILLS_GLOBAL_PATH, constants.CLAUDE_SKILLS_GLOBAL_PATH)
+ local_dirs = _installed_hf_cli_dirs(constants.AGENTS_SKILLS_LOCAL_PATH, constants.CLAUDE_SKILLS_LOCAL_PATH)
+
+ if not global_dirs and not local_dirs:
+ # Don't push the skill on pre-release / dev versions (same rule as the PyPI update check).
+ if any(tag in __version__ for tag in ["rc", "dev"]):
+ return
+ out.hint(
+ f"The `{DEFAULT_SKILL_ID}` skill is not installed. Run `hf skills add -g`"
+ " to teach your AI agents how to use the `hf` CLI."
+ )
+ return
+
+ # Suggest the command matching where the outdated skill is installed (global takes precedence).
+ if any(_is_skill_outdated(skill_dir) for skill_dir in global_dirs):
+ scope = " -g"
+ elif any(_is_skill_outdated(skill_dir) for skill_dir in local_dirs):
+ scope = ""
+ else:
+ return
+ out.hint(
+ f"The installed `{DEFAULT_SKILL_ID}` skill was generated by another `hf` version."
+ f" Run `hf skills update {DEFAULT_SKILL_ID}{scope}` to refresh it."
+ )
+
+
+def _installed_hf_cli_dirs(*roots: Path) -> list[Path]:
+ dirs = [(root.expanduser() / DEFAULT_SKILL_ID).resolve() for root in roots]
+ return [skill_dir for skill_dir in dict.fromkeys(dirs) if skill_dir.is_dir()]
+
+
+def _is_skill_outdated(skill_dir: Path) -> bool:
+ """Whether the installed `hf-cli` SKILL.md was generated by another `hf` version."""
+ try:
+ content = (skill_dir / "SKILL.md").read_text(encoding="utf-8")
+ except OSError:
+ return False
+ match = _GENERATED_VERSION_RE.search(content)
+ return match is None or match.group(1) != __version__
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_uv_script_header.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_uv_script_header.py
new file mode 100644
index 0000000000000000000000000000000000000000..9545bcee17b788c7478a95990839877b97faa63e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/_uv_script_header.py
@@ -0,0 +1,230 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Read launch defaults from a UV script's optional `[tool.hf-jobs]` PEP 723 table.
+
+See the CLI guide for the header format and `cli/jobs.py` for the merge rules.
+"""
+
+import re
+import sys
+from collections.abc import Generator
+from contextlib import contextmanager
+from dataclasses import dataclass, field, fields
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlsplit
+
+import httpx2
+
+from huggingface_hub import constants
+from huggingface_hub.errors import CLIError
+from huggingface_hub.utils import SoftTemporaryDirectory, get_session
+
+
+# Reference regex from PEP 723 (https://peps.python.org/pep-0723/#reference-implementation)
+_PEP_723_REGEX = re.compile(r"(?m)^# /// (?P[a-zA-Z0-9-]+)$\s(?P(^#(| .*)$\s)+)^# ///$")
+
+TABLE_NAME = "tool.hf-jobs"
+
+
+@dataclass
+class UvScriptHeader:
+ """The `[tool.hf-jobs]` table of a UV script: the launch configuration the script carries."""
+
+ image: str | None = None
+ flavor: str | None = None
+ python: str | None = None
+ timeout: str | None = None
+ name: str | None = None
+ namespace: str | None = None
+ network_group: str | None = None
+ env: dict[str, str] = field(default_factory=dict)
+ secrets: list[str] = field(default_factory=list)
+ labels: dict[str, str] = field(default_factory=dict)
+ volumes: list[str] = field(default_factory=list)
+ network_aliases: list[str] = field(default_factory=list)
+
+
+VALID_KEYS = tuple(f.name for f in fields(UvScriptHeader))
+
+
+@dataclass
+class UvScript:
+ """A UV script ready to be submitted, with the launch config it carries (if any)."""
+
+ script: str
+ """Path (or command) to pass to the Jobs API. A URL script is downloaded, so this is a local path."""
+
+ header: UvScriptHeader | None = None
+ """Parsed `[tool.hf-jobs]` table, or `None` if the script has none."""
+
+
+@contextmanager
+def load_uv_script(script: str) -> Generator[UvScript, None, None]:
+ """Resolve a `hf jobs uv run` script argument and read its `[tool.hf-jobs]` header.
+
+ - a local file is read in place
+ - an `http(s)` URL is downloaded to a temporary directory and from there shipped to the Job like
+ any local script: the URL is fetched exactly once, at submit time, instead of being fetched
+ again by the container (the header has to be read client-side anyway, since `image`, `flavor`,
+ `secrets`, ... are all decided before the container exists). The download lives until the
+ `with` block exits, i.e. until the Job is submitted.
+ - anything else is a command (e.g. `lighteval`) and carries no header
+ """
+ # Scripts are read as UTF-8, not with the locale encoding: a downloaded script is written as raw
+ # bytes and a header with non-ASCII values must not fail to decode depending on the platform.
+ if script.startswith(("http://", "https://")):
+ with SoftTemporaryDirectory(prefix="hf-jobs-uv-") as tmp_dir:
+ local_path = _download_script(script, Path(tmp_dir))
+ yield UvScript(
+ script=str(local_path), header=parse_uv_script_header(local_path.read_text(encoding="utf-8"))
+ )
+ return
+ path = Path(script)
+ if path.is_file():
+ yield UvScript(script=script, header=parse_uv_script_header(path.read_text(encoding="utf-8")))
+ return
+ yield UvScript(script=script)
+
+
+def parse_uv_script_header(text: str) -> UvScriptHeader | None:
+ """Parse the `[tool.hf-jobs]` table out of a script's PEP 723 header. Returns `None` if absent."""
+ content = _extract_pep_723_block(text)
+ # Cheap pre-check: scripts that don't use the feature never need a TOML parser.
+ if content is None or "hf-jobs" not in content:
+ return None
+ table = _load_toml(content).get("tool", {}).get("hf-jobs")
+ if table is None:
+ return None
+ if not isinstance(table, dict):
+ raise CLIError(f"'{TABLE_NAME}' must be a table in the script's PEP 723 header (i.e. `[{TABLE_NAME}]`).")
+
+ if unknown := sorted(key for key in table if key not in VALID_KEYS):
+ raise CLIError(
+ f"Unknown key(s) {', '.join(repr(key) for key in unknown)} in the script's [{TABLE_NAME}] table."
+ f" Valid keys are: {', '.join(VALID_KEYS)}."
+ )
+ return UvScriptHeader(
+ image=_as_str(table, "image"),
+ flavor=_as_str(table, "flavor"),
+ python=_as_str(table, "python"),
+ timeout=_as_timeout(table),
+ name=_as_str(table, "name"),
+ namespace=_as_str(table, "namespace"),
+ network_group=_as_str(table, "network_group"),
+ env=_as_str_table(table, "env"),
+ secrets=_as_secret_names(table),
+ labels=_as_str_table(table, "labels"),
+ volumes=_as_str_list(table, "volumes"),
+ network_aliases=_as_str_list(table, "network_aliases"),
+ )
+
+
+def _download_script(url: str, dest_dir: Path) -> Path:
+ """Download a remote UV script into `dest_dir` and return its local path."""
+ name = Path(urlsplit(url).path).name
+ local_path = dest_dir / (name if name.endswith(".py") else "script.py")
+ try:
+ # Timeout so that an unreachable URL fails the CLI instead of hanging it.
+ response = get_session().get(url, timeout=constants.DEFAULT_REQUEST_TIMEOUT)
+ response.raise_for_status()
+ except httpx2.HTTPError as e:
+ raise CLIError(f"Could not download the UV script from '{url}': {e}") from e
+ local_path.write_bytes(response.content)
+ return local_path
+
+
+def _extract_pep_723_block(text: str) -> str | None:
+ """Return the content of the first PEP 723 `script` block, with the `# ` prefixes stripped."""
+ # The PEP 723 regex anchors on '\n': normalize line endings first, otherwise a script written on
+ # Windows (or fetched over HTTP) silently parses as "no header at all".
+ text = text.replace("\r\n", "\n").replace("\r", "\n")
+ for match in _PEP_723_REGEX.finditer(text):
+ if match.group("type") == "script":
+ return "".join(
+ line[2:] if line.startswith("# ") else line[1:]
+ for line in match.group("content").splitlines(keepends=True)
+ )
+ return None
+
+
+def _load_toml(content: str) -> dict[str, Any]:
+ if sys.version_info >= (3, 11):
+ import tomllib
+ else: # tomllib was added in Python 3.11
+ try:
+ import tomli as tomllib # type: ignore[import-not-found,no-redef,unused-ignore]
+ except ImportError as e:
+ raise CLIError(
+ f"Reading the [{TABLE_NAME}] table of a UV script requires a TOML parser."
+ " Upgrade to Python 3.11+ or install one with `pip install tomli`."
+ ) from e
+ try:
+ return tomllib.loads(content)
+ except tomllib.TOMLDecodeError as e:
+ raise CLIError(f"Invalid TOML in the script's PEP 723 header: {e}") from e
+
+
+def _type_error(key: str, value: Any, expected: str) -> CLIError:
+ return CLIError(f"'{key}' in the script's [{TABLE_NAME}] table must be {expected}, got: {value!r}.")
+
+
+def _as_str(table: dict[str, Any], key: str) -> str | None:
+ value = table.get(key)
+ if value is None:
+ return None
+ if not isinstance(value, str):
+ raise _type_error(key, value, "a string")
+ return value
+
+
+def _as_timeout(table: dict[str, Any]) -> str | None:
+ value = table.get("timeout")
+ if value is None:
+ return None
+ if isinstance(value, bool) or not isinstance(value, (str, int)):
+ raise _type_error("timeout", value, 'a duration string (e.g. "30m") or a number of seconds')
+ return str(value)
+
+
+def _as_str_table(table: dict[str, Any], key: str) -> dict[str, str]:
+ value = table.get(key)
+ if value is None:
+ return {}
+ if not isinstance(value, dict):
+ raise _type_error(key, value, "a table of strings")
+ for name, item in value.items():
+ # Stringifying a bool or an int here would send a value the author never wrote.
+ if not isinstance(item, str):
+ raise _type_error(f"{key}.{name}", item, "a string")
+ return value
+
+
+def _as_str_list(table: dict[str, Any], key: str) -> list[str]:
+ value = table.get(key)
+ if value is None:
+ return []
+ if not isinstance(value, list) or any(not isinstance(item, str) for item in value):
+ raise _type_error(key, value, "a list of strings")
+ return value
+
+
+def _as_secret_names(table: dict[str, Any]) -> list[str]:
+ if isinstance(table.get("secrets"), dict):
+ raise CLIError(
+ f"'secrets' in the script's [{TABLE_NAME}] table must be a list of names (e.g. `secrets = [\"HF_TOKEN\"]`),"
+ " not a table: scripts travel publicly, so secret values are always read from the environment of"
+ " whoever runs the script."
+ )
+ return _as_str_list(table, "secrets")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/auth.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/auth.py
new file mode 100644
index 0000000000000000000000000000000000000000..d460f283558f7aaa31a271a6bda34a4d9c513cea
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/auth.py
@@ -0,0 +1,174 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to authenticate to the Hugging Face Hub and interact with your repositories."""
+
+from typing import Annotated
+
+import click
+
+from huggingface_hub.constants import ENDPOINT
+from huggingface_hub.hf_api import whoami
+
+from .._login import _save_oauth_token, auth_list, auth_switch, login, logout
+from ..errors import CLIError
+from ..utils import get_stored_tokens, get_token, logging, select_choice
+from ..utils._oauth_device import poll_device_token, request_device_code
+from ._cli_utils import TokenOpt, typer_factory
+from ._framework import Option
+from ._output import OutputFormat, out
+
+
+logger = logging.get_logger(__name__)
+
+
+auth_cli = typer_factory(help="Manage authentication (login, logout, etc.).")
+
+
+@auth_cli.command(
+ "login",
+ examples=[
+ "hf auth login",
+ "hf auth login --token $HF_TOKEN",
+ "hf auth login --token $HF_TOKEN --add-to-git-credential",
+ "hf auth login --force",
+ ],
+)
+def auth_login(
+ token: TokenOpt = None,
+ add_to_git_credential: Annotated[
+ bool,
+ Option(
+ help="Save to git credential helper. Useful only if you plan to run git commands directly.",
+ ),
+ ] = False,
+ force: Annotated[
+ bool,
+ Option(
+ help="Force re-login even if already logged in.",
+ ),
+ ] = False,
+) -> None:
+ """Login from your browser, or using a token from huggingface.co/settings/tokens."""
+ if token is not None or out.mode == OutputFormat.human:
+ # `--token` bypasses any prompt; in human mode the gh-style menu lives in `login()`.
+ login(token=token, add_to_git_credential=add_to_git_credential, skip_if_logged_in=not force)
+ return
+
+ # Logging in is an interactive flow: besides human mode, only agent mode is supported.
+ if out.mode != OutputFormat.agent:
+ raise CLIError(
+ "`hf auth login` is interactive and does not support --format json/quiet. "
+ "Pass --token for a non-interactive login."
+ )
+
+ # agent mode: never prompt; print instructions the agent can relay to its user.
+ if not force and get_token() is not None:
+ out.text(agent="Already logged in. Use `hf auth login --force` to re-login.")
+ return
+ device_info = request_device_code()
+ out.text(
+ agent=(
+ f"Ask the user to open {device_info['verification_uri_complete']} in a browser and enter the code "
+ f"{device_info['user_code']}. The code expires in {device_info['expires_in']} seconds. "
+ "Waiting for authorization..."
+ )
+ )
+ response = poll_device_token(device_info)
+ token_name, username = _save_oauth_token(response)
+ out.text(agent=f"Login successful: logged in as {username} (token saved as '{token_name}').")
+
+
+@auth_cli.command(
+ "logout",
+ examples=["hf auth logout", "hf auth logout --token-name my-token"],
+)
+def auth_logout(
+ token_name: Annotated[
+ str | None,
+ Option(help="Name of token to logout"),
+ ] = None,
+) -> None:
+ """Logout from a specific token."""
+ logout(token_name=token_name)
+
+
+def _select_token_name() -> str | None:
+ token_names = list(get_stored_tokens().keys())
+
+ if not token_names:
+ logger.error("No stored tokens found. Please login first.")
+ return None
+
+ if out.mode != OutputFormat.human:
+ raise CLIError("Use --token-name to select a token in non-interactive mode.")
+ return token_names[select_choice("Select a token to switch to:", token_names)]
+
+
+@auth_cli.command(
+ "switch",
+ examples=["hf auth switch", "hf auth switch --token-name my-token"],
+)
+def auth_switch_cmd(
+ token_name: Annotated[
+ str | None,
+ Option(
+ help="Name of the token to switch to",
+ ),
+ ] = None,
+ add_to_git_credential: Annotated[
+ bool,
+ Option(
+ help="Save to git credential helper. Useful only if you plan to run git commands directly.",
+ ),
+ ] = False,
+) -> None:
+ """Switch between access tokens."""
+ if token_name is None:
+ token_name = _select_token_name()
+ if token_name is None:
+ print("No token name provided. Aborting.")
+ raise click.exceptions.Exit()
+ auth_switch(token_name, add_to_git_credential=add_to_git_credential)
+
+
+@auth_cli.command("list | ls", examples=["hf auth list"])
+def auth_list_cmd() -> None:
+ """List all stored access tokens."""
+ auth_list()
+
+
+@auth_cli.command("token", examples=["hf auth token", "hf auth token | xargs curl -H 'Authorization: Bearer {}'"])
+def auth_token() -> None:
+ """Print the current access token to stdout."""
+ token = get_token()
+ if token is None:
+ out.error("Not logged in. Run `hf auth login` first.")
+ raise click.exceptions.Exit(code=1)
+ print(token)
+ out.hint("Run `hf auth whoami` to see which account this token belongs to.")
+
+
+@auth_cli.command("whoami", examples=["hf auth whoami", "hf auth whoami --format json"])
+def auth_whoami() -> None:
+ """Find out which huggingface.co account you are logged in as."""
+
+ token = get_token()
+ if token is None:
+ out.error("Not logged in. Run `hf auth login` first.")
+ raise click.exceptions.Exit(code=1)
+
+ info = whoami(token)
+ orgs = ",".join(org["name"] for org in info["orgs"]) or None
+ endpoint = ENDPOINT if ENDPOINT != "https://huggingface.co" else None
+ out.result("Logged in", user=info["name"], orgs=orgs, endpoint=endpoint)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/buckets.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/buckets.py
new file mode 100644
index 0000000000000000000000000000000000000000..525045492c42f8577a95aff2b175eb94432ed598
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/buckets.py
@@ -0,0 +1,754 @@
+# Copyright 2025-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with buckets via the CLI."""
+
+from collections.abc import Iterable, Iterator
+from typing import Annotated
+
+import click
+
+from huggingface_hub import logging
+from huggingface_hub._buckets import (
+ BUCKET_PREFIX,
+ BucketFile,
+ BucketFolder,
+ FilterMatcher,
+ _parse_bucket_uri,
+)
+
+from ..hf_api import REPO_REGIONS
+from ._cli_utils import (
+ SearchOpt,
+ TokenOpt,
+ get_hf_api,
+ typer_factory,
+)
+from ._cp import make_cp
+from ._file_listing import format_size, print_file_listing
+from ._framework import Argument, Option
+from ._output import OutputFormat, out
+
+
+logger = logging.get_logger(__name__)
+
+
+buckets_cli = typer_factory(help="Commands to interact with buckets.")
+
+
+@buckets_cli.command(
+ name="create",
+ examples=[
+ "hf buckets create my-bucket",
+ "hf buckets create user/my-bucket",
+ "hf buckets create hf://buckets/user/my-bucket",
+ "hf buckets create user/my-bucket --private",
+ "hf buckets create user/my-bucket --exist-ok",
+ "hf buckets create user/my-bucket --region us",
+ ],
+)
+def create(
+ bucket_id: Annotated[
+ str,
+ Argument(
+ help="Bucket ID: bucket_name, namespace/bucket_name, or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ private: Annotated[
+ bool,
+ Option(
+ "--private",
+ help="Create a private bucket.",
+ ),
+ ] = False,
+ region: Annotated[
+ REPO_REGIONS | None,
+ Option(
+ "--region",
+ help="Cloud region in which to create the bucket. Can be one of 'us' or 'eu'. Requires Team plan or above.",
+ ),
+ ] = None,
+ exist_ok: Annotated[
+ bool,
+ Option(
+ "--exist-ok",
+ help="Do not raise an error if the bucket already exists.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Create a new bucket."""
+ api = get_hf_api(token=token)
+
+ if bucket_id.startswith(BUCKET_PREFIX):
+ parsed = _parse_bucket_uri(bucket_id)
+ if parsed.path_in_repo:
+ raise click.BadParameter(
+ f"Cannot specify a prefix for bucket creation: {bucket_id}."
+ f" Use namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+ bucket_id = parsed.id
+
+ bucket_url = api.create_bucket(
+ bucket_id,
+ private=private if private else None,
+ region=region,
+ exist_ok=exist_ok,
+ )
+ out.result("Bucket created", uri=bucket_url.uri.to_uri(), url=bucket_url.url)
+
+
+def _is_bucket_id(argument: str) -> bool:
+ """Check if argument is a bucket ID (namespace/name) vs just a namespace."""
+ if argument.startswith(BUCKET_PREFIX):
+ path = argument[len(BUCKET_PREFIX) :]
+ else:
+ path = argument
+ return "/" in path
+
+
+@buckets_cli.command(
+ name="list | ls",
+ examples=[
+ "hf buckets list",
+ "hf buckets list huggingface",
+ 'hf buckets list --search "my-prefix"',
+ "hf buckets list user/my-bucket",
+ "hf buckets list user/my-bucket -R",
+ "hf buckets list user/my-bucket -h",
+ "hf buckets list user/my-bucket --tree",
+ "hf buckets list user/my-bucket --tree -h",
+ "hf buckets list hf://buckets/user/my-bucket",
+ "hf buckets list user/my-bucket/sub -R",
+ ],
+)
+def list_cmd(
+ argument: Annotated[
+ str | None,
+ Argument(
+ help=(
+ "Namespace (user or org) to list buckets, or bucket ID"
+ " (namespace/bucket_name(/prefix) or hf://buckets/...) to list files."
+ ),
+ ),
+ ] = None,
+ human_readable: Annotated[
+ bool,
+ Option(
+ "--human-readable",
+ "-h",
+ help="Show sizes in human readable format.",
+ ),
+ ] = False,
+ as_tree: Annotated[
+ bool,
+ Option(
+ "--tree",
+ help="List files in tree format (only for listing files).",
+ ),
+ ] = False,
+ recursive: Annotated[
+ bool,
+ Option(
+ "--recursive",
+ "-R",
+ help="List files recursively (only for listing files).",
+ ),
+ ] = False,
+ search: SearchOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List buckets or files in a bucket.
+
+ When called with no argument or a namespace, lists buckets.
+ When called with a bucket ID (namespace/bucket_name), lists files in the bucket.
+ """
+ # Determine mode: listing buckets or listing files
+ is_file_mode = argument is not None and _is_bucket_id(argument)
+
+ if is_file_mode:
+ if search is not None:
+ raise click.BadParameter("Cannot use --search when listing files.")
+ _list_files(
+ argument=argument, # type: ignore
+ human_readable=human_readable,
+ as_tree=as_tree,
+ recursive=recursive,
+ token=token,
+ )
+ else:
+ _list_buckets(
+ namespace=argument,
+ search=search,
+ human_readable=human_readable,
+ as_tree=as_tree,
+ recursive=recursive,
+ token=token,
+ )
+
+
+def _list_buckets(
+ namespace: str | None,
+ search: str | None,
+ human_readable: bool,
+ as_tree: bool,
+ recursive: bool,
+ token: str | None,
+) -> None:
+ """List buckets in a namespace."""
+ # Validate incompatible flags
+ if as_tree:
+ raise click.BadParameter("Cannot use --tree when listing buckets.")
+ if recursive:
+ raise click.BadParameter("Cannot use --recursive when listing buckets.")
+
+ # Handle hf://buckets/namespace format
+ if namespace is not None and namespace.startswith(BUCKET_PREFIX):
+ namespace = namespace[len(BUCKET_PREFIX) :]
+ # Strip trailing slash if any
+ namespace = namespace.rstrip("/")
+
+ api = get_hf_api(token=token)
+ items = [
+ {
+ "id": bucket.id,
+ "private": bucket.private,
+ "size": format_size(bucket.size, human_readable) if human_readable else bucket.size,
+ "total_files": bucket.total_files,
+ "created_at": bucket.created_at,
+ }
+ for bucket in api.list_buckets(namespace=namespace, search=search)
+ ]
+ out.table(items, alignments={"size": "right"})
+
+
+def _iter_under_prefix(items: Iterable[BucketFile | BucketFolder], prefix: str) -> Iterator[BucketFile | BucketFolder]:
+ """Drop entries that are not `prefix` itself or below it.
+
+ The server matches `prefix` lexically, so listing "logs" also returns "logs.json" or "logs_backup/...".
+ Filesystem semantics require path components => keep only what's actually under the prefix.
+ """
+ prefix = prefix.rstrip("/")
+ for item in items:
+ if not prefix or item.path == prefix or item.path.startswith(f"{prefix}/"):
+ yield item
+
+
+def _list_files(
+ argument: str,
+ human_readable: bool,
+ as_tree: bool,
+ recursive: bool,
+ token: str | None,
+) -> None:
+ """List files in a bucket."""
+ if as_tree and out.mode == OutputFormat.json:
+ raise click.BadParameter("Cannot use --tree with --format json.")
+
+ api = get_hf_api(token=token)
+ parsed = _parse_bucket_uri(argument)
+ items = list(
+ _iter_under_prefix(
+ api.list_bucket_tree(
+ parsed.id,
+ prefix=parsed.path_in_repo or None,
+ recursive=recursive,
+ ),
+ parsed.path_in_repo,
+ )
+ )
+
+ print_file_listing(items, human_readable=human_readable, as_tree=as_tree, recursive=recursive)
+
+
+@buckets_cli.command(
+ name="info",
+ examples=[
+ "hf buckets info user/my-bucket",
+ "hf buckets info hf://buckets/user/my-bucket",
+ ],
+)
+def info(
+ bucket_id: Annotated[
+ str,
+ Argument(
+ help="Bucket ID: namespace/bucket_name or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a bucket."""
+ api = get_hf_api(token=token)
+ parsed = _parse_bucket_uri(bucket_id)
+ bucket = api.bucket_info(parsed.id)
+ out.dict(bucket, id_key="id")
+
+
+@buckets_cli.command(
+ name="delete",
+ examples=[
+ "hf buckets delete user/my-bucket",
+ "hf buckets delete hf://buckets/user/my-bucket",
+ "hf buckets delete user/my-bucket --yes",
+ "hf buckets delete user/my-bucket --missing-ok",
+ ],
+)
+def delete(
+ bucket_id: Annotated[
+ str,
+ Argument(
+ help="Bucket ID: namespace/bucket_name or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ missing_ok: Annotated[
+ bool,
+ Option(
+ "--missing-ok",
+ help="Do not raise an error if the bucket does not exist.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete a bucket.
+
+ This deletes the entire bucket and all its contents. Use `hf buckets rm` to remove individual files.
+ """
+ if bucket_id.startswith(BUCKET_PREFIX):
+ parsed = _parse_bucket_uri(bucket_id)
+ if parsed.path_in_repo:
+ raise click.BadParameter(
+ f"Cannot specify a prefix for bucket deletion: {bucket_id}."
+ f" Use namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+ bucket_id = parsed.id
+ elif "/" not in bucket_id:
+ raise click.BadParameter(
+ f"Invalid bucket ID: {bucket_id}."
+ f" Must be in format namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+
+ out.confirm(f"Are you sure you want to delete bucket '{bucket_id}'?", yes=yes)
+
+ api = get_hf_api(token=token)
+ api.delete_bucket(bucket_id, missing_ok=missing_ok)
+ out.result("Bucket deleted", bucket_id=bucket_id)
+
+
+@buckets_cli.command(
+ name="remove | rm",
+ examples=[
+ "hf buckets remove user/my-bucket/file.txt",
+ "hf buckets rm hf://buckets/user/my-bucket/file.txt",
+ "hf buckets rm user/my-bucket/logs/ --recursive",
+ 'hf buckets rm user/my-bucket --recursive --include "*.tmp"',
+ "hf buckets rm user/my-bucket/data/ --recursive --dry-run",
+ ],
+)
+def remove(
+ argument: Annotated[
+ str,
+ Argument(
+ help=(
+ "Bucket path: namespace/bucket_name/path or hf://buckets/namespace/bucket_name/path."
+ " With --recursive, namespace/bucket_name is also accepted to target all files."
+ ),
+ ),
+ ],
+ recursive: Annotated[
+ bool,
+ Option(
+ "--recursive",
+ "-R",
+ help="Remove files recursively under the given prefix.",
+ ),
+ ] = False,
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ dry_run: Annotated[
+ bool,
+ Option(
+ "--dry-run",
+ help="Preview what would be deleted without actually deleting.",
+ ),
+ ] = False,
+ include: Annotated[
+ list[str] | None,
+ Option(
+ help="Include only files matching pattern (can specify multiple). Requires --recursive.",
+ ),
+ ] = None,
+ exclude: Annotated[
+ list[str] | None,
+ Option(
+ help="Exclude files matching pattern (can specify multiple). Requires --recursive.",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Remove files from a bucket.
+
+ To delete an entire bucket, use `hf buckets delete` instead.
+ """
+ parsed = _parse_bucket_uri(argument)
+ bucket_id = parsed.id
+ prefix = parsed.path_in_repo
+
+ if prefix == "" and not recursive:
+ raise click.BadParameter(
+ f"No file path specified. To remove files, provide a path"
+ f" (e.g. '{bucket_id}/FILE') or use --recursive to remove all files."
+ f" To delete the entire bucket, use `hf buckets delete {bucket_id}`."
+ )
+
+ if (include or exclude) and not recursive:
+ raise click.BadParameter("--include and --exclude require --recursive.")
+
+ api = get_hf_api(token=token)
+
+ if recursive:
+ status = out.status("Listing files from remote")
+
+ all_files: list[BucketFile] = []
+ for item in _iter_under_prefix(
+ api.list_bucket_tree(
+ bucket_id,
+ prefix=prefix or None,
+ recursive=True,
+ ),
+ prefix,
+ ):
+ if isinstance(item, BucketFile):
+ all_files.append(item)
+ status.update(f"Listing files from remote ({len(all_files)} files)")
+ status.done(f"Listing files from remote ({len(all_files)} files)")
+
+ if include or exclude:
+ matcher = FilterMatcher(include_patterns=include, exclude_patterns=exclude)
+ matched_files = [f for f in all_files if matcher.matches(f.path)]
+ else:
+ matched_files = all_files
+
+ file_paths = [f.path for f in matched_files]
+ total_size = sum(f.size for f in matched_files)
+ size_str = format_size(total_size, human_readable=True)
+
+ if not file_paths:
+ out.text("No files to remove.")
+ return
+
+ count_label = f"{len(file_paths)} file(s) totaling {size_str}"
+
+ if not yes and not dry_run:
+ out.text("\n".join(f" {path}" for path in file_paths))
+ out.confirm(f"Remove {count_label} from '{bucket_id}'?", yes=False)
+
+ if dry_run:
+ out.text("\n".join(f"delete: {BUCKET_PREFIX}{bucket_id}/{path}" for path in file_paths))
+ out.text(f"(dry run) {count_label} would be removed.")
+ return
+
+ api.batch_bucket_files(bucket_id, delete=file_paths)
+ out.result(
+ f"Removed {count_label} from '{bucket_id}'",
+ bucket_id=bucket_id,
+ files_deleted=len(file_paths),
+ size=size_str,
+ )
+
+ else:
+ file_path = prefix
+ if not file_path:
+ raise click.BadParameter("File path cannot be empty.")
+
+ if dry_run:
+ out.text(f"delete: {BUCKET_PREFIX}{bucket_id}/{file_path}")
+ out.text("(dry run) 1 file would be removed.")
+ return
+
+ out.confirm(f"Remove '{file_path}' from '{bucket_id}'?", yes=yes)
+
+ api.batch_bucket_files(bucket_id, delete=[file_path])
+ out.result("File removed", path=file_path, bucket_id=bucket_id)
+
+
+@buckets_cli.command(
+ name="move",
+ examples=[
+ "hf buckets move user/old-bucket user/new-bucket",
+ "hf buckets move user/my-bucket my-org/my-bucket",
+ "hf buckets move hf://buckets/user/old-bucket hf://buckets/user/new-bucket",
+ ],
+)
+def move(
+ from_id: Annotated[
+ str,
+ Argument(
+ help="Source bucket ID: namespace/bucket_name or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ to_id: Annotated[
+ str,
+ Argument(
+ help="Destination bucket ID: namespace/bucket_name or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ token: TokenOpt = None,
+) -> None:
+ """Move (rename) a bucket to a new name or namespace."""
+ # Parse from_id
+ parsed_from = _parse_bucket_uri(from_id)
+ if parsed_from.path_in_repo:
+ raise click.BadParameter(
+ f"Cannot specify a prefix for bucket move: {from_id}."
+ f" Use namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+
+ # Parse to_id
+ parsed_to = _parse_bucket_uri(to_id)
+ if parsed_to.path_in_repo:
+ raise click.BadParameter(
+ f"Cannot specify a prefix for bucket move: {to_id}."
+ f" Use namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+
+ api = get_hf_api(token=token)
+ api.move_bucket(from_id=parsed_from.id, to_id=parsed_to.id)
+ out.result("Bucket moved", from_id=parsed_from.id, to_id=parsed_to.id)
+
+
+@buckets_cli.command(
+ name="settings",
+ examples=[
+ "hf buckets settings user/my-bucket --private",
+ "hf buckets settings user/my-bucket --public",
+ "hf buckets settings hf://buckets/user/my-bucket --private",
+ ],
+)
+def settings(
+ bucket_id: Annotated[
+ str,
+ Argument(
+ help="Bucket ID: namespace/bucket_name or hf://buckets/namespace/bucket_name",
+ ),
+ ],
+ private: Annotated[
+ bool,
+ Option(
+ "--private",
+ help="Make the bucket private.",
+ ),
+ ] = False,
+ public: Annotated[
+ bool,
+ Option(
+ "--public",
+ help="Make the bucket public.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Update bucket settings (visibility)."""
+ if private == public:
+ raise click.BadParameter("Specify exactly one of --private or --public.")
+
+ if bucket_id.startswith(BUCKET_PREFIX):
+ parsed = _parse_bucket_uri(bucket_id)
+ if parsed.path_in_repo:
+ raise click.BadParameter(
+ f"Cannot specify a prefix for bucket settings: {bucket_id}."
+ f" Use namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+ bucket_id = parsed.id
+ elif "/" not in bucket_id:
+ raise click.BadParameter(
+ f"Invalid bucket ID: {bucket_id}."
+ f" Must be in format namespace/bucket_name or {BUCKET_PREFIX}namespace/bucket_name."
+ )
+
+ api = get_hf_api(token=token)
+ api.update_bucket_settings(bucket_id, private=private)
+ out.result("Bucket settings updated", bucket_id=bucket_id, private=private)
+ out.hint(f"Run `hf buckets info {bucket_id}` to view bucket details.")
+
+
+# =============================================================================
+# Sync command
+# =============================================================================
+
+
+@buckets_cli.command(
+ name="sync",
+ examples=[
+ "hf buckets sync ./data hf://buckets/user/my-bucket",
+ "hf buckets sync hf://buckets/user/my-bucket ./data",
+ "hf buckets sync ./data hf://buckets/user/my-bucket --delete",
+ 'hf buckets sync hf://buckets/user/my-bucket ./data --include "*.safetensors" --exclude "*.tmp"',
+ "hf buckets sync ./data hf://buckets/user/my-bucket --plan sync-plan.jsonl",
+ "hf buckets sync --apply sync-plan.jsonl",
+ "hf buckets sync ./data hf://buckets/user/my-bucket --dry-run",
+ "hf buckets sync ./data hf://buckets/user/my-bucket --dry-run | jq .",
+ ],
+)
+def sync(
+ source: Annotated[
+ str | None,
+ Argument(
+ help="Source path: local directory or hf://buckets/namespace/bucket_name(/prefix)",
+ ),
+ ] = None,
+ dest: Annotated[
+ str | None,
+ Argument(
+ help="Destination path: local directory or hf://buckets/namespace/bucket_name(/prefix)",
+ ),
+ ] = None,
+ delete: Annotated[
+ bool,
+ Option(
+ help="Delete destination files not present in source.",
+ ),
+ ] = False,
+ ignore_times: Annotated[
+ bool,
+ Option(
+ "--ignore-times",
+ help="Skip files only based on size, ignoring modification times.",
+ ),
+ ] = False,
+ ignore_sizes: Annotated[
+ bool,
+ Option(
+ "--ignore-sizes",
+ help="Skip files only based on modification times, ignoring sizes.",
+ ),
+ ] = False,
+ plan: Annotated[
+ str | None,
+ Option(
+ help="Save sync plan to JSONL file for review instead of executing.",
+ ),
+ ] = None,
+ apply: Annotated[
+ str | None,
+ Option(
+ help="Apply a previously saved plan file.",
+ ),
+ ] = None,
+ dry_run: Annotated[
+ bool,
+ Option(
+ "--dry-run",
+ help="Print sync plan to stdout as JSONL without executing.",
+ ),
+ ] = False,
+ include: Annotated[
+ list[str] | None,
+ Option(
+ help="Include files matching pattern (can specify multiple).",
+ ),
+ ] = None,
+ exclude: Annotated[
+ list[str] | None,
+ Option(
+ help="Exclude files matching pattern (can specify multiple).",
+ ),
+ ] = None,
+ filter_from: Annotated[
+ str | None,
+ Option(
+ help="Read include/exclude patterns from file.",
+ ),
+ ] = None,
+ existing: Annotated[
+ bool,
+ Option(
+ "--existing",
+ help="Skip creating new files on receiver (only update existing files).",
+ ),
+ ] = False,
+ ignore_existing: Annotated[
+ bool,
+ Option(
+ "--ignore-existing",
+ help="Skip updating files that exist on receiver (only create new files).",
+ ),
+ ] = False,
+ verbose: Annotated[
+ bool,
+ Option(
+ "--verbose",
+ "-v",
+ help="Show detailed logging with reasoning.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Sync files between local directory and a bucket."""
+ api = get_hf_api(token=token)
+ api.sync_bucket(
+ source=source,
+ dest=dest,
+ delete=delete,
+ ignore_times=ignore_times,
+ ignore_sizes=ignore_sizes,
+ existing=existing,
+ ignore_existing=ignore_existing,
+ include=include,
+ exclude=exclude,
+ filter_from=filter_from,
+ plan=plan,
+ apply=apply,
+ dry_run=dry_run,
+ verbose=verbose,
+ quiet=out.is_quiet(),
+ )
+ if plan and not out.is_quiet():
+ out.hint(f"Run `hf buckets sync --apply {plan}` to execute this plan.")
+
+
+# =============================================================================
+# Cp command
+# =============================================================================
+
+
+# `hf buckets cp` is an alias for the top-level `hf cp` command (see `cli/_cp.py`).
+buckets_cli.command(
+ name="cp",
+ examples=[
+ # Download (repo or bucket -> local / stdout)
+ "hf buckets cp hf://buckets/username/my-bucket/config.json config.json",
+ "hf buckets cp hf://buckets/username/my-bucket/data.csv data/",
+ "hf buckets cp hf://buckets/username/my-bucket/config.json -",
+ # Upload (local / stdin -> bucket)
+ "hf buckets cp model.safetensors hf://buckets/username/my-bucket/model.safetensors",
+ "hf buckets cp config.json hf://buckets/username/my-bucket/logs/",
+ "hf buckets cp - hf://buckets/username/my-bucket/config.json",
+ # Remote to remote (repo or bucket -> bucket)
+ "hf buckets cp hf://buckets/username/my-bucket/data.csv hf://buckets/username/dest-bucket/",
+ "hf buckets cp hf://buckets/username/source-bucket/logs/ hf://buckets/username/dest-bucket/logs/",
+ ],
+)(make_cp("buckets"))
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/cache.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7ea5344c38822eedfbfa49ff65706aa1cfdd729
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/cache.py
@@ -0,0 +1,896 @@
+# Copyright 2025-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains the 'hf cache' command group with cache management subcommands."""
+
+import re
+import time
+from collections import defaultdict
+from collections.abc import Callable, Mapping
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Annotated, Any
+
+import click
+
+from huggingface_hub.errors import CLIError
+
+from ..utils import (
+ ANSI,
+ CachedFileInfo,
+ CachedRepoInfo,
+ CachedRevisionInfo,
+ CacheNotFound,
+ HFCacheInfo,
+ HfUri,
+ _format_size,
+ _shared_blobs,
+ parse_hf_uri,
+ scan_cache_dir,
+)
+from ..utils._parsing import parse_duration, parse_size
+from ._cli_utils import RepoIdArg, RepoTypeOpt, RevisionOpt, TokenOpt, get_hf_api, typer_factory
+from ._framework import Argument, Option
+from ._output import out
+
+
+cache_cli = typer_factory(help="Manage local cache directory.")
+
+
+#### Cache helper utilities
+
+
+@dataclass(frozen=True)
+class _DeletionResolution:
+ revisions: frozenset[str]
+ selected: dict[CachedRepoInfo, frozenset[CachedRevisionInfo]]
+ missing: tuple[str, ...]
+ files: dict[CachedFileInfo, str] = field(default_factory=dict) # selected files, mapped to their display label
+
+
+_FILTER_PATTERN = re.compile(r"(?P[a-zA-Z_]+)\s*(?P==|!=|>=|<=|>|<|=)\s*(?P.+)")
+_ALLOWED_OPERATORS = {"=", "!=", ">", "<", ">=", "<="}
+_FILTER_KEYS = {"accessed", "modified", "refs", "size", "type"}
+_SORT_KEYS = {"accessed", "modified", "name", "size"}
+_SORT_PATTERN = re.compile(r"(?P[a-zA-Z_]+)(?::(?Pasc|desc))?")
+_SORT_DEFAULT_ORDER = {
+ # Default ordering: accessed/modified/size are descending (newest/biggest first), name is ascending
+ "accessed": "desc",
+ "modified": "desc",
+ "size": "desc",
+ "name": "asc",
+}
+
+
+# Dynamically generate SortOptions enum from _SORT_KEYS
+_sort_options_dict = {}
+for key in sorted(_SORT_KEYS):
+ _sort_options_dict[key] = key
+ _sort_options_dict[f"{key}_asc"] = f"{key}:asc"
+ _sort_options_dict[f"{key}_desc"] = f"{key}:desc"
+
+SortOptions = Enum("SortOptions", _sort_options_dict, type=str, module=__name__) # type: ignore
+
+
+@dataclass(frozen=True)
+class CacheDeletionCounts:
+ """Simple counters summarizing cache deletions for CLI messaging."""
+
+ repo_count: int
+ partial_revision_count: int
+ total_revision_count: int
+
+
+CacheEntry = tuple[CachedRepoInfo, CachedRevisionInfo | None]
+RepoRefsMap = dict[CachedRepoInfo, frozenset[str]]
+
+
+def summarize_deletions(
+ selected_by_repo: Mapping[CachedRepoInfo, frozenset[CachedRevisionInfo]],
+) -> CacheDeletionCounts:
+ """Summarize deletions across repositories."""
+ repo_count = 0
+ total_revisions = 0
+ revisions_in_full_repos = 0
+
+ for repo, revisions in selected_by_repo.items():
+ total_revisions += len(revisions)
+ if len(revisions) == len(repo.revisions):
+ repo_count += 1
+ revisions_in_full_repos += len(revisions)
+
+ partial_revision_count = total_revisions - revisions_in_full_repos
+ return CacheDeletionCounts(repo_count, partial_revision_count, total_revisions)
+
+
+def _prune_summary(revision_count: int, incomplete_count: int, shared_blob_count: int) -> str:
+ """Build the human-readable summary of what `hf cache prune` is about to delete."""
+ parts: list[str] = []
+ if revision_count:
+ parts.append(f"{revision_count} unreferenced revision(s)")
+ if incomplete_count:
+ parts.append(f"{incomplete_count} incomplete download(s)")
+ if shared_blob_count:
+ parts.append(f"{shared_blob_count} unreferenced shared blob(s)")
+ if len(parts) > 1:
+ return ", ".join(parts[:-1]) + " and " + parts[-1]
+ return "".join(parts)
+
+
+def print_cache_selected_revisions(selected_by_repo: Mapping[CachedRepoInfo, frozenset[CachedRevisionInfo]]) -> None:
+ """Pretty-print selected cache revisions during confirmation prompts."""
+ for repo in sorted(selected_by_repo.keys(), key=lambda repo: (repo.repo_type, repo.repo_id.lower())):
+ repo_key = f"{repo.repo_type}/{repo.repo_id}"
+ revisions = sorted(selected_by_repo[repo], key=lambda rev: rev.commit_hash)
+ if len(revisions) == len(repo.revisions):
+ out.text(f" - {repo_key} (entire repo)")
+ continue
+
+ out.text(f" - {repo_key}:")
+ for revision in revisions:
+ refs = " ".join(sorted(revision.refs)) or "(detached)"
+ out.text(f" {revision.commit_hash} [{refs}] {revision.size_on_disk_str}")
+
+
+def build_cache_index(
+ hf_cache_info: HFCacheInfo,
+) -> tuple[
+ dict[str, CachedRepoInfo],
+ dict[str, tuple[CachedRepoInfo, CachedRevisionInfo]],
+]:
+ """Create lookup tables so CLI commands can resolve repo ids and revisions quickly."""
+ repo_lookup: dict[str, CachedRepoInfo] = {}
+ revision_lookup: dict[str, tuple[CachedRepoInfo, CachedRevisionInfo]] = {}
+ for repo in hf_cache_info.repos:
+ repo_key = repo.cache_id.lower()
+ repo_lookup[repo_key] = repo
+ for revision in repo.revisions:
+ revision_lookup[revision.commit_hash.lower()] = (repo, revision)
+ return repo_lookup, revision_lookup
+
+
+def _parse_hf_uri_target(target: str) -> HfUri:
+ """Parse and validate an hf:// target passed to `hf cache rm`."""
+ uri = parse_hf_uri(target)
+ if not uri.is_repo:
+ raise CLIError("Only repository hf:// URIs are supported by `hf cache rm`.")
+ if uri.revision is not None:
+ hint = (
+ "Drop '@' to delete the file from all cached revisions."
+ if uri.path_in_repo
+ else "Pass the revision hash instead, see 'hf cache ls --revisions'."
+ )
+ raise CLIError(f"Revisions in hf:// URIs are not supported by `hf cache rm`. {hint}")
+ return uri
+
+
+def _cache_id(uri: HfUri) -> str:
+ """Return the `type/id` cache id of a repo URI."""
+ return f"{uri.type}/{uri.id}"
+
+
+def collect_cache_entries(
+ hf_cache_info: HFCacheInfo, *, include_revisions: bool
+) -> tuple[list[CacheEntry], RepoRefsMap]:
+ """Flatten cache metadata into rows consumed by `hf cache ls`."""
+ entries: list[CacheEntry] = []
+ repo_refs_map: RepoRefsMap = {}
+ sorted_repos = sorted(hf_cache_info.repos, key=lambda repo: (repo.repo_type, repo.repo_id.lower()))
+ for repo in sorted_repos:
+ repo_refs_map[repo] = frozenset({ref for revision in repo.revisions for ref in revision.refs})
+ if include_revisions:
+ for revision in sorted(repo.revisions, key=lambda rev: rev.commit_hash):
+ entries.append((repo, revision))
+ else:
+ entries.append((repo, None))
+ if include_revisions:
+ entries.sort(
+ key=lambda entry: (
+ entry[0].cache_id,
+ entry[1].commit_hash if entry[1] is not None else "",
+ )
+ )
+ else:
+ entries.sort(key=lambda entry: entry[0].cache_id)
+ return entries, repo_refs_map
+
+
+def compile_cache_filter(
+ expr: str, repo_refs_map: RepoRefsMap
+) -> Callable[[CachedRepoInfo, CachedRevisionInfo | None, float], bool]:
+ """Convert a `hf cache ls` filter expression into the yes/no test we apply to each cache entry before displaying it."""
+ match = _FILTER_PATTERN.fullmatch(expr.strip())
+ if not match:
+ raise ValueError(f"Invalid filter expression: '{expr}'.")
+
+ key = match.group("key").lower()
+ op = match.group("op")
+ value_raw = match.group("value").strip()
+
+ if op not in _ALLOWED_OPERATORS:
+ raise ValueError(f"Unsupported operator '{op}' in filter '{expr}'. Must be one of {list(_ALLOWED_OPERATORS)}.")
+
+ if key not in _FILTER_KEYS:
+ raise ValueError(f"Unsupported filter key '{key}' in '{expr}'. Must be one of {list(_FILTER_KEYS)}.")
+ # at this point we know that key is in `_FILTER_KEYS`
+ if key == "size":
+ size_threshold = parse_size(value_raw)
+ return lambda repo, revision, _: _compare_numeric(
+ revision.size_on_disk if revision is not None else repo.size_on_disk,
+ op,
+ size_threshold,
+ )
+
+ if key in {"modified", "accessed"}:
+ seconds = parse_duration(value_raw.strip())
+
+ def _time_filter(repo: CachedRepoInfo, revision: CachedRevisionInfo | None, now: float) -> bool:
+ timestamp = (
+ repo.last_accessed
+ if key == "accessed"
+ else revision.last_modified
+ if revision is not None
+ else repo.last_modified
+ )
+ if timestamp is None:
+ return False
+ return _compare_numeric(now - timestamp, op, seconds)
+
+ return _time_filter
+
+ if key == "type":
+ expected = value_raw.lower()
+
+ if op != "=":
+ raise ValueError(f"Only '=' is supported for 'type' filters. Got '{op}'.")
+
+ def _type_filter(repo: CachedRepoInfo, revision: CachedRevisionInfo | None, _: float) -> bool:
+ return repo.repo_type.lower() == expected
+
+ return _type_filter
+
+ else: # key == "refs"
+ if op != "=":
+ raise ValueError(f"Only '=' is supported for 'refs' filters. Got {op}.")
+
+ def _refs_filter(repo: CachedRepoInfo, revision: CachedRevisionInfo | None, _: float) -> bool:
+ refs = revision.refs if revision is not None else repo_refs_map.get(repo, frozenset())
+ return value_raw.lower() in [ref.lower() for ref in refs]
+
+ return _refs_filter
+
+
+def _compare_numeric(left: float | None, op: str, right: float) -> bool:
+ """Evaluate numeric comparisons for filters."""
+ if left is None:
+ return False
+
+ comparisons = {
+ "=": left == right,
+ "!=": left != right,
+ ">": left > right,
+ "<": left < right,
+ ">=": left >= right,
+ "<=": left <= right,
+ }
+
+ if op not in comparisons:
+ raise ValueError(f"Unsupported numeric comparison operator: {op}")
+
+ return comparisons[op]
+
+
+def compile_cache_sort(sort_expr: str) -> tuple[Callable[[CacheEntry], tuple[Any, ...]], bool]:
+ """Convert a `hf cache ls` sort expression into a key function for sorting entries.
+
+ Returns:
+ A tuple of (key_function, reverse_flag) where reverse_flag indicates whether
+ to sort in descending order (True) or ascending order (False).
+ """
+ match = _SORT_PATTERN.fullmatch(sort_expr.strip().lower())
+ if not match:
+ raise ValueError(f"Invalid sort expression: '{sort_expr}'. Expected format: 'key' or 'key:asc' or 'key:desc'.")
+
+ key = match.group("key").lower()
+ explicit_order = match.group("order")
+
+ if key not in _SORT_KEYS:
+ raise ValueError(f"Unsupported sort key '{key}' in '{sort_expr}'. Must be one of {list(_SORT_KEYS)}.")
+
+ # Use explicit order if provided, otherwise use default for the key
+ order = explicit_order if explicit_order else _SORT_DEFAULT_ORDER[key]
+ reverse = order == "desc"
+
+ def _sort_key(entry: CacheEntry) -> tuple[Any, ...]:
+ repo, revision = entry
+
+ if key == "name":
+ # Sort by cache_id (repo type/id)
+ value: Any = repo.cache_id.lower()
+ return (value,)
+
+ if key == "size":
+ # Use revision size if available, otherwise repo size
+ value = revision.size_on_disk if revision is not None else repo.size_on_disk
+ return (value,)
+
+ if key == "accessed":
+ # For revisions, accessed is not available per-revision, use repo's last_accessed
+ # For repos, use repo's last_accessed
+ value = repo.last_accessed if repo.last_accessed is not None else 0.0
+ return (value,)
+
+ if key == "modified":
+ # Use revision's last_modified if available, otherwise repo's last_modified
+ if revision is not None:
+ value = revision.last_modified if revision.last_modified is not None else 0.0
+ else:
+ value = repo.last_modified if repo.last_modified is not None else 0.0
+ return (value,)
+
+ # Should never reach here due to validation above
+ raise ValueError(f"Unsupported sort key: {key}")
+
+ return _sort_key, reverse
+
+
+def _resolve_deletion_targets(hf_cache_info: HFCacheInfo, targets: list[str]) -> _DeletionResolution:
+ """Resolve the deletion targets into a deletion resolution."""
+ targets = [stripped for target in targets if (stripped := target.strip())]
+ uris = {target: _parse_hf_uri_target(target) for target in targets if target.startswith("hf://")}
+ file_uris = {target: uri for target, uri in uris.items() if uri.path_in_repo}
+ if file_uris:
+ if len(file_uris) != len(set(targets)):
+ raise CLIError("File targets cannot be mixed with repository or revision targets.")
+ return _resolve_file_targets(hf_cache_info, file_uris)
+
+ repo_lookup, revision_lookup = build_cache_index(hf_cache_info)
+
+ selected: dict[CachedRepoInfo, set[CachedRevisionInfo]] = defaultdict(set)
+ revisions: set[str] = set()
+ missing: list[str] = []
+
+ for target in targets:
+ lowered = target.lower()
+
+ if re.fullmatch(r"[0-9a-fA-F]{40}", lowered):
+ match = revision_lookup.get(lowered)
+ if match is None:
+ missing.append(target)
+ continue
+ repo, revision = match
+ selected[repo].add(revision)
+ revisions.add(revision.commit_hash)
+ continue
+
+ cache_id = _cache_id(uris[target]) if target in uris else target
+ matched_repo = repo_lookup.get(cache_id.lower())
+ if matched_repo is None:
+ missing.append(target)
+ continue
+
+ for revision in matched_repo.revisions:
+ selected[matched_repo].add(revision)
+ revisions.add(revision.commit_hash)
+
+ frozen_selected = {repo: frozenset(revs) for repo, revs in selected.items()}
+ return _DeletionResolution(
+ revisions=frozenset(revisions),
+ selected=frozen_selected,
+ missing=tuple(missing),
+ )
+
+
+def _resolve_file_targets(hf_cache_info: HFCacheInfo, file_uris: dict[str, HfUri]) -> _DeletionResolution:
+ """Select the cached files matching hf:// file URIs, in every cached revision of their repo."""
+ repo_lookup, _ = build_cache_index(hf_cache_info)
+ files: dict[CachedFileInfo, str] = {}
+ missing: list[str] = []
+ for target, uri in file_uris.items():
+ repo = repo_lookup.get(_cache_id(uri).lower())
+ if repo is None:
+ missing.append(target)
+ continue
+ matches = {
+ file: f"{repo.cache_id}@{revision.commit_hash}/{uri.path_in_repo}"
+ for revision in repo.revisions
+ for file in revision.files
+ if file.file_path.relative_to(revision.snapshot_path).as_posix() == uri.path_in_repo
+ }
+ if not matches:
+ missing.append(target)
+ files.update(matches)
+ return _DeletionResolution(revisions=frozenset(), selected={}, missing=tuple(missing), files=files)
+
+
+#### Cache CLI commands
+
+
+@cache_cli.command(
+ "list | ls",
+ examples=[
+ "hf cache ls",
+ "hf cache ls --revisions",
+ 'hf cache ls --filter "size>1GB" --limit 20',
+ "hf cache ls --format json",
+ ],
+)
+def ls(
+ cache_dir: Annotated[
+ str | None,
+ Option(
+ help="Cache directory to scan (defaults to Hugging Face cache).",
+ ),
+ ] = None,
+ revisions: Annotated[
+ bool,
+ Option(
+ help="Include revisions in the output instead of aggregated repositories.",
+ ),
+ ] = False,
+ filter: Annotated[
+ list[str] | None,
+ Option(
+ "-f",
+ "--filter",
+ help="Filter entries (e.g. 'size>1GB', 'type=model', 'accessed>7d'). Can be used multiple times.",
+ ),
+ ] = None,
+ sort: Annotated[
+ SortOptions | None,
+ Option(
+ help="Sort entries by key. Supported keys: 'accessed', 'modified', 'name', 'size'. "
+ "Append ':asc' or ':desc' to explicitly set the order (e.g., 'modified:asc'). "
+ "Defaults: 'accessed', 'modified', 'size' default to 'desc' (newest/biggest first); "
+ "'name' defaults to 'asc' (alphabetical).",
+ ),
+ ] = None,
+ limit: Annotated[
+ int | None,
+ Option(
+ help="Limit the number of results returned. Returns only the top N entries after sorting.",
+ ),
+ ] = None,
+ show_warnings: Annotated[
+ bool,
+ Option(
+ help="Show warnings about cache inconsistencies.",
+ ),
+ ] = False,
+) -> None:
+ """List cached repositories or revisions."""
+ try:
+ hf_cache_info = scan_cache_dir(cache_dir)
+ except CacheNotFound as exc:
+ raise CLIError(f"Cache directory not found: {exc.cache_dir}") from exc
+
+ filters = filter or []
+
+ entries, repo_refs_map = collect_cache_entries(hf_cache_info, include_revisions=revisions)
+ try:
+ filter_fns = [compile_cache_filter(expr, repo_refs_map) for expr in filters]
+ except ValueError as exc:
+ raise click.BadParameter(str(exc)) from exc
+
+ now = time.time()
+ for fn in filter_fns:
+ entries = [entry for entry in entries if fn(entry[0], entry[1], now)]
+
+ # Apply sorting if requested
+ if sort:
+ try:
+ sort_key_fn, reverse = compile_cache_sort(sort.value)
+ entries.sort(key=sort_key_fn, reverse=reverse)
+ except ValueError as exc:
+ raise click.BadParameter(str(exc)) from exc
+
+ # Apply limit if requested
+ if limit is not None:
+ if limit < 0:
+ raise click.BadParameter(f"Limit must be a positive integer, got {limit}.")
+ entries = entries[:limit]
+
+ if revisions:
+ items = [
+ {
+ "id": repo.cache_id,
+ "repo_id": repo.repo_id,
+ "repo_type": repo.repo_type,
+ "revision": revision.commit_hash,
+ "snapshot_path": str(revision.snapshot_path),
+ "size": revision.size_on_disk_str,
+ "last_modified": revision.last_modified_str,
+ "refs": sorted(revision.refs),
+ }
+ for repo, revision in entries
+ if revision is not None
+ ]
+ out.table(
+ items,
+ headers=["id", "revision", "size", "last_modified", "refs"],
+ id_key="revision",
+ alignments={"size": "right"},
+ )
+ else:
+ items = [
+ {
+ "id": repo.cache_id,
+ "repo_id": repo.repo_id,
+ "repo_type": repo.repo_type,
+ "size": repo.size_on_disk_str,
+ "last_accessed": repo.last_accessed_str or "",
+ "last_modified": repo.last_modified_str,
+ "refs": sorted(repo_refs_map.get(repo, frozenset())),
+ }
+ for repo, _ in entries
+ ]
+ out.table(
+ items,
+ headers=["id", "size", "last_accessed", "last_modified", "refs"],
+ id_key="id",
+ alignments={"size": "right"},
+ )
+
+ if entries:
+ unique_repos = {repo for repo, _ in entries}
+ repo_count = len(unique_repos)
+ if revisions:
+ revision_count = sum(1 for _, rev in entries if rev is not None)
+ total_size = sum(rev.size_on_disk for _, rev in entries if rev is not None)
+ else:
+ revision_count = sum(len(repo.revisions) for repo in unique_repos)
+ if filters or limit is not None:
+ total_size = sum(repo.size_on_disk for repo in unique_repos)
+ else:
+ # Per-repo sizes double-count blobs shared across repos: report the physical total.
+ total_size = hf_cache_info.size_on_disk
+ out.text(
+ ANSI.bold(
+ f"\nFound {repo_count} repo(s) for a total of {revision_count} revision(s)"
+ f" and {_format_size(total_size)} on disk."
+ )
+ )
+
+ if len(hf_cache_info.warnings):
+ if show_warnings:
+ for warning in hf_cache_info.warnings:
+ out.warning(str(warning).rstrip(".") + ". Repo ignored.")
+ else:
+ out.warning(
+ f"Found {len(hf_cache_info.warnings)} cache inconsistencies. Re-run with `--show-warnings` to display them."
+ )
+
+ incomplete_files = hf_cache_info.incomplete_files
+ if incomplete_files:
+ out.hint(
+ f"Found {len(incomplete_files)} incomplete download(s) totalling "
+ f"{_format_size(hf_cache_info.incomplete_size_on_disk)}. "
+ "Remove them with 'hf cache prune'."
+ )
+
+
+@cache_cli.command(
+ examples=[
+ "hf cache rm model/gpt2",
+ "hf cache rm hf://models/openai-community/gpt2",
+ "hf cache rm hf://models/openai-community/gpt2/model.safetensors",
+ "hf cache rm ",
+ "hf cache rm model/gpt2 --dry-run",
+ "hf cache rm model/gpt2 --yes",
+ ],
+)
+def rm(
+ targets: Annotated[
+ list[str],
+ Argument(
+ help="One or more repo IDs (e.g. model/bert-base-uncased), hf:// URIs (repo or file), or revision hashes to delete.",
+ ),
+ ],
+ cache_dir: Annotated[
+ str | None,
+ Option(
+ help="Cache directory to scan (defaults to Hugging Face cache).",
+ ),
+ ] = None,
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ dry_run: Annotated[
+ bool,
+ Option(
+ help="Preview deletions without removing anything.",
+ ),
+ ] = False,
+) -> None:
+ """Remove cached repositories, revisions or files."""
+ try:
+ hf_cache_info = scan_cache_dir(cache_dir)
+ except CacheNotFound as exc:
+ raise CLIError(f"Cache directory not found: {exc.cache_dir}") from exc
+
+ resolution = _resolve_deletion_targets(hf_cache_info, targets)
+
+ if resolution.missing:
+ details = "\n".join(f" - {entry}" for entry in resolution.missing)
+ out.warning(f"Could not find in cache:\n{details}")
+
+ if resolution.files:
+ strategy = hf_cache_info.delete_files(*resolution.files)
+ out.text(f"About to delete {len(resolution.files)} file(s) totalling {strategy.expected_freed_size_str}.")
+ for label in sorted(resolution.files.values()):
+ out.text(f" - {label}")
+ if dry_run:
+ out.result(
+ "Dry run: no files were deleted.",
+ dry_run=True,
+ files=len(resolution.files),
+ size=strategy.expected_freed_size_str,
+ )
+ return
+ out.confirm("Proceed with deletion?", yes=yes)
+ strategy.execute()
+ out.result(
+ f"Deleted {len(resolution.files)} file(s); freed {strategy.expected_freed_size_str}.",
+ files_deleted=len(resolution.files),
+ freed=strategy.expected_freed_size_str,
+ )
+ return
+
+ if len(resolution.revisions) == 0:
+ out.text("Nothing to delete.")
+ raise click.exceptions.Exit(code=0)
+
+ strategy = hf_cache_info.delete_revisions(*sorted(resolution.revisions))
+ counts = summarize_deletions(resolution.selected)
+
+ summary_parts: list[str] = []
+ if counts.repo_count:
+ summary_parts.append(f"{counts.repo_count} repo(s)")
+ if counts.partial_revision_count:
+ summary_parts.append(f"{counts.partial_revision_count} revision(s)")
+ if not summary_parts:
+ summary_parts.append(f"{counts.total_revision_count} revision(s)")
+
+ summary_text = " and ".join(summary_parts)
+ out.text(f"About to delete {summary_text} totalling {strategy.expected_freed_size_str}.")
+ print_cache_selected_revisions(resolution.selected)
+
+ if dry_run:
+ out.result(
+ "Dry run: no files were deleted.",
+ dry_run=True,
+ repos=counts.repo_count,
+ revisions=counts.total_revision_count,
+ size=strategy.expected_freed_size_str,
+ )
+ return
+
+ out.confirm("Proceed with deletion?", yes=yes)
+
+ strategy.execute()
+ counts = summarize_deletions(resolution.selected)
+ out.result(
+ f"Deleted {counts.repo_count} repo(s) and {counts.total_revision_count} revision(s);"
+ f" freed {strategy.expected_freed_size_str}.",
+ repos_deleted=counts.repo_count,
+ revisions_deleted=counts.total_revision_count,
+ freed=strategy.expected_freed_size_str,
+ )
+
+
+@cache_cli.command(examples=["hf cache prune", "hf cache prune --dry-run"])
+def prune(
+ cache_dir: Annotated[
+ str | None,
+ Option(
+ help="Cache directory to scan (defaults to Hugging Face cache).",
+ ),
+ ] = None,
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ dry_run: Annotated[
+ bool,
+ Option(
+ help="Preview deletions without removing anything.",
+ ),
+ ] = False,
+) -> None:
+ """Remove detached revisions and incomplete downloads from the cache."""
+ try:
+ hf_cache_info = scan_cache_dir(cache_dir)
+ except CacheNotFound as exc:
+ raise CLIError(f"Cache directory not found: {exc.cache_dir}") from exc
+
+ selected: dict[CachedRepoInfo, frozenset[CachedRevisionInfo]] = {}
+ revisions: set[str] = set()
+ for repo in hf_cache_info.repos:
+ detached = frozenset(revision for revision in repo.revisions if len(revision.refs) == 0)
+ if not detached:
+ continue
+ selected[repo] = detached
+ revisions.update(revision.commit_hash for revision in detached)
+
+ incomplete_files = hf_cache_info.incomplete_files
+ # Shared blobs left behind when a repo folder is deleted manually or by an older client.
+ unreferenced_blobs = (
+ _shared_blobs.unreferenced_shared_blobs(hf_cache_info.cache_dir) if hf_cache_info.cache_dir is not None else {}
+ )
+
+ if len(revisions) == 0 and not incomplete_files and not unreferenced_blobs:
+ out.text("No unreferenced revisions, incomplete downloads or shared blobs found. Nothing to prune.")
+ return
+
+ strategy = hf_cache_info.delete_revisions(*sorted(revisions))
+ counts = summarize_deletions(selected)
+ total_freed = (
+ strategy.expected_freed_size + hf_cache_info.incomplete_size_on_disk + sum(unreferenced_blobs.values())
+ )
+
+ summary = _prune_summary(counts.total_revision_count, len(incomplete_files), len(unreferenced_blobs))
+ out.text(f"About to delete {summary} ({_format_size(total_freed)} total).")
+ print_cache_selected_revisions(selected)
+
+ if dry_run:
+ out.result(
+ "Dry run: no files were deleted.",
+ dry_run=True,
+ revisions=counts.total_revision_count,
+ incomplete=len(incomplete_files), # might be overstated but it's fine
+ shared_blobs=len(unreferenced_blobs),
+ size=_format_size(total_freed),
+ )
+ return
+
+ out.confirm("Proceed?", yes=yes)
+
+ strategy.execute()
+ for incomplete_file in incomplete_files:
+ try:
+ incomplete_file.file_path.unlink()
+ except FileNotFoundError:
+ pass # already removed (e.g. by a full-repo deletion above)
+ except OSError as exc:
+ out.warning(f"Could not delete incomplete file {incomplete_file.file_path}: {exc}")
+ if hf_cache_info.cache_dir is not None:
+ for store_path in unreferenced_blobs:
+ _shared_blobs.sweep_shared_blob(store_path, cache_dir=hf_cache_info.cache_dir)
+ out.result(
+ f"Deleted {summary}; freed {_format_size(total_freed)}.",
+ revisions_deleted=counts.total_revision_count,
+ incomplete_deleted=len(incomplete_files),
+ shared_blobs_deleted=len(unreferenced_blobs),
+ freed=_format_size(total_freed),
+ )
+
+
+@cache_cli.command(
+ examples=[
+ "hf cache verify gpt2",
+ "hf cache verify gpt2 --revision refs/pr/1",
+ "hf cache verify my-dataset --repo-type dataset",
+ ],
+)
+def verify(
+ repo_id: RepoIdArg,
+ repo_type: RepoTypeOpt = RepoTypeOpt.model,
+ revision: RevisionOpt = None,
+ cache_dir: Annotated[
+ str | None,
+ Option(
+ help="Cache directory to use when verifying files from cache (defaults to Hugging Face cache).",
+ ),
+ ] = None,
+ local_dir: Annotated[
+ str | None,
+ Option(
+ help="If set, verify files under this directory instead of the cache.",
+ ),
+ ] = None,
+ fail_on_missing_files: Annotated[
+ bool,
+ Option(
+ "--fail-on-missing-files",
+ help="Fail if some files exist on the remote but are missing locally.",
+ ),
+ ] = False,
+ fail_on_extra_files: Annotated[
+ bool,
+ Option(
+ "--fail-on-extra-files",
+ help="Fail if some files exist locally but are not present on the remote revision.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Verify checksums for a single repo revision from cache or a local directory.
+
+ Examples:
+ - Verify main revision in cache: `hf cache verify gpt2`
+ - Verify specific revision: `hf cache verify gpt2 --revision refs/pr/1`
+ - Verify dataset: `hf cache verify karpathy/fineweb-edu-100b-shuffle --repo-type dataset`
+ - Verify local dir: `hf cache verify deepseek-ai/DeepSeek-OCR --local-dir /path/to/repo`
+ """
+
+ if local_dir is not None and cache_dir is not None:
+ out.error("Cannot pass both --local-dir and --cache-dir. Use one or the other.")
+ raise click.exceptions.Exit(code=2)
+
+ api = get_hf_api(token=token)
+
+ result = api.verify_repo_checksums(
+ repo_id=repo_id,
+ repo_type=repo_type.value if hasattr(repo_type, "value") else str(repo_type),
+ revision=revision,
+ local_dir=local_dir,
+ cache_dir=cache_dir,
+ token=token,
+ )
+
+ exit_code = 0
+
+ if result.mismatches:
+ details = "\n".join(
+ f" - {m['path']}: expected {m['expected']} ({m['algorithm']}), got {m['actual']}"
+ for m in result.mismatches
+ )
+ out.text(f"❌ Checksum verification failed for the following file(s):\n{details}")
+ exit_code = 1
+
+ if result.missing_paths:
+ if fail_on_missing_files:
+ details = "\n".join(f" - {p}" for p in result.missing_paths)
+ out.text(f"❌ Missing files (present remotely, absent locally):\n{details}")
+ exit_code = 1
+ else:
+ out.warning(
+ f"{len(result.missing_paths)} remote file(s) are missing locally. "
+ "Use --fail-on-missing-files for details."
+ )
+
+ if result.extra_paths:
+ if fail_on_extra_files:
+ details = "\n".join(f" - {p}" for p in result.extra_paths)
+ out.text(f"❌ Extra files (present locally, absent remotely):\n{details}")
+ exit_code = 1
+ else:
+ out.warning(
+ f"{len(result.extra_paths)} local file(s) do not exist on the remote repo. "
+ "Use --fail-on-extra-files for details."
+ )
+
+ verified_location = result.verified_path
+
+ if exit_code != 0:
+ out.error(
+ f"Verification failed for '{repo_id}' ({repo_type.value}) in {verified_location}.\n Revision: {result.revision}"
+ )
+ raise click.exceptions.Exit(code=exit_code)
+
+ out.result(
+ f"Verified {result.checked_count} file(s) for {repo_type.value} '{repo_id}'. All checksums match.",
+ repo_id=repo_id,
+ repo_type=repo_type.value,
+ checked=result.checked_count,
+ path=str(verified_location),
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/collections.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/collections.py
new file mode 100644
index 0000000000000000000000000000000000000000..984b11cdc081a3e8e37cc74e0d21fe6320322437
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/collections.py
@@ -0,0 +1,293 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with collections on the Hugging Face Hub."""
+
+import enum
+from typing import Annotated, get_args
+
+from huggingface_hub.hf_api import CollectionItemType_T, CollectionSort_T
+
+from ._cli_utils import LimitOpt, TokenOpt, get_hf_api, typer_factory
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+# Build enums dynamically from Literal types to avoid duplication
+_COLLECTION_ITEM_TYPES = get_args(CollectionItemType_T)
+CollectionItemType = enum.Enum("CollectionItemType", {t: t for t in _COLLECTION_ITEM_TYPES}, type=str) # type: ignore[misc]
+
+_COLLECTION_SORT_OPTIONS = get_args(CollectionSort_T)
+CollectionSort = enum.Enum("CollectionSort", {s: s for s in _COLLECTION_SORT_OPTIONS}, type=str) # type: ignore[misc]
+
+
+collections_cli = typer_factory(help="Interact with collections on the Hub.")
+
+
+@collections_cli.command(
+ "list | ls",
+ examples=[
+ "hf collections ls",
+ "hf collections ls --owner nvidia",
+ "hf collections ls --item models/teknium/OpenHermes-2.5-Mistral-7B --limit 10",
+ ],
+)
+def collections_ls(
+ owner: Annotated[
+ str | None,
+ Option(help="Filter by owner username or organization."),
+ ] = None,
+ item: Annotated[
+ str | None,
+ Option(
+ help='Filter collections containing a specific item (e.g., "models/gpt2", "datasets/squad", "papers/2311.12983").'
+ ),
+ ] = None,
+ sort: Annotated[
+ CollectionSort | None,
+ Option(help="Sort results by last modified, trending, or upvotes."),
+ ] = None,
+ limit: LimitOpt = 10,
+ token: TokenOpt = None,
+) -> None:
+ """List collections on the Hub."""
+ api = get_hf_api(token=token)
+ sort_key = sort.value if sort else None
+ results = [
+ _dataclass_to_dict(collection)
+ for collection in api.list_collections(
+ owner=owner,
+ item=item,
+ sort=sort_key, # type: ignore[arg-type]
+ limit=limit,
+ )
+ ]
+ out.table(results)
+
+
+@collections_cli.command(
+ "info",
+ examples=[
+ "hf collections info username/my-collection-slug",
+ ],
+)
+def collections_info(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a collection on the Hub."""
+ api = get_hf_api(token=token)
+ collection = api.get_collection(collection_slug)
+ out.dict(collection)
+
+
+@collections_cli.command(
+ "create",
+ examples=[
+ 'hf collections create "My Models"',
+ 'hf collections create "My Models" --description "A collection of my favorite models" --private',
+ 'hf collections create "Org Collection" --namespace my-org',
+ ],
+)
+def collections_create(
+ title: Annotated[str, Argument(help="The title of the collection.")],
+ namespace: Annotated[
+ str | None,
+ Option(help="The namespace (username or organization). Defaults to the authenticated user."),
+ ] = None,
+ description: Annotated[
+ str | None,
+ Option(help="A description for the collection (max 150 characters)."),
+ ] = None,
+ private: Annotated[
+ bool,
+ Option(help="Create a private collection."),
+ ] = False,
+ exists_ok: Annotated[
+ bool,
+ Option(help="Do not raise an error if the collection already exists."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Create a new collection on the Hub."""
+ api = get_hf_api(token=token)
+ collection = api.create_collection(
+ title=title,
+ namespace=namespace,
+ description=description,
+ private=private,
+ exists_ok=exists_ok,
+ )
+ out.result("Collection created", slug=collection.slug, url=collection.url)
+
+
+@collections_cli.command(
+ "update",
+ examples=[
+ 'hf collections update username/my-collection --title "New Title"',
+ 'hf collections update username/my-collection --description "Updated description"',
+ "hf collections update username/my-collection --private --theme green",
+ ],
+)
+def collections_update(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ title: Annotated[
+ str | None,
+ Option(help="The new title for the collection."),
+ ] = None,
+ description: Annotated[
+ str | None,
+ Option(help="The new description for the collection (max 150 characters)."),
+ ] = None,
+ position: Annotated[
+ int | None,
+ Option(help="The new position of the collection in the owner's list."),
+ ] = None,
+ private: Annotated[
+ bool | None,
+ Option(help="Whether the collection should be private."),
+ ] = None,
+ theme: Annotated[
+ str | None,
+ Option(help="The theme color for the collection (e.g., 'green', 'blue')."),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update a collection's metadata on the Hub."""
+ api = get_hf_api(token=token)
+ collection = api.update_collection_metadata(
+ collection_slug=collection_slug,
+ title=title,
+ description=description,
+ position=position,
+ private=private,
+ theme=theme,
+ )
+ out.result("Collection updated", slug=collection.slug, url=collection.url)
+
+
+@collections_cli.command(
+ "delete",
+ examples=[
+ "hf collections delete username/my-collection",
+ "hf collections delete username/my-collection --missing-ok",
+ ],
+)
+def collections_delete(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ missing_ok: Annotated[
+ bool,
+ Option(help="Do not raise an error if the collection doesn't exist."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete a collection from the Hub."""
+ api = get_hf_api(token=token)
+ api.delete_collection(collection_slug, missing_ok=missing_ok)
+ out.result("Collection deleted", slug=collection_slug)
+
+
+@collections_cli.command(
+ "add-item",
+ examples=[
+ "hf collections add-item username/my-collection moonshotai/kimi-k2 model",
+ 'hf collections add-item username/my-collection Qwen/DeepPlanning dataset --note "Useful dataset"',
+ "hf collections add-item username/my-collection Tongyi-MAI/Z-Image space",
+ ],
+)
+def collections_add_item(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ item_id: Annotated[str, Argument(help="The ID of the item to add (repo_id for repos, paper ID for papers).")],
+ item_type: Annotated[
+ CollectionItemType,
+ Argument(help="The type of item (model, dataset, space, paper, collection, or bucket)."),
+ ],
+ note: Annotated[
+ str | None,
+ Option(help="A note to attach to the item (max 500 characters)."),
+ ] = None,
+ exists_ok: Annotated[
+ bool,
+ Option(help="Do not raise an error if the item is already in the collection."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Add an item to a collection."""
+ api = get_hf_api(token=token)
+ collection = api.add_collection_item(
+ collection_slug=collection_slug,
+ item_id=item_id,
+ item_type=item_type.value, # type: ignore[arg-type]
+ note=note,
+ exists_ok=exists_ok,
+ )
+ out.result("Item added to collection", slug=collection_slug, url=collection.url)
+
+
+@collections_cli.command(
+ "update-item",
+ examples=[
+ 'hf collections update-item username/my-collection ITEM_OBJECT_ID --note "Updated note"',
+ "hf collections update-item username/my-collection ITEM_OBJECT_ID --position 0",
+ ],
+)
+def collections_update_item(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ item_object_id: Annotated[
+ str,
+ Argument(help="The ID of the item in the collection (from 'item_object_id' field, not the repo_id)."),
+ ],
+ note: Annotated[
+ str | None,
+ Option(help="A new note for the item (max 500 characters)."),
+ ] = None,
+ position: Annotated[
+ int | None,
+ Option(help="The new position of the item in the collection."),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update an item in a collection."""
+ api = get_hf_api(token=token)
+ api.update_collection_item(
+ collection_slug=collection_slug,
+ item_object_id=item_object_id,
+ note=note,
+ position=position,
+ )
+ out.result("Item updated in collection", slug=collection_slug)
+
+
+@collections_cli.command("delete-item")
+def collections_delete_item(
+ collection_slug: Annotated[str, Argument(help="The collection slug (e.g., 'username/collection-slug').")],
+ item_object_id: Annotated[
+ str,
+ Argument(
+ help="The ID of the item in the collection (retrieved from `item_object_id` field returned by 'hf collections info'."
+ ),
+ ],
+ missing_ok: Annotated[
+ bool,
+ Option(help="Do not raise an error if the item doesn't exist."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete an item from a collection."""
+ api = get_hf_api(token=token)
+ api.delete_collection_item(
+ collection_slug=collection_slug,
+ item_object_id=item_object_id,
+ missing_ok=missing_ok,
+ )
+ out.result("Item deleted from collection", slug=collection_slug)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/datasets.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..15625504da845c384f7ab77b2b553e69be76d30f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/datasets.py
@@ -0,0 +1,274 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with datasets on the Hugging Face Hub."""
+
+import enum
+from typing import Annotated, get_args
+
+import click
+
+from huggingface_hub._dataset_viewer import execute_raw_sql_query
+from huggingface_hub.errors import CLIError, RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.hf_api import DatasetSort_T, ExpandDatasetProperty_T
+from huggingface_hub.repocard import DatasetCard
+
+from ._cli_utils import (
+ REPO_LIST_DEFAULT_LIMIT,
+ AuthorOpt,
+ FilterOpt,
+ LimitOpt,
+ RevisionOpt,
+ SearchOpt,
+ TokenOpt,
+ get_hf_api,
+ make_expand_properties_parser,
+ typer_factory,
+)
+from ._file_listing import list_repo_files_cmd
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+_EXPAND_PROPERTIES = sorted(get_args(ExpandDatasetProperty_T))
+_SORT_OPTIONS = get_args(DatasetSort_T)
+DatasetSortEnum = enum.Enum("DatasetSortEnum", {s: s for s in _SORT_OPTIONS}, type=str) # type: ignore[misc]
+
+
+ExpandOpt = Annotated[
+ str | None,
+ Option(
+ help=f"Comma-separated properties to return. When used, only the listed properties (and id) are returned. Example: '--expand=downloads,likes,tags'. Valid: {', '.join(_EXPAND_PROPERTIES)}.",
+ callback=make_expand_properties_parser(_EXPAND_PROPERTIES),
+ ),
+]
+
+
+datasets_cli = typer_factory(help="Interact with datasets on the Hub.")
+
+
+@datasets_cli.command(
+ "list | ls",
+ examples=[
+ "hf datasets ls",
+ "hf datasets ls --sort downloads --limit 10",
+ 'hf datasets ls --search "code"',
+ "hf datasets ls --filter benchmark:official",
+ "hf datasets ls HuggingFaceFW/fineweb",
+ "hf datasets ls HuggingFaceFW/fineweb -R",
+ "hf datasets ls HuggingFaceFW/fineweb --tree -h",
+ ],
+)
+def datasets_ls(
+ repo_id: Annotated[
+ str | None,
+ Argument(help="Dataset ID (e.g. `username/repo-name`) to list files from. If omitted, lists datasets."),
+ ] = None,
+ search: SearchOpt = None,
+ author: AuthorOpt = None,
+ filter: FilterOpt = None,
+ sort: Annotated[
+ DatasetSortEnum | None,
+ Option(help="Sort results."),
+ ] = None,
+ limit: LimitOpt = REPO_LIST_DEFAULT_LIMIT,
+ expand: ExpandOpt = None,
+ human_readable: Annotated[
+ bool,
+ Option("--human-readable", "-h", help="Show sizes in human readable format (only for listing files)."),
+ ] = False,
+ as_tree: Annotated[
+ bool,
+ Option("--tree", help="List files in tree format (only for listing files)."),
+ ] = False,
+ recursive: Annotated[
+ bool,
+ Option("--recursive", "-R", help="List files recursively (only for listing files)."),
+ ] = False,
+ revision: RevisionOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List datasets on the Hub, or files in a dataset repo.
+
+ When called with no argument, lists datasets on the Hub.
+ When called with a dataset ID, lists files in that dataset repo.
+ """
+ if repo_id is not None:
+ if search is not None:
+ raise click.BadParameter("Cannot use --search when listing files.")
+ if author is not None:
+ raise click.BadParameter("Cannot use --author when listing files.")
+ if filter is not None:
+ raise click.BadParameter("Cannot use --filter when listing files.")
+ if sort is not None:
+ raise click.BadParameter("Cannot use --sort when listing files.")
+ if limit != REPO_LIST_DEFAULT_LIMIT:
+ raise click.BadParameter("Cannot use --limit when listing files.")
+ if expand is not None:
+ raise click.BadParameter("Cannot use --expand when listing files.")
+ return list_repo_files_cmd(
+ repo_id=repo_id,
+ repo_type="dataset",
+ human_readable=human_readable,
+ as_tree=as_tree,
+ recursive=recursive,
+ revision=revision,
+ token=token,
+ )
+
+ if as_tree:
+ raise click.BadParameter("Cannot use --tree when listing datasets.")
+ if recursive:
+ raise click.BadParameter("Cannot use --recursive when listing datasets.")
+ if human_readable:
+ raise click.BadParameter("Cannot use --human-readable when listing datasets.")
+ if revision is not None:
+ raise click.BadParameter("Cannot use --revision when listing datasets.")
+
+ api = get_hf_api(token=token)
+ sort_key = sort.value if sort else None
+ results = [
+ _dataclass_to_dict(dataset_info)
+ for dataset_info in api.list_datasets(
+ filter=filter,
+ author=author,
+ search=search,
+ sort=sort_key,
+ limit=limit,
+ expand=expand, # type: ignore
+ )
+ ]
+ out.table(results)
+
+
+@datasets_cli.command(
+ "leaderboard",
+ examples=[
+ "hf datasets leaderboard SWE-bench/SWE-bench_Verified",
+ "hf datasets leaderboard SWE-bench/SWE-bench_Verified --limit 5 --format json",
+ "hf datasets ls --filter benchmark:official # list available leaderboards",
+ ],
+)
+def datasets_leaderboard(
+ dataset_id: Annotated[str, Argument(help="The benchmark dataset ID (e.g. `SWE-bench/SWE-bench_Verified`).")],
+ limit: LimitOpt = 20,
+ token: TokenOpt = None,
+) -> None:
+ """List model scores from a dataset leaderboard. This command helps find the best models for a task or compare models by benchmark scores. Use 'hf datasets ls --filter benchmark:official' to list available leaderboards."""
+ api = get_hf_api(token=token)
+ leaderboard = api.get_dataset_leaderboard(repo_id=dataset_id)
+ results = [_dataclass_to_dict(entry) for entry in leaderboard[:limit]]
+ out.table(
+ results,
+ headers=["rank", "model_id", "value", "source"],
+ id_key="model_id",
+ )
+ out.hint("Use 'hf datasets ls --filter benchmark:official' to list available leaderboards.")
+ if leaderboard:
+ out.hint(f"Use 'hf models info {leaderboard[0].model_id}' to get details about a model.")
+
+
+@datasets_cli.command(
+ "info",
+ examples=[
+ "hf datasets info HuggingFaceFW/fineweb",
+ "hf datasets info my-dataset --expand downloads,likes,tags",
+ ],
+)
+def datasets_info(
+ dataset_id: Annotated[str, Argument(help="The dataset ID (e.g. `username/repo-name`).")],
+ revision: RevisionOpt = None,
+ expand: ExpandOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a dataset on the Hub."""
+ api = get_hf_api(token=token)
+ try:
+ info = api.dataset_info(repo_id=dataset_id, revision=revision, expand=expand) # type: ignore
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"Dataset '{dataset_id}' not found.") from e
+ except RevisionNotFoundError as e:
+ raise CLIError(f"Revision '{revision}' not found on '{dataset_id}'.") from e
+ out.dict(info)
+
+
+@datasets_cli.command(
+ "parquet",
+ examples=[
+ "hf datasets parquet cfahlgren1/hub-stats",
+ "hf datasets parquet cfahlgren1/hub-stats --subset models",
+ "hf datasets parquet cfahlgren1/hub-stats --split train",
+ "hf datasets parquet cfahlgren1/hub-stats --format json",
+ ],
+)
+def datasets_parquet(
+ dataset_id: Annotated[str, Argument(help="The dataset ID (e.g. `username/repo-name`).")],
+ subset: Annotated[str | None, Option("--subset", help="Filter parquet entries by subset/config.")] = None,
+ split: Annotated[str | None, Option(help="Filter parquet entries by split.")] = None,
+ token: TokenOpt = None,
+) -> None:
+ """List parquet file URLs available for a dataset."""
+ api = get_hf_api(token=token)
+ entries = api.list_dataset_parquet_files(repo_id=dataset_id, config=subset)
+ filtered = [entry for entry in entries if split is None or entry.split == split]
+ results = [
+ {"subset": entry.config, "split": entry.split, "url": entry.url, "size": entry.size} for entry in filtered
+ ]
+ out.table(results, headers=["subset", "split", "url", "size"], id_key="url")
+
+
+@datasets_cli.command(
+ "sql",
+ examples=[
+ "hf datasets sql \"SELECT COUNT(*) AS rows FROM read_parquet('https://huggingface.co/api/datasets/cfahlgren1/hub-stats/parquet/models/train/0.parquet')\"",
+ "hf datasets sql \"SELECT * FROM read_parquet('https://huggingface.co/api/datasets/cfahlgren1/hub-stats/parquet/models/train/0.parquet') LIMIT 5\" --format json",
+ ],
+)
+def datasets_sql(
+ sql: Annotated[str, Argument(help="Raw SQL query to execute.")],
+ token: TokenOpt = None,
+) -> None:
+ """Execute a raw SQL query with DuckDB against dataset parquet URLs."""
+ try:
+ result = execute_raw_sql_query(sql_query=sql, token=token)
+ except ImportError as e:
+ raise CLIError(str(e)) from e
+ out.table(result)
+
+
+@datasets_cli.command(
+ "card",
+ examples=[
+ "hf datasets card HuggingFaceFW/fineweb",
+ "hf datasets card HuggingFaceFW/fineweb --metadata",
+ "hf datasets card HuggingFaceFW/fineweb --metadata --format json",
+ "hf datasets card HuggingFaceFW/fineweb --text",
+ ],
+)
+def datasets_card(
+ dataset_id: Annotated[str, Argument(help="The dataset ID (e.g. `username/repo-name`).")],
+ metadata: Annotated[bool, Option("--metadata", help="Output only the metadata from the card.")] = False,
+ text: Annotated[bool, Option("--text", help="Output only the text body (no metadata).")] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Get the dataset card (README) for a dataset on the Hub."""
+ if metadata and text:
+ raise CLIError("--metadata and --text are mutually exclusive.")
+ card = DatasetCard.load(dataset_id, token=token)
+ if metadata:
+ out.dict(card.data.to_dict())
+ elif text:
+ out.text(card.text)
+ else:
+ out.text(card.content)
+ out.hint(f"Use `hf datasets card {dataset_id} --metadata` to extract only the card metadata.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/discussions.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/discussions.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fd3b4217bfa0d143ba7906d12791b013369c912
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/discussions.py
@@ -0,0 +1,497 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with discussions and pull requests on the Hugging Face Hub."""
+
+import enum
+import sys
+from pathlib import Path
+from typing import Annotated
+
+import click
+
+from huggingface_hub import constants
+
+from ._cli_utils import (
+ AuthorOpt,
+ LimitOpt,
+ RepoIdArg,
+ RepoType,
+ RepoTypeOpt,
+ TokenOpt,
+ get_hf_api,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+class DiscussionStatus(str, enum.Enum):
+ open = "open"
+ closed = "closed"
+ merged = "merged"
+ draft = "draft"
+ all = "all"
+
+
+class DiscussionKind(str, enum.Enum):
+ all = "all"
+ discussion = "discussion"
+ pull_request = "pull_request"
+
+
+# "merged" and "draft" are valid Discussion statuses but the Hub API filter
+# (DiscussionStatusFilter) only accepts "all", "open", "closed". When the user
+# asks for merged/draft we fetch with api_status=None (i.e. all) and filter
+# client-side.
+_CLIENT_SIDE_STATUSES = {"merged", "draft"}
+
+
+DiscussionNumArg = Annotated[
+ int,
+ Argument(
+ help="The discussion or pull request number.",
+ min=1,
+ ),
+]
+
+
+def _read_body(body: str | None, body_file: Path | None) -> str | None:
+ """Resolve body text from --body or --body-file (supports '-' for stdin)."""
+ if body is not None and body_file is not None:
+ raise click.BadParameter("Cannot use both --body and --body-file.")
+ if body_file is not None:
+ if str(body_file) == "-":
+ return sys.stdin.read()
+ return body_file.read_text(encoding="utf-8")
+ return body
+
+
+discussions_cli = typer_factory(help="Manage discussions and pull requests on the Hub.")
+
+
+@discussions_cli.command(
+ "list | ls",
+ examples=[
+ "hf discussions list username/my-model",
+ "hf discussions list username/my-model --kind pull_request --status merged",
+ "hf discussions list username/my-dataset --type dataset --status closed",
+ "hf discussions list username/my-model --author alice --format json",
+ ],
+)
+def discussion_list(
+ repo_id: RepoIdArg,
+ status: Annotated[
+ DiscussionStatus,
+ Option(
+ "-s",
+ "--status",
+ help="Filter by status (open, closed, merged, draft, all).",
+ ),
+ ] = DiscussionStatus.open,
+ kind: Annotated[
+ DiscussionKind,
+ Option(
+ "-k",
+ "--kind",
+ help="Filter by kind (discussion, pull_request, all).",
+ ),
+ ] = DiscussionKind.all,
+ author: AuthorOpt = None,
+ limit: LimitOpt = 30,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """List discussions and pull requests on a repo."""
+ api = get_hf_api(token=token)
+
+ api_status: constants.DiscussionStatusFilter | None
+ if status == DiscussionStatus.open:
+ api_status = "open"
+ elif status == DiscussionStatus.closed:
+ api_status = "closed"
+ else:
+ api_status = None
+
+ api_discussion_type: constants.DiscussionTypeFilter | None
+ if kind == DiscussionKind.all:
+ api_discussion_type = None
+ else:
+ api_discussion_type = kind.value # type: ignore[assignment]
+
+ discussions = []
+ for d in api.get_repo_discussions(
+ repo_id=repo_id,
+ author=author,
+ discussion_type=api_discussion_type,
+ discussion_status=api_status,
+ repo_type=repo_type.value,
+ ):
+ if status.value in _CLIENT_SIDE_STATUSES and d.status != status.value:
+ continue
+ discussions.append(d)
+ if len(discussions) >= limit:
+ break
+
+ items = [_dataclass_to_dict(d) for d in discussions]
+ out.table(
+ items,
+ headers=["num", "title", "is_pull_request", "status", "author", "created_at"],
+ id_key="num",
+ )
+
+
+@discussions_cli.command(
+ "info",
+ examples=[
+ "hf discussions info username/my-model 5",
+ "hf discussions info username/my-model 5 --format json",
+ ],
+)
+def discussion_info(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a discussion or pull request."""
+ api = get_hf_api(token=token)
+ details = api.get_discussion_details(
+ repo_id=repo_id,
+ discussion_num=num,
+ repo_type=repo_type.value,
+ )
+ out.dict(details)
+
+
+@discussions_cli.command(
+ "create",
+ examples=[
+ 'hf discussions create username/my-model --title "Bug report"',
+ 'hf discussions create username/my-model --title "Feature request" --body "Please add X"',
+ 'hf discussions create username/my-model --title "Fix typo" --pull-request',
+ 'hf discussions create username/my-dataset --type dataset --title "Data quality issue"',
+ ],
+)
+def discussion_create(
+ repo_id: RepoIdArg,
+ title: Annotated[
+ str,
+ Option(
+ "--title",
+ help="The title of the discussion or pull request.",
+ ),
+ ],
+ body: Annotated[
+ str | None,
+ Option(
+ "--body",
+ help="The description (supports Markdown).",
+ ),
+ ] = None,
+ body_file: Annotated[
+ Path | None,
+ Option(
+ "--body-file",
+ help="Read the description from a file. Use '-' for stdin.",
+ ),
+ ] = None,
+ pull_request: Annotated[
+ bool,
+ Option(
+ "--pull-request",
+ "--pr",
+ help="Create a pull request instead of a discussion.",
+ ),
+ ] = False,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Create a new discussion or pull request on a repo."""
+ description = _read_body(body, body_file)
+ api = get_hf_api(token=token)
+ discussion = api.create_discussion(
+ repo_id=repo_id,
+ title=title,
+ description=description,
+ repo_type=repo_type.value,
+ pull_request=pull_request,
+ )
+ kind = "pull request" if pull_request else "discussion"
+ ref = f"refs/pr/{discussion.num}" if pull_request else None
+ out.result(f"Created {kind} #{discussion.num} on {repo_id}", num=discussion.num, url=discussion.url, ref=ref)
+
+
+@discussions_cli.command(
+ "comment",
+ examples=[
+ 'hf discussions comment username/my-model 5 --body "Thanks for reporting!"',
+ 'hf discussions comment username/my-model 5 --body "LGTM!"',
+ ],
+)
+def discussion_comment(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ body: Annotated[
+ str | None,
+ Option(
+ "--body",
+ help="The comment text (supports Markdown).",
+ ),
+ ] = None,
+ body_file: Annotated[
+ Path | None,
+ Option(
+ "--body-file",
+ help="Read the comment from a file. Use '-' for stdin.",
+ ),
+ ] = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Comment on a discussion or pull request."""
+ comment = _read_body(body, body_file)
+ if comment is None:
+ raise click.BadParameter("Either --body or --body-file is required.")
+ api = get_hf_api(token=token)
+ api.comment_discussion(
+ repo_id=repo_id,
+ discussion_num=num,
+ comment=comment,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Commented on #{num} in {repo_id}", num=num, repo=repo_id)
+
+
+@discussions_cli.command(
+ "edit",
+ examples=[
+ 'hf discussions edit username/my-model 5 abc123 --body "Updated comment."',
+ "hf discussions edit username/my-model 5 abc123 --body-file fixed.md",
+ ],
+)
+def discussion_edit(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ comment_id: Annotated[
+ str,
+ Argument(
+ help="The ID of the comment to edit (see 'hf discussions info ... --format json').",
+ ),
+ ],
+ body: Annotated[
+ str | None,
+ Option(
+ "--body",
+ help="The new comment text (supports Markdown).",
+ ),
+ ] = None,
+ body_file: Annotated[
+ Path | None,
+ Option(
+ "--body-file",
+ help="Read the new comment from a file. Use '-' for stdin.",
+ ),
+ ] = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Edit an existing comment on a discussion or pull request."""
+ new_content = _read_body(body, body_file)
+ if new_content is None:
+ raise click.BadParameter("Either --body or --body-file is required.")
+ api = get_hf_api(token=token)
+ api.edit_discussion_comment(
+ repo_id=repo_id,
+ discussion_num=num,
+ comment_id=comment_id,
+ new_content=new_content,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Edited comment {comment_id} on #{num} in {repo_id}", num=num, repo=repo_id, comment_id=comment_id)
+
+
+@discussions_cli.command(
+ "close",
+ examples=[
+ "hf discussions close username/my-model 5",
+ 'hf discussions close username/my-model 5 --comment "Closing as resolved."',
+ ],
+)
+def discussion_close(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ comment: Annotated[
+ str | None,
+ Option(
+ "--comment",
+ help="An optional comment to post when closing.",
+ ),
+ ] = None,
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Close a discussion or pull request."""
+ out.confirm(f"Close #{num} on '{repo_id}'?", yes=yes)
+ api = get_hf_api(token=token)
+ api.change_discussion_status(
+ repo_id=repo_id,
+ discussion_num=num,
+ new_status="closed",
+ comment=comment,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Closed #{num} in {repo_id}", num=num, repo=repo_id)
+
+
+@discussions_cli.command(
+ "reopen",
+ examples=[
+ "hf discussions reopen username/my-model 5",
+ 'hf discussions reopen username/my-model 5 --comment "Reopening for further investigation."',
+ ],
+)
+def discussion_reopen(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ comment: Annotated[
+ str | None,
+ Option(
+ "--comment",
+ help="An optional comment to post when reopening.",
+ ),
+ ] = None,
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Reopen a closed discussion or pull request."""
+ out.confirm(f"Reopen #{num} on '{repo_id}'?", yes=yes)
+ api = get_hf_api(token=token)
+ api.change_discussion_status(
+ repo_id=repo_id,
+ discussion_num=num,
+ new_status="open",
+ comment=comment,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Reopened #{num} in {repo_id}", num=num, repo=repo_id)
+
+
+@discussions_cli.command(
+ "rename",
+ examples=[
+ 'hf discussions rename username/my-model 5 "Updated title"',
+ ],
+)
+def discussion_rename(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ new_title: Annotated[
+ str,
+ Argument(
+ help="The new title.",
+ ),
+ ],
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Rename a discussion or pull request."""
+ api = get_hf_api(token=token)
+ api.rename_discussion(
+ repo_id=repo_id,
+ discussion_num=num,
+ new_title=new_title,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Renamed #{num} in {repo_id}", num=num, repo=repo_id, title=new_title)
+
+
+@discussions_cli.command(
+ "merge",
+ examples=[
+ "hf discussions merge username/my-model 5",
+ 'hf discussions merge username/my-model 5 --comment "Merging, thanks!"',
+ ],
+)
+def discussion_merge(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ comment: Annotated[
+ str | None,
+ Option(
+ "--comment",
+ help="An optional comment to post when merging.",
+ ),
+ ] = None,
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Merge a pull request."""
+ out.confirm(f"Merge #{num} on '{repo_id}'?", yes=yes)
+ api = get_hf_api(token=token)
+ api.merge_pull_request(
+ repo_id=repo_id,
+ discussion_num=num,
+ comment=comment,
+ repo_type=repo_type.value,
+ )
+ out.result(f"Merged #{num} in {repo_id}", num=num, repo=repo_id)
+
+
+@discussions_cli.command(
+ "diff",
+ examples=[
+ "hf discussions diff username/my-model 5",
+ ],
+)
+def discussion_diff(
+ repo_id: RepoIdArg,
+ num: DiscussionNumArg,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+) -> None:
+ """Show the diff of a pull request."""
+ api = get_hf_api(token=token)
+ details = api.get_discussion_details(
+ repo_id=repo_id,
+ discussion_num=num,
+ repo_type=repo_type.value,
+ )
+ if details.diff:
+ out.text(details.diff)
+ else:
+ out.text("No diff available.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/download.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/download.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8788cccb262a38356591888d280668002ba7140
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/download.py
@@ -0,0 +1,228 @@
+# Copyright 202-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to download files from the Hub with the CLI."""
+
+import warnings
+from typing import Annotated
+
+from huggingface_hub import constants
+from huggingface_hub._snapshot_download import snapshot_download
+from huggingface_hub.errors import CLIError
+from huggingface_hub.file_download import DryRunFileInfo, hf_hub_download
+from huggingface_hub.utils import _format_size, parse_hf_uri
+
+from ._cli_utils import RepoIdArg, RepoType, RepoTypeOptionalOpt, RevisionOpt, TokenOpt
+from ._framework import Argument, Option
+from ._output import out
+
+
+DOWNLOAD_EXAMPLES = [
+ "hf download meta-llama/Llama-3.2-1B-Instruct",
+ "hf download meta-llama/Llama-3.2-1B-Instruct config.json tokenizer.json",
+ 'hf download meta-llama/Llama-3.2-1B-Instruct --include "*.safetensors" --exclude "*.bin"',
+ "hf download meta-llama/Llama-3.2-1B-Instruct --local-dir ./models/llama",
+ "hf download HuggingFaceM4/FineVision art/ --repo-type dataset",
+ "hf download hf://datasets/HuggingFaceH4/ultrachat_200k",
+]
+
+
+def download(
+ repo_id: RepoIdArg,
+ filenames: Annotated[
+ list[str] | None,
+ Argument(
+ help="Files to download (e.g. `config.json`, `data/metadata.jsonl`).",
+ ),
+ ] = None,
+ repo_type: RepoTypeOptionalOpt = None,
+ revision: RevisionOpt = None,
+ include: Annotated[
+ list[str] | None,
+ Option(
+ help="Glob patterns to include from files to download. eg: *.json",
+ ),
+ ] = None,
+ exclude: Annotated[
+ list[str] | None,
+ Option(
+ help="Glob patterns to exclude from files to download.",
+ ),
+ ] = None,
+ cache_dir: Annotated[
+ str | None,
+ Option(
+ help="Directory where to save files.",
+ ),
+ ] = None,
+ local_dir: Annotated[
+ str | None,
+ Option(
+ help="If set, the downloaded file will be placed under this directory. Check out https://huggingface.co/docs/huggingface_hub/guides/download#download-files-to-a-local-folder for more details.",
+ ),
+ ] = None,
+ force_download: Annotated[
+ bool,
+ Option(
+ help="If True, the files will be downloaded even if they are already cached.",
+ ),
+ ] = False,
+ dry_run: Annotated[
+ bool,
+ Option(
+ help="If True, perform a dry run without actually downloading the file.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+ max_workers: Annotated[
+ int,
+ Option(
+ help="Maximum number of workers to use for downloading files. Default is 8.",
+ ),
+ ] = 8,
+) -> None:
+ """Download files from the Hub."""
+ if local_dir is not None and cache_dir is not None:
+ raise CLIError(
+ "Cannot use both `--local-dir` and `--cache-dir` at the same time. "
+ "Use `--cache-dir` (or set the HF_HOME environment variable) for shared caching, "
+ "or `--local-dir` for a one-off download to a specific directory."
+ )
+
+ # `repo_id` may be a plain repo id or an `hf://` URI (e.g. `hf://datasets/my-org/my-dataset@v1.0/data/`).
+ # When a URI is provided, it is authoritative for the repo type, revision and (optionally) file path,
+ # so explicit `--repo-type` / `--revision` options are forbidden alongside it.
+ # We branch on the `hf://` prefix (the user's *intent*) rather than on whether the string parses as a
+ # valid URI: a malformed URI then surfaces a precise `HfUriError` (formatted globally in `cli/_errors.py`)
+ # instead of silently falling through to the plain-repo-id path and failing later with an opaque error.
+ if repo_id.startswith(constants.HF_PROTOCOL):
+ if repo_type is not None:
+ raise CLIError(f"'--repo-type' cannot be used with an 'hf://' URI ('{repo_id}').")
+ if revision is not None:
+ raise CLIError(f"'--revision' cannot be used with an 'hf://' URI ('{repo_id}').")
+ uri = parse_hf_uri(repo_id)
+ if uri.is_bucket:
+ raise CLIError("Buckets are not supported by `hf download`. Use `hf sync` instead.")
+ # The URI parser strips trailing slashes, but `hf download` uses a trailing '/' to denote a subfolder
+ # download (e.g. `data/` -> `data/**`). Re-append it when the URI explicitly ended with '/' so a folder
+ # URI keeps routing through the subfolder code path below.
+ path_in_repo = uri.path_in_repo
+ if path_in_repo and repo_id.endswith("/"):
+ path_in_repo += "/"
+ repo_id, repo_type_str, revision = uri.id, uri.type, uri.revision
+ if path_in_repo:
+ if filenames:
+ raise CLIError(
+ f"Cannot combine a file path in the hf:// URI ('{path_in_repo}') with positional filenames {filenames}."
+ )
+ filenames = [path_in_repo]
+ else:
+ repo_type_str = (repo_type or RepoType.model).value
+
+ def run_download() -> str | DryRunFileInfo | list[DryRunFileInfo]:
+ filenames_list = filenames if filenames is not None else []
+
+ # Separate subfolder patterns (ending with '/') from regular filenames
+ # Subfolders like "art/" are converted to include patterns like "art/**"
+ subfolders = [f for f in filenames_list if f.endswith("/")]
+ subfolder_patterns = [f"{f.rstrip('/')}/**" for f in subfolders]
+ regular_filenames = [f for f in filenames_list if not f.endswith("/")]
+
+ # Error if subfolder patterns are combined with --include/--exclude
+ # Guide user to use --include instead of subfolder argument
+ if len(subfolder_patterns) > 0:
+ if include is not None and len(include) > 0:
+ raise CLIError(
+ f"Cannot combine subfolder argument ('{subfolders[0]}') with `--include`. "
+ f'Please use `--include "{subfolders[0]}*"` instead.'
+ )
+ if exclude is not None and len(exclude) > 0:
+ raise CLIError(
+ f"Cannot combine subfolder argument ('{subfolders[0]}') with `--exclude`. "
+ f'Please use `--include "{subfolders[0]}*"` with `--exclude` instead.'
+ )
+
+ # Warn user if patterns are ignored (only if regular filenames are provided)
+ if len(regular_filenames) > 0:
+ if include is not None and len(include) > 0:
+ warnings.warn("Ignoring `--include` since filenames have been explicitly set.")
+ if exclude is not None and len(exclude) > 0:
+ warnings.warn("Ignoring `--exclude` since filenames have been explicitly set.")
+
+ # Single file to download (not a subfolder): use `hf_hub_download`
+ if len(regular_filenames) == 1 and len(subfolder_patterns) == 0:
+ return hf_hub_download(
+ repo_id=repo_id,
+ repo_type=repo_type_str,
+ revision=revision,
+ filename=regular_filenames[0],
+ cache_dir=cache_dir,
+ force_download=force_download,
+ token=token,
+ local_dir=local_dir,
+ library_name="huggingface-cli",
+ dry_run=dry_run,
+ )
+
+ # Otherwise: use `snapshot_download` to ensure all files comes from same revision
+ if len(regular_filenames) == 0 and len(subfolder_patterns) == 0:
+ # No filenames provided: use include/exclude patterns
+ allow_patterns = include
+ ignore_patterns = exclude
+ else:
+ # Combine regular filenames and subfolder patterns as allow_patterns
+ allow_patterns = regular_filenames + subfolder_patterns
+ ignore_patterns = None
+
+ return snapshot_download(
+ repo_id=repo_id,
+ repo_type=repo_type_str,
+ revision=revision,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ force_download=force_download,
+ cache_dir=cache_dir,
+ token=token,
+ local_dir=local_dir,
+ library_name="huggingface-cli",
+ max_workers=max_workers,
+ dry_run=dry_run,
+ )
+
+ def _print_result(result: str | DryRunFileInfo | list[DryRunFileInfo]) -> None:
+ if isinstance(result, str):
+ out.result("Downloaded", path=result)
+ return
+
+ # Print dry run info
+ if isinstance(result, DryRunFileInfo):
+ result = [result]
+ will_download = [r for r in result if r.will_download]
+ total_size = (
+ None if any(r.file_size is None for r in will_download) else sum(r.file_size or 0 for r in will_download)
+ )
+ out.text(
+ f"[dry-run] Will download {len(will_download)} files"
+ f" (out of {len(result)})"
+ f" totalling {_format_size(total_size) if total_size is not None else 'an unknown size'}."
+ )
+ items = [
+ {
+ "file": info.filename,
+ "size": _format_size(info.file_size) if info.will_download and info.file_size is not None else "-",
+ }
+ for info in sorted(result, key=lambda x: x.filename)
+ ]
+ out.table(items)
+
+ _print_result(run_download())
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/extensions.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a92617d18ff71f917277849b824076c40954b25
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/extensions.py
@@ -0,0 +1,766 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains helper utilities for hf CLI extensions."""
+
+import errno
+import json
+import os
+import re
+import shutil
+import subprocess
+import venv
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+from enum import Enum
+from html import unescape
+from pathlib import Path
+from typing import Annotated, Literal
+
+import click
+import httpx2
+
+from huggingface_hub.errors import CLIError, CLIExtensionInstallError, ConfirmationError
+from huggingface_hub.utils import get_session, logging
+
+from ._cli_utils import typer_factory
+from ._framework import Argument, Option
+from ._output import out
+
+
+DEFAULT_EXTENSION_OWNER = "huggingface"
+EXTENSIONS_ROOT = Path("~/.local/share/hf/extensions")
+MANIFEST_FILENAME = "manifest.json"
+EXTENSIONS_HELP = (
+ "Manage hf CLI extensions.\n\n"
+ "Security Warning: extensions are third-party executables or Python packages. "
+ "Install only from sources you trust."
+)
+extensions_cli = typer_factory(help=EXTENSIONS_HELP)
+_EXTENSIONS_GITHUB_TOPIC = "hf-extension"
+_EXTENSIONS_DOWNLOAD_TIMEOUT = 10
+_EXTENSIONS_PIP_INSTALL_TIMEOUT = 300
+
+logger = logging.get_logger(__name__)
+
+
+class _GitHubRateLimitError(CLIError):
+ """The GitHub API refused the request because a rate limit is exhausted.
+
+ Distinguished from other `CLIError`s because it says nothing about the extension being handled:
+ it is not worth retrying on the next one, and it must not be mistaken for an install failure.
+ """
+
+
+class _ExtensionUpdateStatus(str, Enum):
+ UPDATED = "updated"
+ UP_TO_DATE = "up_to_date"
+ SKIPPED = "skipped"
+
+
+@dataclass
+class ExtensionManifest:
+ owner: str
+ repo: str
+ repo_id: str
+ short_name: str
+ executable_path: str
+ type: Literal["binary", "python"]
+ installed_at: datetime
+ description: str | None = None
+ commit_sha: str | None = None
+
+ @classmethod
+ def load(cls, path: Path) -> "ExtensionManifest":
+ manifest_path = path / MANIFEST_FILENAME
+ if not manifest_path.is_file():
+ raise CLIError(f"Manifest file not found at {manifest_path}. Your extension may be corrupted.")
+ data = json.loads(manifest_path.read_text())
+ # Ignore keys not in the dataclass (e.g. fields dropped since the manifest was written).
+ data = {key: value for key, value in data.items() if key in cls.__dataclass_fields__}
+ data["installed_at"] = datetime.fromisoformat(data["installed_at"])
+ return ExtensionManifest(**data)
+
+ def save(self, path: Path) -> None:
+ manifest_path = path / MANIFEST_FILENAME
+ manifest_path.parent.mkdir(parents=True, exist_ok=True)
+ data = asdict(self)
+ data["installed_at"] = self.installed_at.isoformat()
+ manifest_path.write_text(json.dumps(data, indent=2, sort_keys=True))
+
+
+@extensions_cli.command(
+ "install",
+ examples=[
+ "hf extensions install hf-claude",
+ "hf extensions install hanouticelina/hf-claude",
+ "hf extensions install alvarobartt/hf-mem",
+ ],
+)
+def extension_install(
+ ctx: click.Context,
+ repo_id: Annotated[
+ str,
+ Argument(help="GitHub extension repository in `[OWNER/]hf-` format."),
+ ],
+ force: Annotated[bool, Option("--force", help="Overwrite if already installed.")] = False,
+) -> None:
+ """Install an extension from a public GitHub repository.
+
+ Security warning: this installs a third-party executable or Python package.
+ Install only from sources you trust.
+ """
+ owner, repo_name, short_name = _normalize_repo_id(repo_id)
+ root_ctx = ctx.find_root()
+ reserved_commands = set(getattr(root_ctx.command, "commands", {}).keys())
+ if short_name in reserved_commands:
+ raise CLIError(
+ f"Cannot install extension '{short_name}' because it conflicts with an existing `hf {short_name}` command."
+ )
+
+ extension_dir = _get_extension_dir(short_name)
+ if extension_dir.exists() and not force:
+ raise CLIError(f"Extension '{short_name}' is already installed. Use --force to overwrite.")
+
+ if not _github_repo_exists(owner=owner, repo_name=repo_name):
+ raise CLIError(f"GitHub repository '{owner}/{repo_name}' not found. It must exist and be public.")
+ if extension_dir.exists():
+ # --force reinstall: only remove the previous install once the repo is known to exist.
+ shutil.rmtree(extension_dir)
+ manifest = _install_extension(owner=owner, repo_name=repo_name, short_name=short_name)
+ ext_type = manifest.type.capitalize()
+ out.result(
+ f"{ext_type} extension installed",
+ source=f"{owner}/{repo_name}",
+ command=f"hf {short_name}",
+ )
+ out.hint(f"Run it with: hf {short_name}")
+
+
+@extensions_cli.command(
+ "update",
+ examples=[
+ "hf extensions update",
+ "hf extensions update hf-claude",
+ "hf extensions update alvarobartt/hf-mem",
+ ],
+)
+def extension_update(
+ name: Annotated[
+ str | None,
+ Argument(
+ help=(
+ "Extension to update (with or without `hf-` prefix, optionally as `OWNER/hf-`). "
+ "If omitted, all installed extensions are checked and the outdated ones are updated."
+ ),
+ ),
+ ] = None,
+) -> None:
+ """Update installed extension(s) to their latest version."""
+ if name is not None:
+ manifest = _load_installed_extension_for_update(name)
+ update_status = _update_installed_extension(manifest)
+ match update_status:
+ case _ExtensionUpdateStatus.UPDATED:
+ out.result("Extension updated", name=manifest.short_name, source=manifest.repo_id)
+ case _ExtensionUpdateStatus.UP_TO_DATE:
+ out.result(f"Extension '{manifest.short_name}' is already up to date", name=manifest.short_name)
+ case _ExtensionUpdateStatus.SKIPPED:
+ pass # warning already emitted by _update_installed_extension
+ return
+
+ manifests = _list_installed_extensions()
+ if not manifests:
+ out.warning("No extensions installed.")
+ out.hint("Install one with: hf extensions install ")
+ return
+
+ updated = []
+ up_to_date = []
+ for manifest in manifests:
+ out.log(f"Checking '{manifest.short_name}' ({manifest.repo_id})...")
+ try:
+ update_status = _update_installed_extension(manifest)
+ except _GitHubRateLimitError:
+ # Every remaining extension would fail the same way, so stop rather than repeat it.
+ raise
+ except Exception as error:
+ # Keep updating the other extensions even if one fails.
+ out.warning(f"Could not update '{manifest.short_name}' ({manifest.repo_id}): {error}. Skipping.")
+ continue
+ match update_status:
+ case _ExtensionUpdateStatus.UPDATED:
+ updated.append(manifest.short_name)
+ case _ExtensionUpdateStatus.UP_TO_DATE:
+ up_to_date.append(manifest.short_name)
+ case _ExtensionUpdateStatus.SKIPPED:
+ pass # warning already emitted by _update_installed_extension
+ out.result(
+ "Extensions update complete",
+ updated=", ".join(updated) if updated else None,
+ up_to_date=", ".join(up_to_date) if up_to_date else None,
+ )
+
+
+@extensions_cli.command(
+ "exec",
+ context_settings={"allow_extra_args": True, "allow_interspersed_args": False, "ignore_unknown_options": True},
+ examples=[
+ "hf extensions exec claude -- --help",
+ "hf extensions exec claude --model zai-org/GLM-5",
+ ],
+)
+def extension_exec(
+ ctx: click.Context,
+ name: Annotated[
+ str,
+ Argument(help="Extension name (with or without `hf-` prefix)."),
+ ],
+) -> None:
+ """Execute an installed extension."""
+ short_name = _normalize_extension_name(name)
+ executable_path = _resolve_installed_executable_path(short_name)
+
+ if not executable_path.is_file():
+ raise CLIError(f"Extension '{short_name}' is not installed.")
+
+ exit_code = _execute_extension_binary(executable_path=executable_path, args=list(ctx.args))
+ raise click.exceptions.Exit(code=exit_code)
+
+
+@extensions_cli.command("list | ls", examples=["hf extensions list"])
+def extension_list() -> None:
+ """List installed extension commands."""
+ rows = [
+ {
+ "command": f"hf {manifest.short_name}",
+ "source": str(manifest.repo_id),
+ "type": str(manifest.type),
+ "installed": manifest.installed_at.strftime("%Y-%m-%d"),
+ "description": manifest.description,
+ }
+ for manifest in _list_installed_extensions()
+ ]
+ out.table(rows, id_key="command")
+
+
+@extensions_cli.command("search", examples=["hf extensions search"])
+def extension_search() -> None:
+ """Search extensions available on GitHub (tagged with 'hf-extension' topic)."""
+ response = _github_request(
+ "GET",
+ "https://api.github.com/search/repositories",
+ params={"q": f"topic:{_EXTENSIONS_GITHUB_TOPIC}", "sort": "stars", "order": "desc", "per_page": 100},
+ )
+ data = response.json()
+
+ installed = {m.short_name for m in _list_installed_extensions()}
+
+ rows = []
+ for repo in data.get("items", []):
+ short_name = repo["name"].removeprefix("hf-")
+ rows.append(
+ {
+ "name": short_name,
+ "repo": repo["full_name"],
+ "stars": repo.get("stargazers_count", 0),
+ "description": repo.get("description") or "",
+ "installed": "yes" if short_name in installed else "",
+ }
+ )
+
+ out.table(rows, id_key="repo")
+
+
+@extensions_cli.command("remove | rm", examples=["hf extensions remove claude"])
+def extension_remove(
+ name: Annotated[
+ str,
+ Argument(help="Extension name to remove (with or without `hf-` prefix)."),
+ ],
+) -> None:
+ """Remove an installed extension."""
+ short_name = _normalize_extension_name(name)
+ extension_dir = _get_extension_dir(short_name)
+
+ if not extension_dir.is_dir():
+ raise CLIError(f"Extension '{short_name}' is not installed.")
+
+ shutil.rmtree(extension_dir)
+ out.result("Extension removed", name=short_name)
+
+
+### HELPER FUNCTIONS
+
+
+def _list_installed_extensions() -> list[ExtensionManifest]:
+ """Return manifests for all validly-installed extensions, sorted by directory name."""
+ root_dir = EXTENSIONS_ROOT.expanduser()
+ if not root_dir.is_dir():
+ return []
+ manifests = []
+ for extension_dir in sorted(root_dir.iterdir()):
+ if not extension_dir.is_dir() or not extension_dir.name.startswith("hf-"):
+ continue
+ try:
+ manifests.append(ExtensionManifest.load(extension_dir))
+ except Exception as e:
+ logger.debug(f"Failed to load manifest for extension '{extension_dir.name}': {e}")
+ continue
+ return manifests
+
+
+def list_installed_extensions_for_help() -> list[tuple[str, str]]:
+ entries = []
+ for manifest in _list_installed_extensions():
+ tag = f"[extension {manifest.repo_id}]"
+ help_text = f"{manifest.description} {tag}" if manifest.description is not None else tag
+ entries.append((manifest.short_name, help_text))
+ return entries
+
+
+def dispatch_unknown_top_level_extension(args: list[str], known_commands: set[str]) -> int | None:
+ if not args:
+ return None
+
+ command_name = args[0]
+ if command_name.startswith("-"):
+ return None
+ all_known = {a.strip() for cmd in known_commands for a in cmd.split("|")}
+ if command_name in all_known:
+ return None
+
+ try:
+ short_name = _validate_extension_short_name(command_name.removeprefix("hf-"), original_input=command_name)
+ except CLIError:
+ return None
+
+ executable_path: Path | None
+ try:
+ executable_path = _resolve_installed_executable_path(short_name)
+ except Exception:
+ executable_path = _auto_install_official_extension(short_name)
+
+ if executable_path is None or not executable_path.is_file():
+ return None
+
+ return _execute_extension_binary(executable_path=executable_path, args=list(args[1:]))
+
+
+def _auto_install_official_extension(short_name: str) -> Path | None:
+ """Try to auto-install huggingface/hf-. Returns executable path or None."""
+ owner, repo_name = DEFAULT_EXTENSION_OWNER, f"hf-{short_name}"
+ if _get_extension_dir(short_name).exists():
+ return None
+
+ # Every unknown `hf ` reaches this point, typos included, so the existence check must
+ # not spend REST API quota (see `_github_request`).
+ try:
+ if not _github_repo_exists(owner=owner, repo_name=repo_name):
+ return None
+ except Exception:
+ return None
+
+ try:
+ out.confirm(f"'{short_name}' is an official Hugging Face extension ({owner}/{repo_name}). Install it?")
+ except ConfirmationError:
+ return None
+ manifest = _install_extension(owner=owner, repo_name=repo_name, short_name=short_name)
+ return Path(manifest.executable_path).expanduser()
+
+
+def _load_installed_extension_for_update(name: str) -> ExtensionManifest:
+ short_name = _normalize_extension_name(name)
+ extension_dir = _get_extension_dir(short_name)
+ if not extension_dir.is_dir():
+ owner, _, _ = name.strip().rpartition("/")
+ install_target = f"{owner}/hf-{short_name}" if owner else f"hf-{short_name}"
+ raise CLIError(
+ f"Extension '{short_name}' is not installed. Install it first with: hf extensions install {install_target}"
+ )
+ return ExtensionManifest.load(extension_dir)
+
+
+def _update_installed_extension(manifest: ExtensionManifest) -> _ExtensionUpdateStatus:
+ owner, repo_name, short_name = manifest.owner, manifest.repo, manifest.short_name
+
+ try:
+ latest_sha = _fetch_latest_commit_sha(owner=owner, repo_name=repo_name)
+ except _GitHubRateLimitError:
+ # Says nothing about this extension in particular, so let the caller stop the whole run.
+ raise
+ except Exception as error:
+ out.warning(f"Could not check updates for '{short_name}' ({owner}/{repo_name}): {error}. Skipping.")
+ return _ExtensionUpdateStatus.SKIPPED
+
+ if latest_sha == manifest.commit_sha:
+ return _ExtensionUpdateStatus.UP_TO_DATE
+
+ _install_extension(owner=owner, repo_name=repo_name, short_name=short_name, commit_sha=latest_sha)
+ return _ExtensionUpdateStatus.UPDATED
+
+
+def _install_extension(
+ *,
+ owner: str,
+ repo_name: str,
+ short_name: str,
+ commit_sha: str | None = None,
+) -> ExtensionManifest:
+ """Fetch and install an extension (binary or Python package), then persist its manifest.
+
+ Installs in place: an existing install is overwritten without being removed first, so a failed
+ update keeps the previous version working. A failed fresh install is cleaned up entirely.
+ """
+ extension_dir = _get_extension_dir(short_name)
+ fresh_install = not extension_dir.exists()
+ installed = False
+ try:
+ try:
+ binary = _fetch_remote_binary(owner=owner, repo_name=repo_name, short_name=short_name)
+ except Exception:
+ binary = None
+
+ if binary is not None:
+ if os.name == "nt":
+ raise CLIError(
+ f"'{owner}/{repo_name}' is a shell-script extension, which is not supported on Windows. "
+ "Only Python extensions can be installed on Windows."
+ )
+ executable_path = _install_binary_extension(
+ extension_dir=extension_dir, short_name=short_name, binary=binary
+ )
+ else:
+ executable_path = _install_python_extension(
+ extension_dir=extension_dir, owner=owner, repo_name=repo_name, short_name=short_name
+ )
+
+ if commit_sha is None:
+ # The extension itself is on disk by now, fetched from the CDN. The SHA only feeds update
+ # detection, so a failure here must not throw the install away: without it, the next
+ # `hf extensions update` sees no known SHA, considers the extension outdated and reinstalls.
+ try:
+ commit_sha = _fetch_latest_commit_sha(owner=owner, repo_name=repo_name)
+ except Exception as error:
+ out.warning(f"Could not record a version for '{owner}/{repo_name}', installing anyway: {error}")
+
+ manifest = ExtensionManifest(
+ owner=owner,
+ repo=repo_name,
+ repo_id=f"{owner}/{repo_name}",
+ short_name=short_name,
+ executable_path=str(executable_path),
+ type="binary" if binary is not None else "python",
+ installed_at=datetime.now(timezone.utc),
+ description=_try_fetch_remote_description(owner=owner, repo_name=repo_name),
+ commit_sha=commit_sha,
+ )
+ manifest.save(extension_dir)
+ installed = True
+ return manifest
+ except CLIError:
+ raise
+ except subprocess.TimeoutExpired as e:
+ raise CLIExtensionInstallError(
+ f"Pip install timed out after {_EXTENSIONS_PIP_INSTALL_TIMEOUT}s for '{owner}/{repo_name}'. "
+ "See pip output above for details."
+ ) from e
+ except subprocess.CalledProcessError as e:
+ raise CLIExtensionInstallError(
+ f"Failed to install pip package from '{owner}/{repo_name}' (exit code {e.returncode}). "
+ "See pip output above for details."
+ ) from e
+ except Exception as e:
+ raise CLIExtensionInstallError(f"Failed to install extension from '{owner}/{repo_name}': {e}") from e
+ finally:
+ if not installed and fresh_install:
+ shutil.rmtree(extension_dir, ignore_errors=True)
+
+
+def _fetch_latest_commit_sha(*, owner: str, repo_name: str) -> str:
+ """Fetch the latest commit SHA on the default branch, used to detect available updates.
+
+ The only REST API call left in the install and update paths. Raises on failure: callers decide
+ whether a missing SHA is fatal, and report the reason themselves.
+ """
+ response = _github_request(
+ "GET",
+ f"https://api.github.com/repos/{owner}/{repo_name}/commits/HEAD",
+ headers={"Accept": "application/vnd.github.sha"},
+ )
+ return response.text.strip()
+
+
+def _fetch_remote_binary(*, owner: str, repo_name: str, short_name: str) -> bytes:
+ raw_url = f"https://raw.githubusercontent.com/{owner}/{repo_name}/HEAD/hf-{short_name}"
+ response = _github_request("GET", raw_url)
+ return response.content
+
+
+def _install_binary_extension(*, extension_dir: Path, short_name: str, binary: bytes) -> Path:
+ extension_dir.mkdir(parents=True, exist_ok=True)
+ executable_path = extension_dir / _get_executable_name(short_name)
+ executable_path.write_bytes(binary)
+
+ if os.name != "nt":
+ os.chmod(executable_path, 0o755)
+
+ return executable_path
+
+
+def _install_python_extension(*, extension_dir: Path, owner: str, repo_name: str, short_name: str) -> Path:
+ source_url = f"https://github.com/{owner}/{repo_name}/archive/HEAD.zip"
+ venv_dir = extension_dir / "venv"
+ venv_python = _get_venv_bin_path(venv_dir, "python.exe" if os.name == "nt" else "python")
+ uv_path = shutil.which("uv")
+
+ status = out.status()
+ if not venv_python.is_file():
+ status.update(f"Creating virtual environment in {venv_dir}")
+ extension_dir.mkdir(parents=True, exist_ok=True)
+ if uv_path:
+ subprocess.run([uv_path, "venv", str(venv_dir)], check=True)
+ else:
+ venv.EnvBuilder(with_pip=True).create(str(venv_dir))
+ status.done(f"Virtual environment created in {venv_dir}")
+
+ status.update(f"Installing package from {source_url}")
+ if uv_path:
+ # --reinstall: the source URL is the same for every commit, so cached data must be refreshed.
+ install_cmd = [uv_path, "pip", "install", "--reinstall", "--python", str(venv_python), source_url]
+ else:
+ install_cmd = [
+ str(venv_python),
+ "-m",
+ "pip",
+ "install",
+ "--disable-pip-version-check",
+ "--no-input",
+ "--force-reinstall",
+ source_url,
+ ]
+ subprocess.run(install_cmd, check=True, timeout=_EXTENSIONS_PIP_INSTALL_TIMEOUT)
+ status.done(f"Package installed from {source_url}")
+
+ executable_name = _get_executable_name(short_name)
+ venv_executable = _get_venv_bin_path(venv_dir, executable_name)
+ if not venv_executable.is_file():
+ raise CLIError(
+ f"Installed package from '{owner}/{repo_name}' does not expose the required console script "
+ f"'{executable_name}'."
+ )
+ return venv_executable.resolve()
+
+
+_GITHUB_ABOUT_RE = re.compile(r' str | None:
+ """Try to fetch project description either from:
+ - manifest.json
+ - pyproject.toml
+ - the repository's GitHub "About" field
+
+ Only best effort, no error handling.
+ """
+ base = f"https://raw.githubusercontent.com/{owner}/{repo_name}/HEAD"
+
+ # from manifest.json
+ try:
+ response = _github_request("GET", f"{base}/{MANIFEST_FILENAME}")
+ description = response.json().get("description")
+ if isinstance(description, str):
+ return description
+ except Exception:
+ pass
+
+ # from pyproject.toml
+ try:
+ response = _github_request("GET", f"{base}/pyproject.toml")
+
+ # Weak parser but ok for "best effort"
+ for line in response.text.splitlines():
+ line = line.strip()
+ if line.startswith("description"):
+ _, _, value = line.partition("=")
+ return value.strip().strip("\"'")
+ except Exception:
+ pass
+
+ # from the repo page's "About" field. Served by github.com, so it costs no REST API quota.
+ try:
+ response = _github_request("GET", f"https://github.com/{owner}/{repo_name}")
+ if (match := _GITHUB_ABOUT_RE.search(response.text)) is not None:
+ # GitHub renders " - /", and unrelated boilerplate when About is empty.
+ content = unescape(match.group(1))
+ if (description := content.removesuffix(f" - {owner}/{repo_name}")) != content:
+ return description
+ except Exception:
+ pass
+
+ return None
+
+
+def _get_extension_dir(short_name: str) -> Path:
+ # Callers validate at the parse boundary already; re-validate here as defense-in-depth since
+ # this path is rmtree'd on removal.
+ _validate_extension_short_name(short_name, original_input=short_name)
+ return EXTENSIONS_ROOT.expanduser() / f"hf-{short_name}"
+
+
+def _github_request(
+ method: str, url: str, *, params: dict | None = None, headers: dict | None = None
+) -> httpx2.Response:
+ """Perform a GitHub request.
+
+ Shared by every GitHub/Raw fetch in this module so the timeout, redirect and rate-limit policy are
+ shared.
+
+ Unauthenticated `api.github.com` calls are capped per IP address, and that quota is shared with
+ everything else calling GitHub from the same network (NAT, VPN, CI egress). Remote refs are therefore
+ always addressed as `HEAD`, which GitHub resolves to the repo's default branch on
+ `raw.githubusercontent.com`, on the archive endpoint and on the REST API alike: no
+ `GET /repos/{owner}/{repo}` lookup is needed to discover the branch name.
+ """
+ response = get_session().request(
+ method,
+ url,
+ params=params,
+ headers=headers,
+ follow_redirects=True,
+ timeout=_EXTENSIONS_DOWNLOAD_TIMEOUT,
+ )
+ # Primary limits report an exhausted quota, secondary ones only ask to back off.
+ if response.status_code in (403, 429) and (
+ response.headers.get("x-ratelimit-remaining") == "0" or "retry-after" in response.headers
+ ):
+ raise _GitHubRateLimitError(_rate_limit_message(response.headers))
+ response.raise_for_status()
+ return response
+
+
+def _rate_limit_message(headers: httpx2.Headers) -> str:
+ """Message for a GitHub rate-limit rejection, built from the response headers.
+
+ The cap itself is deliberately not quoted: it depends on the endpoint (60/hour on the core API,
+ 10/minute on search), so only the wait is reported.
+
+ `retry-after` wins over `x-ratelimit-reset`: GitHub rides the primary window's reset timestamp on
+ every REST response, secondary rate limits included, so on a back-off it would point at an hourly
+ window that is not the one being enforced.
+ """
+ message = (
+ "GitHub API rate limit exceeded. Unauthenticated requests are capped per IP address, and that "
+ "quota is shared with anything else calling GitHub from your network."
+ )
+ if (retry_after := headers.get("retry-after")) is not None:
+ message += f" Retry in {retry_after}s."
+ elif (reset := headers.get("x-ratelimit-reset")) is not None:
+ try:
+ reset_at = datetime.fromtimestamp(int(reset), tz=timezone.utc).astimezone()
+ except (OverflowError, OSError, ValueError):
+ pass
+ else:
+ message += f" It resets at {reset_at:%H:%M:%S %Z}."
+ return message
+
+
+def _github_repo_exists(*, owner: str, repo_name: str) -> bool:
+ """Whether a public GitHub repo exists.
+
+ Resolved against `github.com` rather than `GET /repos/{owner}/{repo}` to keep the check off the
+ metered REST API (see `_github_request`). Only a 404 answers `False`: any other failure is reported
+ as such, so callers never present an unreachable GitHub as a missing repo.
+ """
+ try:
+ _github_request("HEAD", f"https://github.com/{owner}/{repo_name}")
+ except httpx2.HTTPError as error:
+ if isinstance(error, httpx2.HTTPStatusError) and error.response.status_code == 404:
+ return False
+ raise CLIError(f"Could not reach GitHub to check whether '{owner}/{repo_name}' exists: {error}") from error
+ return True
+
+
+def _get_executable_name(short_name: str) -> str:
+ name = f"hf-{short_name}"
+ if os.name == "nt":
+ name += ".exe"
+ return name
+
+
+def _resolve_installed_executable_path(short_name: str) -> Path:
+ extension_dir = _get_extension_dir(short_name)
+ manifest = ExtensionManifest.load(extension_dir)
+ return Path(manifest.executable_path).expanduser()
+
+
+def _get_venv_bin_path(venv_dir: Path, executable_name: str) -> Path:
+ if os.name == "nt":
+ return venv_dir / "Scripts" / executable_name
+ return venv_dir / "bin" / executable_name
+
+
+_ALLOWED_EXTENSION_NAME = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]*$")
+
+
+def _validate_extension_short_name(short_name: str, *, original_input: str) -> str:
+ name = short_name.strip()
+ if not name:
+ raise CLIError("Extension name cannot be empty.")
+ if any(sep in name for sep in ("/", "\\")):
+ raise CLIError(f"Invalid extension name '{original_input}'.")
+ if ".." in name or ":" in name:
+ raise CLIError(f"Invalid extension name '{original_input}'.")
+ if not _ALLOWED_EXTENSION_NAME.fullmatch(name):
+ raise CLIError(
+ f"Invalid extension name '{original_input}'. Allowed characters: letters, digits, '.', '_' and '-'."
+ )
+ return name
+
+
+def _normalize_repo_id(repo_id: str) -> tuple[str, str, str]:
+ if "://" in repo_id:
+ raise CLIError("Only GitHub repositories in `[OWNER/]hf-` format are supported.")
+
+ parts = repo_id.split("/")
+ if len(parts) == 1:
+ owner = DEFAULT_EXTENSION_OWNER
+ repo_name = parts[0]
+ elif len(parts) == 2 and all(parts):
+ owner, repo_name = parts
+ else:
+ raise CLIError(f"Expected `[OWNER/]REPO` format, got '{repo_id}'.")
+
+ if not repo_name.startswith("hf-"):
+ raise CLIError(f"Extension repository name must start with 'hf-', got '{repo_name}'.")
+
+ short_name = repo_name.removeprefix("hf-")
+ if not short_name:
+ raise CLIError("Invalid extension repository name 'hf-'.")
+ _validate_extension_short_name(short_name, original_input=repo_id)
+
+ return owner, repo_name, short_name
+
+
+def _normalize_extension_name(name: str) -> str:
+ repo_name = name.strip().rsplit("/", 1)[-1]
+ return _validate_extension_short_name(repo_name.removeprefix("hf-"), original_input=name)
+
+
+def _execute_extension_binary(executable_path: Path, args: list[str]) -> int:
+ try:
+ return subprocess.call([str(executable_path)] + args)
+ except OSError as e:
+ if os.name == "nt" or e.errno != errno.ENOEXEC:
+ raise
+ return subprocess.call(["sh", str(executable_path)] + args)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/hf.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..3231707c982f959d8464dbb2ff58f43b1684f629
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/hf.py
@@ -0,0 +1,183 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import traceback
+from typing import Annotated, Any
+
+import click
+
+from huggingface_hub import __version__, constants
+from huggingface_hub.cli._cli_utils import (
+ LazyHfCommand,
+ LazyHfGroup,
+ check_cli_update,
+ fallback_typer_group_factory,
+ typer_factory,
+)
+from huggingface_hub.cli._output import out
+from huggingface_hub.utils import logging
+
+from ._completion import _COMPLETE_VAR, InstallCompletionOpt, ShowCompletionOpt
+from ._framework import Option
+
+
+def _dispatch_unknown_top_level_extension(args: list[str], built_in_commands: set[str]) -> int | None:
+ from .extensions import dispatch_unknown_top_level_extension
+
+ return dispatch_unknown_top_level_extension(args, built_in_commands)
+
+
+def _list_installed_extensions_for_help() -> list[tuple[str, str]]:
+ from .extensions import list_installed_extensions_for_help
+
+ return list_installed_extensions_for_help()
+
+
+def check_skill_update() -> None:
+ from ._skills import check_skill_update as _check_skill_update
+
+ _check_skill_update()
+
+
+app = typer_factory(
+ help="Hugging Face Hub CLI",
+ cls=fallback_typer_group_factory(
+ _dispatch_unknown_top_level_extension,
+ extra_commands_provider=_list_installed_extensions_for_help,
+ ),
+)
+
+
+def _version_callback(value: bool) -> None:
+ if value:
+ print(__version__)
+ raise click.exceptions.Exit()
+
+
+def _skills_callback(value: bool) -> None:
+ if value:
+ from .skills import skills_preview
+
+ skills_preview()
+ raise click.exceptions.Exit()
+
+
+@app.group_callback(invoke_without_command=True)
+def app_callback(
+ version: Annotated[
+ bool | None, Option("-v", "--version", callback=_version_callback, is_eager=True, hidden=True)
+ ] = None,
+ skills: Annotated[
+ bool,
+ Option(
+ "--skills",
+ callback=_skills_callback,
+ is_eager=True,
+ help="Print the `hf-cli` SKILL.md to stdout (alias for `hf skills preview`).",
+ ),
+ ] = False,
+ install_completion: InstallCompletionOpt = False,
+ show_completion: ShowCompletionOpt = False,
+) -> None:
+ pass
+
+
+_LAZY_COMMANDS: list[tuple[str, str, str, dict[str, Any]]] = [
+ ("sync", "buckets", "sync", {}),
+ ("cp", "_cp", "make_cp", {"examples_attribute": "CP_EXAMPLES", "is_factory": True}),
+ ("download", "download", "download", {"examples_attribute": "DOWNLOAD_EXAMPLES"}),
+ ("upload", "upload", "upload", {"examples_attribute": "UPLOAD_EXAMPLES"}),
+ ("env", "system", "env", {"topic": "help"}),
+ ("update", "system", "update", {"topic": "help"}),
+ ("version", "system", "version", {"topic": "help"}),
+ ("lfs-enable-largefiles", "lfs", "lfs_enable_largefiles", {"hidden": True}),
+ ("lfs-multipart-upload", "lfs", "lfs_multipart_upload", {"hidden": True}),
+]
+
+
+_LAZY_GROUPS: list[tuple[str, str, str, dict[str, Any]]] = [
+ ("auth", "auth", "auth_cli", {}),
+ ("buckets", "buckets", "buckets_cli", {}),
+ ("cache", "cache", "cache_cli", {}),
+ ("collections", "collections", "collections_cli", {}),
+ ("datasets", "datasets", "datasets_cli", {}),
+ ("discussions", "discussions", "discussions_cli", {}),
+ ("jobs", "jobs", "jobs_cli", {}),
+ ("models", "models", "models_cli", {}),
+ ("papers", "papers", "papers_cli", {}),
+ ("repos", "repos", "repos_cli", {}),
+ ("sandbox", "sandbox", "sandbox_cli", {}),
+ ("skills", "skills", "skills_cli", {}),
+ ("spaces", "spaces", "spaces_cli", {}),
+ ("webhooks", "webhooks", "webhooks_cli", {}),
+ ("endpoints", "inference_endpoints", "ie_cli", {}),
+ ("extensions | ext", "extensions", "extensions_cli", {}),
+]
+
+for name, module, attribute, extras in _LAZY_COMMANDS:
+ app.add_command(
+ LazyHfCommand(
+ name,
+ module=f"huggingface_hub.cli.{module}",
+ attribute=attribute,
+ **extras,
+ ),
+ name,
+ )
+
+for name, module, attribute, extras in _LAZY_GROUPS:
+ app.add_command(
+ LazyHfGroup(
+ name,
+ module=f"huggingface_hub.cli.{module}",
+ attribute=attribute,
+ **extras,
+ ),
+ name,
+ )
+
+
+def main():
+ # Shell-completion requests must stay fast and emit nothing but candidates:
+ # skip the startup work and let click handle the env var inside `app()`.
+ if _COMPLETE_VAR not in os.environ:
+ if not constants.HF_DEBUG:
+ logging.set_verbosity_info()
+ check_cli_update("huggingface_hub")
+ # Don't nag while the user is already managing skills, nor on `hf update` which handles the
+ # skill itself (it would print a redundant or contradictory hint before doing so).
+ if sys.argv[1:2] not in (["skills"], ["update"], ["--skills"]):
+ check_skill_update()
+
+ try:
+ # Click glob-expands args on Windows by default, even quoted ones like `--exclude "*.bin"`.
+ app(windows_expand_args=False)
+ except Exception as e:
+ from ._errors import format_known_exception
+
+ message = format_known_exception(e)
+ if message:
+ out.error(message)
+ if constants.HF_DEBUG:
+ traceback.print_exc()
+ else:
+ out.hint("set HF_DEBUG=1 as environment variable for full traceback.")
+ sys.exit(1)
+ raise
+
+
+if __name__ == "__main__":
+ main()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/inference_endpoints.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/inference_endpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..88efee8f00857ca99dae4151ab1afadeeb91700c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/inference_endpoints.py
@@ -0,0 +1,940 @@
+"""CLI commands for Hugging Face Inference Endpoints."""
+
+import shlex
+from typing import Annotated
+
+import click
+
+from huggingface_hub._inference_endpoints import (
+ InferenceEndpointHardware,
+ InferenceEndpointScalingMetric,
+ InferenceEndpointType,
+ _set_parallelism_in_image,
+)
+from huggingface_hub.errors import CLIError, HfHubHTTPError
+
+from ._cli_utils import (
+ EnvFileOpt,
+ EnvOpt,
+ RevisionOpt,
+ SearchOpt,
+ SecretsFileOpt,
+ SecretsOpt,
+ SoftChoice,
+ TokenOpt,
+ get_hf_api,
+ parse_env_map,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+ie_cli = typer_factory(help="Manage Hugging Face Inference Endpoints.")
+
+catalog_app = typer_factory(help="Interact with the Inference Endpoints catalog.")
+
+
+NameArg = Annotated[
+ str,
+ Argument(help="Endpoint name."),
+]
+NameOpt = Annotated[
+ str | None,
+ Option(help="Endpoint name."),
+]
+
+NamespaceOpt = Annotated[
+ str | None,
+ Option(
+ help="The namespace associated with the Inference Endpoint. Defaults to the current user's namespace.",
+ ),
+]
+
+# Maps the CLI `--engine` values to the `model.image` variant keys of the API payload. `huggingface` and
+# `huggingfaceNeuron` are left out on purpose: they are the managed Hugging Face images and take no image URL.
+ENGINE_IMAGE_KEYS = {
+ "custom": "custom",
+ "hf-serve": "hfServe",
+ "llamacpp": "llamacpp",
+ "sglang": "sGLang",
+ "tei": "tei",
+ "tgi": "tgi",
+ "tgi-neuron": "tgiNeuron",
+ "vllm": "vLLM",
+ "vllm-neuron": "vLLMNeuron",
+}
+
+EngineOpt = Annotated[
+ str | None,
+ Option(
+ "--engine",
+ click_type=SoftChoice(list(ENGINE_IMAGE_KEYS)),
+ help="Managed engine image to run --custom-image with (e.g. 'vllm'). Defaults to an arbitrary container.",
+ ),
+]
+
+HealthRouteOpt = Annotated[
+ str | None,
+ Option(
+ help="Health check route exposed by the container (e.g. '/health'). Requires --custom-image.",
+ ),
+]
+
+PortOpt = Annotated[
+ int | None,
+ Option(
+ help="Port the container listens on (e.g. 30000). Requires --custom-image.",
+ ),
+]
+
+TensorParallelSizeOpt = Annotated[
+ int | None,
+ Option(
+ "--tensor-parallel-size",
+ help="Number of accelerators to shard a single model copy across (vLLM and SGLang engines only).",
+ ),
+]
+
+DataParallelSizeOpt = Annotated[
+ int | None,
+ Option(
+ "--data-parallel-size",
+ help="Number of model copies to run, one per accelerator (vLLM engine only).",
+ ),
+]
+
+
+@ie_cli.command("list | ls", examples=["hf endpoints ls", "hf endpoints ls --namespace my-org"])
+def ls(
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Lists all Inference Endpoints for the given namespace."""
+ api = get_hf_api(token=token)
+ try:
+ endpoints = api.list_inference_endpoints(namespace=namespace, token=token)
+ except HfHubHTTPError as error:
+ out.error(f"Listing failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ results = []
+ for endpoint in endpoints:
+ raw = endpoint.raw
+ status = raw.get("status", {})
+ model = raw.get("model", {})
+ compute = raw.get("compute", {})
+ provider = raw.get("provider", {})
+ results.append(
+ {
+ "name": raw.get("name", ""),
+ "model": model.get("repository", "") if isinstance(model, dict) else "",
+ "status": status.get("state", "") if isinstance(status, dict) else "",
+ "task": model.get("task", "") if isinstance(model, dict) else "",
+ "framework": model.get("framework", "") if isinstance(model, dict) else "",
+ "instance": compute.get("instanceType", "") if isinstance(compute, dict) else "",
+ "vendor": provider.get("vendor", "") if isinstance(provider, dict) else "",
+ "region": provider.get("region", "") if isinstance(provider, dict) else "",
+ }
+ )
+ out.table(results, id_key="name")
+
+
+# A denylist like the Endpoints UI uses, so a new server-side status shows up instead of silently disappearing.
+_UNDEPLOYABLE_STATUSES = {"deprecated", "not_available"}
+
+
+def _is_deployable(hw: InferenceEndpointHardware) -> bool:
+ """Whether the namespace can deploy one replica on this hardware right now: usable status and enough quota."""
+ return (
+ hw.status not in _UNDEPLOYABLE_STATUSES and hw.max_accelerators - hw.used_accelerators >= hw.num_accelerators
+ )
+
+
+@ie_cli.command(
+ "hardware",
+ examples=[
+ "hf endpoints hardware",
+ "hf endpoints hardware --vendor aws --accelerator gpu",
+ ],
+)
+def hardware(
+ namespace: NamespaceOpt = None,
+ vendor: Annotated[
+ str | None,
+ Option(help="Only show hardware hosted by this cloud provider (e.g. 'aws')."),
+ ] = None,
+ region: Annotated[
+ str | None,
+ Option(help="Only show hardware available in this cloud region (e.g. 'us-east-1')."),
+ ] = None,
+ accelerator: Annotated[
+ str | None,
+ Option(help="Only show hardware with this accelerator (e.g. 'cpu', 'gpu', 'neuron')."),
+ ] = None,
+ instance_type: Annotated[
+ str | None,
+ Option(help="Only show hardware of this instance type (e.g. 'nvidia-l4')."),
+ ] = None,
+ show_all: Annotated[
+ bool,
+ Option(
+ "-a",
+ "--all",
+ help="Also show hardware that cannot be deployed on right now (unavailable, deprecated or out of quota).",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """List the hardware available to deploy an Inference Endpoint on.
+
+ Only the hardware the namespace can deploy on right now is listed: a usable status, and enough accelerator
+ quota left for one replica. Use `--all` to list every combination the API returns.
+
+ Quota is per namespace, so pass the same `--namespace` you will pass to `hf endpoints deploy`. Prices are in
+ USD, per replica per hour.
+ """
+ api = get_hf_api(token=token)
+ hardware_list = api.list_inference_endpoints_hardware(namespace=namespace, token=token)
+
+ # Both sides lowercased: relying on the server's casing would turn a change into a silently empty result.
+ matching = [
+ hw
+ for hw in hardware_list
+ if (vendor is None or hw.vendor.lower() == vendor.lower())
+ and (region is None or hw.region.lower() == region.lower())
+ and (accelerator is None or hw.accelerator.lower() == accelerator.lower())
+ and (instance_type is None or hw.instance_type.lower() == instance_type.lower())
+ ]
+ visible = [hw for hw in matching if show_all or _is_deployable(hw)]
+ # Smallest size first per instance type; 'instance_size' can't be the key, 'x16' sorts before 'x2'.
+ visible.sort(key=lambda hw: (hw.vendor, hw.region, hw.accelerator, hw.instance_type, hw.num_accelerators))
+
+ # Add a quota column, keeping the rest of the dict whole since '--format json' emits it.
+ items = [_dataclass_to_dict(hw) | {"quota": f"{hw.used_accelerators}/{hw.max_accelerators}"} for hw in visible]
+ out.table(
+ items,
+ # Redundant and always-null columns are dropped here but stay in the items for '--format json'.
+ headers=[
+ "vendor",
+ "region",
+ "accelerator",
+ "instance_type",
+ "instance_size",
+ "memory_gb",
+ "gpu_memory_gb",
+ "price_per_hour",
+ "quota",
+ "status",
+ ],
+ id_key="id",
+ )
+ # The example must be one 'deploy' accepts, and with '--all' the first row may not be.
+ if (hw := next((hw for hw in visible if _is_deployable(hw)), None)) is not None:
+ out.hint(
+ f"Deploy on one of these, e.g.: hf endpoints deploy my-endpoint --repo --framework "
+ f"--vendor {hw.vendor} --region {hw.region} --accelerator {hw.accelerator} "
+ f"--instance-type {hw.instance_type} --instance-size {hw.instance_size}"
+ )
+ elif visible: # only reachable with '--all'
+ out.hint("None of these can be deployed on right now, see the QUOTA and STATUS columns.")
+ elif matching: # all matches were dropped as undeployable
+ out.hint("Use '--all' to also show hardware that cannot be deployed on right now.")
+ else:
+ # Nothing matched at all: a filter value is probably a typo, so name the ones that exist nowhere.
+ unknown = [
+ f"{flag} '{value}' (valid: {', '.join(sorted(valid))})"
+ for flag, value, valid in (
+ ("--vendor", vendor, {hw.vendor for hw in hardware_list}),
+ ("--region", region, {hw.region for hw in hardware_list}),
+ ("--accelerator", accelerator, {hw.accelerator for hw in hardware_list}),
+ ("--instance-type", instance_type, {hw.instance_type for hw in hardware_list}),
+ )
+ if value is not None and value.lower() not in {v.lower() for v in valid}
+ ]
+ out.hint(
+ f"No such hardware: {'; '.join(unknown)}."
+ if unknown
+ # Each value exists, they just never occur together.
+ else "No hardware matches all of these filters at once. Try dropping one."
+ )
+
+
+@ie_cli.command(
+ name="deploy",
+ examples=[
+ "hf endpoints deploy my-endpoint --repo gpt2 --framework pytorch ...",
+ "hf endpoints deploy my-endpoint --repo openai/gpt-oss-120b --framework custom --engine vllm "
+ "--custom-image vllm/vllm-openai:v0.23.0 --tensor-parallel-size 8 ...",
+ ],
+)
+def deploy(
+ name: NameArg,
+ repo: Annotated[
+ str,
+ Option(
+ help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
+ ),
+ ],
+ framework: Annotated[
+ str,
+ Option(
+ help="The machine learning framework used for the model (e.g. 'vllm').",
+ ),
+ ],
+ accelerator: Annotated[
+ str,
+ Option(
+ help="The hardware accelerator to be used for inference (e.g. 'cpu').",
+ ),
+ ],
+ instance_size: Annotated[
+ str,
+ Option(
+ help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
+ ),
+ ],
+ instance_type: Annotated[
+ str,
+ Option(
+ help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
+ ),
+ ],
+ region: Annotated[
+ str,
+ Option(
+ help="The cloud region in which the Inference Endpoint will be created (e.g. 'us-east-1').",
+ ),
+ ],
+ vendor: Annotated[
+ str,
+ Option(
+ help="The cloud provider or vendor where the Inference Endpoint will be hosted (e.g. 'aws').",
+ ),
+ ],
+ *,
+ namespace: NamespaceOpt = None,
+ task: Annotated[
+ str | None,
+ Option(
+ help="The task on which to deploy the model (e.g. 'text-classification').",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+ min_replica: Annotated[
+ int,
+ Option(
+ help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
+ ),
+ ] = 1,
+ max_replica: Annotated[
+ int,
+ Option(
+ help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
+ ),
+ ] = 1,
+ scale_to_zero_timeout: Annotated[
+ int | None,
+ Option(
+ help="The duration in minutes before an inactive endpoint is scaled to zero.",
+ ),
+ ] = None,
+ scaling_metric: Annotated[
+ InferenceEndpointScalingMetric | None,
+ Option(
+ help="The metric reference for scaling.",
+ ),
+ ] = None,
+ scaling_threshold: Annotated[
+ float | None,
+ Option(
+ help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
+ ),
+ ] = None,
+ revision: RevisionOpt = None,
+ custom_image: Annotated[
+ str | None,
+ Option(
+ "--custom-image",
+ help="Docker image URL for the container to run (e.g. 'nexagi/sglang:v0.5.12'). Requires '--framework custom'.",
+ ),
+ ] = None,
+ engine: EngineOpt = None,
+ health_route: HealthRouteOpt = None,
+ port: PortOpt = None,
+ container_registry_username: Annotated[
+ str | None,
+ Option(
+ help="Username used to authenticate with the registry hosting --custom-image.",
+ ),
+ ] = None,
+ container_registry_password: Annotated[
+ str | None,
+ Option(
+ help="Optional password used to authenticate with the registry hosting --custom-image.",
+ ),
+ ] = None,
+ tensor_parallel_size: TensorParallelSizeOpt = None,
+ data_parallel_size: DataParallelSizeOpt = None,
+ container_command: Annotated[
+ str | None,
+ Option(
+ "--container-command",
+ help=(
+ "Override the container entrypoint, as a quoted string split into tokens "
+ '(e.g. "python -m sglang.launch_server").'
+ ),
+ ),
+ ] = None,
+ container_args: Annotated[
+ str | None,
+ Option(
+ "--container-args",
+ help=(
+ "Arguments appended to the container entrypoint, as a quoted string split into tokens "
+ '(e.g. "--tp 8 --reasoning-parser qwen3").'
+ ),
+ ),
+ ] = None,
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ endpoint_type: Annotated[
+ str | None,
+ Option(
+ "--type",
+ click_type=SoftChoice(InferenceEndpointType),
+ help="Endpoint access type. Defaults to 'authenticated' (token-gated, publicly reachable).",
+ ),
+ ] = None,
+) -> None:
+ """Deploy an Inference Endpoint from a Hub repository.
+
+ Run `hf endpoints hardware` to list the valid `--vendor`, `--region`, `--accelerator`, `--instance-type` and
+ `--instance-size` combinations.
+ """
+ custom_image_dict = _build_custom_image(
+ custom_image,
+ engine=engine,
+ health_route=health_route,
+ port=port,
+ container_registry_username=container_registry_username,
+ container_registry_password=container_registry_password,
+ tensor_parallel_size=tensor_parallel_size,
+ data_parallel_size=data_parallel_size,
+ )
+
+ env_map = {key: value or "" for key, value in parse_env_map(env, env_file).items()}
+ secrets_map = {key: value or "" for key, value in parse_env_map(secrets, secrets_file).items()}
+
+ # Only forward the values the user actually set and let `create_inference_endpoint` own the defaults.
+ params: dict = {}
+ if endpoint_type is not None:
+ params["type"] = endpoint_type
+ if custom_image_dict is not None:
+ params["custom_image"] = custom_image_dict
+ if container_registry_username is not None:
+ params["container_registry_username"] = container_registry_username
+ if container_registry_password is not None:
+ params["container_registry_password"] = container_registry_password
+ if container_command:
+ params["container_command"] = shlex.split(container_command)
+ if container_args:
+ params["container_args"] = shlex.split(container_args)
+ if env_map:
+ params["env"] = env_map
+ if secrets_map:
+ params["secrets"] = secrets_map
+
+ api = get_hf_api(token=token)
+ endpoint = api.create_inference_endpoint(
+ name=name,
+ repository=repo,
+ framework=framework,
+ accelerator=accelerator,
+ instance_size=instance_size,
+ instance_type=instance_type,
+ region=region,
+ vendor=vendor,
+ namespace=namespace,
+ task=task,
+ token=token,
+ min_replica=min_replica,
+ max_replica=max_replica,
+ scaling_metric=scaling_metric,
+ scaling_threshold=scaling_threshold,
+ scale_to_zero_timeout=scale_to_zero_timeout,
+ revision=revision,
+ **params,
+ )
+ out.dict(endpoint.raw)
+ out.hint(f"Use 'hf endpoints describe {name}' to check the deployment status.")
+
+
+@catalog_app.command(
+ name="deploy",
+ examples=[
+ "hf endpoints catalog deploy --repo meta-llama/Llama-3.2-1B-Instruct",
+ "hf endpoints catalog deploy --recipe sizzling-biryani-g4xsi1ac",
+ ],
+)
+def deploy_from_catalog(
+ repo: Annotated[
+ str | None,
+ Option(
+ help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b')."
+ " Deploys its default recipe. Mutually exclusive with --recipe.",
+ ),
+ ] = None,
+ recipe: Annotated[
+ str | None,
+ Option(
+ help="The id of the catalog recipe to deploy, as listed by 'hf endpoints catalog ls'."
+ " Mutually exclusive with --repo.",
+ ),
+ ] = None,
+ name: NameOpt = None,
+ accelerator: Annotated[
+ str | None,
+ Option(
+ click_type=SoftChoice(["cpu", "gpu", "neuron"]),
+ help="The hardware accelerator to be used for inference. Only with --repo.",
+ ),
+ ] = None,
+ gguf_file: Annotated[
+ str | None,
+ Option(
+ help="The GGUF file to deploy, for models that have one recipe per quant. Only with --repo.",
+ ),
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Deploy an Inference Endpoint from the Model Catalog.
+
+ Catalog models are deployed through a recipe: a hardware and engine combination that has been tested for them.
+ Pass --repo to deploy the default recipe of a model, optionally narrowed down with --accelerator and
+ --gguf-file, or pass --recipe to deploy an exact recipe listed by `hf endpoints catalog ls`.
+ """
+ # `--repo` used to be required, so a bare `catalog deploy` used to fail in click. Keep the message flag-shaped.
+ if (repo is None) == (recipe is None):
+ raise CLIError("Provide exactly one of --repo or --recipe. List recipes with 'hf endpoints catalog ls'.")
+
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.create_inference_endpoint_from_catalog(
+ repo_id=repo,
+ recipe_id=recipe,
+ name=name,
+ accelerator=accelerator,
+ gguf_file=gguf_file,
+ namespace=namespace,
+ token=token,
+ )
+ except HfHubHTTPError as error:
+ out.error(f"Deployment failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ out.dict(endpoint.raw)
+ out.hint(f"Use 'hf endpoints describe {endpoint.name}' to check the deployment status.")
+
+
+def list_catalog(
+ accelerator: Annotated[
+ str | None,
+ Option(
+ click_type=SoftChoice(["cpu", "gpu", "neuron"]),
+ help="Only show recipes running on this accelerator.",
+ ),
+ ] = None,
+ engine: Annotated[
+ str | None,
+ Option(
+ click_type=SoftChoice(["llamacpp", "sglang", "tei", "vllm"]),
+ help="Only show recipes running this inference engine.",
+ ),
+ ] = None,
+ license: Annotated[
+ str | None,
+ Option(help="Only show models under this license (e.g. 'Apache 2.0')."),
+ ] = None,
+ task: Annotated[
+ str | None,
+ Option(help="Only show models for this task (e.g. 'text-generation')."),
+ ] = None,
+ search: SearchOpt = None,
+ limit: Annotated[int | None, Option(help="Limit the number of models to return.")] = None,
+ token: TokenOpt = None,
+) -> None:
+ """List the models available in the Model Catalog.
+
+ One row per recipe, i.e. per tested way of deploying a model. Deploy one with
+ `hf endpoints catalog deploy --recipe `.
+ """
+ api = get_hf_api(token=token)
+ try:
+ models = api.list_inference_catalog(
+ accelerator=accelerator,
+ engine=engine,
+ license=license,
+ task=task,
+ search=search,
+ limit=limit,
+ token=token,
+ )
+ except HfHubHTTPError as error:
+ out.error(f"Catalog fetch failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ items = [
+ {
+ "repo_id": model.repo_id,
+ "task": model.task,
+ "license": model.license,
+ "accelerator": recipe.accelerator,
+ "engine": recipe.engine,
+ "gguf_file": recipe.gguf_file,
+ "recipe_id": recipe.id,
+ }
+ for model in models
+ for recipe in model.recipes
+ ]
+ out.table(items, id_key="recipe_id")
+ if items:
+ out.hint(f"Deploy one with: hf endpoints catalog deploy --recipe {items[0]['recipe_id']}")
+
+
+catalog_app.command(name="list | ls", examples=["hf endpoints catalog ls", "hf endpoints catalog ls --engine vllm"])(
+ list_catalog
+)
+ie_cli.command(name="list-catalog", hidden=True)(list_catalog)
+
+
+ie_cli.add_group(catalog_app, name="catalog")
+
+
+@ie_cli.command(examples=["hf endpoints describe my-endpoint"])
+def describe(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Get information about an existing endpoint."""
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.get_inference_endpoint(name=name, namespace=namespace, token=token)
+ except HfHubHTTPError as error:
+ out.error(f"Fetch failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ out.dict(endpoint.raw)
+
+
+@ie_cli.command(
+ examples=[
+ "hf endpoints update my-endpoint --min-replica 2",
+ "hf endpoints update my-endpoint --tensor-parallel-size 8",
+ 'hf endpoints update my-endpoint --container-args "--enable-auto-tool-choice --tool-call-parser lfm2"',
+ ]
+)
+def update(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ repo: Annotated[
+ str | None,
+ Option(
+ help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
+ ),
+ ] = None,
+ accelerator: Annotated[
+ str | None,
+ Option(
+ help="The hardware accelerator to be used for inference (e.g. 'cpu').",
+ ),
+ ] = None,
+ instance_size: Annotated[
+ str | None,
+ Option(
+ help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
+ ),
+ ] = None,
+ instance_type: Annotated[
+ str | None,
+ Option(
+ help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
+ ),
+ ] = None,
+ framework: Annotated[
+ str | None,
+ Option(
+ help="The machine learning framework used for the model (e.g. 'custom').",
+ ),
+ ] = None,
+ revision: Annotated[
+ str | None,
+ Option(
+ help="The specific model revision to deploy on the Inference Endpoint (e.g. '6c0e6080953db56375760c0471a8c5f2929baf11').",
+ ),
+ ] = None,
+ task: Annotated[
+ str | None,
+ Option(
+ help="The task on which to deploy the model (e.g. 'text-classification').",
+ ),
+ ] = None,
+ custom_image: Annotated[
+ str | None,
+ Option(
+ "--custom-image",
+ help=(
+ "Docker image URL for the container to run (e.g. 'nexagi/sglang:v0.5.12'). Replaces the image "
+ "currently configured on the endpoint rather than patching it, so pass the engine and container "
+ "settings you want to keep along with it, run 'hf endpoints describe NAME' first to see them."
+ ),
+ ),
+ ] = None,
+ engine: EngineOpt = None,
+ health_route: HealthRouteOpt = None,
+ port: PortOpt = None,
+ tensor_parallel_size: TensorParallelSizeOpt = None,
+ data_parallel_size: DataParallelSizeOpt = None,
+ container_command: Annotated[
+ str | None,
+ Option(
+ "--container-command",
+ help=(
+ "Override the container entrypoint, as a quoted string split into tokens "
+ '(e.g. "python -m sglang.launch_server"). Replaces the current value; '
+ "pass an empty string to clear it."
+ ),
+ ),
+ ] = None,
+ container_args: Annotated[
+ str | None,
+ Option(
+ "--container-args",
+ help=(
+ "Arguments appended to the container entrypoint, as a quoted string split into tokens "
+ '(e.g. "--enable-auto-tool-choice --tool-call-parser lfm2"). Replaces the arguments currently '
+ "set on the endpoint rather than adding to them, so include the ones you want to keep, run "
+ "'hf endpoints describe NAME' first to see them. Pass an empty string to clear them."
+ ),
+ ),
+ ] = None,
+ min_replica: Annotated[
+ int | None,
+ Option(
+ help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
+ ),
+ ] = None,
+ max_replica: Annotated[
+ int | None,
+ Option(
+ help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
+ ),
+ ] = None,
+ scale_to_zero_timeout: Annotated[
+ int | None,
+ Option(
+ help="The duration in minutes before an inactive endpoint is scaled to zero.",
+ ),
+ ] = None,
+ scaling_metric: Annotated[
+ InferenceEndpointScalingMetric | None,
+ Option(
+ help="The metric reference for scaling.",
+ ),
+ ] = None,
+ scaling_threshold: Annotated[
+ float | None,
+ Option(
+ help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update an existing endpoint."""
+ # Exactly one path carries the sizes: into the image built here when `--custom-image` gives us one, otherwise
+ # as kwargs, for `update_inference_endpoint` to merge into the image the endpoint currently runs.
+ custom_image_dict = _build_custom_image(
+ custom_image,
+ engine=engine,
+ health_route=health_route,
+ port=port,
+ tensor_parallel_size=tensor_parallel_size if custom_image is not None else None,
+ data_parallel_size=data_parallel_size if custom_image is not None else None,
+ )
+
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.update_inference_endpoint(
+ name=name,
+ namespace=namespace,
+ repository=repo,
+ framework=framework,
+ revision=revision,
+ task=task,
+ custom_image=custom_image_dict,
+ container_command=shlex.split(container_command) if container_command is not None else None,
+ container_args=shlex.split(container_args) if container_args is not None else None,
+ tensor_parallel_size=tensor_parallel_size if custom_image is None else None,
+ data_parallel_size=data_parallel_size if custom_image is None else None,
+ accelerator=accelerator,
+ instance_size=instance_size,
+ instance_type=instance_type,
+ min_replica=min_replica,
+ max_replica=max_replica,
+ scale_to_zero_timeout=scale_to_zero_timeout,
+ scaling_metric=scaling_metric,
+ scaling_threshold=scaling_threshold,
+ token=token,
+ )
+ except HfHubHTTPError as error:
+ out.error(f"Update failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+ out.dict(endpoint.raw)
+
+
+@ie_cli.command(examples=["hf endpoints delete my-endpoint"])
+def delete(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ yes: Annotated[
+ bool,
+ Option("--yes", help="Skip confirmation prompts."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete an Inference Endpoint permanently."""
+ out.confirm(f"Delete endpoint '{name}'?", yes=yes)
+
+ api = get_hf_api(token=token)
+ try:
+ api.delete_inference_endpoint(name=name, namespace=namespace, token=token)
+ except HfHubHTTPError as error:
+ out.error(f"Delete failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ out.result(f"Deleted '{name}'.", name=name)
+
+
+@ie_cli.command(examples=["hf endpoints pause my-endpoint"])
+def pause(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Pause an Inference Endpoint."""
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.pause_inference_endpoint(name=name, namespace=namespace, token=token)
+ except HfHubHTTPError as error:
+ out.error(f"Pause failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ out.dict(endpoint.raw)
+
+
+@ie_cli.command(examples=["hf endpoints resume my-endpoint"])
+def resume(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ fail_if_already_running: Annotated[
+ bool,
+ Option(
+ "--fail-if-already-running",
+ help="If `True`, the method will raise an error if the Inference Endpoint is already running.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Resume an Inference Endpoint."""
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.resume_inference_endpoint(
+ name=name,
+ namespace=namespace,
+ token=token,
+ running_ok=not fail_if_already_running,
+ )
+ except HfHubHTTPError as error:
+ out.error(f"Resume failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+ out.dict(endpoint.raw)
+
+
+@ie_cli.command(examples=["hf endpoints scale-to-zero my-endpoint"])
+def scale_to_zero(
+ name: NameArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Scale an Inference Endpoint to zero."""
+ api = get_hf_api(token=token)
+ try:
+ endpoint = api.scale_to_zero_inference_endpoint(name=name, namespace=namespace, token=token)
+ except HfHubHTTPError as error:
+ out.error(f"Scale To Zero failed: {error}")
+ raise click.exceptions.Exit(code=error.response.status_code) from error
+
+ out.dict(endpoint.raw)
+
+
+def _build_custom_image(
+ custom_image: str | None,
+ *,
+ engine: str | None = None,
+ health_route: str | None = None,
+ port: int | None = None,
+ container_registry_username: str | None = None,
+ container_registry_password: str | None = None,
+ tensor_parallel_size: int | None = None,
+ data_parallel_size: int | None = None,
+) -> dict | None:
+ """Build the `custom_image` argument of `HfApi` from the flat image flags.
+
+ A bare image URL describes a custom container and is passed flat, `HfApi` keys it as `custom`. `--engine`
+ selects a managed engine image instead, whose config is keyed by the API's variant name. An engine this
+ version doesn't know about is forwarded as typed, so the API names the bad variant.
+ """
+ if custom_image is None:
+ # These flags all describe the container image, and the only image these commands can build is the one
+ # --custom-image names. Container command/args are top-level model fields, hence not listed here.
+ image_flags = {
+ "--engine": engine,
+ "--health-route": health_route,
+ "--port": port,
+ "--container-registry-username": container_registry_username,
+ "--container-registry-password": container_registry_password,
+ "--tensor-parallel-size": tensor_parallel_size,
+ "--data-parallel-size": data_parallel_size,
+ }
+ if used := [flag for flag, value in image_flags.items() if value is not None]:
+ raise CLIError(f"--custom-image is required when using {', '.join(used)}.")
+ return None
+
+ if container_registry_password is not None and container_registry_username is None:
+ raise CLIError("--container-registry-password requires --container-registry-username.")
+ if engine is not None and container_registry_username is not None:
+ raise CLIError("Container registry credentials can only be set for a custom container without --engine.")
+
+ if engine is None and (tensor_parallel_size is not None or data_parallel_size is not None):
+ # Without an engine the config is a plain custom container, whose parallelism fields the API ignores
+ # rather than rejects. Fail here instead of deploying something that quietly runs on one accelerator.
+ raise CLIError("--tensor-parallel-size and --data-parallel-size require --engine (e.g. --engine vllm).")
+
+ config: dict = {"url": custom_image}
+ if health_route is not None:
+ config["healthRoute"] = health_route
+ if port is not None:
+ config["port"] = port
+ if engine is None:
+ return config
+ # Delegate the sizes so this and `update_inference_endpoint` share one rule, including the warning when
+ # the engine does not declare the field.
+ return _set_parallelism_in_image(
+ {ENGINE_IMAGE_KEYS.get(engine, engine): config},
+ tensor_parallel_size=tensor_parallel_size,
+ data_parallel_size=data_parallel_size,
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/jobs.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/jobs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9096fcb49757a33c4f68c1f1506a0d25f6febb9
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/jobs.py
@@ -0,0 +1,2187 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with jobs on the Hugging Face Hub."""
+
+import itertools
+import multiprocessing
+import multiprocessing.pool
+import shlex
+import shutil
+import time
+from collections.abc import Callable, Collection, Generator, Iterable
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from queue import Empty, Queue
+from typing import Annotated, Any, TypeVar
+from urllib.parse import urlsplit
+
+from huggingface_hub import HfApi, JobHardware, JobInfo, JobNetwork, JobStage, Volume, constants
+from huggingface_hub._jobs_api import (
+ DEFAULT_UV_IMAGE,
+ TERMINAL_JOB_STAGES,
+ _default_job_name_from_image,
+ _default_job_name_from_script,
+)
+from huggingface_hub.errors import CLIError
+from huggingface_hub.utils import logging
+from huggingface_hub.utils._cache_manager import _format_size
+from huggingface_hub.utils._hf_uris import _split_mount
+from huggingface_hub.utils._parsing import format_duration, parse_duration
+
+from ._cli_utils import (
+ EnvFileOpt,
+ EnvOpt,
+ FormatOpt,
+ JsonOpt,
+ QuietOpt,
+ SecretsFileOpt,
+ SecretsOpt,
+ SoftChoice,
+ SshDryRunOpt,
+ SshIdentityFileOpt,
+ TokenOpt,
+ _get_extended_environ,
+ exec_ssh,
+ get_hf_api,
+ parse_env_map,
+ parse_volumes,
+ set_output_format,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import OutputFormat, _dataclass_to_dict, out
+from ._uv_script_header import TABLE_NAME, UvScriptHeader, load_uv_script
+
+
+logger = logging.get_logger(__name__)
+
+
+def _parse_namespace_from_job_id(job_id: str, namespace: str | None) -> tuple[str, str | None]:
+ """Extract namespace from job_id if provided in 'namespace/job_id' format.
+
+ Allows users to pass job IDs copied from the Hub UI (e.g. 'username/job_id')
+ instead of only bare job IDs. If the namespace is also provided explicitly via
+ --namespace and conflicts, a CLIError is raised.
+ """
+ if not job_id:
+ raise CLIError("Job ID cannot be empty.")
+
+ if job_id.count("/") > 1:
+ raise CLIError(f"Job ID must be in the form 'job_id' or 'namespace/job_id': '{job_id}'.")
+
+ if "/" not in job_id:
+ return job_id, namespace
+
+ extracted_namespace, parsed_job_id = job_id.split("/", 1)
+ if not extracted_namespace or not parsed_job_id:
+ raise CLIError(f"Job ID must be in the form 'job_id' or 'namespace/job_id': '{job_id}'.")
+
+ if namespace is not None and namespace != extracted_namespace:
+ raise CLIError(
+ f"Conflicting namespace: got --namespace='{namespace}' but job ID implies namespace='{extracted_namespace}'"
+ )
+
+ return parsed_job_id, extracted_namespace
+
+
+def _parse_and_sync_job_volumes(
+ volumes: list[str] | None, *, api: HfApi, namespace: str | None
+) -> list[Volume] | None:
+ """Parse `-v` specs for Jobs commands.
+
+ Same as [`parse_volumes`] but the source side can also be a local directory: it is synced to a
+ bucket via [`HfApi.sync_job_volume`] and the resulting bucket subfolder is mounted (read-only
+ unless ':rw' is specified).
+ """
+ if not volumes:
+ return None
+
+ result: list[Volume] = []
+ for raw_spec in volumes:
+ if raw_spec.startswith(constants.HF_PROTOCOL):
+ result.extend(parse_volumes([raw_spec]) or [])
+ continue
+
+ # Not a 'hf://' URI: treat the source as a local directory.
+ source, mount_path, read_only = _split_mount(raw_spec, raw=raw_spec)
+ if mount_path is None:
+ raise CLIError(
+ f"Missing mount path in volume spec '{raw_spec}'. Expected 'LOCAL_DIR:/MOUNT_PATH[:ro|:rw]' (e.g. './data:/data')."
+ )
+ if not Path(source).expanduser().is_dir():
+ raise CLIError(
+ f"Volume source '{source}' is not an existing local directory. "
+ "To mount a repo or bucket instead, use the 'hf://' syntax (e.g. 'hf://buckets/my-org/my-bucket:/data')."
+ )
+ volume = api.sync_job_volume(
+ source,
+ mount_path,
+ read_only=read_only if read_only is not None else True,
+ namespace=namespace,
+ )
+ if volume.read_only is False:
+ out.hint(
+ f"Volume '{mount_path}' is mounted read-write. Once the job is over, pull back its data with:\n"
+ f" hf buckets sync hf://buckets/{volume.source}/{volume.path} {source}"
+ )
+ result.append(volume)
+ return result
+
+
+@dataclass
+class _UvJobConfig:
+ """Resolved launch configuration of a UV Job: CLI flags merged with the script's `[tool.hf-jobs]` table."""
+
+ script: str
+ """What is passed to the Jobs API. Same as the CLI argument, except for a URL script (downloaded locally)."""
+
+ script_args: list[str] = field(default_factory=list)
+ image: str | None = None
+ flavor: str | None = None
+ python: str | None = None
+ timeout: str | None = None
+ namespace: str | None = None
+ network_group: str | None = None
+ env: dict[str, str | None] = field(default_factory=dict)
+ secrets: dict[str, str | None] = field(default_factory=dict)
+ labels: dict[str, str] = field(default_factory=dict)
+ volume_specs: list[str] = field(default_factory=list)
+ volumes: list[Volume] | None = None
+ network_aliases: list[str] = field(default_factory=list)
+
+ from_script: set[str] = field(default_factory=set)
+ """Keys whose value comes from the script's `[tool.hf-jobs]` table, for display purposes."""
+
+
+@contextmanager
+def _resolve_uv_job_config(
+ *,
+ api: HfApi,
+ script: str,
+ script_args: list[str] | None,
+ dependencies: list[str] | None,
+ image: str | None,
+ flavor: str | None,
+ python: str | None,
+ env: list[str] | None,
+ env_file: str | None,
+ secrets: list[str] | None,
+ secrets_file: str | None,
+ timeout: str | None,
+ attempts: int | None,
+ name: str | None,
+ label: list[str] | None,
+ volume: list[str] | None,
+ namespace: str | None,
+ network_group: str | None,
+ network_aliases: list[str] | None,
+ dry_run: bool,
+) -> Generator[_UvJobConfig, None, None]:
+ """Merge the CLI flags with the `[tool.hf-jobs]` table the script carries, if any.
+
+ An explicit CLI flag always wins over the script; `env`, `labels`, `secrets` and `volumes` are merged
+ entry by entry (by key, by name and by mount path respectively), so a script value and a CLI value
+ only ever conflict when they target the same entry. `network_aliases` is the exception: the aliases
+ a Job claims form a set, so `--network-alias` replaces the script's list rather than adding to it.
+
+ A URL script is downloaded to a temporary file that lives until the `with` block exits: submit the
+ Job inside the block.
+ """
+ with load_uv_script(script) as source:
+ header = source.header or UvScriptHeader()
+ from_script: set[str] = set()
+
+ def pick(key: str, cli_value: str | None) -> str | None:
+ """Keep the CLI value, and fall back to the script's."""
+ if cli_value is not None:
+ return cli_value
+ if (script_value := getattr(header, key)) is not None:
+ from_script.add(key)
+ return script_value
+ return None
+
+ image = pick("image", image)
+ flavor = pick("flavor", flavor)
+ python = pick("python", python)
+ timeout = pick("timeout", timeout)
+ namespace = pick("namespace", namespace)
+ network_group = pick("network_group", network_group)
+
+ if not network_aliases and header.network_aliases:
+ from_script.add("network_aliases")
+ network_aliases = header.network_aliases
+
+ env_map = parse_env_map(env, env_file)
+ script_env = {key: value for key, value in header.env.items() if key not in env_map}
+ from_script.update(f"env.{key}" for key in script_env)
+ env_map = {**script_env, **env_map}
+
+ secrets_map = _parse_secrets_map(secrets, secrets_file, dry_run=dry_run)
+ script_secrets = _resolve_script_secrets(header.secrets, secrets_map, dry_run=dry_run)
+ from_script.update(f"secrets.{key}" for key in script_secrets)
+ secrets_map = {**script_secrets, **secrets_map}
+
+ labels_map = _parse_labels_map(label, name=name) or {}
+ # `name = ...` in the script's table is shorthand for the `name` label.
+ script_labels = {**header.labels, **({"name": header.name} if header.name is not None else {})}
+ script_labels = {key: value for key, value in script_labels.items() if key not in labels_map}
+ from_script.update(f"labels.{key}" for key in script_labels)
+ labels_map = {**script_labels, **labels_map}
+
+ volume_specs, script_volume_specs = _merge_volume_specs(volume or [], header.volumes)
+ from_script.update(f"volumes.{spec}" for spec in script_volume_specs)
+
+ config = _UvJobConfig(
+ script=source.script,
+ script_args=script_args or [],
+ image=image,
+ flavor=flavor,
+ python=python,
+ timeout=timeout,
+ namespace=namespace,
+ network_group=network_group,
+ env=env_map,
+ secrets=secrets_map,
+ labels=labels_map,
+ volume_specs=volume_specs,
+ # A dry run must not have side effects: local directories are only synced to a bucket for real runs.
+ volumes=None if dry_run else _parse_and_sync_job_volumes(volume_specs, api=api, namespace=namespace),
+ network_aliases=network_aliases or [],
+ from_script=from_script,
+ )
+ if "name" not in config.labels:
+ # `script` (not `config.script`) so that a URL keeps naming the Job after the URL, not after
+ # the temporary file it was downloaded to.
+ config.labels["name"] = _default_job_name_from_script(
+ script,
+ config.script_args,
+ config_parts=_name_hash_parts(
+ flavor=config.flavor,
+ timeout=config.timeout,
+ namespace=config.namespace,
+ env=config.env,
+ secrets=config.secrets,
+ volume_specs=config.volume_specs,
+ network_group=config.network_group,
+ network_aliases=config.network_aliases,
+ extra=[
+ config.image or DEFAULT_UV_IMAGE,
+ config.python or "",
+ *(dependencies or []),
+ *([str(attempts)] if attempts is not None else []),
+ ],
+ ),
+ )
+ yield config
+
+
+def _parse_secrets_map(
+ secrets: list[str] | None, secrets_file: str | None, *, dry_run: bool = False
+) -> dict[str, str | None]:
+ """Parse `--secrets`/`--secrets-file`, failing if a bare `--secrets NAME` is not set locally.
+
+ Same behavior as for the secrets requested by a script (see `_resolve_script_secrets`): a missing secret
+ is an error rather than silently dropped, except in `--dry-run` where it is displayed as ``.
+ """
+ secrets_map = parse_env_map(secrets, secrets_file)
+ missing = [name for value in secrets or [] if "=" not in value and (name := value.strip()) not in secrets_map]
+ if missing and not dry_run:
+ raise CLIError(
+ f"The following secret(s) are not set in your environment: {', '.join(missing)}."
+ f" Export them locally (e.g. `export {missing[0]}=...`) or use `--secrets-file` otherwise."
+ )
+ return {**secrets_map, **dict.fromkeys(missing)}
+
+
+def _resolve_script_secrets(
+ names: list[str], cli_secrets: dict[str, str | None], *, dry_run: bool = False
+) -> dict[str, str | None]:
+ """Resolve the secrets requested by a script from the caller's environment.
+
+ A script only lists secret *names*: values always come from whoever runs it (`HF_TOKEN` also
+ resolves from `hf auth login`). A requested secret that is not set locally is an error rather than
+ an empty value: forgetting to export a secret before launching is easy, and debugging the Job that
+ results from it is not.
+
+ In `--dry-run` a missing secret is not an error but a `None` value, displayed as `` in the
+ summary: nothing is submitted, so the point is to see the full configuration of a script - including
+ one whose secrets are not provisioned locally (yet).
+ """
+ if not names:
+ return {}
+ environ = _get_extended_environ()
+ resolved: dict[str, str | None] = {}
+ missing: list[str] = []
+ for name in names:
+ if name in cli_secrets: # an explicit `--secrets NAME=...` wins
+ continue
+ if name in environ:
+ resolved[name] = environ[name]
+ else:
+ missing.append(name)
+ if missing and not dry_run:
+ raise CLIError(
+ f"The script requires the following secret(s), which are not set in your environment: {', '.join(missing)}."
+ f" Export them locally (e.g. `export {missing[0]}=...`) or use `--secrets-file` otherwise."
+ )
+ if resolved:
+ out.warning(
+ f"The script's [{TABLE_NAME}] table requests {', '.join(resolved)}: the value(s) from your local"
+ f" environment {'would be' if dry_run else 'will be'} sent to the Job."
+ )
+ return {**resolved, **dict.fromkeys(missing)}
+
+
+def _merge_volume_specs(cli_specs: list[str], script_specs: list[str]) -> tuple[list[str], list[str]]:
+ """Merge `-v` specs with the ones from the script. Returns `(all specs, specs from the script)`.
+
+ A CLI mount overrides the script's mount at the same path.
+ """
+ cli_mount_paths = {_split_mount(spec, raw=spec)[1] for spec in cli_specs}
+ kept = [spec for spec in script_specs if _split_mount(spec, raw=spec)[1] not in cli_mount_paths]
+ return kept + cli_specs, kept
+
+
+def _name_hash_parts(
+ *,
+ flavor: str | None,
+ timeout: str | None,
+ namespace: str | None,
+ env: dict[str, str | None],
+ secrets: dict[str, str | None],
+ volume_specs: list[str],
+ network_group: str | None = None,
+ network_aliases: list[str] | None = None,
+ extra: list[str] | None = None,
+) -> list[str]:
+ """The resolved launch values hashed into a Job's default name, on top of its image/script.
+
+ Same inputs for `hf jobs run` and `hf jobs uv run`, so that both name Jobs after what they actually
+ submit (`extra` carries what is specific to UV runs). Env *values* are part of the hash since they
+ change what the Job does; secrets only contribute their names, never their values.
+ """
+ return [
+ flavor or JobHardware.CPU_BASIC.value,
+ timeout or "",
+ namespace or "",
+ network_group or "",
+ *(extra or []),
+ *(f"{key}={value}" for key, value in sorted(env.items())),
+ *sorted(secrets),
+ *volume_specs,
+ *(network_aliases or []),
+ ]
+
+
+_FROM_SCRIPT = " (from script)"
+
+
+def _print_job_summary(values: dict[str, Any], *, from_script: Collection[str] = (), dry_run: bool = False) -> None:
+ """Echo the launch configuration of a Job before submitting it.
+
+ Printed on every run, so that what is sent to the Jobs API is always visible - in particular the
+ values injected by a script's `[tool.hf-jobs]` table, which are marked as such. Goes to stderr for a
+ real run, and to stdout for `--dry-run`, where it is the output of the command.
+ """
+ rows = [
+ (key, _format_job_value(key, value, from_script))
+ for key, value in values.items()
+ if value not in (None, "", [], {}, False)
+ ]
+ width = max(len(key) for key, _ in rows)
+ lines = ["Job configuration:"]
+ for key, value in rows:
+ value += _FROM_SCRIPT if key in from_script else ""
+ # Only the first line of a multi-line value is prefixed with the key.
+ lines += [
+ f" {(key if index == 0 else '').ljust(width)} {line}" for index, line in enumerate(value.split("\n"))
+ ]
+ summary = "\n".join(lines)
+ if dry_run:
+ out.text(f"{summary}\n(dry run) Job not submitted.")
+ else:
+ out.log(summary)
+
+
+def _format_job_value(key: str, value: Any, from_script: Collection[str]) -> str:
+ """Format config entries, redacting secrets and marking values inherited from the script."""
+ match key:
+ case "env":
+ # Keep long prompts or JSON values readable in the summary.
+ entries = {}
+ for name, item in value.items():
+ text = item or ""
+ entries[name] = f"{name}={text[:59] + '…' if len(text) > 60 else text}"
+ case "secrets":
+ entries = {name: f"{name}={'***' if item is not None else ''}" for name, item in value.items()}
+ case "labels":
+ entries = {name: f"{name}={item}" for name, item in value.items()}
+ case "volumes":
+ entries = {spec: spec for spec in value}
+ case _:
+ return str(value)
+ return "\n".join(text + (_FROM_SCRIPT if f"{key}.{name}" in from_script else "") for name, text in entries.items())
+
+
+STATS_UPDATE_MIN_INTERVAL = 0.1 # we set a limit here since there is one update per second per job
+
+# Common job-related options
+ImageArg = Annotated[
+ str,
+ Argument(
+ help="The Docker image to use.",
+ ),
+]
+
+ImageOpt = Annotated[
+ str | None,
+ Option(
+ help="Use a custom Docker image with `uv` installed.",
+ ),
+]
+
+FlavorOpt = Annotated[
+ str | None,
+ Option(
+ help="Flavor for the hardware. Run 'hf jobs hardware' to list available flavors. Defaults to `cpu-basic`.",
+ click_type=SoftChoice(JobHardware),
+ ),
+]
+
+LabelsOpt = Annotated[
+ list[str] | None,
+ Option(
+ "-l",
+ "--label",
+ help="Set labels. E.g. --label KEY=VALUE or --label LABEL",
+ ),
+]
+
+NameOpt = Annotated[
+ str | None,
+ Option(
+ "--name",
+ help="Name the Job. Stored as the `name` label. Names do not have to be unique. Defaults to the image or script name plus a short hash of the resolved launch configuration.",
+ ),
+]
+
+TimeoutOpt = Annotated[
+ str | None,
+ Option(
+ help="Max duration: int with s (seconds, default), m (minutes), h (hours) or d (days).",
+ ),
+]
+
+AttemptsOpt = Annotated[
+ int | None,
+ Option("--attempts", min=1, help="Maximum attempts, including the first run. Defaults to 1."),
+]
+
+DetachOpt = Annotated[
+ bool,
+ Option(
+ "-d",
+ "--detach",
+ help="Run the Job in the background and print the Job ID.",
+ ),
+]
+
+DryRunOpt = Annotated[
+ bool,
+ Option(
+ "--dry-run",
+ help="Print the resolved Job configuration without submitting the Job.",
+ ),
+]
+
+NamespaceOpt = Annotated[
+ str | None,
+ Option(
+ help="The namespace where the job will be running. Defaults to the current user's namespace.",
+ ),
+]
+
+ResourceGroupIdOpt = Annotated[
+ str | None,
+ Option(
+ "--resource-group-id",
+ help="The ID of the resource group to create the Job in. Used to control access to resources within an organization and for cost attribution/spending-limit features.",
+ ),
+]
+
+ExposeOpt = Annotated[
+ list[int] | None,
+ Option(
+ "--expose",
+ help="Expose a container port through the jobs proxy. Repeat the flag for multiple ports (e.g. `--expose 8000 --expose 8001`). Each exposed port is reachable on the public jobs domain; access requires an HF token with read access to the job's namespace.",
+ ),
+]
+
+ExposePublicOpt = Annotated[
+ list[int] | None,
+ Option(
+ "--expose-public",
+ help="Expose a container port through the jobs proxy without authentication. Repeat the flag for multiple ports. No need to also pass the port with `--expose`.",
+ ),
+]
+
+
+SshEnabledOpt = Annotated[
+ bool,
+ Option(
+ "--ssh",
+ help="Make the job's container reachable over SSH. Connect with `hf jobs ssh `. Requires an SSH public key registered on https://huggingface.co/settings/keys.",
+ ),
+]
+
+NetworkGroupOpt = Annotated[
+ str | None,
+ Option(
+ "--network-group",
+ help="Join a network group. Jobs in the same namespace and resource group sharing a group are placed together and reach each other on every port. Inside each member, `$HF_NETWORK_GROUP_HOSTNAME` resolves to every member. Lowercase alphanumerics and dashes, 46 characters max.",
+ ),
+]
+
+NetworkAliasOpt = Annotated[
+ list[str] | None,
+ Option(
+ "--network-alias",
+ help="Claim an alias in the network group. Members reach the jobs claiming it at `${HF_NETWORK_GROUP_PREFIX}`. Repeat the flag for several aliases. Lowercase alphanumerics and dashes, 34 characters max, unique within the job. Requires `--network-group`.",
+ ),
+]
+
+WithOpt = Annotated[
+ list[str] | None,
+ Option(
+ "--with",
+ help="Run with the given packages installed",
+ ),
+]
+
+PythonOpt = Annotated[
+ str | None,
+ Option(
+ "-p",
+ "--python",
+ help="The Python interpreter to use for the run environment",
+ ),
+]
+
+SuspendOpt = Annotated[
+ bool | None,
+ Option(
+ help="Suspend (pause) the scheduled Job",
+ ),
+]
+
+ConcurrencyOpt = Annotated[
+ bool | None,
+ Option(
+ help="Allow multiple instances of this Job to run concurrently",
+ ),
+]
+
+ScheduleArg = Annotated[
+ str,
+ Argument(
+ help="One of @annually, @yearly, @monthly, @weekly, @daily, @hourly, or a CRON schedule expression.",
+ ),
+]
+
+ScriptArg = Annotated[
+ str,
+ Argument(
+ help="UV script to run (local file or URL)",
+ ),
+]
+
+ScriptArgsArg = Annotated[
+ list[str] | None,
+ Argument(
+ help="Arguments for the script",
+ ),
+]
+
+
+CommandArg = Annotated[
+ list[str],
+ Argument(
+ help="The command to run.",
+ ),
+]
+
+JobIdArg = Annotated[
+ str,
+ Argument(
+ help="Job ID (or 'namespace/job_id')",
+ ),
+]
+
+JobIdsArg = Annotated[
+ list[str] | None,
+ Argument(
+ help="Job IDs (or 'namespace/job_id')",
+ ),
+]
+
+ScheduledJobIdArg = Annotated[
+ str,
+ Argument(
+ help="Scheduled Job ID (or 'namespace/scheduled_job_id')",
+ ),
+]
+
+JobVolumesOpt = Annotated[
+ list[str] | None,
+ Option(
+ "-v",
+ "--volume",
+ help="Mount one or more volumes. Format: hf://[TYPE/]SOURCE:/MOUNT_PATH[:ro|:rw] or LOCAL_DIR:/MOUNT_PATH[:ro|:rw]. "
+ "TYPE is one of: models, datasets, spaces, buckets. "
+ "TYPE defaults to models if omitted. "
+ "models, datasets and spaces are always mounted read-only. buckets are read+write by default. "
+ "A local directory source is first synced to a bucket and mounted read-only by default. "
+ "E.g. -v hf://datasets/org/ds:/data or -v hf://buckets/org/b:/mnt:ro or -v ./inputs:/inputs",
+ ),
+]
+
+
+jobs_cli = typer_factory(help="Run and manage Jobs on the Hub.")
+
+
+def _stream_logs_and_check_status(api: HfApi, job: JobInfo) -> None:
+ """Stream Job logs until the Job ends, then fail the command if the Job did not complete successfully."""
+ for log in api.fetch_job_logs(job_id=job.id, namespace=job.owner.name, follow=True):
+ out.text(log)
+ # The log stream can end while the Job is still scheduling or shutting down: settle the final state.
+ final = api.wait_for_job(job_id=job.id, namespace=job.owner.name)
+ if final.status.stage != JobStage.COMPLETED:
+ message = f": {final.status.message}" if final.status.message else ""
+ raise CLIError(f"Job {final.id} finished with stage '{final.status.stage}'{message}")
+ out.text(f"Job {final.id} completed")
+
+
+def _hint_or_follow_started_job(api: HfApi, job: JobInfo, *, detach: bool) -> None:
+ """Print hints about how to reach a started Job, then stream its logs unless `detach` is set."""
+ job_ref = f"{job.owner.name}/{job.id}"
+ if isinstance(job.status.expose_urls, list):
+ urls = "\n".join(f" {url}" for url in job.status.expose_urls)
+ out.hint(f"Exposed ports are reachable at:\n{urls}")
+ if isinstance(job.status.ssh_url, str):
+ out.hint(f"Use `hf jobs ssh {job_ref}` to open an SSH session into the job.")
+ if isinstance(job.network, JobNetwork):
+ group = job.network.group
+ out.hint(
+ f"Joined network group '{group}'. Jobs of this namespace and resource group started with "
+ f"`--network-group {group}` reach each other at `$HF_NETWORK_GROUP_HOSTNAME` (every member) "
+ "or `${HF_NETWORK_GROUP_PREFIX}` (members claiming an alias)."
+ )
+ if detach:
+ out.hint(f"Use `hf jobs logs -f {job_ref}` to stream logs, or `hf jobs inspect {job_ref}` to check status.")
+ out.hint(f"Use `hf jobs wait {job_ref}` to block until it finishes.")
+ return
+ _stream_logs_and_check_status(api, job)
+
+
+@jobs_cli.command(
+ "run",
+ context_settings={"ignore_unknown_options": True},
+ examples=[
+ "hf jobs run --name hello-world python:3.12 python -c 'print(\"Hello!\")'",
+ "hf jobs run --detach python:3.12 python script.py",
+ "hf jobs run -e FOO=foo python:3.12 python script.py",
+ "hf jobs run --secrets HF_TOKEN python:3.12 python script.py",
+ "hf jobs run -v hf://org/my-model:/data -v hf://buckets/org/b:/mnt python:3.12 python script.py",
+ ],
+)
+def jobs_run(
+ image: ImageArg,
+ command: CommandArg,
+ env: EnvOpt = None,
+ secrets: SecretsOpt = None,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ volume: JobVolumesOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ flavor: FlavorOpt = None,
+ timeout: TimeoutOpt = None,
+ attempts: AttemptsOpt = None,
+ detach: DetachOpt = False,
+ dry_run: DryRunOpt = False,
+ expose: ExposeOpt = None,
+ expose_public: ExposePublicOpt = None,
+ ssh: SshEnabledOpt = False,
+ network_group: NetworkGroupOpt = None,
+ network_alias: NetworkAliasOpt = None,
+ resource_group_id: ResourceGroupIdOpt = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+ format: FormatOpt = None,
+ json_output: JsonOpt = False,
+ quiet: QuietOpt = False,
+) -> None:
+ """Run a Job."""
+ set_output_format(format, json_output, quiet)
+ env_map = parse_env_map(env, env_file)
+ secrets_map = _parse_secrets_map(secrets, secrets_file, dry_run=dry_run)
+ labels_map = _parse_labels_map(label, name=name) or {}
+ labels_map.setdefault(
+ "name",
+ _default_job_name_from_image(
+ image,
+ command,
+ config_parts=_name_hash_parts(
+ flavor=flavor,
+ timeout=timeout,
+ extra=[str(attempts)] if attempts is not None else None,
+ namespace=namespace,
+ env=env_map,
+ secrets=secrets_map,
+ volume_specs=volume or [],
+ network_group=network_group,
+ network_aliases=network_alias,
+ ),
+ ),
+ )
+
+ api = get_hf_api(token=token)
+ volumes = None if dry_run else _parse_and_sync_job_volumes(volume, api=api, namespace=namespace)
+ _print_job_summary(
+ {
+ "image": image,
+ "command": shlex.join(command),
+ "flavor": flavor or JobHardware.CPU_BASIC.value,
+ "timeout": timeout,
+ "attempts": attempts,
+ "env": env_map,
+ "secrets": secrets_map,
+ "volumes": volume or [],
+ "labels": labels_map,
+ "expose": " ".join(str(port) for port in expose or []),
+ "expose_public": " ".join(str(port) for port in expose_public or []),
+ "ssh": ssh,
+ "network_group": network_group,
+ "network_aliases": " ".join(network_alias or []),
+ "resource_group_id": resource_group_id,
+ "namespace": namespace,
+ },
+ dry_run=dry_run,
+ )
+ if dry_run:
+ return
+ job = api.run_job(
+ image=image,
+ command=command,
+ env=env_map,
+ secrets=secrets_map,
+ labels=labels_map,
+ volumes=volumes,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ expose=expose,
+ expose_public=expose_public,
+ ssh=ssh,
+ network_group=network_group,
+ network_aliases=network_alias,
+ resource_group_id=resource_group_id,
+ namespace=namespace,
+ )
+ out.result("Job started", id=job.id, name=(job.labels or {}).get("name"), url=job.url)
+ if not _has_explicit_name(name, label):
+ auto_name = (job.labels or {}).get("name")
+ out.hint(
+ f"Job auto-named '{auto_name}'. Pass `--name` or run "
+ f"`hf jobs labels {job.owner.name}/{job.id} --name NAME` to rename it."
+ )
+ _hint_or_follow_started_job(api, job, detach=detach)
+
+
+@jobs_cli.command(
+ "logs",
+ examples=[
+ "hf jobs logs ",
+ "hf jobs logs -f ",
+ "hf jobs logs --tail 20 ",
+ "hf jobs logs -f --tail 100 ",
+ ],
+)
+def jobs_logs(
+ job_id: JobIdArg,
+ follow: Annotated[
+ bool,
+ Option(
+ "-f",
+ "--follow",
+ help="Follow log output (stream until the job completes). Without this flag, only currently available logs are printed.",
+ ),
+ ] = False,
+ tail: Annotated[
+ int | None,
+ Option(
+ "-n",
+ "--tail",
+ help="Number of lines to show from the end of the logs. When combined with --follow, starts streaming from the last N lines.",
+ ),
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Fetch the logs of a Job.
+
+ By default, prints currently available logs and exits (non-blocking).
+ Use --follow/-f to stream logs in real-time until the job completes.
+ Use --tail/-n to limit the number of lines returned (server-side when supported).
+
+ Note: following exits when the log stream ends, regardless of whether the Job
+ succeeded or failed. Run `hf jobs inspect ` to check the final status.
+ """
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+
+ api = get_hf_api(token=token)
+ logs = api.fetch_job_logs(job_id=job_id, namespace=namespace, follow=follow, tail=tail)
+ for log in logs:
+ out.text(log)
+ job_ref = f"{namespace}/{job_id}" if namespace else job_id
+ if follow:
+ out.hint(f"Stream ended. Run `hf jobs inspect {job_ref}` to check the final status (e.g. COMPLETED or ERROR).")
+ else:
+ out.hint(
+ f"If the job is still running, use `hf jobs logs -f {job_ref}` to stream new logs until it completes."
+ )
+
+
+def _clear_line(n: int) -> None:
+ LINE_UP = "\033[1A"
+ LINE_CLEAR = "\x1b[2K"
+ for i in range(n):
+ print(LINE_UP, end=LINE_CLEAR)
+
+
+def _format_job_stats_rows(job_id: str, metrics: dict[str, Any], table_headers: list[str]) -> list[list[str | int]]:
+ row: list[str | int] = [
+ job_id,
+ f"{metrics['cpu_usage_pct']}%",
+ round(metrics["cpu_millicores"] / 1000.0, 1),
+ f"{round(100 * metrics['memory_used_bytes'] / metrics['memory_total_bytes'], 2)}%",
+ f"{_format_size(metrics['memory_used_bytes'])}B / {_format_size(metrics['memory_total_bytes'])}B",
+ f"{_format_size(metrics['rx_bps'])}bps / {_format_size(metrics['tx_bps'])}bps",
+ ]
+ if metrics["gpus"] and isinstance(metrics["gpus"], dict):
+ rows: list[list[str | int]] = [row] + [[""] * len(row) for _ in range(len(metrics["gpus"]) - 1)]
+ for row, gpu_id in zip(rows, sorted(metrics["gpus"])):
+ gpu = metrics["gpus"][gpu_id]
+ row += [
+ f"{gpu['utilization']}%",
+ f"{round(100 * gpu['memory_used_bytes'] / gpu['memory_total_bytes'], 2)}%",
+ f"{_format_size(gpu['memory_used_bytes'])}B / {_format_size(gpu['memory_total_bytes'])}B",
+ ]
+ return rows
+ row += ["N/A"] * (len(table_headers) - len(row))
+ return [row]
+
+
+def _get_jobs_stats_rows(
+ job_id: str, metrics_stream: Iterable[dict[str, Any]], table_headers: list[str]
+) -> Iterable[tuple[bool, str, list[list[str | int]]]]:
+ for metrics in metrics_stream:
+ yield False, job_id, _format_job_stats_rows(job_id, metrics, table_headers)
+ yield True, job_id, []
+
+
+@jobs_cli.command(
+ "stats",
+ examples=[
+ "hf jobs stats",
+ "hf jobs stats ",
+ "hf jobs stats -f ",
+ ],
+)
+def jobs_stats(
+ job_ids: JobIdsArg = None,
+ follow: Annotated[
+ bool,
+ Option(
+ "-f",
+ "--follow",
+ help="Follow stats output (live view until the Jobs complete). Without this flag, a single snapshot is printed.",
+ ),
+ ] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Fetch the resource usage statistics and metrics of Jobs.
+
+ By default, prints a snapshot of the current stats and exits (non-blocking).
+ Use --follow/-f to display live stats until the Jobs complete.
+ """
+ if follow and out.mode != OutputFormat.human:
+ raise CLIError(
+ f"`--follow` displays a live terminal view and is not supported with '{out.mode.value}' output."
+ " Run without `--follow` to get a snapshot."
+ )
+ if job_ids is not None:
+ parsed_ids = []
+ for job_id in job_ids:
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ parsed_ids.append(job_id)
+ job_ids = parsed_ids
+ api = get_hf_api(token=token)
+ if namespace is None:
+ namespace = api.whoami()["name"]
+ if job_ids is None:
+ job_ids = [
+ job.id
+ for job in api.list_jobs(namespace=namespace)
+ if (job.status.stage if job.status else "UNKNOWN") in ("RUNNING", "UPDATING")
+ ]
+ if len(job_ids) == 0:
+ out.table([])
+ out.hint("No running jobs. Use `hf jobs ps -a` to list finished (and failed) jobs.")
+ return
+ table_headers = [
+ "JOB ID",
+ "CPU %",
+ "NUM CPU",
+ "MEM %",
+ "MEM USAGE",
+ "NET I/O",
+ "GPU UTIL %",
+ "GPU MEM %",
+ "GPU MEM USAGE",
+ ]
+ if not follow:
+
+ def fetch_latest_metrics(job_id: str) -> dict[str, Any] | None:
+ return next(iter(api.fetch_job_metrics(job_id=job_id, namespace=namespace)), None)
+
+ with multiprocessing.pool.ThreadPool(len(job_ids)) as pool:
+ latest_metrics = pool.map(fetch_latest_metrics, job_ids)
+ items = []
+ for job_id, metrics in zip(job_ids, latest_metrics):
+ if metrics is None:
+ out.warning(f"No stats available for Job {job_id}. Is it running?")
+ else:
+ items.append({"job_id": job_id, **metrics})
+ if out.mode == OutputFormat.human and items:
+ rows = [row for item in items for row in _format_job_stats_rows(item["job_id"], item, table_headers)]
+ out.text(_tabulate(rows, headers=table_headers))
+ job_refs = " ".join(f"{namespace}/{item['job_id']}" for item in items)
+ out.hint(f"Use `hf jobs stats -f {job_refs}` to follow live stats.")
+ else:
+ out.table(items, id_key="job_id")
+ return
+ with multiprocessing.pool.ThreadPool(len(job_ids)) as pool:
+ rows_per_job_id: dict[str, list[list[str | int]]] = {}
+ for job_id in job_ids:
+ row: list[str | int] = [job_id]
+ row += ["-- / --" if ("/" in header or "USAGE" in header) else "--" for header in table_headers[1:]]
+ rows_per_job_id[job_id] = [row]
+ last_update_time = time.time()
+ total_rows = [row for job_id in rows_per_job_id for row in rows_per_job_id[job_id]]
+ # In-place refresh (cursor-up + clear) requires a fixed line count and layout —
+ # `out.table`'s mode-dependent formatting would break it.
+ print(_tabulate(total_rows, headers=table_headers))
+
+ kwargs_list = [
+ {
+ "job_id": job_id,
+ "metrics_stream": api.fetch_job_metrics(job_id=job_id, namespace=namespace, follow=True),
+ "table_headers": table_headers,
+ }
+ for job_id in job_ids
+ ]
+ for done, job_id, rows in iflatmap_unordered(pool, _get_jobs_stats_rows, kwargs_list=kwargs_list):
+ if done:
+ rows_per_job_id.pop(job_id, None)
+ else:
+ rows_per_job_id[job_id] = rows
+ now = time.time()
+ if now - last_update_time >= STATS_UPDATE_MIN_INTERVAL:
+ _clear_line(2 + len(total_rows))
+ total_rows = [row for job_id in rows_per_job_id for row in rows_per_job_id[job_id]]
+ print(_tabulate(total_rows, headers=table_headers))
+ last_update_time = now
+ job_refs = " ".join(f"{namespace}/{job_id}" for job_id in job_ids)
+ out.hint(f"Stream ended. Run `hf jobs inspect {job_refs}` to check the final status (e.g. COMPLETED or ERROR).")
+
+
+@jobs_cli.command(
+ "list | ls | ps",
+ examples=[
+ "hf jobs ls",
+ "hf jobs ls -a",
+ "hf jobs ls --status running,scheduling",
+ "hf jobs ls --name training-v2",
+ "hf jobs ls --label env=prod --label team=ml",
+ "hf jobs ls --all --label hf-sandbox=1",
+ ],
+)
+def jobs_ps(
+ all: Annotated[
+ bool,
+ Option(
+ "-a",
+ "--all",
+ help="Show all Jobs (default shows running and scheduling). Cannot be combined with --status.",
+ ),
+ ] = False,
+ status: Annotated[
+ list[str] | None,
+ Option(
+ "--status",
+ click_type=SoftChoice(JobStage),
+ help="Only show Jobs with the given status. Comma-separated or repeated, e.g. `--status running,scheduling`.",
+ ),
+ ] = None,
+ label: Annotated[
+ list[str] | None,
+ Option(
+ "-l",
+ "--label",
+ help="Only show Jobs with the given `key=value` label. Repeat to require several labels, e.g. `--label env=prod --label team=ml`.",
+ ),
+ ] = None,
+ name: Annotated[
+ str | None,
+ Option(
+ "--name",
+ help="Only show Jobs with the given name (shortcut for `--label name=NAME`).",
+ ),
+ ] = None,
+ limit: Annotated[
+ int,
+ Option(
+ "--limit",
+ help="Maximum number of Jobs to display. Set to 0 to show all (no limit).",
+ ),
+ ] = 100,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List Jobs.
+
+ Use `--status` to filter by status (see [`JobStage`] for possible values) and `--label` to filter by `key=value`
+ labels. A Job must match every filter to be listed.
+ """
+ api = get_hf_api(token=token)
+
+ if all and status:
+ raise CLIError("`-a`/`--all` cannot be combined with `--status`.")
+
+ # Status filtering (default to active Jobs, unless `--all` or `--status` is provided).
+ raw_statuses: list[str] = []
+ for value in status or []:
+ raw_statuses.extend(part.strip() for part in value.split(",") if part.strip())
+
+ server_statuses: list[str] | None
+ if raw_statuses:
+ server_statuses = raw_statuses
+ elif all:
+ server_statuses = None
+ else:
+ server_statuses = [JobStage.RUNNING.value, JobStage.SCHEDULING.value]
+
+ # Labels filtering
+ labels: dict[str, str] = {}
+ for item in label or []:
+ if "=" not in item:
+ raise CLIError(f"Invalid label filter '{item}': must be in the form 'key=value'")
+ key, value = item.split("=")
+ labels[key] = value
+
+ # `--name` is a shortcut for the `name` label.
+ if name is not None:
+ if "name" in labels:
+ raise CLIError("Cannot filter by both `--name` and `--label name=...`.")
+ labels["name"] = name
+
+ jobs_iter = api.list_jobs(namespace=namespace, status=server_statuses, labels=labels or None)
+
+ # Apply the display limit. Fetch one extra Job to detect (and warn about) truncation.
+ truncated = False
+ if limit > 0:
+ jobs = list(itertools.islice(jobs_iter, limit + 1))
+ if len(jobs) > limit:
+ truncated = True
+ jobs = jobs[:limit]
+ else:
+ jobs = list(jobs_iter)
+
+ # Build display items. Augment the raw api dict with curated, table-friendly columns.
+ job_items: list[dict[str, Any]] = []
+ for job in jobs:
+ job_item = _dataclass_to_dict(job)
+ durations = job_item.get("durations") or {}
+ cmd = job_item.get("command") or []
+ job_item["job_id"] = job_item.get("id", "")
+ job_item["name"] = (job_item.get("labels") or {}).get("name") or "N/A"
+ job_item["image/space"] = job_item.get("docker_image") or "N/A"
+ job_item["command"] = " ".join(cmd) if cmd else "N/A"
+ job_item["created"] = job_item["created_at"][:19].replace("T", " ") if job_item.get("created_at") else "N/A"
+ job_item["status"] = (job_item.get("status") or {}).get("stage", "UNKNOWN")
+ job_item["runtime"] = format_duration(durations.get("running_secs"))
+ job_items.append(job_item)
+
+ out.table(
+ job_items,
+ headers=["job_id", "name", "image/space", "command", "created", "status", "runtime"],
+ id_key="job_id",
+ )
+ if truncated:
+ out.hint(f"Output truncated to {limit} Jobs. Use `--limit 0` to show all (or `--limit N`).")
+ if not job_items:
+ if raw_statuses or labels:
+ filters_msg = ", ".join(
+ [*(f"status={s}" for s in raw_statuses), *(f"label={k}={v}" for k, v in labels.items())]
+ )
+ out.text(f"No jobs matched filters: {filters_msg}")
+ elif not all:
+ out.hint("No running jobs. Use `-a`/`--all` to include finished (and failed) jobs.")
+
+
+@jobs_cli.command("hardware", examples=["hf jobs hardware"])
+def jobs_hardware() -> None:
+ """List available hardware options for Jobs"""
+ api = get_hf_api()
+ hardware_list = api.list_jobs_hardware()
+ items = []
+ for hw in hardware_list:
+ accelerator_info = ""
+ if hw.accelerator:
+ accelerator_info = f"{hw.accelerator.quantity}x {hw.accelerator.model} ({hw.accelerator.vram})"
+ cost_min = f"${hw.unit_cost_usd:.4f}" if hw.unit_cost_usd else "free"
+ cost_hour = f"${hw.unit_cost_usd * 60:.2f}" if hw.unit_cost_usd else "free"
+ items.append(
+ {
+ "name": hw.name,
+ "pretty name": hw.pretty_name,
+ "cpu": hw.cpu,
+ "ram": hw.ram,
+ "storage": hw.ephemeral_storage,
+ "accelerator": accelerator_info,
+ "cost/min": cost_min,
+ "cost/hour": cost_hour,
+ }
+ )
+ out.table(items)
+ out.hint("Use `hf jobs run --flavor ...` to request a specific hardware flavor.")
+
+
+@jobs_cli.command("inspect", examples=["hf jobs inspect "])
+def jobs_inspect(
+ job_ids: Annotated[
+ list[str],
+ Argument(
+ help="Job IDs to inspect (or 'namespace/job_id')",
+ ),
+ ],
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Display detailed information on one or more Jobs"""
+ parsed_ids = []
+ for job_id in job_ids:
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ parsed_ids.append(job_id)
+ job_ids = parsed_ids
+ api = get_hf_api(token=token)
+ jobs = [api.inspect_job(job_id=job_id, namespace=namespace) for job_id in job_ids]
+ out.table([_surface_name(_dataclass_to_dict(job), labels=job.labels) for job in jobs], id_key="id")
+
+
+@jobs_cli.command("rerun", examples=["hf jobs rerun ", "hf jobs rerun --detach "])
+def jobs_rerun(
+ job_id: JobIdArg,
+ detach: DetachOpt = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Run a new Job with an existing Job's spec."""
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ api = get_hf_api(token=token)
+ job = api.rerun_job(job_id=job_id, namespace=namespace)
+ out.result("Job started", id=job.id, name=(job.labels or {}).get("name"), url=job.url)
+ _hint_or_follow_started_job(api, job, detach=detach)
+
+
+@jobs_cli.command(
+ "expose",
+ examples=[
+ "hf jobs expose 8000",
+ "hf jobs expose 8000 --public 9000",
+ "hf jobs expose --clear",
+ ],
+)
+def jobs_expose(
+ job_id: JobIdArg,
+ ports: Annotated[
+ list[int] | None,
+ Argument(
+ help="Ports to expose through the jobs proxy. Access requires an HF token with read access to the job's namespace."
+ ),
+ ] = None,
+ public: Annotated[
+ list[int] | None,
+ Option(
+ "--public",
+ help="Expose a port without authentication. Repeat the flag for multiple ports. No need to also pass the port as a positional argument.",
+ ),
+ ] = None,
+ clear: Annotated[bool, Option("--clear", help="Close all exposed ports.")] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Replace exposed ports on a running Job."""
+ if clear and (ports or public):
+ raise CLIError("`--clear` cannot be combined with ports or `--public`.")
+ if not clear and not ports and not public:
+ raise CLIError("Pass at least one port or `--public` port, or `--clear` to close all ports.")
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ api = get_hf_api(token=token)
+ job = api.update_job_expose(job_id=job_id, expose=ports, expose_public=public, namespace=namespace)
+ out.result("Exposed ports updated", id=job.id, ports=job.expose or [], public_ports=job.expose_public or [])
+
+
+@jobs_cli.command("cancel", examples=["hf jobs cancel "])
+def jobs_cancel(
+ job_id: JobIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Cancel a Job"""
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ api = get_hf_api(token=token)
+ api.cancel_job(job_id=job_id, namespace=namespace)
+ out.result("Job cancelled", id=job_id)
+
+
+@jobs_cli.command(
+ "wait",
+ examples=[
+ "hf jobs wait ",
+ "hf jobs wait ",
+ "hf jobs ls -q | xargs hf jobs wait",
+ ],
+)
+def jobs_wait(
+ job_ids: Annotated[
+ list[str],
+ Argument(
+ help="Job IDs to wait for (or 'namespace/job_id').",
+ ),
+ ],
+ timeout: Annotated[
+ str | None,
+ Option(
+ help="Max time to wait: int with s (seconds, default), m (minutes), h (hours) or d (days).",
+ ),
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Wait for one or more Jobs to reach a terminal state.
+
+ Blocks until every Job has finished, then exits with code 0 if all Jobs completed
+ successfully, or a non-zero exit code if any Job was canceled, errored or deleted.
+
+ All Jobs must belong to the same namespace.
+ """
+ parsed_ids = []
+ namespaces = set()
+ for job_id in job_ids:
+ parsed_id, parsed_namespace = _parse_namespace_from_job_id(job_id, namespace)
+ parsed_ids.append(parsed_id)
+ namespaces.add(parsed_namespace)
+ if len(namespaces) > 1:
+ raise CLIError(
+ "All Job IDs must be in the same namespace, got: "
+ + ", ".join(str(ns) for ns in sorted(namespaces, key=str))
+ )
+ namespace = namespaces.pop()
+ timeout_secs = parse_duration(timeout) if timeout is not None else None
+
+ api = get_hf_api(token=token)
+ status = out.status(f"Waiting for {len(parsed_ids)} Job(s) to finish...")
+ try:
+ jobs = api.wait_for_job(parsed_ids, timeout=timeout_secs, namespace=namespace)
+ except TimeoutError:
+ status.done("Timed out.")
+ raise CLIError(f"Timed out after {timeout} waiting for Job(s) to finish.") from None
+ status.done(f"{len(jobs)} Job(s) finished.")
+
+ out.table([{"id": job.id, "stage": str(job.status.stage), "message": job.status.message} for job in jobs])
+ failed = [job for job in jobs if job.status.stage != JobStage.COMPLETED]
+ if failed:
+ raise CLIError(
+ f"{len(failed)} of {len(jobs)} Job(s) did not complete successfully: "
+ + ", ".join(f"{job.id} ({job.status.stage})" for job in failed)
+ )
+
+
+@jobs_cli.command(
+ "labels",
+ examples=[
+ "hf jobs labels --name training-v2",
+ "hf jobs labels --label env=prod --label team=ml",
+ "hf jobs labels --clear",
+ ],
+)
+def jobs_labels(
+ job_id: JobIdArg,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ clear: Annotated[bool, Option("--clear", help="Remove all labels from the job.")] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update labels on a Job. Passing --label replaces all existing labels; passing --name alone keeps them."""
+ if not label and name is None and not clear:
+ raise CLIError(
+ "Please set a name with --name or at least one label with --label. To remove all labels, pass --clear."
+ )
+ if (label or name is not None) and clear:
+ raise CLIError(
+ "Cannot set a name or labels and clear them at the same time. Please use --name/--label or --clear, not both."
+ )
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ api = get_hf_api(token=token)
+ if name is not None and not label:
+ # Naming a Job should not wipe its existing labels: fetch them and merge the name in.
+ current_labels = api.inspect_job(job_id=job_id, namespace=namespace).labels or {}
+ labels = {**current_labels, "name": name}
+ else:
+ labels = _parse_labels_map(label, name=name) or {}
+ job = api.update_job_labels(job_id=job_id, labels=labels, namespace=namespace)
+ out.result("Labels updated", id=job.id, name=labels.get("name"))
+
+
+@jobs_cli.command(
+ "ssh",
+ examples=[
+ "hf jobs ssh ",
+ "hf jobs ssh --dry-run",
+ "hf jobs ssh -i ~/.ssh/id_ed25519",
+ ],
+)
+def jobs_ssh(
+ job_id: JobIdArg,
+ identity_file: SshIdentityFileOpt = None,
+ dry_run: SshDryRunOpt = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """SSH into a running Job.
+
+ If the Job is not yet running, waits until it reaches the RUNNING state before
+ connecting. Requires the Job to be started with SSH enabled (`hf jobs run --ssh ...`)
+ and your SSH public key to be registered at https://huggingface.co/settings/keys.
+ """
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ api = get_hf_api(token=token)
+ job = api.inspect_job(job_id=job_id, namespace=namespace)
+ if job.status.ssh_url is None:
+ raise CLIError("SSH is not enabled on this job. Start a job with SSH support using `hf jobs run --ssh ...`.")
+ if job.status.stage in TERMINAL_JOB_STAGES:
+ raise CLIError(f"Cannot SSH into job '{job.id}': job has already finished (stage: '{job.status.stage}').")
+ if job.status.stage != JobStage.RUNNING:
+ status = out.status(f"Waiting for job '{job.id}' to be running (stage: '{job.status.stage}')...")
+ job = api.wait_for_job(job_id=job.id, namespace=namespace, stages=[JobStage.RUNNING])
+ if job.status.stage != JobStage.RUNNING:
+ status.done("Job finished.")
+ raise CLIError(
+ f"Cannot SSH into job '{job.id}': job finished before reaching RUNNING (stage: '{job.status.stage}')."
+ )
+ status.done("Job is running.")
+ ssh_url = urlsplit(job.status.ssh_url)
+ exec_ssh(
+ f"{ssh_url.username}@{ssh_url.hostname}", # type: ignore
+ port=ssh_url.port,
+ identity_file=identity_file,
+ dry_run=dry_run,
+ )
+
+
+uv_app = typer_factory(help="Run UV scripts (Python with inline dependencies) on HF infrastructure.")
+jobs_cli.add_group(uv_app, name="uv")
+
+
+@uv_app.command(
+ "run",
+ context_settings={"ignore_unknown_options": True},
+ examples=[
+ "hf jobs uv run --name my-script my_script.py",
+ "hf jobs uv run --detach my_script.py",
+ "hf jobs uv run --flavor a10g-small ml_training.py",
+ "hf jobs uv run --with transformers train.py",
+ "hf jobs uv run -v hf://org/my-model:/data -v hf://buckets/org/b:/mnt script.py",
+ "hf jobs uv run --dry-run script.py",
+ ],
+)
+def jobs_uv_run(
+ script: ScriptArg,
+ script_args: ScriptArgsArg = None,
+ image: ImageOpt = None,
+ flavor: FlavorOpt = None,
+ env: EnvOpt = None,
+ secrets: SecretsOpt = None,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ volume: JobVolumesOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ timeout: TimeoutOpt = None,
+ attempts: AttemptsOpt = None,
+ detach: DetachOpt = False,
+ dry_run: DryRunOpt = False,
+ expose: ExposeOpt = None,
+ expose_public: ExposePublicOpt = None,
+ ssh: SshEnabledOpt = False,
+ network_group: NetworkGroupOpt = None,
+ network_alias: NetworkAliasOpt = None,
+ resource_group_id: ResourceGroupIdOpt = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+ with_: WithOpt = None,
+ python: PythonOpt = None,
+ format: FormatOpt = None,
+ json_output: JsonOpt = False,
+ quiet: QuietOpt = False,
+) -> None:
+ """Run a UV script (local file or URL) on HF infrastructure"""
+ set_output_format(format, json_output, quiet)
+ api = get_hf_api(token=token)
+ with _resolve_uv_job_config(
+ api=api,
+ script=script,
+ script_args=script_args,
+ dependencies=with_,
+ image=image,
+ flavor=flavor,
+ python=python,
+ env=env,
+ env_file=env_file,
+ secrets=secrets,
+ secrets_file=secrets_file,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ label=label,
+ volume=volume,
+ namespace=namespace,
+ network_group=network_group,
+ network_aliases=network_alias,
+ dry_run=dry_run,
+ ) as config:
+ _print_job_summary(
+ {
+ "script": script,
+ "args": shlex.join(config.script_args),
+ "with": " ".join(with_ or []),
+ "image": config.image or DEFAULT_UV_IMAGE,
+ "flavor": config.flavor or JobHardware.CPU_BASIC.value,
+ "python": config.python,
+ "timeout": config.timeout,
+ "attempts": attempts,
+ "env": config.env,
+ "secrets": config.secrets,
+ "volumes": config.volume_specs,
+ "labels": config.labels,
+ "expose": " ".join(str(port) for port in expose or []),
+ "expose_public": " ".join(str(port) for port in expose_public or []),
+ "ssh": ssh,
+ "network_group": config.network_group,
+ "network_aliases": " ".join(config.network_aliases),
+ "resource_group_id": resource_group_id,
+ "namespace": config.namespace,
+ },
+ from_script=config.from_script,
+ dry_run=dry_run,
+ )
+ if dry_run:
+ return
+ job = api.run_uv_job(
+ script=config.script,
+ script_args=config.script_args,
+ dependencies=with_,
+ python=config.python,
+ image=config.image,
+ env=config.env,
+ secrets=config.secrets,
+ labels=config.labels,
+ volumes=config.volumes,
+ flavor=config.flavor,
+ timeout=config.timeout,
+ attempts=attempts,
+ expose=expose,
+ expose_public=expose_public,
+ ssh=ssh,
+ network_group=config.network_group,
+ network_aliases=config.network_aliases or None,
+ resource_group_id=resource_group_id,
+ namespace=config.namespace,
+ )
+ out.result("Job started", id=job.id, name=(job.labels or {}).get("name"), url=job.url)
+ if not _has_explicit_name(name, label, config.from_script):
+ auto_name = (job.labels or {}).get("name")
+ out.hint(
+ f"Job auto-named '{auto_name}'. Pass `--name` or run "
+ f"`hf jobs labels {job.owner.name}/{job.id} --name NAME` to rename it."
+ )
+ _hint_or_follow_started_job(api, job, detach=detach)
+
+
+class ScheduledJobStatusFilter(str, Enum):
+ """Possible values for `hf jobs scheduled ls --status`.
+
+ Scheduled Jobs are not "running": they are either active (i.e. they will trigger new runs) or suspended.
+ """
+
+ ACTIVE = "active"
+ SUSPENDED = "suspended"
+
+
+scheduled_app = typer_factory(help="Create and manage scheduled Jobs on the Hub.")
+jobs_cli.add_group(scheduled_app, name="scheduled")
+
+
+@scheduled_app.command(
+ "run",
+ context_settings={"ignore_unknown_options": True},
+ examples=['hf jobs scheduled run "0 0 * * *" --name daily-script python:3.12 python script.py'],
+)
+def scheduled_run(
+ schedule: ScheduleArg,
+ image: ImageArg,
+ command: CommandArg,
+ suspend: SuspendOpt = None,
+ concurrency: ConcurrencyOpt = None,
+ env: EnvOpt = None,
+ secrets: SecretsOpt = None,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ volume: JobVolumesOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ flavor: FlavorOpt = None,
+ timeout: TimeoutOpt = None,
+ attempts: AttemptsOpt = None,
+ dry_run: DryRunOpt = False,
+ expose: ExposeOpt = None,
+ expose_public: ExposePublicOpt = None,
+ resource_group_id: ResourceGroupIdOpt = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+ format: FormatOpt = None,
+ json_output: JsonOpt = False,
+ quiet: QuietOpt = False,
+) -> None:
+ """Schedule a Job."""
+ set_output_format(format, json_output, quiet)
+ env_map = parse_env_map(env, env_file)
+ secrets_map = _parse_secrets_map(secrets, secrets_file, dry_run=dry_run)
+ labels_map = _parse_labels_map(label, name=name) or {}
+ labels_map.setdefault(
+ "name",
+ _default_job_name_from_image(
+ image,
+ command,
+ config_parts=_name_hash_parts(
+ flavor=flavor,
+ timeout=timeout,
+ extra=[str(attempts)] if attempts is not None else None,
+ namespace=namespace,
+ env=env_map,
+ secrets=secrets_map,
+ volume_specs=volume or [],
+ ),
+ ),
+ )
+
+ api = get_hf_api(token=token)
+ volumes = None if dry_run else _parse_and_sync_job_volumes(volume, api=api, namespace=namespace)
+ _print_job_summary(
+ {
+ "schedule": schedule,
+ "suspend": suspend,
+ "concurrency": concurrency,
+ "image": image,
+ "command": shlex.join(command),
+ "flavor": flavor or JobHardware.CPU_BASIC.value,
+ "timeout": timeout,
+ "attempts": attempts,
+ "env": env_map,
+ "secrets": secrets_map,
+ "volumes": volume or [],
+ "labels": labels_map,
+ "expose": " ".join(str(port) for port in expose or []),
+ "expose_public": " ".join(str(port) for port in expose_public or []),
+ "resource_group_id": resource_group_id,
+ "namespace": namespace,
+ },
+ dry_run=dry_run,
+ )
+ if dry_run:
+ return
+ scheduled_job = api.create_scheduled_job(
+ image=image,
+ command=command,
+ schedule=schedule,
+ suspend=suspend,
+ concurrency=concurrency,
+ env=env_map,
+ secrets=secrets_map,
+ labels=labels_map,
+ volumes=volumes,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ expose=expose,
+ expose_public=expose_public,
+ resource_group_id=resource_group_id,
+ namespace=namespace,
+ )
+ out.result("Scheduled Job created", id=scheduled_job.id, name=(scheduled_job.job_spec.labels or {}).get("name"))
+ if not _has_explicit_name(name, label):
+ auto_name = (scheduled_job.job_spec.labels or {}).get("name")
+ out.hint(
+ f"Scheduled Job auto-named '{auto_name}'. Pass `--name` or run "
+ f"`hf jobs scheduled labels {scheduled_job.owner.name}/{scheduled_job.id} --name NAME` to rename it."
+ )
+ out.hint(f"Use `hf jobs scheduled inspect {scheduled_job.owner.name}/{scheduled_job.id}` to view its details.")
+
+
+@scheduled_app.command(
+ "list | ls | ps",
+ examples=[
+ "hf jobs scheduled ls",
+ "hf jobs scheduled ls -a",
+ "hf jobs scheduled ls --status suspended",
+ "hf jobs scheduled ls --name daily-script",
+ "hf jobs scheduled ls --label env=prod --label team=ml",
+ ],
+)
+def scheduled_ps(
+ all: Annotated[
+ bool,
+ Option(
+ "-a",
+ "--all",
+ help="Show all scheduled Jobs (default hides suspended). Cannot be combined with --status.",
+ ),
+ ] = False,
+ status: Annotated[
+ list[str] | None,
+ Option(
+ "--status",
+ click_type=SoftChoice(ScheduledJobStatusFilter),
+ help=(
+ "Only show scheduled Jobs with the given status. Comma-separated or repeated, e.g."
+ " `--status suspended`."
+ ),
+ ),
+ ] = None,
+ label: Annotated[
+ list[str] | None,
+ Option(
+ "-l",
+ "--label",
+ help=(
+ "Only show scheduled Jobs with the given `key=value` label. Repeat to require several labels, e.g."
+ " `--label env=prod --label team=ml`."
+ ),
+ ),
+ ] = None,
+ name: Annotated[
+ str | None,
+ Option(
+ "--name",
+ help="Only show scheduled Jobs with the given name (shortcut for `--label name=NAME`).",
+ ),
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List scheduled Jobs.
+
+ Use `--status` to filter by status (`active` or `suspended`) and `--label` to filter by `key=value` labels.
+ A scheduled Job must match every filter to be listed.
+ """
+ api = get_hf_api(token=token)
+
+ if all and status:
+ raise CLIError("`-a`/`--all` cannot be combined with `--status`.")
+
+ # Status filtering (default to active scheduled Jobs, unless `--all` or `--status` is provided).
+ raw_statuses: list[str] = []
+ for value in status or []:
+ raw_statuses.extend(part.strip().lower() for part in value.split(",") if part.strip())
+
+ unknown_statuses = [s for s in raw_statuses if s not in tuple(ScheduledJobStatusFilter)]
+ if unknown_statuses:
+ raise CLIError(
+ f"Invalid status filter(s) {unknown_statuses}: expected one of"
+ f" {[s.value for s in ScheduledJobStatusFilter]}."
+ )
+
+ if raw_statuses:
+ show_active = ScheduledJobStatusFilter.ACTIVE in raw_statuses
+ show_suspended = ScheduledJobStatusFilter.SUSPENDED in raw_statuses
+ else:
+ show_active = True
+ show_suspended = all
+
+ # Labels filtering
+ labels: dict[str, str] = {}
+ for raw_label in label or []:
+ if "=" not in raw_label:
+ raise CLIError(f"Invalid label filter '{raw_label}': must be in the form 'key=value'")
+ key, value = raw_label.split("=", 1)
+ labels[key] = value
+
+ # `--name` is a shortcut for the `name` label.
+ if name is not None:
+ if "name" in labels:
+ raise CLIError("Cannot filter by both `--name` and `--label name=...`.")
+ labels["name"] = name
+
+ scheduled_jobs = api.list_scheduled_jobs(namespace=namespace, labels=labels or None)
+
+ filtered_jobs = []
+ for scheduled_job in scheduled_jobs:
+ suspend = scheduled_job.suspend or False
+ if suspend and not show_suspended:
+ continue
+ if not suspend and not show_active:
+ continue
+ filtered_jobs.append(scheduled_job)
+
+ # Build display items. Augment with curated columns.
+ items: list[dict[str, Any]] = []
+ for sj in filtered_jobs:
+ item = _dataclass_to_dict(sj)
+ job_spec = item.get("job_spec") or {}
+ status_dict = item.get("status") or {}
+ last_job = status_dict.get("last_job")
+ cmd = job_spec.get("command") or []
+ item["name"] = (job_spec.get("labels") or {}).get("name") or "N/A"
+ item["image/space"] = job_spec.get("docker_image") or "N/A"
+ item["command"] = " ".join(cmd) if cmd else "N/A"
+ item["last_run"] = last_job["at"][:19].replace("T", " ") if last_job and last_job.get("at") else "N/A"
+ item["next_run"] = (
+ status_dict["next_job_run_at"][:19].replace("T", " ") if status_dict.get("next_job_run_at") else "N/A"
+ )
+ item["suspend"] = item.get("suspend") or False
+ items.append(item)
+
+ out.table(
+ items,
+ headers=["id", "name", "schedule", "image/space", "command", "last_run", "next_run", "suspend"],
+ id_key="id",
+ )
+ if not items:
+ if raw_statuses or labels:
+ filters_msg = ", ".join(
+ [*(f"status={s}" for s in raw_statuses), *(f"label={k}={v}" for k, v in labels.items())]
+ )
+ out.text(f"No scheduled jobs matched filters: {filters_msg}")
+ elif not all:
+ out.hint("No active scheduled jobs. Use `-a`/`--all` to include suspended ones.")
+ if items:
+ first_item_id = items[0]["id"]
+ out.hint(f"Use `hf jobs scheduled inspect {first_item_id}` to view details about a scheduled job.")
+ out.hint(f"Use `hf jobs scheduled trigger {first_item_id}` to trigger a scheduled job immediately.")
+
+
+@scheduled_app.command("inspect", examples=["hf jobs scheduled inspect "])
+def scheduled_inspect(
+ scheduled_job_ids: Annotated[
+ list[str],
+ Argument(
+ help="Scheduled Job IDs to inspect (or 'namespace/scheduled_job_id')",
+ ),
+ ],
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Display detailed information on one or more scheduled Jobs"""
+ parsed_ids = []
+ for job_id in scheduled_job_ids:
+ job_id, namespace = _parse_namespace_from_job_id(job_id, namespace)
+ parsed_ids.append(job_id)
+ scheduled_job_ids = parsed_ids
+ api = get_hf_api(token=token)
+ scheduled_jobs = [
+ api.inspect_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace)
+ for scheduled_job_id in scheduled_job_ids
+ ]
+ out.table([_surface_name(_dataclass_to_dict(sj), labels=sj.job_spec.labels) for sj in scheduled_jobs], id_key="id")
+
+
+@scheduled_app.command("delete", examples=["hf jobs scheduled delete "])
+def scheduled_delete(
+ scheduled_job_id: ScheduledJobIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Delete a scheduled Job."""
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ api.delete_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace)
+ out.result("Scheduled Job deleted", id=scheduled_job_id)
+
+
+@scheduled_app.command("suspend", examples=["hf jobs scheduled suspend "])
+def scheduled_suspend(
+ scheduled_job_id: ScheduledJobIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Suspend (pause) a scheduled Job."""
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ api.suspend_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace)
+ out.result("Scheduled Job suspended", id=scheduled_job_id)
+ out.hint(f"Use `hf jobs scheduled resume {scheduled_job_id}` to resume it.")
+
+
+@scheduled_app.command("resume", examples=["hf jobs scheduled resume "])
+def scheduled_resume(
+ scheduled_job_id: ScheduledJobIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Resume (unpause) a scheduled Job."""
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ api.resume_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace)
+ out.result("Scheduled Job resumed", id=scheduled_job_id)
+
+
+@scheduled_app.command("reschedule", examples=['hf jobs scheduled reschedule "0 9 * * 1"'])
+def scheduled_reschedule(
+ scheduled_job_id: ScheduledJobIdArg,
+ schedule: ScheduleArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Change when a scheduled Job runs."""
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ job = api.update_scheduled_job_schedule(scheduled_job_id=scheduled_job_id, schedule=schedule, namespace=namespace)
+ out.result("Scheduled Job rescheduled", id=job.id, schedule=job.schedule)
+ out.hint(f"Use `hf jobs scheduled inspect {job.owner.name}/{job.id}` to see its next run.")
+
+
+@scheduled_app.command("trigger", examples=["hf jobs scheduled trigger "])
+def scheduled_trigger(
+ scheduled_job_id: ScheduledJobIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Trigger a scheduled Job to run immediately (does not change the schedule)."""
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ job = api.trigger_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace)
+ out.result("Scheduled Job triggered", id=job.id, url=job.url)
+ out.hint(f"Use `hf jobs logs -f {job.owner.name}/{job.id}` to stream logs.")
+
+
+@scheduled_app.command(
+ "labels",
+ examples=[
+ "hf jobs scheduled labels --name daily-script",
+ "hf jobs scheduled labels --label env=prod --label team=ml",
+ "hf jobs scheduled labels --clear",
+ ],
+)
+def scheduled_labels(
+ scheduled_job_id: ScheduledJobIdArg,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ clear: Annotated[bool, Option("--clear", help="Remove all labels from the scheduled job.")] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update labels on a scheduled Job. Passing --label replaces all existing labels; passing --name alone keeps them."""
+ if not label and name is None and not clear:
+ raise CLIError(
+ "Please set a name with --name or at least one label with --label. To remove all labels, pass --clear."
+ )
+ if (label or name is not None) and clear:
+ raise CLIError(
+ "Cannot set a name or labels and clear them at the same time. Please use --name/--label or --clear, not both."
+ )
+ scheduled_job_id, namespace = _parse_namespace_from_job_id(scheduled_job_id, namespace)
+ api = get_hf_api(token=token)
+ if name is not None and not label:
+ # Naming a scheduled Job should not wipe its existing labels: fetch them and merge the name in.
+ current_labels = (
+ api.inspect_scheduled_job(scheduled_job_id=scheduled_job_id, namespace=namespace).job_spec.labels or {}
+ )
+ labels = {**current_labels, "name": name}
+ else:
+ labels = _parse_labels_map(label, name=name) or {}
+ scheduled_job = api.update_scheduled_job_labels(
+ scheduled_job_id=scheduled_job_id, labels=labels, namespace=namespace
+ )
+ out.result("Labels updated", id=scheduled_job.id, name=labels.get("name"))
+
+
+scheduled_uv_app = typer_factory(help="Schedule UV scripts on HF infrastructure.")
+scheduled_app.add_group(scheduled_uv_app, name="uv")
+
+
+@scheduled_uv_app.command(
+ "run",
+ context_settings={"ignore_unknown_options": True},
+ examples=[
+ 'hf jobs scheduled uv run "0 0 * * *" --name daily-script script.py',
+ 'hf jobs scheduled uv run "0 0 * * *" script.py --with pandas',
+ ],
+)
+def scheduled_uv_run(
+ schedule: ScheduleArg,
+ script: ScriptArg,
+ script_args: ScriptArgsArg = None,
+ suspend: SuspendOpt = None,
+ concurrency: ConcurrencyOpt = None,
+ image: ImageOpt = None,
+ flavor: FlavorOpt = None,
+ env: EnvOpt = None,
+ secrets: SecretsOpt = None,
+ name: NameOpt = None,
+ label: LabelsOpt = None,
+ volume: JobVolumesOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ timeout: TimeoutOpt = None,
+ attempts: AttemptsOpt = None,
+ dry_run: DryRunOpt = False,
+ expose: ExposeOpt = None,
+ expose_public: ExposePublicOpt = None,
+ resource_group_id: ResourceGroupIdOpt = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+ with_: WithOpt = None,
+ python: PythonOpt = None,
+ format: FormatOpt = None,
+ json_output: JsonOpt = False,
+ quiet: QuietOpt = False,
+) -> None:
+ """Run a UV script (local file or URL) on HF infrastructure"""
+ set_output_format(format, json_output, quiet)
+ api = get_hf_api(token=token)
+ with _resolve_uv_job_config(
+ api=api,
+ script=script,
+ script_args=script_args,
+ dependencies=with_,
+ image=image,
+ flavor=flavor,
+ python=python,
+ env=env,
+ env_file=env_file,
+ secrets=secrets,
+ secrets_file=secrets_file,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ label=label,
+ volume=volume,
+ namespace=namespace,
+ # Scheduled Jobs have no network group: a script asking for one is an error, not a silent drop.
+ network_group=None,
+ network_aliases=None,
+ dry_run=dry_run,
+ ) as config:
+ if config.network_group or config.network_aliases:
+ raise CLIError(
+ f"Scheduled Jobs do not support network groups: remove 'network_group'/'network_aliases' from the"
+ f" script's [{TABLE_NAME}] table to schedule it."
+ )
+ _print_job_summary(
+ {
+ "schedule": schedule,
+ "suspend": suspend,
+ "concurrency": concurrency,
+ "script": script,
+ "args": shlex.join(config.script_args),
+ "with": " ".join(with_ or []),
+ "image": config.image or DEFAULT_UV_IMAGE,
+ "flavor": config.flavor or JobHardware.CPU_BASIC.value,
+ "python": config.python,
+ "timeout": config.timeout,
+ "attempts": attempts,
+ "env": config.env,
+ "secrets": config.secrets,
+ "volumes": config.volume_specs,
+ "labels": config.labels,
+ "expose": " ".join(str(port) for port in expose or []),
+ "expose_public": " ".join(str(port) for port in expose_public or []),
+ "resource_group_id": resource_group_id,
+ "namespace": config.namespace,
+ },
+ from_script=config.from_script,
+ dry_run=dry_run,
+ )
+ if dry_run:
+ return
+ job = api.create_scheduled_uv_job(
+ script=config.script,
+ script_args=config.script_args,
+ schedule=schedule,
+ suspend=suspend,
+ concurrency=concurrency,
+ dependencies=with_,
+ python=config.python,
+ image=config.image,
+ env=config.env,
+ secrets=config.secrets,
+ labels=config.labels,
+ volumes=config.volumes,
+ flavor=config.flavor,
+ timeout=config.timeout,
+ attempts=attempts,
+ expose=expose,
+ expose_public=expose_public,
+ resource_group_id=resource_group_id,
+ namespace=config.namespace,
+ )
+ out.result("Scheduled Job created", id=job.id, name=(job.job_spec.labels or {}).get("name"))
+ if not _has_explicit_name(name, label, config.from_script):
+ auto_name = (job.job_spec.labels or {}).get("name")
+ out.hint(
+ f"Scheduled Job auto-named '{auto_name}'. Pass `--name` or run "
+ f"`hf jobs scheduled labels {job.owner.name}/{job.id} --name NAME` to rename it."
+ )
+ out.hint(f"Use `hf jobs scheduled inspect {job.owner.name}/{job.id}` to view its details.")
+
+
+### UTILS
+
+
+def _surface_name(item: dict[str, Any], *, labels: dict[str, str] | None) -> dict[str, Any]:
+ """Promote the `name` label to a top-level `name` field for display.
+
+ The `name` is kept inside `labels` too (the dataclasses and API payloads are unchanged); this
+ only makes it a first-class column/field in command outputs. No-op when there is no name.
+ """
+ name = (labels or {}).get("name")
+ if name is None:
+ return item
+ return {"name": name, **item}
+
+
+def _has_explicit_name(name: str | None, label: list[str] | None, from_script: Collection[str] = ()) -> bool:
+ """Whether the Job was named on purpose: via `--name`, a `name=` label, or the script's `[tool.hf-jobs]` table."""
+ return (
+ name is not None
+ or "labels.name" in from_script
+ or any(item.split("=", 1)[0] == "name" for item in label or [])
+ )
+
+
+def _parse_labels_map(labels: list[str] | None, *, name: str | None = None) -> dict[str, str] | None:
+ """Parse label key-value pairs from CLI arguments.
+
+ Args:
+ labels: List of label strings in KEY=VALUE format. If KEY only, then VALUE is set to empty string.
+
+ Returns:
+ Dictionary mapping label keys to values, or None if no labels or name provided.
+ """
+ if not labels and name is None:
+ return None
+ labels_map: dict[str, str] = {}
+ for label_var in labels or []:
+ key, value = label_var.split("=", 1) if "=" in label_var else (label_var, "")
+ labels_map[key] = value
+ if name is not None:
+ if "name" in labels_map:
+ raise CLIError("--name and --label name=... cannot both be provided.")
+ labels_map["name"] = name
+ return labels_map
+
+
+def _tabulate(rows: list[list[str | int]], headers: list[str]) -> str:
+ """
+ Inspired by:
+
+ - stackoverflow.com/a/8356620/593036
+ - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
+ """
+ col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
+ terminal_width = max(shutil.get_terminal_size().columns, len(headers) * 12)
+ while len(headers) + sum(col_widths) > terminal_width:
+ col_to_minimize = col_widths.index(max(col_widths))
+ col_widths[col_to_minimize] //= 2
+ if len(headers) + sum(col_widths) <= terminal_width:
+ col_widths[col_to_minimize] = terminal_width - sum(col_widths) - len(headers) + col_widths[col_to_minimize]
+ row_format = ("{{:{}}} " * len(headers)).format(*col_widths)
+ lines = []
+ lines.append(row_format.format(*headers))
+ lines.append(row_format.format(*["-" * w for w in col_widths]))
+ for row in rows:
+ row_format_args = [
+ str(x)[: col_width - 3] + "..." if len(str(x)) > col_width else str(x)
+ for x, col_width in zip(row, col_widths)
+ ]
+ lines.append(row_format.format(*row_format_args))
+ return "\n".join(lines)
+
+
+T = TypeVar("T")
+
+
+def _write_generator_to_queue(queue: Queue[T], func: Callable[..., Iterable[T]], kwargs: dict) -> None:
+ for result in func(**kwargs):
+ queue.put(result)
+
+
+def iflatmap_unordered(
+ pool: multiprocessing.pool.ThreadPool,
+ func: Callable[..., Iterable[T]],
+ *,
+ kwargs_list: list[dict],
+) -> Iterable[T]:
+ """
+ Takes a function that returns an iterable of items, and run it in parallel using threads to return the flattened iterable of items as they arrive.
+
+ This is inspired by those three `map()` variants, and is the mix of all three:
+
+ * `imap()`: like `map()` but returns an iterable instead of a list of results
+ * `imap_unordered()`: like `imap()` but the output is sorted by time of arrival
+ * `flatmap()`: like `map()` but given a function which returns a list, `flatmap()` returns the flattened list that is the concatenation of all the output lists
+ """
+ queue: Queue[T] = Queue()
+ async_results = [pool.apply_async(_write_generator_to_queue, (queue, func, kwargs)) for kwargs in kwargs_list]
+ try:
+ while True:
+ try:
+ yield queue.get(timeout=0.05)
+ except Empty:
+ if all(async_result.ready() for async_result in async_results) and queue.empty():
+ break
+ except KeyboardInterrupt:
+ pass
+ finally:
+ # we get the result in case there's an error to raise
+ try:
+ [async_result.get(timeout=0.05) for async_result in async_results]
+ except multiprocessing.TimeoutError:
+ pass
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/lfs.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/lfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b5e8b3ea68e3aabe023e9475e5c97c2fae28209
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/lfs.py
@@ -0,0 +1,175 @@
+"""
+Implementation of a custom transfer agent for the transfer type "multipart" for
+git-lfs.
+
+Inspired by:
+github.com/cbartz/git-lfs-swift-transfer-agent/blob/master/git_lfs_swift_transfer.py
+
+Spec is: github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
+
+
+To launch debugger while developing:
+
+``` [lfs "customtransfer.multipart"]
+path = /path/to/huggingface_hub/.venv/bin/python args = -m debugpy --listen 5678
+--wait-for-client
+/path/to/huggingface_hub/src/huggingface_hub/commands/huggingface_cli.py
+lfs-multipart-upload ```"""
+
+import json
+import os
+import subprocess
+import sys
+from typing import Annotated
+
+from huggingface_hub.errors import CLIError
+from huggingface_hub.lfs import LFS_MULTIPART_UPLOAD_COMMAND
+
+from ..utils import get_session, hf_raise_for_status, logging
+from ..utils._lfs import SliceFileObj
+from ._framework import Argument
+from ._output import out
+
+
+logger = logging.get_logger(__name__)
+
+
+def lfs_enable_largefiles(
+ path: Annotated[
+ str,
+ Argument(
+ help="Local path to repository you want to configure.",
+ ),
+ ],
+) -> None:
+ """
+ Configure your repository to enable upload of files > 5GB.
+
+ This command sets up git-lfs to use the custom multipart transfer agent
+ which enables efficient uploading of large files in chunks.
+ """
+ local_path = os.path.abspath(path)
+ if not os.path.isdir(local_path):
+ raise CLIError("This does not look like a valid git repo.")
+ subprocess.run(
+ "git config lfs.customtransfer.multipart.path hf".split(),
+ check=True,
+ cwd=local_path,
+ )
+ subprocess.run(
+ f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split(),
+ check=True,
+ cwd=local_path,
+ )
+ out.result("Local repo set up for largefiles", path=local_path)
+
+
+def write_msg(msg: dict):
+ """Write out the message in Line delimited JSON."""
+ msg_str = json.dumps(msg) + "\n"
+ sys.stdout.write(msg_str)
+ sys.stdout.flush()
+
+
+def read_msg() -> dict | None:
+ """Read Line delimited JSON from stdin."""
+ msg = json.loads(sys.stdin.readline().strip())
+
+ if "terminate" in (msg.get("type"), msg.get("event")):
+ # terminate message received
+ return None
+
+ if msg.get("event") not in ("download", "upload"):
+ logger.critical("Received unexpected message")
+ sys.exit(1)
+
+ return msg
+
+
+def lfs_multipart_upload() -> None:
+ """Internal git-lfs custom transfer agent for multipart uploads.
+
+ This function implements the custom transfer protocol for git-lfs multipart uploads.
+ Handles chunked uploads of large files to Hugging Face Hub.
+ """
+ # Immediately after invoking a custom transfer process, git-lfs
+ # sends initiation data to the process over stdin.
+ # This tells the process useful information about the configuration.
+ init_msg = json.loads(sys.stdin.readline().strip())
+ if not (init_msg.get("event") == "init" and init_msg.get("operation") == "upload"):
+ write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}})
+ sys.exit(1)
+
+ # The transfer process should use the information it needs from the
+ # initiation structure, and also perform any one-off setup tasks it
+ # needs to do. It should then respond on stdout with a simple empty
+ # confirmation structure, as follows:
+ write_msg({})
+
+ # After the initiation exchange, git-lfs will send any number of
+ # transfer requests to the stdin of the transfer process, in a serial sequence.
+ while True:
+ msg = read_msg()
+ if msg is None:
+ # When all transfers have been processed, git-lfs will send
+ # a terminate event to the stdin of the transfer process.
+ # On receiving this message the transfer process should
+ # clean up and terminate. No response is expected.
+ sys.exit(0)
+
+ oid = msg["oid"]
+ filepath = msg["path"]
+ completion_url = msg["action"]["href"]
+ header = msg["action"]["header"]
+ chunk_size = int(header.pop("chunk_size"))
+ presigned_urls: list[str] = list(header.values())
+
+ # Send a "started" progress event to allow other workers to start.
+ # Otherwise they're delayed until first "progress" event is reported,
+ # i.e. after the first 5GB by default (!)
+ write_msg(
+ {
+ "event": "progress",
+ "oid": oid,
+ "bytesSoFar": 1,
+ "bytesSinceLast": 0,
+ }
+ )
+
+ parts = []
+ with open(filepath, "rb") as file:
+ for i, presigned_url in enumerate(presigned_urls):
+ with SliceFileObj(
+ file,
+ seek_from=i * chunk_size,
+ read_limit=chunk_size,
+ ) as data:
+ r = get_session().put(presigned_url, data=data)
+ hf_raise_for_status(r)
+ parts.append(
+ {
+ "etag": r.headers.get("etag"),
+ "partNumber": i + 1,
+ }
+ )
+ # In order to support progress reporting while data is uploading / downloading,
+ # the transfer process should post messages to stdout
+ write_msg(
+ {
+ "event": "progress",
+ "oid": oid,
+ "bytesSoFar": (i + 1) * chunk_size,
+ "bytesSinceLast": chunk_size,
+ }
+ )
+
+ r = get_session().post(
+ completion_url,
+ json={
+ "oid": oid,
+ "parts": parts,
+ },
+ )
+ hf_raise_for_status(r)
+
+ write_msg({"event": "complete", "oid": oid})
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/models.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0e8259eed1df7b94ffa8359a7b748423a468476
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/models.py
@@ -0,0 +1,265 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with models on the Hugging Face Hub."""
+
+import enum
+from typing import Annotated, cast, get_args
+
+import click
+
+from huggingface_hub.errors import CLIError, RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.hf_api import ExpandModelProperty_T, ModelSort_T
+from huggingface_hub.inference._providers import PROVIDER_T
+from huggingface_hub.repocard import ModelCard
+
+from ._cli_utils import (
+ REPO_LIST_DEFAULT_LIMIT,
+ AuthorOpt,
+ FilterOpt,
+ LimitOpt,
+ RevisionOpt,
+ SearchOpt,
+ SoftChoice,
+ TokenOpt,
+ get_hf_api,
+ make_expand_properties_parser,
+ typer_factory,
+)
+from ._file_listing import list_repo_files_cmd
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+_EXPAND_PROPERTIES = sorted(get_args(ExpandModelProperty_T))
+_SORT_OPTIONS = get_args(ModelSort_T)
+ModelSortEnum = enum.Enum("ModelSortEnum", {s: s for s in _SORT_OPTIONS}, type=str) # type: ignore[misc]
+InferenceProviderEnum = enum.Enum( # type: ignore[misc]
+ "InferenceProviderEnum", {p: p for p in sorted(get_args(PROVIDER_T))}, type=str
+)
+
+
+ExpandOpt = Annotated[
+ str | None,
+ Option(
+ help=f"Comma-separated properties to return. When used, only the listed properties (and id) are returned. Example: '--expand=downloads,likes,tags'. Valid: {', '.join(_EXPAND_PROPERTIES)}.",
+ callback=make_expand_properties_parser(_EXPAND_PROPERTIES),
+ ),
+]
+
+
+models_cli = typer_factory(help="Interact with models on the Hub.")
+
+
+@models_cli.command(
+ "list | ls",
+ examples=[
+ "hf models ls --sort downloads --limit 10",
+ 'hf models ls --search "llama" --author meta-llama',
+ "hf models ls --pipeline-tag text-generation --warm",
+ "hf models ls --num-parameters min:6B,max:128B --sort likes",
+ "hf models ls --no-gated --author google",
+ "hf models ls --apps llama.cpp --apps vllm",
+ "hf models ls --inference-provider fireworks-ai --sort downloads",
+ "hf models ls --warm --search llama",
+ "hf models ls meta-llama/Llama-3.2-1B-Instruct",
+ "hf models ls meta-llama/Llama-3.2-1B-Instruct -R",
+ "hf models ls meta-llama/Llama-3.2-1B-Instruct --tree -h",
+ ],
+)
+def models_ls(
+ repo_id: Annotated[
+ str | None,
+ Argument(help="Model ID (e.g. `username/repo-name`) to list files from. If omitted, lists models."),
+ ] = None,
+ search: SearchOpt = None,
+ author: AuthorOpt = None,
+ filter: FilterOpt = None,
+ pipeline_tag: Annotated[
+ str | None,
+ Option("--pipeline-tag", help="Filter by pipeline tag (canonical task), e.g. 'summarization'."),
+ ] = None,
+ gated: Annotated[
+ bool | None,
+ Option(
+ "--gated/--no-gated",
+ help="Filter by gated status. '--gated' for gated only, '--no-gated' for non-gated only.",
+ ),
+ ] = None,
+ apps: Annotated[
+ list[str] | None,
+ Option("--apps", help="Filter by app(s) that can run the model, e.g. 'ollama' or 'vllm'."),
+ ] = None,
+ num_parameters: Annotated[
+ str | None,
+ Option(help="Filter by parameter count, e.g. 'min:6B,max:128B'."),
+ ] = None,
+ inference_provider: Annotated[
+ list[str] | None,
+ Option(
+ "--inference-provider",
+ click_type=SoftChoice(InferenceProviderEnum),
+ help="Filter by inference provider(s) serving the model, e.g. 'fireworks-ai'.",
+ ),
+ ] = None,
+ warm: Annotated[
+ bool,
+ Option("--warm", help="Only list models currently served by at least one inference provider."),
+ ] = False,
+ sort: Annotated[
+ ModelSortEnum | None,
+ Option(help="Sort results."),
+ ] = None,
+ limit: LimitOpt = REPO_LIST_DEFAULT_LIMIT,
+ expand: ExpandOpt = None,
+ human_readable: Annotated[
+ bool,
+ Option("--human-readable", "-h", help="Show sizes in human readable format (only for listing files)."),
+ ] = False,
+ as_tree: Annotated[
+ bool,
+ Option("--tree", help="List files in tree format (only for listing files)."),
+ ] = False,
+ recursive: Annotated[
+ bool,
+ Option("--recursive", "-R", help="List files recursively (only for listing files)."),
+ ] = False,
+ revision: RevisionOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List models on the Hub, or files in a model repo.
+
+ When called with no argument, lists models on the Hub.
+ When called with a model ID, lists files in that model repo.
+ """
+ if repo_id is not None:
+ if search is not None:
+ raise click.BadParameter("Cannot use --search when listing files.")
+ if author is not None:
+ raise click.BadParameter("Cannot use --author when listing files.")
+ if filter is not None:
+ raise click.BadParameter("Cannot use --filter when listing files.")
+ if pipeline_tag is not None:
+ raise click.BadParameter("Cannot use --pipeline-tag when listing files.")
+ if gated is not None:
+ raise click.BadParameter("Cannot use --gated/--no-gated when listing files.")
+ if apps is not None:
+ raise click.BadParameter("Cannot use --apps when listing files.")
+ if num_parameters is not None:
+ raise click.BadParameter("Cannot use --num-parameters when listing files.")
+ if inference_provider is not None:
+ raise click.BadParameter("Cannot use --inference-provider when listing files.")
+ if warm:
+ raise click.BadParameter("Cannot use --warm when listing files.")
+ if sort is not None:
+ raise click.BadParameter("Cannot use --sort when listing files.")
+ if limit != REPO_LIST_DEFAULT_LIMIT:
+ raise click.BadParameter("Cannot use --limit when listing files.")
+ if expand is not None:
+ raise click.BadParameter("Cannot use --expand when listing files.")
+ return list_repo_files_cmd(
+ repo_id=repo_id,
+ repo_type="model",
+ human_readable=human_readable,
+ as_tree=as_tree,
+ recursive=recursive,
+ revision=revision,
+ token=token,
+ )
+
+ if as_tree:
+ raise click.BadParameter("Cannot use --tree when listing models.")
+ if recursive:
+ raise click.BadParameter("Cannot use --recursive when listing models.")
+ if human_readable:
+ raise click.BadParameter("Cannot use --human-readable when listing models.")
+ if revision is not None:
+ raise click.BadParameter("Cannot use --revision when listing models.")
+ if warm and inference_provider is not None:
+ raise click.BadParameter("Cannot use --warm together with --inference-provider.")
+ api = get_hf_api(token=token)
+ sort_key = sort.value if sort else None
+ results = [
+ _dataclass_to_dict(model_info)
+ for model_info in api.list_models(
+ filter=filter,
+ author=author,
+ search=search,
+ pipeline_tag=pipeline_tag,
+ gated=gated,
+ apps=apps,
+ num_parameters=num_parameters,
+ inference="warm" if warm else None,
+ inference_provider=cast(list[PROVIDER_T] | None, inference_provider),
+ sort=sort_key,
+ limit=limit,
+ expand=expand, # type: ignore
+ )
+ ]
+ out.table(results)
+ if (inference_provider is not None or warm) and not expand:
+ out.hint(
+ "Use `--expand inferenceProviderMapping` to see which provider serves each model and the provider-specific model id."
+ )
+
+
+@models_cli.command(
+ "info",
+ examples=[
+ "hf models info meta-llama/Llama-3.2-1B-Instruct",
+ "hf models info Qwen/Qwen3.5-9B --expand downloads,likes,tags",
+ ],
+)
+def models_info(
+ model_id: Annotated[str, Argument(help="The model ID (e.g. `username/repo-name`).")],
+ revision: RevisionOpt = None,
+ expand: ExpandOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a model on the Hub."""
+ api = get_hf_api(token=token)
+ try:
+ info = api.model_info(repo_id=model_id, revision=revision, expand=expand) # type: ignore
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"Model '{model_id}' not found.") from e
+ except RevisionNotFoundError as e:
+ raise CLIError(f"Revision '{revision}' not found on '{model_id}'.") from e
+ out.dict(info)
+
+
+@models_cli.command(
+ "card",
+ examples=[
+ "hf models card google/gemma-4-31B-it",
+ "hf models card google/gemma-4-31B-it --metadata",
+ "hf models card google/gemma-4-31B-it --metadata --format json",
+ "hf models card google/gemma-4-31B-it --text",
+ ],
+)
+def models_card(
+ model_id: Annotated[str, Argument(help="The model ID (e.g. `username/repo-name`).")],
+ metadata: Annotated[bool, Option("--metadata", help="Output only the metadata from the card.")] = False,
+ text: Annotated[bool, Option("--text", help="Output only the text body (no metadata).")] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Get the model card (README) for a model on the Hub."""
+ if metadata and text:
+ raise CLIError("--metadata and --text are mutually exclusive.")
+ card = ModelCard.load(model_id, token=token)
+ if metadata:
+ out.dict(card.data.to_dict())
+ elif text:
+ out.text(card.text)
+ else:
+ out.text(card.content)
+ out.hint(f"Use `hf models card {model_id} --metadata` to extract only the card metadata.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/papers.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/papers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f33db0d90c9febdaea8e26186c13aadc6ea3708
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/papers.py
@@ -0,0 +1,167 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with papers on the Hugging Face Hub."""
+
+import datetime
+import enum
+from typing import Annotated, get_args
+
+from huggingface_hub.errors import CLIError, HfHubHTTPError
+from huggingface_hub.hf_api import DailyPapersSort_T
+
+from ._cli_utils import (
+ LimitOpt,
+ TokenOpt,
+ get_hf_api,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+_SORT_OPTIONS = get_args(DailyPapersSort_T)
+PaperSortEnum = enum.Enum("PaperSortEnum", {s: s for s in _SORT_OPTIONS}, type=str) # type: ignore[misc]
+
+
+def _parse_date(value: str | None) -> str | None:
+ """Parse date option, converting 'today' to current date."""
+ if value is None:
+ return None
+ if value.lower() == "today":
+ return datetime.date.today().isoformat()
+ return value
+
+
+papers_cli = typer_factory(help="Interact with papers on the Hub.")
+
+
+@papers_cli.command(
+ "list | ls",
+ examples=[
+ "hf papers ls",
+ "hf papers ls --sort trending",
+ "hf papers ls --date 2025-01-23",
+ "hf papers ls --week 2025-W09",
+ "hf papers ls --submitter akhaliq",
+ "hf papers ls --format json",
+ ],
+)
+def papers_ls(
+ date: Annotated[
+ str | None,
+ Option(
+ help="Date in ISO format (YYYY-MM-DD) or 'today'.",
+ callback=_parse_date,
+ ),
+ ] = None,
+ week: Annotated[
+ str | None,
+ Option(help="ISO week to filter by, e.g. '2025-W09'."),
+ ] = None,
+ month: Annotated[
+ str | None,
+ Option(help="Month to filter by in ISO format (YYYY-MM), e.g. '2025-02'."),
+ ] = None,
+ submitter: Annotated[
+ str | None,
+ Option(help="Filter by username of the submitter."),
+ ] = None,
+ sort: Annotated[
+ PaperSortEnum | None,
+ Option(help="Sort results."),
+ ] = None,
+ limit: LimitOpt = 50,
+ token: TokenOpt = None,
+) -> None:
+ """List daily papers on the Hub."""
+ api = get_hf_api(token=token)
+ sort_key = sort.value if sort else None
+ results = []
+ for paper_info in api.list_daily_papers(
+ date=date,
+ week=week,
+ month=month,
+ submitter=submitter,
+ sort=sort_key,
+ limit=limit,
+ ):
+ item = _dataclass_to_dict(paper_info)
+ submitted_by = item.get("submitted_by") or {}
+ item["submitted_by_name"] = submitted_by.get("fullname") or submitted_by.get("username") or ""
+ results.append(item)
+ out.table(
+ results,
+ headers=["id", "title", "upvotes", "comments", "published_at", "submitted_by_name"],
+ )
+
+
+@papers_cli.command(
+ "search",
+ examples=[
+ 'hf papers search "vision language"',
+ 'hf papers search "attention mechanism" --limit 10',
+ 'hf papers search "diffusion" --format json',
+ ],
+)
+def papers_search(
+ query: Annotated[str, Argument(help="Search query string.")],
+ limit: LimitOpt = 20,
+ token: TokenOpt = None,
+) -> None:
+ """Search papers on the Hub."""
+ api = get_hf_api(token=token)
+ results = [_dataclass_to_dict(paper_info) for paper_info in api.list_papers(query=query, limit=limit)]
+ out.table(results, headers=["id", "title", "summary", "upvotes", "published_at"])
+
+
+@papers_cli.command(
+ "info",
+ examples=[
+ "hf papers info 2601.15621",
+ ],
+)
+def papers_info(
+ paper_id: Annotated[str, Argument(help="The arXiv paper ID (e.g. '2502.08025').")],
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a paper on the Hub."""
+ api = get_hf_api(token=token)
+ try:
+ info = api.paper_info(id=paper_id)
+ except HfHubHTTPError as e:
+ if e.response.status_code == 404:
+ raise CLIError(f"Paper '{paper_id}' not found on the Hub.") from e
+ raise
+ out.dict(info)
+
+
+@papers_cli.command(
+ "read",
+ examples=[
+ "hf papers read 2601.15621",
+ ],
+)
+def papers_read(
+ paper_id: Annotated[str, Argument(help="The arXiv paper ID (e.g. '2502.08025').")],
+ token: TokenOpt = None,
+) -> None:
+ """Read a paper as markdown."""
+ api = get_hf_api(token=token)
+ try:
+ content = api.read_paper(id=paper_id)
+ except HfHubHTTPError as e:
+ if e.response.status_code == 404:
+ raise CLIError(f"Paper '{paper_id}' not found on the Hub.") from e
+ raise
+ out.text(content)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/repos.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/repos.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa5fd78b283106787c247343c5d7904f9af147fe
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/repos.py
@@ -0,0 +1,625 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with repositories on the Hugging Face Hub."""
+
+import enum
+from typing import Annotated, cast
+
+from huggingface_hub import SpaceHardware
+from huggingface_hub.cli._cli_utils import SoftChoice
+from huggingface_hub.errors import CLIError, HfHubHTTPError, RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.hf_api import REPO_REGIONS
+
+from ._city_game import run_city_game
+from ._cli_utils import (
+ REPO_LIST_DEFAULT_LIMIT,
+ EnvFileOpt,
+ EnvOpt,
+ LimitOpt,
+ PrivateOpt,
+ RepoIdArg,
+ RepoType,
+ RepoTypeOpt,
+ RevisionOpt,
+ SearchOpt,
+ SecretsFileOpt,
+ SecretsOpt,
+ TokenOpt,
+ VolumesOpt,
+ env_map_to_key_value_list,
+ get_hf_api,
+ parse_env_map,
+ parse_volumes,
+ typer_factory,
+)
+from ._cp import make_cp
+from ._file_listing import format_size
+from ._framework import Argument, Option
+from ._output import OutputFormat, out
+
+
+repos_cli = typer_factory(help="Manage repos on the Hub.")
+
+
+class RepoTypeAll(str, enum.Enum):
+ model = "model"
+ dataset = "dataset"
+ space = "space"
+ bucket = "bucket"
+
+
+class GatedChoices(str, enum.Enum):
+ auto = "auto"
+ manual = "manual"
+ false = "false"
+
+
+PublicOpt = Annotated[
+ bool | None,
+ Option(
+ "--public",
+ help="Whether to make the repo public. Ignored if the repo already exists.",
+ ),
+]
+
+ProtectedOpt = Annotated[
+ bool | None,
+ Option(
+ "--protected",
+ help="Whether to make the Space protected (Spaces only). Ignored if the repo already exists.",
+ ),
+]
+SpaceHardwareOpt = Annotated[
+ str | None,
+ Option(
+ "--flavor",
+ help="Space hardware flavor (e.g. 'cpu-basic', 't4-medium', 'l4x4'). Only for Spaces.",
+ click_type=SoftChoice(SpaceHardware),
+ ),
+]
+
+SpaceSleepTimeOpt = Annotated[
+ int | None,
+ Option(
+ "--sleep-time",
+ help="Seconds of inactivity before the Space is put to sleep. Use -1 to disable. Only for Spaces.",
+ ),
+]
+
+
+tag_cli = typer_factory(help="Manage tags for a repo on the Hub.")
+branch_cli = typer_factory(help="Manage branches for a repo on the Hub.")
+repos_cli.add_group(tag_cli, name="tag")
+repos_cli.add_group(branch_cli, name="branch")
+
+
+@repos_cli.command(
+ "list | ls",
+ examples=[
+ "hf repos ls",
+ "hf repos ls --explore",
+ "hf repos ls --namespace my-org --search bert",
+ ],
+)
+def repo_list(
+ namespace: Annotated[
+ str | None,
+ Option(
+ help="Organization name. If not provided, lists repos for the authenticated user.",
+ ),
+ ] = None,
+ repo_type: Annotated[
+ RepoTypeAll | None,
+ Option(
+ "--type",
+ "--repo-type",
+ help="Filter by repository type (model, dataset, space, or bucket).",
+ ),
+ ] = None,
+ search: SearchOpt = None,
+ limit: LimitOpt = REPO_LIST_DEFAULT_LIMIT,
+ explore: Annotated[
+ bool,
+ Option("--explore", help="Explore your repos as an interactive 3D city."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """List all repos (models, datasets, spaces, buckets) with storage info."""
+ api = get_hf_api(token=token)
+ repos = list(api.list_user_repos(namespace=namespace))
+ if repo_type is not None:
+ repos = [r for r in repos if r.type == repo_type.value]
+ if search is not None:
+ search_lower = search.lower()
+ repos = [r for r in repos if search_lower in r.id.lower()]
+ total = len(repos)
+
+ if explore:
+ if out.mode == OutputFormat.human:
+ run_city_game(repos)
+ return
+ raise CLIError("Repository exploration is only available in terminal.")
+
+ if limit > 0:
+ repos = repos[:limit]
+ items = [
+ {
+ "id": r.id,
+ "type": r.type,
+ "updated": r.updated_at.strftime("%Y-%m-%d"),
+ "visibility": r.visibility,
+ "storage": format_size(r.storage, human_readable=True),
+ "%_of_total": f"{r.storage_percent:.1f}%",
+ }
+ for r in repos
+ ]
+ out.table(items, id_key="id", alignments={"storage": "right", "%_of_total": "right"})
+ if limit > 0 and total > limit:
+ out.hint(f"Showing {limit} of {total} repos. Use `--limit 0` to list all.")
+
+
+@repos_cli.command(
+ "create",
+ examples=[
+ "hf repos create my-model",
+ "hf repos create my-dataset --repo-type dataset --private",
+ "hf repos create my-space --type space --sdk gradio --flavor t4-medium --secrets HF_TOKEN -e THEME=dark --protected",
+ "hf repos create my-jupyterlab --type space --template SpacesExamples/jupyterlab",
+ "hf repos create my-space --type space --sdk gradio -v hf://org/my-model:/models -v hf://buckets/org/b:/data",
+ "hf repos create my-model --region us",
+ ],
+)
+def repo_create(
+ repo_id: RepoIdArg,
+ repo_type: RepoTypeOpt = RepoType.model,
+ sdk: Annotated[
+ str | None,
+ Option(
+ "--sdk",
+ "--space-sdk",
+ help="Hugging Face Spaces SDK type. Required when --type is set to 'space'.",
+ ),
+ ] = None,
+ template: Annotated[
+ str | None,
+ Option(
+ "--template",
+ help=(
+ "Create a Space from an official template. Pass a template repo id (e.g. "
+ "'SpacesExamples/jupyterlab') or its short name (e.g. 'JupyterLab'). List available templates with "
+ "`hf spaces templates`. Spaces only."
+ ),
+ ),
+ ] = None,
+ private: PrivateOpt = None,
+ public: PublicOpt = None,
+ protected: ProtectedOpt = None,
+ token: TokenOpt = None,
+ exist_ok: Annotated[
+ bool,
+ Option(
+ help="Do not raise an error if repo already exists.",
+ ),
+ ] = False,
+ resource_group_id: Annotated[
+ str | None,
+ Option(
+ help="Resource group in which to create the repo. Resource groups is only available for Enterprise Hub organizations.",
+ ),
+ ] = None,
+ region: Annotated[
+ REPO_REGIONS | None,
+ Option(
+ "--region",
+ help="Cloud region in which to create the repo. Can be one of 'us' or 'eu'. Requires Team plan or above.",
+ ),
+ ] = None,
+ hardware: SpaceHardwareOpt = None,
+ sleep_time: SpaceSleepTimeOpt = None,
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ volume: VolumesOpt = None,
+) -> None:
+ """Create a new repo on the Hub."""
+ api = get_hf_api(token=token)
+ repo_url = api.create_repo(
+ repo_id=repo_id,
+ repo_type=repo_type.value,
+ visibility="private" if private else "public" if public else "protected" if protected else None, # type: ignore [arg-type]
+ token=token,
+ exist_ok=exist_ok,
+ resource_group_id=resource_group_id,
+ region=region,
+ space_sdk=sdk,
+ space_hardware=cast(SpaceHardware | None, hardware),
+ space_sleep_time=sleep_time,
+ space_secrets=env_map_to_key_value_list(parse_env_map(secrets, secrets_file)),
+ space_variables=env_map_to_key_value_list(parse_env_map(env, env_file)),
+ space_volumes=parse_volumes(volume),
+ space_template=template,
+ )
+ out.result("Repo created", repo_id=repo_url.repo_id, url=str(repo_url))
+
+
+@repos_cli.command(
+ "duplicate",
+ examples=[
+ "hf repos duplicate openai/gdpval --type dataset",
+ "hf repos duplicate multimodalart/dreambooth-training my-dreambooth --type space --flavor l4x4 --secrets HF_TOKEN --private",
+ "hf repos duplicate org/my-space my-space --type space -v hf://org/my-model:/models -v hf://buckets/org/b:/data",
+ ],
+)
+def repo_duplicate(
+ from_id: RepoIdArg,
+ to_id: Annotated[
+ str | None,
+ Argument(
+ help="Destination repo ID (e.g. `myorg/my-copy`). Defaults to your namespace with the same repo name.",
+ ),
+ ] = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+ private: PrivateOpt = None,
+ public: PublicOpt = None,
+ protected: ProtectedOpt = None,
+ token: TokenOpt = None,
+ exist_ok: Annotated[
+ bool,
+ Option(
+ help="Do not raise an error if repo already exists.",
+ ),
+ ] = False,
+ resource_group_id: Annotated[
+ str | None,
+ Option(
+ help="Resource group in which to create the new repo. Resource groups is only available for Enterprise Hub organizations.",
+ ),
+ ] = None,
+ hardware: SpaceHardwareOpt = None,
+ sleep_time: SpaceSleepTimeOpt = None,
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ volume: VolumesOpt = None,
+) -> None:
+ """Duplicate a repo on the Hub (model, dataset, or Space)."""
+ api = get_hf_api(token=token)
+ repo_url = api.duplicate_repo(
+ from_id=from_id,
+ to_id=to_id,
+ repo_type=repo_type.value,
+ visibility="private" if private else "public" if public else "protected" if protected else None, # type: ignore [arg-type]
+ token=token,
+ exist_ok=exist_ok,
+ resource_group_id=resource_group_id,
+ space_hardware=cast(SpaceHardware | None, hardware),
+ space_sleep_time=sleep_time,
+ space_secrets=env_map_to_key_value_list(parse_env_map(secrets, secrets_file)),
+ space_variables=env_map_to_key_value_list(parse_env_map(env, env_file)),
+ space_volumes=parse_volumes(volume),
+ )
+ out.result("Repo duplicated", from_id=from_id, to_id=repo_url.repo_id, url=str(repo_url))
+ if repo_url.files_copy_pending:
+ out.warning(
+ f"LFS/Xet files are still being copied in the background. Downloading them from '{repo_url.repo_id}'"
+ " may fail until the copy is complete."
+ )
+
+
+@repos_cli.command("delete", examples=["hf repos delete my-model"])
+def repo_delete(
+ repo_id: RepoIdArg,
+ repo_type: RepoTypeOpt = RepoType.model,
+ token: TokenOpt = None,
+ missing_ok: Annotated[
+ bool,
+ Option(
+ help="If set to True, do not raise an error if repo does not exist.",
+ ),
+ ] = False,
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Answer Yes to prompt automatically.",
+ ),
+ ] = False,
+) -> None:
+ """Delete a repo from the Hub. This is an irreversible operation."""
+ out.confirm(f"You are about to permanently delete {repo_type.value} '{repo_id}'. Proceed?", yes=yes)
+ api = get_hf_api(token=token)
+ api.delete_repo(
+ repo_id=repo_id,
+ repo_type=repo_type.value,
+ missing_ok=missing_ok,
+ )
+ out.result("Repo deleted", repo_id=repo_id)
+
+
+@repos_cli.command("move", examples=["hf repos move old-namespace/my-model new-namespace/my-model"])
+def repo_move(
+ from_id: RepoIdArg,
+ to_id: RepoIdArg,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """Move a repository from a namespace to another namespace."""
+ api = get_hf_api(token=token)
+ api.move_repo(
+ from_id=from_id,
+ to_id=to_id,
+ repo_type=repo_type.value,
+ )
+ out.result("Repo moved", from_id=from_id, to_id=to_id)
+
+
+@repos_cli.command(
+ "settings",
+ examples=[
+ "hf repos settings my-model --private",
+ "hf repos settings my-model --gated auto",
+ "hf repos settings my-space --repo-type space --protected",
+ ],
+)
+def repo_settings(
+ repo_id: RepoIdArg,
+ gated: Annotated[
+ GatedChoices | None,
+ Option(
+ help="The gated status for the repository.",
+ ),
+ ] = None,
+ private: PrivateOpt = None,
+ public: PublicOpt = None,
+ protected: ProtectedOpt = None,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """Update the settings of a repository."""
+ api = get_hf_api(token=token)
+ api.update_repo_settings(
+ repo_id=repo_id,
+ gated=(None if gated is None else False if gated is GatedChoices.false else gated.value),
+ visibility="private" if private else "public" if public else "protected" if protected else None, # type: ignore [arg-type]
+ repo_type=repo_type.value,
+ )
+ out.result("Repo settings updated", repo_id=repo_id)
+
+
+@repos_cli.command(
+ "delete-files",
+ examples=[
+ "hf repos delete-files my-model file.txt",
+ 'hf repos delete-files my-model "*.json"',
+ "hf repos delete-files my-model folder/",
+ ],
+)
+def repo_delete_files(
+ repo_id: RepoIdArg,
+ patterns: Annotated[
+ list[str],
+ Argument(
+ help="Glob patterns to match files to delete. Based on fnmatch, '*' matches files recursively.",
+ ),
+ ],
+ repo_type: RepoTypeOpt = RepoType.model,
+ revision: RevisionOpt = None,
+ commit_message: Annotated[
+ str | None,
+ Option(
+ help="The summary / title / first line of the generated commit.",
+ ),
+ ] = None,
+ commit_description: Annotated[
+ str | None,
+ Option(
+ help="The description of the generated commit.",
+ ),
+ ] = None,
+ create_pr: Annotated[
+ bool,
+ Option(
+ help="Whether to create a new Pull Request for these changes.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete files from a repo on the Hub."""
+ api = get_hf_api(token=token)
+ url = api.delete_files(
+ delete_patterns=patterns,
+ repo_id=repo_id,
+ repo_type=repo_type.value,
+ revision=revision,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ )
+ out.result("Files deleted", repo_id=repo_id, commit_url=url)
+
+
+# `hf repos cp` is an alias for the top-level `hf cp` command (see `cli/_cp.py`).
+repos_cli.command(
+ name="cp",
+ examples=[
+ # Download (repo or bucket -> local / stdout)
+ "hf repos cp hf://username/my-model/config.json config.json",
+ "hf repos cp hf://datasets/username/my-dataset/data.csv data/",
+ "hf repos cp hf://username/my-model/config.json -",
+ # Upload (local / stdin -> repo)
+ "hf repos cp model.safetensors hf://username/my-model/model.safetensors",
+ "hf repos cp config.json hf://username/my-model/logs/",
+ "hf repos cp - hf://username/my-model/config.json",
+ # Remote to remote (repo -> repo)
+ "hf repos cp hf://username/source-model/config.json hf://username/dest-model/config.json",
+ "hf repos cp hf://datasets/username/my-dataset/processed/ hf://datasets/username/dest-dataset/processed/",
+ "hf repos cp hf://username/my-model/logs/ hf://username/archive-model/logs/",
+ ],
+)(make_cp("repos"))
+
+
+@branch_cli.command(
+ "create",
+ examples=[
+ "hf repos branch create my-model dev",
+ "hf repos branch create my-model dev --revision abc123",
+ ],
+)
+def branch_create(
+ repo_id: RepoIdArg,
+ branch: Annotated[
+ str,
+ Argument(
+ help="The name of the branch to create.",
+ ),
+ ],
+ revision: RevisionOpt = None,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+ exist_ok: Annotated[
+ bool,
+ Option(
+ help="If set to True, do not raise an error if branch already exists.",
+ ),
+ ] = False,
+) -> None:
+ """Create a new branch for a repo on the Hub."""
+ api = get_hf_api(token=token)
+ api.create_branch(
+ repo_id=repo_id,
+ branch=branch,
+ revision=revision,
+ repo_type=repo_type.value,
+ exist_ok=exist_ok,
+ )
+ out.result("Branch created", branch=branch, repo_type=repo_type.value, repo_id=repo_id)
+
+
+@branch_cli.command("delete", examples=["hf repos branch delete my-model dev"])
+def branch_delete(
+ repo_id: RepoIdArg,
+ branch: Annotated[
+ str,
+ Argument(
+ help="The name of the branch to delete.",
+ ),
+ ],
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """Delete a branch from a repo on the Hub."""
+ api = get_hf_api(token=token)
+ api.delete_branch(
+ repo_id=repo_id,
+ branch=branch,
+ repo_type=repo_type.value,
+ )
+ out.result("Branch deleted", branch=branch, repo_type=repo_type.value, repo_id=repo_id)
+
+
+@tag_cli.command(
+ "create",
+ examples=[
+ "hf repos tag create my-model v1.0",
+ 'hf repos tag create my-model v1.0 -m "First release"',
+ ],
+)
+def tag_create(
+ repo_id: RepoIdArg,
+ tag: Annotated[
+ str,
+ Argument(
+ help="The name of the tag to create.",
+ ),
+ ],
+ message: Annotated[
+ str | None,
+ Option(
+ "-m",
+ "--message",
+ help="The description of the tag to create.",
+ ),
+ ] = None,
+ revision: RevisionOpt = None,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """Create a tag for a repo."""
+ repo_type_str = repo_type.value
+ api = get_hf_api(token=token)
+ try:
+ api.create_tag(repo_id=repo_id, tag=tag, tag_message=message, revision=revision, repo_type=repo_type_str)
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"{repo_type_str.capitalize()} '{repo_id}' not found.") from e
+ except RevisionNotFoundError as e:
+ raise CLIError(f"Revision '{revision}' not found.") from e
+ except HfHubHTTPError as e:
+ if e.response.status_code == 409:
+ raise CLIError(f"Tag '{tag}' already exists on '{repo_id}'.") from e
+ raise
+ out.result("Tag created", tag=tag, repo_type=repo_type_str, repo_id=repo_id)
+
+
+@tag_cli.command("list | ls", examples=["hf repos tag list my-model"])
+def tag_list(
+ repo_id: RepoIdArg,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """List tags for a repo."""
+ repo_type_str = repo_type.value
+ api = get_hf_api(token=token)
+ try:
+ refs = api.list_repo_refs(repo_id=repo_id, repo_type=repo_type_str)
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"{repo_type_str.capitalize()} '{repo_id}' not found.") from e
+ items = [{"name": t.name, "target_commit": t.target_commit, "ref": t.ref} for t in refs.tags]
+ out.table(items)
+
+
+@tag_cli.command("delete", examples=["hf repos tag delete my-model v1.0"])
+def tag_delete(
+ repo_id: RepoIdArg,
+ tag: Annotated[
+ str,
+ Argument(
+ help="The name of the tag to delete.",
+ ),
+ ],
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Answer Yes to prompt automatically",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+ repo_type: RepoTypeOpt = RepoType.model,
+) -> None:
+ """Delete a tag for a repo."""
+ repo_type_str = repo_type.value
+ out.text(f"You are about to delete tag {tag} on {repo_type_str} {repo_id}")
+ out.confirm("Proceed?", yes=yes)
+ api = get_hf_api(token=token)
+ try:
+ api.delete_tag(repo_id=repo_id, tag=tag, repo_type=repo_type_str)
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"{repo_type_str.capitalize()} '{repo_id}' not found.") from e
+ except RevisionNotFoundError as e:
+ raise CLIError(f"Tag '{tag}' not found on '{repo_id}'.") from e
+ out.result("Tag deleted", tag=tag, repo_type=repo_type_str, repo_id=repo_id)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/sandbox.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/sandbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d93fd9e50c57962c97b5ab253a212ff5bfa1ffc
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/sandbox.py
@@ -0,0 +1,492 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to run and manage sandboxes on Hugging Face Jobs."""
+
+import sys
+import time
+from contextlib import contextmanager
+from typing import Annotated, Any, Iterator
+
+import click
+
+from huggingface_hub._sandbox import (
+ DEFAULT_IDLE_TIMEOUT,
+ DEFAULT_IMAGE,
+ DEFAULT_SANDBOXES_PER_HOST,
+ MODE_LABEL,
+ MODE_POOL,
+ POOL_LABEL,
+ SANDBOX_LABEL,
+ SHARED_ID_SEP,
+ Sandbox,
+ SandboxPool,
+ SandboxProcess,
+ _split_sandbox_id,
+)
+from huggingface_hub._sandbox_cache import delete_pool_cache
+from huggingface_hub.errors import CLIError, SandboxError
+
+from ._cli_utils import (
+ EnvFileOpt,
+ EnvOpt,
+ SecretsFileOpt,
+ SecretsOpt,
+ TokenOpt,
+ VolumesOpt,
+ get_hf_api,
+ parse_env_map,
+ parse_volumes,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import out
+from .jobs import FlavorOpt, LabelsOpt, NamespaceOpt, _parse_labels_map
+
+
+sandbox_cli = typer_factory(help="Run and manage experimental sandboxes on Hugging Face Jobs.")
+pool_cli = typer_factory(
+ help="Warm host VM pools and spawn experimental shared sandboxes for workloads within the same trust boundary."
+)
+sandbox_cli.add_group(pool_cli, name="pool")
+process_cli = typer_factory(help="List and stop background processes running in a sandbox.")
+sandbox_cli.add_group(process_cli, name="process")
+
+SandboxIdArg = Annotated[str, Argument(help="The sandbox id as printed by `hf sandbox create`.")]
+
+
+@contextmanager
+def _connect(sandbox_id: str, *, namespace: str | None, token: str | None) -> Iterator[Sandbox]:
+ """Reattach to a sandbox and close the HTTP client when the command is done."""
+ sandbox = Sandbox.connect(sandbox_id, namespace=namespace, token=token)
+ try:
+ yield sandbox
+ finally:
+ sandbox.close()
+
+
+@sandbox_cli.command(
+ "create",
+ examples=[
+ "hf sandbox create",
+ "hf sandbox create ubuntu:24.04",
+ "hf sandbox create --flavor a10g-small",
+ "hf sandbox create --label controller-run=run-42",
+ "hf sandbox create --pool pool-ab12cd34ef56 --env LOG_LEVEL=debug",
+ ],
+)
+def sandbox_create(
+ image: Annotated[str | None, Argument(help="Docker image (needs /bin/sh).")] = None,
+ pool: Annotated[
+ str | None,
+ Option("--pool", help="Spawn a cheap shared sandbox in this pool (from `hf sandbox pool create`)."),
+ ] = None,
+ flavor: FlavorOpt = None,
+ idle_timeout: Annotated[
+ str | None,
+ Option(help="Auto-terminate the sandbox after this much inactivity (e.g. '10m'). Defaults to 10m."),
+ ] = None,
+ env: EnvOpt = None,
+ secrets: SecretsOpt = None,
+ label: LabelsOpt = None,
+ env_file: EnvFileOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ volume: VolumesOpt = None,
+ namespace: NamespaceOpt = None,
+ forward_hf_token: Annotated[
+ bool, Option("--forward-hf-token", help="Inject your HF token as HF_TOKEN in the sandbox.")
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Create a sandbox: a dedicated VM by default, or a cheap shared one with `--pool`.
+
+ Env and idle-timeout apply to the sandbox in both modes. With `--pool`, the image and
+ flavor come from the pool, so passing them here is an error; `--secrets` is also
+ rejected since pooled sandboxes have no encrypted-secrets channel (use `--env`). Define
+ a pool first with `hf sandbox pool create`.
+ """
+ start = time.time()
+ idle = idle_timeout if idle_timeout is not None else DEFAULT_IDLE_TIMEOUT
+
+ if pool is not None:
+ if image is not None or flavor is not None or volume:
+ raise CLIError("--pool fixes the image/flavor (and volumes aren't supported); drop those options.")
+ if secrets or secrets_file:
+ raise CLIError(
+ "--pool has no encrypted-secrets channel. Pass the values with --env/--env-file: on a pooled "
+ "sandbox they are delivered to the host at creation and are not stored in the job metadata, "
+ "but they are not encrypted at rest."
+ )
+ if label:
+ raise CLIError("--label is only supported for dedicated sandboxes.")
+ sbx = SandboxPool.connect(pool, namespace=namespace, token=token).create(
+ env=parse_env_map(env, env_file),
+ idle_timeout=idle,
+ forward_hf_token=forward_hf_token,
+ )
+ out.result("Sandbox ready", id=sbx.id, host=sbx.host_id, pool=pool, elapsed=f"{time.time() - start:.1f}s")
+ out.hint(f"Run a command with `hf sandbox exec {sbx.id} -- echo hello`.")
+ out.hint(f"Terminate it with `hf sandbox kill {sbx.id}`.")
+ return
+
+ sandbox = Sandbox.create(
+ image=image or DEFAULT_IMAGE,
+ flavor=flavor or "cpu-basic",
+ idle_timeout=idle,
+ env=parse_env_map(env, env_file),
+ secrets=parse_env_map(secrets, secrets_file),
+ volumes=parse_volumes(volume),
+ namespace=namespace,
+ forward_hf_token=forward_hf_token,
+ labels=_parse_labels_map(label),
+ token=token,
+ )
+ # Release the HTTP client (the sandbox keeps running)
+ sandbox.close()
+ out.result("Sandbox ready", id=sandbox.id, image=sandbox.image, elapsed=f"{time.time() - start:.1f}s")
+ out.hint(f"Run a command with `hf sandbox exec {sandbox.id} -- echo hello`.")
+ out.hint(f"Terminate it with `hf sandbox kill {sandbox.id}`.")
+
+
+@sandbox_cli.command(
+ "exec",
+ context_settings={"ignore_unknown_options": True},
+ examples=[
+ 'hf sandbox exec -- python -c "print(42)"',
+ "hf sandbox exec -w /app -- pytest -x",
+ ],
+)
+def sandbox_exec(
+ sandbox_id: SandboxIdArg,
+ command: Annotated[list[str], Argument(help="The command to run.")],
+ workdir: Annotated[str | None, Option("-w", "--workdir", help="Working directory.")] = None,
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ exec_timeout: Annotated[
+ float | None, Option("--timeout", help="Kill the command after this many seconds.")
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Run a command in a sandbox, streaming output. Exits with the command's exit code.
+
+ To start a long-running command in the background instead of waiting for it, use
+ `hf sandbox spawn`.
+ """
+
+ def write_stdout(data: str) -> None:
+ sys.stdout.write(data)
+ sys.stdout.flush()
+
+ def write_stderr(data: str) -> None:
+ sys.stderr.write(data)
+ sys.stderr.flush()
+
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ result = sandbox.run(
+ list(command),
+ env=parse_env_map(env, env_file),
+ cwd=workdir,
+ timeout=exec_timeout,
+ on_stdout=write_stdout,
+ on_stderr=write_stderr,
+ capture_output=False,
+ check=False,
+ )
+ if result.timed_out:
+ out.error(f"Command timed out after {exec_timeout}s.")
+ raise click.exceptions.Exit(code=result.exit_code or 124) # 124: conventional timeout exit code
+ if result.exit_code != 0:
+ raise click.exceptions.Exit(code=result.exit_code if result.exit_code is not None else 1)
+
+
+@sandbox_cli.command(
+ "spawn",
+ context_settings={"ignore_unknown_options": True},
+ examples=[
+ "hf sandbox spawn -- python -m http.server 8000",
+ "hf sandbox spawn -w /app -- uvicorn app:app",
+ ],
+)
+def sandbox_spawn(
+ sandbox_id: SandboxIdArg,
+ command: Annotated[list[str], Argument(help="The command to run in the background.")],
+ workdir: Annotated[str | None, Option("-w", "--workdir", help="Working directory.")] = None,
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Start a long-running command in the background and return its pid (don't wait).
+
+ List a sandbox's processes with `hf sandbox process ls` and stop one with
+ `hf sandbox process kill`.
+ """
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ process = sandbox.run(list(command), env=parse_env_map(env, env_file), cwd=workdir, background=True)
+ out.result("Process started", sandbox=sandbox_id, pid=process.pid)
+ out.hint(f"List processes with `hf sandbox process ls {sandbox_id}`.")
+ out.hint(f"Stop it with `hf sandbox process kill {sandbox_id} {process.pid}`.")
+
+
+@sandbox_cli.command(
+ "cp",
+ examples=[
+ "hf sandbox cp data.csv :/data/data.csv",
+ "hf sandbox cp :/app/result.json result.json",
+ ],
+)
+def sandbox_cp(
+ src: Annotated[str, Argument(help="Source: a local path or :.")],
+ dst: Annotated[str, Argument(help="Destination: a local path or :.")],
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Copy a file between the local machine and a sandbox (docker-style)."""
+
+ def parse(ref: str) -> tuple[str | None, str]:
+ # Only treat as a sandbox ref when the part before ':' looks like a sandbox id
+ # (more than one char): this leaves local paths and Windows drive letters like
+ # 'C:\data\file.csv' or 'C:/data/file.csv' (single-letter prefix) untouched.
+ if ":" in ref and not ref.startswith((".", "/", "~")):
+ sandbox_id, path = ref.split(":", 1)
+ if len(sandbox_id) > 1:
+ return sandbox_id, path
+ return None, ref
+
+ src_sandbox, src_path = parse(src)
+ dst_sandbox, dst_path = parse(dst)
+ if (src_sandbox is None) == (dst_sandbox is None):
+ raise CLIError("Exactly one of SRC and DST must be a sandbox path (:).")
+ if src_sandbox is not None:
+ with _connect(src_sandbox, namespace=namespace, token=token) as sandbox:
+ sandbox.files.download(src_path, dst_path)
+ else:
+ assert dst_sandbox is not None
+ with _connect(dst_sandbox, namespace=namespace, token=token) as sandbox:
+ sandbox.files.upload(src_path, dst_path)
+ out.result("Copied", src=src, dst=dst)
+
+
+@sandbox_cli.command(
+ "kill",
+ examples=[
+ "hf sandbox kill ",
+ "hf sandbox kill # kills a whole shared host (all its sandboxes)",
+ "hf sandbox kill --all",
+ ],
+)
+def sandbox_kill(
+ sandbox_id: Annotated[str | None, Argument(help="The sandbox or host id to terminate.")] = None,
+ all_: Annotated[bool, Option("--all", help="Terminate every sandbox and host in the namespace.")] = False,
+ yes: Annotated[bool, Option("-y", "--yes", help="Answer Yes to prompts automatically.")] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Terminate a sandbox, a whole shared host, or everything (--all)."""
+ api = get_hf_api(token=token)
+
+ if all_:
+ jobs = list(api.list_jobs(status=["RUNNING", "SCHEDULING"], labels={SANDBOX_LABEL: "1"}, namespace=namespace))
+ if not jobs:
+ out.text("No running sandboxes.")
+ return
+ out.confirm(f"Terminate {len(jobs)} sandbox job(s) (including shared hosts and all their sandboxes)?", yes=yes)
+ for job in jobs:
+ api.cancel_job(job_id=job.id, namespace=job.owner.name)
+ out.result("Terminated", jobs=len(jobs))
+ return
+
+ if sandbox_id is None:
+ raise CLIError("Provide a sandbox id, a host id, or --all.")
+
+ sid, ns = _split_sandbox_id(sandbox_id, namespace)
+ if SHARED_ID_SEP in sid:
+ # One shared sandbox: remove it from its host (frees a slot; host keeps running).
+ try:
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ sandbox.kill()
+ except SandboxError as e:
+ raise CLIError(str(e)) from e
+ out.result("Sandbox terminated", id=sandbox_id)
+ return
+
+ # A bare id: either a dedicated sandbox job or a shared host job.
+ job = api.inspect_job(job_id=sid, namespace=ns)
+ if (job.labels or {}).get(MODE_LABEL) == MODE_POOL:
+ out.confirm(f"Terminate shared host {sid} and all of its sandboxes?", yes=yes)
+ api.cancel_job(job_id=job.id, namespace=job.owner.name)
+ out.result("Host terminated", id=sid)
+ return
+ try:
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ sandbox.kill()
+ except SandboxError as e:
+ raise CLIError(str(e)) from e
+ out.result("Sandbox terminated", id=sandbox_id)
+
+
+@pool_cli.command(
+ "create",
+ examples=[
+ "hf sandbox pool create",
+ "hf sandbox pool create python:3.12 --flavor cpu-basic",
+ "hf sandbox pool create --per-host 50 --idle-timeout 30m",
+ ],
+)
+def pool_create(
+ image: Annotated[str | None, Argument(help="Docker image for the hosts (needs /bin/sh).")] = None,
+ flavor: FlavorOpt = None,
+ per_host: Annotated[
+ int,
+ Option("--per-host", min=1, help=f"Sandboxes packed per host VM (default {DEFAULT_SANDBOXES_PER_HOST})."),
+ ] = DEFAULT_SANDBOXES_PER_HOST,
+ max_hosts: Annotated[
+ int | None, Option("--max-hosts", min=1, help="Optional cap on the number of host VMs.")
+ ] = None,
+ idle_timeout: Annotated[
+ str | None,
+ Option(help="Shut a host down once it has had no sandboxes for this long (e.g. '10m'). Defaults to 10m."),
+ ] = None,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Warm a pool: boot one host VM now, tagged so it can be found later by its pool id."""
+ start = time.time()
+ image = image or DEFAULT_IMAGE
+ pool = SandboxPool(
+ image=image,
+ flavor=flavor or "cpu-basic",
+ sandboxes_per_host=per_host,
+ max_hosts=max_hosts,
+ idle_timeout=idle_timeout if idle_timeout is not None else DEFAULT_IDLE_TIMEOUT,
+ namespace=namespace,
+ token=token,
+ )
+ pool_id = pool.name
+ host_ids = pool.host_ids
+ out.result(
+ "Pool created",
+ id=pool_id,
+ image=image,
+ flavor=flavor or "cpu-basic",
+ host=host_ids[0],
+ elapsed=f"{time.time() - start:.1f}s",
+ )
+ out.hint(f"Spawn a sandbox with `hf sandbox create --pool {pool_id}`.")
+ out.hint(f"Delete the pool (and its hosts) with `hf sandbox pool delete {pool_id}`.")
+
+
+@pool_cli.command("ls | list", examples=["hf sandbox pool ls"])
+def pool_ls(
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List running sandbox pools (grouped from their host VMs)."""
+ api = get_hf_api(token=token)
+ pools: dict[str, dict[str, Any]] = {}
+ for job in api.list_jobs(status="RUNNING", labels={MODE_LABEL: MODE_POOL}, namespace=namespace):
+ pid = (job.labels or {}).get(POOL_LABEL)
+ if not pid:
+ continue
+ env = job.environment if isinstance(job.environment, dict) else {}
+ info = pools.setdefault(
+ pid,
+ {
+ "id": pid,
+ "image": job.docker_image or job.space_id,
+ "flavor": job.flavor,
+ "per_host": env.get("SBX_CAPACITY", ""),
+ "hosts": 0,
+ },
+ )
+ info["hosts"] += 1
+ rows = list(pools.values())
+ out.table(rows, id_key="id")
+ if not rows:
+ out.hint("Create one with `hf sandbox pool create`.")
+ else:
+ out.hint("Spawn a sandbox with `hf sandbox create --pool `.")
+
+
+@pool_cli.command(
+ "delete | rm",
+ examples=["hf sandbox pool delete "],
+)
+def pool_delete(
+ pool_id: Annotated[str, Argument(help="Pool id to delete.")],
+ yes: Annotated[bool, Option("-y", "--yes", help="Answer Yes to prompts automatically.")] = False,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Terminate every host VM of a pool (and therefore all its sandboxes)."""
+ api = get_hf_api(token=token)
+ hosts = list(
+ api.list_jobs(
+ status=["RUNNING", "SCHEDULING"],
+ labels={MODE_LABEL: MODE_POOL, POOL_LABEL: pool_id},
+ namespace=namespace,
+ )
+ )
+ if not hosts:
+ delete_pool_cache(pool_id)
+ out.text(f"No running hosts for pool '{pool_id}'.")
+ return
+ out.confirm(f"Terminate {len(hosts)} host(s) of pool '{pool_id}' (and all their sandboxes)?", yes=yes)
+ for job in hosts:
+ api.cancel_job(job_id=job.id, namespace=job.owner.name)
+ delete_pool_cache(pool_id)
+ out.result("Pool deleted", id=pool_id, hosts_terminated=len(hosts))
+
+
+def _fmt_cmd(cmd: str | list[str]) -> str:
+ return cmd if isinstance(cmd, str) else " ".join(cmd)
+
+
+def _fmt_status(process: SandboxProcess) -> str:
+ if process.running:
+ return "running"
+ return "exited" if process.exit_code is None else f"exited ({process.exit_code})"
+
+
+@process_cli.command("ls | list", examples=["hf sandbox process ls "])
+def process_ls(
+ sandbox_id: SandboxIdArg,
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List the background processes running in a sandbox (started with `hf sandbox spawn`)."""
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ processes = sandbox.processes()
+ rows = [{"pid": p.pid, "status": _fmt_status(p), "cmd": _fmt_cmd(p.cmd)} for p in processes]
+ out.table(rows, id_key="pid")
+ if not rows:
+ out.hint(f"Start one with `hf sandbox spawn {sandbox_id} -- `.")
+ else:
+ out.hint(f"Stop one with `hf sandbox process kill {sandbox_id} `.")
+
+
+@process_cli.command("kill", examples=["hf sandbox process kill "])
+def process_kill(
+ sandbox_id: SandboxIdArg,
+ pid: Annotated[int, Argument(help="The pid as printed by `hf sandbox process ls`.")],
+ namespace: NamespaceOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Stop a background process running in a sandbox."""
+ with _connect(sandbox_id, namespace=namespace, token=token) as sandbox:
+ process = next((p for p in sandbox.processes() if p.pid == pid), None)
+ if process is None:
+ raise CLIError(f"No process with pid {pid} in sandbox {sandbox_id}.")
+ process.kill()
+ out.result("Process stopped", sandbox=sandbox_id, pid=pid)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/skills.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/skills.py
new file mode 100644
index 0000000000000000000000000000000000000000..39390ddd92597edd7321bd403a5eabc631549820
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/skills.py
@@ -0,0 +1,516 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to manage skills for AI assistants."""
+
+import os
+import shutil
+from pathlib import Path
+from typing import Annotated
+
+from click import Command, Context, Group
+
+from huggingface_hub import constants
+from huggingface_hub.errors import CLIError
+
+from ..utils import disable_progress_bars
+from . import _skills
+from ._cli_utils import TokenOpt, _has_local_formatting_option, get_hf_api, typer_factory
+from ._framework import Argument, Option
+from ._output import out
+from ._skills import DEFAULT_SKILL_ID
+
+
+_SKILL_DESCRIPTION = (
+ "Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing"
+ " models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub."
+ " Use when: handling authentication;"
+ " managing local cache;"
+ " managing Hugging Face Buckets;"
+ " running or scheduling jobs on Hugging Face infrastructure;"
+ " managing Hugging Face repos;"
+ " discussions and pull requests;"
+ " browsing models, datasets and spaces;"
+ " reading, searching, or browsing academic papers;"
+ " managing collections;"
+ " querying datasets;"
+ " configuring spaces;"
+ " setting up webhooks;"
+ " or deploying and managing HF Inference Endpoints."
+ " Make sure to use this skill whenever the user mentions"
+ " 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli',"
+ " or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general."
+ " Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces."
+ " Use even if the user doesn't explicitly ask for a CLI command."
+ " Replaces the deprecated `huggingface-cli`."
+)
+
+_SKILL_YAML_PREFIX = f"""\
+---
+name: hf-cli
+description: "{_SKILL_DESCRIPTION}"
+---
+
+Install: `curl -LsSf https://hf.co/cli/install.sh | bash -s`.
+
+The Hugging Face Hub CLI tool `hf` is available. IMPORTANT: The `hf` command replaces the deprecated `huggingface-cli` command.
+
+Use `hf --help` to view available functions. Note that auth commands are now all under `hf auth` e.g. `hf auth whoami`.
+"""
+
+_SKILL_TIPS = """
+## Mounting repos as local filesystems
+
+To mount Hub repositories or buckets as local filesystems — no download, no copy, no waiting — use `hf-mount`. Files are fetched on demand. GitHub: https://github.com/huggingface/hf-mount
+
+Install: `brew install hf-mount`, or download a binary from https://github.com/huggingface/hf-mount/releases
+
+Some command examples:
+- `hf-mount start repo openai-community/gpt2 /tmp/gpt2` — mount a repo (read-only)
+- `hf-mount start --hf-token $HF_TOKEN bucket myuser/my-bucket /tmp/data` — mount a bucket (read-write)
+- `hf-mount status` / `hf-mount stop /tmp/data` — list or unmount
+
+## Tips
+
+- Use `hf --help` for full options, descriptions, usage, and real-world examples
+- Authenticate with `HF_TOKEN` env var (recommended) or with `--token`
+- Update the CLI with `hf update` (uses the correct command for the detected install method)
+"""
+
+# Flags worth explaining in the common-options glossary. Self-explanatory flags
+# (--namespace, --yes, --private, …) are omitted even if they appear frequently.
+_COMMON_FLAG_ALLOWLIST = {"--token", "--quiet", "--type", "--format", "--revision"}
+# Keep token out of inline command signatures to encourage env based auth.
+_INLINE_FLAG_EXCLUDE = {"--token"}
+
+_COMMON_FLAG_HELP_OVERRIDES: dict[str, str] = {
+ "--format": "Output format: `--format json` (or `--json`) or `--format table` (default).",
+ "--token": "Use a User Access Token. Prefer setting `HF_TOKEN` env var instead of passing `--token`.",
+}
+
+# Global formatting flags injected into the skill markdown for commands that
+# accept them. They aren't real click params on the command (they're consumed
+# globally — see ``_consume_format_flags_for_leaf`` in ``_cli_utils.py``) so we
+# add them synthetically here.
+_GLOBAL_FORMAT_INLINE_FLAGS = ["--format [auto|human|agent|json|quiet]"]
+_GLOBAL_COMMON_FLAGS: dict[str, tuple[str, str]] = {
+ "--format": ("--format", "Output format."),
+ "--quiet": ("-q / --quiet", "Quiet output (one ID per line)."),
+}
+
+skills_cli = typer_factory(help="Manage skills for AI assistants.")
+
+
+def _type_hint(param) -> str:
+ """Value hint for an option: enum choices inline as ``[a|b|c]``, otherwise the TYPE name.
+
+ e.g. `--sort [downloads|likes|trending_score]` instead of `--sort CHOICE`.
+ """
+ choices = getattr(param.type, "choices", None)
+ if choices:
+ return "[" + "|".join(str(c) for c in choices) + "]"
+ return getattr(param.type, "name", "").upper() or "VALUE"
+
+
+def _format_params(cmd: Command) -> str:
+ """Format required params: positional as UPPER_CASE, options as ``--name TYPE``."""
+ parts = []
+ for p in cmd.params:
+ if not p.required or p.human_readable_name == "--help":
+ continue
+ if p.name and p.name.startswith("_"):
+ continue
+ long_name = next((o for o in getattr(p, "opts", []) if o.startswith("--")), None)
+ if long_name is not None:
+ type_name = _type_hint(p)
+ parts.append(f"{long_name} {type_name}")
+ elif p.name:
+ parts.append(p.human_readable_name)
+ return " ".join(parts)
+
+
+def _collect_leaf_commands(group: Group, ctx: Context, path_parts: list[str]) -> list[tuple[list[str], Command]]:
+ """Recursively walk a Click Group, returning (full_path_parts, cmd) for every leaf command."""
+ leaves: list[tuple[list[str], Command]] = []
+ sub_ctx = Context(group, parent=ctx, info_name=path_parts[-1])
+ for name in group.list_commands(sub_ctx):
+ cmd = group.get_command(sub_ctx, name)
+ if cmd is None or cmd.hidden:
+ continue
+ if materialize := getattr(cmd, "materialize", None):
+ cmd = materialize()
+ assert cmd is not None
+ child_path = [*path_parts, name]
+ if isinstance(cmd, Group):
+ leaves.extend(_collect_leaf_commands(cmd, sub_ctx, child_path))
+ else:
+ leaves.append((child_path, cmd))
+ return leaves
+
+
+def _iter_optional_params(cmd: Command):
+ """Yield (param, long_name, short_name) for each optional, non-internal param."""
+ for p in cmd.params:
+ if p.required or p.human_readable_name == "--help":
+ continue
+ if p.name and p.name.startswith("_"):
+ continue
+ long_name = None
+ short_name = None
+ for opt in getattr(p, "opts", []):
+ if opt.startswith("--"):
+ long_name = long_name or opt
+ elif opt.startswith("-"):
+ short_name = opt
+ if long_name:
+ yield p, long_name, short_name
+
+
+def _accepts_global_format_flags(cmd: Command) -> bool:
+ """Return True if the leaf command accepts the global '--format' / '--json' / '-q' flags."""
+ if cmd.context_settings.get("ignore_unknown_options"):
+ return False
+ return not _has_local_formatting_option(cmd)
+
+
+def _get_flag_names(cmd: Command, *, exclude: set[str] | None = None) -> list[str]:
+ """Return long-form flag names (--foo) for optional, non-internal params.
+
+ Boolean flags are bare ('--dry-run'). Value-taking options include a type hint ('--include TEXT', '--max-workers INTEGER').
+ Synthetic global formatting flags are appended for commands that accept them.
+ """
+ flags: list[str] = []
+ for p, long_name, _short in _iter_optional_params(cmd):
+ if exclude and long_name in exclude:
+ continue
+ if getattr(p, "is_flag", False):
+ flags.append(long_name)
+ else:
+ type_name = _type_hint(p)
+ flags.append(f"{long_name} {type_name}")
+ if _accepts_global_format_flags(cmd):
+ flags.extend(flag for flag in _GLOBAL_FORMAT_INLINE_FLAGS if not (exclude and flag.split()[0] in exclude))
+ return flags
+
+
+def _compute_common_flags(
+ leaf_commands: list[tuple[list[str], Command]],
+) -> dict[str, tuple[str, str]]:
+ """Collect display info for flags in the allowlist."""
+ flag_info: dict[str, tuple[str, str]] = {}
+
+ for _path, cmd in leaf_commands:
+ for p, long_name, short_name in _iter_optional_params(cmd):
+ if long_name not in _COMMON_FLAG_ALLOWLIST:
+ continue
+ # Prefer the version with a short form (e.g. "-q / --quiet" over just "--quiet")
+ if long_name not in flag_info or (short_name and " / " not in flag_info[long_name][0]):
+ display = f"{short_name} / {long_name}" if short_name else long_name
+ help_text = (getattr(p, "help", None) or "").split("\n")[0].strip()
+ flag_info[long_name] = (display, help_text)
+
+ # Inject the global formatting flags as common flags whenever any leaf
+ # command accepts them (the vast majority do).
+ if any(_accepts_global_format_flags(cmd) for _path, cmd in leaf_commands):
+ for long_name, entry in _GLOBAL_COMMON_FLAGS.items():
+ flag_info.setdefault(long_name, entry)
+
+ return flag_info
+
+
+def _render_leaf(path_parts: list[str], cmd: Command) -> str:
+ """Render a single leaf command as a markdown list entry."""
+ help_text = (cmd.help or "").split("\n")[0].strip()
+ params = _format_params(cmd)
+ parts = ["hf", *path_parts] + ([params] if params else [])
+ entry = f"- `{' '.join(parts)}` — {help_text}"
+ flags = _get_flag_names(cmd, exclude=_INLINE_FLAG_EXCLUDE)
+ if flags:
+ entry += f" `[{' '.join(flags)}]`"
+ return entry
+
+
+def build_skill_md() -> str:
+ # Lazy import to avoid circular dependency (hf.py imports skills_cli from this module)
+ from huggingface_hub import __version__
+ from huggingface_hub.cli.hf import app
+
+ click_app = app # the app is already a click.Group
+ ctx = Context(click_app, info_name="hf")
+
+ top_level: list[tuple[list[str], Command]] = []
+ groups: list[tuple[str, Group]] = []
+ for name in sorted(click_app.list_commands(ctx)): # type: ignore[attr-defined]
+ cmd = click_app.get_command(ctx, name) # type: ignore[attr-defined]
+ if cmd is None or cmd.hidden:
+ continue
+ if materialize := getattr(cmd, "materialize", None):
+ cmd = materialize()
+ assert cmd is not None
+ if isinstance(cmd, Group):
+ groups.append((name, cmd))
+ else:
+ top_level.append(([name], cmd))
+
+ group_leaves: list[tuple[str, list[tuple[list[str], Command]]]] = []
+ all_leaf_commands: list[tuple[list[str], Command]] = list(top_level)
+ for name, group in groups:
+ leaves = _collect_leaf_commands(group, ctx, [name])
+ group_leaves.append((name, leaves))
+ all_leaf_commands.extend(leaves)
+
+ common_flags = _compute_common_flags(all_leaf_commands)
+
+ # wrap in list to widen list[LiteralString] -> list[str] for `ty`
+ lines: list[str] = list(_SKILL_YAML_PREFIX.splitlines())
+ lines.append("")
+ lines.append(f"Generated with `huggingface_hub v{__version__}`. Run `hf skills add --force` to regenerate.")
+ lines.append("")
+ lines.append("## Commands")
+ lines.append("")
+
+ for path_parts, cmd in top_level:
+ lines.append(_render_leaf(path_parts, cmd))
+
+ groups_dict = dict(groups)
+ for name, leaves in group_leaves:
+ group_cmd = groups_dict[name]
+ help_text = (group_cmd.help or "").split("\n")[0].strip()
+ lines.append("")
+ lines.append(f"### `hf {name}` — {help_text}")
+ lines.append("")
+ for path_parts, cmd in leaves:
+ lines.append(_render_leaf(path_parts, cmd))
+
+ if common_flags:
+ lines.append("")
+ lines.append("## Common options")
+ lines.append("")
+ for long_name, (display, help_text) in sorted(common_flags.items()):
+ help_text = _COMMON_FLAG_HELP_OVERRIDES.get(long_name, help_text)
+ if help_text:
+ lines.append(f"- `{display}` — {help_text}")
+ else:
+ lines.append(f"- `{display}`")
+
+ lines.extend(_SKILL_TIPS.splitlines())
+
+ return "\n".join(lines)
+
+
+def _remove_existing(path: Path, force: bool) -> None:
+ """Remove existing file/directory/symlink if force is True, otherwise raise an error."""
+ if not (path.exists() or path.is_symlink()):
+ return
+ if not force:
+ raise CLIError(f"Skill already exists at {path}.\nRe-run with --force to overwrite.")
+ if path.is_dir() and not path.is_symlink():
+ shutil.rmtree(path)
+ else:
+ path.unlink()
+
+
+def _install_to(skills_dir: Path, skill_name: str, force: bool) -> Path:
+ """Install a marketplace skill into a skills directory. Returns the installed path."""
+ try:
+ if skill_name.strip() == DEFAULT_SKILL_ID:
+ return _skills.install_generated_skill(build_skill_md(), skills_dir, force=force)
+ return _skills.add_skill(skill_name, skills_dir, force=force)
+ except FileExistsError as exc:
+ raise CLIError(f"{exc}\nRe-run with --force to overwrite.") from exc
+
+
+def _create_symlink(agent_skills_dir: Path, skill_name: str, central_skill_path: Path, force: bool) -> Path:
+ """Create a relative symlink from agent directory to the central skill location."""
+ agent_skills_dir = agent_skills_dir.expanduser().resolve()
+ agent_skills_dir.mkdir(parents=True, exist_ok=True)
+ link_path = agent_skills_dir / skill_name
+
+ _remove_existing(link_path, force)
+ try:
+ link_path.symlink_to(os.path.relpath(central_skill_path, agent_skills_dir))
+ except OSError:
+ # Windows needs Developer Mode or admin rights for symlinks.
+ # Fall back to a copy: `hf skills update` walks both roots, so the copy stays in sync.
+ shutil.copytree(central_skill_path, link_path)
+
+ return link_path
+
+
+def _resolve_update_roots(*, global_: bool, dest: Path | None) -> list[Path]:
+ if dest is not None:
+ if global_:
+ raise CLIError("--dest cannot be combined with --global.")
+ return [dest.expanduser().resolve()]
+
+ if global_:
+ roots = [constants.AGENTS_SKILLS_GLOBAL_PATH, constants.CLAUDE_SKILLS_GLOBAL_PATH]
+ else:
+ roots = [constants.AGENTS_SKILLS_LOCAL_PATH, constants.CLAUDE_SKILLS_LOCAL_PATH]
+ return [root.expanduser().resolve() for root in roots]
+
+
+@skills_cli.command("preview")
+def skills_preview() -> None:
+ """Print the generated `hf-cli` SKILL.md to stdout."""
+ print(build_skill_md())
+
+
+@skills_cli.command(
+ "list | ls",
+ examples=[
+ "hf skills list",
+ "hf skills list --format json",
+ ],
+)
+def skills_list(
+ token: TokenOpt = None,
+) -> None:
+ """List available skills from the Hugging Face marketplace."""
+ install_locations: list[tuple[str, Path]] = [
+ ("project", constants.AGENTS_SKILLS_LOCAL_PATH),
+ ("project (claude)", constants.CLAUDE_SKILLS_LOCAL_PATH),
+ ("global", constants.AGENTS_SKILLS_GLOBAL_PATH),
+ ("global (claude)", constants.CLAUDE_SKILLS_GLOBAL_PATH),
+ ]
+ installed: dict[str, set[str]] = {}
+ for label, root in install_locations:
+ for skill_dir in _skills._iter_unique_skill_dirs([root]):
+ installed.setdefault(skill_dir.name.lower(), set()).add(label)
+
+ api = get_hf_api(token=token)
+ with disable_progress_bars():
+ skills = _skills._load_marketplace_skills(api)
+ results = [
+ {
+ "name": skill.name,
+ "description": skill.description or "",
+ **{
+ label: "yes" if label in installed.get(skill.name.lower(), set()) else ""
+ for label, _ in install_locations
+ },
+ }
+ for skill in skills
+ ]
+ out.table(
+ results,
+ id_key="name",
+ alignments={"project": "right", "global": "right", "project (claude)": "right", "global (claude)": "right"},
+ )
+
+
+@skills_cli.command(
+ "add",
+ examples=[
+ "hf skills add",
+ "hf skills add huggingface-gradio --dest=~/my-skills",
+ "hf skills add --global",
+ "hf skills add huggingface-gradio --global",
+ ],
+)
+def skills_add(
+ name: Annotated[
+ str,
+ Argument(help="Marketplace skill name.", show_default=False),
+ ] = DEFAULT_SKILL_ID,
+ global_: Annotated[
+ bool,
+ Option(
+ "--global",
+ "-g",
+ help="Install globally (user-level) instead of in the current project directory.",
+ ),
+ ] = False,
+ dest: Annotated[
+ Path | None,
+ Option(
+ help="Install into a custom destination (path to skills directory).",
+ ),
+ ] = None,
+ force: Annotated[
+ bool,
+ Option(
+ "--force",
+ help="Overwrite existing skills in the destination.",
+ ),
+ ] = False,
+) -> None:
+ """Install a Hugging Face skill for an AI assistant.
+
+ The default `hf-cli` skill is generated locally from the installed CLI version;
+ other skills are downloaded from the Hugging Face marketplace.
+ Default location is in the current directory (.agents/skills) or user-level (~/.agents/skills).
+ The skill is also symlinked into Claude Code's skills directory (`.claude/skills` or `~/.claude/skills`,
+ honoring `CLAUDE_CONFIG_DIR` when set), unless `--dest` is used.
+ """
+ if dest is not None:
+ if global_:
+ raise CLIError("--dest cannot be combined with --global.")
+ skill_dest = _install_to(dest, name, force)
+ print(f"Installed '{name}' to {skill_dest}")
+ return
+
+ # Install to central location, then expose it to Claude Code which only reads `.claude/skills`
+ central_path = constants.AGENTS_SKILLS_GLOBAL_PATH if global_ else constants.AGENTS_SKILLS_LOCAL_PATH
+ central_skill_path = _install_to(central_path, name, force)
+ print(f"Installed '{name}' to central location: {central_skill_path}")
+
+ claude_path = constants.CLAUDE_SKILLS_GLOBAL_PATH if global_ else constants.CLAUDE_SKILLS_LOCAL_PATH
+ link_path = _create_symlink(claude_path, name, central_skill_path, force)
+ print(f"Created symlink: {link_path}" if link_path.is_symlink() else f"Copied '{name}' to {link_path}")
+ if not global_:
+ out.hint(f"Run `hf skills add {name} --global` to install the skill globally instead.")
+
+
+@skills_cli.command(
+ "update",
+ examples=[
+ "hf skills update",
+ "hf skills update hf-cli",
+ "hf skills update huggingface-gradio --dest=~/my-skills",
+ "hf skills update -g",
+ ],
+)
+def skills_update(
+ name: Annotated[
+ str | None,
+ Argument(help="Optional installed skill name to update.", show_default=False),
+ ] = None,
+ global_: Annotated[
+ bool,
+ Option(
+ "--global",
+ "-g",
+ help="Use global skills directories instead of the current project.",
+ ),
+ ] = False,
+ dest: Annotated[
+ Path | None,
+ Option(
+ help="Update skills in a custom skills directory.",
+ ),
+ ] = None,
+) -> None:
+ """Update installed Hugging Face marketplace skills."""
+ roots = _resolve_update_roots(global_=global_, dest=dest)
+
+ results = _skills.update_skills(roots, selector=name, hf_cli_content=build_skill_md())
+ if not results:
+ print("No installed skills found.")
+ for result in results:
+ detail = f" ({result.detail})" if result.detail else ""
+ print(f"{result.name}: {result.status}{detail}")
+
+ if not global_ and dest is None:
+ command = f"hf skills update {name} --global" if name else "hf skills update --global"
+ out.hint(f"Run `{command}` to update globally installed skills instead.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/spaces.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/spaces.py
new file mode 100644
index 0000000000000000000000000000000000000000..772053a98c31888f81a5359085dcaf278a9bb573
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/spaces.py
@@ -0,0 +1,1121 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to interact with spaces on the Hugging Face Hub."""
+
+import enum
+import functools
+import itertools
+import os
+import shlex
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+from collections import deque
+from pathlib import Path
+from typing import TYPE_CHECKING, Annotated, Literal, get_args
+
+import click
+from packaging import version
+from typing_extensions import assert_never
+
+from huggingface_hub._hot_reload.client import multi_replica_reload_events
+from huggingface_hub._hot_reload.types import ApiGetReloadEventSourceData, ReloadRegion
+from huggingface_hub._space_api import SpaceHardware, SpaceStage
+from huggingface_hub.cli._cli_utils import SoftChoice
+from huggingface_hub.errors import CLIError, RemoteEntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.file_download import hf_hub_download
+from huggingface_hub.hf_api import ExpandSpaceProperty_T, HfApi, SpaceSort_T
+from huggingface_hub.repocard import SpaceCard
+from huggingface_hub.utils import disable_progress_bars
+from huggingface_hub.utils._parsing import parse_duration
+
+from ._cli_utils import (
+ REPO_LIST_DEFAULT_LIMIT,
+ AuthorOpt,
+ EnvFileOpt,
+ EnvOpt,
+ FilterOpt,
+ LimitOpt,
+ RevisionOpt,
+ SearchOpt,
+ SecretsFileOpt,
+ SecretsOpt,
+ SshDryRunOpt,
+ SshIdentityFileOpt,
+ TokenOpt,
+ VolumesOpt,
+ exec_ssh,
+ get_hf_api,
+ make_expand_properties_parser,
+ parse_env_map,
+ parse_volumes,
+ typer_factory,
+)
+from ._file_listing import list_repo_files_cmd
+from ._framework import Argument, Option
+from ._output import _dataclass_to_dict, out
+
+
+HOT_RELOADING_MIN_GRADIO = "6.1.0"
+
+
+_EXPAND_PROPERTIES = sorted(get_args(ExpandSpaceProperty_T))
+_SORT_OPTIONS = get_args(SpaceSort_T)
+SpaceSortEnum = enum.Enum("SpaceSortEnum", {s: s for s in _SORT_OPTIONS}, type=str) # type: ignore[misc]
+
+
+ExpandOpt = Annotated[
+ str | None,
+ Option(
+ help=f"Comma-separated properties to return. When used, only the listed properties (and id) are returned. Example: '--expand=likes,tags'. Valid: {', '.join(_EXPAND_PROPERTIES)}.",
+ callback=make_expand_properties_parser(_EXPAND_PROPERTIES),
+ ),
+]
+
+spaces_cli = typer_factory(help="Interact with spaces on the Hub.")
+volumes_cli = typer_factory(help="Manage volumes for a Space on the Hub.")
+secrets_cli = typer_factory(help="Manage secrets for a Space on the Hub.")
+variables_cli = typer_factory(help="Manage environment variables for a Space on the Hub.")
+zero_gpu_cli = typer_factory(help="Manage your ZeroGPU usage on the Hub.")
+spaces_cli.add_group(volumes_cli, name="volumes")
+spaces_cli.add_group(secrets_cli, name="secrets")
+spaces_cli.add_group(variables_cli, name="variables")
+spaces_cli.add_group(zero_gpu_cli, name="zero-gpu")
+
+
+@spaces_cli.command(
+ "list | ls",
+ examples=[
+ "hf spaces ls --limit 10",
+ 'hf spaces ls --search "chatbot" --author huggingface',
+ "hf spaces ls victor/deepsite",
+ "hf spaces ls victor/deepsite -R",
+ "hf spaces ls victor/deepsite --tree -h",
+ ],
+)
+def spaces_ls(
+ repo_id: Annotated[
+ str | None,
+ Argument(help="Space ID (e.g. `username/repo-name`) to list files from. If omitted, lists spaces."),
+ ] = None,
+ search: SearchOpt = None,
+ author: AuthorOpt = None,
+ filter: FilterOpt = None,
+ sort: Annotated[
+ SpaceSortEnum | None,
+ Option(help="Sort results."),
+ ] = None,
+ limit: LimitOpt = REPO_LIST_DEFAULT_LIMIT,
+ expand: ExpandOpt = None,
+ human_readable: Annotated[
+ bool,
+ Option("--human-readable", "-h", help="Show sizes in human readable format (only for listing files)."),
+ ] = False,
+ as_tree: Annotated[
+ bool,
+ Option("--tree", help="List files in tree format (only for listing files)."),
+ ] = False,
+ recursive: Annotated[
+ bool,
+ Option("--recursive", "-R", help="List files recursively (only for listing files)."),
+ ] = False,
+ revision: RevisionOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """List spaces on the Hub, or files in a space repo.
+
+ When called with no argument, lists spaces on the Hub.
+ When called with a space ID, lists files in that space repo.
+ """
+ if repo_id is not None:
+ if search is not None:
+ raise click.BadParameter("Cannot use --search when listing files.")
+ if author is not None:
+ raise click.BadParameter("Cannot use --author when listing files.")
+ if filter is not None:
+ raise click.BadParameter("Cannot use --filter when listing files.")
+ if sort is not None:
+ raise click.BadParameter("Cannot use --sort when listing files.")
+ if limit != REPO_LIST_DEFAULT_LIMIT:
+ raise click.BadParameter("Cannot use --limit when listing files.")
+ if expand is not None:
+ raise click.BadParameter("Cannot use --expand when listing files.")
+ return list_repo_files_cmd(
+ repo_id=repo_id,
+ repo_type="space",
+ human_readable=human_readable,
+ as_tree=as_tree,
+ recursive=recursive,
+ revision=revision,
+ token=token,
+ )
+
+ if as_tree:
+ raise click.BadParameter("Cannot use --tree when listing spaces.")
+ if recursive:
+ raise click.BadParameter("Cannot use --recursive when listing spaces.")
+ if human_readable:
+ raise click.BadParameter("Cannot use --human-readable when listing spaces.")
+ if revision is not None:
+ raise click.BadParameter("Cannot use --revision when listing spaces.")
+ api = get_hf_api(token=token)
+ sort_key = sort.value if sort else None
+ results = [
+ _dataclass_to_dict(space_info)
+ for space_info in api.list_spaces(
+ filter=filter,
+ author=author,
+ search=search,
+ sort=sort_key,
+ limit=limit,
+ expand=expand, # type: ignore[arg-type]
+ )
+ ]
+ out.table(results)
+
+
+@spaces_cli.command(
+ "info",
+ examples=[
+ "hf spaces info enzostvs/deepsite",
+ "hf spaces info gradio/theme_builder --expand sdk,runtime,likes",
+ ],
+)
+def spaces_info(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ revision: RevisionOpt = None,
+ expand: ExpandOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Get info about a space on the Hub."""
+ api = get_hf_api(token=token)
+ try:
+ info = api.space_info(repo_id=space_id, revision=revision, expand=expand) # type: ignore[arg-type]
+ except RepositoryNotFoundError as e:
+ raise CLIError(f"Space '{space_id}' not found.") from e
+ except RevisionNotFoundError as e:
+ raise CLIError(f"Revision '{revision}' not found on '{space_id}'.") from e
+ out.dict(info)
+
+
+@spaces_cli.command(
+ "card",
+ examples=[
+ "hf spaces card mteb/leaderboard",
+ "hf spaces card mteb/leaderboard --metadata",
+ "hf spaces card mteb/leaderboard --metadata --format json",
+ "hf spaces card mteb/leaderboard --text",
+ ],
+)
+def spaces_card(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ metadata: Annotated[bool, Option("--metadata", help="Output only the metadata from the card.")] = False,
+ text: Annotated[bool, Option("--text", help="Output only the text body (no metadata).")] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Get the Space card (README) for a Space on the Hub."""
+ if metadata and text:
+ raise CLIError("--metadata and --text are mutually exclusive.")
+ card = SpaceCard.load(space_id, token=token)
+ if metadata:
+ out.dict(card.data.to_dict())
+ elif text:
+ out.text(card.text)
+ else:
+ out.text(card.content)
+ out.hint(f"Use `hf spaces card {space_id} --metadata` to extract only the card metadata.")
+
+
+@spaces_cli.command(
+ "templates",
+ examples=["hf spaces templates"],
+)
+def spaces_templates(
+ token: TokenOpt = None,
+) -> None:
+ """List the available Space templates.
+
+ The `repo_id` (or `name`) of a template can be passed to `hf repos create --template ...` to
+ create a new Space from that template.
+ """
+ api = get_hf_api(token=token)
+ templates = [_dataclass_to_dict(template) for template in api.list_space_templates()]
+ out.table(templates, id_key="name")
+ out.hint(
+ "Create a Space from a template with `hf repos create --type space --space-sdk --template `."
+ )
+
+
+@spaces_cli.command(
+ "search",
+ examples=[
+ 'hf spaces search "generate image"',
+ 'hf spaces search "identify objects in pictures" --sdk gradio --limit 5',
+ 'hf spaces search "remove background from photo" --description --json',
+ ],
+)
+def spaces_search(
+ query: Annotated[str, Argument(help="Search query.")],
+ filter: FilterOpt = None,
+ sdk: Annotated[list[str] | None, Option(help="Filter by SDK (e.g. gradio, docker, static).")] = None,
+ include_non_running: Annotated[bool, Option(help="Include non-running spaces in results.")] = False,
+ description: Annotated[bool, Option(help="Show AI-generated descriptions.")] = False,
+ limit: LimitOpt = 10,
+ token: TokenOpt = None,
+) -> None:
+ """Search spaces on the Hub using semantic search."""
+ api = get_hf_api(token=token)
+ results = api.search_spaces(
+ query=query,
+ filter=filter,
+ sdk=sdk,
+ include_non_running=include_non_running,
+ token=token,
+ )
+ items = []
+ for r in itertools.islice(results, limit):
+ item: dict = {
+ "id": r.id,
+ "title": r.title,
+ "sdk": r.sdk,
+ "likes": r.likes,
+ "stage": r.runtime.stage if r.runtime else None,
+ "category": r.ai_category,
+ "score": round(r.semantic_relevancy_score, 2) if r.semantic_relevancy_score is not None else None,
+ }
+ if description:
+ item["description"] = r.ai_short_description
+ items.append(item)
+ out.table(items)
+ if not description:
+ out.hint("Use --description to show AI-generated descriptions.")
+
+
+@spaces_cli.command(
+ "wait",
+ examples=[
+ "hf spaces wait username/my-space",
+ "hf spaces wait username/my-space --timeout 5m",
+ ],
+)
+def spaces_wait(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ timeout: Annotated[
+ str | None,
+ Option(
+ help="Max time to wait: int with s (seconds, default), m (minutes), h (hours) or d (days).",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Wait for a Space to finish building/starting.
+
+ Blocks until the Space leaves an intermediate stage (BUILDING, APP_STARTING, etc.)
+ and reaches a settled stage. Exits with code 0 if the Space is RUNNING,
+ or a non-zero exit code otherwise (e.g. BUILD_ERROR, RUNTIME_ERROR).
+ """
+ timeout_secs = parse_duration(timeout) if timeout is not None else None
+ api = get_hf_api(token=token)
+ status = out.status("Waiting for Space to be ready...")
+ try:
+ runtime = api.wait_for_space(space_id, timeout=timeout_secs)
+ except TimeoutError:
+ status.done("Timed out.")
+ raise CLIError(f"Timed out after {timeout} waiting for Space '{space_id}' to be ready.") from None
+ status.done(f"Space reached stage '{runtime.stage}'.")
+ if runtime.stage != SpaceStage.RUNNING:
+ raise CLIError(f"Space '{space_id}' is not running (stage='{runtime.stage}').")
+ out.result("Space ready", space_id=space_id, stage=str(runtime.stage))
+ out.hint(f"Use `hf spaces logs {space_id}` to view run logs.")
+
+
+@spaces_cli.command(
+ "dev-mode",
+ examples=[
+ "hf spaces dev-mode my-user-name/deepsite",
+ ],
+)
+def dev_mode(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ stop: Annotated[bool, Option(help="Stop dev mode.")] = False,
+ token: TokenOpt = None,
+):
+ """
+ Enable or disable dev mode on a Space.
+
+ Spaces Dev Mode eases the debugging of your application and makes iterating on Spaces faster by allowing you to
+ restart your application without stopping the Space container itself. Enabling dev mode requires a PRO or Team &
+ Enterprise plan. Disabling it (`--stop`) is always allowed.
+
+ See docs: https://huggingface.co/docs/hub/spaces-dev-mode
+ """
+ api = get_hf_api(token=token)
+ if stop:
+ api.disable_space_dev_mode(space_id)
+ print(f"Dev mode disabled for '{space_id}'")
+ return
+ api.enable_space_dev_mode(space_id)
+ runtime = api.wait_for_space(space_id)
+ if runtime.stage != SpaceStage.RUNNING:
+ out.warning(f"Dev mode is not ready (stage='{runtime.stage}')")
+ return
+ info = api.space_info(space_id)
+ folder = getattr(info.card_data, "dev-mode-folder", "" if info.sdk == "docker" else "/home/user/app")
+ folder_query_param = f"folder={folder}" if folder else ""
+ print("Connect to dev environment:")
+ print("")
+ print("Web:")
+ vscode_web_url = f"https://huggingface.co/spaces/{info.id}/dev-mode/vscode-web"
+ if folder_query_param:
+ vscode_web_url += f"?{folder_query_param}"
+ ssh_host = f"{info.subdomain}@ssh.hf.space"
+ print(f" * VSCode: {vscode_web_url}")
+ print("")
+ print("Local:")
+ print("1. Add your SSH key to https://huggingface.co/settings/keys")
+ print(f"2. SSH with `hf spaces ssh {space_id}` (or `ssh -i {ssh_host}`)")
+ print(" Or open")
+ print(f" * VSCode: vscode://vscode-remote/ssh-remote+{ssh_host}{folder}")
+ print(f" * Cursor: cursor://vscode-remote/ssh-remote+{ssh_host}{folder}")
+ print("")
+ print("PS: Dev mode stops after 48h of inactivity, don't forget to save your changes regularly.")
+
+
+@spaces_cli.command(
+ "ssh",
+ examples=[
+ "hf spaces ssh username/my-space",
+ "hf spaces ssh username/my-space --dry-run",
+ "hf spaces ssh username/my-space -i ~/.ssh/id_ed25519",
+ "hf spaces ssh username/my-space --auto",
+ ],
+)
+def spaces_ssh(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ identity_file: SshIdentityFileOpt = None,
+ dry_run: SshDryRunOpt = False,
+ auto: Annotated[
+ bool,
+ Option("--auto", help="Enable Dev Mode without prompting if not already enabled."),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """SSH into a Space's Dev Mode container.
+
+ Requires Dev Mode to be running on the Space and your SSH public key to be registered at https://huggingface.co/settings/keys.
+
+ See: https://huggingface.co/docs/hub/spaces-dev-mode
+ """
+ api = get_hf_api(token=token)
+ info = api.space_info(space_id)
+ if info.runtime is None or not info.runtime.dev_mode:
+ out.confirm(
+ f"Dev Mode is disabled on '{space_id}'. Enable it now?", yes=auto, default=True, confirm_param="--auto"
+ )
+ api.enable_space_dev_mode(space_id)
+ runtime = api.wait_for_space(space_id)
+ if runtime.stage != SpaceStage.RUNNING:
+ raise CLIError(f"Space '{space_id}' is not running (stage='{runtime.stage}').")
+ info = api.space_info(space_id)
+ exec_ssh(f"{info.subdomain}@ssh.hf.space", identity_file=identity_file, dry_run=dry_run)
+
+
+@spaces_cli.command(
+ "pause",
+ examples=[
+ "hf spaces pause username/my-space",
+ ],
+)
+def spaces_pause(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ token: TokenOpt = None,
+) -> None:
+ """Pause a Space."""
+ api = get_hf_api(token=token)
+ runtime = api.pause_space(space_id)
+ out.result("Space paused", space_id=space_id, stage=runtime.stage)
+ out.hint(f"Use `hf spaces restart {space_id}` to restart it.")
+ out.hint(
+ f"Mount a Volume or bucket to persist data across restarts: `hf spaces volumes set {space_id} -v hf://...`"
+ )
+
+
+@spaces_cli.command(
+ "restart",
+ examples=[
+ "hf spaces restart username/my-space",
+ "hf spaces restart username/my-space --factory-reboot",
+ ],
+)
+def spaces_restart(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ factory_reboot: Annotated[
+ bool,
+ Option(
+ "--factory-reboot",
+ help="Rebuild the Space from scratch without using the build cache.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Restart a Space."""
+ api = get_hf_api(token=token)
+ runtime = api.restart_space(space_id, factory_reboot=factory_reboot)
+ out.result(
+ "Space restart triggered",
+ space_id=space_id,
+ stage=runtime.stage,
+ factory_reboot=factory_reboot,
+ )
+ out.hint(f"Use `hf spaces wait {space_id}` to wait until the Space is ready.")
+ out.hint(
+ f"Mount a Volume or bucket to persist data across restarts: `hf spaces volumes set {space_id} -v hf://...`"
+ )
+
+
+@spaces_cli.command(
+ "hardware",
+ examples=[
+ "hf spaces hardware",
+ ],
+)
+def spaces_hardware(token: TokenOpt = None) -> None:
+ """List available hardware options for Spaces."""
+ api = get_hf_api(token=token)
+ hardware_list = api.list_spaces_hardware()
+ items = []
+ for hw in hardware_list:
+ accelerator = (
+ f"{hw.accelerator.quantity}x {hw.accelerator.model} ({hw.accelerator.vram})" if hw.accelerator else None
+ )
+ cost_min = f"${hw.unit_cost_usd:.4f}" if hw.unit_cost_usd else "free"
+ cost_hour = f"${hw.unit_cost_usd * 60:.2f}" if hw.unit_cost_usd else "free"
+ items.append(
+ {
+ "name": hw.name,
+ "pretty name": hw.pretty_name,
+ "cpu": hw.cpu,
+ "ram": hw.ram,
+ "accelerator": accelerator,
+ "cost/min": cost_min,
+ "cost/hour": cost_hour,
+ }
+ )
+ out.table(items)
+ out.hint("Use `hf spaces settings --hardware ` to request hardware for a Space.")
+
+
+@zero_gpu_cli.command(
+ "quota",
+ examples=[
+ "hf spaces zero-gpu quota",
+ "hf spaces zero-gpu quota --format json",
+ ],
+)
+def spaces_zero_gpu_quota(token: TokenOpt = None) -> None:
+ """Show your ZeroGPU quota (remaining GPU time and reset date).
+
+ Values are in GPU-seconds. With `--quiet`, only the remaining GPU-seconds are printed.
+ """
+ api = get_hf_api(token=token)
+ quota = api.get_zero_gpu_quota()
+ out.result(
+ "ZeroGPU quota (in GPU-seconds)",
+ remaining=quota.remaining,
+ base=quota.base,
+ resets_at=quota.resets_at.isoformat() if quota.resets_at else None,
+ overquota_used=quota.overquota_used,
+ )
+ if quota.remaining < 0.1 * quota.base:
+ status = "exhausted" if quota.remaining <= 0 else "running low"
+ out.hint(
+ f"Your ZeroGPU quota is {status}. PRO, Team and Enterprise users can purchase credits to keep using"
+ " ZeroGPU beyond the daily quota: https://huggingface.co/settings/billing?add-credits=true"
+ )
+
+
+@spaces_cli.command(
+ "settings",
+ examples=[
+ "hf spaces settings username/my-space --sleep-time 300",
+ "hf spaces settings username/my-space --hardware t4-medium",
+ ],
+)
+def spaces_settings(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ sleep_time: Annotated[
+ int | None,
+ Option(
+ "--sleep-time",
+ help="Idle time in seconds after which the Space goes to sleep. Use -1 to never sleep. Only available on upgraded hardware.",
+ ),
+ ] = None,
+ hardware: Annotated[
+ str | None,
+ Option(
+ "--hardware",
+ help="Space hardware flavor (e.g. 'cpu-basic', 't4-medium', 'l4x4'). Run 'hf spaces hardware' to list available options.",
+ click_type=SoftChoice(SpaceHardware),
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update the settings of a Space."""
+ api = get_hf_api(token=token)
+ if hardware is not None:
+ runtime = api.request_space_hardware(space_id, hardware=hardware, sleep_time=sleep_time) # type: ignore[arg-type]
+ elif sleep_time is not None:
+ runtime = api.set_space_sleep_time(space_id, sleep_time=sleep_time)
+ else:
+ raise CLIError("Specify at least one setting to update.")
+ out.result(
+ "Space settings updated",
+ space_id=space_id,
+ hardware=runtime.requested_hardware,
+ sleep_time=runtime.sleep_time,
+ )
+ out.hint(f"Use `hf spaces info {space_id}` to verify the runtime configuration.")
+
+
+@spaces_cli.command(
+ "logs",
+ examples=[
+ "hf spaces logs username/my-space",
+ "hf spaces logs username/my-space --build",
+ "hf spaces logs -f username/my-space",
+ "hf spaces logs -n 50 username/my-space",
+ ],
+)
+def spaces_logs(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ build: Annotated[
+ bool,
+ Option(
+ "--build",
+ help="Fetch the container build logs instead of the run logs. Useful when a Space is stuck in BUILD_ERROR.",
+ ),
+ ] = False,
+ follow: Annotated[
+ bool,
+ Option(
+ "-f",
+ "--follow",
+ help="Follow log output (stream until the server closes the stream). Without this flag, only currently available logs are printed.",
+ ),
+ ] = False,
+ tail: Annotated[
+ int | None,
+ Option(
+ "-n",
+ "--tail",
+ help="Number of lines to show from the end of the logs.",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Fetch the run or build logs of a Space.
+
+ By default, prints currently available run logs and exits (non-blocking, like
+ `docker logs`). Use --follow/-f to stream until the server closes the stream.
+ Use --build to see the container build logs instead (useful when a Space is
+ stuck in BUILD_ERROR).
+ """
+ if follow and tail is not None:
+ raise CLIError(
+ "Cannot use --follow and --tail together. Use --follow to stream logs or --tail to show recent logs."
+ )
+
+ api = get_hf_api(token=token)
+ logs = api.fetch_space_logs(space_id, build=build, follow=follow)
+ if tail is not None:
+ logs = deque(logs, maxlen=tail)
+ found_logs = False
+ for line in logs:
+ clean_line = line.strip()
+ out.text(clean_line)
+ if clean_line:
+ found_logs = True
+ if not found_logs and not build:
+ out.hint(f"No run logs found for space {space_id}. Try passing --build to fetch build logs instead.")
+
+
+@spaces_cli.command(
+ "hot-reload",
+ examples=[
+ "hf spaces hot-reload username/repo-name app.py # Open an interactive editor to the remote app.py file",
+ "hf spaces hot-reload username/repo-name -f app.py # Take local version from ./app.py and patch app.py remotely",
+ "hf spaces hot-reload username/repo-name app.py -f src/app.py # Take local version from ./src/app.py",
+ ],
+)
+def spaces_hot_reload(
+ space_id: Annotated[
+ str,
+ Argument(
+ help="The space ID (e.g. `username/repo-name`).",
+ ),
+ ],
+ filename: Annotated[
+ str | None,
+ Argument(
+ help="Path to the Python file in the Space repository. Can be omitted when --local-file is specified and path in repository matches."
+ ),
+ ] = None,
+ local_file: Annotated[
+ Path | None,
+ Option(
+ "--local-file",
+ "-f",
+ help="Path of local file. Interactive editor mode if not specified",
+ ),
+ ] = None,
+ skip_checks: Annotated[bool, Option(help="Skip hot-reload compatibility checks.")] = False,
+ skip_summary: Annotated[bool, Option(help="Skip summary display after hot-reload is triggered")] = False,
+ token: TokenOpt = None,
+) -> None:
+ """
+ Hot-reload any Python file of a Space without a full rebuild + restart.
+
+ ⚠ This feature is experimental ⚠
+
+ Only works with Gradio SDK (6.1+)
+ Opens an interactive editor unless --local-file/-f is specified.
+
+ This command patches the live Python process using https://github.com/breuleux/jurigged
+ (AST-based diffing, in-place function updates, etc.), integrated with Gradio's native hot-reload support
+ (meaning that Gradio demo object changes are reflected in the UI)
+
+ The command creates a remote commit.
+ If you are working from a local clone, run `git pull --autostash` afterwards
+ to bring the commit back and keep your local git state in sync.
+ """
+
+ click.secho("This feature is experimental and subject to change", fg="bright_black")
+
+ api = get_hf_api(token=token)
+
+ if not skip_checks:
+ space_info = api.space_info(space_id)
+ if space_info.sdk != "gradio":
+ raise CLIError(f"Hot-reloading is only available on Gradio SDK. Found {space_info.sdk} SDK")
+ if (card_data := space_info.card_data) is None:
+ raise CLIError(f"Unable to read cardData for Space {space_id}")
+ if (sdk_version := card_data.sdk_version) is None:
+ raise CLIError(f"Unable to read sdk_version from {space_id} cardData")
+ if version.parse(sdk_version) < version.Version(HOT_RELOADING_MIN_GRADIO):
+ raise CLIError(f"Hot-reloading requires Gradio >= {HOT_RELOADING_MIN_GRADIO} (found {sdk_version})")
+ if (current_sha := space_info.sha) is None:
+ raise CLIError(f"Unexpected `None` running SHA for Space {space_id}")
+ else:
+ current_sha = None
+
+ if local_file:
+ local_path = str(local_file)
+ filename = local_file.as_posix() if filename is None else filename
+ elif filename:
+ if not skip_checks:
+ try:
+ api.auth_check(
+ repo_type="space",
+ repo_id=space_id,
+ write=True,
+ )
+ except RepositoryNotFoundError as e:
+ raise CLIError(
+ f"Write access check to {space_id} repository failed. Make sure that you are authenticated"
+ ) from e
+ temp_dir = tempfile.TemporaryDirectory()
+ local_path = os.path.join(temp_dir.name, filename)
+ with disable_progress_bars():
+ try:
+ hf_hub_download(repo_type="space", repo_id=space_id, filename=filename, local_dir=temp_dir.name)
+ except RemoteEntryNotFoundError:
+ click.secho(f"{filename} not found in remote repository. Assuming new file", fg="bright_black")
+
+ editor_res = _editor_open(local_path)
+ if editor_res == "no-tty":
+ persistent_temp_dir = tempfile.mkdtemp()
+ shutil.copytree(temp_dir.name, persistent_temp_dir, dirs_exist_ok=True)
+ local_path = os.path.join(persistent_temp_dir, filename)
+ click.secho("No TTY detected. Non-interactive fallback:")
+ click.secho(f"- Edit {local_path}")
+ click.secho(f"- Run `hf spaces hot-reload {space_id} {filename} -f {local_path}`")
+ return
+ if editor_res == "no-editor":
+ raise CLIError("No editor found in local environment. Use -f flag to hot-reload from local path")
+ if editor_res != 0:
+ raise CLIError(f"Editor returned a non-zero exit code while attempting to edit {local_path}")
+ else:
+ raise CLIError("Either filename or --local-file/-f must be specified")
+
+ commit_info = api.upload_file(
+ repo_type="space",
+ repo_id=space_id,
+ path_or_fileobj=local_path,
+ path_in_repo=filename,
+ parent_commit=current_sha,
+ _hot_reload=True,
+ )
+
+ if local_file is not None and local_file.resolve().is_relative_to(Path.cwd()):
+ click.secho(f"Created commit {commit_info.oid} in remote Space repository.")
+ click.secho("Consider running `git pull --autostash` to stay synced if you are working from a local clone.")
+
+ if not skip_summary:
+ click.secho("Hot-reload summary:")
+ _spaces_hot_reload_summary(
+ api=api,
+ space_id=space_id,
+ current_sha=current_sha,
+ commit_sha=commit_info.oid,
+ local_path=local_path if local_file else filename,
+ filename=filename,
+ token=token,
+ )
+
+
+def _spaces_hot_reload_summary(
+ api: HfApi,
+ space_id: str,
+ current_sha: str | None,
+ commit_sha: str,
+ filename: str,
+ local_path: str,
+ token: str | None,
+) -> None:
+ while (space_info := api.space_info(space_id)).sha == current_sha:
+ if current_sha is None or current_sha == commit_sha:
+ break
+ click.secho("Waiting for up-to-date Space infos", fg="bright_black", err=True)
+ time.sleep(2)
+ if space_info.sha != commit_sha:
+ raise CLIError(f"Expected SHA {commit_sha} after hot-reload but got {space_info.sha}")
+ if (runtime := space_info.runtime) is None:
+ raise CLIError(f"Unable to read SpaceRuntime from {space_id} infos")
+ if (hot_reloading := runtime.hot_reloading) is None:
+ raise CLIError(f"Space {space_id} current running version has not been hot-reloaded")
+ if hot_reloading.status != "created":
+ click.echo(f"Failed creating hot-reloaded commit. {hot_reloading.replica_statuses=}")
+ return
+
+ if (space_host := space_info.host) is None:
+ raise CLIError("Unexpected None host on hotReloaded Space")
+ if (space_subdomain := space_info.subdomain) is None:
+ raise CLIError("Unexpected None subdomain on hotReloaded Space")
+
+ def render_region(region: ReloadRegion) -> str:
+ res = f"{local_path}, "
+ if region["startLine"] == region["endLine"]:
+ res += f"line {region['startLine'] - 1}"
+ else:
+ res += f"lines {region['startLine'] - 1}-{region['endLine']}"
+ return res
+
+ def display_event(event: ApiGetReloadEventSourceData) -> None:
+ if event["data"]["kind"] == "error":
+ click.secho("✘ Unexpected hot-reloading error", bold=True)
+ click.secho(event["data"]["traceback"], italic=True)
+ elif event["data"]["kind"] == "exception":
+ click.secho(f"✘ Exception at {render_region(event['data']['region'])}", bold=True)
+ click.secho(event["data"]["traceback"], italic=True)
+ elif event["data"]["kind"] == "add":
+ click.secho(f"✔︎ Created {event['data']['objectName']} {event['data']['objectType']}", bold=True)
+ elif event["data"]["kind"] == "delete":
+ click.secho(f"∅ Deleted {event['data']['objectName']} {event['data']['objectType']}", bold=True)
+ elif event["data"]["kind"] == "update":
+ click.secho(f"✔︎ Updated {event['data']['objectName']} {event['data']['objectType']}", bold=True)
+ elif event["data"]["kind"] == "run":
+ click.secho(f"▶ Run {render_region(event['data']['region'])}", bold=True)
+ click.secho(event["data"]["codeLines"], italic=True)
+ elif event["data"]["kind"] == "ui":
+ if event["data"]["updated"]:
+ click.secho("⟳ UI updated", bold=True)
+ else:
+ click.secho("∅ UI untouched", bold=True)
+ elif event["data"]["kind"] == "file":
+ if event["data"]["created"]:
+ click.secho(f"✔︎ {filename} created", bold=True)
+ else:
+ click.secho(f"✔︎ {filename} updated", bold=True)
+ else:
+ click.secho(f"❓ Unknown update event: {event=}")
+ if TYPE_CHECKING:
+ assert_never(event["data"]["kind"])
+
+ for replica_stream_event in multi_replica_reload_events(
+ commit_sha=commit_sha,
+ host=space_host,
+ subdomain=space_subdomain,
+ replica_hashes=[hash for hash, _ in hot_reloading.replica_statuses],
+ token=token,
+ ):
+ if replica_stream_event["kind"] == "event":
+ display_event(replica_stream_event["event"])
+ elif replica_stream_event["kind"] == "replicaHash":
+ click.secho(f"---- Replica {replica_stream_event['hash']} ----")
+ elif replica_stream_event["kind"] == "fullMatch":
+ click.echo("✔︎ Same as first replica")
+ elif replica_stream_event["kind"] == "warning":
+ click.secho(f"⚠ {replica_stream_event['message']}", fg="bright_black")
+ else:
+ assert_never(replica_stream_event)
+
+
+PREFERRED_EDITORS = (
+ ("code", "code --wait"),
+ ("nvim", "nvim"),
+ ("nano", "nano"),
+ ("vim", "vim"),
+ ("vi", "vi"),
+)
+
+
+@functools.cache
+def _get_editor_command() -> str | None:
+ for env in ("HF_EDITOR", "VISUAL", "EDITOR"):
+ if command := os.getenv(env, "").strip():
+ return command
+ for binary_path, editor_command in PREFERRED_EDITORS:
+ if shutil.which(binary_path) is not None:
+ return editor_command
+ return None
+
+
+def _editor_open(local_path: str) -> int | Literal["no-tty", "no-editor"]:
+ if not (sys.stdin.isatty() and sys.stdout.isatty()):
+ return "no-tty"
+ if (editor_command := _get_editor_command()) is None:
+ return "no-editor"
+ command = [*shlex.split(editor_command), local_path]
+ res = subprocess.run(command, start_new_session=True)
+ return res.returncode
+
+
+@volumes_cli.command(
+ "list | ls",
+ examples=[
+ "hf spaces volumes ls username/my-space",
+ ],
+)
+def volumes_ls(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ token: TokenOpt = None,
+) -> None:
+ """List volumes mounted in a Space."""
+ api = get_hf_api(token=token)
+ info = api.space_info(space_id)
+ if info.runtime is None:
+ raise CLIError(f"Runtime not available for Space '{space_id}'.")
+ volumes = info.runtime.volumes or []
+ items = [_dataclass_to_dict(v) for v in volumes]
+ out.table(items)
+ out.hint(
+ f"Use `hf spaces volumes set {space_id} -v hf:///:/` to set volumes for a Space."
+ )
+
+
+@volumes_cli.command(
+ "set",
+ examples=[
+ "hf spaces volumes set username/my-space -v hf://models/username/my-model:/models",
+ "hf spaces volumes set username/my-space -v hf://buckets/username/my-bucket:/data -v hf://datasets/username/my-dataset:/datasets:ro",
+ ],
+)
+def volumes_set(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ volume: VolumesOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Set (replace) volumes for a Space."""
+ volumes = parse_volumes(volume)
+ if not volumes:
+ raise CLIError("At least one volume must be specified with -v/--volume.")
+ api = get_hf_api(token=token)
+ api.set_space_volumes(space_id, volumes=volumes)
+ out.result("Volumes set", space_id=space_id, volumes=[v.to_uri() for v in volumes])
+ out.hint(f"Use `hf spaces volumes ls {space_id}` to list volumes for a Space.")
+
+
+@volumes_cli.command(
+ "delete",
+ examples=[
+ "hf spaces volumes delete username/my-space",
+ "hf spaces volumes delete username/my-space --yes",
+ ],
+)
+def volumes_delete(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Answer Yes to prompt automatically.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Remove all volumes from a Space."""
+ out.confirm(f"You are about to remove all volumes from Space '{space_id}'. Proceed?", yes=yes)
+ api = get_hf_api(token=token)
+ api.delete_space_volumes(space_id)
+ out.result("Volumes deleted", space_id=space_id)
+ out.hint(
+ f"Use `hf spaces volumes set {space_id} -v hf:///:/` to set volumes for a Space."
+ )
+
+
+@secrets_cli.command(
+ "list | ls",
+ examples=["hf spaces secrets ls username/my-space"],
+)
+def secrets_ls(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ token: TokenOpt = None,
+) -> None:
+ """List secrets for a Space. Secret values are write-only and not returned."""
+ api = get_hf_api(token=token)
+ secrets = api.get_space_secrets(space_id)
+ items = [_dataclass_to_dict(s) for s in secrets.values()]
+ out.table(items)
+ out.hint(f"Use `hf spaces secrets add {space_id} -s KEY=VALUE` to add secrets to a Space.")
+
+
+@secrets_cli.command(
+ "add",
+ examples=[
+ "hf spaces secrets add username/my-space -s HF_TOKEN",
+ "hf spaces secrets add username/my-space -s OPENAI_API_KEY=sk-... -s ANTHROPIC_API_KEY=sk-...",
+ "hf spaces secrets add username/my-space --secrets-file .env.secrets",
+ ],
+)
+def secrets_add(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Add or update secrets for a Space."""
+ secrets_map = parse_env_map(secrets, secrets_file)
+ if not secrets_map:
+ raise CLIError("At least one secret must be specified with -s/--secrets or --secrets-file.")
+ api = get_hf_api(token=token)
+ for key, value in secrets_map.items():
+ api.add_space_secret(space_id, key=key, value=value or "")
+ out.result("Secrets added", space_id=space_id, keys=list(secrets_map))
+ out.hint(f"Use `hf spaces secrets delete {space_id} ` to remove a secret from a Space.")
+
+
+@secrets_cli.command(
+ "delete",
+ examples=[
+ "hf spaces secrets delete username/my-space HF_TOKEN",
+ "hf spaces secrets delete username/my-space HF_TOKEN --yes",
+ ],
+)
+def secrets_delete(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ key: Annotated[str, Argument(help="Name of the secret to remove.")],
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Answer Yes to prompt automatically.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Remove a secret from a Space."""
+ out.confirm(
+ f"You are about to remove secret '{key}' from Space '{space_id}'. The value cannot be recovered. Proceed?",
+ yes=yes,
+ )
+ api = get_hf_api(token=token)
+ api.delete_space_secret(space_id, key=key)
+ out.result("Secret deleted", space_id=space_id, key=key)
+ out.hint(f"Use `hf spaces secrets add {space_id} -s {key}=` to re-add a secret to a Space.")
+
+
+@variables_cli.command(
+ "list | ls",
+ examples=["hf spaces variables ls username/my-space"],
+)
+def variables_ls(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ token: TokenOpt = None,
+) -> None:
+ """List environment variables for a Space."""
+ api = get_hf_api(token=token)
+ variables = api.get_space_variables(space_id)
+ items = [_dataclass_to_dict(v) for v in variables.values()]
+ out.table(items)
+ out.hint(f"Use `hf spaces variables add {space_id} -e KEY=VALUE` to add variables to a Space.")
+
+
+@variables_cli.command(
+ "add",
+ examples=[
+ "hf spaces variables add username/my-space -e DEBUG=1",
+ "hf spaces variables add username/my-space -e MODEL_ID=gpt2 -e MAX_TOKENS=512",
+ "hf spaces variables add username/my-space --env-file .env",
+ ],
+)
+def variables_add(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ env: EnvOpt = None,
+ env_file: EnvFileOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Add or update environment variables for a Space."""
+ env_map = parse_env_map(env, env_file)
+ if not env_map:
+ raise CLIError("At least one variable must be specified with -e/--env or --env-file.")
+ api = get_hf_api(token=token)
+ for key, value in env_map.items():
+ api.add_space_variable(space_id, key=key, value=value or "")
+ out.result("Variables added", space_id=space_id, keys=list(env_map))
+ out.hint(f"Use `hf spaces variables ls {space_id}` to list variables for a Space.")
+
+
+@variables_cli.command(
+ "delete",
+ examples=[
+ "hf spaces variables delete username/my-space DEBUG",
+ "hf spaces variables delete username/my-space DEBUG --yes",
+ ],
+)
+def variables_delete(
+ space_id: Annotated[str, Argument(help="The space ID (e.g. `username/repo-name`).")],
+ key: Annotated[str, Argument(help="Name of the variable to remove.")],
+ yes: Annotated[
+ bool,
+ Option(
+ "-y",
+ "--yes",
+ help="Answer Yes to prompt automatically.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Remove an environment variable from a Space."""
+ out.confirm(
+ f"You are about to remove variable '{key}' from Space '{space_id}'. Proceed?",
+ yes=yes,
+ )
+ api = get_hf_api(token=token)
+ api.delete_space_variable(space_id, key=key)
+ out.result("Variable deleted", space_id=space_id, key=key)
+ out.hint(f"Use `hf spaces variables ls {space_id}` to list remaining variables for a Space.")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/system.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/system.py
new file mode 100644
index 0000000000000000000000000000000000000000..46a8bbd2f3ab2ae857587e60ece6771c8fa619da
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/system.py
@@ -0,0 +1,91 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to print information about the environment and version."""
+
+import shutil
+import subprocess
+import sys
+
+import click
+
+from huggingface_hub import __version__, constants
+
+from ..utils import dump_environment_info, installation_method
+from ._cli_utils import _fetch_latest_pypi_version, run_update
+from ._output import out
+
+
+def env() -> None:
+ """Print information about the environment."""
+ dump_environment_info()
+
+
+def version() -> None:
+ """Print information about the hf version."""
+ out.result("hf version", version=__version__)
+
+
+def update() -> None:
+ """Update the `hf` CLI to the latest version."""
+ from ._skills import DEFAULT_SKILL_ID, _installed_hf_cli_dirs
+
+ out.text(f"Current version: {__version__}")
+ out.text("Checking for updates to latest version...")
+ latest_version = _fetch_latest_pypi_version("huggingface_hub")
+ if latest_version is not None and __version__ == latest_version:
+ out.text(f"hf is up to date ({__version__})")
+ return
+
+ # Windows refuses to replace the `hf.exe` launcher of a running process: pip would fail with a
+ # "file in use" error (WinError 32) *after* having uninstalled the current version, leaving a dead
+ # `hf.exe` and no `huggingface_hub` module behind. Print the command instead of running it, the same
+ # way pip refuses to upgrade itself on Windows.
+ if sys.platform == "win32" and installation_method() == "pip":
+ command = subprocess.list2cmdline([sys.executable, "-m", "pip", "install", "-U", "huggingface_hub"])
+ out.error(
+ "On Windows, a pip-installed `hf` cannot update itself: pip is not allowed to replace `hf.exe` while it "
+ f"is running. Run this command instead:\n {command}"
+ )
+ raise click.exceptions.Exit(code=1)
+
+ # The standalone installer installs the `hf-cli` skill by default. If it's not installed at this
+ # point, the user opted out (or removed it): tell the installer to leave it alone instead of
+ # silently undoing that choice.
+ skill_installed = bool(
+ _installed_hf_cli_dirs(constants.AGENTS_SKILLS_GLOBAL_PATH, constants.CLAUDE_SKILLS_GLOBAL_PATH)
+ )
+
+ returncode = run_update(exclude_skill=not skill_installed)
+ if returncode != 0:
+ raise click.exceptions.Exit(code=returncode)
+
+ if not skill_installed:
+ out.hint("Run `hf skills add -g` to teach your AI agents how to use the `hf` CLI.")
+ return
+
+ # Refresh the globally installed skill so agents see the new command surface. Runs in a
+ # subprocess: the skill is generated from the CLI code, which has just been replaced on disk
+ # while this process still runs the previous version.
+ out.text(f"Updating the `{DEFAULT_SKILL_ID}` skill...")
+ subprocess.call([*_hf_argv(), "skills", "update", DEFAULT_SKILL_ID, "-g"])
+
+
+def _hf_argv() -> list[str]:
+ """argv prefix to invoke the freshly updated `hf` CLI."""
+ if installation_method() == "brew":
+ # Homebrew installs the new version in a new prefix: the current interpreter is the old one.
+ hf_bin = shutil.which("hf")
+ if hf_bin is not None:
+ return [hf_bin]
+ return [sys.executable, "-m", "huggingface_hub.cli.hf"]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/upload.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/upload.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a25bb4d708ba30ee870c02a37ca2ae772147b57
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/upload.py
@@ -0,0 +1,291 @@
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains command to upload a repo or file with the CLI."""
+
+import os
+import time
+import warnings
+from typing import Annotated
+
+import click
+
+from huggingface_hub import constants, logging
+from huggingface_hub._commit_scheduler import CommitScheduler
+from huggingface_hub.errors import CLIError, RevisionNotFoundError
+from huggingface_hub.utils import parse_hf_uri
+
+from ._cli_utils import (
+ PrivateOpt,
+ RepoIdArg,
+ RepoType,
+ RepoTypeOptionalOpt,
+ RevisionOpt,
+ TokenOpt,
+ get_hf_api,
+)
+from ._framework import Argument, Option
+from ._output import out
+
+
+logger = logging.get_logger(__name__)
+
+
+UPLOAD_EXAMPLES = [
+ "hf upload my-cool-model . .",
+ "hf upload Wauplin/my-cool-model ./models/model.safetensors",
+ "hf upload Wauplin/my-cool-dataset ./data /train --repo-type=dataset",
+ 'hf upload Wauplin/my-cool-model ./models . --commit-message="Epoch 34/50" --commit-description="Val accuracy: 68%"',
+ "hf upload bigcode/the-stack . . --repo-type dataset --create-pr",
+]
+
+
+def upload(
+ repo_id: RepoIdArg,
+ local_path: Annotated[
+ str | None,
+ Argument(
+ help="Local path to the file or folder to upload. Wildcard patterns are supported. Defaults to current directory.",
+ ),
+ ] = None,
+ path_in_repo: Annotated[
+ str | None,
+ Argument(
+ help="Path of the file or folder in the repo. Defaults to the relative path of the file or folder.",
+ ),
+ ] = None,
+ repo_type: RepoTypeOptionalOpt = None,
+ revision: RevisionOpt = None,
+ private: PrivateOpt = None,
+ include: Annotated[
+ list[str] | None,
+ Option(
+ help="Glob patterns to match files to upload.",
+ ),
+ ] = None,
+ exclude: Annotated[
+ list[str] | None,
+ Option(
+ help="Glob patterns to exclude from files to upload.",
+ ),
+ ] = None,
+ delete: Annotated[
+ list[str] | None,
+ Option(
+ help="Glob patterns for file to be deleted from the repo while committing.",
+ ),
+ ] = None,
+ commit_message: Annotated[
+ str | None,
+ Option(
+ help="The summary / title / first line of the generated commit.",
+ ),
+ ] = None,
+ commit_description: Annotated[
+ str | None,
+ Option(
+ help="The description of the generated commit.",
+ ),
+ ] = None,
+ create_pr: Annotated[
+ bool,
+ Option(
+ help="Whether to upload content as a new Pull Request.",
+ ),
+ ] = False,
+ every: Annotated[
+ float | None,
+ Option(
+ help="If set, a background job is scheduled to create commits every `every` minutes.",
+ ),
+ ] = None,
+ token: TokenOpt = None,
+) -> None:
+ """Upload a file or a folder to the Hub.
+
+ Folders with many files are split into several commits. If a folder upload is interrupted, re-run the same command
+ to resume it.
+ """
+
+ if every is not None and every <= 0:
+ raise click.BadParameter("--every must be a positive value", param_hint="every")
+
+ # `repo_id` may be a plain repo id or an `hf://` URI (e.g. `hf://datasets/my-org/my-dataset@v1.0/data/`).
+ # When a URI is provided, it is authoritative for the repo type, revision and (optionally) path in repo,
+ # so explicit `--repo-type` / `--revision` options are forbidden alongside it.
+ # We branch on the `hf://` prefix (the user's *intent*) rather than on whether the string parses as a
+ # valid URI: a malformed URI then surfaces a precise `HfUriError` (formatted globally in `cli/_errors.py`)
+ # instead of silently falling through to the plain-repo-id path and failing later with an opaque error.
+ if repo_id.startswith(constants.HF_PROTOCOL):
+ if repo_type is not None:
+ raise CLIError(f"'--repo-type' cannot be used with an 'hf://' URI ('{repo_id}').")
+ if revision is not None:
+ raise CLIError(f"'--revision' cannot be used with an 'hf://' URI ('{repo_id}').")
+ uri = parse_hf_uri(repo_id)
+ if uri.is_bucket:
+ raise CLIError("Buckets are not supported by `hf upload`. Use `hf sync` instead.")
+ repo_id, repo_type_str, revision = uri.id, uri.type, uri.revision
+ if uri.path_in_repo:
+ if path_in_repo is not None:
+ raise CLIError(
+ f"Cannot combine a path in the hf:// URI ('{uri.path_in_repo}') with the `path_in_repo` argument ('{path_in_repo}')."
+ )
+ path_in_repo = uri.path_in_repo
+ else:
+ repo_type_str = (repo_type or RepoType.model).value
+
+ api = get_hf_api(token=token)
+
+ # Resolve local_path and path_in_repo based on implicit/explicit rules
+ resolved_local_path, resolved_path_in_repo, resolved_include = _resolve_upload_paths(
+ repo_id=repo_id, local_path=local_path, path_in_repo=path_in_repo, include=include
+ )
+
+ def run_upload() -> str:
+ if os.path.isfile(resolved_local_path):
+ if resolved_include is not None and len(resolved_include) > 0 and isinstance(resolved_include, list):
+ warnings.warn("Ignoring --include since a single file is uploaded.")
+ if exclude is not None and len(exclude) > 0:
+ warnings.warn("Ignoring --exclude since a single file is uploaded.")
+ if delete is not None and len(delete) > 0:
+ warnings.warn("Ignoring --delete since a single file is uploaded.")
+
+ # Schedule commits if `every` is set
+ if every is not None:
+ allow_patterns: list[str] | None
+ ignore_patterns: list[str] | None
+ if os.path.isfile(resolved_local_path):
+ # If file => watch entire folder + use allow_patterns.
+ # `CommitScheduler` matches patterns against paths relative to `folder_path` and
+ # re-uploads them under `path_in_repo`, so the pattern is the file name and only
+ # the directory part of `path_in_repo` can be honoured.
+ folder_path = os.path.dirname(resolved_local_path)
+ filename = os.path.basename(resolved_local_path)
+ pi, _, dest_filename = resolved_path_in_repo.rpartition("/")
+ # An empty or "." destination name means a directory, not a rename.
+ if dest_filename not in ("", ".", filename):
+ out.warning(
+ f"Scheduled uploads keep the local file name, so the file will be uploaded as"
+ f" '{(pi + '/' if pi else '') + filename}' rather than '{resolved_path_in_repo}'."
+ " Rename the local file to upload it under a different name."
+ )
+ allow_patterns = [filename]
+ ignore_patterns = []
+ else:
+ folder_path = resolved_local_path
+ pi = resolved_path_in_repo
+ allow_patterns = resolved_include
+ ignore_patterns = exclude
+ if delete is not None and len(delete) > 0:
+ warnings.warn("Ignoring --delete when uploading with scheduled commits.")
+
+ scheduler = CommitScheduler(
+ folder_path=folder_path,
+ repo_id=repo_id,
+ repo_type=repo_type_str,
+ revision=revision,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ path_in_repo=pi,
+ private=private,
+ every=every,
+ hf_api=api,
+ )
+ out.text(f"Scheduling commits every {every} minutes to {scheduler.repo_id}.")
+ try:
+ while True:
+ time.sleep(100)
+ except KeyboardInterrupt:
+ scheduler.stop()
+ return "Stopped scheduled commits."
+
+ # Otherwise, create repo and proceed with the upload
+ if not os.path.isfile(resolved_local_path) and not os.path.isdir(resolved_local_path):
+ raise FileNotFoundError(f"No such file or directory: '{resolved_local_path}'.")
+ created = api.create_repo(
+ repo_id=repo_id,
+ repo_type=repo_type_str,
+ exist_ok=True,
+ private=private,
+ space_sdk="gradio" if repo_type_str == "space" else None,
+ # ^ We don't want it to fail when uploading to a Space => let's set Gradio by default.
+ # ^ I'd rather not add CLI args to set it explicitly as we already have `hf repos create` for that.
+ ).repo_id
+
+ # Check if branch already exists and if not, create it
+ if revision is not None and not create_pr:
+ try:
+ api.repo_info(repo_id=created, repo_type=repo_type_str, revision=revision)
+ except RevisionNotFoundError:
+ logger.info(f"Branch '{revision}' not found. Creating it...")
+ api.create_branch(repo_id=created, repo_type=repo_type_str, branch=revision, exist_ok=True)
+ # ^ `exist_ok=True` to avoid race concurrency issues
+
+ # File-based upload
+ if os.path.isfile(resolved_local_path):
+ return api.upload_file(
+ path_or_fileobj=resolved_local_path,
+ path_in_repo=resolved_path_in_repo,
+ repo_id=created,
+ repo_type=repo_type_str,
+ revision=revision,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ )
+
+ # Folder-based upload
+ return api.upload_folder(
+ folder_path=resolved_local_path,
+ path_in_repo=resolved_path_in_repo,
+ repo_id=created,
+ repo_type=repo_type_str,
+ revision=revision,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ allow_patterns=resolved_include,
+ ignore_patterns=exclude,
+ delete_patterns=delete,
+ )
+
+ result = run_upload()
+ out.result("Uploaded", url=result)
+
+
+def _resolve_upload_paths(
+ *, repo_id: str, local_path: str | None, path_in_repo: str | None, include: list[str] | None
+) -> tuple[str, str, list[str] | None]:
+ repo_name = repo_id.split("/")[-1]
+ resolved_include = include
+
+ # An existing path is never a wildcard, even if it contains glob characters (e.g. "model [v2].safetensors")
+ if local_path is not None and not os.path.exists(local_path) and any(c in local_path for c in ["*", "?", "["]):
+ if include is not None:
+ raise ValueError("Cannot set --include when local_path contains a wildcard.")
+ if path_in_repo is not None and path_in_repo != ".":
+ raise ValueError("Cannot set path_in_repo when local_path contains a wildcard.")
+ return ".", ".", [local_path]
+
+ if local_path is None and os.path.isfile(repo_name):
+ return repo_name, repo_name, resolved_include
+ if local_path is None and os.path.isdir(repo_name):
+ return repo_name, ".", resolved_include
+ if local_path is None:
+ raise ValueError(f"'{repo_name}' is not a local file or folder. Please set local_path explicitly.")
+
+ if path_in_repo is None and os.path.isfile(local_path):
+ return local_path, os.path.basename(local_path), resolved_include
+ if path_in_repo is None:
+ return local_path, ".", resolved_include
+ return local_path, path_in_repo, resolved_include
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/webhooks.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/webhooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..719258a4ded5a93292d1c7f4ecf0a1e08e1e0f6a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/cli/webhooks.py
@@ -0,0 +1,320 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains commands to manage webhooks on the Hugging Face Hub."""
+
+import enum
+from typing import Annotated, get_args, get_type_hints
+
+import click
+
+from huggingface_hub.constants import WEBHOOK_DOMAIN_T
+from huggingface_hub.hf_api import WebhookWatchedItem
+
+from ._cli_utils import (
+ SecretsFileOpt,
+ SecretsOpt,
+ TokenOpt,
+ get_hf_api,
+ parse_env_map,
+ typer_factory,
+)
+from ._framework import Argument, Option
+from ._output import out
+
+
+# Build enums dynamically from Literal types to avoid duplication
+_WATCHED_TYPES = get_args(get_type_hints(WebhookWatchedItem)["type"])
+WatchedItemType = enum.Enum("WatchedItemType", {t: t for t in _WATCHED_TYPES}, type=str) # type: ignore[misc]
+
+_DOMAIN_TYPES = get_args(WEBHOOK_DOMAIN_T)
+WebhookDomain = enum.Enum("WebhookDomain", {d: d for d in _DOMAIN_TYPES}, type=str) # type: ignore[misc]
+
+
+def _parse_watch(values: list[str]) -> list[WebhookWatchedItem]:
+ """Parse 'type:name' strings into WebhookWatchedItem objects.
+
+ Args:
+ values: List of strings in the format 'type:name'
+ (e.g., 'model:bert-base-uncased', 'org:HuggingFace').
+
+ Returns:
+ List of WebhookWatchedItem objects.
+
+ Raises:
+ click.BadParameter: If any value doesn't match the expected format.
+ """
+ items = []
+ valid_types = tuple(_WATCHED_TYPES)
+ for v in values:
+ if ":" not in v:
+ raise click.BadParameter(
+ f"Expected format 'type:name' (e.g. 'model:bert-base-uncased'), got '{v}'."
+ f" Valid types: {', '.join(valid_types)}."
+ )
+ kind, name = v.split(":", 1)
+ if kind not in valid_types:
+ raise click.BadParameter(f"Invalid type '{kind}'. Valid types: {', '.join(valid_types)}.")
+ items.append(WebhookWatchedItem(type=kind, name=name)) # type: ignore
+ return items
+
+
+def _parse_job_secrets(
+ secrets: list[str] | None, secrets_file: str | None, job_id: str | None
+) -> dict[str, str] | None:
+ """Parse `--secrets` / `--secrets-file` for the Job triggered by a webhook (requires `--job-id`)."""
+ secrets_map = parse_env_map(secrets, secrets_file)
+ if not secrets_map:
+ return None
+ if job_id is None:
+ raise click.BadParameter("--secrets and --secrets-file require --job-id.")
+ return {key: value or "" for key, value in secrets_map.items()}
+
+
+webhooks_cli = typer_factory(help="Manage webhooks on the Hub.")
+
+
+@webhooks_cli.command(
+ "list | ls",
+ examples=[
+ "hf webhooks ls",
+ "hf webhooks ls --format json",
+ "hf webhooks ls --format quiet",
+ ],
+)
+def webhooks_ls(
+ token: TokenOpt = None,
+) -> None:
+ """List all webhooks for the current user."""
+ api = get_hf_api(token=token)
+ results = [
+ {
+ "id": w.id,
+ "url": w.url or "(job)",
+ "disabled": w.disabled,
+ "domains": w.domains or [],
+ "watched": [f"{wi.type}:{wi.name}" for wi in (w.watched or [])],
+ }
+ for w in api.list_webhooks()
+ ]
+ out.table(results)
+
+
+@webhooks_cli.command(
+ "info",
+ examples=[
+ "hf webhooks info abc123",
+ ],
+)
+def webhooks_info(
+ webhook_id: Annotated[str, Argument(help="The ID of the webhook.")],
+ token: TokenOpt = None,
+) -> None:
+ """Show full details for a single webhook."""
+ api = get_hf_api(token=token)
+ webhook = api.get_webhook(webhook_id)
+ out.dict(webhook)
+
+
+@webhooks_cli.command(
+ "create",
+ examples=[
+ "hf webhooks create --url https://example.com/hook --watch model:bert-base-uncased",
+ "hf webhooks create --url https://example.com/hook --watch org:HuggingFace --watch model:gpt2 --domain repo",
+ "hf webhooks create --job-id 687f911eaea852de79c4a50a --watch user:julien-c",
+ "hf webhooks create --job-id 687f911eaea852de79c4a50a --watch bucket:my-org/my-bucket --secrets HF_TOKEN",
+ ],
+)
+def webhooks_create(
+ watch: Annotated[
+ list[str],
+ Option(
+ "--watch",
+ help="Item to watch, in 'type:name' format (e.g. 'model:bert-base-uncased'). Repeatable.",
+ ),
+ ],
+ url: Annotated[
+ str | None,
+ Option(help="URL to send webhook payloads to. Mutually exclusive with --job-id."),
+ ] = None,
+ job_id: Annotated[
+ str | None,
+ Option(
+ "--job-id",
+ help="ID of a Job to trigger (from job.id) instead of pinging a URL. Mutually exclusive with --url.",
+ ),
+ ] = None,
+ domain: Annotated[
+ list[WebhookDomain] | None,
+ Option(
+ "--domain",
+ help="Domain to watch: 'repo' or 'discussions'. Repeatable. Defaults to all domains.",
+ ),
+ ] = None,
+ secret: Annotated[
+ str | None,
+ Option(help="Optional secret used to sign webhook payloads."),
+ ] = None,
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Create a new webhook.
+
+ Provide either --url (to ping a remote server) or --job-id (to trigger a Job), but not both.
+ The source Job's secrets are not copied to the webhook: pass them again with --secrets / --secrets-file.
+ """
+ if url is not None and job_id is not None:
+ raise click.BadParameter("Provide either --url or --job-id, not both.")
+ if url is None and job_id is None:
+ raise click.BadParameter("Provide either --url or --job-id.")
+ job_secrets = _parse_job_secrets(secrets, secrets_file, job_id)
+ api = get_hf_api(token=token)
+ watched_items = _parse_watch(watch)
+ domains = [d.value for d in domain] if domain else None
+ webhook = api.create_webhook(
+ url=url,
+ job_id=job_id,
+ watched=watched_items, # type: ignore
+ domains=domains, # type: ignore
+ secret=secret,
+ secrets=job_secrets,
+ )
+ out.result("Webhook created", id=webhook.id)
+
+
+@webhooks_cli.command(
+ "update",
+ examples=[
+ "hf webhooks update abc123 --url https://new-url.com/hook",
+ "hf webhooks update abc123 --watch model:gpt2 --domain repo",
+ "hf webhooks update abc123 --secret newsecret",
+ "hf webhooks update abc123 --job-id 687f911eaea852de79c4a50a --secrets HF_TOKEN",
+ ],
+)
+def webhooks_update(
+ webhook_id: Annotated[str, Argument(help="The ID of the webhook to update.")],
+ url: Annotated[
+ str | None,
+ Option(help="New URL to send webhook payloads to."),
+ ] = None,
+ job_id: Annotated[
+ str | None,
+ Option(
+ "--job-id",
+ help="ID of the source Job to trigger (can be the current one). Required with --secrets / --secrets-file.",
+ ),
+ ] = None,
+ watch: Annotated[
+ list[str] | None,
+ Option(
+ "--watch",
+ help=(
+ "New list of items to watch, in 'type:name' format. "
+ "Repeatable. Replaces the entire existing watched list."
+ ),
+ ),
+ ] = None,
+ domain: Annotated[
+ list[WebhookDomain] | None,
+ Option(
+ "--domain",
+ help="New list of domains to watch: 'repo' or 'discussions'. Repeatable.",
+ ),
+ ] = None,
+ secret: Annotated[
+ str | None,
+ Option(help="New secret used to sign webhook payloads."),
+ ] = None,
+ secrets: SecretsOpt = None,
+ secrets_file: SecretsFileOpt = None,
+ token: TokenOpt = None,
+) -> None:
+ """Update an existing webhook. Only provided options are changed.
+
+ Job secrets passed with --secrets replace stored values with the same name; others are kept.
+ """
+ if url is not None and job_id is not None:
+ raise click.BadParameter("Provide either --url or --job-id, not both.")
+ job_secrets = _parse_job_secrets(secrets, secrets_file, job_id)
+ api = get_hf_api(token=token)
+ watched_items = _parse_watch(watch) if watch else None
+ domains = [d.value for d in domain] if domain else None
+ webhook = api.update_webhook(
+ webhook_id,
+ url=url,
+ job_id=job_id,
+ watched=watched_items, # type: ignore
+ domains=domains, # type: ignore
+ secret=secret,
+ secrets=job_secrets,
+ )
+ out.result("Webhook updated", id=webhook.id)
+
+
+@webhooks_cli.command(
+ "enable",
+ examples=[
+ "hf webhooks enable abc123",
+ ],
+)
+def webhooks_enable(
+ webhook_id: Annotated[str, Argument(help="The ID of the webhook to enable.")],
+ token: TokenOpt = None,
+) -> None:
+ """Enable a disabled webhook."""
+ api = get_hf_api(token=token)
+ webhook = api.enable_webhook(webhook_id)
+ out.result("Webhook enabled", id=webhook.id)
+
+
+@webhooks_cli.command(
+ "disable",
+ examples=[
+ "hf webhooks disable abc123",
+ ],
+)
+def webhooks_disable(
+ webhook_id: Annotated[str, Argument(help="The ID of the webhook to disable.")],
+ token: TokenOpt = None,
+) -> None:
+ """Disable an active webhook."""
+ api = get_hf_api(token=token)
+ webhook = api.disable_webhook(webhook_id)
+ out.result("Webhook disabled", id=webhook.id)
+
+
+@webhooks_cli.command(
+ "delete",
+ examples=[
+ "hf webhooks delete abc123",
+ "hf webhooks delete abc123 --yes",
+ ],
+)
+def webhooks_delete(
+ webhook_id: Annotated[str, Argument(help="The ID of the webhook to delete.")],
+ yes: Annotated[
+ bool,
+ Option(
+ "--yes",
+ "-y",
+ help="Skip confirmation prompt.",
+ ),
+ ] = False,
+ token: TokenOpt = None,
+) -> None:
+ """Delete a webhook permanently."""
+ out.confirm(f"Are you sure you want to delete webhook '{webhook_id}'?", yes=yes)
+ api = get_hf_api(token=token)
+ api.delete_webhook(webhook_id)
+ out.result("Webhook deleted", id=webhook_id)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/community.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/community.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2ad0ac1f70723410b7d293135de8bdd0bc8de8e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/community.py
@@ -0,0 +1,363 @@
+"""
+Data structures to interact with Discussions and Pull Requests on the Hub.
+
+See [the Discussions and Pull Requests guide](https://huggingface.co/docs/hub/repositories-pull-requests-discussions)
+for more information on Pull Requests, Discussions, and the community tab.
+"""
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Literal, TypedDict
+
+from . import constants
+from .utils import parse_datetime
+
+
+DiscussionStatus = Literal["open", "closed", "merged", "draft"]
+
+
+@dataclass
+class Discussion:
+ """
+ A Discussion or Pull Request on the Hub.
+
+ This dataclass is not intended to be instantiated directly.
+
+ Attributes:
+ title (`str`):
+ The title of the Discussion / Pull Request
+ status (`str`):
+ The status of the Discussion / Pull Request.
+ It must be one of:
+ * `"open"`
+ * `"closed"`
+ * `"merged"` (only for Pull Requests )
+ * `"draft"` (only for Pull Requests )
+ num (`int`):
+ The number of the Discussion / Pull Request.
+ repo_id (`str`):
+ The id (`"{namespace}/{repo_name}"`) of the repo on which
+ the Discussion / Pull Request was open.
+ repo_type (`str`):
+ The type of the repo on which the Discussion / Pull Request was open.
+ Possible values are: `"model"`, `"dataset"`, `"space"`.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ is_pull_request (`bool`):
+ Whether or not this is a Pull Request.
+ created_at (`datetime`):
+ The `datetime` of creation of the Discussion / Pull Request.
+ endpoint (`str`):
+ Endpoint of the Hub. Default is https://huggingface.co.
+ git_reference (`str`, *optional*):
+ (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+ url (`str`):
+ (property) URL of the discussion on the Hub.
+ """
+
+ title: str
+ status: DiscussionStatus
+ num: int
+ repo_id: str
+ repo_type: str
+ author: str
+ is_pull_request: bool
+ created_at: datetime
+ endpoint: str
+
+ @property
+ def git_reference(self) -> str | None:
+ """
+ If this is a Pull Request , returns the git reference to which changes can be pushed.
+ Returns `None` otherwise.
+ """
+ if self.is_pull_request:
+ return f"refs/pr/{self.num}"
+ return None
+
+ @property
+ def url(self) -> str:
+ """Returns the URL of the discussion on the Hub."""
+ if self.repo_type is None or self.repo_type == constants.REPO_TYPE_MODEL:
+ return f"{self.endpoint}/{self.repo_id}/discussions/{self.num}"
+ return f"{self.endpoint}/{self.repo_type}s/{self.repo_id}/discussions/{self.num}"
+
+
+@dataclass
+class DiscussionWithDetails(Discussion):
+ """
+ Subclass of [`Discussion`].
+
+ Attributes:
+ title (`str`):
+ The title of the Discussion / Pull Request
+ status (`str`):
+ The status of the Discussion / Pull Request.
+ It can be one of:
+ * `"open"`
+ * `"closed"`
+ * `"merged"` (only for Pull Requests )
+ * `"draft"` (only for Pull Requests )
+ num (`int`):
+ The number of the Discussion / Pull Request.
+ repo_id (`str`):
+ The id (`"{namespace}/{repo_name}"`) of the repo on which
+ the Discussion / Pull Request was open.
+ repo_type (`str`):
+ The type of the repo on which the Discussion / Pull Request was open.
+ Possible values are: `"model"`, `"dataset"`, `"space"`.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ is_pull_request (`bool`):
+ Whether or not this is a Pull Request.
+ created_at (`datetime`):
+ The `datetime` of creation of the Discussion / Pull Request.
+ events (`list` of [`DiscussionEvent`]):
+ The list of [`DiscussionEvents`] in this Discussion or Pull Request.
+ conflicting_files (`Union[list[str], bool, None]`, *optional*):
+ A list of conflicting files if this is a Pull Request.
+ `None` if `self.is_pull_request` is `False`.
+ `True` if there are conflicting files but the list can't be retrieved.
+ target_branch (`str`, *optional*):
+ The branch into which changes are to be merged if this is a
+ Pull Request . `None` if `self.is_pull_request` is `False`.
+ merge_commit_oid (`str`, *optional*):
+ If this is a merged Pull Request , this is set to the OID / SHA of
+ the merge commit, `None` otherwise.
+ diff (`str`, *optional*):
+ The git diff if this is a Pull Request , `None` otherwise.
+ endpoint (`str`):
+ Endpoint of the Hub. Default is https://huggingface.co.
+ git_reference (`str`, *optional*):
+ (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+ url (`str`):
+ (property) URL of the discussion on the Hub.
+ """
+
+ events: list["DiscussionEvent"]
+ conflicting_files: list[str] | bool | None
+ target_branch: str | None
+ merge_commit_oid: str | None
+ diff: str | None
+
+
+class DiscussionEventArgs(TypedDict):
+ id: str
+ type: str
+ created_at: datetime
+ author: str
+ _event: dict
+
+
+@dataclass
+class DiscussionEvent:
+ """
+ An event in a Discussion or Pull Request.
+
+ Use concrete classes:
+ * [`DiscussionComment`]
+ * [`DiscussionStatusChange`]
+ * [`DiscussionCommit`]
+ * [`DiscussionTitleChange`]
+
+ Attributes:
+ id (`str`):
+ The ID of the event. An hexadecimal string.
+ type (`str`):
+ The type of the event.
+ created_at (`datetime`):
+ A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+ object holding the creation timestamp for the event.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ """
+
+ id: str
+ type: str
+ created_at: datetime
+ author: str
+
+ _event: dict
+ """Stores the original event data, in case we need to access it later."""
+
+
+@dataclass
+class DiscussionComment(DiscussionEvent):
+ """A comment in a Discussion / Pull Request.
+
+ Subclass of [`DiscussionEvent`].
+
+
+ Attributes:
+ id (`str`):
+ The ID of the event. An hexadecimal string.
+ type (`str`):
+ The type of the event.
+ created_at (`datetime`):
+ A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+ object holding the creation timestamp for the event.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ content (`str`):
+ The raw markdown content of the comment. Mentions, links and images are not rendered.
+ edited (`bool`):
+ Whether or not this comment has been edited.
+ hidden (`bool`):
+ Whether or not this comment has been hidden.
+ """
+
+ content: str
+ edited: bool
+ hidden: bool
+
+ @property
+ def rendered(self) -> str:
+ """The rendered comment, as a HTML string"""
+ return self._event["data"]["latest"]["html"]
+
+ @property
+ def last_edited_at(self) -> datetime:
+ """The last edit time, as a `datetime` object."""
+ return parse_datetime(self._event["data"]["latest"]["updatedAt"])
+
+ @property
+ def last_edited_by(self) -> str:
+ """The username of the user who last edited the comment."""
+ return self._event["data"]["latest"].get("author", {}).get("name", "deleted")
+
+ @property
+ def edit_history(self) -> list[dict]:
+ """The edit history of the comment"""
+ return self._event["data"]["history"]
+
+ @property
+ def number_of_edits(self) -> int:
+ return len(self.edit_history)
+
+
+@dataclass
+class DiscussionStatusChange(DiscussionEvent):
+ """A change of status in a Discussion / Pull Request.
+
+ Subclass of [`DiscussionEvent`].
+
+ Attributes:
+ id (`str`):
+ The ID of the event. An hexadecimal string.
+ type (`str`):
+ The type of the event.
+ created_at (`datetime`):
+ A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+ object holding the creation timestamp for the event.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ new_status (`str`):
+ The status of the Discussion / Pull Request after the change.
+ It can be one of:
+ * `"open"`
+ * `"closed"`
+ * `"merged"` (only for Pull Requests )
+ """
+
+ new_status: str
+
+
+@dataclass
+class DiscussionCommit(DiscussionEvent):
+ """A commit in a Pull Request.
+
+ Subclass of [`DiscussionEvent`].
+
+ Attributes:
+ id (`str`):
+ The ID of the event. An hexadecimal string.
+ type (`str`):
+ The type of the event.
+ created_at (`datetime`):
+ A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+ object holding the creation timestamp for the event.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ summary (`str`):
+ The summary of the commit.
+ oid (`str`):
+ The OID / SHA of the commit, as a hexadecimal string.
+ """
+
+ summary: str
+ oid: str
+
+
+@dataclass
+class DiscussionTitleChange(DiscussionEvent):
+ """A rename event in a Discussion / Pull Request.
+
+ Subclass of [`DiscussionEvent`].
+
+ Attributes:
+ id (`str`):
+ The ID of the event. An hexadecimal string.
+ type (`str`):
+ The type of the event.
+ created_at (`datetime`):
+ A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+ object holding the creation timestamp for the event.
+ author (`str`):
+ The username of the Discussion / Pull Request author.
+ Can be `"deleted"` if the user has been deleted since.
+ old_title (`str`):
+ The previous title for the Discussion / Pull Request.
+ new_title (`str`):
+ The new title.
+ """
+
+ old_title: str
+ new_title: str
+
+
+def deserialize_event(event: dict) -> DiscussionEvent:
+ """Instantiates a [`DiscussionEvent`] from a dict"""
+ event_id: str = event["id"]
+ event_type: str = event["type"]
+ created_at = parse_datetime(event["createdAt"])
+
+ common_args: DiscussionEventArgs = {
+ "id": event_id,
+ "type": event_type,
+ "created_at": created_at,
+ "author": event.get("author", {}).get("name", "deleted"),
+ "_event": event,
+ }
+
+ if event_type == "comment":
+ return DiscussionComment(
+ **common_args,
+ edited=event["data"]["edited"],
+ hidden=event["data"]["hidden"],
+ content=event["data"]["latest"]["raw"],
+ )
+ if event_type == "status-change":
+ return DiscussionStatusChange(
+ **common_args,
+ new_status=event["data"]["status"],
+ )
+ if event_type == "commit":
+ return DiscussionCommit(
+ **common_args,
+ summary=event["data"]["subject"],
+ oid=event["data"]["oid"],
+ )
+ if event_type == "title-change":
+ return DiscussionTitleChange(
+ **common_args,
+ old_title=event["data"]["from"],
+ new_title=event["data"]["to"],
+ )
+
+ return DiscussionEvent(**common_args)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/constants.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec04a7599a8796b5b694e6a83abcb1ce5ce3b038
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/constants.py
@@ -0,0 +1,349 @@
+import os
+import re
+import typing
+from pathlib import Path
+from typing import Literal
+from urllib.parse import urlsplit
+
+
+# Possible values for env variables
+
+
+ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
+ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
+
+
+def _is_true(value: str | None) -> bool:
+ if value is None:
+ return False
+ return value.upper() in ENV_VARS_TRUE_VALUES
+
+
+def _as_int(value: str | None) -> int | None:
+ if value is None:
+ return None
+ return int(value)
+
+
+# Constants for file downloads
+
+PYTORCH_WEIGHTS_NAME = "pytorch_model.bin"
+TF2_WEIGHTS_NAME = "tf_model.h5"
+TF_WEIGHTS_NAME = "model.ckpt"
+FLAX_WEIGHTS_NAME = "flax_model.msgpack"
+CONFIG_NAME = "config.json"
+REPOCARD_NAME = "README.md"
+EVAL_RESULTS_FOLDER = ".eval_results"
+DEFAULT_ETAG_TIMEOUT = 10
+DEFAULT_DOWNLOAD_TIMEOUT = 10
+DEFAULT_REQUEST_TIMEOUT = 10
+DOWNLOAD_CHUNK_SIZE = 10 * 1024 * 1024
+MAX_HTTP_DOWNLOAD_SIZE = 50 * 1000 * 1000 * 1000 # 50 GB
+
+# Constants for serialization
+
+PYTORCH_WEIGHTS_FILE_PATTERN = "pytorch_model{suffix}.bin" # Unsafe pickle: use safetensors instead
+SAFETENSORS_WEIGHTS_FILE_PATTERN = "model{suffix}.safetensors"
+TF2_WEIGHTS_FILE_PATTERN = "tf_model{suffix}.h5"
+
+# Constants for safetensors repos
+
+SAFETENSORS_SINGLE_FILE = "model.safetensors"
+SAFETENSORS_INDEX_FILE = "model.safetensors.index.json"
+SAFETENSORS_MAX_HEADER_LENGTH = 25_000_000
+
+# Timeout of acquiring file lock and logging the attempt
+FILELOCK_LOG_EVERY_SECONDS = 10
+
+# Git-related constants
+
+DEFAULT_REVISION = "main"
+REGEX_COMMIT_OID = re.compile(r"[A-Fa-f0-9]{5,40}")
+
+HUGGINGFACE_CO_URL_HOME = "https://huggingface.co/"
+
+_staging_mode = _is_true(os.environ.get("HUGGINGFACE_CO_STAGING"))
+
+_HF_DEFAULT_ENDPOINT = "https://huggingface.co"
+_HF_DEFAULT_STAGING_ENDPOINT = "https://hub-ci.huggingface.co"
+ENDPOINT = os.getenv("HF_ENDPOINT", _HF_DEFAULT_ENDPOINT).rstrip("/")
+HUGGINGFACE_CO_URL_TEMPLATE = ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
+
+if _staging_mode:
+ ENDPOINT = _HF_DEFAULT_STAGING_ENDPOINT
+ HUGGINGFACE_CO_URL_TEMPLATE = _HF_DEFAULT_STAGING_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
+
+# Hosts considered to be Hugging Face Hub endpoints: the public Hub host, its ``hf.co`` short domain, the staging
+# host, and the host of the configured ``ENDPOINT``. Used to parse web URLs into ``hf://`` URIs and to decide which
+# redirects to follow when resolving files. The auth header is forwarded to these hosts: only add trusted ones.
+HF_URL_HOSTS: frozenset[str] = frozenset(
+ {"hf.co"}
+ | {
+ host.lower()
+ for host in (
+ urlsplit(_HF_DEFAULT_ENDPOINT).hostname,
+ urlsplit(_HF_DEFAULT_STAGING_ENDPOINT).hostname,
+ urlsplit(ENDPOINT).hostname,
+ )
+ if host
+ }
+)
+
+DATASETS_SERVER_ENDPOINT = "https://datasets-server.huggingface.co"
+
+HUGGINGFACE_HEADER_X_REPO_COMMIT = "X-Repo-Commit"
+HUGGINGFACE_HEADER_X_LINKED_ETAG = "X-Linked-Etag"
+HUGGINGFACE_HEADER_X_LINKED_SIZE = "X-Linked-Size"
+HUGGINGFACE_HEADER_X_BILL_TO = "X-HF-Bill-To"
+# Header sent on every HTTP call made while downloading files from the Hub (see `flag_as_download_call`).
+X_HF_DOWNLOAD_COUNTER = "X-HF-Download-Counter"
+
+INFERENCE_ENDPOINT = os.environ.get("HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co")
+
+# See https://huggingface.co/docs/inference-endpoints/index
+INFERENCE_ENDPOINTS_ENDPOINT = "https://api.endpoints.huggingface.cloud/v2"
+INFERENCE_CATALOG_ENDPOINT = "https://endpoints.huggingface.co/api/v1/catalog"
+
+# Proxy for third-party providers
+INFERENCE_PROXY_TEMPLATE = "https://router.huggingface.co/{provider}"
+
+REPO_ID_SEPARATOR = "--"
+# ^ this substring is not allowed in repo_ids on hf.co
+# and is the canonical one we use for serialization of repo ids elsewhere.
+
+
+REPO_TYPE_DATASET = "dataset"
+REPO_TYPE_SPACE = "space"
+REPO_TYPE_MODEL = "model"
+REPO_TYPE_KERNEL = "kernel"
+REPO_TYPES = [None, REPO_TYPE_MODEL, REPO_TYPE_DATASET, REPO_TYPE_SPACE]
+REPO_TYPES_WITH_KERNEL = REPO_TYPES + [REPO_TYPE_KERNEL]
+SPACES_SDK_TYPES = ["gradio", "streamlit", "docker", "static"]
+
+REPO_TYPES_URL_PREFIXES = {
+ REPO_TYPE_DATASET: "datasets/",
+ REPO_TYPE_SPACE: "spaces/",
+ REPO_TYPE_KERNEL: "kernels/",
+}
+REPO_TYPES_MAPPING = {
+ "datasets": REPO_TYPE_DATASET,
+ "spaces": REPO_TYPE_SPACE,
+ "models": REPO_TYPE_MODEL,
+ "kernels": REPO_TYPE_KERNEL,
+}
+
+# HF Hub URIs (``hf://...``). See ``huggingface_hub/utils/_hf_uris.py``
+# and ``docs/source/en/package_reference/hf_uris.md`` for the full grammar.
+HF_PROTOCOL = "hf://"
+HfUriType = Literal["model", "dataset", "space", "kernel", "bucket"]
+# Maps the plural URI prefix that may appear in a HF URI (e.g. ``datasets/``)
+# to the canonical singular type name. Buckets are first-class HF URI types.
+HF_URI_TYPE_PREFIXES: dict[str, HfUriType] = {
+ "models": "model",
+ "datasets": "dataset",
+ "spaces": "space",
+ "kernels": "kernel",
+ "buckets": "bucket",
+}
+
+
+DiscussionTypeFilter = Literal["all", "discussion", "pull_request"]
+DISCUSSION_TYPES: tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionTypeFilter)
+DiscussionStatusFilter = Literal["all", "open", "closed"]
+DISCUSSION_STATUS: tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionStatusFilter)
+
+# Webhook subscription types
+WEBHOOK_DOMAIN_T = Literal["repo", "discussions"]
+
+# default cache
+default_home = os.path.join(os.path.expanduser("~"), ".cache")
+HF_HOME = os.path.expandvars(
+ os.path.expanduser(
+ os.getenv(
+ "HF_HOME",
+ os.path.join(os.getenv("XDG_CACHE_HOME", default_home), "huggingface"),
+ )
+ )
+)
+
+default_cache_path = os.path.join(HF_HOME, "hub")
+default_assets_cache_path = os.path.join(HF_HOME, "assets")
+
+# Legacy env variables
+HUGGINGFACE_HUB_CACHE = os.getenv("HUGGINGFACE_HUB_CACHE", default_cache_path)
+HUGGINGFACE_ASSETS_CACHE = os.getenv("HUGGINGFACE_ASSETS_CACHE", default_assets_cache_path)
+
+# New env variables
+HF_HUB_CACHE = os.path.expandvars(
+ os.path.expanduser(
+ os.getenv(
+ "HF_HUB_CACHE",
+ HUGGINGFACE_HUB_CACHE,
+ )
+ )
+)
+HF_ASSETS_CACHE = os.path.expandvars(
+ os.path.expanduser(
+ os.getenv(
+ "HF_ASSETS_CACHE",
+ HUGGINGFACE_ASSETS_CACHE,
+ )
+ )
+)
+
+HF_HUB_OFFLINE = _is_true(os.environ.get("HF_HUB_OFFLINE") or os.environ.get("TRANSFORMERS_OFFLINE"))
+
+
+def is_offline_mode() -> bool:
+ """Returns whether we are in offline mode for the Hub.
+
+ When offline mode is enabled, all HTTP requests made with `get_session` will raise an `OfflineModeIsEnabled` exception.
+
+ Example:
+ ```py
+ from huggingface_hub import is_offline_mode
+
+ def list_files(repo_id: str):
+ if is_offline_mode():
+ ... # list files from local cache (degraded experience but still functional)
+ else:
+ ... # list files from Hub (complete experience)
+ ```
+ """
+ return HF_HUB_OFFLINE
+
+
+# File created to mark that the version check has been done.
+# Check is performed once per 24 hours at most.
+CHECK_FOR_UPDATE_DONE_PATH = os.path.join(HF_HOME, ".check_for_update_done")
+
+# File created to mark that the `hf-cli` skill check has been done.
+# Check is performed once per 24 hours at most.
+CHECK_FOR_SKILL_UPDATE_DONE_PATH = os.path.join(HF_HOME, ".check_for_skill_update_done")
+
+# File caching the AI agent harnesses registry fetched from `{ENDPOINT}/api/agent-harnesses`.
+# Refreshed once per 24 hours at most (see `utils/_detect_agent.py`).
+AGENT_HARNESSES_PATH = os.path.join(HF_HOME, ".agent_harnesses.json")
+
+# Skills directories for AI agents: `.agents/skills` is read by most agents, `.claude/skills` by Claude Code.
+# Local = current project, global = user-level (see `hf skills add`).
+# Claude Code's user-level directory defaults to `~/.claude` but can be relocated with `CLAUDE_CONFIG_DIR`.
+AGENTS_SKILLS_LOCAL_PATH = Path(".agents/skills")
+AGENTS_SKILLS_GLOBAL_PATH = Path("~/.agents/skills")
+CLAUDE_SKILLS_LOCAL_PATH = Path(".claude/skills")
+CLAUDE_SKILLS_GLOBAL_PATH = Path(os.environ.get("CLAUDE_CONFIG_DIR") or "~/.claude") / "skills"
+
+# Set to skip the CLI update check (PyPI query + "new version available" warning at startup).
+HF_HUB_DISABLE_UPDATE_CHECK = _is_true(os.environ.get("HF_HUB_DISABLE_UPDATE_CHECK"))
+
+# If set, log level will be set to DEBUG and all requests made to the Hub will be logged
+# as curl commands for reproducibility.
+HF_DEBUG = _is_true(os.environ.get("HF_DEBUG"))
+
+# Opt-out from telemetry requests
+HF_HUB_DISABLE_TELEMETRY = (
+ _is_true(os.environ.get("HF_HUB_DISABLE_TELEMETRY")) # HF-specific env variable
+ or _is_true(os.environ.get("DISABLE_TELEMETRY"))
+ or _is_true(os.environ.get("DO_NOT_TRACK")) # https://donottrack.sh/
+)
+
+HF_TOKEN_PATH = os.path.expandvars(
+ os.path.expanduser(
+ os.getenv(
+ "HF_TOKEN_PATH",
+ os.path.join(HF_HOME, "token"),
+ )
+ )
+)
+HF_STORED_TOKENS_PATH = os.path.join(os.path.dirname(HF_TOKEN_PATH), "stored_tokens")
+
+if _staging_mode:
+ # In staging mode, we use a different cache to ensure we don't mix up production and staging data or tokens
+ # In practice in `huggingface_hub` tests, we monkeypatch these values with temporary directories. The following
+ # lines are only used in third-party libraries tests (e.g. `transformers`, `diffusers`, etc.).
+ _staging_home = os.path.join(os.path.expanduser("~"), ".cache", "huggingface_staging")
+ HUGGINGFACE_HUB_CACHE = os.path.join(_staging_home, "hub")
+ HF_TOKEN_PATH = os.path.join(_staging_home, "token")
+
+# Here, `True` will disable progress bars globally without possibility of enabling it
+# programmatically. `False` will enable them without possibility of disabling them.
+# If environment variable is not set (None), then the user is free to enable/disable
+# them programmatically.
+# TL;DR: env variable has priority over code
+__HF_HUB_DISABLE_PROGRESS_BARS = os.environ.get("HF_HUB_DISABLE_PROGRESS_BARS")
+HF_HUB_DISABLE_PROGRESS_BARS: bool | None = (
+ _is_true(__HF_HUB_DISABLE_PROGRESS_BARS) if __HF_HUB_DISABLE_PROGRESS_BARS is not None else None
+)
+
+# Disable symlinks in the cache (files are copied instead of symlinked)
+HF_HUB_DISABLE_SYMLINKS: bool = _is_true(os.environ.get("HF_HUB_DISABLE_SYMLINKS"))
+
+# Disable warning on machines that do not support symlinks (e.g. Windows non-developer)
+HF_HUB_DISABLE_SYMLINKS_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_SYMLINKS_WARNING"))
+
+# Disable warning when using experimental features
+HF_HUB_DISABLE_EXPERIMENTAL_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_EXPERIMENTAL_WARNING"))
+
+# Disable sending the cached token by default is all HTTP requests to the Hub
+HF_HUB_DISABLE_IMPLICIT_TOKEN: bool = _is_true(os.environ.get("HF_HUB_DISABLE_IMPLICIT_TOKEN"))
+
+HF_XET_HIGH_PERFORMANCE: bool = _is_true(os.environ.get("HF_XET_HIGH_PERFORMANCE"))
+
+# Bucket and mount path used when launching Jobs
+HF_JOBS_ARTIFACTS_BUCKET_NAME: str = "jobs-artifacts"
+HF_JOBS_ARTIFACTS_MOUNT_PATH: str = "/data"
+
+# hf_transfer is not used anymore. Let's warn user is case they set the env variable.
+# Note: we use FutureWarning (shown by default) instead of DeprecationWarning (silenced
+# by default for end users) so users running standard `python` actually see the message.
+if _is_true(os.environ.get("HF_HUB_ENABLE_HF_TRANSFER")) and not HF_XET_HIGH_PERFORMANCE:
+ import warnings
+
+ warnings.warn(
+ "The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. "
+ "Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. "
+ "Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.",
+ FutureWarning,
+ )
+
+# Used to override the etag timeout on a system level
+HF_HUB_ETAG_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_ETAG_TIMEOUT")) or DEFAULT_ETAG_TIMEOUT
+
+# Used to override the get request timeout on a system level
+# Also used as a default timeout for other requests if not specified (kept the naming for legacy reasons)
+HF_HUB_DOWNLOAD_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_DOWNLOAD_TIMEOUT")) or DEFAULT_DOWNLOAD_TIMEOUT
+
+# Allows to add information about the requester in the user-agent (e.g. partner name)
+HF_HUB_USER_AGENT_ORIGIN: str | None = os.environ.get("HF_HUB_USER_AGENT_ORIGIN")
+
+# If OAuth didn't work after 2 redirects, there's likely a third-party cookie issue in the Space iframe view.
+# In this case, we redirect the user to the non-iframe view.
+OAUTH_MAX_REDIRECTS = 2
+
+# OAuth-related environment variables injected by the Space
+OAUTH_CLIENT_ID = os.environ.get("OAUTH_CLIENT_ID")
+OAUTH_CLIENT_SECRET = os.environ.get("OAUTH_CLIENT_SECRET")
+OAUTH_SCOPES = os.environ.get("OAUTH_SCOPES")
+OPENID_PROVIDER_URL = os.environ.get("OPENID_PROVIDER_URL")
+
+# OAuth client ID of the Device Code login flow (RFC 8628) used by `hf auth login` / `login()`.
+# Overridable for Hub deployments (staging, Enterprise) where the default client ID is not provisioned.
+DEVICE_CODE_OAUTH_CLIENT_ID = os.environ.get("HF_DEVICE_CODE_OAUTH_CLIENT_ID", "26be6b09-91c5-47da-9861-d2d2bb7a7e36")
+
+# Xet constants
+HUGGINGFACE_HEADER_X_XET_ENDPOINT = "X-Xet-Cas-Url"
+HUGGINGFACE_HEADER_X_XET_ACCESS_TOKEN = "X-Xet-Access-Token"
+HUGGINGFACE_HEADER_X_XET_EXPIRATION = "X-Xet-Token-Expiration"
+HUGGINGFACE_HEADER_X_XET_HASH = "X-Xet-Hash"
+HUGGINGFACE_HEADER_X_XET_REFRESH_ROUTE = "X-Xet-Refresh-Route"
+HUGGINGFACE_HEADER_LINK_XET_AUTH_KEY = "xet-auth"
+
+default_xet_cache_path = os.path.join(HF_HOME, "xet")
+HF_XET_CACHE = os.getenv("HF_XET_CACHE", default_xet_cache_path)
+HF_HUB_DISABLE_XET: bool = _is_true(os.environ.get("HF_HUB_DISABLE_XET"))
+
+# Disable the cache-wide shared blob store (see `huggingface_hub.utils._shared_blobs`).
+HF_HUB_DISABLE_SHARED_BLOBS: bool = _is_true(os.environ.get("HF_HUB_DISABLE_SHARED_BLOBS"))
+
+# Bucket hosting the static sandbox server binary (see huggingface_hub.Sandbox)
+SANDBOX_SERVER_BUCKET: str = "huggingface/sbx-server"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/dataclasses.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/dataclasses.py
new file mode 100644
index 0000000000000000000000000000000000000000..8777d51814c66be4f75812dd98ceccedb64a2a9b
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/dataclasses.py
@@ -0,0 +1,696 @@
+import collections.abc
+import inspect
+import types
+from collections.abc import Callable
+from dataclasses import MISSING, Field, field, fields, make_dataclass
+from functools import lru_cache, wraps
+from typing import (
+ Annotated,
+ Any,
+ ForwardRef,
+ Literal,
+ Type,
+ TypeVar,
+ Union,
+ get_args,
+ get_origin,
+ overload,
+)
+
+
+try:
+ # Python 3.11+
+ from typing import NotRequired, Required # type: ignore
+except ImportError:
+ try:
+ # In case typing_extensions is installed
+ from typing_extensions import NotRequired, Required # type: ignore
+ except ImportError:
+ # Fallback: create dummy types that will never match
+ Required = type("Required", (), {}) # type: ignore
+ NotRequired = type("NotRequired", (), {}) # type: ignore
+
+from .errors import (
+ StrictDataclassClassValidationError,
+ StrictDataclassDefinitionError,
+ StrictDataclassFieldValidationError,
+)
+
+
+Validator_T = Callable[[Any], None]
+T = TypeVar("T")
+TypedDictType = TypeVar("TypedDictType", bound=dict[str, Any])
+
+_TYPED_DICT_DEFAULT_VALUE = object() # used as default value in TypedDict fields (to distinguish from None)
+
+
+# The overload decorator helps type checkers understand the different return types
+@overload
+def strict(cls: Type[T]) -> Type[T]: ...
+
+
+@overload
+def strict(*, accept_kwargs: bool = False) -> Callable[[Type[T]], Type[T]]: ...
+
+
+def strict(cls: Type[T] | None = None, *, accept_kwargs: bool = False) -> Type[T] | Callable[[Type[T]], Type[T]]:
+ """
+ Decorator to add strict validation to a dataclass.
+
+ This decorator must be used on top of `@dataclass` to ensure IDEs and static typing tools
+ recognize the class as a dataclass.
+
+ Can be used with or without arguments:
+ - `@strict`
+ - `@strict(accept_kwargs=True)`
+
+ Args:
+ cls:
+ The class to convert to a strict dataclass.
+ accept_kwargs (`bool`, *optional*):
+ If True, allows arbitrary keyword arguments in `__init__`. Defaults to False.
+
+ Returns:
+ The enhanced dataclass with strict validation on field assignment.
+
+ Example:
+ ```py
+ >>> from dataclasses import dataclass
+ >>> from huggingface_hub.dataclasses import as_validated_field, strict, validated_field
+
+ >>> @as_validated_field
+ >>> def positive_int(value: int):
+ ... if not value >= 0:
+ ... raise ValueError(f"Value must be positive, got {value}")
+
+ >>> @strict(accept_kwargs=True)
+ ... @dataclass
+ ... class User:
+ ... name: str
+ ... age: int = positive_int(default=10)
+
+ # Initialize
+ >>> User(name="John")
+ User(name='John', age=10)
+
+ # Extra kwargs are accepted
+ >>> User(name="John", age=30, lastname="Doe")
+ User(name='John', age=30, *lastname='Doe')
+
+ # Invalid type => raises
+ >>> User(name="John", age="30")
+ huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+ TypeError: Field 'age' expected int, got str (value: '30')
+
+ # Invalid value => raises
+ >>> User(name="John", age=-1)
+ huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+ ValueError: Value must be positive, got -1
+ ```
+ """
+
+ def wrap(cls: Type[T]) -> Type[T]:
+ if not hasattr(cls, "__dataclass_fields__"):
+ raise StrictDataclassDefinitionError(
+ f"Class '{cls.__name__}' must be a dataclass before applying @strict."
+ )
+
+ # List and store validators
+ field_validators: dict[str, list[Validator_T]] = {}
+ for f in fields(cls): # type: ignore
+ validators = []
+ validators.append(_create_type_validator(f))
+ custom_validator = f.metadata.get("validator")
+ if custom_validator is not None:
+ if not isinstance(custom_validator, list):
+ custom_validator = [custom_validator]
+ for validator in custom_validator:
+ if not _is_validator(validator):
+ raise StrictDataclassDefinitionError(
+ f"Invalid validator for field '{f.name}': {validator}. Must be a callable taking a single argument."
+ )
+ validators.extend(custom_validator)
+ field_validators[f.name] = validators
+ cls.__validators__ = field_validators # type: ignore
+
+ # Override __setattr__ to validate fields on assignment
+ original_setattr = cls.__setattr__
+
+ def __strict_setattr__(self: Any, name: str, value: Any) -> None:
+ """Custom __setattr__ method for strict dataclasses."""
+ # Run all validators
+ for validator in self.__validators__.get(name, []):
+ try:
+ validator(value)
+ except (ValueError, TypeError) as e:
+ raise StrictDataclassFieldValidationError(field=name, cause=e) from e
+
+ # If validation passed, set the attribute
+ original_setattr(self, name, value)
+
+ cls.__setattr__ = __strict_setattr__ # type: ignore
+
+ if accept_kwargs:
+ # (optional) Override __init__ to accept arbitrary keyword arguments
+ original_init = cls.__init__
+
+ @wraps(original_init)
+ def __init__(self, *args, **kwargs: Any) -> None:
+ # Extract only the fields that are part of the dataclass
+ dataclass_fields = {f.name for f in fields(cls)} # type: ignore
+ standard_kwargs = {k: v for k, v in kwargs.items() if k in dataclass_fields}
+
+ # User shouldn't define custom `__init__` when `accepts_kwargs`, and instead
+ # are advised to move field manipulation to `__post_init__` (e.g., derive new field from existing ones)
+ # We need to call bare `__init__` here without `__post_init__` but the``original_init`` would call
+ # post-init right away with no kwargs.
+ if len(args) > 0:
+ raise ValueError(
+ f"When `accept_kwargs=True`, {cls.__name__} accepts only keyword arguments, "
+ f"but found `{len(args)}` positional args."
+ )
+
+ for f in fields(cls): # type: ignore
+ if f.name in standard_kwargs:
+ setattr(self, f.name, standard_kwargs[f.name])
+ elif f.default is not MISSING:
+ setattr(self, f.name, f.default)
+ elif f.default_factory is not MISSING:
+ setattr(self, f.name, f.default_factory())
+ else:
+ raise TypeError(f"Missing required field - '{f.name}'")
+
+ # Pass any additional kwargs to `__post_init__` and let the object
+ # decide whether to set the attr or use for different purposes (e.g. BC checks)
+ additional_kwargs = {}
+ for name, value in kwargs.items():
+ if name not in dataclass_fields:
+ additional_kwargs[name] = value
+
+ self.__post_init__(**additional_kwargs)
+
+ cls.__init__ = __init__ # type: ignore
+
+ # Define a default __post_init__ if not defined
+ if not hasattr(cls, "__post_init__"):
+
+ def __post_init__(self, **kwargs: Any) -> None:
+ """Default __post_init__ to accept additional kwargs."""
+ for name, value in kwargs.items():
+ setattr(self, name, value)
+
+ cls.__post_init__ = __post_init__ # type: ignore
+
+ # (optional) Override __repr__ to include additional kwargs
+ original_repr = cls.__repr__
+
+ @wraps(original_repr)
+ def __repr__(self) -> str:
+ # Call the original __repr__ to get the standard fields
+ standard_repr = original_repr(self)
+
+ # Get additional kwargs
+ additional_kwargs = [
+ # add a '*' in front of additional kwargs to let the user know they are not part of the dataclass
+ f"*{k}={v!r}"
+ for k, v in self.__dict__.items()
+ if k not in cls.__dataclass_fields__ # type: ignore [attr-defined]
+ ]
+ additional_repr = ", ".join(additional_kwargs)
+
+ # Combine both representations
+ return f"{standard_repr[:-1]}, {additional_repr})" if additional_kwargs else standard_repr
+
+ if cls.__dataclass_params__.repr is True: # type: ignore [attr-defined]
+ cls.__repr__ = __repr__ # type: ignore
+
+ # List all public methods starting with `validate_` => class validators.
+ class_validators = []
+
+ for name in dir(cls):
+ if not name.startswith("validate_"):
+ continue
+ method = getattr(cls, name)
+ if not callable(method):
+ continue
+ if len(inspect.signature(method).parameters) != 1:
+ raise StrictDataclassDefinitionError(
+ f"Class '{cls.__name__}' has a class validator '{name}' that takes more than one argument."
+ " Class validators must take only 'self' as an argument. Methods starting with 'validate_'"
+ " are considered to be class validators."
+ )
+ class_validators.append(method)
+
+ cls.__class_validators__ = class_validators # type: ignore
+
+ # Add `validate` method to the class, but first check if it already exists
+ def validate(self: T) -> None:
+ """Run class validators on the instance."""
+ for validator in cls.__class_validators__: # type: ignore [attr-defined]
+ try:
+ validator(self)
+ except (ValueError, TypeError) as e:
+ raise StrictDataclassClassValidationError(validator=validator.__name__, cause=e) from e
+
+ # Hack to be able to raise if `.validate()` already exists except if it was created by this decorator on a parent class
+ # (in which case we just override it)
+ validate.__is_defined_by_strict_decorator__ = True # type: ignore [attr-defined]
+
+ if hasattr(cls, "validate"):
+ if not getattr(cls.validate, "__is_defined_by_strict_decorator__", False): # type: ignore [attr-defined]
+ raise StrictDataclassDefinitionError(
+ f"Class '{cls.__name__}' already implements a method called 'validate'."
+ " This method name is reserved when using the @strict decorator on a dataclass."
+ " If you want to keep your own method, please rename it."
+ )
+
+ cls.validate = validate # type: ignore
+
+ # Run class validators after initialization
+ initial_init = cls.__init__
+
+ @wraps(initial_init)
+ def init_with_validate(self, *args, **kwargs) -> None:
+ """Run class validators after initialization."""
+ initial_init(self, *args, **kwargs) # type: ignore [call-arg]
+ cls.validate(self) # type: ignore [attr-defined]
+
+ setattr(cls, "__init__", init_with_validate)
+
+ return cls
+
+ # Return wrapped class or the decorator itself
+ return wrap(cls) if cls is not None else wrap
+
+
+def validate_typed_dict(schema: type[TypedDictType], data: dict) -> None:
+ """
+ Validate that a dictionary conforms to the types defined in a TypedDict class.
+
+ Under the hood, the typed dict is converted to a strict dataclass and validated using the `@strict` decorator.
+
+ Args:
+ schema (`type[TypedDictType]`):
+ The TypedDict class defining the expected structure and types.
+ data (`dict`):
+ The dictionary to validate.
+
+ Raises:
+ `StrictDataclassFieldValidationError`:
+ If any field in the dictionary does not conform to the expected type.
+
+ Example:
+ ```py
+ >>> from typing import Annotated, TypedDict
+ >>> from huggingface_hub.dataclasses import validate_typed_dict
+
+ >>> def positive_int(value: int):
+ ... if not value >= 0:
+ ... raise ValueError(f"Value must be positive, got {value}")
+
+ >>> class User(TypedDict):
+ ... name: str
+ ... age: Annotated[int, positive_int]
+
+ >>> # Valid data
+ >>> validate_typed_dict(User, {"name": "John", "age": 30})
+
+ >>> # Invalid type for age
+ >>> validate_typed_dict(User, {"name": "John", "age": "30"})
+ huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+ TypeError: Field 'age' expected int, got str (value: '30')
+
+ >>> # Invalid value for age
+ >>> validate_typed_dict(User, {"name": "John", "age": -1})
+ huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+ ValueError: Value must be positive, got -1
+ ```
+ """
+ # Convert typed dict to dataclass
+ strict_cls = _build_strict_cls_from_typed_dict(schema)
+
+ # Validate the data by instantiating the strict dataclass
+ strict_cls(**data) # will raise if validation fails
+
+
+@lru_cache
+def _build_strict_cls_from_typed_dict(schema: type[TypedDictType]) -> Type:
+ # Extract type hints from the TypedDict class
+ type_hints = _get_typed_dict_annotations(schema)
+
+ # If the TypedDict is not total, wrap fields as NotRequired (unless explicitly Required or NotRequired)
+ if not getattr(schema, "__total__", True):
+ for key, value in type_hints.items():
+ origin = get_origin(value)
+
+ if origin is Annotated:
+ base, *meta = get_args(value)
+ if not _is_required_or_notrequired(base):
+ base = NotRequired[base]
+ type_hints[key] = Annotated[tuple([base] + list(meta))] # type: ignore
+ elif not _is_required_or_notrequired(value):
+ type_hints[key] = NotRequired[value]
+
+ # Convert type hints to dataclass fields
+ fields = []
+ for key, value in type_hints.items():
+ if get_origin(value) is Annotated:
+ base, *meta = get_args(value)
+ fields.append((key, base, field(default=_TYPED_DICT_DEFAULT_VALUE, metadata={"validator": meta[0]})))
+ else:
+ fields.append((key, value, field(default=_TYPED_DICT_DEFAULT_VALUE)))
+
+ # Create a strict dataclass from the TypedDict fields
+ return strict(make_dataclass(schema.__name__, fields))
+
+
+def _get_typed_dict_annotations(schema: type[TypedDictType]) -> dict[str, Any]:
+ """Extract type annotations from a TypedDict class."""
+ try:
+ # Available in Python 3.14+
+ import annotationlib
+
+ return annotationlib.get_annotations(schema)
+ except ImportError:
+ return {
+ # We do not use `get_type_hints` here to avoid evaluating ForwardRefs (which might fail).
+ # ForwardRefs are not validated by @strict anyway.
+ name: value if value is not None else type(None)
+ for name, value in schema.__dict__.get("__annotations__", {}).items()
+ }
+
+
+def validated_field(
+ validator: list[Validator_T] | Validator_T,
+ default: Any = MISSING,
+ default_factory: Any = MISSING,
+ init: bool = True,
+ repr: bool = True,
+ hash: bool | None = None,
+ compare: bool = True,
+ metadata: dict | None = None,
+ **kwargs: Any,
+) -> Any:
+ """
+ Create a dataclass field with a custom validator.
+
+ Useful to apply several checks to a field. If only applying one rule, check out the [`as_validated_field`] decorator.
+
+ Args:
+ validator (`Callable` or `list[Callable]`):
+ A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
+ Can be a list of validators to apply multiple checks.
+ **kwargs:
+ Additional arguments to pass to `dataclasses.field()`.
+
+ Returns:
+ A field with the validator attached in metadata
+ """
+ if not isinstance(validator, list):
+ validator = [validator]
+ if metadata is None:
+ metadata = {}
+ metadata["validator"] = validator
+ return field( # type: ignore
+ default=default, # type: ignore
+ default_factory=default_factory, # type: ignore
+ init=init,
+ repr=repr,
+ hash=hash,
+ compare=compare,
+ metadata=metadata,
+ **kwargs,
+ )
+
+
+def as_validated_field(validator: Validator_T):
+ """
+ Decorates a validator function as a [`validated_field`] (i.e. a dataclass field with a custom validator).
+
+ Args:
+ validator (`Callable`):
+ A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
+ """
+
+ def _inner(
+ default: Any = MISSING,
+ default_factory: Any = MISSING,
+ init: bool = True,
+ repr: bool = True,
+ hash: bool | None = None,
+ compare: bool = True,
+ metadata: dict | None = None,
+ **kwargs: Any,
+ ):
+ return validated_field(
+ validator,
+ default=default,
+ default_factory=default_factory,
+ init=init,
+ repr=repr,
+ hash=hash,
+ compare=compare,
+ metadata=metadata,
+ **kwargs,
+ )
+
+ return _inner
+
+
+def type_validator(name: str, value: Any, expected_type: Any) -> None:
+ """Validate that 'value' matches 'expected_type'."""
+ origin = get_origin(expected_type)
+ args = get_args(expected_type)
+
+ if expected_type is Any:
+ return
+ elif expected_type is None:
+ _validate_none(name, value)
+ elif validator := _BASIC_TYPE_VALIDATORS.get(origin):
+ validator(name, value, args)
+ elif isinstance(expected_type, type): # simple types
+ _validate_simple_type(name, value, expected_type)
+ elif isinstance(expected_type, ForwardRef) or isinstance(expected_type, str):
+ return
+ elif origin is Required:
+ if value is _TYPED_DICT_DEFAULT_VALUE:
+ raise TypeError(f"Field '{name}' is required but missing.")
+ type_validator(name, value, args[0])
+ elif origin is NotRequired:
+ if value is _TYPED_DICT_DEFAULT_VALUE:
+ return
+ type_validator(name, value, args[0])
+ else:
+ raise TypeError(f"Unsupported type for field '{name}': {expected_type}")
+
+
+def _validate_none(name: str, value: Any) -> None:
+ """Validate None type.
+
+ 'None' is not a type, it's a special value. Type should be `NoneType` instead.
+ But in type annotations 'None' is accepted so we must support it.
+ """
+ if value is not None:
+ raise TypeError(f"Field '{name}' expected None, got {type(value).__name__}")
+
+
+def _validate_union(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate that value matches one of the types in a Union."""
+ errors = []
+ for t in args:
+ try:
+ type_validator(name, value, t)
+ return # Valid if any type matches
+ except TypeError as e:
+ errors.append(str(e))
+
+ raise TypeError(
+ f"Field '{name}' with value {repr(value)} doesn't match any type in {args}. Errors: {'; '.join(errors)}"
+ )
+
+
+def _validate_literal(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate Literal type."""
+ if isinstance(value, bool):
+ if value not in [arg for arg in args if isinstance(arg, bool)]:
+ raise TypeError(f"Field '{name}' expected one of {args}, got {value}")
+ elif isinstance(value, int):
+ if value not in [arg for arg in args if isinstance(arg, int) and not isinstance(arg, bool)]:
+ raise TypeError(f"Field '{name}' expected one of {args}, got {value}")
+ elif value not in args:
+ raise TypeError(f"Field '{name}' expected one of {args}, got {value}")
+
+
+def _validate_list(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate list or list[T] type."""
+ if not isinstance(value, list):
+ raise TypeError(f"Field '{name}' expected a list, got {type(value).__name__}")
+
+ # If no type argument is provided (i.e., bare `typing.List`), skip item validation
+ if not args:
+ return
+
+ # Validate each item in the list
+ item_type = args[0]
+ for i, item in enumerate(value):
+ try:
+ type_validator(f"{name}[{i}]", item, item_type)
+ except TypeError as e:
+ raise TypeError(f"Invalid item at index {i} in list '{name}'") from e
+
+
+def _validate_dict(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate dict or dict[K, V] type."""
+ if not isinstance(value, dict):
+ raise TypeError(f"Field '{name}' expected a dict, got {type(value).__name__}")
+
+ # If no type arguments are provided (i.e., bare `typing.Dict`), skip key/value validation
+ if not args:
+ return
+
+ # Validate keys and values
+ key_type, value_type = args
+ for k, v in value.items():
+ try:
+ type_validator(f"{name}.key", k, key_type)
+ type_validator(f"{name}[{k!r}]", v, value_type)
+ except TypeError as e:
+ raise TypeError(f"Invalid key or value in dict '{name}'") from e
+
+
+def _validate_tuple(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate Tuple type."""
+ if not isinstance(value, tuple):
+ raise TypeError(f"Field '{name}' expected a tuple, got {type(value).__name__}")
+
+ # Handle variable-length tuples: tuple[T, ...]
+ if len(args) == 2 and args[1] is Ellipsis:
+ for i, item in enumerate(value):
+ try:
+ type_validator(f"{name}[{i}]", item, args[0])
+ except TypeError as e:
+ raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
+ # Handle fixed-length tuples: tuple[T1, T2, ...]
+ elif len(args) != len(value):
+ raise TypeError(f"Field '{name}' expected a tuple of length {len(args)}, got {len(value)}")
+ else:
+ for i, (item, expected) in enumerate(zip(value, args)):
+ try:
+ type_validator(f"{name}[{i}]", item, expected)
+ except TypeError as e:
+ raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
+
+
+def _validate_set(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate set or set[T] type."""
+ if not isinstance(value, set):
+ raise TypeError(f"Field '{name}' expected a set, got {type(value).__name__}")
+
+ # If no type argument is provided (i.e., bare `typing.Set`), skip item validation
+ if not args:
+ return
+
+ # Validate each item in the set
+ item_type = args[0]
+ for i, item in enumerate(value):
+ try:
+ type_validator(f"{name} item", item, item_type)
+ except TypeError as e:
+ raise TypeError(f"Invalid item in set '{name}'") from e
+
+
+def _validate_sequence(name: str, value: Any, args: tuple[Any, ...]) -> None:
+ """Validate Sequence or Sequence[T] type."""
+ if not isinstance(value, collections.abc.Sequence):
+ raise TypeError(f"Field '{name}' expected a Sequence, got {type(value).__name__}")
+
+ # If no type argument is provided (i.e., just `Sequence`), skip item validation
+ if not args:
+ return
+
+ # Validate each item in the sequence
+ item_type = args[0]
+ for i, item in enumerate(value):
+ try:
+ type_validator(f"{name}[{i}]", item, item_type)
+ except TypeError as e:
+ raise TypeError(f"Invalid item at index {i} in sequence '{name}'") from e
+
+
+def _validate_simple_type(name: str, value: Any, expected_type: type) -> None:
+ """Validate simple type (int, str, etc.)."""
+ if expected_type is int and isinstance(value, bool):
+ raise TypeError(
+ f"Field '{name}' expected {expected_type.__name__}, got {type(value).__name__} (value: {repr(value)})"
+ )
+ if not isinstance(value, expected_type):
+ raise TypeError(
+ f"Field '{name}' expected {expected_type.__name__}, got {type(value).__name__} (value: {repr(value)})"
+ )
+
+
+def _create_type_validator(field: Field) -> Validator_T:
+ """Create a type validator function for a field."""
+ # Hacky: we cannot use a lambda here because of reference issues
+
+ def validator(value: Any) -> None:
+ type_validator(field.name, value, field.type)
+
+ return validator
+
+
+def _is_validator(validator: Any) -> bool:
+ """Check if a function is a validator.
+
+ A validator is a Callable that can be called with a single positional argument.
+ The validator can have more arguments with default values.
+
+ Basically, returns True if `validator(value)` is possible.
+ """
+ if not callable(validator):
+ return False
+
+ signature = inspect.signature(validator)
+ parameters = list(signature.parameters.values())
+ if len(parameters) == 0:
+ return False
+ if parameters[0].kind not in (
+ inspect.Parameter.POSITIONAL_OR_KEYWORD,
+ inspect.Parameter.POSITIONAL_ONLY,
+ inspect.Parameter.VAR_POSITIONAL,
+ ):
+ return False
+ for parameter in parameters[1:]:
+ if parameter.default == inspect.Parameter.empty:
+ return False
+ return True
+
+
+def _is_required_or_notrequired(type_hint: Any) -> bool:
+ """Helper to check if a type is Required/NotRequired."""
+ return type_hint in (Required, NotRequired) or (get_origin(type_hint) in (Required, NotRequired))
+
+
+_BASIC_TYPE_VALIDATORS: dict[Any, Callable[[str, Any, tuple[Any, ...]], None]] = {
+ Union: _validate_union,
+ Literal: _validate_literal,
+ list: _validate_list,
+ dict: _validate_dict,
+ tuple: _validate_tuple,
+ set: _validate_set,
+ collections.abc.Sequence: _validate_sequence,
+}
+
+# TODO: make it first class citizen when bumping to Python 3.10+
+_BASIC_TYPE_VALIDATORS[types.UnionType] = _validate_union # x | y syntax, available only Python 3.10+
+
+
+__all__ = [
+ "strict",
+ "validate_typed_dict",
+ "validated_field",
+ "Validator_T",
+ "StrictDataclassClassValidationError",
+ "StrictDataclassDefinitionError",
+ "StrictDataclassFieldValidationError",
+]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/errors.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..d06b9de9002a1e2c17a3894bad601a6adf49c0de
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/errors.py
@@ -0,0 +1,629 @@
+"""Contains all custom errors."""
+
+from enum import Enum
+from pathlib import Path
+
+from httpx2 import HTTPError, Response
+
+
+# CACHE ERRORS
+
+
+class CacheNotFound(Exception):
+ """Exception thrown when the Huggingface cache is not found."""
+
+ cache_dir: str | Path
+
+ def __init__(self, msg: str, cache_dir: str | Path, *args, **kwargs):
+ super().__init__(msg, *args, **kwargs)
+ self.cache_dir = cache_dir
+
+
+class CorruptedCacheException(Exception):
+ """Exception for any unexpected structure in the Huggingface cache-system."""
+
+
+class CachedRepoTreeNotFoundError(Exception):
+ """Raised by [`get_cached_repo_tree`] when no tree listing is cached for the requested revision.
+
+ The tree listing is populated as a side effect of [`snapshot_download`].
+ """
+
+
+# HEADERS ERRORS
+
+
+class LocalTokenNotFoundError(EnvironmentError):
+ """Raised if local token is required but not found."""
+
+
+# OIDC ERRORS
+
+
+class OIDCError(Exception):
+ """Raised when keyless CI/CD auth via OIDC token exchange ("Trusted Publishers") cannot proceed.
+
+ Typically because `HF_OIDC_RESOURCE` is set but no id token is available: not running in a
+ supported CI provider and `HF_OIDC_ID_TOKEN` is unset.
+
+ See https://huggingface.co/docs/hub/trusted-publishers.
+ """
+
+
+# DEVICE CODE OAUTH ERRORS
+
+
+class OAuthErrorCode(str, Enum):
+ """Known OAuth `error` codes returned by the Hub's token endpoint (RFC 6749 / RFC 8628)."""
+
+ AUTHORIZATION_PENDING = "authorization_pending"
+ SLOW_DOWN = "slow_down"
+ EXPIRED_TOKEN = "expired_token"
+ ACCESS_DENIED = "access_denied"
+ INVALID_GRANT = "invalid_grant"
+
+
+class DeviceCodeError(Exception):
+ """Raised when the Device Code OAuth login flow (RFC 8628) or an OAuth token refresh fails.
+
+ Covers failures at any step: requesting the device code, polling for the token,
+ authorization denied/expired, or unexpected server responses.
+
+ Attributes:
+ error_code (`str`, *optional*):
+ The OAuth `error` code returned by the server, if any. Known values are listed in
+ [`OAuthErrorCode`] but the server may return other codes.
+ """
+
+ def __init__(self, message: str, error_code: str | None = None):
+ super().__init__(message)
+ self.error_code = error_code
+
+
+# HTTP ERRORS
+
+
+class OfflineModeIsEnabled(ConnectionError):
+ """Raised when a request is made but `HF_HUB_OFFLINE=1` is set as environment variable."""
+
+
+class HfHubHTTPError(HTTPError, OSError):
+ """
+ HTTPError to inherit from for any custom HTTP Error raised in HF Hub.
+
+ Any HTTPError is converted at least into a `HfHubHTTPError`. If some information is
+ sent back by the server, it will be added to the error message.
+
+ Added details:
+ - Request ID sourced from headers in order of precedence: "X-Request-Id", "X-Amzn-Trace-Id", "X-Amz-Cf-Id".
+ - Server error message from the header "X-Error-Message".
+ - Server error message if we can found one in the response body.
+
+ Example:
+ ```py
+ import httpx2
+ from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
+
+ response = get_session().post(...)
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as e:
+ print(str(e)) # formatted message
+ e.request_id, e.server_message # details returned by server
+
+ # Complete the error message with additional information once it's raised
+ e.append_to_message("\n`create_commit` expects the repository to exist.")
+ raise
+ ```
+ """
+
+ def __init__(
+ self,
+ message: str,
+ *,
+ response: Response,
+ server_message: str | None = None,
+ ):
+ self.request_id = (
+ response.headers.get("x-request-id")
+ or response.headers.get("X-Amzn-Trace-Id")
+ or response.headers.get("x-amz-cf-id")
+ )
+ self.server_message = server_message
+ self.response = response
+ self.request = response.request
+ super().__init__(message)
+
+ def append_to_message(self, additional_message: str) -> None:
+ """Append additional information to the `HfHubHTTPError` initial message."""
+ self.args = (self.args[0] + additional_message,) + self.args[1:]
+
+ @classmethod
+ def _reconstruct_hf_hub_http_error(
+ cls, message: str, response: Response, server_message: str | None
+ ) -> "HfHubHTTPError":
+ return cls(message, response=response, server_message=server_message)
+
+ def __reduce_ex__(self, protocol):
+ """Fix pickling of Exception subclass with kwargs. We need to override __reduce_ex__ of the parent class"""
+ return (self.__class__._reconstruct_hf_hub_http_error, (str(self), self.response, self.server_message))
+
+
+# INFERENCE CLIENT ERRORS
+
+
+class InferenceTimeoutError(HTTPError, TimeoutError):
+ """Error raised when a model is unavailable or the request times out."""
+
+
+# INFERENCE ENDPOINT ERRORS
+
+
+class InferenceEndpointError(Exception):
+ """Generic exception when dealing with Inference Endpoints."""
+
+
+class InferenceEndpointTimeoutError(InferenceEndpointError, TimeoutError):
+ """Exception for timeouts while waiting for Inference Endpoint."""
+
+
+# SAFETENSORS ERRORS
+
+
+class SafetensorsParsingError(Exception):
+ """Raised when failing to parse a safetensors file metadata.
+
+ This can be the case if the file is not a safetensors file or does not respect the specification.
+ """
+
+
+class NotASafetensorsRepoError(Exception):
+ """Raised when a repo is not a Safetensors repo i.e. doesn't have either a `model.safetensors` or a
+ `model.safetensors.index.json` file.
+ """
+
+
+# TEXT GENERATION ERRORS
+
+
+class TextGenerationError(HTTPError):
+ """Generic error raised if text-generation went wrong."""
+
+
+# Text Generation Inference Errors
+class ValidationError(TextGenerationError):
+ """Server-side validation error."""
+
+
+class GenerationError(TextGenerationError):
+ pass
+
+
+class OverloadedError(TextGenerationError):
+ pass
+
+
+class IncompleteGenerationError(TextGenerationError):
+ pass
+
+
+class UnknownError(TextGenerationError):
+ pass
+
+
+# VALIDATION ERRORS
+
+
+class HFValidationError(ValueError):
+ """Generic exception thrown by `huggingface_hub` validators.
+
+ Inherits from [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).
+ """
+
+
+class HfUriError(ValueError):
+ """Raised when an `hf://...` URI is malformed.
+
+ See [`parse_hf_uri`] and the
+ [HF URIs reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_uris)
+ for the canonical syntax.
+
+ Inherits from [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).
+ """
+
+ def __init__(self, uri: str, msg: str):
+ self.uri = uri
+ self.msg = msg
+ full_msg = f"Invalid HF URI '{uri}'. {msg}" if uri else f"Invalid HF URI. {msg}"
+ super().__init__(full_msg)
+
+
+# FILE METADATA ERRORS
+
+
+class DryRunError(OSError):
+ """Error triggered when a dry run is requested but cannot be performed (e.g. invalid repo)."""
+
+
+class FileMetadataError(OSError):
+ """Error triggered when the metadata of a file on the Hub cannot be retrieved (missing ETag or commit_hash).
+
+ Inherits from `OSError` for backward compatibility.
+ """
+
+
+# BUCKET ERRORS
+
+
+class BucketNotFoundError(HfHubHTTPError):
+ """
+ Raised when trying to access a bucket that does not exist.
+
+ Attributes:
+ bucket_id (`str` or `None`):
+ The bucket id (namespace/name) that was not found, if it could be determined from the request URL.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import bucket_info
+ >>> bucket_info("")
+ (...)
+ huggingface_hub.errors.BucketNotFoundError: 404 Client Error. (Request ID: XXX)
+
+ Bucket Not Found for url: https://huggingface.co/api/buckets/namespace/name.
+ Please make sure you specified the correct bucket id (namespace/name).
+ If the bucket is private, make sure you are authenticated and your token has the required permissions.
+ ```
+ """
+
+ bucket_id: str | None = None
+
+
+class BucketBatchError(HfHubHTTPError):
+ """
+ Raised when the server reports that some operations of a bucket batch failed.
+
+ Attributes:
+ failures (`list[dict[str, str]]`):
+ The failed operations as listed by the server, e.g. `[{"path": "config.json", "error": "..."}]`.
+ """
+
+ failures: list[dict[str, str]] = []
+
+
+# JOB ERRORS
+
+
+class JobNotFoundError(HfHubHTTPError):
+ """
+ Raised when trying to access a Job that does not exist.
+
+ Attributes:
+ job_id (`str`):
+ The job id that was not found.
+ """
+
+ job_id: str
+
+
+# REPOSITORY ERRORS
+
+
+class RepositoryNotFoundError(HfHubHTTPError):
+ """
+ Raised when trying to access a hf.co URL with an invalid repository name, or
+ with a private repo name the user does not have access to.
+
+ Attributes:
+ repo_id (`str` or `None`):
+ The repo id that was not found, if it could be determined from the request URL.
+ repo_type (`str` or `None`):
+ The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import model_info
+ >>> model_info("")
+ (...)
+ huggingface_hub.errors.RepositoryNotFoundError: 401 Client Error. (Request ID: PvMw_VjBMjVdMz53WKIzP)
+
+ Repository Not Found for url: https://huggingface.co/api/models/%3Cnon_existent_repository%3E.
+ Please make sure you specified the correct `repo_id` and `repo_type`.
+ If the repo is private, make sure you are authenticated and your token has the required permissions.
+ Invalid username or password.
+ ```
+ """
+
+ repo_id: str | None = None
+ repo_type: str | None = None
+
+
+class GatedRepoError(RepositoryNotFoundError):
+ """
+ Raised when trying to access a gated repository for which the user is not on the
+ authorized list.
+
+ Note: derives from `RepositoryNotFoundError` to ensure backward compatibility.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import model_info
+ >>> model_info("")
+ (...)
+ huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: ViT1Bf7O_026LGSQuVqfa)
+
+ Cannot access gated repo for url https://huggingface.co/api/models/ardent-figment/gated-model.
+ Access to model ardent-figment/gated-model is restricted and you are not in the authorized list.
+ Visit https://huggingface.co/ardent-figment/gated-model to ask for access.
+ ```
+ """
+
+
+class DisabledRepoError(HfHubHTTPError):
+ """
+ Raised when trying to access a repository that has been disabled by its author.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import dataset_info
+ >>> dataset_info("laion/laion-art")
+ (...)
+ huggingface_hub.errors.DisabledRepoError: 403 Client Error. (Request ID: Root=1-659fc3fa-3031673e0f92c71a2260dbe2;bc6f4dfb-b30a-4862-af0a-5cfe827610d8)
+
+ Cannot access repository for url https://huggingface.co/api/datasets/laion/laion-art.
+ Access to this resource is disabled.
+ ```
+ """
+
+
+# REVISION ERROR
+
+
+class RevisionNotFoundError(HfHubHTTPError):
+ """
+ Raised when trying to access a hf.co URL with a valid repository but an invalid
+ revision.
+
+ Attributes:
+ repo_id (`str` or `None`):
+ The repo id, if it could be determined from the request URL.
+ repo_type (`str` or `None`):
+ The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import hf_hub_download
+ >>> hf_hub_download('bert-base-cased', 'config.json', revision='')
+ (...)
+ huggingface_hub.errors.RevisionNotFoundError: 404 Client Error. (Request ID: Mwhe_c3Kt650GcdKEFomX)
+
+ Revision Not Found for url: https://huggingface.co/bert-base-cased/resolve/%3Cnon-existent-revision%3E/config.json.
+ ```
+ """
+
+ repo_id: str | None = None
+ repo_type: str | None = None
+
+
+class RevisionResolutionError(Exception):
+ """
+ Raised by [`HfApi.resolve_revision`] when a revision cannot be resolved to a commit hash: the Hub could not be
+ reached (offline mode, connection error, timeout, Hub downtime, ...) and no matching entry was found in the
+ local cache.
+ """
+
+
+# ENTRY ERRORS
+class EntryNotFoundError(Exception):
+ """
+ Raised when entry not found, either locally or remotely.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import hf_hub_download
+ >>> hf_hub_download('bert-base-cased', '')
+ (...)
+ huggingface_hub.errors.RemoteEntryNotFoundError (...)
+ >>> hf_hub_download('bert-base-cased', '', local_files_only=True)
+ (...)
+ huggingface_hub.utils.errors.LocalEntryNotFoundError (...)
+ ```
+ """
+
+
+class RemoteEntryNotFoundError(HfHubHTTPError, EntryNotFoundError):
+ """
+ Raised when trying to access a hf.co URL with a valid repository and revision
+ but an invalid filename.
+
+ Attributes:
+ repo_id (`str` or `None`):
+ The repo id, if it could be determined from the request URL.
+ repo_type (`str` or `None`):
+ The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import hf_hub_download
+ >>> hf_hub_download('bert-base-cased', '')
+ (...)
+ huggingface_hub.errors.EntryNotFoundError: 404 Client Error. (Request ID: 53pNl6M0MxsnG5Sw8JA6x)
+
+ Entry Not Found for url: https://huggingface.co/bert-base-cased/resolve/main/%3Cnon-existent-file%3E.
+ ```
+ """
+
+ repo_id: str | None = None
+ repo_type: str | None = None
+
+
+class LocalEntryNotFoundError(FileNotFoundError, EntryNotFoundError):
+ """
+ Raised when trying to access a file or snapshot that is not on the disk when network is
+ disabled or unavailable (connection issue). The entry may exist on the Hub.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import hf_hub_download
+ >>> hf_hub_download('bert-base-cased', '', local_files_only=True)
+ (...)
+ huggingface_hub.errors.LocalEntryNotFoundError: Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable hf.co look-ups and downloads online, set 'local_files_only' to False.
+ ```
+ """
+
+ def __init__(self, message: str):
+ super().__init__(message)
+
+
+class IncompleteSnapshotError(LocalEntryNotFoundError):
+ """
+ Raised by [`snapshot_download`] when the Hub cannot be reached (offline, connection issue, or
+ `local_files_only=True`) and the cached snapshot is known to be incomplete: some files listed in
+ the repository's cached tree listing are missing from the local snapshot.
+
+ This is a subclass of [`LocalEntryNotFoundError`] for backward compatibility.
+
+ The `snapshot_path` attribute holds the path to the incomplete local snapshot, so a downstream library can locate
+ the latest cached files even though they are known to be incomplete.
+ """
+
+ def __init__(self, message: str, snapshot_path: str):
+ super().__init__(message)
+ self.snapshot_path = snapshot_path
+
+
+# REQUEST ERROR
+class BadRequestError(HfHubHTTPError, ValueError):
+ """
+ Raised by `hf_raise_for_status` when the server returns a HTTP 400 error.
+
+ Example:
+
+ ```py
+ >>> resp = httpx2.post("hf.co/api/check", ...)
+ >>> hf_raise_for_status(resp, endpoint_name="check")
+ huggingface_hub.errors.BadRequestError: Bad request for check endpoint: {details} (Request ID: XXX)
+ ```
+ """
+
+
+# DDUF file format ERROR
+
+
+class DDUFError(Exception):
+ """Base exception for errors related to the DDUF format."""
+
+
+class DDUFCorruptedFileError(DDUFError):
+ """Exception thrown when the DDUF file is corrupted."""
+
+
+class DDUFExportError(DDUFError):
+ """Base exception for errors during DDUF export."""
+
+
+class DDUFInvalidEntryNameError(DDUFExportError):
+ """Exception thrown when the entry name is invalid."""
+
+
+# STRICT DATACLASSES ERRORS
+
+
+class StrictDataclassError(Exception):
+ """Base exception for strict dataclasses."""
+
+
+class StrictDataclassDefinitionError(StrictDataclassError):
+ """Exception thrown when a strict dataclass is defined incorrectly."""
+
+
+class StrictDataclassFieldValidationError(StrictDataclassError):
+ """Exception thrown when a strict dataclass fails validation for a given field."""
+
+ def __init__(self, field: str, cause: Exception):
+ error_message = f"Validation error for field '{field}':"
+ error_message += f"\n {cause.__class__.__name__}: {cause}"
+ super().__init__(error_message)
+
+
+class StrictDataclassClassValidationError(StrictDataclassError):
+ """Exception thrown when a strict dataclass fails validation on a class validator."""
+
+ def __init__(self, validator: str, cause: Exception):
+ error_message = f"Class validation error for validator '{validator}':"
+ error_message += f"\n {cause.__class__.__name__}: {cause}"
+ super().__init__(error_message)
+
+
+# XET ERRORS
+
+
+class XetDownloadError(Exception):
+ """Exception thrown when the download from Xet Storage fails."""
+
+
+# LFS ERRORS
+
+
+class FileDuplicationError(Exception):
+ """Raised when duplicating files across repos fails."""
+
+
+# CLI ERRORS
+
+
+class CLIError(Exception):
+ """CLI error with clean message (no traceback by default)."""
+
+
+class ConfirmationError(CLIError):
+ """Raised when a confirmation prompt is declined (non-interactive mode)."""
+
+
+class CLIExtensionInstallError(CLIError):
+ """Error during CLI extension installation."""
+
+
+# SANDBOX ERRORS
+
+
+class SandboxError(Exception):
+ """Base exception for sandbox operations (see `huggingface_hub.Sandbox`).
+
+ Attributes:
+ status_code: The HTTP status returned by the in-sandbox server, if the error
+ originated from an API response (e.g. `404` for a missing file). `None` otherwise.
+ """
+
+ def __init__(self, message: str, *, status_code: int | None = None) -> None:
+ super().__init__(message)
+ self.status_code = status_code
+
+
+class SandboxCommandError(SandboxError):
+ """Raised when a command run in a sandbox exits with a non-zero code.
+
+ Attributes:
+ cmd: The command that failed.
+ result: The full `SandboxCommandResult` (exit_code, stdout, stderr, ...).
+ """
+
+ def __init__(self, cmd, result) -> None:
+ self.cmd = cmd
+ self.result = result
+ stderr_tail = result.stderr[-1000:] if result.stderr else ""
+ if result.timed_out:
+ reason = "timed out"
+ elif result.signal is not None:
+ reason = f"was killed by signal {result.signal}"
+ else:
+ reason = f"exited with code {result.exit_code}"
+ super().__init__(f"Command {cmd!r} {reason}. stderr:\n{stderr_tail}")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/fastai_utils.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/fastai_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2604e7b1e598d3d1c0b9dc8115e5e3c0a3fc3de2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/fastai_utils.py
@@ -0,0 +1,414 @@
+import json
+import os
+from pathlib import Path
+from pickle import DEFAULT_PROTOCOL, PicklingError
+from typing import Any
+
+from packaging import version
+
+from huggingface_hub import constants, snapshot_download
+from huggingface_hub.hf_api import HfApi
+from huggingface_hub.utils import (
+ SoftTemporaryDirectory,
+ get_fastai_version,
+ get_fastcore_version,
+ get_python_version,
+)
+
+from .utils import logging, validate_hf_hub_args
+
+
+logger = logging.get_logger(__name__)
+
+
+def _check_fastai_fastcore_versions(
+ fastai_min_version: str = "2.4",
+ fastcore_min_version: str = "1.3.27",
+):
+ """
+ Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
+
+ Args:
+ fastai_min_version (`str`, *optional*):
+ The minimum fastai version supported.
+ fastcore_min_version (`str`, *optional*):
+ The minimum fastcore version supported.
+
+ > [!TIP]
+ > Raises the following error:
+ >
+ > - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+ > if the fastai or fastcore libraries are not available or are of an invalid version.
+ """
+
+ if (get_fastcore_version() or get_fastai_version()) == "N/A":
+ raise ImportError(
+ f"fastai>={fastai_min_version} and fastcore>={fastcore_min_version} are"
+ f" required. Currently using fastai=={get_fastai_version()} and"
+ f" fastcore=={get_fastcore_version()}."
+ )
+
+ current_fastai_version = version.Version(get_fastai_version())
+ current_fastcore_version = version.Version(get_fastcore_version())
+
+ if current_fastai_version < version.Version(fastai_min_version):
+ raise ImportError(
+ "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+ f" fastai>={fastai_min_version} version, but you are using fastai version"
+ f" {get_fastai_version()} which is incompatible. Upgrade with `pip install"
+ " fastai==2.5.6`."
+ )
+
+ if current_fastcore_version < version.Version(fastcore_min_version):
+ raise ImportError(
+ "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+ f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+ f" version {get_fastcore_version()} which is incompatible. Upgrade with"
+ " `pip install fastcore==1.3.27`."
+ )
+
+
+def _check_fastai_fastcore_pyproject_versions(
+ storage_folder: str,
+ fastai_min_version: str = "2.4",
+ fastcore_min_version: str = "1.3.27",
+):
+ """
+ Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
+ that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
+ or does not contain versions for fastai and fastcore, then it logs a warning.
+
+ Args:
+ storage_folder (`str`):
+ Folder to look for the `pyproject.toml` file.
+ fastai_min_version (`str`, *optional*):
+ The minimum fastai version supported.
+ fastcore_min_version (`str`, *optional*):
+ The minimum fastcore version supported.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+ > if the `toml` module is not installed.
+ > - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+ > if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
+ """
+
+ try:
+ import toml
+ except ModuleNotFoundError:
+ raise ImportError(
+ "`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module."
+ " Install it with `pip install toml`."
+ )
+
+ # Checks that a `pyproject.toml`, with `build-system` and `requires` sections, exists in the repository. If so, get a list of required packages.
+ if not os.path.isfile(f"{storage_folder}/pyproject.toml"):
+ logger.warning(
+ "There is no `pyproject.toml` in the repository that contains the fastai"
+ " `Learner`. The `pyproject.toml` would allow us to verify that your fastai"
+ " and fastcore versions are compatible with those of the model you want to"
+ " load."
+ )
+ return
+ pyproject_toml = toml.load(f"{storage_folder}/pyproject.toml")
+
+ if "build-system" not in pyproject_toml.keys():
+ logger.warning(
+ "There is no `build-system` section in the pyproject.toml of the repository"
+ " that contains the fastai `Learner`. The `build-system` would allow us to"
+ " verify that your fastai and fastcore versions are compatible with those"
+ " of the model you want to load."
+ )
+ return
+ build_system_toml = pyproject_toml["build-system"]
+
+ if "requires" not in build_system_toml.keys():
+ logger.warning(
+ "There is no `requires` section in the pyproject.toml of the repository"
+ " that contains the fastai `Learner`. The `requires` would allow us to"
+ " verify that your fastai and fastcore versions are compatible with those"
+ " of the model you want to load."
+ )
+ return
+ package_versions = build_system_toml["requires"]
+
+ # Extracts contains fastai and fastcore versions from `pyproject.toml` if available.
+ # If the package is specified but not the version (e.g. "fastai" instead of "fastai=2.4"), the default versions are the highest.
+ fastai_packages = [pck for pck in package_versions if pck.startswith("fastai")]
+ if len(fastai_packages) == 0:
+ logger.warning("The repository does not have a fastai version specified in the `pyproject.toml`.")
+ # fastai_version is an empty string if not specified
+ else:
+ fastai_version = str(fastai_packages[0]).partition("=")[2]
+ if fastai_version != "" and version.Version(fastai_version) < version.Version(fastai_min_version):
+ raise ImportError(
+ "`from_pretrained_fastai` requires"
+ f" fastai>={fastai_min_version} version but the model to load uses"
+ f" {fastai_version} which is incompatible."
+ )
+
+ fastcore_packages = [pck for pck in package_versions if pck.startswith("fastcore")]
+ if len(fastcore_packages) == 0:
+ logger.warning("The repository does not have a fastcore version specified in the `pyproject.toml`.")
+ # fastcore_version is an empty string if not specified
+ else:
+ fastcore_version = str(fastcore_packages[0]).partition("=")[2]
+ if fastcore_version != "" and version.Version(fastcore_version) < version.Version(fastcore_min_version):
+ raise ImportError(
+ "`from_pretrained_fastai` requires"
+ f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+ f" version {fastcore_version} which is incompatible."
+ )
+
+
+README_TEMPLATE = """---
+tags:
+- fastai
+---
+
+# Amazing!
+
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+
+
+---
+
+
+# Model card
+
+## Model description
+More information needed
+
+## Intended uses & limitations
+More information needed
+
+## Training and evaluation data
+More information needed
+"""
+
+PYPROJECT_TEMPLATE = f"""[build-system]
+requires = ["setuptools>=40.8.0", "wheel", "python={get_python_version()}", "fastai={get_fastai_version()}", "fastcore={get_fastcore_version()}"]
+build-backend = "setuptools.build_meta:__legacy__"
+"""
+
+
+def _create_model_card(repo_dir: Path):
+ """
+ Creates a model card for the repository.
+
+ Args:
+ repo_dir (`Path`):
+ Directory where model card is created.
+ """
+ readme_path = repo_dir / "README.md"
+
+ if not readme_path.exists():
+ with readme_path.open("w", encoding="utf-8") as f:
+ f.write(README_TEMPLATE)
+
+
+def _create_model_pyproject(repo_dir: Path):
+ """
+ Creates a `pyproject.toml` for the repository.
+
+ Args:
+ repo_dir (`Path`):
+ Directory where `pyproject.toml` is created.
+ """
+ pyproject_path = repo_dir / "pyproject.toml"
+
+ if not pyproject_path.exists():
+ with pyproject_path.open("w", encoding="utf-8") as f:
+ f.write(PYPROJECT_TEMPLATE)
+
+
+def _save_pretrained_fastai(
+ learner,
+ save_directory: str | Path,
+ config: dict[str, Any] | None = None,
+):
+ """
+ Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
+
+ Args:
+ learner (`Learner`):
+ The `fastai.Learner` you'd like to save.
+ save_directory (`str` or `Path`):
+ Specific directory in which you want to save the fastai learner.
+ config (`dict`, *optional*):
+ Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
+
+ > [!TIP]
+ > Raises the following error:
+ >
+ > - [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
+ > if the config file provided is not a dictionary.
+ """
+ _check_fastai_fastcore_versions()
+
+ os.makedirs(save_directory, exist_ok=True)
+
+ # if the user provides config then we update it with the fastai and fastcore versions in CONFIG_TEMPLATE.
+ if config is not None:
+ if not isinstance(config, dict):
+ raise RuntimeError(f"Provided config should be a dict. Got: '{type(config)}'")
+ path = os.path.join(save_directory, constants.CONFIG_NAME)
+ with open(path, "w") as f:
+ json.dump(config, f)
+
+ _create_model_card(Path(save_directory))
+ _create_model_pyproject(Path(save_directory))
+
+ # learner.export saves the model in `self.path`.
+ learner.path = Path(save_directory)
+ os.makedirs(save_directory, exist_ok=True)
+ try:
+ learner.export(
+ fname="model.pkl",
+ pickle_protocol=DEFAULT_PROTOCOL,
+ )
+ except PicklingError:
+ raise PicklingError(
+ "You are using a lambda function, i.e., an anonymous function. `pickle`"
+ " cannot pickle function objects and requires that all functions have"
+ " names. One possible solution is to name the function."
+ )
+
+
+@validate_hf_hub_args
+def from_pretrained_fastai(
+ repo_id: str,
+ revision: str | None = None,
+):
+ """
+ Load pretrained fastai model from the Hub or from a local directory.
+
+ Args:
+ repo_id (`str`):
+ The location where the pickled fastai.Learner is. It can be either of the two:
+ - Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
+ You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
+ Revision is the specific model version to use. Since we use a git-based system for storing models and other
+ artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
+ - Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
+ indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
+ revision (`str`, *optional*):
+ Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
+
+ Returns:
+ The `fastai.Learner` model in the `repo_id` repo.
+ """
+ _check_fastai_fastcore_versions()
+
+ # Load the `repo_id` repo.
+ # `snapshot_download` returns the folder where the model was stored.
+ # `cache_dir` will be the default '/root/.cache/huggingface/hub'
+ if not os.path.isdir(repo_id):
+ storage_folder = snapshot_download(
+ repo_id=repo_id,
+ revision=revision,
+ library_name="fastai",
+ library_version=get_fastai_version(),
+ )
+ else:
+ storage_folder = repo_id
+
+ _check_fastai_fastcore_pyproject_versions(storage_folder)
+
+ from fastai.learner import load_learner # type: ignore
+
+ return load_learner(os.path.join(storage_folder, "model.pkl"))
+
+
+@validate_hf_hub_args
+def push_to_hub_fastai(
+ learner,
+ *,
+ repo_id: str,
+ commit_message: str = "Push FastAI model using huggingface_hub.",
+ private: bool | None = None,
+ token: str | None = None,
+ config: dict | None = None,
+ branch: str | None = None,
+ create_pr: bool | None = None,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ delete_patterns: list[str] | str | None = None,
+ api_endpoint: str | None = None,
+):
+ """
+ Upload learner checkpoint files to the Hub.
+
+ Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+ `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+ details.
+
+ Args:
+ learner (`Learner`):
+ The `fastai.Learner' you'd like to push to the Hub.
+ repo_id (`str`):
+ The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
+ commit_message (`str`, *optional*):
+ Message to commit while pushing. Will default to :obj:`"add model"`.
+ private (`bool`, *optional*):
+ Whether or not the repository created should be private.
+ If `None` (default), will default to been public except if the organization's default is private.
+ token (`str`, *optional*):
+ The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
+ config (`dict`, *optional*):
+ Configuration object to be saved alongside the model weights.
+ branch (`str`, *optional*):
+ The git branch on which to push the model. This defaults to
+ the default branch as specified in your repository, which
+ defaults to `"main"`.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request from `branch` with that commit.
+ Defaults to `False`.
+ api_endpoint (`str`, *optional*):
+ The API endpoint to use when pushing the model to the hub.
+ allow_patterns (`list[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are pushed.
+ ignore_patterns (`list[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not pushed.
+ delete_patterns (`list[str]` or `str`, *optional*):
+ If provided, remote files matching any of the patterns will be deleted from the repo.
+
+ Returns:
+ The url of the commit of your model in the given repository.
+
+ > [!TIP]
+ > Raises the following error:
+ >
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if the user is not log on to the Hugging Face Hub.
+ """
+ _check_fastai_fastcore_versions()
+ api = HfApi(endpoint=api_endpoint)
+ repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
+
+ # Push the files to the repo in a single commit
+ with SoftTemporaryDirectory() as tmp:
+ saved_path = Path(tmp) / repo_id
+ _save_pretrained_fastai(learner, saved_path, config=config)
+ return api.upload_folder(
+ repo_id=repo_id,
+ token=token,
+ folder_path=saved_path,
+ commit_message=commit_message,
+ revision=branch,
+ create_pr=create_pr,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ delete_patterns=delete_patterns,
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/file_download.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/file_download.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e332ac07862d37deafd16bdf5a3abb3613097c2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/file_download.py
@@ -0,0 +1,2161 @@
+import copy
+import errno
+import os
+import re
+import shutil
+import stat
+import time
+import uuid
+import warnings
+from contextlib import ExitStack
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, BinaryIO, Literal, NoReturn, cast, overload
+from urllib.parse import quote
+
+import httpx2
+from tqdm import tqdm as base_tqdm
+
+from . import constants
+from ._local_folder import (
+ _create_cachedir_tag,
+ _validate_relative_filename,
+ get_local_download_paths,
+ read_download_metadata,
+ write_download_metadata,
+)
+from ._revision import ResolvedRevision
+from ._tree_cache import read_tree_cache, tree_cache_folder_for_local_dir
+from .errors import (
+ FileMetadataError,
+ GatedRepoError,
+ HfHubHTTPError,
+ LocalEntryNotFoundError,
+ RemoteEntryNotFoundError,
+ RepositoryNotFoundError,
+ RevisionNotFoundError,
+)
+from .utils import (
+ OfflineModeIsEnabled,
+ SoftTemporaryDirectory,
+ WeakFileLock,
+ XetFileData,
+ build_hf_headers,
+ hf_raise_for_status,
+ logging,
+ parse_xet_file_data_from_response,
+ validate_hf_hub_args,
+)
+from .utils._http import (
+ _DEFAULT_RETRY_ON_EXCEPTIONS,
+ _DEFAULT_RETRY_ON_STATUS_CODES,
+ _adjust_range_header,
+ _httpx2_follow_hub_redirects_with_backoff,
+ _is_same_or_hub_host,
+ flag_as_download_call,
+ http_stream_backoff,
+)
+from .utils._paths import as_extended_path
+from .utils._runtime import is_xet_available
+from .utils._shared_blobs import (
+ has_shared_blob,
+ publish_blob_to_shared_store,
+ shared_blobs_enabled,
+ try_link_from_shared_store,
+)
+from .utils._xet import XetTokenType, is_valid_xet_hash, xet_connection_info_refresh_url
+from .utils.sha import sha_fileobj
+from .utils.tqdm import _get_progress_bar_context
+
+
+logger = logging.get_logger(__name__)
+
+# Return value when trying to load a file from cache but the file does not exist in the distant repo.
+_CACHED_NO_EXIST = object()
+_CACHED_NO_EXIST_T = Any
+
+# Regex to get filename from a "Content-Disposition" header for CDN-served files
+HEADER_FILENAME_PATTERN = re.compile(r'filename="(?P.*?)";')
+
+# Regex to check if the revision IS directly a commit_hash
+REGEX_COMMIT_HASH = re.compile(r"[0-9a-f]{40}")
+
+# Regex to check if the file etag IS a valid sha256
+REGEX_SHA256 = re.compile(r"[0-9a-f]{64}")
+
+_are_symlinks_supported_in_dir: dict[str, bool] = {}
+
+# Internal retry timeout for metadata fetch when no local file exists
+_ETAG_RETRY_TIMEOUT = 60
+
+
+def are_symlinks_supported(cache_dir: str | Path | None = None) -> bool:
+ """Return whether the symlinks are supported on the machine.
+
+ Since symlinks support can change depending on the mounted disk, we need to check
+ on the precise cache folder. By default, the default HF cache directory is checked.
+
+ Args:
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+
+ Returns: [bool] Whether symlinks are supported in the directory.
+ """
+ # Defaults to HF cache
+ if cache_dir is None:
+ cache_dir = constants.HF_HUB_CACHE
+ cache_dir = str(Path(cache_dir).expanduser().resolve()) # make it unique
+
+ # If symlinks are explicitly disabled by the user, always return False
+ if constants.HF_HUB_DISABLE_SYMLINKS:
+ return False
+
+ # Cache symlink compatibility per directory, publishing the result only after the probe completes.
+ if cache_dir not in _are_symlinks_supported_in_dir:
+ os.makedirs(cache_dir, exist_ok=True)
+ with SoftTemporaryDirectory(dir=cache_dir) as tmpdir:
+ src_path = Path(tmpdir) / "dummy_file_src"
+ src_path.touch()
+ dst_path = Path(tmpdir) / "dummy_file_dst"
+
+ # Relative source path as in `_create_symlink``
+ relative_src = os.path.relpath(src_path, start=os.path.dirname(dst_path))
+ try:
+ os.symlink(relative_src, dst_path)
+ _are_symlinks_supported_in_dir[cache_dir] = True
+ except OSError:
+ # Likely running on Windows
+ _are_symlinks_supported_in_dir[cache_dir] = False
+
+ if not constants.HF_HUB_DISABLE_SYMLINKS_WARNING:
+ message = (
+ "`huggingface_hub` cache-system uses symlinks by default to"
+ " efficiently store duplicated files but your machine does not"
+ f" support them in {cache_dir}. Caching files will still work"
+ " but in a degraded version that might require more space on"
+ " your disk. This warning can be disabled by setting the"
+ " `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For"
+ " more details, see"
+ " https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations."
+ )
+ if os.name == "nt":
+ message += (
+ "\nTo support symlinks on Windows, you either need to"
+ " activate Developer Mode or to run Python as an"
+ " administrator. In order to activate developer mode,"
+ " see this article:"
+ " https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development"
+ )
+ warnings.warn(message)
+
+ return _are_symlinks_supported_in_dir[cache_dir]
+
+
+@dataclass(frozen=True)
+class HfFileMetadata:
+ """Data structure containing information about a file versioned on the Hub.
+
+ Returned by [`get_hf_file_metadata`] based on a URL.
+
+ Args:
+ commit_hash (`str`, *optional*):
+ The commit_hash related to the file.
+ etag (`str`, *optional*):
+ Etag of the file on the server.
+ location (`str`):
+ Location where to download the file. Can be a Hub url or not (CDN).
+ size (`size`):
+ Size of the file. In case of an LFS file, contains the size of the actual
+ LFS file, not the pointer.
+ xet_file_data (`XetFileData`, *optional*):
+ Xet information for the file. This is only set if the file is stored using Xet storage.
+ """
+
+ commit_hash: str | None
+ etag: str | None
+ location: str
+ size: int | None
+ xet_file_data: XetFileData | None
+
+
+@dataclass
+class DryRunFileInfo:
+ """Information returned when performing a dry run of a file download.
+
+ Returned by [`hf_hub_download`] when `dry_run=True`.
+
+ Args:
+ commit_hash (`str`):
+ The commit_hash related to the file.
+ file_size (`int`, *optional*):
+ Size of the file, if known. In case of an LFS file, contains the size of the actual LFS file, not the
+ pointer.
+ filename (`str`):
+ Name of the file in the repo.
+ is_cached (`bool`):
+ Whether the file is already cached locally.
+ will_download (`bool`):
+ Whether the file will be downloaded if `hf_hub_download` is called with `dry_run=False`.
+ In practice, will_download is `True` if the file is not cached or if `force_download=True`.
+ """
+
+ commit_hash: str
+ file_size: int | None
+ filename: str
+ local_path: str
+ is_cached: bool
+ will_download: bool
+
+
+@validate_hf_hub_args
+def hf_hub_url(
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ endpoint: str | None = None,
+) -> str:
+ """Construct the URL of a file from the given information.
+
+ The resolved address can either be a huggingface.co-hosted url, or a link to
+ Cloudfront (a Content Delivery Network, or CDN) for large files which are
+ more than a few MBs.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) name and a repo name separated
+ by a `/`.
+ filename (`str`):
+ The name of the file in the repo.
+ subfolder (`str`, *optional*):
+ An optional value corresponding to a folder inside the repo.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"`, `"space"` or `"kernel"` if downloading from a dataset, space or kernel repo,
+ `None` or `"model"` if downloading from a model. Default is `None`.
+ revision (`str`, *optional*):
+ An optional Git revision id which can be a branch name, a tag, or a
+ commit hash.
+ endpoint (`str`, *optional*):
+ The Hub endpoint to send the request to. Defaults to the value of `HF_ENDPOINT`.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import hf_hub_url
+
+ >>> hf_hub_url(
+ ... repo_id="julien-c/EsperBERTo-small", filename="pytorch_model.bin"
+ ... )
+ 'https://huggingface.co/julien-c/EsperBERTo-small/resolve/main/pytorch_model.bin'
+ ```
+
+ > [!TIP]
+ > Notes:
+ >
+ > Cloudfront is replicated over the globe so downloads are way faster for
+ > the end user (and it also lowers our bandwidth costs).
+ >
+ > Cloudfront aggressively caches files by default (default TTL is 24
+ > hours), however this is not an issue here because we implement a
+ > git-based versioning system on huggingface.co, which means that we store
+ > the files on S3/Cloudfront in a content-addressable way (i.e., the file
+ > name is its hash). Using content-addressable filenames means cache can't
+ > ever be stale.
+ >
+ > In terms of client-side caching from this library, we base our caching
+ > on the objects' entity tag (`ETag`), which is an identifier of a
+ > specific version of a resource [1]_. An object's ETag is: its git-sha1
+ > if stored in git, or its sha256 if stored in git-lfs.
+
+ References:
+
+ - [1] https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag
+ """
+ if subfolder == "":
+ subfolder = None
+ if subfolder is not None:
+ filename = f"{subfolder}/{filename}"
+
+ if repo_type not in constants.REPO_TYPES_WITH_KERNEL:
+ raise ValueError("Invalid repo type")
+
+ if repo_type in constants.REPO_TYPES_URL_PREFIXES:
+ repo_id = constants.REPO_TYPES_URL_PREFIXES[repo_type] + repo_id # type: ignore
+
+ if revision is None:
+ revision = constants.DEFAULT_REVISION
+ url = constants.HUGGINGFACE_CO_URL_TEMPLATE.format(
+ repo_id=repo_id, revision=quote(revision, safe=""), filename=quote(filename)
+ )
+ # Update endpoint if provided
+ if endpoint is not None and url.startswith(constants.ENDPOINT):
+ url = endpoint + url[len(constants.ENDPOINT) :]
+ return url
+
+
+def _get_file_length_from_http_response(response: httpx2.Response) -> int | None:
+ """
+ Get the length of the file from the HTTP response headers.
+
+ This function extracts the file size from the HTTP response headers, either from the
+ `Content-Range` or `Content-Length` header, if available (in that order).
+
+ Args:
+ response (`httpx2.Response`):
+ The HTTP response object.
+
+ Returns:
+ `int` or `None`: The length of the file in bytes, or None if not available.
+ """
+
+ # If HTTP response contains compressed body (e.g. gzip), the `Content-Length` header will
+ # contain the length of the compressed body, not the uncompressed file size.
+ # And at the start of transmission there's no way to know the uncompressed file size for gzip,
+ # thus we return None in that case.
+ content_encoding = response.headers.get("Content-Encoding", "identity").lower()
+ if content_encoding != "identity":
+ # gzip/br/deflate/zstd etc
+ return None
+
+ content_range = response.headers.get("Content-Range")
+ if content_range is not None:
+ return int(content_range.rsplit("/")[-1])
+
+ content_length = response.headers.get("Content-Length")
+ if content_length is not None:
+ return int(content_length)
+
+ return None
+
+
+@validate_hf_hub_args
+def http_get(
+ url: str,
+ temp_file: BinaryIO,
+ *,
+ resume_size: int = 0,
+ headers: dict[str, Any] | None = None,
+ expected_size: int | None = None,
+ displayed_filename: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ _nb_retries: int = 5,
+ _tqdm_bar: base_tqdm | None = None,
+) -> None:
+ """
+ Download a remote file. Do not gobble up errors, and will return errors tailored to the Hugging Face Hub.
+
+ If ConnectionError (SSLError) or ReadTimeout happen while streaming data from the server, it is most likely a
+ transient error (network outage?). We log a warning message and try to resume the download a few times before
+ giving up. The method gives up after 5 attempts if no new data has being received from the server.
+
+ Args:
+ url (`str`):
+ The URL of the file to download.
+ temp_file (`BinaryIO`):
+ The file-like object where to save the file. Content is written from the object's current position, so the
+ caller may hand over a file that already contains data of its own (see [`HfFileSystem.get_file`]).
+ resume_size (`int`, *optional*):
+ The number of bytes already downloaded. If set to 0 (default), the whole file is download. If set to a
+ positive number, the download will resume at the given position.
+ headers (`dict`, *optional*):
+ Dictionary of HTTP Headers to send with the request.
+ expected_size (`int`, *optional*):
+ The expected size of the file to download. If set, the download will raise an error if the size of the
+ received content is different from the expected one.
+ displayed_filename (`str`, *optional*):
+ The filename of the file that is being downloaded. Value is used only to display a nice progress bar. If
+ not set, the filename is guessed from the URL or the `Content-Disposition` header.
+ """
+ if expected_size is not None and resume_size == expected_size:
+ # If the file is already fully downloaded, we don't need to download it again.
+ return
+
+ initial_headers = headers
+ headers = copy.deepcopy(headers) or {}
+ if resume_size > 0:
+ headers["Range"] = _adjust_range_header(headers.get("Range"), resume_size)
+ elif expected_size and expected_size > constants.MAX_HTTP_DOWNLOAD_SIZE:
+ # Any files over 50GB will not be available through basic http requests.
+ raise ValueError(
+ "The file is too large to be downloaded using the regular download method. "
+ " Install `hf_xet` with `pip install hf_xet` for xet-powered downloads."
+ )
+
+ # Keep the response and progress bar open while recursive retries reuse them.
+ with ExitStack() as stack:
+ progress = _tqdm_bar
+ new_resume_size = resume_size
+ try:
+ response = stack.enter_context(
+ http_stream_backoff(
+ method="GET",
+ url=url,
+ headers=headers,
+ timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ retry_on_exceptions=(),
+ retry_on_status_codes=(408, 429),
+ )
+ )
+ hf_raise_for_status(response)
+
+ # If we requested a Range but got 200 back, the server ignored our Range header
+ # (e.g. CloudFront with Accept-Encoding: gzip). Reset file to avoid corruption.
+ if resume_size > 0 and response.status_code == 200:
+ # Rewind to where this download started, which is not necessarily the start of the file: the caller
+ # may have handed over a file object that already contains data of its own.
+ temp_file.seek(max(temp_file.tell() - resume_size, 0))
+ temp_file.truncate()
+ if _tqdm_bar is not None:
+ # When the progress bar is reused across retries, its counter has already been advanced by `resume_size`
+ # worth of chunks from earlier attempts. Those bytes are gone from disk now, so roll the counter back
+ # to keep the upcoming full re-download from double-counting (e.g. ending at 130/100 on a 100-byte file).
+ _tqdm_bar.update(-resume_size)
+ if callable(update_transfer := getattr(_tqdm_bar, "update_transfer", None)):
+ update_transfer(-resume_size)
+ resume_size = 0
+
+ total: int | None = _get_file_length_from_http_response(response)
+ if expected_size is None:
+ expected_size = total
+ elif total is None:
+ # Hub serves compressible text files (e.g. vocab.json) with `Content-Encoding: gzip` and
+ # `Transfer-Encoding: chunked`, so the response carries no `Content-Length`. Fall back to the caller's
+ # `expected_size` (always known from the metadata HEAD on the hf_hub path) so the progress bar, and any
+ # aggregating wrapper such as snapshot_download's `_AggregatedTqdm` — still sees the file size.
+ total = expected_size
+
+ if displayed_filename is None:
+ displayed_filename = url
+ content_disposition = response.headers.get("Content-Disposition")
+ if content_disposition is not None:
+ match = HEADER_FILENAME_PATTERN.search(content_disposition)
+ if match is not None:
+ # Means file is on CDN
+ displayed_filename = match.groupdict()["filename"]
+
+ # Truncate filename if too long to display
+ if len(displayed_filename) > 40:
+ displayed_filename = f"(…){displayed_filename[-40:]}"
+
+ consistency_error_message = (
+ f"Consistency check failed: file should be of size {expected_size} but has size"
+ f" {{actual_size}} ({displayed_filename}).\nThis is usually due to network issues while downloading the file."
+ " Please retry with `force_download=True`."
+ )
+ progress_cm = _get_progress_bar_context(
+ desc=displayed_filename,
+ log_level=logger.getEffectiveLevel(),
+ total=total,
+ initial=resume_size,
+ name="huggingface_hub.http_get",
+ tqdm_class=cast(Any, tqdm_class),
+ _tqdm_bar=cast(Any, _tqdm_bar),
+ )
+
+ progress = stack.enter_context(progress_cm)
+ new_resume_size = resume_size
+ for chunk in response.iter_bytes(chunk_size=constants.DOWNLOAD_CHUNK_SIZE):
+ if chunk: # filter out keep-alive new chunks
+ progress.update(len(chunk))
+ if callable(update_transfer := getattr(progress, "update_transfer", None)):
+ update_transfer(len(chunk))
+ temp_file.write(chunk)
+ new_resume_size += len(chunk)
+ # Some data has been downloaded from the server so we reset the number of retries.
+ _nb_retries = 5
+ except (httpx2.ConnectError, httpx2.TimeoutException, httpx2.RemoteProtocolError) as e:
+ # Retry transient failures both when opening the stream and while reading its body.
+ if _nb_retries <= 0:
+ logger.warning("Error while downloading from %s: %s\nMax retries exceeded.", url, str(e))
+ raise
+ logger.warning("Error while downloading from %s: %s\nTrying to resume download...", url, str(e))
+ time.sleep(1)
+ return http_get(
+ url=url,
+ temp_file=temp_file,
+ resume_size=new_resume_size,
+ headers=initial_headers,
+ expected_size=expected_size,
+ tqdm_class=tqdm_class,
+ _nb_retries=_nb_retries - 1,
+ # Reuse the existing progress bar across retries so a custom `tqdm_class` (e.g. snapshot_download's `_AggregatedTqdm`,
+ # which mutates a shared parent bar in `__init__`) is not re-instantiated and does not double-count `total`/`initial`.
+ _tqdm_bar=progress,
+ )
+
+ # Compare against the bytes downloaded by this call rather than the absolute file position: the two differ
+ # whenever the caller passed a file object that was not positioned at 0.
+ if expected_size is not None and expected_size != new_resume_size:
+ raise OSError(
+ consistency_error_message.format(
+ actual_size=new_resume_size,
+ )
+ )
+
+
+def xet_get(
+ *,
+ incomplete_path: Path,
+ xet_file_data: XetFileData,
+ headers: dict[str, str],
+ expected_size: int | None = None,
+ displayed_filename: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ _tqdm_bar: base_tqdm | None = None,
+) -> None:
+ """
+ Download a file using Xet storage service.
+
+ Args:
+ incomplete_path (`Path`):
+ The path to the file to download.
+ xet_file_data (`XetFileData`):
+ The file metadata needed to make the request to the xet storage service.
+ headers (`dict[str, str]`):
+ The headers to send to the xet storage service.
+ expected_size (`int`, *optional*):
+ The expected size of the file to download. If set, the download will raise an error if the size of the
+ received content is different from the expected one.
+ displayed_filename (`str`, *optional*):
+ The filename of the file that is being downloaded. Value is used only to display a nice progress bar. If
+ not set, the filename is guessed from the URL or the `Content-Disposition` header.
+
+ **How it works:**
+ The file download system uses Xet storage, which is a content-addressable storage system that breaks files into chunks
+ for efficient storage and transfer.
+
+ ``session.new_file_download_group()`` manages downloading files by:
+ - Registering download tasks (each with its unique content hash) and starting download immediately in the background
+ - Connecting to a storage server (CAS server) that knows how files are chunked
+ - Using authentication to ensure secure access
+ - Providing progress updates during download
+
+ Authentication works transparently: the download group is seeded with a short-lived xet
+ access token (fetched once per repo revision and cached across files) and accepts a
+ ``token_refresh_url`` that is used to refresh it as needed.
+
+ The download process works like this:
+ 1. Download tasks run in parallel:
+ 1.1. Prepare to write the file to disk or to a stream (e.g. truncate file, set up cache)
+ 1.2. Ask the server "how is this file split into chunks?" using the file's unique hash
+ The server responds with:
+ - Which chunks make up the complete file
+ - Where each chunk can be downloaded from
+ 1.3. For each needed chunk:
+ - Checks if we already have it in our local cache
+ - If not, download it from cloud storage (S3)
+ - Save it to cache for future use
+ - Assemble the chunks in order to recreate the original file
+
+ """
+ try:
+ from hf_xet import XetFileInfo # type: ignore[no-redef]
+ except ImportError:
+ raise ValueError(
+ "To use optimized download using Xet storage, you need to install the hf_xet package. "
+ 'Try `pip install "huggingface_hub[hf_xet]"` or `pip install hf_xet`.'
+ )
+
+ if not displayed_filename:
+ displayed_filename = incomplete_path.name
+
+ # Truncate filename if too long to display
+ if len(displayed_filename) > 40:
+ displayed_filename = f"{displayed_filename[:40]}(…)"
+
+ from .utils._xet import abort_xet_session, get_xet_session, refresh_xet_connection_info, xet_headers_without_auth
+ from .utils._xet_progress_reporting import XetDownloadProgressReporter
+
+ xet_headers = xet_headers_without_auth(headers)
+
+ # Fetched once per repo revision and cached; otherwise each download group would request its own
+ # token, i.e. one Hub API call per file (rate-limited on large snapshot downloads, see #4722).
+ connection_info = refresh_xet_connection_info(file_data=xet_file_data, headers=headers)
+
+ session = get_xet_session()
+
+ with XetDownloadProgressReporter(
+ reconstruction_desc=f"{displayed_filename}: reconstructing file",
+ transfer_desc=f"{displayed_filename}: downloading bytes",
+ total=expected_size,
+ log_level=logger.getEffectiveLevel(),
+ name="huggingface_hub.xet_get",
+ tqdm_class=tqdm_class,
+ external_reconstruction_bar=_tqdm_bar,
+ ) as progress:
+ try:
+ with session.new_file_download_group(
+ endpoint=connection_info.endpoint,
+ token=connection_info.access_token,
+ token_expiry_unix_secs=connection_info.expiration_unix_epoch,
+ token_refresh_url=xet_file_data.refresh_route,
+ token_refresh_headers=headers,
+ custom_headers=xet_headers,
+ progress_callback=progress.update_progress,
+ ) as group:
+ group.start_download_file(
+ XetFileInfo(xet_file_data.file_hash, expected_size), str(incomplete_path.absolute())
+ )
+ except KeyboardInterrupt:
+ abort_xet_session()
+ raise
+
+
+def _normalize_etag(etag: str | None) -> str | None:
+ """Normalize ETag HTTP header, so it can be used to create nice filepaths.
+
+ The HTTP spec allows two forms of ETag:
+ ETag: W/""
+ ETag: ""
+
+ For now, we only expect the second form from the server, but we want to be future-proof so we support both. For
+ more context, see `TestNormalizeEtag` tests and https://github.com/huggingface/huggingface_hub/pull/1428.
+
+ Args:
+ etag (`str`, *optional*): HTTP header
+
+ Returns:
+ `str` or `None`: string that can be used as a nice directory name.
+ Returns `None` if input is None.
+ """
+ if etag is None:
+ return None
+ return etag.lstrip("W/").strip('"')
+
+
+def _create_relative_symlink(src: str, dst: str, new_blob: bool = False) -> None:
+ """Alias method used in `transformers` conversion script."""
+ return _create_symlink(src=src, dst=dst, new_blob=new_blob)
+
+
+def _create_symlink(src: str, dst: str, new_blob: bool = False) -> None:
+ """Create a symbolic link named dst pointing to src.
+
+ By default, it will try to create a symlink using a relative path. Relative paths have 2 advantages:
+ - If the cache_folder is moved (example: back-up on a shared drive), relative paths within the cache folder will
+ not break.
+ - Relative paths seems to be better handled on Windows. Issue was reported 3 times in less than a week when
+ changing from relative to absolute paths. See https://github.com/huggingface/huggingface_hub/issues/1398,
+ https://github.com/huggingface/diffusers/issues/2729 and https://github.com/huggingface/transformers/pull/22228.
+ NOTE: The issue with absolute paths doesn't happen on admin mode.
+ When creating a symlink from the cache to a local folder, it is possible that a relative path cannot be created.
+ This happens when paths are not on the same volume. In that case, we use absolute paths.
+
+
+ The result layout looks something like
+ └── [ 128] snapshots
+ ├── [ 128] 2439f60ef33a0d46d85da5001d52aeda5b00ce9f
+ │ ├── [ 52] README.md -> ../../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812
+ │ └── [ 76] pytorch_model.bin -> ../../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+
+ If symlinks cannot be created on this platform (most likely to be Windows), the workaround is to avoid symlinks by
+ having the actual file in `dst`. If it is a new file (`new_blob=True`), we move it to `dst`. If it is not a new file
+ (`new_blob=False`), we don't know if the blob file is already referenced elsewhere. To avoid breaking existing
+ cache, the file is duplicated on the disk.
+
+ In case symlinks are not supported, a warning message is displayed to the user once when loading `huggingface_hub`.
+ The warning message can be disabled with the `DISABLE_SYMLINKS_WARNING` environment variable.
+ """
+ try:
+ os.remove(dst)
+ except OSError:
+ pass
+
+ abs_src = os.path.abspath(os.path.expanduser(src))
+ abs_dst = os.path.abspath(os.path.expanduser(dst))
+ abs_dst_folder = os.path.dirname(abs_dst)
+
+ # Use relative_dst in priority
+ try:
+ relative_src = os.path.relpath(abs_src, abs_dst_folder)
+ except ValueError:
+ # Raised on Windows if src and dst are not on the same volume. This is the case when creating a symlink to a
+ # local_dir instead of within the cache directory.
+ # See https://docs.python.org/3/library/os.path.html#os.path.relpath
+ relative_src = None
+
+ try:
+ commonpath = os.path.commonpath([abs_src, abs_dst])
+ _support_symlinks = are_symlinks_supported(commonpath)
+ except ValueError:
+ # Raised if src and dst are not on the same volume. Symlinks will still work on Linux/Macos.
+ # See https://docs.python.org/3/library/os.path.html#os.path.commonpath
+ _support_symlinks = os.name != "nt" and not constants.HF_HUB_DISABLE_SYMLINKS
+ except PermissionError:
+ # Permission error means src and dst are not in the same volume (e.g. destination path has been provided
+ # by the user via `local_dir`. Let's test symlink support there)
+ _support_symlinks = are_symlinks_supported(abs_dst_folder)
+ except OSError as e:
+ # OS error (errno=30) means that the commonpath is readonly on Linux/MacOS.
+ if e.errno == errno.EROFS:
+ _support_symlinks = are_symlinks_supported(abs_dst_folder)
+ else:
+ raise
+
+ # Symlinks are supported => let's create a symlink.
+ if _support_symlinks:
+ src_rel_or_abs = relative_src or abs_src
+ logger.debug(f"Creating pointer from {src_rel_or_abs} to {abs_dst}")
+ try:
+ os.symlink(src_rel_or_abs, abs_dst)
+ return
+ except FileExistsError:
+ if os.path.islink(abs_dst) and os.path.realpath(abs_dst) == os.path.realpath(abs_src):
+ # `abs_dst` already exists and is a symlink to the `abs_src` blob. It is most likely that the file has
+ # been cached twice concurrently (exactly between `os.remove` and `os.symlink`). Do nothing.
+ return
+ else:
+ # Very unlikely to happen. Means a file `dst` has been created exactly between `os.remove` and
+ # `os.symlink` and is not a symlink to the `abs_src` blob file. Raise exception.
+ raise
+ except PermissionError:
+ # Permission error means src and dst are not in the same volume (e.g. download to local dir) and symlink
+ # is supported on both volumes but not between them. Let's just make a hard copy in that case.
+ pass
+
+ # Symlinks are not supported => let's move or copy the file.
+ if new_blob:
+ logger.debug(f"Symlink not supported. Moving file from {abs_src} to {abs_dst}")
+ shutil.move(abs_src, abs_dst, copy_function=_copy_no_matter_what)
+ else:
+ logger.debug(f"Symlink not supported. Copying file from {abs_src} to {abs_dst}")
+ shutil.copyfile(abs_src, abs_dst)
+
+
+def _cache_commit_hash_for_specific_revision(storage_folder: str, revision: str, commit_hash: str) -> None:
+ """Cache reference between a revision (tag, branch or truncated commit hash) and the corresponding commit hash.
+
+ Does nothing if `revision` is already a proper `commit_hash` or reference is already cached.
+ """
+ if revision != commit_hash:
+ ref_path = Path(storage_folder) / "refs" / revision
+ ref_path.parent.mkdir(parents=True, exist_ok=True)
+ if not ref_path.exists() or commit_hash != ref_path.read_text():
+ # Update ref only if has been updated. Could cause useless error in case
+ # repo is already cached and user doesn't have write access to cache folder.
+ # See https://github.com/huggingface/huggingface_hub/issues/1216.
+ # Write atomically (tmp file + rename) so that concurrent readers never see
+ # a partially written ref.
+ tmp_path = ref_path.with_name(f"{ref_path.name}.{uuid.uuid4().hex[:8]}.tmp")
+ tmp_path.write_text(commit_hash)
+ os.replace(tmp_path, ref_path)
+
+
+@validate_hf_hub_args
+def repo_folder_name(*, repo_id: str, repo_type: str) -> str:
+ """Return a serialized version of a hf.co repo name and type, safe for disk storage
+ as a single non-nested folder.
+
+ Example: models--julien-c--EsperBERTo-small
+ """
+ # remove all `/` occurrences to correctly convert repo to directory name
+ parts = [f"{repo_type}s", *repo_id.split("/")]
+ return constants.REPO_ID_SEPARATOR.join(parts)
+
+
+def _check_disk_space(expected_size: int, target_dir: str | Path) -> None:
+ """Check disk usage and log a warning if there is not enough disk space to download the file.
+
+ Args:
+ expected_size (`int`):
+ The expected size of the file in bytes.
+ target_dir (`str`):
+ The directory where the file will be stored after downloading.
+ """
+
+ target_dir = Path(target_dir) # format as `Path`
+ for path in [target_dir] + list(target_dir.parents): # first check target_dir, then each parents one by one
+ try:
+ target_dir_free = shutil.disk_usage(path).free
+ if target_dir_free < expected_size:
+ warnings.warn(
+ "Not enough free disk space to download the file. "
+ f"The expected file size is: {expected_size / 1e6:.2f} MB. "
+ f"The target location {target_dir} only has {target_dir_free / 1e6:.2f} MB free disk space."
+ )
+ return
+ except OSError: # raise on anything: file does not exist or space disk cannot be checked
+ pass
+
+
+@overload
+def hf_hub_download(
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ user_agent: dict | str | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[False] = False,
+) -> str: ...
+
+
+@overload
+def hf_hub_download(
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ user_agent: dict | str | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[True] = True,
+) -> DryRunFileInfo: ...
+
+
+@overload
+def hf_hub_download(
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ user_agent: dict | str | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: bool = False,
+) -> str | DryRunFileInfo: ...
+
+
+@validate_hf_hub_args
+@flag_as_download_call
+def hf_hub_download(
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ user_agent: dict | str | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: bool = False,
+) -> str | DryRunFileInfo:
+ """Download a given file if it's not already present in the local cache.
+
+ The new cache file layout looks like this:
+ - The cache directory contains one subfolder per repo_id (namespaced by repo type)
+ - inside each repo folder:
+ - refs is a list of the latest known revision => commit_hash pairs
+ - blobs contains the actual file blobs (identified by their git-sha or sha256, depending on
+ whether they're LFS files or not)
+ - snapshots contains one subfolder per commit, each "commit" contains the subset of the files
+ that have been resolved at that particular commit. Each filename is a symlink to the blob
+ at that particular commit.
+
+ ```
+ [ 96] .
+ └── [ 160] models--julien-c--EsperBERTo-small
+ ├── [ 160] blobs
+ │ ├── [321M] 403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ │ ├── [ 398] 7cb18dc9bafbfcf74629a4b760af1b160957a83e
+ │ └── [1.4K] d7edf6bd2a681fb0175f7735299831ee1b22b812
+ ├── [ 96] refs
+ │ └── [ 40] main
+ └── [ 128] snapshots
+ ├── [ 128] 2439f60ef33a0d46d85da5001d52aeda5b00ce9f
+ │ ├── [ 52] README.md -> ../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812
+ │ └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ └── [ 128] bbc77c8132af1cc5cf678da3f1ddf2de43606d48
+ ├── [ 52] README.md -> ../../blobs/7cb18dc9bafbfcf74629a4b760af1b160957a83e
+ └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ ```
+
+ If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+ option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+ to store some metadata related to the downloaded files. While this mechanism is not as robust as the main
+ cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ filename (`str`):
+ The name of the file in the repo.
+ subfolder (`str`, *optional*):
+ An optional value corresponding to a folder inside the model repo.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"`, `"space"` or `"kernel"` if downloading from a dataset, space or kernel repo,
+ `None` or `"model"` if downloading from a model. Default is `None`.
+ revision (`str`, *optional*):
+ An optional Git revision id which can be a branch name, a tag, or a
+ commit hash.
+ library_name (`str`, *optional*):
+ The name of the library to which the object corresponds.
+ library_version (`str`, *optional*):
+ The version of the library.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+ local_dir (`str` or `Path`, *optional*):
+ If provided, the downloaded file will be placed under this directory.
+ user_agent (`dict`, `str`, *optional*):
+ The user-agent info in the form of a dictionary or a string.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether the file should be downloaded even if it already exists in
+ the local cache.
+ etag_timeout (`float`, *optional*, defaults to `10`):
+ When fetching ETag, how many seconds to wait for the server to send
+ data before giving up, which is passed to `httpx2.request`.
+ token (`str`, `bool`, *optional*):
+ A token to be used for the download.
+ - If `True`, the token is read from the HuggingFace config
+ folder.
+ - If a string, it's used as the authentication token.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, avoid downloading the file and return the path to the
+ local cached file if it exists.
+ headers (`dict`, *optional*):
+ Additional headers to be sent with the request.
+ endpoint (`str`, *optional*):
+ The Hub endpoint to send the request to. Defaults to the value of `HF_ENDPOINT`.
+ tqdm_class (`tqdm`, *optional*):
+ If provided, overwrites the default behavior for the progress bar. Passed
+ argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
+ Defaults to the custom HF progress bar that can be disabled by setting
+ `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
+ dry_run (`bool`, *optional*, defaults to `False`):
+ If `True`, perform a dry run without actually downloading the file. Returns a
+ [`DryRunFileInfo`] object containing information about what would be downloaded.
+
+ Returns:
+ `str` or [`DryRunFileInfo`]:
+ - If `dry_run=False`: Local path of file or if networking is off, last version of file cached on disk.
+ - If `dry_run=True`: A [`DryRunFileInfo`] object containing download information.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]
+ If the repository to download from cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+ [`~utils.RevisionNotFoundError`]
+ If the revision to download from cannot be found.
+ [`~utils.RemoteEntryNotFoundError`]
+ If the file to download cannot be found.
+ [`~utils.LocalEntryNotFoundError`]
+ If network is disabled or unavailable and file is not found in cache.
+ [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+ If `token=True` but the token cannot be found.
+ [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError)
+ If ETag cannot be determined.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If some parameter value is invalid.
+
+ """
+ if constants.HF_HUB_ETAG_TIMEOUT != constants.DEFAULT_ETAG_TIMEOUT:
+ # Respect environment variable above user value
+ etag_timeout = constants.HF_HUB_ETAG_TIMEOUT
+
+ if revision is None:
+ revision = constants.DEFAULT_REVISION
+ elif isinstance(revision, ResolvedRevision):
+ # Revision has already been resolved to a commit hash (see [`HfApi.resolve_revision`]) => use it directly.
+ # This pins the download to an immutable commit and lets us skip network calls when it's already cached.
+ revision = revision.resolved
+
+ if cache_dir is None:
+ cache_dir = constants.HF_HUB_CACHE
+ cache_dir = str(Path(cache_dir).expanduser().resolve())
+
+ if local_dir is not None:
+ local_dir = str(Path(local_dir).expanduser().resolve())
+
+ if subfolder == "":
+ subfolder = None
+ if subfolder is not None:
+ # This is used to create a URL, and not a local path, hence the forward slash.
+ filename = f"{subfolder}/{filename}"
+
+ if repo_type is None:
+ repo_type = "model"
+ if repo_type not in constants.REPO_TYPES_WITH_KERNEL:
+ raise ValueError(
+ f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES_WITH_KERNEL)}"
+ )
+
+ hf_headers = build_hf_headers(
+ token=token,
+ library_name=library_name,
+ library_version=library_version,
+ user_agent=user_agent,
+ headers=headers,
+ )
+
+ if local_dir is not None:
+ return _hf_hub_download_to_local_dir(
+ # Destination
+ local_dir=local_dir,
+ # File info
+ repo_id=repo_id,
+ repo_type=repo_type,
+ filename=filename,
+ revision=revision,
+ # HTTP info
+ endpoint=endpoint,
+ etag_timeout=etag_timeout,
+ headers=hf_headers,
+ token=token,
+ # Additional options
+ cache_dir=cache_dir,
+ force_download=force_download,
+ local_files_only=local_files_only,
+ tqdm_class=tqdm_class,
+ dry_run=dry_run,
+ )
+ else:
+ return _hf_hub_download_to_cache_dir(
+ # Destination
+ cache_dir=cache_dir,
+ # File info
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ # HTTP info
+ endpoint=endpoint,
+ etag_timeout=etag_timeout,
+ headers=hf_headers,
+ token=token,
+ # Additional options
+ local_files_only=local_files_only,
+ force_download=force_download,
+ tqdm_class=tqdm_class,
+ dry_run=dry_run,
+ )
+
+
+def _hf_hub_download_to_cache_dir(
+ *,
+ # Destination
+ cache_dir: str,
+ # File info
+ repo_id: str,
+ filename: str,
+ repo_type: str,
+ revision: str,
+ # HTTP info
+ endpoint: str | None,
+ etag_timeout: float,
+ headers: dict[str, str],
+ token: bool | str | None,
+ # Additional options
+ local_files_only: bool,
+ force_download: bool,
+ tqdm_class: type[base_tqdm] | None,
+ dry_run: bool,
+) -> str | DryRunFileInfo:
+ """Download a given file to a cache folder, if not already present.
+
+ Method should not be called directly. Please use `hf_hub_download` instead.
+ """
+ locks_dir = os.path.join(cache_dir, ".locks")
+ storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+
+ _validate_relative_filename(filename)
+ # cross-platform transcription of filename, to be used as a local file path.
+ relative_filename = os.path.join(*filename.split("/"))
+
+ # if user provides a commit_hash and they already have the file on disk, shortcut everything.
+ if REGEX_COMMIT_HASH.fullmatch(revision):
+ pointer_path = _get_pointer_path(storage_folder, revision, relative_filename)
+ if os.path.exists(pointer_path):
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=revision,
+ file_size=os.path.getsize(pointer_path),
+ filename=filename,
+ is_cached=True,
+ local_path=pointer_path,
+ will_download=force_download,
+ )
+ if not force_download:
+ return pointer_path
+
+ # Try to get metadata (etag, commit_hash, url, size) from the server.
+ # If we can't, a HEAD request error is returned.
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=endpoint,
+ etag_timeout=etag_timeout,
+ headers=headers,
+ token=token,
+ local_files_only=local_files_only,
+ storage_folder=storage_folder,
+ relative_filename=relative_filename,
+ tree_cache_folder=storage_folder,
+ )
+
+ # etag can be None for several reasons:
+ # 1. we passed local_files_only.
+ # 2. we don't have a connection
+ # 3. Hub is down (HTTP 500, 503, 504)
+ # 4. repo is not found -for example private or gated- and invalid/missing token sent
+ # 5. Hub is blocked by a firewall or proxy is not set correctly.
+ # => Try to get the last downloaded one from the specified revision.
+ #
+ # If the specified revision is a commit hash, look inside "snapshots".
+ # If the specified revision is a branch or tag, look inside "refs".
+ if head_call_error is not None:
+ # Couldn't make a HEAD call => let's try to find a local file
+ if not force_download:
+ commit_hash = None
+ if REGEX_COMMIT_HASH.fullmatch(revision):
+ commit_hash = revision
+ else:
+ ref_path = os.path.join(storage_folder, "refs", revision)
+ if os.path.isfile(ref_path):
+ with open(ref_path) as f:
+ commit_hash = f.read()
+
+ # Return pointer file if exists
+ if commit_hash is not None:
+ pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename)
+ if os.path.exists(pointer_path):
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=os.path.getsize(pointer_path),
+ filename=filename,
+ is_cached=True,
+ local_path=pointer_path,
+ will_download=force_download,
+ )
+ if not force_download:
+ return pointer_path
+
+ if isinstance(head_call_error, _DEFAULT_RETRY_ON_EXCEPTIONS) or (
+ isinstance(head_call_error, HfHubHTTPError)
+ and head_call_error.response.status_code in _DEFAULT_RETRY_ON_STATUS_CODES
+ ):
+ logger.info("No local file found. Retrying..")
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = (
+ _get_metadata_or_catch_error(
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=endpoint,
+ etag_timeout=_ETAG_RETRY_TIMEOUT,
+ headers=headers,
+ token=token,
+ local_files_only=local_files_only,
+ storage_folder=storage_folder,
+ relative_filename=relative_filename,
+ retry_on_errors=True,
+ )
+ )
+
+ # If still error, raise
+ if head_call_error is not None:
+ _raise_on_head_call_error(head_call_error, force_download, local_files_only)
+
+ # From now on, etag, commit_hash and url are not None.
+ assert etag is not None, "etag must have been retrieved from server"
+ assert commit_hash is not None, "commit_hash must have been retrieved from server"
+ assert url_to_download is not None, "file location must have been retrieved from server"
+ blob_path = os.path.join(storage_folder, "blobs", etag)
+ pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename)
+
+ if dry_run:
+ # A usable shared store entry counts as cached: the real call would symlink it without downloading.
+ # Symlink support is not probed here (probing writes to the cache), so the preview may be optimistic.
+ is_cached = (
+ os.path.exists(pointer_path)
+ or os.path.exists(blob_path)
+ or (
+ xet_file_data is not None
+ and not constants.HF_HUB_DISABLE_SYMLINKS
+ and has_shared_blob(xet_hash=xet_file_data.file_hash, cache_dir=cache_dir, expected_size=expected_size)
+ )
+ )
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=expected_size,
+ filename=filename,
+ is_cached=is_cached,
+ local_path=pointer_path,
+ will_download=force_download or not is_cached,
+ )
+
+ # Pointer already exists -> update the ref best-effort, then return without
+ # attempting to write to the cache (which may be mounted read-only).
+ if not force_download and os.path.exists(pointer_path):
+ try:
+ _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash)
+ except OSError:
+ pass
+ return pointer_path
+
+ os.makedirs(os.path.dirname(blob_path), exist_ok=True)
+ os.makedirs(os.path.dirname(pointer_path), exist_ok=True)
+
+ # Tag cache_dir so backup tools can skip it (CACHEDIR.TAG standard).
+ _create_cachedir_tag(Path(cache_dir))
+
+ # if passed revision is not identical to commit_hash
+ # then revision has to be a branch name or tag name.
+ # In that case store a ref.
+ _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash)
+
+ # Prevent parallel downloads of the same file with a lock.
+ # etag could be duplicated across repos.
+ # Note: the lock is best-effort to avoid downloading the same file twice. Cache correctness
+ # does not depend on it: each download writes to a process-unique temporary file that is
+ # atomically renamed into place (see `_download_to_tmp_and_move`).
+ lock_path = os.path.join(locks_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type), f"{etag}.lock")
+
+ lock_path = as_extended_path(lock_path)
+ blob_path = as_extended_path(blob_path)
+
+ Path(lock_path).parent.mkdir(parents=True, exist_ok=True)
+
+ # Blob exists but pointer must be (safely) created -> take the lock
+ if not force_download and os.path.exists(blob_path):
+ with WeakFileLock(lock_path):
+ if not os.path.exists(pointer_path):
+ _create_symlink(blob_path, pointer_path, new_blob=False)
+ return pointer_path
+
+ # Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)
+
+ # Xet hash of the blob in the shared store, None when the store must not be used for this download
+ # (see `utils/_shared_blobs.py`).
+ shared_blob_hash = (
+ xet_file_data.file_hash
+ if xet_file_data is not None and shared_blobs_enabled() and are_symlinks_supported(cache_dir)
+ else None
+ )
+
+ with WeakFileLock(lock_path):
+ blob_reused_from_store = (
+ shared_blob_hash is not None
+ and not force_download
+ and not os.path.exists(blob_path)
+ and try_link_from_shared_store(
+ blob_path=blob_path, xet_hash=shared_blob_hash, cache_dir=cache_dir, expected_size=expected_size
+ )
+ )
+ blob_is_shared = blob_reused_from_store
+ if not blob_reused_from_store:
+ will_download = force_download or not os.path.exists(blob_path)
+ _download_to_tmp_and_move(
+ incomplete_path=Path(blob_path + ".incomplete"),
+ destination_path=Path(blob_path),
+ url_to_download=url_to_download,
+ headers=headers,
+ expected_size=expected_size,
+ filename=filename,
+ force_download=force_download,
+ etag=etag,
+ xet_file_data=xet_file_data,
+ tqdm_class=tqdm_class,
+ )
+ if shared_blob_hash is not None and will_download and is_xet_available():
+ # Only Xet-verified downloads are published, never the plain HTTP fallback.
+ blob_is_shared = publish_blob_to_shared_store(
+ blob_path=blob_path,
+ xet_hash=shared_blob_hash,
+ cache_dir=cache_dir,
+ expected_size=expected_size,
+ replace_existing=force_download,
+ )
+ if not os.path.exists(pointer_path):
+ # A shared blob is a symlink: if the snapshot symlink fails, copy through it instead of moving it away.
+ _create_symlink(blob_path, pointer_path, new_blob=not blob_is_shared)
+
+ return pointer_path
+
+
+def _hf_hub_download_to_local_dir(
+ *,
+ # Destination
+ local_dir: str | Path,
+ # File info
+ repo_id: str,
+ repo_type: str,
+ filename: str,
+ revision: str,
+ # HTTP info
+ endpoint: str | None,
+ etag_timeout: float,
+ headers: dict[str, str],
+ token: bool | str | None,
+ # Additional options
+ cache_dir: str,
+ force_download: bool,
+ local_files_only: bool,
+ tqdm_class: type[base_tqdm] | None,
+ dry_run: bool,
+) -> str | DryRunFileInfo:
+ """Download a given file to a local folder, if not already present.
+
+ Method should not be called directly. Please use `hf_hub_download` instead.
+ """
+ local_dir = Path(as_extended_path(local_dir))
+ paths = get_local_download_paths(local_dir=local_dir, filename=filename)
+ local_metadata = read_download_metadata(local_dir=local_dir, filename=filename)
+
+ # Local file exists + metadata exists + commit_hash matches => return file
+ if (
+ REGEX_COMMIT_HASH.fullmatch(revision)
+ and paths.file_path.is_file()
+ and local_metadata is not None
+ and local_metadata.commit_hash == revision
+ ):
+ local_file = str(paths.file_path)
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=revision,
+ file_size=os.path.getsize(local_file),
+ filename=filename,
+ is_cached=True,
+ local_path=local_file,
+ will_download=force_download,
+ )
+ if not force_download:
+ return local_file
+
+ # Local file doesn't exist or commit_hash doesn't match => we need the etag
+ # - Xet file => might be cached in /tree listing
+ # - otherwise => HEAD /resolve call
+ tree_cache_folder = tree_cache_folder_for_local_dir(str(local_dir))
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = _get_metadata_or_catch_error(
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=endpoint,
+ etag_timeout=etag_timeout,
+ headers=headers,
+ token=token,
+ local_files_only=local_files_only,
+ tree_cache_folder=tree_cache_folder,
+ )
+
+ if head_call_error is not None:
+ # No HEAD call but local file exists => default to local file
+ if paths.file_path.is_file():
+ if dry_run or not force_download:
+ logger.warning(
+ f"Couldn't access the Hub to check for update but local file already exists. Defaulting to existing file. (error: {head_call_error})"
+ )
+ local_path = str(paths.file_path)
+ if dry_run and local_metadata is not None:
+ return DryRunFileInfo(
+ commit_hash=local_metadata.commit_hash,
+ file_size=os.path.getsize(local_path),
+ filename=filename,
+ is_cached=True,
+ local_path=local_path,
+ will_download=force_download,
+ )
+ if not force_download:
+ return local_path
+ elif not force_download:
+ if isinstance(head_call_error, _DEFAULT_RETRY_ON_EXCEPTIONS) or (
+ isinstance(head_call_error, HfHubHTTPError)
+ and head_call_error.response.status_code in _DEFAULT_RETRY_ON_STATUS_CODES
+ ):
+ logger.info("No local file found. Retrying..")
+ (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_call_error) = (
+ _get_metadata_or_catch_error(
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=endpoint,
+ etag_timeout=_ETAG_RETRY_TIMEOUT,
+ headers=headers,
+ token=token,
+ local_files_only=local_files_only,
+ retry_on_errors=True,
+ )
+ )
+
+ # If still error, raise
+ if head_call_error is not None:
+ _raise_on_head_call_error(head_call_error, force_download, local_files_only)
+
+ # From now on, etag, commit_hash and url are not None.
+ assert etag is not None, "etag must have been retrieved from server"
+ assert commit_hash is not None, "commit_hash must have been retrieved from server"
+ assert url_to_download is not None, "file location must have been retrieved from server"
+
+ # Local file exists => check if it's up-to-date
+ if not force_download and paths.file_path.is_file():
+ # etag matches => update metadata and return file
+ if local_metadata is not None and local_metadata.etag == etag:
+ write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=expected_size,
+ filename=filename,
+ is_cached=True,
+ local_path=str(paths.file_path),
+ will_download=False,
+ )
+ return str(paths.file_path)
+
+ # metadata is outdated + etag is a sha256
+ # => means it's an LFS file (large)
+ # => let's compute local hash and compare
+ # => if match, update metadata and return file
+ if local_metadata is None and REGEX_SHA256.fullmatch(etag) is not None:
+ with open(paths.file_path, "rb") as f:
+ file_hash = sha_fileobj(f).hex()
+ if file_hash == etag:
+ write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=expected_size,
+ filename=filename,
+ is_cached=True,
+ local_path=str(paths.file_path),
+ will_download=False,
+ )
+ return str(paths.file_path)
+
+ # Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)
+
+ # If we are lucky enough, the file is already in the cache => copy it
+ if not force_download:
+ cached_path = try_to_load_from_cache(
+ repo_id=repo_id,
+ filename=filename,
+ cache_dir=cache_dir,
+ revision=commit_hash,
+ repo_type=repo_type,
+ )
+ if isinstance(cached_path, str):
+ if dry_run:
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=expected_size,
+ filename=filename,
+ is_cached=True,
+ local_path=str(paths.file_path),
+ will_download=False,
+ )
+ with WeakFileLock(paths.lock_path):
+ paths.file_path.parent.mkdir(parents=True, exist_ok=True)
+ shutil.copyfile(cached_path, paths.file_path)
+ write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+ return str(paths.file_path)
+
+ if dry_run:
+ is_cached = paths.file_path.is_file()
+ return DryRunFileInfo(
+ commit_hash=commit_hash,
+ file_size=expected_size,
+ filename=filename,
+ is_cached=is_cached,
+ local_path=str(paths.file_path),
+ will_download=force_download or not is_cached,
+ )
+
+ # Otherwise, let's download the file!
+ with WeakFileLock(paths.lock_path):
+ paths.file_path.unlink(missing_ok=True) # delete outdated file first
+ _download_to_tmp_and_move(
+ incomplete_path=paths.incomplete_path(etag),
+ destination_path=paths.file_path,
+ url_to_download=url_to_download,
+ headers=headers,
+ expected_size=expected_size,
+ filename=filename,
+ force_download=force_download,
+ etag=etag,
+ xet_file_data=xet_file_data,
+ tqdm_class=tqdm_class,
+ )
+
+ write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+ return str(paths.file_path)
+
+
+@validate_hf_hub_args
+def try_to_load_from_cache(
+ repo_id: str,
+ filename: str,
+ cache_dir: str | Path | None = None,
+ revision: str | None = None,
+ repo_type: str | None = None,
+) -> str | _CACHED_NO_EXIST_T | None:
+ """
+ Explores the cache to return the latest cached file for a given revision if found.
+
+ This function will not raise any exception if the file in not cached.
+
+ Args:
+ cache_dir (`str` or `os.PathLike`):
+ The folder where the cached files lie.
+ repo_id (`str`):
+ The ID of the repo on huggingface.co.
+ filename (`str`):
+ The filename to look for inside `repo_id`.
+ revision (`str`, *optional*):
+ The specific model version to use. Will default to `"main"` if it's not provided and no `commit_hash` is
+ provided either.
+ repo_type (`str`, *optional*):
+ The type of the repository. Will default to `"model"`.
+
+ Returns:
+ `Optional[str]` or `_CACHED_NO_EXIST`:
+ Will return `None` if the file was not cached. Otherwise:
+ - The exact path to the cached file if it's found in the cache
+ - A special value `_CACHED_NO_EXIST` if the file does not exist at the given commit hash and this fact was
+ cached.
+
+ Example:
+
+ ```python
+ from huggingface_hub import try_to_load_from_cache, _CACHED_NO_EXIST
+
+ filepath = try_to_load_from_cache()
+ if isinstance(filepath, str):
+ # file exists and is cached
+ ...
+ elif filepath is _CACHED_NO_EXIST:
+ # non-existence of file is cached
+ ...
+ else:
+ # file is not cached
+ ...
+ ```
+ """
+ if revision is None:
+ revision = "main"
+ if repo_type is None:
+ repo_type = "model"
+ if repo_type not in constants.REPO_TYPES_WITH_KERNEL:
+ raise ValueError(
+ f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES_WITH_KERNEL)}"
+ )
+ if cache_dir is None:
+ cache_dir = constants.HF_HUB_CACHE
+
+ repo_cache = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+ if not os.path.isdir(repo_cache):
+ # No cache for this model
+ return None
+
+ refs_dir = os.path.join(repo_cache, "refs")
+ snapshots_dir = os.path.join(repo_cache, "snapshots")
+ no_exist_dir = os.path.join(repo_cache, ".no_exist")
+
+ # Resolve refs (for instance to convert main to the associated commit sha)
+ if os.path.isdir(refs_dir):
+ revision_file = os.path.join(refs_dir, revision)
+ if os.path.isfile(revision_file):
+ with open(revision_file) as f:
+ revision = f.read()
+
+ # Check if file is cached as "no_exist"
+ if os.path.isfile(os.path.join(no_exist_dir, revision, filename)):
+ return _CACHED_NO_EXIST
+
+ # Check if revision folder exists
+ if not os.path.exists(snapshots_dir):
+ return None
+ cached_shas = os.listdir(snapshots_dir)
+ if revision not in cached_shas:
+ # No cache for this revision and we won't try to return a random revision
+ return None
+
+ # Check if file exists in cache
+ cached_file = os.path.join(snapshots_dir, revision, filename)
+ return cached_file if os.path.isfile(cached_file) else None
+
+
+@validate_hf_hub_args
+def get_hf_file_metadata(
+ url: str,
+ token: bool | str | None = None,
+ timeout: float | None = constants.HF_HUB_ETAG_TIMEOUT,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+ retry_on_errors: bool = False,
+) -> HfFileMetadata:
+ """Fetch metadata of a file versioned on the Hub for a given url.
+
+ Args:
+ url (`str`):
+ File url, for example returned by [`hf_hub_url`].
+ token (`str` or `bool`, *optional*):
+ A token to be used for the download.
+ - If `True`, the token is read from the HuggingFace config
+ folder.
+ - If `False` or `None`, no token is provided.
+ - If a string, it's used as the authentication token.
+ timeout (`float`, *optional*, defaults to 10):
+ How many seconds to wait for the server to send metadata before giving up.
+ library_name (`str`, *optional*):
+ The name of the library to which the object corresponds.
+ library_version (`str`, *optional*):
+ The version of the library.
+ user_agent (`dict`, `str`, *optional*):
+ The user-agent info in the form of a dictionary or a string.
+ headers (`dict`, *optional*):
+ Additional headers to be sent with the request.
+ endpoint (`str`, *optional*):
+ Endpoint of the Hub. Defaults to .
+ retry_on_errors (`bool`, *optional*, defaults to `False`):
+ Whether to retry on errors (429, 5xx, timeout, network errors).
+ If False, no retry for fast fallback to local cache.
+
+ Returns:
+ A [`HfFileMetadata`] object containing metadata such as location, etag, size and
+ commit_hash.
+ """
+ hf_headers = build_hf_headers(
+ token=token,
+ library_name=library_name,
+ library_version=library_version,
+ user_agent=user_agent,
+ headers=headers,
+ )
+ hf_headers["Accept-Encoding"] = "identity" # prevent any compression => we want to know the real size of the file
+
+ # Retrieve metadata
+ response = _httpx2_follow_hub_redirects_with_backoff(
+ method="HEAD", url=url, headers=hf_headers, timeout=timeout, retry_on_errors=retry_on_errors
+ )
+ hf_raise_for_status(response)
+
+ # Return
+ return HfFileMetadata(
+ commit_hash=response.headers.get(constants.HUGGINGFACE_HEADER_X_REPO_COMMIT),
+ # We favor a custom header indicating the etag of the linked resource, and we fall back to the regular etag header.
+ etag=_normalize_etag(
+ response.headers.get(constants.HUGGINGFACE_HEADER_X_LINKED_ETAG) or response.headers.get("ETag")
+ ),
+ # Either from response headers (if redirected) or defaults to request url
+ # Do not use directly `url` as we might have followed relative redirects.
+ location=response.headers.get("Location") or str(response.request.url), # type: ignore
+ size=_int_or_none(
+ response.headers.get(constants.HUGGINGFACE_HEADER_X_LINKED_SIZE)
+ or (None if response.is_redirect else response.headers.get("Content-Length"))
+ ),
+ xet_file_data=parse_xet_file_data_from_response(response, endpoint=endpoint), # type: ignore
+ )
+
+
+def _get_metadata_or_catch_error(
+ *,
+ repo_id: str,
+ filename: str,
+ repo_type: str,
+ revision: str,
+ endpoint: str | None,
+ etag_timeout: float | None,
+ headers: dict[str, str], # mutated inplace!
+ token: bool | str | None,
+ local_files_only: bool,
+ relative_filename: str | None = None, # only used to store `.no_exists` in cache
+ storage_folder: str | None = None, # only used to store `.no_exists` in cache
+ retry_on_errors: bool = False,
+ tree_cache_folder: str | None = None, # if set, read the on-disk tree listing to skip the HEAD call
+) -> (
+ # Either an exception is caught and returned
+ tuple[None, None, None, None, None, Exception]
+ |
+ # Or the metadata is returned as
+ # `(url_to_download, etag, commit_hash, expected_size, xet_file_data, None)`
+ tuple[str, str, str, int | None, XetFileData | None, None]
+):
+ """Get metadata for a file on the Hub, safely handling network issues.
+
+ Returns either the etag, commit_hash and expected size of the file, or the error
+ raised while fetching the metadata.
+
+ NOTE: This function mutates `headers` inplace! It removes the `authorization` header
+ if the file is a LFS blob and the domain of the url is different from the
+ domain of the location (typically an S3 bucket).
+ """
+ if local_files_only:
+ return (
+ None,
+ None,
+ None,
+ None,
+ None,
+ OfflineModeIsEnabled(
+ f"Cannot access file since 'local_files_only=True' as been set. (repo_id: {repo_id}, repo_type: {repo_type}, revision: {revision}, filename: {filename})"
+ ),
+ )
+
+ # Skip the per-file HEAD call when the file metadata can be rebuilt from a tree listing cached on disk.
+ if tree_cache_folder is not None and REGEX_COMMIT_HASH.fullmatch(revision):
+ tree_metadata = _xet_file_metadata_from_tree_cache(
+ tree_cache_folder=tree_cache_folder,
+ repo_id=repo_id,
+ repo_type=repo_type,
+ commit_hash=revision,
+ filename=filename,
+ endpoint=endpoint,
+ )
+ if tree_metadata is not None:
+ return tree_metadata
+
+ url = hf_hub_url(repo_id, filename, repo_type=repo_type, revision=revision, endpoint=endpoint)
+ url_to_download: str = url
+ etag: str | None = None
+ commit_hash: str | None = None
+ expected_size: int | None = None
+ head_error_call: Exception | None = None
+ xet_file_data: XetFileData | None = None
+
+ # Try to get metadata from the server.
+ # Do not raise yet if the file is not found or not accessible.
+ if not local_files_only:
+ try:
+ try:
+ metadata = get_hf_file_metadata(
+ url=url,
+ timeout=etag_timeout,
+ headers=headers,
+ token=token,
+ endpoint=endpoint,
+ retry_on_errors=retry_on_errors,
+ )
+ except RemoteEntryNotFoundError as http_error:
+ if storage_folder is not None and relative_filename is not None:
+ # Cache the non-existence of the file
+ commit_hash = http_error.response.headers.get(constants.HUGGINGFACE_HEADER_X_REPO_COMMIT)
+ if commit_hash is not None:
+ no_exist_file_path = Path(storage_folder) / ".no_exist" / commit_hash / relative_filename
+ if not no_exist_file_path.exists():
+ try:
+ no_exist_file_path.parent.mkdir(parents=True, exist_ok=True)
+ no_exist_file_path.touch()
+ except OSError as e:
+ logger.error(
+ f"Could not cache non-existence of file. Will ignore error and continue. Error: {e}"
+ )
+ _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash)
+ raise
+
+ # Commit hash must exist
+ commit_hash = metadata.commit_hash
+ if commit_hash is None:
+ raise FileMetadataError(
+ f"Response from {url} is missing the '{constants.HUGGINGFACE_HEADER_X_REPO_COMMIT}' header, so it"
+ " does not seem to be served by a Hugging Face Hub endpoint. If HF_ENDPOINT is set, check that it"
+ " points to a Hub-compatible endpoint. Otherwise, check your firewall and proxy settings and make"
+ " sure your SSL certificates are updated."
+ )
+
+ # Etag must exist
+ # If we don't have any of those, raise an error.
+ etag = metadata.etag
+ if etag is None:
+ raise FileMetadataError(
+ "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
+ )
+
+ # Xet downloads require a known size, but regular HTTP downloads can recover it from the GET response.
+ expected_size = metadata.size
+ xet_file_data = metadata.xet_file_data
+ if expected_size is None and xet_file_data is not None and is_xet_available():
+ raise FileMetadataError("Distant resource does not have a Content-Length.")
+
+ # In case of a redirect, save an extra redirect on the request.get call,
+ # and ensure we download the exact atomic version even if it changed
+ # between the HEAD and the GET (unlikely, but hey).
+ #
+ # If the final location is on a Hub host (same host, or e.g. huggingface.co reached through an
+ # HF_ENDPOINT mirror redirect) => keep auth. Otherwise it's a signed CDN url => don't send auth.
+ if xet_file_data is None and url != metadata.location:
+ url_to_download = metadata.location
+ if not _is_same_or_hub_host(url, metadata.location):
+ # Remove authorization header when downloading a LFS blob from a CDN
+ headers.pop("authorization", None)
+ except httpx2.ProxyError:
+ # Actually raise on proxy error
+ raise
+ except (httpx2.ConnectError, httpx2.TimeoutException, OfflineModeIsEnabled) as error:
+ # Otherwise, our Internet connection is down.
+ # etag is None
+ head_error_call = error
+ except (RevisionNotFoundError, RemoteEntryNotFoundError):
+ # The repo was found but the revision or entry doesn't exist on the Hub (never existed or got deleted)
+ raise
+ except HfHubHTTPError as error:
+ # Multiple reasons for an http error:
+ # - Repository is private and invalid/missing token sent
+ # - Repository is gated and invalid/missing token sent
+ # - Hub is down (error 500 or 504)
+ # => let's switch to 'local_files_only=True' to check if the files are already cached.
+ # (if it's not the case, the error will be re-raised)
+ head_error_call = error
+ except FileMetadataError as error:
+ # Multiple reasons for a FileMetadataError:
+ # - Wrong network configuration (proxy, firewall, SSL certificates)
+ # - Inconsistency on the Hub
+ # => let's switch to 'local_files_only=True' to check if the files are already cached.
+ # (if it's not the case, the error will be re-raised)
+ head_error_call = error
+
+ if not (local_files_only or etag is not None or head_error_call is not None):
+ raise RuntimeError("etag is empty due to uncovered problems")
+
+ if head_error_call is not None:
+ _detach_tracebacks(head_error_call)
+
+ return (url_to_download, etag, commit_hash, expected_size, xet_file_data, head_error_call) # type: ignore
+
+
+def _detach_tracebacks(error: BaseException) -> None:
+ """Detach the tracebacks of an exception and of its whole `__cause__`/`__context__` chain.
+
+ Returning an exception instead of raising it means keeping its traceback alive. A traceback references the frames
+ it was raised from, and each frame references its caller (up to the user code calling `hf_hub_download`) and its
+ own local variables - including the returned exception itself. This creates a reference cycle that only the cyclic
+ garbage collector can break, so callers falling back to a cached file (i.e. discarding the error) keep their own
+ stack - and everything it references - alive until the next `gc.collect()`.
+
+ Only the frames are dropped: exception types, messages and the cause/context chain are preserved, so re-raising a
+ detached error still reports what went wrong.
+
+ See https://github.com/vllm-project/vllm/pull/50341 for a real-world report of this retention.
+ """
+ seen: set[int] = set()
+ pending = [error]
+ while pending:
+ current = pending.pop()
+ if id(current) in seen: # defensive: exception chains can be cyclic
+ continue
+ seen.add(id(current))
+ current.__traceback__ = None
+ pending.extend(exc for exc in (current.__cause__, current.__context__) if exc is not None)
+
+
+def _xet_file_metadata_from_tree_cache(
+ *,
+ tree_cache_folder: str,
+ repo_id: str,
+ repo_type: str,
+ commit_hash: str,
+ filename: str,
+ endpoint: str | None,
+) -> tuple[str, str, str, int, XetFileData, None] | None:
+ """Rebuild the metadata a HEAD call would return, from the on-disk tree listing cache.
+
+ The optimization is intentionally limited to Xet files when Xet is enabled: that's the only case where
+ skipping the HEAD call pays off, since Xet downloads don't rely on the `/resolve` redirect the HEAD call
+ would resolve. For regular files (or when Xet is unavailable) we return `None` and let the caller make the
+ HEAD call as usual. Also returns `None` when the commit's tree listing or the file is not cached.
+ """
+ if not is_xet_available():
+ return None
+ tree_entries = read_tree_cache(tree_cache_folder, commit_hash)
+ if tree_entries is None:
+ return None
+ entry = tree_entries.get(filename)
+ if (
+ entry is None
+ or entry.xet_hash is None
+ or not is_valid_xet_hash(entry.xet_hash)
+ or entry.lfs_sha256 is None
+ or entry.lfs_size is None
+ ):
+ return None
+
+ xet_file_data = XetFileData(
+ file_hash=entry.xet_hash,
+ refresh_route=xet_connection_info_refresh_url(
+ token_type=XetTokenType.READ,
+ repo_id=repo_id,
+ repo_type=repo_type,
+ revision=commit_hash,
+ endpoint=endpoint,
+ ),
+ )
+ # Mirror the server's HEAD response: ETag/size come from the LFS metadata for LFS-tracked files (which Xet
+ # files are).
+ etag = entry.lfs_sha256
+ file_size = entry.lfs_size
+ location = hf_hub_url(repo_id, filename, repo_type=repo_type, revision=commit_hash, endpoint=endpoint)
+ return (location, etag, commit_hash, file_size, xet_file_data, None)
+
+
+def _raise_on_head_call_error(head_call_error: Exception, force_download: bool, local_files_only: bool) -> NoReturn:
+ """Raise an appropriate error when the HEAD call failed and we cannot locate a local file."""
+ # No head call => we cannot force download.
+ if force_download:
+ if local_files_only:
+ raise ValueError("Cannot pass 'force_download=True' and 'local_files_only=True' at the same time.")
+ elif isinstance(head_call_error, OfflineModeIsEnabled):
+ raise ValueError("Cannot pass 'force_download=True' when offline mode is enabled.") from head_call_error
+ else:
+ raise ValueError("Force download failed due to the above error.") from head_call_error
+
+ # No head call + couldn't find an appropriate file on disk => raise an error.
+ if local_files_only:
+ raise LocalEntryNotFoundError(
+ "Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable"
+ " hf.co look-ups and downloads online, set 'local_files_only' to False."
+ )
+ elif isinstance(head_call_error, (RepositoryNotFoundError, GatedRepoError)) or (
+ isinstance(head_call_error, HfHubHTTPError) and head_call_error.response.status_code == 401
+ ):
+ # Repo not found or gated => let's raise the actual error
+ # Unauthorized => likely a token issue => let's raise the actual error
+ raise head_call_error
+ elif isinstance(head_call_error, FileMetadataError):
+ # The call succeeded but the response lacked the metadata we need => a configuration issue, not connectivity.
+ raise LocalEntryNotFoundError(
+ f"{head_call_error} We also cannot find the requested files in the local cache."
+ ) from head_call_error
+ else:
+ # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
+ raise LocalEntryNotFoundError(
+ "An error happened while trying to locate the file on the Hub and we cannot find the requested files"
+ " in the local cache. Please check your connection and try again or make sure your Internet connection"
+ " is on."
+ ) from head_call_error
+
+
+def _download_to_tmp_and_move(
+ incomplete_path: Path,
+ destination_path: Path,
+ url_to_download: str,
+ headers: dict[str, str],
+ expected_size: int | None,
+ filename: str,
+ force_download: bool,
+ etag: str | None,
+ xet_file_data: XetFileData | None,
+ tqdm_class: type[base_tqdm] | None = None,
+) -> None:
+ """Download content from a URL to a destination path.
+
+ Internal logic:
+ - return early if file is already downloaded
+ - check disk space before downloading
+ - download content to a process-unique temporary file
+ - set correct permissions on temporary file
+ - move the temporary file to the destination path
+
+ Both `incomplete_path` and `destination_path` must be on the same volume to avoid a local copy.
+ """
+ if destination_path.exists() and not force_download:
+ # Do nothing if already exists (except if force_download=True)
+ return
+
+ # Download to a process-unique temporary file before moving it in place. A shared
+ # `.incomplete` file corrupts the cache whenever the surrounding lock is not honored:
+ # on some filesystems (Lustre, GPFS, some NFS mounts) `flock(2)` silently succeeds for every
+ # caller and concurrent processes end up appending to the same file. With a unique file per
+ # process, a broken lock costs only duplicated bandwidth: each process downloads the full
+ # file and atomically renames it to the final destination.
+ # See https://github.com/huggingface/huggingface_hub/pull/4228.
+ # Re-check the length on Windows: the suffix can push a path just under the limit above MAX_PATH.
+ tmp_path = incomplete_path.with_name(f"{incomplete_path.stem}.{uuid.uuid4().hex[:8]}.incomplete")
+ tmp_path = Path(as_extended_path(tmp_path))
+ try:
+ with tmp_path.open("wb") as f:
+ logger.debug(f"Downloading '{filename}' to '{tmp_path}'")
+
+ if expected_size is not None: # might be None if HTTP header not set correctly
+ # Check disk space in both tmp and destination path
+ _check_disk_space(expected_size, tmp_path.parent)
+ _check_disk_space(expected_size, destination_path.parent)
+
+ if xet_file_data is not None and is_xet_available():
+ logger.debug("Xet Storage is enabled for this repo. Downloading file from Xet Storage..")
+ xet_get(
+ incomplete_path=tmp_path,
+ xet_file_data=xet_file_data,
+ headers=headers,
+ expected_size=expected_size,
+ displayed_filename=filename,
+ tqdm_class=tqdm_class,
+ )
+ else:
+ if xet_file_data is not None and not constants.HF_HUB_DISABLE_XET:
+ logger.warning(
+ "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. "
+ "Falling back to regular HTTP download. "
+ "For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`"
+ )
+
+ http_get(
+ url_to_download,
+ f,
+ headers=headers,
+ expected_size=expected_size,
+ tqdm_class=tqdm_class,
+ )
+
+ logger.debug(f"Download complete. Moving file to {destination_path}")
+ _chmod_and_move(tmp_path, destination_path)
+ finally:
+ # No-op on success (file has been moved). On failure, do not keep a partial file around:
+ # it could not be reused anyway since the temporary name is unique to this download.
+ tmp_path.unlink(missing_ok=True)
+
+
+def _int_or_none(value: str | None) -> int | None:
+ try:
+ return int(value) # type: ignore
+ except (TypeError, ValueError):
+ return None
+
+
+def _chmod_and_move(src: Path, dst: Path) -> None:
+ """Set correct permission before moving a blob from tmp directory to cache dir.
+
+ Do not take into account the `umask` from the process as there is no convenient way
+ to get it that is thread-safe.
+
+ See:
+ - About umask: https://docs.python.org/3/library/os.html#os.umask
+ - Thread-safety: https://stackoverflow.com/a/70343066
+ - About solution: https://github.com/huggingface/huggingface_hub/pull/1220#issuecomment-1326211591
+ - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1141
+ - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1215
+ """
+ # Get umask by creating a temporary file in the folder containing the incomplete file.
+ # We know this folder is writable since the incomplete file has just been written there.
+ # Probing next to `dst` is not always possible: when downloading to a local dir, `dst` is the
+ # final file location and its parents might not be writable (e.g. read-only root filesystem).
+ # See https://github.com/huggingface/huggingface_hub/issues/4304.
+ tmp_file = src.parent / f"tmp_{uuid.uuid4()}"
+ try:
+ tmp_file.touch()
+ cache_dir_mode = Path(tmp_file).stat().st_mode
+ os.chmod(str(src), stat.S_IMODE(cache_dir_mode))
+ except OSError as e:
+ logger.warning(
+ f"Could not set the permissions on the file '{src}'. Error: {e}.\nContinuing without setting permissions."
+ )
+ finally:
+ try:
+ tmp_file.unlink()
+ except OSError:
+ # fails if `tmp_file.touch()` failed => do nothing
+ # See https://github.com/huggingface/huggingface_hub/issues/2359
+ pass
+
+ if os.path.lexists(dst):
+ # Replace the entry so a force download never writes through a shared symlink.
+ _replace_no_matter_what(src, dst)
+ else:
+ shutil.move(str(src), str(dst), copy_function=_copy_no_matter_what)
+
+
+def _replace_no_matter_what(src: Path, dst: Path) -> None:
+ """Replace `dst` with `src`.
+
+ Some mounts reject replace-over-existing: stage the new file next to `dst`, move the old entry aside
+ and restore it if the final move fails.
+ """
+ try:
+ os.replace(src, dst)
+ except OSError:
+ staged_dst = dst.with_name(f".{dst.name}.{uuid.uuid4().hex[:8]}.new")
+ backup_dst = dst.with_name(f".{dst.name}.{uuid.uuid4().hex[:8]}.old")
+ backup_holds_previous_entry = False
+ try:
+ shutil.move(str(src), str(staged_dst), copy_function=_copy_no_matter_what)
+ os.rename(dst, backup_dst)
+ backup_holds_previous_entry = True
+ try:
+ shutil.move(str(staged_dst), str(dst), copy_function=_copy_no_matter_what)
+ except OSError as move_error:
+ try:
+ if os.path.lexists(dst):
+ os.unlink(dst)
+ os.rename(backup_dst, dst)
+ backup_holds_previous_entry = False
+ except OSError as restore_error:
+ raise OSError(
+ f"Could not restore previous destination '{dst}' from '{backup_dst}'"
+ ) from restore_error
+ raise move_error
+ try:
+ backup_dst.unlink()
+ backup_holds_previous_entry = False
+ except OSError as cleanup_error:
+ logger.warning(f"Could not remove previous destination backup '{backup_dst}': {cleanup_error}")
+ finally:
+ staged_dst.unlink(missing_ok=True)
+ if not backup_holds_previous_entry:
+ backup_dst.unlink(missing_ok=True)
+
+
+def _copy_no_matter_what(src: str, dst: str) -> None:
+ """Copy file from src to dst.
+
+ If `shutil.copy2` fails, fallback to `shutil.copyfile`.
+ """
+ try:
+ # Copy file with metadata and permission
+ # Can fail e.g. if dst is an S3 mount
+ shutil.copy2(src, dst)
+ except OSError:
+ # Copy only file content
+ shutil.copyfile(src, dst)
+
+
+def _get_pointer_path(storage_folder: str, revision: str, relative_filename: str) -> str:
+ # Using `os.path.abspath` instead of `Path.resolve()` to avoid resolving symlinks
+ snapshot_path = os.path.join(storage_folder, "snapshots")
+ pointer_path = os.path.join(snapshot_path, revision, relative_filename)
+ if Path(os.path.abspath(snapshot_path)) not in Path(os.path.abspath(pointer_path)).parents:
+ raise ValueError(
+ "Invalid pointer path: cannot create pointer path in snapshot folder if"
+ f" `storage_folder='{storage_folder}'`, `revision='{revision}'` and"
+ f" `relative_filename='{relative_filename}'`."
+ )
+ return pointer_path
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_api.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..9482ec61446ee9dd79cd21c1978b33c53499c204
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_api.py
@@ -0,0 +1,15469 @@
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import inspect
+import itertools
+import json
+import re
+import struct
+import time
+import warnings
+from collections import defaultdict
+from collections.abc import Callable, Iterable, Iterator
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timedelta, timezone
+from email.utils import parsedate_to_datetime
+from functools import wraps
+from itertools import islice
+from pathlib import Path
+from secrets import token_hex
+from typing import TYPE_CHECKING, Any, BinaryIO, Literal, TypeVar, cast, overload
+from urllib.parse import quote
+
+import httpcore2
+import httpx2
+from tqdm import tqdm as base_tqdm
+
+from . import constants
+from ._eval_results import EvalResultEntry, parse_eval_result_entries
+from ._inference_endpoints import (
+ InferenceCatalogModel,
+ InferenceEndpoint,
+ InferenceEndpointHardware,
+ InferenceEndpointScalingMetric,
+ InferenceEndpointType,
+ _build_endpoint_image_payload,
+ _set_parallelism_in_image,
+)
+from ._jobs_api import (
+ DEFAULT_UV_IMAGE,
+ TERMINAL_JOB_STAGES,
+ JobHardware,
+ JobHardwareInfo,
+ JobInfo,
+ JobSpec,
+ JobStage,
+ ScheduledJobInfo,
+ _build_expose_payload,
+ _create_job_spec,
+ _default_job_name_from_image,
+ _default_job_name_from_script,
+ _derive_job_volume_name,
+)
+from ._revision import ResolvedRevision
+from ._space_api import (
+ INTERMEDIATE_SPACE_STAGES,
+ SpaceHardware,
+ SpaceRuntime,
+ SpaceSearchResult,
+ SpaceSecret,
+ SpaceTemplate,
+ SpaceVariable,
+ Volume,
+ ZeroGpuQuota,
+)
+from .community import (
+ Discussion,
+ DiscussionComment,
+ DiscussionStatusChange,
+ DiscussionTitleChange,
+ DiscussionWithDetails,
+ deserialize_event,
+)
+from .errors import (
+ BadRequestError,
+ BucketBatchError,
+ EntryNotFoundError,
+ FileDuplicationError,
+ GatedRepoError,
+ HfHubHTTPError,
+ HfUriError,
+ LocalTokenNotFoundError,
+ OfflineModeIsEnabled,
+ RemoteEntryNotFoundError,
+ RepositoryNotFoundError,
+ RevisionNotFoundError,
+ RevisionResolutionError,
+)
+from .repocard_data import DatasetCardData, ModelCardData, SpaceCardData
+from .utils import (
+ DEFAULT_IGNORE_PATTERNS,
+ HfUri,
+ NotASafetensorsRepoError,
+ SafetensorsFileMetadata,
+ SafetensorsParsingError,
+ SafetensorsRepoMetadata,
+ TensorInfo,
+ build_hf_headers,
+ chunk_iterable,
+ experimental,
+ filter_repo_objects,
+ fix_hf_endpoint_in_url,
+ get_session,
+ get_token,
+ hf_raise_for_status,
+ http_backoff,
+ logging,
+ paginate,
+ parse_datetime,
+ parse_hf_uri,
+ parse_xet_file_data_from_response,
+ validate_hf_hub_args,
+)
+from .utils._auth import _get_token_from_environment, _get_token_from_file, _get_token_from_google_colab
+from .utils._http import _httpx2_follow_hub_redirects_with_backoff, flag_as_download_call
+from .utils._runtime import is_xet_available
+from .utils._typing import CallableT
+from .utils.endpoint_helpers import _is_emission_within_threshold
+
+
+if TYPE_CHECKING:
+ from ._buckets import (
+ BucketFile,
+ BucketFileMetadata,
+ BucketFolder,
+ BucketInfo,
+ BucketUrl,
+ SyncPlan,
+ _BucketAddFile,
+ _BucketCopyFile,
+ _BucketDeleteFile,
+ )
+ from ._commit_api import CommitOperation, CommitOperationAdd, CommitOperationCopy, CommitOperationDelete
+ from ._dataset_viewer import DatasetParquetEntry
+ from .file_download import DryRunFileInfo, HfFileMetadata
+ from .inference._providers import PROVIDER_T
+ from .utils._verification import FolderVerification
+ from .utils._xet_progress_reporting import XetUploadProgressReporter
+
+
+R = TypeVar("R") # Return type
+CollectionItemType_T = Literal["model", "dataset", "space", "paper", "collection", "bucket"]
+CollectionSort_T = Literal["lastModified", "trending", "upvotes"]
+RepoVisibility_T = Literal["public", "private", "protected"]
+
+ExpandModelProperty_T = Literal[
+ "author",
+ "baseModels",
+ "cardData",
+ "childrenModelCount",
+ "config",
+ "createdAt",
+ "disabled",
+ "downloads",
+ "downloadsAllTime",
+ "evalResults",
+ "gated",
+ "gguf",
+ "inference",
+ "inferenceProviderMapping",
+ "lastModified",
+ "library_name",
+ "likes",
+ "mask_token",
+ "model-index",
+ "pipeline_tag",
+ "private",
+ "resourceGroup",
+ "safetensors",
+ "sha",
+ "siblings",
+ "spaces",
+ "tags",
+ "transformersInfo",
+ "trendingScore",
+ "usedStorage",
+ "widgetData",
+]
+
+ExpandDatasetProperty_T = Literal[
+ "author",
+ "cardData",
+ "citation",
+ "createdAt",
+ "description",
+ "disabled",
+ "downloads",
+ "downloadsAllTime",
+ "gated",
+ "lastModified",
+ "likes",
+ "mainSize",
+ "paperswithcode_id",
+ "private",
+ "resourceGroup",
+ "sha",
+ "siblings",
+ "tags",
+ "trendingScore",
+ "usedStorage",
+]
+
+ExpandSpaceProperty_T = Literal[
+ "author",
+ "cardData",
+ "createdAt",
+ "datasets",
+ "disabled",
+ "lastModified",
+ "likes",
+ "models",
+ "private",
+ "region",
+ "resourceGroup",
+ "runtime",
+ "sdk",
+ "sha",
+ "siblings",
+ "subdomain",
+ "tags",
+ "trendingScore",
+ "usedStorage",
+]
+
+ModelSort_T = Literal["created_at", "downloads", "last_modified", "likes", "trending_score"]
+DatasetSort_T = Literal["created_at", "downloads", "last_modified", "likes", "trending_score"]
+SpaceSort_T = Literal["created_at", "last_modified", "likes", "trending_score"]
+DailyPapersSort_T = Literal["publishedAt", "trending"]
+
+REPO_REGIONS = Literal["us", "eu"]
+
+USERNAME_PLACEHOLDER = "hf_user"
+_REGEX_DISCUSSION_URL = re.compile(r".*/discussions/(\d+)")
+_REGEX_HTTP_PROTOCOL = re.compile(r"https?://")
+
+_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE = (
+ "\nNote: Creating a commit assumes that the repo already exists on the"
+ " Huggingface Hub. Please use `create_repo` if it's not the case."
+)
+_AUTH_CHECK_NO_REPO_ERROR_MESSAGE = (
+ "\nNote: The repository either does not exist or you do not have access rights."
+ " Please check the repository ID and your access permissions."
+ " If this is a private repository, ensure that your token is correct."
+)
+_BUCKET_PATHS_INFO_BATCH_SIZE = 1000
+_BUCKET_BATCH_ADD_CHUNK_SIZE = 1000
+_BUCKET_BATCH_DELETE_CHUNK_SIZE = 1000
+_BUCKET_BATCH_MAX_LISTED_FAILURES = 10 # in the error message; the full list is on `BucketBatchError.failures`
+
+# Regex used to match special revisions with "/" in them (see #1710)
+SPECIAL_REFS_REVISION_REGEX = re.compile(
+ r"""
+ (^refs\/convert\/\w+) # `refs/convert/parquet` revisions
+ |
+ (^refs\/pr\/\d+) # PR revisions
+ """,
+ re.VERBOSE,
+)
+
+logger = logging.get_logger(__name__)
+
+
+def _resolve_repo_visibility(
+ *,
+ private: bool | None,
+ visibility: RepoVisibility_T | None,
+ repo_type: str | None,
+) -> RepoVisibility_T | None:
+ if private is not None and visibility is not None:
+ raise ValueError("Received both `private` and `visibility` arguments. Please provide only one of them.")
+
+ if visibility is None:
+ if private is None:
+ return None
+ return "private" if private else "public"
+
+ if visibility == "protected" and repo_type != constants.REPO_TYPE_SPACE:
+ raise ValueError("Only Spaces can be 'protected'. Please set visibility to 'public' or 'private'.")
+ return visibility
+
+
+@dataclass
+class LastCommitInfo(dict):
+ oid: str
+ title: str
+ date: datetime
+
+ def __post_init__(self): # hack to make LastCommitInfo backward compatible
+ self.update(asdict(self))
+
+
+@dataclass
+class BlobLfsInfo(dict):
+ size: int
+ sha256: str
+ pointer_size: int
+
+ def __post_init__(self): # hack to make BlobLfsInfo backward compatible
+ self.update(asdict(self))
+
+
+@dataclass
+class BlobSecurityInfo(dict):
+ safe: bool # duplicate information with "status" field, keeping it for backward compatibility
+ status: str
+ av_scan: dict | None
+ pickle_import_scan: dict | None
+
+ def __post_init__(self): # hack to make BlogSecurityInfo backward compatible
+ self.update(asdict(self))
+
+
+@dataclass
+class TransformersInfo(dict):
+ auto_model: str
+ custom_class: str | None = None
+ # possible `pipeline_tag` values: https://github.com/huggingface/huggingface.js/blob/3ee32554b8620644a6287e786b2a83bf5caf559c/packages/tasks/src/pipelines.ts#L72
+ pipeline_tag: str | None = None
+ processor: str | None = None
+
+ def __post_init__(self): # hack to make TransformersInfo backward compatible
+ self.update(asdict(self))
+
+
+@dataclass
+class SafeTensorsInfo(dict):
+ parameters: dict[str, int]
+ total: int
+
+ def __post_init__(self): # hack to make SafeTensorsInfo backward compatible
+ self.update(asdict(self))
+
+
+@dataclass
+class CommitInfo(str):
+ """Data structure containing information about a newly created commit.
+
+ Returned by any method that creates a commit on the Hub: [`create_commit`], [`upload_file`], [`upload_folder`],
+ [`delete_file`], [`delete_folder`]. It inherits from `str` for backward compatibility but using methods specific
+ to `str` is deprecated.
+
+ Attributes:
+ commit_url (`str`):
+ Url where to find the commit.
+
+ commit_message (`str`):
+ The summary (first line) of the commit that has been created.
+
+ commit_description (`str`):
+ Description of the commit that has been created. Can be empty.
+
+ oid (`str`):
+ Commit hash id. Example: `"91c54ad1727ee830252e457677f467be0bfd8a57"`.
+
+ pr_url (`str`, *optional*):
+ Url to the PR that has been created, if any. Populated when `create_pr=True`
+ is passed.
+
+ pr_revision (`str`, *optional*):
+ Revision of the PR that has been created, if any. Populated when
+ `create_pr=True` is passed. Example: `"refs/pr/1"`.
+
+ pr_num (`int`, *optional*):
+ Number of the PR discussion that has been created, if any. Populated when
+ `create_pr=True` is passed. Can be passed as `discussion_num` in
+ [`get_discussion_details`]. Example: `1`.
+
+ repo_url (`RepoUrl`):
+ Repo URL of the commit containing info like repo_id, repo_type, etc.
+ """
+
+ commit_url: str
+ commit_message: str
+ commit_description: str
+ oid: str
+ _endpoint: str | None = field(default=None, repr=False)
+ pr_url: str | None = None
+
+ # Computed from `commit_url` in `__post_init__`
+ repo_url: RepoUrl = field(init=False)
+
+ # Computed from `pr_url` in `__post_init__`
+ pr_revision: str | None = field(init=False)
+ pr_num: int | None = field(init=False)
+
+ def __new__(cls, commit_url: str, *args, **kwargs):
+ return str.__new__(cls, commit_url)
+
+ def __reduce__(self):
+ # without this, pickle/copy rebuild the instance from its string value only, losing the attributes
+ return self.__class__, (
+ self.commit_url,
+ self.commit_message,
+ self.commit_description,
+ self.oid,
+ self._endpoint,
+ self.pr_url,
+ )
+
+ def __post_init__(self):
+ """Populate pr-related fields after initialization.
+
+ See https://docs.python.org/3.10/library/dataclasses.html#post-init-processing.
+ """
+ # Repo info
+ self.repo_url = RepoUrl(self.commit_url.split("/commit/")[0], endpoint=self._endpoint)
+
+ # PR info
+ if self.pr_url is not None:
+ self.pr_revision = _parse_revision_from_pr_url(self.pr_url)
+ self.pr_num = int(self.pr_revision.split("/")[-1])
+ else:
+ self.pr_revision = None
+ self.pr_num = None
+
+
+@dataclass
+class AccessRequest:
+ """Data structure containing information about a user access request.
+
+ Attributes:
+ username (`str`):
+ Username of the user who requested access.
+ fullname (`str`):
+ Fullname of the user who requested access.
+ email (`Optional[str]`):
+ Email of the user who requested access.
+ Can only be `None` in the /accepted list if the user was granted access manually.
+ timestamp (`datetime`):
+ Timestamp of the request.
+ status (`Literal["pending", "accepted", "rejected"]`):
+ Status of the request. Can be one of `["pending", "accepted", "rejected"]`.
+ fields (`dict[str, Any]`, *optional*):
+ Additional fields filled by the user in the gate form.
+ """
+
+ username: str
+ fullname: str
+ email: str | None
+ timestamp: datetime
+ status: Literal["pending", "accepted", "rejected"]
+
+ # Additional fields filled by the user in the gate form
+ fields: dict[str, Any] | None = None
+
+
+@dataclass
+class WebhookWatchedItem:
+ """Data structure containing information about the items watched by a webhook.
+
+ Attributes:
+ type (`Literal["bucket", "dataset", "model", "org", "space", "user"]`):
+ Type of the item to be watched. Can be one of `["bucket", "dataset", "model", "org", "space", "user"]`.
+ name (`str`):
+ Name of the item to be watched. Can be the username, organization name, model name, dataset name, space name
+ or bucket name.
+ """
+
+ type: Literal["bucket", "dataset", "model", "org", "space", "user"]
+ name: str
+
+
+@dataclass
+class WebhookInfo:
+ """Data structure containing information about a webhook.
+
+ One of `url` or `job` is specified, but not both.
+
+ Attributes:
+ id (`str`):
+ ID of the webhook.
+ url (`str`, *optional*):
+ URL of the webhook.
+ job (`JobSpec`, *optional*):
+ Specifications of the Job to trigger.
+ watched (`list[WebhookWatchedItem]`):
+ List of items watched by the webhook, see [`WebhookWatchedItem`].
+ domains (`list[WEBHOOK_DOMAIN_T]`):
+ List of domains the webhook is watching. Can be one of `["repo", "discussions"]`.
+ secret (`str`, *optional*):
+ Secret of the webhook.
+ disabled (`bool`):
+ Whether the webhook is disabled or not.
+ """
+
+ id: str
+ url: str | None
+ job: JobSpec | None
+ watched: list[WebhookWatchedItem]
+ domains: list[constants.WEBHOOK_DOMAIN_T]
+ secret: str | None
+ disabled: bool
+
+
+class RepoUrl(str):
+ """Subclass of `str` describing a repo URL on the Hub.
+
+ `RepoUrl` is returned by `HfApi.create_repo`. It inherits from `str` for backward
+ compatibility. At initialization, the URL is parsed to populate properties:
+ - endpoint (`str`)
+ - namespace (`str`)
+ - repo_name (`str`)
+ - repo_id (`str`)
+ - repo_type (`Literal["model", "dataset", "space"]`)
+ - url (`str`)
+
+ Args:
+ url (`Any`):
+ String value of the repo url.
+ endpoint (`str`, *optional*):
+ Endpoint of the Hub. Defaults to .
+
+ Example:
+ ```py
+ >>> RepoUrl('https://huggingface.co/openai-community/gpt2')
+ RepoUrl('https://huggingface.co/openai-community/gpt2', endpoint='https://huggingface.co', repo_type='model', repo_id='openai-community/gpt2')
+
+ >>> RepoUrl('https://hub-ci.huggingface.co/datasets/dummy_user/dummy_dataset', endpoint='https://hub-ci.huggingface.co')
+ RepoUrl('https://hub-ci.huggingface.co/datasets/dummy_user/dummy_dataset', endpoint='https://hub-ci.huggingface.co', repo_type='dataset', repo_id='dummy_user/dummy_dataset')
+
+ >>> RepoUrl('hf://datasets/my-user/my-dataset')
+ RepoUrl('hf://datasets/my-user/my-dataset', endpoint='https://huggingface.co', repo_type='dataset', repo_id='user/dataset')
+
+ >>> HfApi.create_repo("dummy_model")
+ RepoUrl('https://huggingface.co/Wauplin/dummy_model', endpoint='https://huggingface.co', repo_type='model', repo_id='Wauplin/dummy_model')
+ ```
+
+ Raises:
+ [`~errors.HfUriError`]:
+ If the URL cannot be parsed (e.g. canonical single-segment repo, or unknown `repo_type`).
+ """
+
+ def __new__(cls, url: Any, endpoint: str | None = None):
+ url = fix_hf_endpoint_in_url(url, endpoint=endpoint)
+ return super().__new__(cls, url)
+
+ def __init__(self, url: Any, endpoint: str | None = None) -> None:
+ super().__init__()
+ self.endpoint = endpoint or constants.ENDPOINT
+
+ # Parse with the shared 'parse_hf_uri' parser, which handles 'hf://' URIs as well as Hugging
+ # Face web URLs (including ones on this custom 'endpoint'). If that fails, the input is a bare
+ # '/' id, which we reparse as an 'hf://' URI.
+ raw = str(self)
+ try:
+ parsed = parse_hf_uri(raw, endpoint=self.endpoint)
+ except HfUriError:
+ if "://" in raw:
+ raise # it was a URL: the original error is authoritative, don't retry as a bare id
+ parsed = parse_hf_uri(f"{constants.HF_PROTOCOL}{raw}")
+
+ # Populate fields ('parsed.id' is always '/').
+ self.namespace, self.repo_name = parsed.id.split("/")
+ self.repo_id = parsed.id
+ self.repo_type = parsed.type
+ self.url = str(self) # just in case it's needed
+
+ def __repr__(self) -> str:
+ return f"RepoUrl('{self}', endpoint='{self.endpoint}', repo_type='{self.repo_type}', repo_id='{self.repo_id}')"
+
+
+class DuplicatedRepoUrl(RepoUrl):
+ """[`RepoUrl`] returned by [`duplicate_repo`].
+
+ Attributes:
+ files_copy_pending (`bool`):
+ `True` if the LFS/Xet files of the new repo are still being copied in the background. Until the copy is
+ complete, downloading these files may fail.
+ """
+
+ files_copy_pending: bool = False
+
+
+def _resolve_copy_target_path(
+ src_file_path: str,
+ src_root_path: str | None,
+ is_single_file: bool,
+ destination_path: str,
+ destination_is_directory: bool,
+ destination_exists_as_directory: bool,
+ merge_contents: bool,
+) -> str:
+ basename = src_file_path.rsplit("/", 1)[-1]
+ if is_single_file:
+ if destination_path == "":
+ return basename
+ if destination_is_directory:
+ return f"{destination_path.rstrip('/')}/{basename}"
+ return destination_path
+
+ if src_root_path is None:
+ rel_path = src_file_path
+ elif src_file_path.startswith(src_root_path + "/"):
+ rel_path = src_file_path[len(src_root_path) + 1 :]
+ elif src_file_path == src_root_path:
+ rel_path = src_file_path.rsplit("/", 1)[-1]
+ else:
+ raise ValueError(f"Unexpected source path while copying folder: '{src_file_path}'.")
+
+ if rel_path == "":
+ raise ValueError("Cannot copy an empty relative path.")
+
+ # Rsync-style trailing slash on source means "copy contents of" — skip nesting.
+ # Without trailing slash, match `cp -r` behavior: nest source folder inside
+ # existing destination directory. Non-existing destination always uses rename semantics.
+ if destination_exists_as_directory and src_root_path is not None and not merge_contents:
+ src_dir_basename = src_root_path.rsplit("/", 1)[-1]
+ rel_path = f"{src_dir_basename}/{rel_path}"
+
+ if destination_path == "":
+ return rel_path
+ return f"{destination_path.rstrip('/')}/{rel_path}"
+
+
+@dataclass
+class RepoSibling:
+ """
+ Contains basic information about a repo file inside a repo on the Hub.
+
+ > [!TIP]
+ > All attributes of this class are optional except `rfilename`. This is because only the file names are returned when
+ > listing repositories on the Hub (with [`list_models`], [`list_datasets`] or [`list_spaces`]). If you need more
+ > information like file size, blob id or lfs details, you must request them specifically from one repo at a time
+ > (using [`model_info`], [`dataset_info`] or [`space_info`]) as it adds more constraints on the backend server to
+ > retrieve these.
+
+ Attributes:
+ rfilename (str):
+ file name, relative to the repo root.
+ size (`int`, *optional*):
+ The file's size, in bytes. This attribute is defined when `files_metadata` argument of [`repo_info`] is set
+ to `True`. It's `None` otherwise.
+ blob_id (`str`, *optional*):
+ The file's git OID. This attribute is defined when `files_metadata` argument of [`repo_info`] is set to
+ `True`. It's `None` otherwise.
+ lfs (`BlobLfsInfo`, *optional*):
+ The file's LFS metadata. This attribute is defined when`files_metadata` argument of [`repo_info`] is set to
+ `True` and the file is stored with Git LFS. It's `None` otherwise.
+ """
+
+ rfilename: str
+ size: int | None = None
+ blob_id: str | None = None
+ lfs: BlobLfsInfo | None = None
+
+
+@dataclass
+class RepoFile:
+ """
+ Contains information about a file on the Hub.
+
+ Attributes:
+ path (str):
+ file path relative to the repo root.
+ size (`int`):
+ The file's size, in bytes.
+ blob_id (`str`):
+ The file's git OID.
+ lfs (`BlobLfsInfo`, *optional*):
+ The file's LFS metadata.
+ xet_hash (`str`, *optional*):
+ The file's Xet hash.
+ last_commit (`LastCommitInfo`, *optional*):
+ The file's last commit metadata. Only defined if [`list_repo_tree`] and [`get_paths_info`]
+ are called with `expand=True`.
+ security (`BlobSecurityInfo`, *optional*):
+ The file's security scan metadata. Only defined if [`list_repo_tree`] and [`get_paths_info`]
+ are called with `expand=True`.
+ """
+
+ path: str
+ size: int
+ blob_id: str
+ lfs: BlobLfsInfo | None = None
+ xet_hash: str | None = None
+ last_commit: LastCommitInfo | None = None
+ security: BlobSecurityInfo | None = None
+
+ def __init__(self, **kwargs):
+ self.path = kwargs.pop("path")
+ self.size = kwargs.pop("size")
+ self.blob_id = kwargs.pop("oid")
+ lfs = kwargs.pop("lfs", None)
+ if lfs is not None:
+ lfs = BlobLfsInfo(size=lfs["size"], sha256=lfs["oid"], pointer_size=lfs["pointerSize"])
+ self.lfs = lfs
+ self.xet_hash = kwargs.pop("xetHash", None)
+ last_commit = kwargs.pop("lastCommit", None) or kwargs.pop("last_commit", None)
+ if last_commit is not None:
+ last_commit = LastCommitInfo(
+ oid=last_commit["id"], title=last_commit["title"], date=parse_datetime(last_commit["date"])
+ )
+ self.last_commit = last_commit
+ security = kwargs.pop("securityFileStatus", None)
+ if security is not None:
+ safe = security["status"] == "safe"
+ security = BlobSecurityInfo(
+ safe=safe,
+ status=security["status"],
+ av_scan=security["avScan"],
+ pickle_import_scan=security["pickleImportScan"],
+ )
+ self.security = security
+
+ # backwards compatibility
+ self.rfilename = self.path
+ self.lastCommit = self.last_commit
+
+
+@dataclass
+class RepoFolder:
+ """
+ Contains information about a folder on the Hub.
+
+ Attributes:
+ path (str):
+ folder path relative to the repo root.
+ tree_id (`str`):
+ The folder's git OID.
+ last_commit (`LastCommitInfo`, *optional*):
+ The folder's last commit metadata. Only defined if [`list_repo_tree`] and [`get_paths_info`]
+ are called with `expand=True`.
+ """
+
+ path: str
+ tree_id: str
+ last_commit: LastCommitInfo | None = None
+
+ def __init__(self, **kwargs):
+ self.path = kwargs.pop("path")
+ self.tree_id = kwargs.pop("oid")
+ last_commit = kwargs.pop("lastCommit", None) or kwargs.pop("last_commit", None)
+ if last_commit is not None:
+ last_commit = LastCommitInfo(
+ oid=last_commit["id"], title=last_commit["title"], date=parse_datetime(last_commit["date"])
+ )
+ self.last_commit = last_commit
+
+
+@dataclass
+class InferenceProviderMapping:
+ provider: PROVIDER_T # Provider name
+ hf_model_id: str # ID of the model on the Hugging Face Hub
+ provider_id: str # ID of the model on the provider's side
+ status: Literal["error", "live", "staging"]
+ task: str
+
+ adapter: str | None = None
+ adapter_weights_path: str | None = None
+ type: Literal["single-model", "tag-filter"] | None = None
+
+ def __init__(self, **kwargs):
+ self.provider = kwargs.pop("provider")
+ self.hf_model_id = kwargs.pop("hf_model_id")
+ self.provider_id = kwargs.pop("providerId")
+ self.status = kwargs.pop("status")
+ self.task = kwargs.pop("task")
+
+ self.adapter = kwargs.pop("adapter", None)
+ self.adapter_weights_path = kwargs.pop("adapterWeightsPath", None)
+ self.type = kwargs.pop("type", None)
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class ModelInfo:
+ """
+ Contains information about a model on the Hub. This object is returned by [`model_info`] and [`list_models`].
+
+ > [!TIP]
+ > Most attributes of this class are optional. This is because the data returned by the Hub depends on the query made.
+ > In general, the more specific the query, the more information is returned. On the contrary, when listing models
+ > using [`list_models`] only a subset of the attributes are returned.
+
+ Attributes:
+ id (`str`):
+ ID of model.
+ author (`str`, *optional*):
+ Author of the model.
+ base_models (`list[str]`, *optional*):
+ List of base models this model is derived from.
+ card_data (`ModelCardData`, *optional*):
+ Model Card Metadata as a [`huggingface_hub.repocard_data.ModelCardData`] object.
+ children_model_count (`int`, *optional*):
+ Number of children models derived from this model.
+ config (`dict`, *optional*):
+ Model configuration.
+ created_at (`datetime`, *optional*):
+ Date of creation of the repo on the Hub. Note that the lowest value is `2022-03-02T23:29:04.000Z`,
+ corresponding to the date when we began to store creation dates.
+ disabled (`bool`, *optional*):
+ Is the repo disabled.
+ downloads (`int`):
+ Number of downloads of the model over the last 30 days.
+ downloads_all_time (`int`):
+ Cumulated number of downloads of the model since its creation.
+ eval_results (`list[EvalResultEntry]`, *optional*):
+ Model's evaluation results.
+ gated (`Literal["auto", "manual", False]`, *optional*):
+ Is the repo gated.
+ If so, whether there is manual or automatic approval.
+ gguf (`dict`, *optional*):
+ GGUF information of the model.
+ inference (`Literal["warm"]`, *optional*):
+ Status of the model on Inference Providers. Warm if the model is served by at least one provider.
+ inference_provider_mapping (`list[InferenceProviderMapping]`, *optional*):
+ A list of [`InferenceProviderMapping`] ordered after the user's provider order.
+ last_modified (`datetime`, *optional*):
+ Date of last commit to the repo.
+ library_name (`str`, *optional*):
+ Library associated with the model.
+ likes (`int`):
+ Number of likes of the model.
+ mask_token (`str`, *optional*):
+ Mask token used by the model.
+ model_index (`dict`, *optional*):
+ Model index for evaluation.
+ pipeline_tag (`str`, *optional*):
+ Pipeline tag associated with the model.
+ private (`bool`):
+ Is the repo private.
+ resource_group (`dict`, *optional*):
+ Resource group information for the model.
+ safetensors (`SafeTensorsInfo`, *optional*):
+ Model's safetensors information.
+ security_repo_status (`dict`, *optional*):
+ Model's security scan status.
+ sha (`str`, *optional*):
+ Repo SHA at this particular revision.
+ siblings (`list[RepoSibling]`):
+ List of [`huggingface_hub.hf_api.RepoSibling`] objects that constitute the model.
+ spaces (`list[str]`, *optional*):
+ List of spaces using the model.
+ tags (`list[str]`):
+ List of tags of the model. Compared to `card_data.tags`, contains extra tags computed by the Hub
+ (e.g. supported libraries, model's arXiv).
+ transformers_info (`TransformersInfo`, *optional*):
+ Transformers-specific info (auto class, processor, etc.) associated with the model.
+ trending_score (`int`, *optional*):
+ Trending score of the model.
+ used_storage (`int`, *optional*):
+ Size in bytes of the model on the Hub.
+ widget_data (`Any`, *optional*):
+ Widget data associated with the model.
+ """
+
+ id: str
+ author: str | None
+ base_models: list[str] | None
+ card_data: ModelCardData | None
+ children_model_count: int | None
+ config: dict | None
+ created_at: datetime | None
+ disabled: bool | None
+ downloads: int | None
+ downloads_all_time: int | None
+ eval_results: list[EvalResultEntry] | None
+ gated: Literal["auto", "manual", False] | None
+ gguf: dict | None
+ inference: Literal["warm"] | None
+ inference_provider_mapping: list[InferenceProviderMapping] | None
+ last_modified: datetime | None
+ library_name: str | None
+ likes: int | None
+ mask_token: str | None
+ model_index: dict | None
+ pipeline_tag: str | None
+ private: bool | None
+ resource_group: dict | None
+ safetensors: SafeTensorsInfo | None
+ security_repo_status: dict | None
+ sha: str | None
+ siblings: list[RepoSibling] | None
+ spaces: list[str] | None
+ tags: list[str] | None
+ transformers_info: TransformersInfo | None
+ trending_score: int | None
+ used_storage: int | None
+ widget_data: Any | None
+
+ def __init__(self, **kwargs):
+ self.id = kwargs.pop("id")
+ self.author = kwargs.pop("author", None)
+ self.sha = kwargs.pop("sha", None)
+ last_modified = kwargs.pop("lastModified", None) or kwargs.pop("last_modified", None)
+ self.last_modified = parse_datetime(last_modified) if last_modified else None
+ created_at = kwargs.pop("createdAt", None) or kwargs.pop("created_at", None)
+ self.created_at = parse_datetime(created_at) if created_at else None
+ self.private = kwargs.pop("private", None)
+ self.gated = kwargs.pop("gated", None)
+ self.disabled = kwargs.pop("disabled", None)
+ self.downloads = kwargs.pop("downloads", None)
+ self.downloads_all_time = kwargs.pop("downloadsAllTime", None)
+ self.likes = kwargs.pop("likes", None)
+ self.library_name = kwargs.pop("library_name", None)
+ self.gguf = kwargs.pop("gguf", None)
+
+ self.inference = kwargs.pop("inference", None)
+
+ # little hack to simplify Inference Providers logic and make it backward and forward compatible
+ # right now, API returns a dict on model_info and a list on list_models. Let's harmonize to list.
+ mapping = kwargs.pop("inferenceProviderMapping", None)
+ if isinstance(mapping, list):
+ self.inference_provider_mapping = [
+ InferenceProviderMapping(**{**value, "hf_model_id": self.id}) for value in mapping
+ ]
+ elif isinstance(mapping, dict):
+ self.inference_provider_mapping = [
+ InferenceProviderMapping(**{**value, "hf_model_id": self.id, "provider": provider})
+ for provider, value in mapping.items()
+ ]
+ elif mapping is None:
+ self.inference_provider_mapping = None
+ else:
+ raise ValueError(
+ f"Unexpected type for `inferenceProviderMapping`. Expecting `dict` or `list`. Got {mapping}."
+ )
+
+ self.tags = kwargs.pop("tags", None)
+ self.pipeline_tag = kwargs.pop("pipeline_tag", None)
+ self.mask_token = kwargs.pop("mask_token", None)
+ self.trending_score = kwargs.pop("trendingScore", None)
+ self.used_storage = kwargs.pop("usedStorage", None)
+
+ card_data = kwargs.pop("cardData", None) or kwargs.pop("card_data", None)
+ self.card_data = (
+ ModelCardData(**card_data, ignore_metadata_errors=True) if isinstance(card_data, dict) else card_data
+ )
+
+ self.widget_data = kwargs.pop("widgetData", None)
+ self.model_index = kwargs.pop("model-index", None) or kwargs.pop("model_index", None)
+ self.config = kwargs.pop("config", None)
+ transformers_info = kwargs.pop("transformersInfo", None) or kwargs.pop("transformers_info", None)
+ self.transformers_info = TransformersInfo(**transformers_info) if transformers_info else None
+ siblings = kwargs.pop("siblings", None)
+ self.siblings = (
+ [
+ RepoSibling(
+ rfilename=sibling["rfilename"],
+ size=sibling.get("size"),
+ blob_id=sibling.get("blobId"),
+ lfs=(
+ BlobLfsInfo(
+ size=sibling["lfs"]["size"],
+ sha256=sibling["lfs"]["sha256"],
+ pointer_size=sibling["lfs"]["pointerSize"],
+ )
+ if sibling.get("lfs")
+ else None
+ ),
+ )
+ for sibling in siblings
+ ]
+ if siblings is not None
+ else None
+ )
+ self.spaces = kwargs.pop("spaces", None)
+ safetensors = kwargs.pop("safetensors", None)
+ self.safetensors = (
+ SafeTensorsInfo(
+ parameters=safetensors["parameters"],
+ total=safetensors["total"],
+ )
+ if safetensors
+ else None
+ )
+ self.security_repo_status = kwargs.pop("securityRepoStatus", None)
+ eval_results = kwargs.pop("evalResults", None)
+ self.eval_results = parse_eval_result_entries(eval_results) if eval_results else None
+ self.base_models = kwargs.pop("baseModels", None)
+ self.children_model_count = kwargs.pop("childrenModelCount", None)
+ self.resource_group = kwargs.pop("resourceGroup", None)
+ # backwards compatibility
+ self.lastModified = self.last_modified
+ self.cardData = self.card_data
+ self.transformersInfo = self.transformers_info
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class DatasetInfo:
+ """
+ Contains information about a dataset on the Hub. This object is returned by [`dataset_info`] and [`list_datasets`].
+
+ > [!TIP]
+ > Most attributes of this class are optional. This is because the data returned by the Hub depends on the query made.
+ > In general, the more specific the query, the more information is returned. On the contrary, when listing datasets
+ > using [`list_datasets`] only a subset of the attributes are returned.
+
+ Attributes:
+ id (`str`):
+ ID of dataset.
+ author (`str`):
+ Author of the dataset.
+ card_data (`DatasetCardData`, *optional*):
+ Dataset Card Metadata as a [`huggingface_hub.repocard_data.DatasetCardData`] object.
+ citation (`str`, *optional*):
+ Citation information for the dataset.
+ created_at (`datetime`, *optional*):
+ Date of creation of the repo on the Hub. Note that the lowest value is `2022-03-02T23:29:04.000Z`,
+ corresponding to the date when we began to store creation dates.
+ description (`str`, *optional*):
+ Description of the dataset.
+ disabled (`bool`, *optional*):
+ Is the repo disabled.
+ downloads (`int`):
+ Number of downloads of the dataset over the last 30 days.
+ downloads_all_time (`int`):
+ Cumulated number of downloads of the dataset since its creation.
+ gated (`Literal["auto", "manual", False]`, *optional*):
+ Is the repo gated.
+ If so, whether there is manual or automatic approval.
+ last_modified (`datetime`, *optional*):
+ Date of last commit to the repo.
+ likes (`int`):
+ Number of likes of the dataset.
+ main_size (`int`, *optional*):
+ Size in bytes of the main branch of the dataset.
+ paperswithcode_id (`str`, *optional*):
+ Papers with code ID of the dataset.
+ private (`bool`):
+ Is the repo private.
+ resource_group (`dict`, *optional*):
+ Resource group information for the dataset.
+ sha (`str`):
+ Repo SHA at this particular revision.
+ siblings (`list[RepoSibling]`):
+ List of [`huggingface_hub.hf_api.RepoSibling`] objects that constitute the dataset.
+ tags (`list[str]`):
+ List of tags of the dataset.
+ trending_score (`int`, *optional*):
+ Trending score of the dataset.
+ used_storage (`int`, *optional*):
+ Size in bytes of the dataset on the Hub.
+ """
+
+ id: str
+ author: str | None
+ card_data: DatasetCardData | None
+ citation: str | None
+ created_at: datetime | None
+ description: str | None
+ disabled: bool | None
+ downloads: int | None
+ downloads_all_time: int | None
+ gated: Literal["auto", "manual", False] | None
+ last_modified: datetime | None
+ likes: int | None
+ main_size: int | None
+ paperswithcode_id: str | None
+ private: bool | None
+ resource_group: dict | None
+ sha: str | None
+ siblings: list[RepoSibling] | None
+ tags: list[str] | None
+ trending_score: int | None
+ used_storage: int | None
+
+ def __init__(self, **kwargs):
+ self.id = kwargs.pop("id")
+ self.author = kwargs.pop("author", None)
+ self.sha = kwargs.pop("sha", None)
+ created_at = kwargs.pop("createdAt", None) or kwargs.pop("created_at", None)
+ self.created_at = parse_datetime(created_at) if created_at else None
+ last_modified = kwargs.pop("lastModified", None) or kwargs.pop("last_modified", None)
+ self.last_modified = parse_datetime(last_modified) if last_modified else None
+ self.private = kwargs.pop("private", None)
+ self.gated = kwargs.pop("gated", None)
+ self.disabled = kwargs.pop("disabled", None)
+ self.downloads = kwargs.pop("downloads", None)
+ self.downloads_all_time = kwargs.pop("downloadsAllTime", None)
+ self.likes = kwargs.pop("likes", None)
+ self.main_size = kwargs.pop("mainSize", None)
+ self.paperswithcode_id = kwargs.pop("paperswithcode_id", None)
+ self.tags = kwargs.pop("tags", None)
+ self.trending_score = kwargs.pop("trendingScore", None)
+ self.used_storage = kwargs.pop("usedStorage", None)
+
+ card_data = kwargs.pop("cardData", None) or kwargs.pop("card_data", None)
+ self.card_data = (
+ DatasetCardData(**card_data, ignore_metadata_errors=True) if isinstance(card_data, dict) else card_data
+ )
+ siblings = kwargs.pop("siblings", None)
+ self.siblings = (
+ [
+ RepoSibling(
+ rfilename=sibling["rfilename"],
+ size=sibling.get("size"),
+ blob_id=sibling.get("blobId"),
+ lfs=(
+ BlobLfsInfo(
+ size=sibling["lfs"]["size"],
+ sha256=sibling["lfs"]["sha256"],
+ pointer_size=sibling["lfs"]["pointerSize"],
+ )
+ if sibling.get("lfs")
+ else None
+ ),
+ )
+ for sibling in siblings
+ ]
+ if siblings is not None
+ else None
+ )
+ self.citation = kwargs.pop("citation", None)
+ self.description = kwargs.pop("description", None)
+ self.resource_group = kwargs.pop("resourceGroup", None)
+ # backwards compatibility
+ self.lastModified = self.last_modified
+ self.cardData = self.card_data
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class SpaceInfo:
+ """
+ Contains information about a Space on the Hub. This object is returned by [`space_info`] and [`list_spaces`].
+
+ > [!TIP]
+ > Most attributes of this class are optional. This is because the data returned by the Hub depends on the query made.
+ > In general, the more specific the query, the more information is returned. On the contrary, when listing spaces
+ > using [`list_spaces`] only a subset of the attributes are returned.
+
+ Attributes:
+ id (`str`):
+ ID of the Space.
+ author (`str`, *optional*):
+ Author of the Space.
+ card_data (`SpaceCardData`, *optional*):
+ Space Card Metadata as a [`huggingface_hub.repocard_data.SpaceCardData`] object.
+ created_at (`datetime`, *optional*):
+ Date of creation of the repo on the Hub. Note that the lowest value is `2022-03-02T23:29:04.000Z`,
+ corresponding to the date when we began to store creation dates.
+ datasets (`list[str]`, *optional*):
+ List of datasets used by the Space.
+ disabled (`bool`, *optional*):
+ Is the Space disabled.
+ gated (`Literal["auto", "manual", False]`, *optional*):
+ Is the repo gated.
+ If so, whether there is manual or automatic approval.
+ host (`str`, *optional*):
+ Host URL of the Space.
+ last_modified (`datetime`, *optional*):
+ Date of last commit to the repo.
+ likes (`int`):
+ Number of likes of the Space.
+ models (`list[str]`, *optional*):
+ List of models used by the Space.
+ private (`bool`):
+ Is the repo private.
+ region (`Literal["us", "eu"]`, *optional*):
+ Cloud region in which the Space is stored.
+ resource_group (`dict`, *optional*):
+ Resource group information for the Space.
+ runtime (`SpaceRuntime`, *optional*):
+ Space runtime information as a [`huggingface_hub.hf_api.SpaceRuntime`] object.
+ sdk (`str`, *optional*):
+ SDK used by the Space.
+ sha (`str`, *optional*):
+ Repo SHA at this particular revision.
+ siblings (`list[RepoSibling]`):
+ List of [`huggingface_hub.hf_api.RepoSibling`] objects that constitute the Space.
+ subdomain (`str`, *optional*):
+ Subdomain of the Space.
+ tags (`list[str]`):
+ List of tags of the Space.
+ trending_score (`int`, *optional*):
+ Trending score of the Space.
+ used_storage (`int`, *optional*):
+ Size in bytes of the Space on the Hub.
+ """
+
+ id: str
+ author: str | None
+ card_data: SpaceCardData | None
+ created_at: datetime | None
+ datasets: list[str] | None
+ disabled: bool | None
+ gated: Literal["auto", "manual", False] | None
+ host: str | None
+ last_modified: datetime | None
+ likes: int | None
+ models: list[str] | None
+ private: bool | None
+ region: REPO_REGIONS | None
+ resource_group: dict | None
+ runtime: SpaceRuntime | None
+ sdk: str | None
+ sha: str | None
+ siblings: list[RepoSibling] | None
+ subdomain: str | None
+ tags: list[str] | None
+ trending_score: int | None
+ used_storage: int | None
+
+ def __init__(self, **kwargs):
+ self.id = kwargs.pop("id")
+ self.author = kwargs.pop("author", None)
+ self.sha = kwargs.pop("sha", None)
+ created_at = kwargs.pop("createdAt", None) or kwargs.pop("created_at", None)
+ self.created_at = parse_datetime(created_at) if created_at else None
+ last_modified = kwargs.pop("lastModified", None) or kwargs.pop("last_modified", None)
+ self.last_modified = parse_datetime(last_modified) if last_modified else None
+ self.private = kwargs.pop("private", None)
+ self.gated = kwargs.pop("gated", None)
+ self.disabled = kwargs.pop("disabled", None)
+ self.host = kwargs.pop("host", None)
+ self.subdomain = kwargs.pop("subdomain", None)
+ self.likes = kwargs.pop("likes", None)
+ self.sdk = kwargs.pop("sdk", None)
+ self.tags = kwargs.pop("tags", None)
+ self.trending_score = kwargs.pop("trendingScore", None)
+ self.used_storage = kwargs.pop("usedStorage", None)
+ card_data = kwargs.pop("cardData", None) or kwargs.pop("card_data", None)
+ self.card_data = (
+ SpaceCardData(**card_data, ignore_metadata_errors=True) if isinstance(card_data, dict) else card_data
+ )
+ siblings = kwargs.pop("siblings", None)
+ self.siblings = (
+ [
+ RepoSibling(
+ rfilename=sibling["rfilename"],
+ size=sibling.get("size"),
+ blob_id=sibling.get("blobId"),
+ lfs=(
+ BlobLfsInfo(
+ size=sibling["lfs"]["size"],
+ sha256=sibling["lfs"]["sha256"],
+ pointer_size=sibling["lfs"]["pointerSize"],
+ )
+ if sibling.get("lfs")
+ else None
+ ),
+ )
+ for sibling in siblings
+ ]
+ if siblings is not None
+ else None
+ )
+ runtime = kwargs.pop("runtime", None)
+ self.runtime = SpaceRuntime(runtime) if runtime else None
+ self.models = kwargs.pop("models", None)
+ self.datasets = kwargs.pop("datasets", None)
+ self.region = kwargs.pop("region", None)
+ self.resource_group = kwargs.pop("resourceGroup", None)
+ # backwards compatibility
+ self.lastModified = self.last_modified
+ self.cardData = self.card_data
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class KernelInfo:
+ """
+ Contains information about a kernel repo on the Hub. This object is returned by [`kernel_info`].
+
+ Attributes:
+ id (`str`):
+ ID of the kernel repo.
+ author (`str`, *optional*):
+ Author of the kernel repo.
+ downloads (`int`, *optional*):
+ Number of downloads of the kernel repo over the last 30 days.
+ gated (`Literal["auto", "manual", False]`, *optional*):
+ Is the repo gated. If so, whether there is manual or automatic approval.
+ last_modified (`datetime`, *optional*):
+ Date of last commit to the repo.
+ likes (`int`, *optional*):
+ Number of likes of the kernel repo.
+ private (`bool`, *optional*):
+ Is the repo private.
+ sha (`str`, *optional*):
+ Repo SHA at this particular revision.
+ """
+
+ id: str
+ author: str | None
+ downloads: int | None
+ gated: Literal["auto", "manual", False] | None
+ last_modified: datetime | None
+ likes: int | None
+ private: bool | None
+ sha: str | None
+
+ def __init__(self, **kwargs):
+ self.id = kwargs.pop("id")
+ self.author = kwargs.pop("author", None)
+ self.downloads = kwargs.pop("downloads", None)
+ self.gated = kwargs.pop("gated", None)
+ last_modified = kwargs.pop("lastModified", None) or kwargs.pop("last_modified", None)
+ self.last_modified = parse_datetime(last_modified) if last_modified else None
+ self.likes = kwargs.pop("likes", None)
+ self.private = kwargs.pop("private", None)
+ self.sha = kwargs.pop("sha", None)
+
+ # future compatibility
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class CollectionItem:
+ """
+ Contains information about an item of a Collection (model, dataset, Space, paper, collection or bucket).
+
+ Attributes:
+ item_object_id (`str`):
+ Unique ID of the item in the collection.
+ item_id (`str`):
+ ID of the underlying object on the Hub. Can be either a repo_id, a paper id, a collection slug
+ or a bucket id.
+ e.g. `"jbilcke-hf/ai-comic-factory"`, `"2307.09288"`, `"celinah/cerebras-function-calling-682607169c35fbfa98b30b9a"`.
+ item_type (`str`):
+ Type of the underlying object. Can be one of `"model"`, `"dataset"`, `"space"`, `"paper"`, `"collection"`
+ or `"bucket"`.
+ position (`int`):
+ Position of the item in the collection.
+ note (`str`, *optional*):
+ Note associated with the item, as plain text.
+ """
+
+ item_object_id: str # id in database
+ item_id: str # repo_id or paper id
+ item_type: str
+ position: int
+ note: str | None = None
+
+ def __init__(
+ self,
+ _id: str,
+ id: str,
+ type: CollectionItemType_T,
+ position: int,
+ note: dict | None = None,
+ **kwargs,
+ ) -> None:
+ self.item_object_id: str = _id # id in database
+ self.item_id: str = id # repo_id or paper id
+ # if the item is a collection, override item_id with the slug
+ slug = kwargs.get("slug")
+ if slug is not None:
+ self.item_id = slug # collection slug
+ self.item_type: CollectionItemType_T = type
+ self.position: int = position
+ note_text = note.get("text") if note is not None else None
+ self.note = note_text if isinstance(note_text, str) else None
+
+
+@dataclass
+class Collection:
+ """
+ Contains information about a Collection on the Hub.
+
+ Attributes:
+ slug (`str`):
+ Slug of the collection. E.g. `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ title (`str`):
+ Title of the collection. E.g. `"Recent models"`.
+ owner (`str`):
+ Owner of the collection. E.g. `"TheBloke"`.
+ items (`list[CollectionItem]`):
+ List of items in the collection.
+ last_updated (`datetime`):
+ Date of the last update of the collection.
+ position (`int`):
+ Position of the collection in the list of collections of the owner.
+ private (`bool`):
+ Whether the collection is private or not.
+ theme (`str`):
+ Theme of the collection. E.g. `"green"`.
+ upvotes (`int`):
+ Number of upvotes of the collection.
+ description (`str`, *optional*):
+ Description of the collection, as plain text.
+ url (`str`):
+ (property) URL of the collection on the Hub.
+ """
+
+ slug: str
+ title: str
+ owner: str
+ items: list[CollectionItem]
+ last_updated: datetime
+ position: int
+ private: bool
+ theme: str
+ upvotes: int
+ description: str | None = None
+
+ def __init__(self, **kwargs) -> None:
+ self.slug = kwargs.pop("slug")
+ self.title = kwargs.pop("title")
+ self.owner = kwargs.pop("owner")
+ self.items = [CollectionItem(**item) for item in kwargs.pop("items")]
+ self.last_updated = parse_datetime(kwargs.pop("lastUpdated"))
+ self.position = kwargs.pop("position")
+ self.private = kwargs.pop("private")
+ self.theme = kwargs.pop("theme")
+ self.upvotes = kwargs.pop("upvotes")
+ self.description = kwargs.pop("description", None)
+ endpoint = kwargs.pop("endpoint", None)
+ if endpoint is None:
+ endpoint = constants.ENDPOINT
+ self._url = f"{endpoint}/collections/{self.slug}"
+
+ @property
+ def url(self) -> str:
+ """Returns the URL of the collection on the Hub."""
+ return self._url
+
+
+@dataclass
+class GitRefInfo:
+ """
+ Contains information about a git reference for a repo on the Hub.
+
+ Attributes:
+ name (`str`):
+ Name of the reference (e.g. tag name or branch name).
+ ref (`str`):
+ Full git ref on the Hub (e.g. `"refs/heads/main"` or `"refs/tags/v1.0"`).
+ target_commit (`str`):
+ OID of the target commit for the ref (e.g. `"e7da7f221d5bf496a48136c0cd264e630fe9fcc8"`)
+ """
+
+ name: str
+ ref: str
+ target_commit: str
+
+
+@dataclass
+class GitRefs:
+ """
+ Contains information about all git references for a repo on the Hub.
+
+ Object is returned by [`list_repo_refs`].
+
+ Attributes:
+ branches (`list[GitRefInfo]`):
+ A list of [`GitRefInfo`] containing information about branches on the repo.
+ converts (`list[GitRefInfo]`):
+ A list of [`GitRefInfo`] containing information about "convert" refs on the repo.
+ Converts are refs used (internally) to push preprocessed data in Dataset repos.
+ tags (`list[GitRefInfo]`):
+ A list of [`GitRefInfo`] containing information about tags on the repo.
+ pull_requests (`list[GitRefInfo]`, *optional*):
+ A list of [`GitRefInfo`] containing information about pull requests on the repo.
+ Only returned if `include_prs=True` is set.
+ """
+
+ branches: list[GitRefInfo]
+ converts: list[GitRefInfo]
+ tags: list[GitRefInfo]
+ pull_requests: list[GitRefInfo] | None = None
+
+
+@dataclass
+class GitCommitInfo:
+ """
+ Contains information about a git commit for a repo on the Hub. Check out [`list_repo_commits`] for more details.
+
+ Attributes:
+ commit_id (`str`):
+ OID of the commit (e.g. `"e7da7f221d5bf496a48136c0cd264e630fe9fcc8"`)
+ authors (`list[str]`):
+ List of authors of the commit.
+ created_at (`datetime`):
+ Datetime when the commit was created.
+ title (`str`):
+ Title of the commit. This is a free-text value entered by the authors.
+ message (`str`):
+ Description of the commit. This is a free-text value entered by the authors.
+ formatted_title (`str`):
+ Title of the commit formatted as HTML. Only returned if `formatted=True` is set.
+ formatted_message (`str`):
+ Description of the commit formatted as HTML. Only returned if `formatted=True` is set.
+ """
+
+ commit_id: str
+
+ authors: list[str]
+ created_at: datetime
+ title: str
+ message: str
+
+ formatted_title: str | None
+ formatted_message: str | None
+
+
+@dataclass
+class UserLikes:
+ """
+ Contains information about a user likes on the Hub.
+
+ Attributes:
+ user (`str`):
+ Name of the user for which we fetched the likes.
+ total (`int`):
+ Total number of likes.
+ datasets (`list[str]`):
+ List of datasets liked by the user (as repo_ids).
+ kernels (`list[str]`):
+ List of kernels liked by the user (as repo_ids).
+ models (`list[str]`):
+ List of models liked by the user (as repo_ids).
+ spaces (`list[str]`):
+ List of spaces liked by the user (as repo_ids).
+ """
+
+ # Metadata
+ user: str
+ total: int
+
+ # User likes
+ datasets: list[str]
+ kernels: list[str]
+ models: list[str]
+ spaces: list[str]
+
+
+@dataclass
+class RepoStorageInfo:
+ """
+ Contains storage information about a repository on the Hub.
+
+ Returned by [`list_user_repos`].
+
+ Attributes:
+ id (`str`):
+ ID of the repo (e.g. `username/repo-name`).
+ type (`str`):
+ Type of the repo (`model`, `dataset`, `space`, or `bucket`).
+ updated_at (`datetime`):
+ Last update time of the repo.
+ visibility (`str`):
+ Visibility of the repo (`public` or `private`).
+ storage (`int`):
+ Storage used by the repo in bytes.
+ storage_percent (`float`):
+ Percentage of the namespace's total storage used by this repo.
+ """
+
+ id: str
+ type: str
+ updated_at: datetime
+ visibility: str
+ storage: int
+ storage_percent: float
+
+ def __init__(self, **kwargs: Any) -> None:
+ self.id = kwargs["id"]
+ self.type = kwargs["type"]
+ self.updated_at = parse_datetime(kwargs["updatedAt"])
+ self.visibility = kwargs["visibility"]
+ self.storage = kwargs["storage"]
+ self.storage_percent = kwargs.get("storagePercent") or 0
+
+
+@dataclass
+class Organization:
+ """
+ Contains information about an organization on the Hub.
+
+ Attributes:
+ avatar_url (`str`):
+ URL of the organization's avatar.
+ name (`str`):
+ Name of the organization on the Hub (unique).
+ fullname (`str`):
+ Organization's full name.
+ details (`str`, *optional*):
+ Organization's description.
+ is_verified (`bool`, *optional*):
+ Whether the organization is verified.
+ is_following (`bool`, *optional*):
+ Whether the authenticated user follows this organization.
+ num_users (`int`, *optional*):
+ Number of members in the organization.
+ num_models (`int`, *optional*):
+ Number of models owned by the organization.
+ num_spaces (`int`, *optional*):
+ Number of Spaces owned by the organization.
+ num_datasets (`int`, *optional*):
+ Number of datasets owned by the organization.
+ num_followers (`int`, *optional*):
+ Number of followers of the organization.
+ num_papers (`int`, *optional*):
+ Number of papers authored by the organization.
+ plan (`str`, *optional*):
+ The organization's plan (e.g., "enterprise", "team").
+ """
+
+ avatar_url: str
+ name: str
+ fullname: str
+ details: str | None = None
+ is_verified: bool | None = None
+ is_following: bool | None = None
+ num_users: int | None = None
+ num_models: int | None = None
+ num_spaces: int | None = None
+ num_datasets: int | None = None
+ num_followers: int | None = None
+ num_papers: int | None = None
+ plan: str | None = None
+
+ def __init__(self, **kwargs) -> None:
+ self.avatar_url = kwargs.pop("avatarUrl", "")
+ self.name = kwargs.pop("name", "")
+ self.fullname = kwargs.pop("fullname", "")
+ self.details = kwargs.pop("details", None)
+ self.is_verified = kwargs.pop("isVerified", None)
+ self.is_following = kwargs.pop("isFollowing", None)
+ self.num_users = kwargs.pop("numUsers", None)
+ self.num_models = kwargs.pop("numModels", None)
+ self.num_spaces = kwargs.pop("numSpaces", None)
+ self.num_datasets = kwargs.pop("numDatasets", None)
+ self.num_followers = kwargs.pop("numFollowers", None)
+ self.num_papers = kwargs.pop("numPapers", None)
+ self.plan = kwargs.pop("plan", None)
+
+ # forward compatibility
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class User:
+ """
+ Contains information about a user on the Hub.
+
+ Attributes:
+ username (`str`):
+ Name of the user on the Hub (unique).
+ fullname (`str`):
+ User's full name.
+ avatar_url (`str`):
+ URL of the user's avatar.
+ details (`str`, *optional*):
+ User's details.
+ is_following (`bool`, *optional*):
+ Whether the authenticated user is following this user.
+ is_pro (`bool`, *optional*):
+ Whether the user is a pro user.
+ num_models (`int`, *optional*):
+ Number of models created by the user.
+ num_datasets (`int`, *optional*):
+ Number of datasets created by the user.
+ num_spaces (`int`, *optional*):
+ Number of spaces created by the user.
+ num_discussions (`int`, *optional*):
+ Number of discussions initiated by the user.
+ num_papers (`int`, *optional*):
+ Number of papers authored by the user.
+ num_upvotes (`int`, *optional*):
+ Number of upvotes received by the user.
+ num_likes (`int`, *optional*):
+ Number of likes given by the user.
+ num_following (`int`, *optional*):
+ Number of users this user is following.
+ num_followers (`int`, *optional*):
+ Number of users following this user.
+ orgs (list of [`Organization`]):
+ List of organizations the user is part of.
+ """
+
+ # Metadata
+ username: str
+ fullname: str
+ avatar_url: str
+ details: str | None = None
+ is_following: bool | None = None
+ is_pro: bool | None = None
+ num_models: int | None = None
+ num_datasets: int | None = None
+ num_spaces: int | None = None
+ num_discussions: int | None = None
+ num_papers: int | None = None
+ num_upvotes: int | None = None
+ num_likes: int | None = None
+ num_following: int | None = None
+ num_followers: int | None = None
+ orgs: list[Organization] = field(default_factory=list)
+
+ def __init__(self, **kwargs) -> None:
+ self.username = kwargs.pop("user", "")
+ self.fullname = kwargs.pop("fullname", "")
+ self.avatar_url = kwargs.pop("avatarUrl", "")
+ self.is_following = kwargs.pop("isFollowing", None)
+ self.is_pro = kwargs.pop("isPro", None)
+ self.details = kwargs.pop("details", None)
+ self.num_models = kwargs.pop("numModels", None)
+ self.num_datasets = kwargs.pop("numDatasets", None)
+ self.num_spaces = kwargs.pop("numSpaces", None)
+ self.num_discussions = kwargs.pop("numDiscussions", None)
+ self.num_papers = kwargs.pop("numPapers", None)
+ self.num_upvotes = kwargs.pop("numUpvotes", None)
+ self.num_likes = kwargs.pop("numLikes", None)
+ self.num_following = kwargs.pop("numFollowing", None)
+ self.num_followers = kwargs.pop("numFollowers", None)
+ self.user_type = kwargs.pop("type", None)
+ self.orgs = [Organization(**org) for org in kwargs.pop("orgs", [])]
+
+ # forward compatibility
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class PaperAuthor:
+ """
+ Contains information about a paper author on the Hub.
+
+ Attributes:
+ name (`str`):
+ Name of the author.
+ user (`User`, *optional*):
+ Information about the author as a [`User`] object.
+ status (`str`, *optional*):
+ Status of the author on the Hub.
+ status_last_changed_at (`datetime`, *optional*):
+ Date when the status of the author changed.
+ hidden (`bool`, *optional*):
+ Whether the author is hidden on the Hub.
+ """
+
+ name: str
+ user: User | None
+ status: str | None
+ status_last_changed_at: datetime | None
+ hidden: bool | None
+
+ def __init__(self, **kwargs) -> None:
+ self.name = kwargs.pop("name", "")
+ user = kwargs.pop("user", None)
+ self.user = User(**user) if user else None
+ self.status = kwargs.pop("status", None)
+ status_last_changed_at = kwargs.pop("statusLastChangedAt", None)
+ self.status_last_changed_at = parse_datetime(status_last_changed_at) if status_last_changed_at else None
+ self.hidden = kwargs.pop("hidden", None)
+
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class PaperInfo:
+ """
+ Contains information about a paper on the Hub.
+
+ Attributes:
+ id (`str`):
+ arXiv paper ID.
+ authors (`list[PaperAuthor]`, *optional*):
+ Authors of the paper.
+ published_at (`datetime`, *optional*):
+ Date paper published.
+ title (`str`, *optional*):
+ Title of the paper.
+ summary (`str`, *optional*):
+ Summary of the paper.
+ upvotes (`int`, *optional*):
+ Number of upvotes for the paper on the Hub.
+ discussion_id (`str`, *optional*):
+ Discussion ID for the paper on the Hub.
+ source (`str`, *optional*):
+ Source of the paper.
+ comments (`int`, *optional*):
+ Number of comments for the paper on the Hub.
+ submitted_at (`datetime`, *optional*):
+ Date paper appeared in daily papers on the Hub.
+ submitted_by (`User`, *optional*):
+ Information about who submitted the daily paper.
+ ai_summary (`str`, *optional*):
+ AI summary of the paper.
+ ai_keywords (`list[str]`, *optional*):
+ AI keywords of the paper.
+ organization (`Organization`, *optional*):
+ Information about the organization associated with the paper.
+ project_page (`str`, *optional*):
+ URL of the project page for the paper.
+ github_repo (`str`, *optional*):
+ URL of the GitHub repository for the paper.
+ github_stars (`int`, *optional*):
+ Number of stars of the GitHub repository for the paper.
+ linked_models (`list[ModelInfo]`, *optional*):
+ Models linked to the paper. Only returned by [`paper_info`].
+ num_total_models (`int`, *optional*):
+ Total number of models linked to the paper. Only returned by [`paper_info`].
+ linked_datasets (`list[DatasetInfo]`, *optional*):
+ Datasets linked to the paper. Only returned by [`paper_info`].
+ num_total_datasets (`int`, *optional*):
+ Total number of datasets linked to the paper. Only returned by [`paper_info`].
+ linked_spaces (`list[SpaceInfo]`, *optional*):
+ Spaces linked to the paper. Only returned by [`paper_info`].
+ """
+
+ id: str
+ authors: list[PaperAuthor] | None
+ published_at: datetime | None
+ title: str | None
+ summary: str | None
+ upvotes: int | None
+ discussion_id: str | None
+ source: str | None
+ comments: int | None
+ submitted_at: datetime | None
+ submitted_by: User | None
+ ai_summary: str | None
+ ai_keywords: list[str] | None
+ organization: Organization | None
+ project_page: str | None
+ github_repo: str | None
+ github_stars: int | None
+ linked_models: list[ModelInfo] | None
+ num_total_models: int | None
+ linked_datasets: list[DatasetInfo] | None
+ num_total_datasets: int | None
+ linked_spaces: list[SpaceInfo] | None
+
+ def __init__(self, **kwargs) -> None:
+ paper = kwargs.pop("paper", {})
+ self.id = kwargs.pop("id", None) or paper.pop("id", None)
+ authors = paper.pop("authors", None) or kwargs.pop("authors", None)
+ self.authors = [PaperAuthor(**author) for author in authors] if authors else None
+ published_at = paper.pop("publishedAt", None) or kwargs.pop("publishedAt", None)
+ self.published_at = parse_datetime(published_at) if published_at else None
+ self.title = kwargs.pop("title", None)
+ self.source = kwargs.pop("source", None)
+ self.summary = paper.pop("summary", None) or kwargs.pop("summary", None)
+ self.upvotes = paper.pop("upvotes", None) or kwargs.pop("upvotes", None)
+ self.discussion_id = paper.pop("discussionId", None) or kwargs.pop("discussionId", None)
+ self.comments = kwargs.pop("numComments", 0)
+ submitted_at = kwargs.pop("publishedAt", None) or kwargs.pop("submittedOnDailyAt", None)
+ self.submitted_at = parse_datetime(submitted_at) if submitted_at else None
+ submitted_by = kwargs.pop("submittedBy", None) or kwargs.pop("submittedOnDailyBy", None)
+ self.submitted_by = User(**submitted_by) if submitted_by else None
+ self.ai_summary = kwargs.pop("ai_summary", None)
+ self.ai_keywords = kwargs.pop("ai_keywords", None)
+ organization = kwargs.pop("organization", None)
+ self.organization = Organization(**organization) if organization else None
+ self.project_page = kwargs.pop("projectPage", None)
+ self.github_repo = kwargs.pop("githubRepo", None)
+ self.github_stars = kwargs.pop("githubStars", None)
+ linked_models = kwargs.pop("linkedModels", None)
+ self.linked_models = [ModelInfo(**m) for m in linked_models] if linked_models is not None else None
+ self.num_total_models = kwargs.pop("numTotalModels", None)
+ linked_datasets = kwargs.pop("linkedDatasets", None)
+ self.linked_datasets = [DatasetInfo(**d) for d in linked_datasets] if linked_datasets is not None else None
+ self.num_total_datasets = kwargs.pop("numTotalDatasets", None)
+ linked_spaces = kwargs.pop("linkedSpaces", None)
+ self.linked_spaces = [SpaceInfo(**s) for s in linked_spaces] if linked_spaces is not None else None
+
+ # forward compatibility
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class LFSFileInfo:
+ """
+ Contains information about a file stored as LFS on a repo on the Hub.
+
+ Used in the context of listing and permanently deleting LFS files from a repo to free-up space.
+ See [`list_lfs_files`] and [`permanently_delete_lfs_files`] for more details.
+
+ Git LFS files are tracked using SHA-256 object IDs, rather than file paths, to optimize performance
+ This approach is necessary because a single object can be referenced by multiple paths across different commits,
+ making it impractical to search and resolve these connections. Check out [our documentation](https://huggingface.co/docs/hub/storage-limits#advanced-track-lfs-file-references)
+ to learn how to know which filename(s) is(are) associated with each SHA.
+
+ Attributes:
+ file_oid (`str`):
+ SHA-256 object ID of the file. This is the identifier to pass when permanently deleting the file.
+ filename (`str`):
+ Possible filename for the LFS object. See the note above for more information.
+ oid (`str`):
+ OID of the LFS object.
+ pushed_at (`datetime`):
+ Date the LFS object was pushed to the repo.
+ ref (`str`, *optional*):
+ Ref where the LFS object has been pushed (if any).
+ size (`int`):
+ Size of the LFS object.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> lfs_files = api.list_lfs_files("username/my-cool-repo")
+
+ # Filter files to delete based on a combination of `filename`, `pushed_at`, `ref` or `size`.
+ # e.g. select only LFS files in the "checkpoints" folder
+ >>> lfs_files_to_delete = (lfs_file for lfs_file in lfs_files if lfs_file.filename.startswith("checkpoints/"))
+
+ # Permanently delete LFS files
+ >>> api.permanently_delete_lfs_files("username/my-cool-repo", lfs_files_to_delete)
+ ```
+ """
+
+ file_oid: str
+ filename: str
+ oid: str
+ pushed_at: datetime
+ ref: str | None
+ size: int
+
+ def __init__(self, **kwargs) -> None:
+ self.file_oid = kwargs.pop("fileOid")
+ self.filename = kwargs.pop("filename")
+ self.oid = kwargs.pop("oid")
+ self.pushed_at = parse_datetime(kwargs.pop("pushedAt"))
+ self.ref = kwargs.pop("ref", None)
+ self.size = kwargs.pop("size")
+
+ # forward compatibility
+ self.__dict__.update(**kwargs)
+
+
+@dataclass
+class DatasetLeaderboardEntry:
+ """Contains information about a single entry in a dataset leaderboard on the Hub.
+
+ A leaderboard ranks models based on their evaluation scores on a given benchmark dataset.
+ This object is returned by [`get_dataset_leaderboard`]. To get evaluation results for a
+ specific model across benchmarks, see [`ModelInfo.eval_results`] (via [`model_info`] with
+ `expand=["evalResults"]`) and [`EvalResultEntry`].
+
+ Attributes:
+ rank (`int`):
+ Rank of the model on the leaderboard (1-indexed).
+ model_id (`str`):
+ ID of the model (e.g. `"meta-llama/Llama-3-8b"`).
+ value (`float`):
+ Evaluation score value.
+ filename (`str`):
+ Name of the result file containing the evaluation data.
+ verified (`bool`):
+ Whether the result has been verified.
+ source (`dict[str, Any]`, *optional*):
+ Information about the source of the evaluation result. Contains keys like
+ `"url"`, `"name"`, and `"isExternal"`. Not all entries have a source.
+ author (`User` or `Organization`):
+ The model author, parsed based on the `"type"` field in the API response.
+ pull_request (`int`, *optional*):
+ Pull request number associated with the leaderboard entry, if any.
+ notes (`str`, *optional*):
+ Notes associated with the leaderboard entry, if any.
+ """
+
+ rank: int
+ model_id: str
+ value: float
+ filename: str
+ verified: bool
+ source: dict[str, Any] | None
+ author: User | Organization
+ pull_request: int | None = None
+ notes: str | None = None
+
+ def __init__(self, **kwargs) -> None:
+ self.rank = kwargs.pop("rank")
+ self.model_id = kwargs.pop("modelId")
+ self.value = kwargs.pop("value")
+ self.filename = kwargs.pop("filename")
+ self.verified = kwargs.pop("verified")
+ self.source = kwargs.pop("source", None)
+ author_data = dict(kwargs.pop("author"))
+ author_type = author_data.get("type")
+ if author_type == "org":
+ self.author = Organization(**author_data)
+ else:
+ author_data["user"] = author_data.pop("name", "")
+ self.author = User(**author_data)
+ self.pull_request = kwargs.pop("pullRequest", None)
+ self.notes = kwargs.pop("notes", None)
+
+ # forward compatibility
+ self.__dict__.update(**kwargs)
+
+
+def future_compatible(fn: CallableT) -> CallableT:
+ """Wrap a method of `HfApi` to handle `run_as_future=True`.
+
+ A method flagged as "future_compatible" will be called in a thread if `run_as_future=True` and return a
+ `concurrent.futures.Future` instance. Otherwise, it will be called normally and return the result.
+ """
+ sig = inspect.signature(fn)
+ args_params = list(sig.parameters)[1:] # remove "self" from list
+
+ @wraps(fn)
+ def _inner(self, *args, **kwargs):
+ # Get `run_as_future` value if provided (default to False)
+ if "run_as_future" in kwargs:
+ run_as_future = kwargs["run_as_future"]
+ kwargs["run_as_future"] = False # avoid recursion error
+ else:
+ run_as_future = False
+ for param, value in zip(args_params, args):
+ if param == "run_as_future":
+ run_as_future = value
+ break
+
+ # Call the function in a thread if `run_as_future=True`
+ if run_as_future:
+ return self.run_as_future(fn, self, *args, **kwargs)
+
+ # Otherwise, call the function normally
+ return fn(self, *args, **kwargs)
+
+ _inner.is_future_compatible = True # type: ignore
+ return _inner # type: ignore
+
+
+def _get_safetensors_metadata_size(size_bytes: bytes, filename: str, context_msg: str) -> int:
+ """
+ Parse and validate safetensors metadata size from the first 8 bytes.
+
+ This is a shared helper function used by both remote and local safetensors parsing.
+
+ Args:
+ size_bytes: First 8 bytes of the safetensors file.
+ filename: Filename for error messages.
+ context_msg: Additional context for error messages.
+
+ Returns:
+ The metadata size as an integer.
+
+ Raises:
+ SafetensorsParsingError: If size_bytes is too short or metadata size exceeds limit.
+ """
+ if len(size_bytes) < 8:
+ raise SafetensorsParsingError(
+ f"Failed to parse safetensors header for '{filename}' ({context_msg}): file is too small to be a valid "
+ "safetensors file."
+ )
+
+ metadata_size = struct.unpack(" constants.SAFETENSORS_MAX_HEADER_LENGTH:
+ raise SafetensorsParsingError(
+ f"Failed to parse safetensors header for '{filename}' ({context_msg}): safetensors header is too big. "
+ f"Maximum supported size is {constants.SAFETENSORS_MAX_HEADER_LENGTH} bytes (got {metadata_size})."
+ )
+
+ return metadata_size
+
+
+def _parse_safetensors_header(metadata_as_bytes: bytes, filename: str, context_msg: str) -> SafetensorsFileMetadata:
+ """
+ Parse safetensors metadata from raw header bytes.
+
+ This is a shared helper function used by both remote and local safetensors parsing.
+
+ Args:
+ metadata_as_bytes: Raw bytes of the JSON metadata header (without the 8-byte size prefix).
+ filename: Filename for error messages.
+ context_msg: Additional context for error messages (e.g., repo info or local path).
+
+ Returns:
+ SafetensorsFileMetadata object.
+
+ Raises:
+ SafetensorsParsingError: If the header cannot be parsed.
+ """
+ # Parse json header
+ try:
+ metadata_as_dict = json.loads(metadata_as_bytes.decode(errors="ignore"))
+ except json.JSONDecodeError as e:
+ raise SafetensorsParsingError(
+ f"Failed to parse safetensors header for '{filename}' ({context_msg}): header is not json-encoded string. "
+ "Please make sure this is a correctly formatted safetensors file."
+ ) from e
+
+ try:
+ return SafetensorsFileMetadata(
+ metadata=metadata_as_dict.get("__metadata__", {}),
+ tensors={
+ key: TensorInfo(
+ dtype=tensor["dtype"],
+ shape=tensor["shape"],
+ data_offsets=tuple(tensor["data_offsets"]), # type: ignore
+ )
+ for key, tensor in metadata_as_dict.items()
+ if key != "__metadata__"
+ },
+ )
+ except (KeyError, IndexError) as e:
+ raise SafetensorsParsingError(
+ f"Failed to parse safetensors header for '{filename}' ({context_msg}): header format not recognized. "
+ "Please make sure this is a correctly formatted safetensors file."
+ ) from e
+
+
+class HfApi:
+ """
+ Client to interact with the Hugging Face Hub via HTTP.
+
+ The client is initialized with some high-level settings used in all requests
+ made to the Hub (HF endpoint, authentication, user agents...). Using the `HfApi`
+ client is preferred but not mandatory as all of its public methods are exposed
+ directly at the root of `huggingface_hub`.
+
+ Args:
+ endpoint (`str`, *optional*):
+ Endpoint of the Hub. Defaults to .
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ library_name (`str`, *optional*):
+ The name of the library that is making the HTTP request. Will be added to
+ the user-agent header. Example: `"transformers"`.
+ library_version (`str`, *optional*):
+ The version of the library that is making the HTTP request. Will be added
+ to the user-agent header. Example: `"4.24.0"`.
+ user_agent (`str`, `dict`, *optional*):
+ The user agent info in the form of a dictionary or a single string. It will
+ be completed with information about the installed packages.
+ headers (`dict`, *optional*):
+ Additional headers to be sent with each request. Example: `{"X-My-Header": "value"}`.
+ Headers passed here are taking precedence over the default headers.
+ """
+
+ def __init__(
+ self,
+ endpoint: str | None = None,
+ token: str | bool | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+ headers: dict[str, str] | None = None,
+ ) -> None:
+ self.endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+ self.token = token
+ self.library_name = library_name
+ self.library_version = library_version
+ self.user_agent = user_agent
+ self.headers = headers
+ self._thread_pool: ThreadPoolExecutor | None = None
+
+ # /whoami-v2 is the only endpoint for which we may want to cache results
+ self._whoami_cache: dict[str, dict] = {}
+
+ def run_as_future(self, fn: Callable[..., R], *args, **kwargs) -> Future[R]:
+ """
+ Run a method in the background and return a Future instance.
+
+ The main goal is to run methods without blocking the main thread (e.g. to push data during a training).
+ Background jobs are queued to preserve order but are not ran in parallel. If you need to speed-up your scripts
+ by parallelizing lots of call to the API, you must setup and use your own [ThreadPoolExecutor](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor).
+
+ Note: Most-used methods like [`upload_file`], [`upload_folder`] and [`create_commit`] have a `run_as_future: bool`
+ argument to directly call them in the background. This is equivalent to calling `api.run_as_future(...)` on them
+ but less verbose.
+
+ Args:
+ fn (`Callable`):
+ The method to run in the background.
+ *args, **kwargs:
+ Arguments with which the method will be called.
+
+ Return:
+ `Future`: a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects) instance to
+ get the result of the task.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> future = api.run_as_future(api.whoami) # instant
+ >>> future.done()
+ False
+ >>> future.result() # wait until complete and return result
+ (...)
+ >>> future.done()
+ True
+ ```
+ """
+ if self._thread_pool is None:
+ self._thread_pool = ThreadPoolExecutor(max_workers=1)
+ self._thread_pool
+ return self._thread_pool.submit(fn, *args, **kwargs)
+
+ @validate_hf_hub_args
+ def whoami(self, token: bool | str | None = None, *, cache: bool = False) -> dict:
+ """
+ Call HF API to know "whoami".
+
+ If passing `cache=True`, the result will be cached for subsequent calls for the duration of the Python process. This is useful if you plan to call
+ `whoami` multiple times as this endpoint is heavily rate-limited for security reasons.
+
+ Args:
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ cache (`bool`, *optional*):
+ Whether to cache the result of the `whoami` call for subsequent calls.
+ If an error occurs during the first call, it won't be cached.
+ Defaults to `False`.
+ """
+ # Get the effective token using the helper function get_token
+ token = self.token if token is None else token
+ if token is False:
+ raise ValueError("Cannot use `token=False` with `whoami` method as it requires authentication.")
+ if token is True or token is None:
+ token = get_token()
+ if token is None:
+ raise LocalTokenNotFoundError(
+ "Token is required to call the /whoami-v2 endpoint, but no token found. You must provide a token or be logged in to "
+ "Hugging Face with `hf auth login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens."
+ )
+
+ if cache and (cached_token := self._whoami_cache.get(token)):
+ return cached_token
+
+ # Call Hub
+ output = self._inner_whoami(token=token)
+
+ # Cache result and return
+ if cache:
+ self._whoami_cache[token] = output
+ return output
+
+ def _inner_whoami(self, token: str) -> dict:
+ r = get_session().get(
+ f"{self.endpoint}/api/whoami-v2",
+ headers=self._build_hf_headers(token=token),
+ )
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as e:
+ if e.response.status_code == 401:
+ error_message = "Invalid user token."
+ # Check which token is the effective one and generate the error message accordingly
+ if token == _get_token_from_google_colab():
+ error_message += " The token from Google Colab vault is invalid. Please update it from the UI."
+ elif token == _get_token_from_environment():
+ error_message += (
+ " The token from HF_TOKEN environment variable is invalid. "
+ "Note that HF_TOKEN takes precedence over `hf auth login`."
+ )
+ elif token == _get_token_from_file():
+ error_message += (
+ " The token stored is invalid. Please run `hf auth login --force` to set a new token."
+ )
+ raise HfHubHTTPError(error_message, response=e.response) from e
+ if e.response.status_code == 429:
+ error_message = (
+ "You've hit the rate limit for the /whoami-v2 endpoint, which is intentionally strict for security reasons."
+ " If you're calling it often, consider caching the response with `whoami(..., cache=True)`."
+ )
+ raise HfHubHTTPError(error_message, response=e.response) from e
+ raise
+ return r.json()
+
+ def get_model_tags(self) -> dict:
+ """
+ List all valid model tags as a nested namespace object
+ """
+ path = f"{self.endpoint}/api/models-tags-by-type"
+ r = get_session().get(path)
+ hf_raise_for_status(r)
+ return r.json()
+
+ def get_dataset_tags(self) -> dict:
+ """
+ List all valid dataset tags as a nested namespace object.
+ """
+ path = f"{self.endpoint}/api/datasets-tags-by-type"
+ r = get_session().get(path)
+ hf_raise_for_status(r)
+ return r.json()
+
+ @validate_hf_hub_args
+ def list_models(
+ self,
+ *,
+ # Search-query parameter
+ filter: str | Iterable[str] | None = None,
+ author: str | None = None,
+ apps: str | list[str] | None = None,
+ gated: bool | None = None,
+ inference: Literal["warm"] | None = None,
+ inference_provider: Literal["all"] | PROVIDER_T | list[PROVIDER_T] | None = None,
+ trained_dataset: str | list[str] | None = None,
+ search: str | None = None,
+ pipeline_tag: str | None = None,
+ num_parameters: str | None = None,
+ emissions_thresholds: tuple[float, float] | None = None,
+ # Sorting and pagination parameters
+ sort: ModelSort_T | None = None,
+ limit: int | None = None,
+ # Additional data to fetch
+ expand: list[ExpandModelProperty_T] | None = None,
+ full: bool | None = None,
+ cardData: bool = False,
+ fetch_config: bool = False,
+ token: bool | str | None = None,
+ ) -> Iterable[ModelInfo]:
+ """
+ List models hosted on the Huggingface Hub, given some filters.
+
+ Args:
+ filter (`str` or `Iterable[str]`, *optional*):
+ A string or list of string to filter models on the Hub.
+ Models can be filtered by library, language, task, tags, and more.
+ author (`str`, *optional*):
+ A string which identify the author (user or organization) of the
+ returned models.
+ apps (`str` or `List`, *optional*):
+ A string or list of strings to filter models on the Hub that
+ support the specified apps. Example values include `"ollama"` or `["ollama", "vllm"]`.
+ gated (`bool`, *optional*):
+ A boolean to filter models on the Hub that are gated or not. By default, all models are returned.
+ If `gated=True` is passed, only gated models are returned.
+ If `gated=False` is passed, only non-gated models are returned.
+ inference (`Literal["warm"]`, *optional*):
+ If "warm", filter models on the Hub currently served by at least one provider.
+ inference_provider (`Literal["all"]` or `str`, *optional*):
+ A string to filter models on the Hub that are served by a specific provider.
+ Pass `"all"` to get all models served by at least one provider.
+ trained_dataset (`str` or `List`, *optional*):
+ A string tag or a list of string tags of the trained dataset for a
+ model on the Hub.
+ search (`str`, *optional*):
+ A string that will be contained in the returned model ids.
+ pipeline_tag (`str`, *optional*):
+ A string pipeline tag to filter models on the Hub by, such as `summarization`.
+ num_parameters (`str`, *optional*):
+ Filter models by parameter count. Accepts the same range syntax as the Hub UI and API, for example
+ `"min:6B,max:128B"`, `"min:6B"` or `"max:128B"`.
+ emissions_thresholds (`Tuple`, *optional*):
+ A tuple of two ints or floats representing a minimum and maximum
+ carbon footprint to filter the resulting models with in grams.
+ sort (`ModelSort_T`, *optional*):
+ The key with which to sort the resulting models. Possible values are "created_at", "downloads",
+ "last_modified", "likes" and "trending_score".
+ limit (`int`, *optional*):
+ The limit on the number of models fetched. Leaving this option
+ to `None` fetches all models.
+ expand (`list[ExpandModelProperty_T]`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `full`, `cardData` or `fetch_config` are passed.
+ Possible values are `"author"`, `"cardData"`, `"config"`, `"createdAt"`, `"disabled"`, `"downloads"`, `"downloadsAllTime"`, `"evalResults"`, `"gated"`, `"gguf"`, `"inference"`, `"inferenceProviderMapping"`, `"lastModified"`, `"library_name"`, `"likes"`, `"mask_token"`, `"model-index"`, `"pipeline_tag"`, `"private"`, `"safetensors"`, `"sha"`, `"siblings"`, `"spaces"`, `"tags"`, `"transformersInfo"`, `"trendingScore"`, `"widgetData"`, and `"resourceGroup"`.
+ full (`bool`, *optional*):
+ Whether to fetch all model data, including the `last_modified`,
+ the `sha`, the files and the `tags`. This is set to `True` by
+ default when using a filter.
+ cardData (`bool`, *optional*):
+ Whether to grab the metadata for the model as well. Can contain
+ useful information such as carbon emissions, metrics, and
+ datasets trained on.
+ fetch_config (`bool`, *optional*):
+ Whether to fetch the model configs as well. This is not included
+ in `full` due to its size.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ Returns:
+ `Iterable[ModelInfo]`: an iterable of [`huggingface_hub.hf_api.ModelInfo`] objects.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import HfApi
+
+ >>> api = HfApi()
+
+ # List all models
+ >>> api.list_models()
+
+ # List text classification models
+ >>> api.list_models(filter="text-classification")
+
+ # List models from the KerasHub library
+ >>> api.list_models(filter="keras-hub")
+
+ # List models served by Cohere
+ >>> api.list_models(inference_provider="cohere")
+
+ # List models with "bert" in their name
+ >>> api.list_models(search="bert")
+
+ # List models with "bert" in their name and pushed by google
+ >>> api.list_models(search="bert", author="google")
+
+ # List models with 6B to 128B parameters
+ >>> api.list_models(num_parameters="min:6B,max:128B", sort="likes")
+ ```
+ """
+ if expand and (full or cardData or fetch_config):
+ raise ValueError("`expand` cannot be used if `full`, `cardData` or `fetch_config` are passed.")
+
+ if emissions_thresholds is not None and not cardData:
+ raise ValueError("`emissions_thresholds` were passed without setting `cardData=True`.")
+
+ path = f"{self.endpoint}/api/models"
+ headers = self._build_hf_headers(token=token)
+ params: dict[str, Any] = {}
+
+ # Build the filter list
+ filter_list: list[str] = []
+ if filter:
+ filter_list.extend([filter] if isinstance(filter, str) else filter)
+ if trained_dataset:
+ datasets = [trained_dataset] if isinstance(trained_dataset, str) else trained_dataset
+ filter_list.extend(f"dataset:{d}" if not d.startswith("dataset:") else d for d in datasets)
+ if len(filter_list) > 0:
+ params["filter"] = filter_list
+
+ # Handle other query params
+ if author:
+ params["author"] = author
+ if apps:
+ if isinstance(apps, str):
+ apps = [apps]
+ params["apps"] = apps
+ if gated is not None:
+ params["gated"] = gated
+ if inference is not None:
+ params["inference"] = inference
+ if inference_provider is not None:
+ params["inference_provider"] = inference_provider
+ if pipeline_tag:
+ params["pipeline_tag"] = pipeline_tag
+ if num_parameters is not None:
+ params["num_parameters"] = num_parameters
+ if search:
+ params["search"] = search
+ if sort is not None:
+ params["sort"] = (
+ "lastModified"
+ if sort == "last_modified"
+ else "trendingScore"
+ if sort == "trending_score"
+ else "createdAt"
+ if sort == "created_at"
+ else sort
+ )
+ if limit is not None:
+ params["limit"] = limit
+
+ # Request additional data
+ if full:
+ params["full"] = True
+ if fetch_config:
+ params["config"] = True
+ if cardData:
+ params["cardData"] = True
+ if expand:
+ params["expand"] = expand
+
+ # `items` is a generator
+ items = paginate(path, params=params, headers=headers)
+ if limit is not None:
+ items = islice(items, limit) # Do not iterate over all pages
+ for item in items:
+ if "siblings" not in item:
+ item["siblings"] = None
+ model_info = ModelInfo(**item)
+ if emissions_thresholds is None or _is_emission_within_threshold(model_info, *emissions_thresholds):
+ yield model_info
+
+ @validate_hf_hub_args
+ def list_datasets(
+ self,
+ *,
+ # Search-query parameter
+ filter: str | Iterable[str] | None = None,
+ author: str | None = None,
+ benchmark: Literal[True] | Literal["official"] | str | None = None,
+ dataset_name: str | None = None,
+ gated: bool | None = None,
+ language_creators: str | list[str] | None = None,
+ language: str | list[str] | None = None,
+ multilinguality: str | list[str] | None = None,
+ size_categories: str | list[str] | None = None,
+ task_categories: str | list[str] | None = None,
+ task_ids: str | list[str] | None = None,
+ search: str | None = None,
+ # Sorting and pagination parameters
+ sort: DatasetSort_T | None = None,
+ limit: int | None = None,
+ # Additional data to fetch
+ expand: list[ExpandDatasetProperty_T] | None = None,
+ full: bool | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[DatasetInfo]:
+ """
+ List datasets hosted on the Huggingface Hub, given some filters.
+
+ Args:
+ filter (`str` or `Iterable[str]`, *optional*):
+ A string or list of string to filter datasets on the hub.
+ author (`str`, *optional*):
+ A string which identify the author of the returned datasets.
+ benchmark (`True`, `"official"`, `str`, *optional*):
+ Filter datasets by benchmark. Can be `True` or `"official"` to return official benchmark datasets.
+ For future-compatibility, can also be a string representing the benchmark name (currently only "official" is supported).
+ dataset_name (`str`, *optional*):
+ A string or list of strings that can be used to identify datasets on
+ the Hub by its name, such as `SQAC` or `wikineural`
+ gated (`bool`, *optional*):
+ A boolean to filter datasets on the Hub that are gated or not. By default, all datasets are returned.
+ If `gated=True` is passed, only gated datasets are returned.
+ If `gated=False` is passed, only non-gated datasets are returned.
+ language_creators (`str` or `List`, *optional*):
+ A string or list of strings that can be used to identify datasets on
+ the Hub with how the data was curated, such as `crowdsourced` or
+ `machine_generated`.
+ language (`str` or `List`, *optional*):
+ A string or list of strings representing a two-character language to
+ filter datasets by on the Hub.
+ multilinguality (`str` or `List`, *optional*):
+ A string or list of strings representing a filter for datasets that
+ contain multiple languages.
+ size_categories (`str` or `List`, *optional*):
+ A string or list of strings that can be used to identify datasets on
+ the Hub by the size of the dataset such as `100K>> from huggingface_hub import HfApi
+
+ >>> api = HfApi()
+
+ # List all datasets
+ >>> api.list_datasets()
+
+
+ # List only the text classification datasets
+ >>> api.list_datasets(filter="task_categories:text-classification")
+
+
+ # List only the datasets in russian for language modeling
+ >>> api.list_datasets(
+ ... filter=("language:ru", "task_ids:language-modeling")
+ ... )
+
+ # List FiftyOne datasets (identified by the tag "fiftyone" in dataset card)
+ >>> api.list_datasets(filter="fiftyone")
+ ```
+
+ Example usage with the `search` argument:
+
+ ```python
+ >>> from huggingface_hub import HfApi
+
+ >>> api = HfApi()
+
+ # List all datasets with "text" in their name
+ >>> api.list_datasets(search="text")
+
+ # List all datasets with "text" in their name made by google
+ >>> api.list_datasets(search="text", author="google")
+ ```
+ """
+ if expand and full:
+ raise ValueError("`expand` cannot be used if `full` is passed.")
+
+ path = f"{self.endpoint}/api/datasets"
+ headers = self._build_hf_headers(token=token)
+ params: dict[str, Any] = {}
+
+ # Build `filter` list
+ filter_list = []
+ if filter is not None:
+ if isinstance(filter, str):
+ filter_list.append(filter)
+ else:
+ filter_list.extend(filter)
+ for key, value in (
+ ("language_creators", language_creators),
+ ("language", language),
+ ("multilinguality", multilinguality),
+ ("size_categories", size_categories),
+ ("task_categories", task_categories),
+ ("task_ids", task_ids),
+ ):
+ if value:
+ if isinstance(value, str):
+ value = [value]
+ for value_item in value:
+ if not value_item.startswith(f"{key}:"):
+ data = f"{key}:{value_item}"
+ else:
+ data = value_item
+ filter_list.append(data)
+ if benchmark is not None:
+ if benchmark is True: # alias for official benchmark
+ benchmark = "official"
+ filter_list.append(f"benchmark:{benchmark}")
+ if len(filter_list) > 0:
+ params["filter"] = filter_list
+
+ # Handle other query params
+ if author:
+ params["author"] = author
+ if gated is not None:
+ params["gated"] = gated
+ search_list = []
+ if dataset_name:
+ search_list.append(dataset_name)
+ if search:
+ search_list.append(search)
+ if len(search_list) > 0:
+ params["search"] = search_list
+ if sort is not None:
+ params["sort"] = (
+ "lastModified"
+ if sort == "last_modified"
+ else "trendingScore"
+ if sort == "trending_score"
+ else "createdAt"
+ if sort == "created_at"
+ else sort
+ )
+ if limit is not None:
+ params["limit"] = limit
+
+ # Request additional data
+ if expand:
+ params["expand"] = expand
+ if full:
+ params["full"] = True
+
+ items = paginate(path, params=params, headers=headers)
+ if limit is not None:
+ items = islice(items, limit) # Do not iterate over all pages
+ for item in items:
+ if "siblings" not in item:
+ item["siblings"] = None
+ yield DatasetInfo(**item)
+
+ @validate_hf_hub_args
+ def list_dataset_parquet_files(
+ self,
+ repo_id: str,
+ *,
+ config: str | None = None,
+ token: bool | str | None = None,
+ ) -> list[DatasetParquetEntry]:
+ """List parquet files available for a dataset on the Hub.
+
+ All datasets hosted on the Hub are auto-converted to Parquet by the
+ [Dataset Viewer](https://huggingface.co/docs/dataset-viewer/parquet).
+ This method returns the list of parquet files with their URLs, configs,
+ splits and sizes.
+
+ Args:
+ repo_id (`str`):
+ The dataset repository ID (e.g. `"username/dataset-name"`).
+ config (`str`, *optional*):
+ Filter by a specific config/subset name. When provided, only
+ parquet files for that config are returned.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `list[DatasetParquetEntry]`: a list of [`DatasetParquetEntry`] objects
+ containing config, split, url and size for each parquet file.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_dataset_parquet_files
+ >>> list_dataset_parquet_files("lhoestq/demo1")
+ >>> entries[0]
+ DatasetParquetEntry(config='default', split='train', url='https://huggingface.co/...', size=5038)
+ ```
+ """
+
+ from ._dataset_viewer import DatasetParquetEntry
+
+ if self.endpoint != constants._HF_DEFAULT_ENDPOINT:
+ raise ValueError(
+ "The Dataset Viewer is only available on the Hugging Face Hub"
+ f" (endpoint='{constants._HF_DEFAULT_ENDPOINT}'). It is not supported on"
+ f" third-party endpoints. (endpoint={self.endpoint})"
+ )
+
+ url = f"{constants.DATASETS_SERVER_ENDPOINT}/parquet"
+ params: dict[str, str] = {"dataset": repo_id}
+ if config is not None:
+ params["config"] = config
+ response = get_session().get(url, params=params, headers=self._build_hf_headers(token=token))
+ hf_raise_for_status(response)
+ payload = response.json()
+
+ return [
+ DatasetParquetEntry(
+ config=file_info["config"],
+ split=file_info["split"],
+ url=file_info["url"],
+ size=file_info["size"],
+ )
+ for file_info in payload.get("parquet_files", [])
+ ]
+
+ @validate_hf_hub_args
+ def list_spaces(
+ self,
+ *,
+ # Search-query parameter
+ filter: str | Iterable[str] | None = None,
+ author: str | None = None,
+ search: str | None = None,
+ datasets: str | Iterable[str] | None = None,
+ models: str | Iterable[str] | None = None,
+ linked: bool = False,
+ # Sorting and pagination parameters
+ sort: SpaceSort_T | None = None,
+ limit: int | None = None,
+ # Additional data to fetch
+ expand: list[ExpandSpaceProperty_T] | None = None,
+ full: bool | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[SpaceInfo]:
+ """
+ List spaces hosted on the Huggingface Hub, given some filters.
+
+ Args:
+ filter (`str` or `Iterable`, *optional*):
+ A string tag or list of tags that can be used to identify Spaces on the Hub.
+ author (`str`, *optional*):
+ A string which identify the author of the returned Spaces.
+ search (`str`, *optional*):
+ A string that will be contained in the returned Spaces.
+ datasets (`str` or `Iterable`, *optional*):
+ Whether to return Spaces that make use of a dataset.
+ The name of a specific dataset can be passed as a string.
+ models (`str` or `Iterable`, *optional*):
+ Whether to return Spaces that make use of a model.
+ The name of a specific model can be passed as a string.
+ linked (`bool`, *optional*):
+ Whether to return Spaces that make use of either a model or a dataset.
+ sort (`SpaceSort_T`, *optional*):
+ The key with which to sort the resulting spaces. Possible values are "created_at", "last_modified",
+ "likes" and "trending_score".
+ limit (`int`, *optional*):
+ The limit on the number of Spaces fetched. Leaving this option
+ to `None` fetches all Spaces.
+ expand (`list[ExpandSpaceProperty_T]`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `full` is passed.
+ Possible values are `"author"`, `"cardData"`, `"datasets"`, `"disabled"`, `"lastModified"`, `"createdAt"`, `"likes"`, `"models"`, `"private"`, `"region"`, `"runtime"`, `"sdk"`, `"siblings"`, `"sha"`, `"subdomain"`, `"tags"`, `"trendingScore"`, `"usedStorage"`, and `"resourceGroup"`.
+ full (`bool`, *optional*):
+ Whether to fetch all Spaces data, including the `last_modified`, `siblings`
+ and `card_data` fields.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[SpaceInfo]`: an iterable of [`huggingface_hub.hf_api.SpaceInfo`] objects.
+ """
+ if expand and full:
+ raise ValueError("`expand` cannot be used if `full` is passed.")
+
+ path = f"{self.endpoint}/api/spaces"
+ headers = self._build_hf_headers(token=token)
+ params: dict[str, Any] = {}
+ if filter is not None:
+ params["filter"] = filter
+ if author is not None:
+ params["author"] = author
+ if search is not None:
+ params["search"] = search
+ if sort is not None:
+ params["sort"] = (
+ "lastModified"
+ if sort == "last_modified"
+ else "trendingScore"
+ if sort == "trending_score"
+ else "createdAt"
+ if sort == "created_at"
+ else sort
+ )
+ if limit is not None:
+ params["limit"] = limit
+ if linked:
+ params["linked"] = True
+ if datasets is not None:
+ params["datasets"] = datasets
+ if models is not None:
+ params["models"] = models
+
+ # Request additional data
+ if expand:
+ params["expand"] = expand
+ if full:
+ params["full"] = True
+
+ items = paginate(path, params=params, headers=headers)
+ if limit is not None:
+ items = islice(items, limit) # Do not iterate over all pages
+ for item in items:
+ if "siblings" not in item:
+ item["siblings"] = None
+ yield SpaceInfo(**item)
+
+ @validate_hf_hub_args
+ def search_spaces(
+ self,
+ query: str,
+ *,
+ filter: str | Iterable[str] | None = None,
+ sdk: str | list[str] | None = None,
+ include_non_running: bool = False,
+ token: bool | str | None = None,
+ ) -> Iterable[SpaceSearchResult]:
+ """Search Spaces on the Hub using semantic search.
+
+ This endpoint uses semantic search (embedding-based) for multi-word queries
+ and full-text search for single-word queries.
+
+ Args:
+ query (`str`):
+ The search query string.
+ filter (`str` or `Iterable[str]`, *optional*):
+ A string tag or list of tags to filter by.
+ sdk (`str` or `list[str]`, *optional*):
+ Filter by SDK (e.g. `"gradio"`, `"docker"`, `"static"`).
+ include_non_running (`bool`, *optional*):
+ Whether to include non-running Spaces in results. Defaults to `False`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[SpaceSearchResult]`: an iterable of [`SpaceSearchResult`] objects.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> results = list(api.search_spaces("generate image"))
+ >>> results[0].id
+ 'mrfakename/Z-Image-Turbo'
+ >>> results[0].ai_category
+ 'Image Generation'
+ ```
+ """
+ path = f"{self.endpoint}/api/spaces/semantic-search"
+ headers = self._build_hf_headers(token=token)
+ params: dict[str, Any] = {"q": query}
+ if filter is not None:
+ params["filter"] = filter
+ if sdk is not None:
+ params["sdk"] = sdk
+ if include_non_running:
+ params["includeNonRunning"] = True
+
+ r = get_session().get(path, headers=headers, params=params)
+ hf_raise_for_status(r)
+ for item in r.json():
+ yield SpaceSearchResult(item)
+
+ @validate_hf_hub_args
+ def unlike(
+ self,
+ repo_id: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> None:
+ """
+ Unlike a given repo on the Hub (e.g. remove from favorite list).
+
+ To prevent spam usage, it is not possible to `like` a repository from a script.
+
+ See also [`list_liked_repos`].
+
+ Args:
+ repo_id (`str`):
+ The repository to unlike. Example: `"user/my-cool-model"`.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if unliking a dataset or space, `None` or
+ `"model"` if unliking a model. Default is `None`.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_liked_repos, unlike
+ >>> "gpt2" in list_liked_repos().models # we assume you have already liked gpt2
+ True
+ >>> unlike("gpt2")
+ >>> "gpt2" in list_liked_repos().models
+ False
+ ```
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ response = get_session().delete(
+ url=f"{self.endpoint}/api/{repo_type}s/{repo_id}/like", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def list_liked_repos(
+ self,
+ user: str | None = None,
+ *,
+ token: bool | str | None = None,
+ ) -> UserLikes:
+ """
+ List all public repos liked by a user on huggingface.co.
+
+ This list is public so token is optional. If `user` is not passed, it defaults to
+ the logged in user.
+
+ See also [`unlike`].
+
+ Args:
+ user (`str`, *optional*):
+ Name of the user for which you want to fetch the likes.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`UserLikes`]: object containing the user name and 3 lists of repo ids (1 for
+ models, 1 for datasets and 1 for Spaces).
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If `user` is not passed and no token found (either from argument or from machine).
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_liked_repos
+
+ >>> likes = list_liked_repos("julien-c")
+
+ >>> likes.user
+ "julien-c"
+
+ >>> likes.models
+ ["osanseviero/streamlit_1.15", "Xhaheen/ChatGPT_HF", ...]
+ ```
+ """
+ # User is either provided explicitly or retrieved from current token.
+ if user is None:
+ me = self.whoami(token=token)
+ if me["type"] == "user":
+ user = me["name"]
+ else:
+ raise ValueError(
+ "Cannot list liked repos. You must provide a 'user' as input or be logged in as a user."
+ )
+
+ path = f"{self.endpoint}/api/users/{user}/likes"
+ headers = self._build_hf_headers(token=token)
+
+ likes = list(paginate(path, params={}, headers=headers))
+ # Looping over a list of items similar to:
+ # {
+ # 'createdAt': '2021-09-09T21:53:27.000Z',
+ # 'repo': {
+ # 'name': 'PaddlePaddle/PaddleOCR',
+ # 'type': 'space'
+ # }
+ # }
+ # Let's loop 3 times over the received list. Less efficient but more straightforward to read.
+ return UserLikes(
+ user=user,
+ total=len(likes),
+ kernels=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "kernel"],
+ models=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "model"],
+ datasets=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "dataset"],
+ spaces=[like["repo"]["name"] for like in likes if like["repo"]["type"] == "space"],
+ )
+
+ def list_user_repos(
+ self,
+ namespace: str | None = None,
+ *,
+ token: bool | str | None = None,
+ ) -> Iterable[RepoStorageInfo]:
+ """List all repositories (models, datasets, spaces, buckets) for a user or organization with storage info.
+
+ Uses the `/api/settings/repositories` endpoint for the authenticated user or
+ `/api/organizations/{namespace}/settings/repositories` for an organization.
+
+ Args:
+ namespace (`str`, *optional*):
+ Organization name. If not provided, lists repos for the authenticated user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token. Defaults to the locally saved token.
+
+ Returns:
+ `Iterable[RepoStorageInfo]`: An iterable of [`RepoStorageInfo`] objects.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_user_repos
+
+ >>> repos = list(list_user_repos())
+ >>> repos[0]
+ RepoStorageInfo(id='username/my-model', type='model', ...)
+
+ >>> # List repos from an organization
+ >>> repos = list(list_user_repos(namespace="my-org"))
+ ```
+ """
+ if namespace is not None:
+ path = f"{self.endpoint}/api/organizations/{namespace}/settings/repositories"
+ else:
+ path = f"{self.endpoint}/api/settings/repositories"
+ headers = self._build_hf_headers(token=token)
+ for item in paginate(path, params={}, headers=headers):
+ yield RepoStorageInfo(**item)
+
+ @validate_hf_hub_args
+ def list_repo_likers(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[User]:
+ """
+ List all users who liked a given repo on the hugging Face Hub.
+
+ See also [`list_liked_repos`].
+
+ Args:
+ repo_id (`str`):
+ The repository to retrieve . Example: `"user/my-cool-model"`.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+
+ Returns:
+ `Iterable[User]`: an iterable of [`huggingface_hub.hf_api.User`] objects.
+ """
+
+ # Construct the API endpoint
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/likers"
+ for liker in paginate(path, params={}, headers=self._build_hf_headers(token=token)):
+ yield User(username=liker["user"], fullname=liker["fullname"], avatar_url=liker["avatarUrl"])
+
+ @validate_hf_hub_args
+ def model_info(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ timeout: float | None = None,
+ securityStatus: bool | None = None,
+ files_metadata: bool = False,
+ expand: list[ExpandModelProperty_T] | None = None,
+ token: bool | str | None = None,
+ ) -> ModelInfo:
+ """
+ Get info on one specific model on huggingface.co
+
+ Model can be private if you pass an acceptable token or are logged in.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`, *optional*):
+ The revision of the model repository from which to get the
+ information.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+ securityStatus (`bool`, *optional*):
+ Whether to retrieve the security status from the model
+ repository as well. The security status will be returned in the `security_repo_status` field.
+ files_metadata (`bool`, *optional*):
+ Whether or not to retrieve metadata for files in the repository
+ (size, LFS metadata, etc). Defaults to `False`.
+ expand (`list[ExpandModelProperty_T]`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `securityStatus` or `files_metadata` are passed.
+ Possible values are `"author"`, `"baseModels"`, `"cardData"`, `"childrenModelCount"`, `"config"`, `"createdAt"`, `"disabled"`, `"downloads"`, `"downloadsAllTime"`, `"evalResults"`, `"gated"`, `"gguf"`, `"inference"`, `"inferenceProviderMapping"`, `"lastModified"`, `"library_name"`, `"likes"`, `"mask_token"`, `"model-index"`, `"pipeline_tag"`, `"private"`, `"safetensors"`, `"sha"`, `"siblings"`, `"spaces"`, `"tags"`, `"transformersInfo"`, `"trendingScore"`, `"widgetData"`, `"usedStorage"`, and `"resourceGroup"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`huggingface_hub.hf_api.ModelInfo`]: The model repository information.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+ """
+ if expand and (securityStatus or files_metadata):
+ raise ValueError("`expand` cannot be used if `securityStatus` or `files_metadata` are set.")
+
+ headers = self._build_hf_headers(token=token)
+ path = (
+ f"{self.endpoint}/api/models/{repo_id}"
+ if revision is None
+ else (f"{self.endpoint}/api/models/{repo_id}/revision/{quote(revision, safe='')}")
+ )
+ params: dict = {}
+ if securityStatus:
+ params["securityStatus"] = True
+ if files_metadata:
+ params["blobs"] = True
+ if expand:
+ params["expand"] = expand
+ r = get_session().get(path, headers=headers, timeout=timeout, params=params)
+ hf_raise_for_status(r)
+ data = r.json()
+ return ModelInfo(**data)
+
+ @validate_hf_hub_args
+ def dataset_info(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ timeout: float | None = None,
+ files_metadata: bool = False,
+ expand: list[ExpandDatasetProperty_T] | None = None,
+ token: bool | str | None = None,
+ ) -> DatasetInfo:
+ """
+ Get info on one specific dataset on huggingface.co.
+
+ Dataset can be private if you pass an acceptable token.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`, *optional*):
+ The revision of the dataset repository from which to get the
+ information.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+ files_metadata (`bool`, *optional*):
+ Whether or not to retrieve metadata for files in the repository
+ (size, LFS metadata, etc). Defaults to `False`.
+ expand (`list[ExpandDatasetProperty_T]`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `files_metadata` is passed.
+ Possible values are `"author"`, `"cardData"`, `"citation"`, `"createdAt"`, `"disabled"`, `"description"`, `"downloads"`, `"downloadsAllTime"`, `"gated"`, `"lastModified"`, `"likes"`, `"mainSize"`, `"paperswithcode_id"`, `"private"`, `"siblings"`, `"sha"`, `"tags"`, `"trendingScore"`, `"usedStorage"`, and `"resourceGroup"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`hf_api.DatasetInfo`]: The dataset repository information.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+ """
+ if expand and files_metadata:
+ raise ValueError("`expand` cannot be used if `files_metadata` is set.")
+
+ headers = self._build_hf_headers(token=token)
+ path = (
+ f"{self.endpoint}/api/datasets/{repo_id}"
+ if revision is None
+ else (f"{self.endpoint}/api/datasets/{repo_id}/revision/{quote(revision, safe='')}")
+ )
+ params: dict = {}
+ if files_metadata:
+ params["blobs"] = True
+ if expand:
+ params["expand"] = expand
+
+ r = get_session().get(path, headers=headers, timeout=timeout, params=params)
+ hf_raise_for_status(r)
+ data = r.json()
+ return DatasetInfo(**data)
+
+ @validate_hf_hub_args
+ def get_dataset_leaderboard(
+ self,
+ repo_id: str,
+ *,
+ base_model_only: bool | None = None,
+ token: bool | str | None = None,
+ timeout: float | None = None,
+ ) -> list[DatasetLeaderboardEntry]:
+ """Get the leaderboard for a dataset on the Hub.
+
+ The leaderboard ranks models based on their evaluation scores on the given benchmark
+ dataset. Not all datasets have leaderboards — only benchmark datasets with evaluation
+ results submitted to them. This gives a dataset-centric view of scores; for a model-centric
+ view, use [`model_info`] with `expand=["evalResults"]`.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`. For example: `"allenai/olmOCR-bench"`.
+ base_model_only (`bool` or `None`, *optional*):
+ By default, the leaderboard only includes models that have no declared `base_model` relation
+ (i.e. canonical/root repos), matching the Hub's default leaderboard view. Fine-tuned or derivative
+ repos that declare a parent model are excluded. Pass `base_model_only=False` to disable this filter and
+ include every submitted result, regardless of whether the model declares a base model relation.
+ token (`bool` or `str`, *optional*):
+ A valid user access token. Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+
+ Returns:
+ `list[DatasetLeaderboardEntry]`: A list of [`DatasetLeaderboardEntry`] objects representing
+ the leaderboard entries, sorted by rank.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.HfHubHTTPError`]
+ > If the dataset does not have a leaderboard.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> leaderboard = api.get_dataset_leaderboard("allenai/olmOCR-bench")
+ >>> leaderboard[0].model_id
+ 'datalab-to/chandra-ocr-2'
+ >>> leaderboard[0].rank
+ 1
+
+ # Include fine-tuned / derivative models too
+ >>> full_leaderboard = api.get_dataset_leaderboard("allenai/olmOCR-bench", base_model_only=False)
+ ```
+ """
+ headers = self._build_hf_headers(token=token)
+ path = f"{self.endpoint}/api/datasets/{repo_id}/leaderboard"
+ params = {}
+ if base_model_only is not None:
+ params["base_model"] = base_model_only
+ r = get_session().get(path, headers=headers, params=params, timeout=timeout)
+ hf_raise_for_status(r)
+ data = r.json()
+ return [DatasetLeaderboardEntry(**entry) for entry in data]
+
+ @validate_hf_hub_args
+ def space_info(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ timeout: float | None = None,
+ files_metadata: bool = False,
+ expand: list[ExpandSpaceProperty_T] | None = None,
+ token: bool | str | None = None,
+ ) -> SpaceInfo:
+ """
+ Get info on one specific Space on huggingface.co.
+
+ Space can be private if you pass an acceptable token.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`, *optional*):
+ The revision of the space repository from which to get the
+ information.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+ files_metadata (`bool`, *optional*):
+ Whether or not to retrieve metadata for files in the repository
+ (size, LFS metadata, etc). Defaults to `False`.
+ expand (`list[ExpandSpaceProperty_T]`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `full` is passed.
+ Possible values are `"author"`, `"cardData"`, `"createdAt"`, `"datasets"`, `"disabled"`, `"lastModified"`, `"likes"`, `"models"`, `"private"`, `"region"`, `"runtime"`, `"sdk"`, `"siblings"`, `"sha"`, `"subdomain"`, `"tags"`, `"trendingScore"`, `"usedStorage"`, and `"resourceGroup"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`~hf_api.SpaceInfo`]: The space repository information.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+ """
+ if expand and files_metadata:
+ raise ValueError("`expand` cannot be used if `files_metadata` is set.")
+
+ headers = self._build_hf_headers(token=token)
+ path = (
+ f"{self.endpoint}/api/spaces/{repo_id}"
+ if revision is None
+ else (f"{self.endpoint}/api/spaces/{repo_id}/revision/{quote(revision, safe='')}")
+ )
+ params: dict = {}
+ if files_metadata:
+ params["blobs"] = True
+ if expand:
+ params["expand"] = expand
+
+ r = get_session().get(path, headers=headers, timeout=timeout, params=params)
+ hf_raise_for_status(r)
+ data = r.json()
+ return SpaceInfo(**data)
+
+ @validate_hf_hub_args
+ def kernel_info(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ timeout: float | None = None,
+ token: bool | str | None = None,
+ ) -> KernelInfo:
+ """
+ Get info on one specific kernel on huggingface.co.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ revision (`str`, *optional*):
+ The revision of the kernel repository from which to get the
+ information.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`~hf_api.ModelInfo`]: The kernel repository information.
+ """
+ headers = self._build_hf_headers(token=token)
+ path = (
+ f"{self.endpoint}/api/kernels/{repo_id}"
+ if revision is None
+ else (f"{self.endpoint}/api/kernels/{repo_id}/revision/{quote(revision, safe='')}")
+ )
+ r = get_session().get(path, headers=headers, timeout=timeout)
+ hf_raise_for_status(r)
+ data = r.json()
+ return KernelInfo(**data)
+
+ @validate_hf_hub_args
+ def repo_info(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ repo_type: str | None = None,
+ timeout: float | None = None,
+ files_metadata: bool = False,
+ expand: ExpandModelProperty_T | ExpandDatasetProperty_T | ExpandSpaceProperty_T | None = None,
+ token: bool | str | None = None,
+ ) -> ModelInfo | DatasetInfo | SpaceInfo | KernelInfo:
+ """
+ Get the info object for a given repo of a given type.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the
+ information.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if getting repository info from a dataset or a space,
+ `None` or `"model"` if getting repository info from a model. Default is `None`.
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+ expand (`ExpandModelProperty_T` or `ExpandDatasetProperty_T` or `ExpandSpaceProperty_T`, *optional*):
+ List properties to return in the response. When used, only the properties in the list will be returned.
+ This parameter cannot be used if `files_metadata` is passed.
+ For an exhaustive list of available properties, check out [`model_info`], [`dataset_info`] or [`space_info`].
+ files_metadata (`bool`, *optional*):
+ Whether or not to retrieve metadata for files in the repository
+ (size, LFS metadata, etc). Defaults to `False`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Union[SpaceInfo, DatasetInfo, ModelInfo]`: The repository information, as a
+ [`huggingface_hub.hf_api.DatasetInfo`], [`huggingface_hub.hf_api.ModelInfo`]
+ or [`huggingface_hub.hf_api.SpaceInfo`] object.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+ """
+ match repo_type:
+ case None | "model":
+ method = self.model_info
+ case "dataset":
+ method = self.dataset_info # type: ignore
+ case "space":
+ method = self.space_info # type: ignore
+ case "kernel":
+ # No expand/files_metadata for kernels
+ return self.kernel_info(repo_id, revision=revision, token=token, timeout=timeout)
+ case _:
+ raise ValueError("Unsupported repo type.")
+ return method(
+ repo_id,
+ revision=revision,
+ token=token,
+ timeout=timeout,
+ expand=expand, # type: ignore
+ files_metadata=files_metadata,
+ )
+
+ @validate_hf_hub_args
+ @flag_as_download_call
+ def resolve_revision(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_files_only: bool = False,
+ token: bool | str | None = None,
+ ) -> ResolvedRevision:
+ """Resolve a revision (branch, tag, PR ref) to a commit hash.
+
+ This is meant for libraries that download and load several components of a repo separately (config,
+ weights, tokenizer, ...). Resolving the revision once and passing the returned [`ResolvedRevision`] around
+ guarantees that every subsequent call targets the exact same commit, even if the repo is updated in the
+ meantime. It also saves HTTP calls, as downloads made with a commit hash can be served from the local
+ cache without contacting the Hub.
+
+ The `revision` -> `commit hash` mapping is cached on disk (in the `refs/` folder of the cache), on a
+ best-effort basis. If the Hub cannot be reached later on (offline mode, connection error, timeout, Hub
+ downtime, ...), the cached value is used as a fallback.
+
+ > [!TIP]
+ > If you only need to download a full repo snapshot, a single [`snapshot_download`] call is enough and
+ > already does the right thing. `resolve_revision` is only useful when downloading files separately.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"`, `"space"` or `"kernel"` if the repo is a dataset, space or kernel repo,
+ `None` or `"model"` if it is a model. Default is `None`.
+ revision (`str`, *optional*):
+ The revision to resolve. Can be a branch name, a tag, a PR ref or a commit hash. Defaults to the
+ default branch. If a [`ResolvedRevision`] is passed, it is returned as is - unless it was resolved
+ against another repo, in which case the revision it initially requested is resolved again.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored. Defaults to the value of `HF_HUB_CACHE`.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, resolve the revision from the local cache only, without contacting the Hub.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`ResolvedRevision`]: A `str` subclass holding both the requested revision and the commit hash it resolves to.
+
+ Raises:
+ [`~errors.RevisionResolutionError`]
+ If the revision cannot be resolved: the Hub could not be reached and nothing is cached locally.
+ [`~errors.RevisionNotFoundError`]
+ If the revision does not exist on the Hub.
+ [`~errors.RepositoryNotFoundError`]
+ If the repository cannot be found. This may be because it doesn't exist, or because it is set to
+ `private` and you do not have access.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import hf_hub_download, resolve_revision
+ >>> revision = resolve_revision("openai-community/gpt2")
+ >>> revision
+ ResolvedRevision(initial=None, resolved='607a30d783dfa663caf39e06633721c8d4cfcd7e')
+
+ # Pass it around: every download is pinned to the same commit
+ >>> config = hf_hub_download("openai-community/gpt2", "config.json", revision=revision)
+ >>> weights = hf_hub_download("openai-community/gpt2", "model.safetensors", revision=revision)
+ ```
+ """
+
+ from .file_download import REGEX_COMMIT_HASH, _cache_commit_hash_for_specific_revision, repo_folder_name
+
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ if isinstance(revision, ResolvedRevision):
+ # A commit hash means nothing outside of the repo it was resolved for. `_repo_id=None` means the repo is
+ # unknown (instance built by hand), in which case it is assumed to fit any repo.
+ if revision._repo_id is None or (revision._repo_id, revision._repo_type) == (repo_id, repo_type):
+ return revision # already resolved for this repo => nothing to do
+ revision = revision.initial # resolved for another repo => resolve what was initially requested
+ if revision is not None and REGEX_COMMIT_HASH.fullmatch(revision):
+ return ResolvedRevision(resolved=revision, initial=revision, repo_id=repo_id, repo_type=repo_type)
+
+ if cache_dir is None:
+ cache_dir = constants.HF_HUB_CACHE
+ storage_folder = str(
+ Path(cache_dir).expanduser().resolve() / repo_folder_name(repo_id=repo_id, repo_type=repo_type)
+ )
+
+ error: Exception | None = None
+ if not local_files_only:
+ try:
+ sha = self.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token).sha
+ assert sha is not None, "Repo info returned from server must have a revision sha."
+ try: # best-effort caching, e.g. cache folder might be read-only
+ _cache_commit_hash_for_specific_revision(
+ storage_folder, revision or constants.DEFAULT_REVISION, sha
+ )
+ except OSError as e:
+ logger.warning(f"Ignored error while caching commit hash for '{repo_id}': {e}.")
+ return ResolvedRevision(resolved=sha, initial=revision, repo_id=repo_id, repo_type=repo_type)
+ except httpx2.ProxyError:
+ # Actually raise on proxy error: a misconfigured proxy is not an unreachable Hub
+ raise
+ except (httpx2.TransportError, OfflineModeIsEnabled) as e:
+ # Hub cannot be reached (offline mode, connection error, timeout, ...) => fallback on cache
+ error = e
+ except HfHubHTTPError as e:
+ if e.response.status_code < 500:
+ raise # the Hub answered: repo/revision not found, missing permissions, ... => raise as is
+ error = e # Hub is down => fallback on cache
+
+ ref_path = Path(storage_folder) / "refs" / (revision or constants.DEFAULT_REVISION)
+ if ref_path.is_file():
+ if error is not None:
+ logger.warning(f"Could not reach the Hub ({error}). Using cached commit hash for '{repo_id}'.")
+ return ResolvedRevision(
+ resolved=ref_path.read_text().strip(), initial=revision, repo_id=repo_id, repo_type=repo_type
+ )
+
+ reason = (
+ "'local_files_only=True' is set"
+ if error is None
+ else f"the Hub could not be reached ({error.__class__.__name__}: {error})"
+ )
+ raise RevisionResolutionError(
+ f"Cannot resolve revision '{revision or constants.DEFAULT_REVISION}' for {repo_type} '{repo_id}':"
+ f" {reason} and no matching entry was found in the local cache ('{ref_path}')."
+ ) from error
+
+ @validate_hf_hub_args
+ def repo_exists(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> bool:
+ """
+ Checks if a repository exists on the Hugging Face Hub.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if getting repository info from a dataset or a space,
+ `None` or `"model"` if getting repository info from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ True if the repository exists, False otherwise.
+
+ Examples:
+ ```py
+ >>> from huggingface_hub import repo_exists
+ >>> repo_exists("google/gemma-7b")
+ True
+ >>> repo_exists("google/not-a-repo")
+ False
+ ```
+ """
+ try:
+ self.repo_info(repo_id=repo_id, repo_type=repo_type, token=token)
+ return True
+ except GatedRepoError:
+ return True # we don't have access but it exists
+ except RepositoryNotFoundError:
+ return False
+
+ @validate_hf_hub_args
+ def revision_exists(
+ self,
+ repo_id: str,
+ revision: str,
+ *,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> bool:
+ """
+ Checks if a specific revision exists on a repo on the Hugging Face Hub.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`):
+ The revision of the repository to check.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if getting repository info from a dataset or a space,
+ `None` or `"model"` if getting repository info from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ True if the repository and the revision exists, False otherwise.
+
+ Examples:
+ ```py
+ >>> from huggingface_hub import revision_exists
+ >>> revision_exists("google/gemma-7b", "float16")
+ True
+ >>> revision_exists("google/gemma-7b", "not-a-revision")
+ False
+ ```
+ """
+ try:
+ self.repo_info(repo_id=repo_id, revision=revision, repo_type=repo_type, token=token)
+ return True
+ except RevisionNotFoundError:
+ return False
+ except RepositoryNotFoundError:
+ return False
+
+ @validate_hf_hub_args
+ def file_exists(
+ self,
+ repo_id: str,
+ filename: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ token: str | bool | None = None,
+ ) -> bool:
+ """
+ Checks if a file exists in a repository on the Hugging Face Hub.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ filename (`str`):
+ The name of the file to check, for example:
+ `"config.json"`
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if getting repository info from a dataset or a space,
+ `None` or `"model"` if getting repository info from a model. Default is `None`.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the information. Defaults to `"main"` branch.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ True if the file exists, False otherwise.
+
+ Examples:
+ ```py
+ >>> from huggingface_hub import file_exists
+ >>> file_exists("bigcode/starcoder", "config.json")
+ True
+ >>> file_exists("bigcode/starcoder", "not-a-file")
+ False
+ >>> file_exists("bigcode/not-a-repo", "config.json")
+ False
+ ```
+ """
+
+ from .file_download import get_hf_file_metadata, hf_hub_url
+
+ url = hf_hub_url(
+ repo_id=repo_id, repo_type=repo_type, revision=revision, filename=filename, endpoint=self.endpoint
+ )
+ try:
+ if token is None:
+ token = self.token
+ get_hf_file_metadata(url, token=token)
+ return True
+ except GatedRepoError: # raise specifically on gated repo
+ raise
+ except (RepositoryNotFoundError, RemoteEntryNotFoundError, RevisionNotFoundError):
+ return False
+
+ @validate_hf_hub_args
+ def list_repo_files(
+ self,
+ repo_id: str,
+ *,
+ revision: str | None = None,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> list[str]:
+ """
+ Get the list of files in a given repo.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the information.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or space, `None` or `"model"` if uploading to
+ a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `list[str]`: the list of files in a given repository.
+ """
+ return [
+ f.rfilename
+ for f in self.list_repo_tree(
+ repo_id=repo_id, recursive=True, revision=revision, repo_type=repo_type, token=token
+ )
+ if isinstance(f, RepoFile)
+ ]
+
+ @validate_hf_hub_args
+ def list_repo_tree(
+ self,
+ repo_id: str,
+ path_in_repo: str | None = None,
+ *,
+ recursive: bool = False,
+ expand: bool = False,
+ revision: str | None = None,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> Iterable[RepoFile | RepoFolder]:
+ """
+ List a repo tree's files and folders and get information about them.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ path_in_repo (`str`, *optional*):
+ Relative path of the tree (folder) in the repo, for example:
+ `"checkpoints/1fec34a/results"`. Will default to the root tree (folder) of the repository.
+ recursive (`bool`, *optional*, defaults to `False`):
+ Whether to list tree's files and folders recursively.
+ expand (`bool`, *optional*, defaults to `False`):
+ Whether to fetch more information about the tree's files and folders (e.g. last commit and files' security scan results). This
+ operation is more expensive for the server so only 50 results are returned per page (instead of 1000).
+ As pagination is implemented in `huggingface_hub`, this is transparent for you except for the time it
+ takes to get the results.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the tree. Defaults to `"main"` branch.
+ repo_type (`str`, *optional*):
+ The type of the repository from which to get the tree (`"model"`, `"dataset"`, `"space"` or `"kernel"`).
+ Defaults to `"model"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[Union[RepoFile, RepoFolder]]`:
+ The information about the tree's files and folders, as an iterable of [`RepoFile`] and [`RepoFolder`] objects. The order of the files and folders is
+ not guaranteed.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
+ does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If revision is not found (error 404) on the repo.
+ [`~utils.RemoteEntryNotFoundError`]:
+ If the tree (folder) does not exist (error 404) on the repo.
+
+ Examples:
+
+ Get information about a repo's tree.
+ ```py
+ >>> from huggingface_hub import list_repo_tree
+ >>> repo_tree = list_repo_tree("lysandre/arxiv-nlp")
+ >>> repo_tree
+
+ >>> list(repo_tree)
+ [
+ RepoFile(path='.gitattributes', size=391, blob_id='ae8c63daedbd4206d7d40126955d4e6ab1c80f8f', lfs=None, last_commit=None, security=None),
+ RepoFile(path='README.md', size=391, blob_id='43bd404b159de6fba7c2f4d3264347668d43af25', lfs=None, last_commit=None, security=None),
+ RepoFile(path='config.json', size=554, blob_id='2f9618c3a19b9a61add74f70bfb121335aeef666', lfs=None, last_commit=None, security=None),
+ RepoFile(
+ path='flax_model.msgpack', size=497764107, blob_id='8095a62ccb4d806da7666fcda07467e2d150218e',
+ lfs={'size': 497764107, 'sha256': 'd88b0d6a6ff9c3f8151f9d3228f57092aaea997f09af009eefd7373a77b5abb9', 'pointer_size': 134}, last_commit=None, security=None
+ ),
+ RepoFile(path='merges.txt', size=456318, blob_id='226b0752cac7789c48f0cb3ec53eda48b7be36cc', lfs=None, last_commit=None, security=None),
+ RepoFile(
+ path='pytorch_model.bin', size=548123560, blob_id='64eaa9c526867e404b68f2c5d66fd78e27026523',
+ lfs={'size': 548123560, 'sha256': '9be78edb5b928eba33aa88f431551348f7466ba9f5ef3daf1d552398722a5436', 'pointer_size': 134}, last_commit=None, security=None
+ ),
+ RepoFile(path='vocab.json', size=898669, blob_id='b00361fece0387ca34b4b8b8539ed830d644dbeb', lfs=None, last_commit=None, security=None)]
+ ]
+ ```
+
+ Get even more information about a repo's tree (last commit and files' security scan results)
+ ```py
+ >>> from huggingface_hub import list_repo_tree
+ >>> repo_tree = list_repo_tree("prompthero/openjourney-v4", expand=True)
+ >>> list(repo_tree)
+ [
+ RepoFolder(
+ path='feature_extractor',
+ tree_id='aa536c4ea18073388b5b0bc791057a7296a00398',
+ last_commit={
+ 'oid': '47b62b20b20e06b9de610e840282b7e6c3d51190',
+ 'title': 'Upload diffusers weights (#48)',
+ 'date': datetime.datetime(2023, 3, 21, 9, 5, 27, tzinfo=datetime.timezone.utc)
+ }
+ ),
+ RepoFolder(
+ path='safety_checker',
+ tree_id='65aef9d787e5557373fdf714d6c34d4fcdd70440',
+ last_commit={
+ 'oid': '47b62b20b20e06b9de610e840282b7e6c3d51190',
+ 'title': 'Upload diffusers weights (#48)',
+ 'date': datetime.datetime(2023, 3, 21, 9, 5, 27, tzinfo=datetime.timezone.utc)
+ }
+ ),
+ RepoFile(
+ path='model_index.json',
+ size=582,
+ blob_id='d3d7c1e8c3e78eeb1640b8e2041ee256e24c9ee1',
+ lfs=None,
+ last_commit={
+ 'oid': 'b195ed2d503f3eb29637050a886d77bd81d35f0e',
+ 'title': 'Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#54)',
+ 'date': datetime.datetime(2023, 5, 15, 21, 41, 59, tzinfo=datetime.timezone.utc)
+ },
+ security={
+ 'safe': True,
+ 'av_scan': {'virusFound': False, 'virusNames': None},
+ 'pickle_import_scan': None
+ }
+ )
+ ...
+ ]
+ ```
+ """
+ repo_type = repo_type or constants.REPO_TYPE_MODEL
+ revision = quote(revision, safe="") if revision is not None else constants.DEFAULT_REVISION
+ headers = self._build_hf_headers(token=token)
+
+ encoded_path_in_repo = "/" + quote(path_in_repo, safe="") if path_in_repo else ""
+ tree_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tree/{revision}{encoded_path_in_repo}"
+ for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
+ yield (RepoFile(**path_info) if path_info["type"] == "file" else RepoFolder(**path_info))
+
+ @validate_hf_hub_args
+ def verify_repo_checksums(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ local_dir: str | Path | None = None,
+ cache_dir: str | Path | None = None,
+ token: str | bool | None = None,
+ ) -> FolderVerification:
+ """
+ Verify local files for a repo against Hub checksums.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ repo_type (`str`, *optional*):
+ The type of the repository from which to get the tree (`"model"`, `"dataset"` or `"space"`.
+ Defaults to `"model"`.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the tree. Defaults to `"main"` branch.
+ local_dir (`str` or `Path`, *optional*):
+ The local directory to verify.
+ cache_dir (`str` or `Path`, *optional*):
+ The cache directory to verify.
+ token (Union[bool, str, None], optional):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`FolderVerification`]: a structured result containing the verification details.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
+ does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If revision is not found (error 404) on the repo.
+
+ """
+
+ from .utils._verification import collect_local_files, resolve_local_root, verify_maps
+
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ if local_dir is not None and cache_dir is not None:
+ raise ValueError("Pass either `local_dir` or `cache_dir`, not both.")
+
+ root, remote_revision = resolve_local_root(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ revision=revision,
+ cache_dir=Path(cache_dir) if cache_dir is not None else None,
+ local_dir=Path(local_dir) if local_dir is not None else None,
+ )
+ local_by_path = collect_local_files(root)
+
+ # get remote entries (only files, not folders)
+ remote_by_path: dict[str, RepoFile] = {}
+ for entry in self.list_repo_tree(
+ repo_id=repo_id, recursive=True, revision=remote_revision, repo_type=repo_type, token=token
+ ):
+ if isinstance(entry, RepoFile):
+ remote_by_path[entry.path] = entry
+
+ return verify_maps(
+ remote_by_path=remote_by_path,
+ local_by_path=local_by_path,
+ revision=remote_revision,
+ verified_path=root,
+ )
+
+ @validate_hf_hub_args
+ def list_repo_refs(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ include_pull_requests: bool = False,
+ token: str | bool | None = None,
+ ) -> GitRefs:
+ """
+ Get the list of refs of a given repo (both tags and branches).
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"`, `"space"` or `"kernel"` if listing refs from a dataset, a Space or a Kernel,
+ `None` or `"model"` if listing from a model. Default is `None`.
+ include_pull_requests (`bool`, *optional*):
+ Whether to include refs from pull requests in the list. Defaults to `False`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> api.list_repo_refs("gpt2")
+ GitRefs(branches=[GitRefInfo(name='main', ref='refs/heads/main', target_commit='e7da7f221d5bf496a48136c0cd264e630fe9fcc8')], converts=[], tags=[])
+
+ >>> api.list_repo_refs("bigcode/the-stack", repo_type='dataset')
+ GitRefs(
+ branches=[
+ GitRefInfo(name='main', ref='refs/heads/main', target_commit='18edc1591d9ce72aa82f56c4431b3c969b210ae3'),
+ GitRefInfo(name='v1.1.a1', ref='refs/heads/v1.1.a1', target_commit='f9826b862d1567f3822d3d25649b0d6d22ace714')
+ ],
+ converts=[],
+ tags=[
+ GitRefInfo(name='v1.0', ref='refs/tags/v1.0', target_commit='c37a8cd1e382064d8aced5e05543c5f7753834da')
+ ]
+ )
+ ```
+
+ Returns:
+ [`GitRefs`]: object containing all information about branches and tags for a
+ repo on the Hub.
+ """
+ repo_type = repo_type or constants.REPO_TYPE_MODEL
+ response = get_session().get(
+ f"{self.endpoint}/api/{repo_type}s/{repo_id}/refs",
+ headers=self._build_hf_headers(token=token),
+ params={"include_prs": 1} if include_pull_requests else {},
+ )
+ hf_raise_for_status(response)
+ data = response.json()
+
+ def _format_as_git_ref_info(item: dict) -> GitRefInfo:
+ return GitRefInfo(name=item["name"], ref=item["ref"], target_commit=item["targetCommit"])
+
+ return GitRefs(
+ branches=[_format_as_git_ref_info(item) for item in data["branches"]],
+ converts=[_format_as_git_ref_info(item) for item in data["converts"]],
+ tags=[_format_as_git_ref_info(item) for item in data["tags"]],
+ pull_requests=[_format_as_git_ref_info(item) for item in data["pullRequests"]]
+ if include_pull_requests
+ else None,
+ )
+
+ @validate_hf_hub_args
+ def list_repo_commits(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ revision: str | None = None,
+ formatted: bool = False,
+ ) -> list[GitCommitInfo]:
+ """
+ Get the list of commits of a given revision for a repo on the Hub.
+
+ Commits are sorted by date (last commit first).
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if listing commits from a dataset or a Space, `None` or `"model"` if
+ listing from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ formatted (`bool`):
+ Whether to return the HTML-formatted title and description of the commits. Defaults to False.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+
+ # Commits are sorted by date (last commit first)
+ >>> initial_commit = api.list_repo_commits("gpt2")[-1]
+
+ # Initial commit is always a system commit containing the `.gitattributes` file.
+ >>> initial_commit
+ GitCommitInfo(
+ commit_id='9b865efde13a30c13e0a33e536cf3e4a5a9d71d8',
+ authors=['system'],
+ created_at=datetime.datetime(2019, 2, 18, 10, 36, 15, tzinfo=datetime.timezone.utc),
+ title='initial commit',
+ message='',
+ formatted_title=None,
+ formatted_message=None
+ )
+
+ # Create an empty branch by deriving from initial commit
+ >>> api.create_branch("gpt2", "new_empty_branch", revision=initial_commit.commit_id)
+ ```
+
+ Returns:
+ list[[`GitCommitInfo`]]: list of objects containing information about the commits for a repo on the Hub.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
+ does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If revision is not found (error 404) on the repo.
+ """
+ repo_type = repo_type or constants.REPO_TYPE_MODEL
+ revision = quote(revision, safe="") if revision is not None else constants.DEFAULT_REVISION
+
+ # Paginate over results and return the list of commits.
+ return [
+ GitCommitInfo(
+ commit_id=item["id"],
+ authors=[author["user"] for author in item["authors"]],
+ created_at=parse_datetime(item["date"]),
+ title=item["title"],
+ message=item["message"],
+ formatted_title=item.get("formatted", {}).get("title"),
+ formatted_message=item.get("formatted", {}).get("message"),
+ )
+ for item in paginate(
+ f"{self.endpoint}/api/{repo_type}s/{repo_id}/commits/{revision}",
+ headers=self._build_hf_headers(token=token),
+ params={"expand[]": "formatted"} if formatted else {},
+ )
+ ]
+
+ @validate_hf_hub_args
+ def get_paths_info(
+ self,
+ repo_id: str,
+ paths: list[str] | str,
+ *,
+ expand: bool = False,
+ revision: str | None = None,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> list[RepoFile | RepoFolder]:
+ """
+ Get information about a repo's paths.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ paths (`Union[list[str], str]`, *optional*):
+ The paths to get information about. If a path do not exist, it is ignored without raising
+ an exception.
+ expand (`bool`, *optional*, defaults to `False`):
+ Whether to fetch more information about the paths (e.g. last commit and files' security scan results). This
+ operation is more expensive for the server so only 50 results are returned per page (instead of 1000).
+ As pagination is implemented in `huggingface_hub`, this is transparent for you except for the time it
+ takes to get the results.
+ revision (`str`, *optional*):
+ The revision of the repository from which to get the information. Defaults to `"main"` branch.
+ repo_type (`str`, *optional*):
+ The type of the repository from which to get the information (`"model"`, `"dataset"` or `"space"`.
+ Defaults to `"model"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `list[Union[RepoFile, RepoFolder]]`:
+ The information about the paths, as a list of [`RepoFile`] and [`RepoFolder`] objects.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
+ does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If revision is not found (error 404) on the repo.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import get_paths_info
+ >>> paths_info = get_paths_info("allenai/c4", ["README.md", "en"], repo_type="dataset")
+ >>> paths_info
+ [
+ RepoFile(path='README.md', size=2379, blob_id='f84cb4c97182890fc1dbdeaf1a6a468fd27b4fff', lfs=None, last_commit=None, security=None),
+ RepoFolder(path='en', tree_id='dc943c4c40f53d02b31ced1defa7e5f438d5862e', last_commit=None)
+ ]
+ ```
+ """
+ repo_type = repo_type or constants.REPO_TYPE_MODEL
+ revision = quote(revision, safe="") if revision is not None else constants.DEFAULT_REVISION
+ headers = self._build_hf_headers(token=token)
+
+ response = get_session().post(
+ f"{self.endpoint}/api/{repo_type}s/{repo_id}/paths-info/{revision}",
+ data={
+ "paths": paths if isinstance(paths, list) else [paths],
+ "expand": expand,
+ },
+ headers=headers,
+ )
+ hf_raise_for_status(response)
+ paths_info = response.json()
+ return [
+ RepoFile(**path_info) if path_info["type"] == "file" else RepoFolder(**path_info)
+ for path_info in paths_info
+ ]
+
+ @validate_hf_hub_args
+ def super_squash_history(
+ self,
+ repo_id: str,
+ *,
+ branch: str | None = None,
+ commit_message: str | None = None,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ) -> None:
+ """Squash commit history on a branch for a repo on the Hub.
+
+ Squashing the repo history is useful when you know you'll make hundreds of commits and you don't want to
+ clutter the history. Squashing commits can only be performed from the head of a branch.
+
+ > [!WARNING]
+ > Once squashed, the commit history cannot be retrieved. This is a non-revertible operation.
+
+ > [!WARNING]
+ > Once the history of a branch has been squashed, it is not possible to merge it back into another branch since
+ > their history will have diverged.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ branch (`str`, *optional*):
+ The branch to squash. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The commit message to use for the squashed commit.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if listing commits from a dataset or a Space, `None` or `"model"` if
+ listing from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private but not authenticated or repo
+ does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If the branch to squash cannot be found.
+ [`~utils.BadRequestError`]:
+ If invalid reference for a branch. You cannot squash history on tags.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+
+ # Create repo
+ >>> repo_id = api.create_repo("test-squash").repo_id
+
+ # Make a lot of commits.
+ >>> api.upload_file(repo_id=repo_id, path_in_repo="file.txt", path_or_fileobj=b"content")
+ >>> api.upload_file(repo_id=repo_id, path_in_repo="lfs.bin", path_or_fileobj=b"content")
+ >>> api.upload_file(repo_id=repo_id, path_in_repo="file.txt", path_or_fileobj=b"another_content")
+
+ # Squash history
+ >>> api.super_squash_history(repo_id=repo_id)
+ ```
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError("Invalid repo type")
+ if branch is None:
+ branch = constants.DEFAULT_REVISION
+
+ # Prepare request
+ url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/super-squash/{quote(branch, safe='')}"
+ headers = self._build_hf_headers(token=token)
+ commit_message = commit_message or f"Super-squash branch '{branch}' using huggingface_hub"
+
+ # Super-squash
+ response = get_session().post(url=url, headers=headers, json={"message": commit_message})
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def list_lfs_files(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[LFSFileInfo]:
+ """
+ List all LFS files in a repo on the Hub.
+
+ This is primarily useful to count how much storage a repo is using and to eventually clean up large files
+ with [`permanently_delete_lfs_files`]. Note that this would be a permanent action that will affect all commits
+ referencing this deleted files and that cannot be undone.
+
+ Args:
+ repo_id (`str`):
+ The repository for which you are listing LFS files.
+ repo_type (`str`, *optional*):
+ Type of repository. Set to `"dataset"` or `"space"` if listing from a dataset or space, `None` or
+ `"model"` if listing from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[LFSFileInfo]`: An iterator of [`LFSFileInfo`] objects.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> lfs_files = api.list_lfs_files("username/my-cool-repo")
+
+ # Filter files to delete based on a combination of `filename`, `pushed_at`, `ref` or `size`.
+ # e.g. select only LFS files in the "checkpoints" folder
+ >>> lfs_files_to_delete = (lfs_file for lfs_file in lfs_files if lfs_file.filename.startswith("checkpoints/"))
+
+ # Permanently delete LFS files
+ >>> api.permanently_delete_lfs_files("username/my-cool-repo", lfs_files_to_delete)
+ ```
+ """
+ # Prepare request
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/lfs-files"
+ headers = self._build_hf_headers(token=token)
+
+ # Paginate over LFS items
+ for item in paginate(url, params={}, headers=headers):
+ yield LFSFileInfo(**item)
+
+ @validate_hf_hub_args
+ def permanently_delete_lfs_files(
+ self,
+ repo_id: str,
+ lfs_files: Iterable[LFSFileInfo],
+ *,
+ rewrite_history: bool = True,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Permanently delete LFS files from a repo on the Hub.
+
+ > [!WARNING]
+ > This is a permanent action that will affect all commits referencing the deleted files and might corrupt your
+ > repository. This is a non-revertible operation. Use it only if you know what you are doing.
+
+ Args:
+ repo_id (`str`):
+ The repository for which you are listing LFS files.
+ lfs_files (`Iterable[LFSFileInfo]`):
+ An iterable of [`LFSFileInfo`] items to permanently delete from the repo. Use [`list_lfs_files`] to list
+ all LFS files from a repo.
+ rewrite_history (`bool`, *optional*, default to `True`):
+ Whether to rewrite repository history to remove file pointers referencing the deleted LFS files (recommended).
+ repo_type (`str`, *optional*):
+ Type of repository. Set to `"dataset"` or `"space"` if listing from a dataset or space, `None` or
+ `"model"` if listing from a model. Default is `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> lfs_files = api.list_lfs_files("username/my-cool-repo")
+
+ # Filter files to delete based on a combination of `filename`, `pushed_at`, `ref` or `size`.
+ # e.g. select only LFS files in the "checkpoints" folder
+ >>> lfs_files_to_delete = (lfs_file for lfs_file in lfs_files if lfs_file.filename.startswith("checkpoints/"))
+
+ # Permanently delete LFS files
+ >>> api.permanently_delete_lfs_files("username/my-cool-repo", lfs_files_to_delete)
+ ```
+ """
+ # Prepare request
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/lfs-files/batch"
+ headers = self._build_hf_headers(token=token)
+
+ # Delete LFS items by batches of 1000
+ for batch in chunk_iterable(lfs_files, 1000):
+ shas = [item.file_oid for item in batch]
+ if len(shas) == 0:
+ return
+ payload = {
+ "deletions": {
+ "sha": shas,
+ "rewriteHistory": rewrite_history,
+ }
+ }
+ response = get_session().post(url, headers=headers, json=payload)
+ hf_raise_for_status(response)
+
+ def list_space_templates(self, *, token: str | bool | None = None) -> list[SpaceTemplate]:
+ """List the official Space templates available on the Hub.
+
+ The `repo_id` of a returned template (or its short `name`) can be passed as `space_template`
+ to [`HfApi.create_repo`] to seed a new Space from that template.
+
+ Args:
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `list[SpaceTemplate]`: The list of available Space templates.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import list_space_templates
+ >>> templates = list_space_templates()
+ >>> templates[0]
+ SpaceTemplate(name='Streamlit', repo_id='streamlit/streamlit-template-space', sdk='docker', preferred_private=False)
+ ```
+ """
+ r = get_session().get(f"{self.endpoint}/api/spaces/templates", headers=self._build_hf_headers(token=token))
+ hf_raise_for_status(r)
+ return [SpaceTemplate(item) for item in r.json()["templates"]]
+
+ @validate_hf_hub_args
+ def create_repo(
+ self,
+ repo_id: str,
+ *,
+ token: str | bool | None = None,
+ private: bool | None = None,
+ visibility: RepoVisibility_T | None = None,
+ repo_type: str | None = None,
+ exist_ok: bool = False,
+ resource_group_id: str | None = None,
+ region: REPO_REGIONS | None = None,
+ space_sdk: str | None = None,
+ space_hardware: SpaceHardware | None = None,
+ space_sleep_time: int | None = None,
+ space_secrets: list[dict[str, str]] | None = None,
+ space_variables: list[dict[str, str]] | None = None,
+ space_volumes: list[Volume] | None = None,
+ space_template: str | None = None,
+ ) -> RepoUrl:
+ """Create an empty repo on the HuggingFace Hub.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ private (`bool`, *optional*):
+ Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists. Cannot be passed together with `visibility`.
+ visibility (`Literal["public", "private", "protected"]`, *optional*):
+ Visibility of the repo. Can be `"public"` or `"private"`, or `"protected"` for Spaces. If `None`
+ (default), the repo will be public unless the organization's default is private. This value is ignored
+ if the repo already exists.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ exist_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if repo already exists.
+ resource_group_id (`str`, *optional*):
+ Resource group in which to create the repo. Resource groups is only available for Enterprise Hub organizations and
+ allow to define which members of the organization can access the resource. The ID of a resource group
+ can be found in the URL of the resource's page on the Hub (e.g. `"66670e5163145ca562cb1988"`).
+ To learn more about resource groups, see https://huggingface.co/docs/hub/en/security-resource-groups.
+ region (`Literal["us", "eu"]`, *optional*):
+ Cloud region in which to create the repo. Can be one of `"us"` or `"eu"`. If not specified, the repo will be
+ created in the default region. Requires Team plan or above.
+ space_sdk (`str`, *optional*):
+ Choice of SDK to use if repo_type is "space". Can be "streamlit", "gradio", "docker", or "static".
+ `"static"` Spaces are free for everyone. Other SDKs run on compute: on the free `cpu-basic` hardware,
+ they require a subscription (PRO for users, Team or Enterprise for organizations), while paid hardware
+ only requires billing (a payment method and prepaid credits).
+ space_hardware (`SpaceHardware` or `str`, *optional*):
+ Choice of Hardware if repo_type is "space". See [`SpaceHardware`] for a complete list.
+ space_sleep_time (`int`, *optional*):
+ Number of seconds of inactivity to wait before a Space is put to sleep. Set to `-1` if you don't want
+ your Space to sleep (default behavior for upgraded hardware). For free hardware, you can't configure
+ the sleep time (value is fixed to 48 hours of inactivity).
+ See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
+ space_secrets (`list[dict[str, str]]`, *optional*):
+ A list of secret keys to set in your Space. Each item is in the form `{"key": ..., "value": ..., "description": ...}` where description is optional.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
+ space_variables (`list[dict[str, str]]`, *optional*):
+ A list of public environment variables to set in your Space. Each item is in the form `{"key": ..., "value": ..., "description": ...}` where description is optional.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables.
+ space_volumes (`list[Volume]`, *optional*):
+ A list of [`Volume`] objects to mount in the Space at creation time. Each volume has a `type`
+ (`"bucket"`, `"model"`, `"dataset"`, or `"space"`), a `source` (repo or bucket ID), a `mount_path`
+ (path inside the container), and optional `revision`, `read_only`, and `path` fields.
+ Only applicable if repo_type is "space".
+ space_template (`str`, *optional*):
+ Seed the new Space from an official template. Can be either the template repo id
+ (e.g. `"SpacesExamples/jupyterlab"`) or its short name (e.g. `"JupyterLab"`). Use
+ [`HfApi.list_space_templates`] to list available templates. Only applicable if repo_type is
+ "space". If the template is recommended to be private and visibility is not explicitly set, the Space
+ is created as private.
+
+ Returns:
+ [`RepoUrl`]: URL to the newly created repo. Value is a subclass of `str` containing
+ attributes like `endpoint`, `repo_type` and `repo_id`.
+ """
+ organization, name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
+
+ path = f"{self.endpoint}/api/repos/create"
+
+ if repo_type not in constants.REPO_TYPES_WITH_KERNEL:
+ raise ValueError("Invalid repo type")
+
+ resolved_visibility = _resolve_repo_visibility(private=private, visibility=visibility, repo_type=repo_type)
+
+ resolved_space_template: str | None = None
+ if space_template is not None:
+ if repo_type != constants.REPO_TYPE_SPACE:
+ raise ValueError(f"space_template can only be used with repo_type 'space'. Got repo_type={repo_type}.")
+
+ # space_template passed => resolve it
+ all_templates = self.list_space_templates(token=token)
+ for candidate in all_templates:
+ if candidate.repo_id == space_template:
+ template = candidate
+ break
+ if candidate.name.lower() == space_template.lower():
+ template = candidate
+ break
+ else:
+ raise ValueError(
+ f"Unknown Space template '{space_template}'. Expected one of {', '.join(sorted(t.name for t in all_templates))}. Use `HfApi.list_space_templates()` to list templates."
+ )
+
+ resolved_space_template = template.repo_id
+ # If the chosen template is recommended to be private and the user did not explicitly set a
+ # visibility, default to private (mirrors the recommendation shown in the web UI).
+ if template.preferred_private and private is None and visibility is None:
+ resolved_visibility = "private"
+ # space_sdk can be omitted by the user, we set it following server info
+ if space_sdk is not None and space_sdk != template.sdk:
+ raise ValueError(
+ f"space_sdk must match the SDK of the chosen space_template. Got {space_sdk}, expected {template.sdk}."
+ )
+ space_sdk = template.sdk
+
+ payload: dict[str, Any] = {"name": name, "organization": organization}
+ if resolved_visibility is not None:
+ payload["visibility"] = resolved_visibility
+ if repo_type is not None:
+ payload["type"] = repo_type
+ if repo_type == "space":
+ if space_sdk is None:
+ raise ValueError(
+ "No space_sdk provided. `create_repo` expects space_sdk to be one"
+ f" of {constants.SPACES_SDK_TYPES} when repo_type is 'space'`"
+ )
+ if space_sdk not in constants.SPACES_SDK_TYPES:
+ raise ValueError(f"Invalid space_sdk. Please choose one of {constants.SPACES_SDK_TYPES}.")
+ payload["sdk"] = space_sdk
+ if resolved_space_template is not None:
+ payload["template"] = resolved_space_template
+
+ if space_sdk is not None and repo_type != "space":
+ warnings.warn("Ignoring provided space_sdk because repo_type is not 'space'.")
+
+ space_args: list[tuple[str, str, Any]] = [
+ # input arg, payload key, value
+ ("space_hardware", "hardware", space_hardware),
+ ("space_sleep_time", "sleepTimeSeconds", space_sleep_time),
+ ("space_secrets", "secrets", space_secrets),
+ ("space_variables", "variables", space_variables),
+ ("space_volumes", "volumes", [v.to_dict() for v in space_volumes] if space_volumes else None),
+ ]
+
+ if repo_type == constants.REPO_TYPE_SPACE:
+ for _, key, value in space_args:
+ if value is not None:
+ payload[key] = value
+ if space_sleep_time is not None and space_hardware == SpaceHardware.CPU_BASIC:
+ warnings.warn(
+ "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
+ " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
+ " you want to set a custom sleep time, you need to upgrade to a paid Hardware.",
+ UserWarning,
+ )
+ else:
+ if provided_space_args := [arg for arg, _, value in space_args if value is not None]:
+ warnings.warn(f"Ignoring provided {', '.join(provided_space_args)} because repo_type is not 'space'.")
+
+ if resource_group_id is not None:
+ payload["resourceGroupId"] = resource_group_id
+ if region is not None:
+ payload["region"] = region
+
+ headers = self._build_hf_headers(token=token)
+ while True:
+ r = get_session().post(path, headers=headers, json=payload)
+ if r.status_code == 409 and "Cannot create repo: another conflicting operation is in progress" in r.text:
+ # Since https://github.com/huggingface/moon-landing/pull/7272 (private repo), it is not possible to
+ # concurrently create repos on the Hub for a same user. This is rarely an issue, except when running
+ # tests. To avoid any inconvenience, we retry to create the repo for this specific error.
+ # NOTE: This could have been fixed directly in the tests, but adding it here should fix CIs for all
+ # dependent libraries.
+ # NOTE: If a fix is implemented server-side, we should be able to remove this retry mechanism.
+ logger.debug("Create repo failed due to a concurrency issue. Retrying...")
+ continue
+ break
+
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if exist_ok and err.response.status_code == 409:
+ # Repo already exists and `exist_ok=True`
+ pass
+ elif exist_ok and err.response.status_code in (401, 402, 403):
+ # 401 -> if JWT token without create repo scope
+ # 402 -> if payment required (e.g. if Gradio/Docker Space and free user)
+ # 403 -> if no write permission on the namespace
+ # In all 3 cases, repo might already exist
+ try:
+ self.repo_info(repo_id=repo_id, repo_type=repo_type, token=token)
+ if repo_type is None or repo_type == constants.REPO_TYPE_MODEL:
+ return RepoUrl(f"{self.endpoint}/{repo_id}")
+ return RepoUrl(f"{self.endpoint}/{constants.REPO_TYPES_URL_PREFIXES[repo_type]}{repo_id}")
+ except HfHubHTTPError:
+ raise err
+ else:
+ raise
+
+ d = r.json()
+ return RepoUrl(d["url"], endpoint=self.endpoint)
+
+ @validate_hf_hub_args
+ def delete_repo(
+ self,
+ repo_id: str,
+ *,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ missing_ok: bool = False,
+ ) -> None:
+ """
+ Delete a repo from the HuggingFace Hub. CAUTION: this is irreversible.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model.
+ missing_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if repo does not exist.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]
+ If the repository to delete from cannot be found and `missing_ok` is set to False (default).
+ """
+ organization, name = repo_id.split("/") if "/" in repo_id else (None, repo_id)
+
+ path = f"{self.endpoint}/api/repos/delete"
+
+ if repo_type not in constants.REPO_TYPES_WITH_KERNEL:
+ raise ValueError("Invalid repo type")
+
+ json = {"name": name, "organization": organization}
+ if repo_type is not None:
+ json["type"] = repo_type
+
+ headers = self._build_hf_headers(token=token)
+ r = get_session().request("DELETE", path, headers=headers, json=json)
+ try:
+ hf_raise_for_status(r)
+ except RepositoryNotFoundError:
+ if not missing_ok:
+ raise
+
+ @validate_hf_hub_args
+ def update_repo_settings(
+ self,
+ repo_id: str,
+ *,
+ gated: Literal["auto", "manual", False] | None = None,
+ private: bool | None = None,
+ visibility: RepoVisibility_T | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ ) -> None:
+ """
+ Update the settings of a repository, including gated access and visibility.
+
+ To give more control over how repos are used, the Hub allows repo authors to enable
+ access requests for their repos, and also to change the visibility of the repo.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a /.
+ gated (`Literal["auto", "manual", False]`, *optional*):
+ The gated status for the repository. If set to `None` (default), the `gated` setting of the repository won't be updated.
+ * "auto": The repository is gated, and access requests are automatically approved or denied based on predefined criteria.
+ * "manual": The repository is gated, and access requests require manual approval.
+ * False : The repository is not gated, and anyone can access it.
+ private (`bool`, *optional*):
+ Whether the repository should be private. Cannot be passed together with `visibility`.
+ visibility (`Literal["public", "private", "protected"]`, *optional*):
+ Visibility of the repository. Can be `"public"` or `"private"`, or `"protected"` for Spaces.
+ token (`Union[str, bool, None]`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token,
+ which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass False.
+ repo_type (`str`, *optional*):
+ The type of the repository to update settings from (`"model"`, `"dataset"` or `"space"`).
+ Defaults to `"model"`.
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If gated is not one of "auto", "manual", or False.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If repo_type is not one of the values in constants.REPO_TYPES.
+ [`~utils.HfHubHTTPError`]:
+ If the request to the Hugging Face Hub API fails.
+ [`~utils.RepositoryNotFoundError`]
+ If the repository to download from cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+ """
+
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL # default repo type
+
+ resolved_visibility = _resolve_repo_visibility(private=private, visibility=visibility, repo_type=repo_type)
+
+ # Prepare the JSON payload for the PUT request
+ payload: dict = {}
+
+ if gated is not None:
+ if gated not in ["auto", "manual", False]:
+ raise ValueError(f"Invalid gated status, must be one of 'auto', 'manual', or False. Got '{gated}'.")
+ payload["gated"] = gated
+
+ if resolved_visibility is not None:
+ payload["visibility"] = resolved_visibility
+
+ if len(payload) == 0:
+ raise ValueError("At least one setting must be updated.")
+
+ # Build headers
+ headers = self._build_hf_headers(token=token)
+
+ r = get_session().put(
+ url=f"{self.endpoint}/api/{repo_type}s/{repo_id}/settings",
+ headers=headers,
+ json=payload,
+ )
+ hf_raise_for_status(r)
+
+ def move_repo(
+ self,
+ from_id: str,
+ to_id: str,
+ *,
+ repo_type: str | None = None,
+ token: str | bool | None = None,
+ ):
+ """
+ Moving a repository from namespace1/repo_name1 to namespace2/repo_name2
+
+ Note there are certain limitations. For more information about moving
+ repositories, please see
+ https://hf.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo.
+
+ Args:
+ from_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`. Original repository identifier.
+ to_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`. Final repository identifier.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ if len(from_id.split("/")) != 2:
+ raise ValueError(f"Invalid repo_id: {from_id}. It should have a namespace (:namespace:/:repo_name:)")
+
+ if len(to_id.split("/")) != 2:
+ raise ValueError(f"Invalid repo_id: {to_id}. It should have a namespace (:namespace:/:repo_name:)")
+
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL # Hub won't accept `None`.
+
+ json = {"fromRepo": from_id, "toRepo": to_id, "type": repo_type}
+
+ path = f"{self.endpoint}/api/repos/move"
+ headers = self._build_hf_headers(token=token)
+ r = get_session().post(path, headers=headers, json=json)
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as e:
+ e.append_to_message(
+ "\nFor additional documentation please see"
+ " https://hf.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo."
+ )
+ raise
+
+ @overload
+ def create_commit( # type: ignore
+ self,
+ repo_id: str,
+ operations: Iterable[CommitOperation],
+ *,
+ commit_message: str,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ num_threads: int = 5,
+ parent_commit: str | None = None,
+ run_as_future: Literal[False] = ...,
+ _hot_reload: bool | None = None,
+ ) -> CommitInfo: ...
+
+ @overload
+ def create_commit(
+ self,
+ repo_id: str,
+ operations: Iterable[CommitOperation],
+ *,
+ commit_message: str,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ num_threads: int = 5,
+ parent_commit: str | None = None,
+ run_as_future: Literal[True] = ...,
+ _hot_reload: bool | None = None,
+ ) -> Future[CommitInfo]: ...
+
+ @validate_hf_hub_args
+ @future_compatible
+ def create_commit(
+ self,
+ repo_id: str,
+ operations: Iterable[CommitOperation],
+ *,
+ commit_message: str,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ num_threads: int = 5,
+ parent_commit: str | None = None,
+ run_as_future: bool = False,
+ _hot_reload: bool | None = None,
+ ) -> CommitInfo | Future[CommitInfo]:
+ """
+ Creates a commit in the given repo, deleting & uploading files as needed.
+
+ > [!WARNING]
+ > The input list of `CommitOperation` will be mutated during the commit process. Do not reuse the same objects
+ > for multiple commits.
+
+ > [!WARNING]
+ > `create_commit` assumes that the repo already exists on the Hub. If you get a
+ > Client error 404, please make sure you are authenticated, that your token has the required permissions,
+ > and that `repo_id` and `repo_type` are set correctly. If repo does not exist,
+ > create it first using [`~hf_api.create_repo`].
+
+ > [!WARNING]
+ > `create_commit` is limited to 25k LFS files and a 1GB payload for regular files.
+
+ Args:
+ repo_id (`str`):
+ The repository in which the commit will be created, for example:
+ `"username/custom_transformers"`
+
+ operations (`Iterable` of [`~hf_api.CommitOperation`]):
+ An iterable of operations to include in the commit, either:
+
+ - [`~hf_api.CommitOperationAdd`] to upload a file
+ - [`~hf_api.CommitOperationDelete`] to delete a file
+ - [`~hf_api.CommitOperationCopy`] to copy a file
+
+ Operation objects will be mutated to include information relative to the upload. Do not reuse the
+ same objects for multiple commits.
+
+ commit_message (`str`):
+ The summary (first line) of the commit that will be created.
+
+ commit_description (`str`, *optional*):
+ The description of the commit that will be created
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`.
+ If `revision` is not set, PR is opened against the `"main"` branch. If
+ `revision` is set and is a branch, PR is opened against this branch. If
+ `revision` is set and is not a branch name (example: a commit oid), an
+ `RevisionNotFoundError` is returned by the server.
+
+ num_threads (`int`, *optional*):
+ Number of concurrent threads for uploading files. Defaults to 5.
+ Setting it to 2 means at most 2 files will be uploaded concurrently.
+
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string.
+ Shorthands (7 first characters) are also supported. If specified and `create_pr` is `False`,
+ the commit will fail if `revision` does not point to `parent_commit`. If specified and `create_pr`
+ is `True`, the pull request will be created from `parent_commit`. Specifying `parent_commit`
+ ensures the repo has not changed before committing the changes, and can be especially useful
+ if the repo is updated / committed to concurrently.
+ run_as_future (`bool`, *optional*):
+ Whether or not to run this method in the background. Background jobs are run sequentially without
+ blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
+ object. Defaults to `False`.
+
+ Returns:
+ [`CommitInfo`] or `Future`:
+ Instance of [`CommitInfo`] containing information about the newly created commit (commit hash, commit
+ url, pr url, commit message,...). If `run_as_future=True` is passed, returns a Future object which will
+ contain the result when executed.
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If commit message is empty.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If parent commit is not a valid commit OID.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If a README.md file with an invalid metadata section is committed. In this case, the commit will fail
+ early, before trying to upload any file.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If `create_pr` is `True` and revision is neither `None` nor `"main"`.
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+ """
+
+ from ._commit_api import (
+ CommitOperationAdd,
+ CommitOperationCopy,
+ _fetch_files_to_copy,
+ _send_commit,
+ _warn_on_overwriting_operations,
+ )
+
+ if parent_commit is not None and not constants.REGEX_COMMIT_OID.fullmatch(parent_commit):
+ raise ValueError(
+ f"`parent_commit` is not a valid commit OID. It must match the following regex: {constants.REGEX_COMMIT_OID}"
+ )
+
+ if commit_message is None or len(commit_message) == 0:
+ raise ValueError("`commit_message` can't be empty, please pass a value.")
+
+ commit_description = commit_description if commit_description is not None else ""
+ repo_type = repo_type if repo_type is not None else constants.REPO_TYPE_MODEL
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ unquoted_revision = revision or constants.DEFAULT_REVISION
+ revision = quote(unquoted_revision, safe="")
+ create_pr = create_pr if create_pr is not None else False
+ _hot_reload = _hot_reload if _hot_reload is not None else False
+
+ headers = self._build_hf_headers(token=token)
+
+ operations = list(operations)
+ additions = [op for op in operations if isinstance(op, CommitOperationAdd)]
+ copies = [op for op in operations if isinstance(op, CommitOperationCopy)]
+ nb_additions = len(additions)
+ nb_copies = len(copies)
+ nb_deletions = len(operations) - nb_additions - nb_copies
+
+ for addition in additions:
+ if addition._is_committed:
+ raise ValueError(
+ f"CommitOperationAdd {addition} has already being committed and cannot be reused. Please create a"
+ " new CommitOperationAdd object if you want to create a new commit."
+ )
+
+ if repo_type != "dataset":
+ for addition in additions:
+ if addition.path_in_repo.endswith((".arrow", ".parquet")):
+ warnings.warn(
+ f"It seems that you are about to commit a data file ({addition.path_in_repo}) to a {repo_type}"
+ " repository. You are sure this is intended? If you are trying to upload a dataset, please"
+ " set `repo_type='dataset'` or `--repo-type=dataset` in a CLI."
+ )
+
+ logger.debug(
+ f"About to commit to the hub: {len(additions)} addition(s), {len(copies)} copy(ies) and"
+ f" {nb_deletions} deletion(s)."
+ )
+
+ # If updating a README.md file, make sure the metadata format is valid
+ # It's better to fail early than to fail after all the files have been uploaded.
+ for addition in additions:
+ if addition.path_in_repo == "README.md":
+ with addition.as_file() as file:
+ content = file.read().decode()
+ self._validate_yaml(content, repo_type=repo_type, token=token)
+ # Skip other additions after `README.md` has been processed
+ break
+
+ # If updating twice the same file or update then delete a file in a single commit
+ _warn_on_overwriting_operations(operations)
+
+ self.preupload_lfs_files(
+ repo_id=repo_id,
+ additions=additions,
+ token=token,
+ repo_type=repo_type,
+ revision=unquoted_revision, # first-class methods take unquoted revision
+ create_pr=create_pr,
+ num_threads=num_threads,
+ free_memory=False, # do not remove `CommitOperationAdd.path_or_fileobj` on LFS files for "normal" users
+ )
+
+ files_to_copy = _fetch_files_to_copy(
+ copies=copies,
+ repo_type=repo_type,
+ repo_id=repo_id,
+ headers=headers,
+ revision=unquoted_revision,
+ endpoint=self.endpoint,
+ )
+
+ self._duplicate_lfs_files(
+ repo_id=repo_id, copies=copies, files_to_copy=files_to_copy, token=token, repo_type=repo_type
+ )
+
+ # Remove no-op operations (files that have not changed)
+ operations_without_no_op = []
+ for operation in operations:
+ if (
+ isinstance(operation, CommitOperationAdd)
+ and operation._remote_oid is not None
+ and operation._remote_oid == operation._local_oid
+ ):
+ # File already exists on the Hub and has not changed: we can skip it.
+ logger.debug(f"Skipping upload for '{operation.path_in_repo}' as the file has not changed.")
+ continue
+ if (
+ isinstance(operation, CommitOperationCopy)
+ and operation._dest_oid is not None
+ and operation._dest_oid == operation._src_oid
+ ):
+ # Source and destination files are identical - skip
+ logger.debug(
+ f"Skipping copy for '{operation.src_path_in_repo}' -> '{operation.path_in_repo}' as the content of the source file is the same as the destination file."
+ )
+ continue
+ operations_without_no_op.append(operation)
+ if len(operations) != len(operations_without_no_op):
+ logger.info(
+ f"Removing {len(operations) - len(operations_without_no_op)} file(s) from commit that have not changed."
+ )
+
+ # Return early if empty commit
+ if len(operations_without_no_op) == 0:
+ logger.warning("No files have been modified since last commit. Skipping to prevent empty commit.")
+
+ # Get latest commit info
+ try:
+ info = self.repo_info(repo_id=repo_id, repo_type=repo_type, revision=unquoted_revision, token=token)
+ except RepositoryNotFoundError as e:
+ e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
+ raise
+
+ # Return commit info based on latest commit
+ url_prefix = self.endpoint
+ if repo_type is not None and repo_type != constants.REPO_TYPE_MODEL:
+ url_prefix = f"{url_prefix}/{repo_type}s"
+ return CommitInfo(
+ commit_url=f"{url_prefix}/{repo_id}/commit/{info.sha}",
+ commit_message=commit_message,
+ commit_description=commit_description,
+ oid=info.sha, # type: ignore
+ _endpoint=self.endpoint,
+ )
+
+ try:
+ commit_info = _send_commit(
+ operations=operations_without_no_op,
+ files_to_copy=files_to_copy,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ repo_type=repo_type,
+ repo_id=repo_id,
+ headers=headers,
+ revision=revision,
+ endpoint=self.endpoint,
+ parent_commit=parent_commit,
+ create_pr=create_pr,
+ hot_reload=_hot_reload,
+ )
+ except RepositoryNotFoundError as e:
+ e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
+ raise
+ except RemoteEntryNotFoundError as e:
+ if nb_deletions > 0 and "A file with this name doesn't exist" in str(e):
+ e.append_to_message(
+ "\nMake sure to differentiate file and folder paths in delete"
+ " operations with a trailing '/' or using `is_folder=True/False`."
+ )
+ raise
+
+ # Mark additions as committed (cannot be reused in another commit)
+ for addition in additions:
+ addition._is_committed = True
+
+ return commit_info
+
+ def preupload_lfs_files(
+ self,
+ repo_id: str,
+ additions: Iterable[CommitOperationAdd],
+ *,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ num_threads: int = 5,
+ free_memory: bool = True,
+ gitignore_content: str | None = None,
+ ):
+ """Pre-upload LFS files to S3 in preparation on a future commit.
+
+ This method is useful if you are generating the files to upload on-the-fly and you don't want to store them
+ in memory before uploading them all at once.
+
+ > [!WARNING]
+ > This is a power-user method. You shouldn't need to call it directly to make a normal commit.
+ > Use [`create_commit`] directly instead.
+
+ > [!WARNING]
+ > Commit operations will be mutated during the process. In particular, the attached `path_or_fileobj` will be
+ > removed after the upload to save memory (and replaced by an empty `bytes` object). Do not reuse the same
+ > objects except to pass them to [`create_commit`]. If you don't want to remove the attached content from the
+ > commit operation object, pass `free_memory=False`.
+
+ Args:
+ repo_id (`str`):
+ The repository in which you will commit the files, for example: `"username/custom_transformers"`.
+
+ additions (`Iterable` of [`CommitOperationAdd`]):
+ The list of files to upload. Warning: the objects in this list will be mutated to include information
+ relative to the upload. Do not reuse the same objects for multiple commits.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ The type of repository to upload to (e.g. `"model"` -default-, `"dataset"` or `"space"`).
+
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+
+ create_pr (`boolean`, *optional*):
+ Whether or not you plan to create a Pull Request with that commit. Defaults to `False`.
+
+ num_threads (`int`, *optional*):
+ Number of concurrent threads for uploading files. Defaults to 5.
+ Setting it to 2 means at most 2 files will be uploaded concurrently.
+
+ free_memory (`bool`, *optional*, defaults to `True`):
+ If `True`, the `path_or_fileobj` attribute of each `CommitOperationAdd` is replaced by an empty
+ `bytes` object after upload to save memory. Set to `False` if you need to reuse the operation
+ objects outside of a subsequent [`create_commit`] call.
+
+ gitignore_content (`str`, *optional*):
+ The content of the `.gitignore` file to know which files should be ignored. The order of priority
+ is to first check if `gitignore_content` is passed, then check if the `.gitignore` file is present
+ in the list of files to commit and finally default to the `.gitignore` file already hosted on the Hub
+ (if any).
+
+ Example:
+ ```py
+ >>> from huggingface_hub import CommitOperationAdd, preupload_lfs_files, create_commit, create_repo
+
+ >>> repo_id = create_repo("test_preupload").repo_id
+
+ # Generate and preupload LFS files one by one
+ >>> operations = [] # List of all `CommitOperationAdd` objects that will be generated
+ >>> for i in range(5):
+ ... content = ... # generate binary content
+ ... addition = CommitOperationAdd(path_in_repo=f"shard_{i}_of_5.bin", path_or_fileobj=content)
+ ... preupload_lfs_files(repo_id, additions=[addition]) # upload + free memory
+ ... operations.append(addition)
+
+ # Create commit
+ >>> create_commit(repo_id, operations=operations, commit_message="Commit all shards")
+ ```
+ """
+
+ from ._commit_api import _fetch_upload_modes, _upload_files
+
+ repo_type = repo_type if repo_type is not None else constants.REPO_TYPE_MODEL
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ revision = quote(revision, safe="") if revision is not None else constants.DEFAULT_REVISION
+ create_pr = create_pr if create_pr is not None else False
+ headers = self._build_hf_headers(token=token)
+
+ # Check if a `gitignore` file is being committed to the Hub.
+ additions = list(additions)
+ if gitignore_content is None:
+ for addition in additions:
+ if addition.path_in_repo == ".gitignore":
+ with addition.as_file() as f:
+ gitignore_content = f.read().decode()
+ break
+
+ # Filter out already uploaded files
+ new_additions = [addition for addition in additions if not addition._is_uploaded]
+
+ # Check which new files are LFS
+ # For some items, we might have already fetched the upload mode.
+ additions_no_upload_mode = [addition for addition in new_additions if addition._upload_mode is None]
+ if len(additions_no_upload_mode) > 0:
+ try:
+ _fetch_upload_modes(
+ additions=additions_no_upload_mode,
+ repo_type=repo_type,
+ repo_id=repo_id,
+ headers=headers,
+ revision=revision,
+ endpoint=self.endpoint,
+ create_pr=create_pr or False,
+ gitignore_content=gitignore_content,
+ )
+ except RepositoryNotFoundError as e:
+ e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
+ raise
+
+ # Filter out regular files
+ new_lfs_additions = [addition for addition in new_additions if addition._upload_mode == "lfs"]
+
+ # Filter out files listed in .gitignore
+ new_lfs_additions_to_upload = []
+ for addition in new_lfs_additions:
+ if addition._should_ignore:
+ logger.debug(f"Skipping upload for LFS file '{addition.path_in_repo}' (ignored by gitignore file).")
+ else:
+ new_lfs_additions_to_upload.append(addition)
+ if len(new_lfs_additions) != len(new_lfs_additions_to_upload):
+ logger.info(
+ f"Skipped upload for {len(new_lfs_additions) - len(new_lfs_additions_to_upload)} LFS file(s) "
+ "(ignored by gitignore file)."
+ )
+ # If no LFS files remain to upload, keep previous behavior and log explicitly
+ if len(new_lfs_additions_to_upload) == 0:
+ logger.debug("No LFS files to upload.")
+ return
+ # Prepare upload parameters
+ upload_kwargs = {
+ "additions": new_lfs_additions_to_upload,
+ "repo_type": repo_type,
+ "repo_id": repo_id,
+ "headers": headers,
+ "endpoint": self.endpoint,
+ # If `create_pr`, we don't want to check user permission on the revision as users with read permission
+ # should still be able to create PRs even if they don't have write permission on the target branch of the
+ # PR (i.e. `revision`).
+ "revision": revision if not create_pr else None,
+ }
+ _upload_files(
+ **upload_kwargs, # type: ignore[arg-type]
+ num_threads=num_threads,
+ create_pr=create_pr,
+ )
+ for addition in new_lfs_additions_to_upload:
+ addition._is_uploaded = True
+ if free_memory:
+ addition.path_or_fileobj = b""
+
+ @validate_hf_hub_args
+ def _duplicate_lfs_files(
+ self,
+ repo_id: str,
+ copies: Iterable[CommitOperationCopy],
+ *,
+ files_to_copy: dict,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ ) -> None:
+ """Duplicate LFS files from source repositories to the destination repository.
+
+ This method is the equivalent of [`preupload_lfs_files`] for cross-repo copy operations. It must be called
+ before [`create_commit`] to ensure that LFS files from the source repositories are available in the destination
+ repository before the commit is created.
+
+ Args:
+ repo_id (`str`):
+ The destination repository in which you will commit the files, for example:
+ `"username/custom_transformers"`.
+
+ copies (`Iterable` of [`CommitOperationCopy`]):
+ The list of copy operations describing which files to duplicate. Only cross-repo copies (where
+ `src_repo_id` is set) with LFS files will be processed. Warning: the objects in this list will be
+ mutated to include information relative to the duplication. Do not reuse the same objects for multiple
+ commits.
+
+ files_to_copy (`dict`):
+ Pre-fetched file info from [`_fetch_files_to_copy`]. LFS metadata is extracted from this dict instead
+ of making additional API calls. Keys are `_CopySource` tuples, values are `RepoFile` (for LFS files)
+ or `bytes` (for regular files).
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ The type of the destination repository (e.g. `"model"` -default-, `"dataset"` or `"space"`).
+ """
+
+ from ._commit_api import DUPLICATE_LFS_BATCH_SIZE, _CopySource
+
+ repo_type = repo_type if repo_type is not None else constants.REPO_TYPE_MODEL
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ headers = self._build_hf_headers(token=token)
+
+ copies = list(copies)
+ # Filter to cross-repo copies that haven't been duplicated yet
+ cross_repo_copies = [op for op in copies if op.src_repo_id is not None and not op._is_duplicated]
+ if not cross_repo_copies:
+ logger.debug("No cross-repo LFS files to duplicate.")
+ return
+
+ # The /lfs-files/duplicate endpoint lives on the *source* repo and takes the destination as `target`.
+ cross_repo_copies.sort(key=lambda op: (op.src_repo_id or "", op.src_repo_type or "", op.src_revision or ""))
+
+ for (src_repo_id, src_repo_type, src_revision), group in itertools.groupby(
+ cross_repo_copies, key=lambda op: (op.src_repo_id, op.src_repo_type, op.src_revision)
+ ):
+ operations = list(group)
+
+ lfs_files: list[dict] = []
+ seen_oids: set[str] = set()
+
+ for op in operations:
+ key = _CopySource(op.src_repo_id, op.src_repo_type, op.src_path_in_repo, op.src_revision)
+ src_file = files_to_copy.get(key)
+ if src_file is None or isinstance(src_file, bytes):
+ continue
+ if not src_file.lfs:
+ continue
+ if not src_file.xet_hash:
+ raise ValueError(
+ f"Cannot duplicate LFS file '{src_file.path}' from {src_repo_type}s/{src_repo_id}: file has no xet hash."
+ f" (file: {src_file})"
+ )
+ oid = src_file.lfs.sha256
+ if oid not in seen_oids:
+ seen_oids.add(oid)
+ lfs_files.append({"xetHash": src_file.xet_hash, "sha256": oid, "filename": src_file.path})
+
+ if not lfs_files:
+ continue
+
+ # Call the duplicate endpoint on the *source* repo, in batches
+ duplicate_url = f"{self.endpoint}/api/{src_repo_type}s/{src_repo_id}/lfs-files/duplicate"
+ for batch in chunk_iterable(lfs_files, DUPLICATE_LFS_BATCH_SIZE):
+ response = get_session().post(
+ duplicate_url,
+ headers=headers,
+ json={"target": {"type": repo_type, "name": repo_id}, "files": list(batch)},
+ )
+ hf_raise_for_status(response)
+ data = response.json()
+ failures = data.get("failed", [])
+ if failures:
+ messages = [f" - {f['sha256']}: {f['error']}" for f in failures]
+ raise FileDuplicationError(
+ f"Failed to duplicate files from {src_repo_type}s/{src_repo_id} "
+ f"to {repo_type}s/{repo_id}:\n" + "\n".join(messages)
+ )
+
+ for op in cross_repo_copies:
+ op._is_duplicated = True
+
+ @overload
+ def upload_file( # type: ignore
+ self,
+ *,
+ path_or_fileobj: str | Path | bytes | BinaryIO,
+ path_in_repo: str,
+ repo_id: str,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ run_as_future: Literal[False] = ...,
+ _hot_reload: bool | None = None,
+ ) -> CommitInfo: ...
+
+ @overload
+ def upload_file(
+ self,
+ *,
+ path_or_fileobj: str | Path | bytes | BinaryIO,
+ path_in_repo: str,
+ repo_id: str,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ run_as_future: Literal[True] = ...,
+ _hot_reload: bool | None = None,
+ ) -> Future[CommitInfo]: ...
+
+ @validate_hf_hub_args
+ @future_compatible
+ def upload_file(
+ self,
+ *,
+ path_or_fileobj: str | Path | bytes | BinaryIO,
+ path_in_repo: str,
+ repo_id: str,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ run_as_future: bool = False,
+ _hot_reload: bool | None = None,
+ ) -> CommitInfo | Future[CommitInfo]:
+ """
+ Upload a local file (up to 50 GB) to the given repo. The upload is done
+ through a HTTP post request, and doesn't require git or git-lfs to be
+ installed.
+
+ Args:
+ path_or_fileobj (`str`, `Path`, `bytes`, or `IO`):
+ Path to a file on the local machine or binary data stream /
+ fileobj / buffer.
+ path_in_repo (`str`):
+ Relative filepath in the repo, for example:
+ `"checkpoints/1fec34a/weights.bin"`
+ repo_id (`str`):
+ The repository to which the file will be uploaded, for example:
+ `"username/custom_transformers"`
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit
+ commit_description (`str`, *optional*):
+ The description of the generated commit
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`.
+ If `revision` is not set, PR is opened against the `"main"` branch. If
+ `revision` is set and is a branch, PR is opened against this branch. If
+ `revision` is set and is not a branch name (example: a commit oid), an
+ `RevisionNotFoundError` is returned by the server.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed to concurrently.
+ run_as_future (`bool`, *optional*):
+ Whether or not to run this method in the background. Background jobs are run sequentially without
+ blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
+ object. Defaults to `False`.
+
+
+ Returns:
+ [`CommitInfo`] or `Future`:
+ Instance of [`CommitInfo`] containing information about the newly created commit (commit hash, commit
+ url, pr url, commit message,...). If `run_as_future=True` is passed, returns a Future object which will
+ contain the result when executed.
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+
+ > [!WARNING]
+ > `upload_file` assumes that the repo already exists on the Hub. If you get a
+ > Client error 404, please make sure you are authenticated, that your token has the required permissions,
+ > and that `repo_id` and `repo_type` are set correctly. If repo does not exist,
+ > create it first using [`~hf_api.create_repo`].
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import upload_file
+
+ >>> with open("./local/filepath", "rb") as fobj:
+ ... upload_file(
+ ... path_or_fileobj=fileobj,
+ ... path_in_repo="remote/file/path.h5",
+ ... repo_id="username/my-dataset",
+ ... repo_type="dataset",
+ ... token="my_token",
+ ... )
+
+ >>> upload_file(
+ ... path_or_fileobj=".\\\\local\\\\file\\\\path",
+ ... path_in_repo="remote/file/path.h5",
+ ... repo_id="username/my-model",
+ ... token="my_token",
+ ... )
+
+ >>> upload_file(
+ ... path_or_fileobj=".\\\\local\\\\file\\\\path",
+ ... path_in_repo="remote/file/path.h5",
+ ... repo_id="username/my-model",
+ ... token="my_token",
+ ... create_pr=True,
+ ... )
+ ```
+ """
+
+ from ._commit_api import CommitOperationAdd
+
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+
+ commit_message = (
+ commit_message if commit_message is not None else f"Upload {path_in_repo} with huggingface_hub"
+ )
+ operation = CommitOperationAdd(
+ path_or_fileobj=path_or_fileobj,
+ path_in_repo=path_in_repo,
+ )
+
+ return self.create_commit(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ operations=[operation],
+ commit_message=commit_message,
+ commit_description=commit_description,
+ token=token,
+ revision=revision,
+ create_pr=create_pr,
+ _hot_reload=_hot_reload,
+ parent_commit=parent_commit,
+ )
+
+ @overload
+ def upload_folder( # type: ignore
+ self,
+ *,
+ repo_id: str,
+ folder_path: str | Path,
+ path_in_repo: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ delete_patterns: list[str] | str | None = None,
+ run_as_future: Literal[False] = ...,
+ ) -> CommitInfo: ...
+
+ @overload
+ def upload_folder( # type: ignore
+ self,
+ *,
+ repo_id: str,
+ folder_path: str | Path,
+ path_in_repo: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ delete_patterns: list[str] | str | None = None,
+ run_as_future: Literal[True] = ...,
+ ) -> Future[CommitInfo]: ...
+
+ @validate_hf_hub_args
+ @future_compatible
+ def upload_folder(
+ self,
+ *,
+ repo_id: str,
+ folder_path: str | Path,
+ path_in_repo: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ delete_patterns: list[str] | str | None = None,
+ run_as_future: bool = False,
+ ) -> CommitInfo | Future[CommitInfo]:
+ """
+ Upload a local folder to the given repo. The upload is done through a HTTP requests, and doesn't require git or
+ git-lfs to be installed.
+
+ The structure of the folder will be preserved. Files with the same name already present in the repository will
+ be overwritten. Others will be left untouched.
+
+ Use the `allow_patterns` and `ignore_patterns` arguments to specify which files to upload. These parameters
+ accept either a single pattern or a list of patterns. Patterns are Standard Wildcards (globbing patterns) as
+ documented [here](https://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm). If both `allow_patterns` and
+ `ignore_patterns` are provided, both constraints apply. By default, all files from the folder are uploaded.
+
+ Use the `delete_patterns` argument to specify remote files you want to delete. Input type is the same as for
+ `allow_patterns` (see above). If `path_in_repo` is also provided, the patterns are matched against paths
+ relative to this folder. For example, `upload_folder(..., path_in_repo="experiment", delete_patterns="logs/*")`
+ will delete any remote file under `./experiment/logs/`. Note that the `.gitattributes` file will not be deleted
+ even if it matches the patterns.
+
+ Any `.git/` folder present in any subdirectory will be ignored. However, please be aware that the `.gitignore`
+ file is not taken into account.
+
+ When `hf_xet` is installed (the default), files are uploaded through a streamed pipeline: uploads start while
+ the folder is still being checked against the Hub, files are hashed while being chunked for upload (single
+ read pass), and large folders are automatically committed in several batches to stay below server limits
+ (follow-up commits get a ` (part N)` suffix on the commit message). If the upload is interrupted, re-running
+ the same call resumes it: already-committed files are skipped and already-uploaded data is deduplicated. When
+ `hf_xet` is not installed, falls back to a single commit created with [`create_commit`].
+
+ Args:
+ repo_id (`str`):
+ The repository to which the file will be uploaded, for example:
+ `"username/custom_transformers"`
+ folder_path (`str` or `Path`):
+ Path to the folder to upload on the local file system
+ path_in_repo (`str`, *optional*):
+ Relative path of the directory in the repo, for example:
+ `"checkpoints/1fec34a/results"`. Will default to the root folder of the repository.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit. Defaults to:
+ `f"Upload {path_in_repo} with huggingface_hub"`
+ commit_description (`str` *optional*):
+ The description of the generated commit
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`. The PR is always
+ opened against the default branch: setting both `create_pr=True` and `revision` raises a
+ `ValueError`. Note that each call with `create_pr=True` opens a new pull request: to resume an
+ interrupted upload into the existing PR, re-run with `revision="refs/pr/N"` instead.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed to concurrently. If the upload is split into
+ several commits (large folders), `parent_commit` only applies to the first one.
+ allow_patterns (`list[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are uploaded.
+ ignore_patterns (`list[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not uploaded.
+ delete_patterns (`list[str]` or `str`, *optional*):
+ If provided, remote files matching any of the patterns will be deleted from the repo while committing
+ new files. This is useful if you don't know which files have already been uploaded.
+ Note: to avoid discrepancies the `.gitattributes` file is not deleted even if it matches the pattern.
+ run_as_future (`bool`, *optional*):
+ Whether or not to run this method in the background. Background jobs are run sequentially without
+ blocking the main thread. Passing `run_as_future=True` will return a [Future](https://docs.python.org/3/library/concurrent.futures.html#future-objects)
+ object. Defaults to `False`.
+
+ Returns:
+ [`CommitInfo`] or `Future`:
+ Instance of [`CommitInfo`] containing information about the newly created commit (commit hash, commit
+ url, pr url, commit message,...). If `run_as_future=True` is passed, returns a Future object which will
+ contain the result when executed.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+
+ > [!WARNING]
+ > `upload_folder` assumes that the repo already exists on the Hub. If you get a Client error 404, please make
+ > sure you are authenticated, that your token has the required permissions, and that `repo_id` and `repo_type`
+ > are set correctly. If repo does not exist, create it first using [`~hf_api.create_repo`].
+
+ Example:
+
+ ```python
+ # Upload checkpoints folder except the log files
+ >>> upload_folder(
+ ... folder_path="local/checkpoints",
+ ... path_in_repo="remote/experiment/checkpoints",
+ ... repo_id="username/my-dataset",
+ ... repo_type="datasets",
+ ... token="my_token",
+ ... ignore_patterns="**/logs/*.txt",
+ ... )
+
+ # Upload checkpoints folder including logs while deleting existing logs from the repo
+ # Useful if you don't know exactly which log files have already being pushed
+ >>> upload_folder(
+ ... folder_path="local/checkpoints",
+ ... path_in_repo="remote/experiment/checkpoints",
+ ... repo_id="username/my-dataset",
+ ... repo_type="datasets",
+ ... token="my_token",
+ ... delete_patterns="**/logs/*.txt",
+ ... )
+
+ # Upload checkpoints folder while creating a PR
+ >>> upload_folder(
+ ... folder_path="local/checkpoints",
+ ... path_in_repo="remote/experiment/checkpoints",
+ ... repo_id="username/my-dataset",
+ ... repo_type="datasets",
+ ... token="my_token",
+ ... create_pr=True,
+ ... )
+ ```
+ """
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if create_pr and revision is not None and revision != constants.DEFAULT_REVISION:
+ raise ValueError(
+ f"Cannot use `create_pr=True` with `revision='{revision}'`: pull requests created by"
+ " `upload_folder` are always opened against the default branch. Don't set `revision` when"
+ " `create_pr=True`."
+ )
+
+ # By default, upload folder to the root directory in repo.
+ if path_in_repo is None:
+ path_in_repo = ""
+
+ # Do not upload .git folder
+ if ignore_patterns is None:
+ ignore_patterns = []
+ elif isinstance(ignore_patterns, str):
+ ignore_patterns = [ignore_patterns]
+ ignore_patterns += DEFAULT_IGNORE_PATTERNS
+
+ delete_operations = self._prepare_folder_deletions(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ revision=constants.DEFAULT_REVISION if create_pr else revision,
+ token=token,
+ path_in_repo=path_in_repo,
+ delete_patterns=delete_patterns,
+ )
+ add_operations = self._prepare_upload_folder_additions(
+ folder_path,
+ path_in_repo,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ token=token,
+ repo_type=repo_type,
+ )
+
+ # Optimize operations: if some files will be overwritten, we don't need to delete them first
+ if len(add_operations) > 0:
+ added_paths = {op.path_in_repo for op in add_operations}
+ delete_operations = [
+ delete_op for delete_op in delete_operations if delete_op.path_in_repo not in added_paths
+ ]
+ commit_operations = delete_operations + add_operations
+
+ commit_message = commit_message or "Upload folder using huggingface_hub"
+
+ if is_xet_available():
+ from ._upload_pipeline import pipelined_upload
+
+ # Streamed multi-commit pipeline: uploads and commits overlap, large folders are
+ # committed in adaptive batches, interrupted uploads resume by re-running.
+ return pipelined_upload(
+ self,
+ repo_id=repo_id,
+ repo_type=repo_type or constants.REPO_TYPE_MODEL,
+ add_operations=add_operations,
+ delete_operations=delete_operations,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ token=token,
+ revision=revision,
+ create_pr=create_pr or False,
+ parent_commit=parent_commit,
+ )
+
+ # Legacy single-commit path (hf_xet not installed).
+ if len(add_operations) > 30:
+ log = logger.warning if len(add_operations) > 200 else logger.info
+ log(
+ "It seems you are trying to upload a large folder at once. This might take some time and then fail if "
+ "the folder is too large. Installing `hf_xet` (`pip install hf_xet`) enables a more robust, resumable "
+ "upload that handles large folders in multiple commits."
+ )
+ return self.create_commit(
+ repo_type=repo_type,
+ repo_id=repo_id,
+ operations=commit_operations,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ token=token,
+ revision=revision,
+ create_pr=create_pr,
+ parent_commit=parent_commit,
+ )
+
+ @validate_hf_hub_args
+ def delete_file(
+ self,
+ path_in_repo: str,
+ repo_id: str,
+ *,
+ token: str | bool | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ ) -> CommitInfo:
+ """
+ Deletes a file in the given repo.
+
+ Args:
+ path_in_repo (`str`):
+ Relative filepath in the repo, for example:
+ `"checkpoints/1fec34a/weights.bin"`
+ repo_id (`str`):
+ The repository from which the file will be deleted, for example:
+ `"username/custom_transformers"`
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if the file is in a dataset or
+ space, `None` or `"model"` if in a model. Default is `None`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit. Defaults to
+ `f"Delete {path_in_repo} with huggingface_hub"`.
+ commit_description (`str`, *optional*):
+ The description of the generated commit
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`.
+ If `revision` is not set, PR is opened against the `"main"` branch. If
+ `revision` is set and is a branch, PR is opened against this branch. If
+ `revision` is set and is not a branch name (example: a commit oid), an
+ `RevisionNotFoundError` is returned by the server.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed to concurrently.
+
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the revision to download from cannot be found.
+ > - [`~utils.EntryNotFoundError`]
+ > If the file to download cannot be found.
+
+ """
+
+ from ._commit_api import CommitOperationDelete
+
+ commit_message = (
+ commit_message if commit_message is not None else f"Delete {path_in_repo} with huggingface_hub"
+ )
+
+ operations = [CommitOperationDelete(path_in_repo=path_in_repo)]
+
+ return self.create_commit(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ token=token,
+ operations=operations,
+ revision=revision,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ parent_commit=parent_commit,
+ )
+
+ @validate_hf_hub_args
+ def delete_files(
+ self,
+ repo_id: str,
+ delete_patterns: list[str],
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ ) -> CommitInfo:
+ """
+ Delete files from a repository on the Hub.
+
+ If a folder path is provided, the entire folder is deleted as well as
+ all files it contained.
+
+ Args:
+ repo_id (`str`):
+ The repository from which the folder will be deleted, for example:
+ `"username/custom_transformers"`
+ delete_patterns (`list[str]`):
+ List of files or folders to delete. Each string can either be
+ a file path, a folder path, or a wildcard pattern. Patterns are Standard
+ Wildcards (globbing patterns) as documented [here](https://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm).
+ The pattern matching is based on [`fnmatch`](https://docs.python.org/3/library/fnmatch.html).
+ Note that `fnmatch` matches `*` across path boundaries, unlike traditional Unix shell globbing.
+ E.g. `["file.txt", "folder/", "data/*.parquet"]`
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ to the stored token.
+ repo_type (`str`, *optional*):
+ Type of the repo to delete files from. Can be `"model"`,
+ `"dataset"` or `"space"`. Defaults to `"model"`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The summary (first line) of the generated commit. Defaults to
+ `f"Delete files using huggingface_hub"`.
+ commit_description (`str`, *optional*):
+ The description of the generated commit.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`.
+ If `revision` is not set, PR is opened against the `"main"` branch. If
+ `revision` is set and is a branch, PR is opened against this branch. If
+ `revision` is set and is not a branch name (example: a commit oid), an
+ `RevisionNotFoundError` is returned by the server.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed to concurrently.
+ """
+ operations = self._prepare_folder_deletions(
+ repo_id=repo_id, repo_type=repo_type, delete_patterns=delete_patterns, path_in_repo="", revision=revision
+ )
+
+ if commit_message is None:
+ commit_message = f"Delete files {' '.join(delete_patterns)} with huggingface_hub"
+
+ return self.create_commit(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ token=token,
+ operations=operations,
+ revision=revision,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ parent_commit=parent_commit,
+ )
+
+ @validate_hf_hub_args
+ def delete_folder(
+ self,
+ path_in_repo: str,
+ repo_id: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ ) -> CommitInfo:
+ """
+ Deletes a folder in the given repo.
+
+ Simple wrapper around [`create_commit`] method.
+
+ Args:
+ path_in_repo (`str`):
+ Relative folder path in the repo, for example: `"checkpoints/1fec34a"`.
+ repo_id (`str`):
+ The repository from which the folder will be deleted, for example:
+ `"username/custom_transformers"`
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ to the stored token.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if the folder is in a dataset or
+ space, `None` or `"model"` if in a model. Default is `None`.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit. Defaults to
+ `f"Delete folder {path_in_repo} with huggingface_hub"`.
+ commit_description (`str`, *optional*):
+ The description of the generated commit.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request with that commit. Defaults to `False`.
+ If `revision` is not set, PR is opened against the `"main"` branch. If
+ `revision` is set and is a branch, PR is opened against this branch. If
+ `revision` is set and is not a branch name (example: a commit oid), an
+ `RevisionNotFoundError` is returned by the server.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed to concurrently.
+ """
+
+ from ._commit_api import CommitOperationDelete
+
+ return self.create_commit(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ token=token,
+ operations=[CommitOperationDelete(path_in_repo=path_in_repo, is_folder=True)],
+ revision=revision,
+ commit_message=(
+ commit_message if commit_message is not None else f"Delete folder {path_in_repo} with huggingface_hub"
+ ),
+ commit_description=commit_description,
+ create_pr=create_pr,
+ parent_commit=parent_commit,
+ )
+
+ @validate_hf_hub_args
+ def get_hf_file_metadata(
+ self,
+ *,
+ url: str,
+ token: bool | str | None = None,
+ timeout: float | None = constants.HF_HUB_ETAG_TIMEOUT,
+ ) -> HfFileMetadata:
+ """Fetch metadata of a file versioned on the Hub for a given url.
+
+ Args:
+ url (`str`):
+ File url, for example returned by [`hf_hub_url`].
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ timeout (`float`, *optional*, defaults to 10):
+ How many seconds to wait for the server to send metadata before giving up.
+
+ Returns:
+ A [`HfFileMetadata`] object containing metadata such as location, etag, size and commit_hash.
+ """
+
+ from .file_download import get_hf_file_metadata
+
+ if token is None:
+ # Cannot do `token = token or self.token` as token can be `False`.
+ token = self.token
+
+ return get_hf_file_metadata(
+ url=url,
+ token=token,
+ timeout=timeout,
+ library_name=self.library_name,
+ library_version=self.library_version,
+ user_agent=self.user_agent,
+ endpoint=self.endpoint,
+ )
+
+ @overload
+ def hf_hub_download(
+ self,
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[False] = False,
+ ) -> str: ...
+
+ @overload
+ def hf_hub_download(
+ self,
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[True],
+ ) -> DryRunFileInfo: ...
+
+ @validate_hf_hub_args
+ def hf_hub_download(
+ self,
+ repo_id: str,
+ filename: str,
+ *,
+ subfolder: str | None = None,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ force_download: bool = False,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: bool = False,
+ ) -> str | DryRunFileInfo:
+ """Download a given file if it's not already present in the local cache.
+
+ The new cache file layout looks like this:
+ - The cache directory contains one subfolder per repo_id (namespaced by repo type)
+ - inside each repo folder:
+ - refs is a list of the latest known revision => commit_hash pairs
+ - blobs contains the actual file blobs (identified by their git-sha or sha256, depending on
+ whether they're LFS files or not)
+ - snapshots contains one subfolder per commit, each "commit" contains the subset of the files
+ that have been resolved at that particular commit. Each filename is a symlink to the blob
+ at that particular commit.
+
+ ```
+ [ 96] .
+ └── [ 160] models--julien-c--EsperBERTo-small
+ ├── [ 160] blobs
+ │ ├── [321M] 403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ │ ├── [ 398] 7cb18dc9bafbfcf74629a4b760af1b160957a83e
+ │ └── [1.4K] d7edf6bd2a681fb0175f7735299831ee1b22b812
+ ├── [ 96] refs
+ │ └── [ 40] main
+ └── [ 128] snapshots
+ ├── [ 128] 2439f60ef33a0d46d85da5001d52aeda5b00ce9f
+ │ ├── [ 52] README.md -> ../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812
+ │ └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ └── [ 128] bbc77c8132af1cc5cf678da3f1ddf2de43606d48
+ ├── [ 52] README.md -> ../../blobs/7cb18dc9bafbfcf74629a4b760af1b160957a83e
+ └── [ 76] pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+ ```
+
+ If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+ option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+ to store some metadata related to the downloaded files. While this mechanism is not as robust as the main
+ cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ filename (`str`):
+ The name of the file in the repo.
+ subfolder (`str`, *optional*):
+ An optional value corresponding to a folder inside the repository.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+ `None` or `"model"` if downloading from a model. Default is `None`.
+ revision (`str`, *optional*):
+ An optional Git revision id which can be a branch name, a tag, or a
+ commit hash.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+ local_dir (`str` or `Path`, *optional*):
+ If provided, the downloaded file will be placed under this directory.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether the file should be downloaded even if it already exists in
+ the local cache.
+ etag_timeout (`float`, *optional*, defaults to `10`):
+ When fetching ETag, how many seconds to wait for the server to send
+ data before giving up which is passed to `httpx2.request`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, avoid downloading the file and return the path to the
+ local cached file if it exists.
+ tqdm_class (`tqdm`, *optional*):
+ If provided, overwrites the default behavior for the progress bar. Passed
+ argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
+ Defaults to the custom HF progress bar that can be disabled by setting
+ `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
+ dry_run (`bool`, *optional*, defaults to `False`):
+ If `True`, perform a dry run without actually downloading the file. Returns a
+ [`DryRunFileInfo`] object containing information about what would be downloaded.
+
+ Returns:
+ `str` or [`DryRunFileInfo`]:
+ - If `dry_run=False`: Local path of file or if networking is off, last version of file cached on disk.
+ - If `dry_run=True`: A [`DryRunFileInfo`] object containing download information.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]
+ If the repository to download from cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+ [`~utils.RevisionNotFoundError`]
+ If the revision to download from cannot be found.
+ [`~utils.RemoteEntryNotFoundError`]
+ If the file to download cannot be found.
+ [`~utils.LocalEntryNotFoundError`]
+ If network is disabled or unavailable and file is not found in cache.
+ [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+ If `token=True` but the token cannot be found.
+ [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError)
+ If ETag cannot be determined.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If some parameter value is invalid.
+ """
+ from .file_download import hf_hub_download
+
+ if token is None:
+ # Cannot do `token = token or self.token` as token can be `False`.
+ token = self.token
+
+ return hf_hub_download(
+ repo_id=repo_id,
+ filename=filename,
+ subfolder=subfolder,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=self.endpoint,
+ library_name=self.library_name,
+ library_version=self.library_version,
+ cache_dir=cache_dir,
+ local_dir=local_dir,
+ user_agent=self.user_agent,
+ force_download=force_download,
+ etag_timeout=etag_timeout,
+ token=token,
+ headers=self.headers,
+ local_files_only=local_files_only,
+ tqdm_class=cast(Any, tqdm_class),
+ dry_run=dry_run,
+ )
+
+ @overload
+ def snapshot_download(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ force_download: bool = False,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ max_workers: int = 8,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[False] = False,
+ ) -> str: ...
+
+ @overload
+ def snapshot_download(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ force_download: bool = False,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ max_workers: int = 8,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: Literal[True],
+ ) -> list[DryRunFileInfo]: ...
+
+ @validate_hf_hub_args
+ def snapshot_download(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ cache_dir: str | Path | None = None,
+ local_dir: str | Path | None = None,
+ etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+ force_download: bool = False,
+ token: bool | str | None = None,
+ local_files_only: bool = False,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ max_workers: int = 8,
+ tqdm_class: type[base_tqdm] | None = None,
+ dry_run: bool = False,
+ ) -> str | list[DryRunFileInfo]:
+ """Download repo files.
+
+ Download a whole snapshot of a repo's files at the specified revision. This is useful when you want all files from
+ a repo, because you don't know which ones you will need a priori. All files are nested inside a folder in order
+ to keep their actual filename relative to that folder. You can also filter which files to download using
+ `allow_patterns` and `ignore_patterns`.
+
+ If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+ option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+ to store some metadata related to the downloaded files.While this mechanism is not as robust as the main
+ cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+ An alternative would be to clone the repo but this requires git and git-lfs to be installed and properly
+ configured. It is also not possible to filter which files to download when cloning a repository using git.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+ `None` or `"model"` if downloading from a model. Default is `None`.
+ revision (`str`, *optional*):
+ An optional Git revision id which can be a branch name, a tag, or a
+ commit hash.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+ local_dir (`str` or `Path`, *optional*):
+ If provided, the downloaded files will be placed under this directory.
+ etag_timeout (`float`, *optional*, defaults to `10`):
+ When fetching ETag, how many seconds to wait for the server to send
+ data before giving up which is passed to `httpx2.request`.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether the file should be downloaded even if it already exists in the local cache.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, avoid downloading the file and return the path to the
+ local cached file if it exists.
+ allow_patterns (`list[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are downloaded.
+ ignore_patterns (`list[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not downloaded.
+ max_workers (`int`, *optional*):
+ Number of concurrent threads to download files (1 thread = 1 file download).
+ Defaults to 8.
+ tqdm_class (`tqdm`, *optional*):
+ If provided, overwrites the default behavior for the progress bar. Passed
+ argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
+ Note that the `tqdm_class` is not passed to each individual download.
+ Defaults to the custom HF progress bar that can be disabled by setting
+ `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
+ dry_run (`bool`, *optional*, defaults to `False`):
+ If `True`, perform a dry run without actually downloading the files. Returns a list of
+ [`DryRunFileInfo`] objects containing information about what would be downloaded.
+
+ Returns:
+ `str` or list of [`DryRunFileInfo`]:
+ - If `dry_run=False`: Folder path of the repo snapshot.
+ - If `dry_run=True`: A list of [`DryRunFileInfo`] objects containing download information.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]
+ If the repository to download from cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+ [`~utils.RevisionNotFoundError`]
+ If the revision to download from cannot be found.
+ [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+ If `token=True` and the token cannot be found.
+ [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) if
+ ETag cannot be determined.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ if some parameter value is invalid.
+ """
+ from ._snapshot_download import snapshot_download
+
+ if token is None:
+ # Cannot do `token = token or self.token` as token can be `False`.
+ token = self.token
+
+ return snapshot_download(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=self.endpoint,
+ cache_dir=cache_dir,
+ local_dir=local_dir,
+ library_name=self.library_name,
+ library_version=self.library_version,
+ user_agent=self.user_agent,
+ etag_timeout=etag_timeout,
+ force_download=force_download,
+ token=token,
+ local_files_only=local_files_only,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ max_workers=max_workers,
+ tqdm_class=cast(Any, tqdm_class),
+ headers=self.headers,
+ dry_run=dry_run,
+ )
+
+ def get_safetensors_metadata(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ token: bool | str | None = None,
+ timeout: float | None = constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ ) -> SafetensorsRepoMetadata:
+ """
+ Parse metadata for a safetensors repo on the Hub.
+
+ We first check if the repo has a single safetensors file or a sharded safetensors repo. If it's a single
+ safetensors file, we parse the metadata from this file. If it's a sharded safetensors repo, we parse the
+ metadata from the index file and then parse the metadata from each shard.
+
+ To parse metadata from a single safetensors file, use [`parse_safetensors_file_metadata`].
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if the file is in a dataset or space, `None` or `"model"` if in a
+ model. Default is `None`.
+ revision (`str`, *optional*):
+ The git revision to fetch the file from. Can be a branch name, a tag, or a commit hash. Defaults to the
+ head of the `"main"` branch.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ timeout (`float`, *optional*, defaults to 10):
+ How many seconds to wait for the server to send data before giving up, passed to each request that
+ fetches a safetensors file header. Set to `None` to disable the timeout (not recommended, as a stalled
+ connection can hang the call indefinitely).
+
+ Returns:
+ [`SafetensorsRepoMetadata`]: information related to safetensors repo.
+
+ Raises:
+ [`NotASafetensorsRepoError`]
+ If the repo is not a safetensors repo i.e. doesn't have either a
+ `model.safetensors` or a `model.safetensors.index.json` file.
+ [`SafetensorsParsingError`]
+ If a safetensors file header couldn't be parsed correctly.
+
+ Example:
+ ```py
+ # Parse repo with single weights file
+ >>> metadata = get_safetensors_metadata("bigscience/bloomz-560m")
+ >>> metadata
+ SafetensorsRepoMetadata(
+ metadata=None,
+ sharded=False,
+ weight_map={'h.0.input_layernorm.bias': 'model.safetensors', ...},
+ files_metadata={'model.safetensors': SafetensorsFileMetadata(...)}
+ )
+ >>> metadata.files_metadata["model.safetensors"].metadata
+ {'format': 'pt'}
+
+ # Parse repo with sharded model
+ >>> metadata = get_safetensors_metadata("bigscience/bloom")
+ Parse safetensors files: 100%|██████████████████████████████████████████| 72/72 [00:12<00:00, 5.78it/s]
+ >>> metadata
+ SafetensorsRepoMetadata(metadata={'total_size': 352494542848}, sharded=True, weight_map={...}, files_metadata={...})
+ >>> len(metadata.files_metadata)
+ 72 # All safetensors files have been fetched
+
+ # Parse repo with sharded model
+ >>> get_safetensors_metadata("runwayml/stable-diffusion-v1-5")
+ NotASafetensorsRepoError: 'runwayml/stable-diffusion-v1-5' is not a safetensors repo. Couldn't find 'model.safetensors.index.json' or 'model.safetensors' files.
+ ```
+ """
+ if self.file_exists( # Single safetensors file => non-sharded model
+ repo_id=repo_id,
+ filename=constants.SAFETENSORS_SINGLE_FILE,
+ repo_type=repo_type,
+ revision=revision,
+ token=token,
+ ):
+ file_metadata = self.parse_safetensors_file_metadata(
+ repo_id=repo_id,
+ filename=constants.SAFETENSORS_SINGLE_FILE,
+ repo_type=repo_type,
+ revision=revision,
+ token=token,
+ timeout=timeout,
+ )
+ return SafetensorsRepoMetadata(
+ metadata=None,
+ sharded=False,
+ weight_map={
+ tensor_name: constants.SAFETENSORS_SINGLE_FILE for tensor_name in file_metadata.tensors.keys()
+ },
+ files_metadata={constants.SAFETENSORS_SINGLE_FILE: file_metadata},
+ )
+ elif self.file_exists( # Multiple safetensors files => sharded with index
+ repo_id=repo_id,
+ filename=constants.SAFETENSORS_INDEX_FILE,
+ repo_type=repo_type,
+ revision=revision,
+ token=token,
+ ):
+ # Fetch index
+ index_file = self.hf_hub_download(
+ repo_id=repo_id,
+ filename=constants.SAFETENSORS_INDEX_FILE,
+ repo_type=repo_type,
+ revision=revision,
+ token=token,
+ )
+ with open(index_file) as f:
+ index = json.load(f)
+
+ weight_map = index.get("weight_map", {})
+
+ # Fetch metadata per shard
+ files_metadata = {}
+
+ def _parse(filename: str) -> None:
+ files_metadata[filename] = self.parse_safetensors_file_metadata(
+ repo_id=repo_id,
+ filename=filename,
+ repo_type=repo_type,
+ revision=revision,
+ token=token,
+ timeout=timeout,
+ )
+
+ from .utils import hf_thread_map
+ from .utils import tqdm as hf_tqdm
+
+ hf_thread_map(
+ _parse,
+ set(weight_map.values()),
+ desc="Parse safetensors files",
+ tqdm_class=hf_tqdm,
+ )
+
+ return SafetensorsRepoMetadata(
+ metadata=index.get("metadata", None),
+ sharded=True,
+ weight_map=weight_map,
+ files_metadata=files_metadata,
+ )
+ else:
+ # Not a safetensors repo
+ raise NotASafetensorsRepoError(
+ f"'{repo_id}' is not a safetensors repo. Couldn't find '{constants.SAFETENSORS_INDEX_FILE}' or '{constants.SAFETENSORS_SINGLE_FILE}' files."
+ )
+
+ def parse_safetensors_file_metadata(
+ self,
+ repo_id: str,
+ filename: str,
+ *,
+ repo_type: str | None = None,
+ revision: str | None = None,
+ token: bool | str | None = None,
+ timeout: float | None = constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ ) -> SafetensorsFileMetadata:
+ """
+ Parse metadata from a safetensors file on the Hub.
+
+ To parse metadata from all safetensors files in a repo at once, use [`get_safetensors_metadata`].
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Args:
+ repo_id (`str`):
+ A user or an organization name and a repo name separated by a `/`.
+ filename (`str`):
+ The name of the file in the repo.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if the file is in a dataset or space, `None` or `"model"` if in a
+ model. Default is `None`.
+ revision (`str`, *optional*):
+ The git revision to fetch the file from. Can be a branch name, a tag, or a commit hash. Defaults to the
+ head of the `"main"` branch.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ timeout (`float`, *optional*, defaults to 10):
+ How many seconds to wait for the server to send data before giving up. Set to `None` to disable the
+ timeout (not recommended, as a stalled connection can hang the call indefinitely).
+
+ Returns:
+ [`SafetensorsFileMetadata`]: information related to a safetensors file.
+
+ Raises:
+ [`NotASafetensorsRepoError`]:
+ If the repo is not a safetensors repo i.e. doesn't have either a
+ `model.safetensors` or a `model.safetensors.index.json` file.
+ [`SafetensorsParsingError`]:
+ If a safetensors file header couldn't be parsed correctly.
+ """
+
+ from .file_download import hf_hub_url
+
+ url = hf_hub_url(
+ repo_id=repo_id, filename=filename, repo_type=repo_type, revision=revision, endpoint=self.endpoint
+ )
+ _headers = self._build_hf_headers(token=token)
+
+ context_msg = f"repo '{repo_id}', revision '{revision or constants.DEFAULT_REVISION}'"
+
+ # 1. Fetch first 100kb
+ # Empirically, 97% of safetensors files have a metadata size < 100kb (over the top 1000 models on the Hub).
+ # We assume fetching 100kb is faster than making 2 GET requests. Therefore we always fetch the first 100kb to
+ # avoid the 2nd GET in most cases.
+ # See https://github.com/huggingface/huggingface_hub/pull/1855#discussion_r1404286419.
+ response = get_session().get(url, headers={**_headers, "range": "bytes=0-100000"}, timeout=timeout)
+ hf_raise_for_status(response)
+
+ # 2. Parse and validate metadata size using shared helper
+ metadata_size = _get_safetensors_metadata_size(response.content[:8], filename, context_msg)
+
+ # 3.a. Get metadata from payload, if fully contained in the response (minus the 8-byte size prefix)
+ if metadata_size <= len(response.content) - 8:
+ metadata_as_bytes = response.content[8 : 8 + metadata_size]
+ else: # 3.b. Request full metadata
+ response = get_session().get(
+ url, headers={**_headers, "range": f"bytes=8-{metadata_size + 7}"}, timeout=timeout
+ )
+ hf_raise_for_status(response)
+ metadata_as_bytes = response.content
+
+ # 4. Parse json header using shared helper
+ return _parse_safetensors_header(metadata_as_bytes, filename, context_msg)
+
+ @validate_hf_hub_args
+ def create_branch(
+ self,
+ repo_id: str,
+ *,
+ branch: str,
+ revision: str | None = None,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ exist_ok: bool = False,
+ ) -> None:
+ """
+ Create a new branch for a repo on the Hub, starting from the specified revision (defaults to `main`).
+ To find a revision suiting your needs, you can use [`list_repo_refs`] or [`list_repo_commits`].
+
+ Args:
+ repo_id (`str`):
+ The repository in which the branch will be created.
+ Example: `"user/my-cool-model"`.
+
+ branch (`str`):
+ The name of the branch to create.
+
+ revision (`str`, *optional*):
+ The git revision to create the branch from. It can be a branch name or
+ the OID/SHA of a commit, as a hexadecimal string. Defaults to the head
+ of the `"main"` branch.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if creating a branch on a dataset or
+ space, `None` or `"model"` if tagging a model. Default is `None`.
+
+ exist_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if branch already exists.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+ [`~utils.BadRequestError`]:
+ If invalid reference for a branch. Ex: `refs/pr/5` or 'refs/foo/bar'.
+ [`~utils.HfHubHTTPError`]:
+ If the branch already exists on the repo (error 409) and `exist_ok` is
+ set to `False`.
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ branch = quote(branch, safe="")
+
+ # Prepare request
+ branch_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/branch/{branch}"
+ headers = self._build_hf_headers(token=token)
+ payload = {}
+ if revision is not None:
+ payload["startingPoint"] = revision
+
+ # Create branch
+ response = get_session().post(url=branch_url, headers=headers, json=payload)
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as e:
+ if exist_ok and e.response.status_code == 409:
+ return
+ elif exist_ok and e.response.status_code == 403:
+ # No write permission on the namespace but branch might already exist
+ try:
+ refs = self.list_repo_refs(repo_id=repo_id, repo_type=repo_type, token=token)
+ for branch_ref in refs.branches:
+ if branch_ref.name == branch:
+ return # Branch already exists => do not raise
+ except HfHubHTTPError:
+ pass # We raise the original error if the branch does not exist
+ raise
+
+ @validate_hf_hub_args
+ def delete_branch(
+ self,
+ repo_id: str,
+ *,
+ branch: str,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> None:
+ """
+ Delete a branch from a repo on the Hub.
+
+ Args:
+ repo_id (`str`):
+ The repository in which a branch will be deleted.
+ Example: `"user/my-cool-model"`.
+
+ branch (`str`):
+ The name of the branch to delete.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if creating a branch on a dataset or
+ space, `None` or `"model"` if tagging a model. Default is `None`.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+ [`~utils.HfHubHTTPError`]:
+ If trying to delete a protected branch. Ex: `main` cannot be deleted.
+ [`~utils.HfHubHTTPError`]:
+ If trying to delete a branch that does not exist.
+
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ branch = quote(branch, safe="")
+
+ # Prepare request
+ branch_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/branch/{branch}"
+ headers = self._build_hf_headers(token=token)
+
+ # Delete branch
+ response = get_session().delete(url=branch_url, headers=headers)
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def create_tag(
+ self,
+ repo_id: str,
+ *,
+ tag: str,
+ tag_message: str | None = None,
+ revision: str | None = None,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ exist_ok: bool = False,
+ ) -> None:
+ """
+ Tag a given commit of a repo on the Hub.
+
+ Args:
+ repo_id (`str`):
+ The repository in which a commit will be tagged.
+ Example: `"user/my-cool-model"`.
+
+ tag (`str`):
+ The name of the tag to create.
+
+ tag_message (`str`, *optional*):
+ The description of the tag to create.
+
+ revision (`str`, *optional*):
+ The git revision to tag. It can be a branch name or the OID/SHA of a
+ commit, as a hexadecimal string. Shorthands (7 first characters) are
+ also supported. Defaults to the head of the `"main"` branch.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if tagging a dataset or
+ space, `None` or `"model"` if tagging a model. Default is
+ `None`.
+
+ exist_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if tag already exists.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If revision is not found (error 404) on the repo.
+ [`~utils.HfHubHTTPError`]:
+ If the branch already exists on the repo (error 409) and `exist_ok` is
+ set to `False`.
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ revision = quote(revision, safe="") if revision is not None else constants.DEFAULT_REVISION
+
+ # Prepare request
+ tag_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tag/{revision}"
+ headers = self._build_hf_headers(token=token)
+ payload = {"tag": tag}
+ if tag_message is not None:
+ payload["message"] = tag_message
+
+ # Tag
+ response = get_session().post(url=tag_url, headers=headers, json=payload)
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as e:
+ if not (e.response.status_code == 409 and exist_ok):
+ raise
+
+ @validate_hf_hub_args
+ def delete_tag(
+ self,
+ repo_id: str,
+ *,
+ tag: str,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> None:
+ """
+ Delete a tag from a repo on the Hub.
+
+ Args:
+ repo_id (`str`):
+ The repository in which a tag will be deleted.
+ Example: `"user/my-cool-model"`.
+
+ tag (`str`):
+ The name of the tag to delete.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if tagging a dataset or space, `None` or
+ `"model"` if tagging a model. Default is `None`.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If repository is not found (error 404): wrong repo_id/repo_type, private
+ but not authenticated or repo does not exist.
+ [`~utils.RevisionNotFoundError`]:
+ If tag is not found.
+ """
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ tag = quote(tag, safe="")
+
+ # Prepare request
+ tag_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/tag/{tag}"
+ headers = self._build_hf_headers(token=token)
+
+ # Un-tag
+ response = get_session().delete(url=tag_url, headers=headers)
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def get_full_repo_name(
+ self,
+ model_id: str,
+ *,
+ organization: str | None = None,
+ token: bool | str | None = None,
+ ):
+ """
+ Returns the repository name for a given model ID and optional
+ organization.
+
+ Args:
+ model_id (`str`):
+ The name of the model.
+ organization (`str`, *optional*):
+ If passed, the repository name will be in the organization
+ namespace instead of the user namespace.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `str`: The repository name in the user's namespace
+ ({username}/{model_id}) if no organization is passed, and under the
+ organization namespace ({organization}/{model_id}) otherwise.
+ """
+ if organization is None:
+ if "/" in model_id:
+ username = model_id.split("/")[0]
+ else:
+ username = self.whoami(token=token)["name"] # type: ignore
+ return f"{username}/{model_id}"
+ else:
+ return f"{organization}/{model_id}"
+
+ @validate_hf_hub_args
+ def get_repo_discussions(
+ self,
+ repo_id: str,
+ *,
+ author: str | None = None,
+ discussion_type: constants.DiscussionTypeFilter | None = None,
+ discussion_status: constants.DiscussionStatusFilter | None = None,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterator[Discussion]:
+ """
+ Fetches Discussions and Pull Requests for the given repo.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ author (`str`, *optional*):
+ Pass a value to filter by discussion author. `None` means no filter.
+ Default is `None`.
+ discussion_type (`str`, *optional*):
+ Set to `"pull_request"` to fetch only pull requests, `"discussion"`
+ to fetch only discussions. Set to `"all"` or `None` to fetch both.
+ Default is `None`.
+ discussion_status (`str`, *optional*):
+ Set to `"open"` (respectively `"closed"`) to fetch only open
+ (respectively closed) discussions. Set to `"all"` or `None`
+ to fetch both.
+ Default is `None`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if fetching from a dataset or
+ space, `None` or `"model"` if fetching from a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterator[Discussion]`: An iterator of [`Discussion`] objects.
+
+ Example:
+ Collecting all discussions of a repo in a list:
+
+ ```python
+ >>> from huggingface_hub import get_repo_discussions
+ >>> discussions_list = list(get_repo_discussions(repo_id="bert-base-uncased"))
+ ```
+
+ Iterating over discussions of a repo:
+
+ ```python
+ >>> from huggingface_hub import get_repo_discussions
+ >>> for discussion in get_repo_discussions(repo_id="bert-base-uncased"):
+ ... print(discussion.num, discussion.title)
+ ```
+ """
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ if discussion_type is not None and discussion_type not in constants.DISCUSSION_TYPES:
+ raise ValueError(f"Invalid discussion_type, must be one of {constants.DISCUSSION_TYPES}")
+ if discussion_status is not None and discussion_status not in constants.DISCUSSION_STATUS:
+ raise ValueError(f"Invalid discussion_status, must be one of {constants.DISCUSSION_STATUS}")
+
+ headers = self._build_hf_headers(token=token)
+ path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions"
+
+ params: dict[str, str | int] = {}
+ if discussion_type is not None:
+ params["type"] = discussion_type
+ if discussion_status is not None:
+ params["status"] = discussion_status
+ if author is not None:
+ params["author"] = author
+
+ def _fetch_discussion_page(page_index: int):
+ params["p"] = page_index
+ resp = get_session().get(path, headers=headers, params=params)
+ hf_raise_for_status(resp)
+ paginated_discussions = resp.json()
+ total = paginated_discussions["count"]
+ start = paginated_discussions["start"]
+ discussions = paginated_discussions["discussions"]
+ has_next = (start + len(discussions)) < total
+ return discussions, has_next
+
+ has_next, page_index = True, 0
+
+ while has_next:
+ discussions, has_next = _fetch_discussion_page(page_index=page_index)
+ for discussion in discussions:
+ yield Discussion(
+ title=discussion["title"],
+ num=discussion["num"],
+ author=discussion.get("author", {}).get("name", "deleted"),
+ created_at=parse_datetime(discussion["createdAt"]),
+ status=discussion["status"],
+ repo_id=discussion["repo"]["name"],
+ repo_type=discussion["repo"]["type"],
+ is_pull_request=discussion["isPullRequest"],
+ endpoint=self.endpoint,
+ )
+ page_index = page_index + 1
+
+ @validate_hf_hub_args
+ def get_discussion_details(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ *,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> DiscussionWithDetails:
+ """Fetches a Discussion's / Pull Request 's details from the Hub.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns: [`DiscussionWithDetails`]
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ if not isinstance(discussion_num, int) or discussion_num <= 0:
+ raise ValueError("Invalid discussion_num, must be a positive integer")
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions/{discussion_num}"
+ headers = self._build_hf_headers(token=token)
+ resp = get_session().get(path, params={"diff": "1"}, headers=headers)
+ hf_raise_for_status(resp)
+
+ discussion_details = resp.json()
+ is_pull_request = discussion_details["isPullRequest"]
+
+ target_branch = discussion_details["changes"]["base"] if is_pull_request else None
+ conflicting_files = discussion_details["filesWithConflicts"] if is_pull_request else None
+ merge_commit_oid = discussion_details["changes"].get("mergeCommitId", None) if is_pull_request else None
+
+ return DiscussionWithDetails(
+ title=discussion_details["title"],
+ num=discussion_details["num"],
+ author=discussion_details.get("author", {}).get("name", "deleted"),
+ created_at=parse_datetime(discussion_details["createdAt"]),
+ status=discussion_details["status"],
+ repo_id=discussion_details["repo"]["name"],
+ repo_type=discussion_details["repo"]["type"],
+ is_pull_request=discussion_details["isPullRequest"],
+ events=[deserialize_event(evt) for evt in discussion_details["events"]],
+ conflicting_files=conflicting_files,
+ target_branch=target_branch,
+ merge_commit_oid=merge_commit_oid,
+ diff=discussion_details.get("diff"),
+ endpoint=self.endpoint,
+ )
+
+ @validate_hf_hub_args
+ def create_discussion(
+ self,
+ repo_id: str,
+ title: str,
+ *,
+ token: bool | str | None = None,
+ description: str | None = None,
+ repo_type: str | None = None,
+ pull_request: bool = False,
+ ) -> DiscussionWithDetails:
+ """Creates a Discussion or Pull Request.
+
+ Pull Requests created programmatically will be in `"draft"` status.
+
+ Creating a Pull Request with changes can also be done at once with [`HfApi.create_commit`].
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ title (`str`):
+ The title of the discussion. It can be up to 200 characters long,
+ and must be at least 3 characters long. Leading and trailing whitespaces
+ will be stripped.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ description (`str`, *optional*):
+ An optional description for the Pull Request.
+ Defaults to `"Discussion opened with the huggingface_hub Python library"`
+ pull_request (`bool`, *optional*):
+ Whether to create a Pull Request or discussion. If `True`, creates a Pull Request.
+ If `False`, creates a discussion. Defaults to `False`.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+
+ Returns: [`DiscussionWithDetails`]
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access."""
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ if description is not None:
+ description = description.strip()
+ description = (
+ description
+ if description
+ else (
+ f"{'Pull Request' if pull_request else 'Discussion'} opened with the"
+ " [huggingface_hub Python"
+ " library](https://huggingface.co/docs/huggingface_hub)"
+ )
+ )
+
+ headers = self._build_hf_headers(token=token)
+ resp = get_session().post(
+ f"{self.endpoint}/api/{repo_type}s/{repo_id}/discussions",
+ json={
+ "title": title.strip(),
+ "description": description,
+ "pullRequest": pull_request,
+ },
+ headers=headers,
+ )
+ hf_raise_for_status(resp)
+ num = resp.json()["num"]
+ return self.get_discussion_details(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=num,
+ token=token,
+ )
+
+ @validate_hf_hub_args
+ def create_pull_request(
+ self,
+ repo_id: str,
+ title: str,
+ *,
+ token: bool | str | None = None,
+ description: str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionWithDetails:
+ """Creates a Pull Request . Pull Requests created programmatically will be in `"draft"` status.
+
+ Creating a Pull Request with changes can also be done at once with [`HfApi.create_commit`];
+
+ This is a wrapper around [`HfApi.create_discussion`].
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ title (`str`):
+ The title of the discussion. It can be up to 200 characters long,
+ and must be at least 3 characters long. Leading and trailing whitespaces
+ will be stripped.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ description (`str`, *optional*):
+ An optional description for the Pull Request.
+ Defaults to `"Discussion opened with the huggingface_hub Python library"`
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+
+ Returns: [`DiscussionWithDetails`]
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access."""
+ return self.create_discussion(
+ repo_id=repo_id,
+ title=title,
+ token=token,
+ description=description,
+ repo_type=repo_type,
+ pull_request=True,
+ )
+
+ def _post_discussion_changes(
+ self,
+ *,
+ repo_id: str,
+ discussion_num: int,
+ resource: str,
+ body: dict | None = None,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> httpx2.Response:
+ """Internal utility to POST changes to a Discussion or Pull Request"""
+ if not isinstance(discussion_num, int) or discussion_num <= 0:
+ raise ValueError("Invalid discussion_num, must be a positive integer")
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ repo_id = f"{repo_type}s/{repo_id}"
+
+ path = f"{self.endpoint}/api/{repo_id}/discussions/{discussion_num}/{resource}"
+
+ headers = self._build_hf_headers(token=token)
+ resp = get_session().post(path, headers=headers, json=body)
+ hf_raise_for_status(resp)
+ return resp
+
+ @validate_hf_hub_args
+ def comment_discussion(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ comment: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionComment:
+ """Creates a new comment on the given Discussion.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ comment (`str`):
+ The content of the comment to create. Comments support markdown formatting.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionComment`]: the newly created comment
+
+
+ Examples:
+ ```python
+
+ >>> comment = \"\"\"
+ ... Hello @otheruser!
+ ...
+ ... # This is a title
+ ...
+ ... **This is bold**, *this is italic* and ~this is strikethrough~
+ ... And [this](http://url) is a link
+ ... \"\"\"
+
+ >>> HfApi().comment_discussion(
+ ... repo_id="username/repo_name",
+ ... discussion_num=34
+ ... comment=comment
+ ... )
+ # DiscussionComment(id='deadbeef0000000', type='comment', ...)
+
+ ```
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ resp = self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource="comment",
+ body={"comment": comment},
+ )
+ return deserialize_event(resp.json()["newMessage"]) # type: ignore
+
+ @validate_hf_hub_args
+ def rename_discussion(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ new_title: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionTitleChange:
+ """Renames a Discussion.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ new_title (`str`):
+ The new title for the discussion
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionTitleChange`]: the title change event
+
+
+ Examples:
+ ```python
+ >>> new_title = "New title, fixing a typo"
+ >>> HfApi().rename_discussion(
+ ... repo_id="username/repo_name",
+ ... discussion_num=34
+ ... new_title=new_title
+ ... )
+ # DiscussionTitleChange(id='deadbeef0000000', type='title-change', ...)
+
+ ```
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ resp = self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource="title",
+ body={"title": new_title},
+ )
+ return deserialize_event(resp.json()["newTitle"]) # type: ignore
+
+ @validate_hf_hub_args
+ def change_discussion_status(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ new_status: Literal["open", "closed"],
+ *,
+ token: bool | str | None = None,
+ comment: str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionStatusChange:
+ """Closes or re-opens a Discussion or Pull Request.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ new_status (`str`):
+ The new status for the discussion, either `"open"` or `"closed"`.
+ comment (`str`, *optional*):
+ An optional comment to post with the status change.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionStatusChange`]: the status change event
+
+
+ Examples:
+ ```python
+ >>> new_title = "New title, fixing a typo"
+ >>> HfApi().rename_discussion(
+ ... repo_id="username/repo_name",
+ ... discussion_num=34
+ ... new_title=new_title
+ ... )
+ # DiscussionStatusChange(id='deadbeef0000000', type='status-change', ...)
+
+ ```
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ if new_status not in ["open", "closed"]:
+ raise ValueError("Invalid status, valid statuses are: 'open' and 'closed'")
+ body: dict[str, str] = {"status": new_status}
+ if comment and comment.strip():
+ body["comment"] = comment.strip()
+ resp = self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource="status",
+ body=body,
+ )
+ return deserialize_event(resp.json()["newStatus"]) # type: ignore
+
+ @validate_hf_hub_args
+ def merge_pull_request(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ *,
+ token: bool | str | None = None,
+ comment: str | None = None,
+ repo_type: str | None = None,
+ ):
+ """Merges a Pull Request.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ comment (`str`, *optional*):
+ An optional comment to post with the status change.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionStatusChange`]: the status change event
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource="merge",
+ body={"comment": comment.strip()} if comment and comment.strip() else None,
+ )
+
+ @validate_hf_hub_args
+ def edit_discussion_comment(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ comment_id: str,
+ new_content: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionComment:
+ """Edits a comment on a Discussion / Pull Request.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ comment_id (`str`):
+ The ID of the comment to edit.
+ new_content (`str`):
+ The new content of the comment. Comments support markdown formatting.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionComment`]: the edited comment
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ resp = self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource=f"comment/{comment_id.lower()}/edit",
+ body={"content": new_content},
+ )
+ return deserialize_event(resp.json()["updatedComment"]) # type: ignore
+
+ @validate_hf_hub_args
+ def hide_discussion_comment(
+ self,
+ repo_id: str,
+ discussion_num: int,
+ comment_id: str,
+ *,
+ token: bool | str | None = None,
+ repo_type: str | None = None,
+ ) -> DiscussionComment:
+ """Hides a comment on a Discussion / Pull Request.
+
+ > [!WARNING]
+ > Hidden comments' content cannot be retrieved anymore. Hiding a comment is irreversible.
+
+ Args:
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ discussion_num (`int`):
+ The number of the Discussion or Pull Request . Must be a strictly positive integer.
+ comment_id (`str`):
+ The ID of the comment to edit.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if uploading to a dataset or
+ space, `None` or `"model"` if uploading to a model. Default is
+ `None`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`DiscussionComment`]: the hidden comment
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the HuggingFace API returned an error
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if some parameter value is invalid
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it doesn't exist,
+ > or because it is set to `private` and you do not have access.
+ """
+ warnings.warn(
+ "Hidden comments' content cannot be retrieved anymore. Hiding a comment is irreversible.",
+ UserWarning,
+ )
+ resp = self._post_discussion_changes(
+ repo_id=repo_id,
+ repo_type=repo_type,
+ discussion_num=discussion_num,
+ token=token,
+ resource=f"comment/{comment_id.lower()}/hide",
+ )
+ return deserialize_event(resp.json()["updatedComment"]) # type: ignore
+
+ @validate_hf_hub_args
+ def add_space_secret(
+ self,
+ repo_id: str,
+ key: str,
+ value: str,
+ *,
+ description: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """Adds or updates a secret in a Space.
+
+ Secrets allow to set secret keys or tokens to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ key (`str`):
+ Secret key. Example: `"GITHUB_API_KEY"`
+ value (`str`):
+ Secret value. Example: `"your_github_api_key"`.
+ description (`str`, *optional*):
+ Secret description. Example: `"Github API key to access the Github API"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ payload = {"key": key, "value": value}
+ if description is not None:
+ payload["description"] = description
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/secrets",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(r)
+
+ @validate_hf_hub_args
+ def delete_space_secret(self, repo_id: str, key: str, *, token: bool | str | None = None) -> None:
+ """Deletes a secret from a Space.
+
+ Secrets allow to set secret keys or tokens to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ key (`str`):
+ Secret key. Example: `"GITHUB_API_KEY"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ r = get_session().request(
+ "DELETE",
+ f"{self.endpoint}/api/spaces/{repo_id}/secrets",
+ headers=self._build_hf_headers(token=token),
+ json={"key": key},
+ )
+ hf_raise_for_status(r)
+
+ @validate_hf_hub_args
+ def get_space_secrets(self, repo_id: str, *, token: bool | str | None = None) -> dict[str, SpaceSecret]:
+ """Gets all secrets from a Space.
+
+ Secret values are write-only and cannot be read back. Only the key, description, and last update time
+ are returned.
+
+ Secrets allow to set secret keys or tokens to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to query. Example: `"bigcode/in-the-stack"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `dict[str, SpaceSecret]`: Dictionary of [`SpaceSecret`] objects keyed by secret name.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> api.get_space_secrets("username/my-space")
+ {'HF_TOKEN': SpaceSecret(key='HF_TOKEN', description='...', updated_at=datetime.datetime(...))}
+ ```
+ """
+ r = get_session().get(
+ f"{self.endpoint}/api/spaces/{repo_id}/secrets",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(r)
+ return {k: SpaceSecret(k, v) for k, v in r.json().items()}
+
+ @validate_hf_hub_args
+ def get_space_variables(self, repo_id: str, *, token: bool | str | None = None) -> dict[str, SpaceVariable]:
+ """Gets all variables from a Space.
+
+ Variables allow to set environment variables to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to query. Example: `"bigcode/in-the-stack"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ r = get_session().get(
+ f"{self.endpoint}/api/spaces/{repo_id}/variables",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(r)
+ return {k: SpaceVariable(k, v) for k, v in r.json().items()}
+
+ @validate_hf_hub_args
+ def add_space_variable(
+ self,
+ repo_id: str,
+ key: str,
+ value: str,
+ *,
+ description: str | None = None,
+ token: bool | str | None = None,
+ ) -> dict[str, SpaceVariable]:
+ """Adds or updates a variable in a Space.
+
+ Variables allow to set environment variables to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ key (`str`):
+ Variable key. Example: `"MODEL_REPO_ID"`
+ value (`str`):
+ Variable value. Example: `"the_model_repo_id"`.
+ description (`str`):
+ Description of the variable. Example: `"Model Repo ID of the implemented model"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ payload = {"key": key, "value": value}
+ if description is not None:
+ payload["description"] = description
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/variables",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(r)
+ return {k: SpaceVariable(k, v) for k, v in r.json().items()}
+
+ @validate_hf_hub_args
+ def delete_space_variable(
+ self, repo_id: str, key: str, *, token: bool | str | None = None
+ ) -> dict[str, SpaceVariable]:
+ """Deletes a variable from a Space.
+
+ Variables allow to set environment variables to a Space without hardcoding them.
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ key (`str`):
+ Variable key. Example: `"MODEL_REPO_ID"`
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ r = get_session().request(
+ "DELETE",
+ f"{self.endpoint}/api/spaces/{repo_id}/variables",
+ headers=self._build_hf_headers(token=token),
+ json={"key": key},
+ )
+ hf_raise_for_status(r)
+ return {k: SpaceVariable(k, v) for k, v in r.json().items()}
+
+ @validate_hf_hub_args
+ def get_space_runtime(self, repo_id: str, *, token: bool | str | None = None) -> SpaceRuntime:
+ """Gets runtime information about a Space.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ Returns:
+ [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
+ """
+ r = get_session().get(
+ f"{self.endpoint}/api/spaces/{repo_id}/runtime", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ def list_spaces_hardware(self, token: bool | str | None = None) -> list[JobHardwareInfo]:
+ """List available hardware options for Spaces.
+
+ Returns:
+ `list[JobHardwareInfo]`: A list of available hardware configurations.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import list_spaces_hardware
+ >>> hardware_list = list_spaces_hardware()
+ >>> hardware_list[0]
+ JobHardwareInfo(name='cpu-basic', pretty_name='CPU Basic', cpu='2 vCPU', ram='16 GB', ...)
+ >>> hardware_list[0].name
+ 'cpu-basic'
+ ```
+ """
+ response = get_session().get(
+ f"{self.endpoint}/api/spaces/hardware", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(response)
+ return [JobHardwareInfo(**hardware) for hardware in response.json()]
+
+ def get_zero_gpu_quota(self, *, token: bool | str | None = None) -> ZeroGpuQuota:
+ """Get the ZeroGPU quota of the authenticated user.
+
+ Useful to track ZeroGPU usage when calling ZeroGPU Spaces programmatically (e.g. from an app, an agent or
+ an MCP server). See https://huggingface.co/docs/hub/spaces-zerogpu for more details.
+
+ Args:
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ A fine-grained token must have the "Billing > Read billing usage and payment method status"
+ permission and an OAuth token must have the `read-billing` scope.
+
+ Returns:
+ [`ZeroGpuQuota`]: The ZeroGPU quota of the authenticated user.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import get_zero_gpu_quota
+ >>> quota = get_zero_gpu_quota()
+ >>> quota
+ ZeroGpuQuota(base=2400, remaining=1810, resets_at=datetime.datetime(2026, 9, 30, 9, 12, 3, tzinfo=datetime.timezone.utc), overquota_used=0)
+ >>> print(f"{quota.remaining / 60:.1f} minutes left")
+ 30.2 minutes left
+ ```
+ """
+ r = get_session().get(
+ f"{self.endpoint}/api/spaces/zero-gpu/quota", headers=self._build_hf_headers(token=token)
+ )
+ if r.status_code == 401:
+ # Handled before `hf_raise_for_status` which would treat it as a missing 'zero-gpu/quota' Space repo.
+ raise HfHubHTTPError(
+ "401 Unauthorized: a valid token is required to get the ZeroGPU quota. Log in with `hf auth login` or"
+ " pass a token. See https://huggingface.co/settings/tokens.",
+ response=r,
+ )
+ hf_raise_for_status(r)
+ return ZeroGpuQuota(r.json())
+
+ @validate_hf_hub_args
+ def request_space_hardware(
+ self,
+ repo_id: str,
+ hardware: SpaceHardware,
+ *,
+ token: bool | str | None = None,
+ sleep_time: int | None = None,
+ ) -> SpaceRuntime:
+ """Request new hardware for a Space.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ hardware (`str` or [`SpaceHardware`]):
+ Hardware on which to run the Space. Example: `"t4-medium"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ sleep_time (`int`, *optional*):
+ Number of seconds of inactivity to wait before a Space is put to sleep. Set to `-1` if you don't want
+ your Space to sleep (default behavior for upgraded hardware). For free hardware, you can't configure
+ the sleep time (value is fixed to 48 hours of inactivity).
+ See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
+ Returns:
+ [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
+
+ > [!TIP]
+ > It is also possible to request hardware directly when creating the Space repo! See [`create_repo`] for details.
+ """
+ if sleep_time is not None and hardware == SpaceHardware.CPU_BASIC:
+ warnings.warn(
+ "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
+ " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
+ " you want to set a custom sleep time, you need to upgrade to a paid Hardware.",
+ UserWarning,
+ )
+ payload: dict[str, Any] = {"flavor": hardware}
+ if sleep_time is not None:
+ payload["sleepTimeSeconds"] = sleep_time
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/hardware",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ @validate_hf_hub_args
+ def set_space_sleep_time(self, repo_id: str, sleep_time: int, *, token: bool | str | None = None) -> SpaceRuntime:
+ """Set a custom sleep time for a Space running on upgraded hardware..
+
+ Your Space will go to sleep after X seconds of inactivity. You are not billed when your Space is in "sleep"
+ mode. If a new visitor lands on your Space, it will "wake it up". Only upgraded hardware can have a
+ configurable sleep time. To know more about the sleep stage, please refer to
+ https://huggingface.co/docs/hub/spaces-gpus#sleep-time.
+
+ Args:
+ repo_id (`str`):
+ ID of the repo to update. Example: `"bigcode/in-the-stack"`.
+ sleep_time (`int`, *optional*):
+ Number of seconds of inactivity to wait before a Space is put to sleep. Set to `-1` if you don't want
+ your Space to pause (default behavior for upgraded hardware). For free hardware, you can't configure
+ the sleep time (value is fixed to 48 hours of inactivity).
+ See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ Returns:
+ [`SpaceRuntime`]: Runtime information about a Space including Space stage and hardware.
+
+ > [!TIP]
+ > It is also possible to set a custom sleep time when requesting hardware with [`request_space_hardware`].
+ """
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/sleeptime",
+ headers=self._build_hf_headers(token=token),
+ json={"seconds": sleep_time},
+ )
+ hf_raise_for_status(r)
+ runtime = SpaceRuntime(r.json())
+
+ hardware = runtime.requested_hardware or runtime.hardware
+ if hardware == SpaceHardware.CPU_BASIC:
+ warnings.warn(
+ "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
+ " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
+ " you want to set a custom sleep time, you need to upgrade to a paid Hardware.",
+ UserWarning,
+ )
+ return runtime
+
+ @validate_hf_hub_args
+ def pause_space(self, repo_id: str, *, token: bool | str | None = None) -> SpaceRuntime:
+ """Pause your Space.
+
+ A paused Space stops executing until manually restarted by its owner. This is different from the sleeping
+ state in which free Spaces go after 48h of inactivity. Paused time is not billed to your account, no matter the
+ hardware you've selected. To restart your Space, use [`restart_space`] and go to your Space settings page.
+
+ For more details, please visit [the docs](https://huggingface.co/docs/hub/spaces-gpus#pause).
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to pause. Example: `"Salesforce/BLIP2"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`SpaceRuntime`]: Runtime information about your Space including `stage=PAUSED` and requested hardware.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
+ are not authenticated.
+ [`~utils.HfHubHTTPError`]:
+ 403 Forbidden: only the owner of a Space can pause it. If you want to manage a Space that you don't
+ own, either ask the owner by opening a Discussion or duplicate the Space.
+ [`~utils.BadRequestError`]:
+ If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
+ a static Space, you can set it to private.
+ """
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/pause", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ @validate_hf_hub_args
+ def enable_space_dev_mode(self, repo_id: str, *, token: bool | str | None = None) -> SpaceRuntime:
+ """Enable dev mode on a Space.
+
+ Spaces Dev Mode eases the debugging of your application and makes iterating on Spaces faster by allowing you
+ to restart your application without stopping the Space container itself. This feature is available as part of
+ a PRO or Team & Enterprise plan. See https://huggingface.co/docs/hub/spaces-dev-mode for more details.
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to enable dev mode. Example: `"Salesforce/BLIP2"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`SpaceRuntime`]: Runtime information about your Space.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
+ are not authenticated.
+ [`~utils.HfHubHTTPError`]:
+ 403 Forbidden: only the owner of a Space can set dev mode. If you want to handle a Space that you don't
+ own, either ask the owner by opening a Discussion or duplicate the Space.
+ [`~utils.BadRequestError`]:
+ If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
+ a static Space, you can set it to private.
+ """
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/dev-mode",
+ headers=self._build_hf_headers(token=token),
+ json={"enabled": True},
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ @validate_hf_hub_args
+ def disable_space_dev_mode(
+ self,
+ repo_id: str,
+ *,
+ token: bool | str | None = None,
+ ) -> SpaceRuntime:
+ """Disable dev mode on a Space.
+
+ Spaces Dev Mode eases the debugging of your application and makes iterating on Spaces faster by allowing you
+ to restart your application without stopping the Space container itself. Enabling dev mode requires a PRO or
+ Team & Enterprise plan, but disabling it is always allowed (e.g. after a subscription has expired). See
+ https://huggingface.co/docs/hub/spaces-dev-mode for more details.
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to disable dev mode. Example: `"Salesforce/BLIP2"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`SpaceRuntime`]: Runtime information about your Space.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
+ are not authenticated.
+ [`~utils.HfHubHTTPError`]:
+ 403 Forbidden: only the owner of a Space can set dev mode. If you want to handle a Space that you don't
+ own, either ask the owner by opening a Discussion or duplicate the Space.
+ [`~utils.BadRequestError`]:
+ If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
+ a static Space, you can set it to private.
+ """
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/dev-mode",
+ headers=self._build_hf_headers(token=token),
+ json={"enabled": False},
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ @validate_hf_hub_args
+ def restart_space(
+ self, repo_id: str, *, token: bool | str | None = None, factory_reboot: bool = False
+ ) -> SpaceRuntime:
+ """Restart your Space.
+
+ This is the only way to programmatically restart a Space if you've put it on Pause (see [`pause_space`]). You
+ must be the owner of the Space to restart it. If you are using an upgraded hardware, your account will be
+ billed as soon as the Space is restarted. You can trigger a restart no matter the current state of a Space.
+
+ For more details, please visit [the docs](https://huggingface.co/docs/hub/spaces-gpus#pause).
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to restart. Example: `"Salesforce/BLIP2"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ factory_reboot (`bool`, *optional*):
+ If `True`, the Space will be rebuilt from scratch without caching any requirements.
+
+ Returns:
+ [`SpaceRuntime`]: Runtime information about your Space.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If your Space is not found (error 404). Most probably wrong repo_id or your space is private but you
+ are not authenticated.
+ [`~utils.HfHubHTTPError`]:
+ 403 Forbidden: only the owner of a Space can restart it. If you want to restart a Space that you don't
+ own, either ask the owner by opening a Discussion or duplicate the Space.
+ [`~utils.BadRequestError`]:
+ If your Space is a static Space. Static Spaces are always running and never billed. If you want to hide
+ a static Space, you can set it to private.
+ """
+ params = {}
+ if factory_reboot:
+ params["factory"] = "true"
+ r = get_session().post(
+ f"{self.endpoint}/api/spaces/{repo_id}/restart", headers=self._build_hf_headers(token=token), params=params
+ )
+ hf_raise_for_status(r)
+ return SpaceRuntime(r.json())
+
+ def _stream_sse_events(
+ self,
+ *,
+ url: str,
+ log_label: str,
+ timeout: int,
+ follow: bool,
+ token: bool | str | None = None,
+ skip_previous_events_on_retry: bool = True,
+ tolerated_status_codes: tuple[int, ...] = (),
+ tolerated_exception_types: tuple[type[Exception], ...] = (),
+ on_iteration_end: Callable[[], bool] | None = None,
+ params: dict[str, Any] | None = None,
+ ) -> Iterable[dict[str, Any]]:
+ # Shared SSE streaming loop with retry/backoff and event-index dedup.
+ # Used by Spaces logs and Jobs logs/metrics. Two retry styles:
+ # - on_iteration_end is None: retries are the only backstop (Spaces).
+ # - on_iteration_end is set: it polls authoritative state after every
+ # failed iteration; ReadTimeouts/tolerated errors fall through to it
+ # instead of consuming retries (Jobs).
+ nb_tries = 0
+ max_retries = 5 if follow else 0
+ min_wait_time = 1
+ max_wait_time = 10
+ sleep_time = 0
+ start_event_idx = 0
+ error_to_retry: Exception | None = None
+ while True:
+ if error_to_retry is not None:
+ logger.warning(f"'{error_to_retry}' thrown while requesting {log_label}")
+ logger.warning(f"Retrying in {sleep_time}s [Retry {nb_tries}/{max_retries}].")
+ error_to_retry = None
+ time.sleep(sleep_time)
+ try:
+ with get_session().stream(
+ "GET",
+ url,
+ headers=self._build_hf_headers(token=token),
+ timeout=timeout,
+ params=params,
+ ) as response:
+ if response.status_code == 200:
+ # no-follow: stop at the first event logged after the server's Date (1s resolution)
+ replay_end = None
+ if not follow and (date := response.headers.get("date")):
+ replay_end = parsedate_to_datetime(date) + timedelta(seconds=1)
+ event_idx = -1
+ for line in response.iter_lines():
+ if line and line.startswith("data: {"):
+ event_idx += 1
+ if event_idx >= start_event_idx:
+ if skip_previous_events_on_retry:
+ start_event_idx += 1
+ event = json.loads(line[len("data: ") :])
+ if (
+ replay_end
+ and (ts := event.get("timestamp"))
+ and parse_datetime(ts) > replay_end
+ ):
+ break
+ yield event
+ break
+ elif response.status_code not in tolerated_status_codes:
+ hf_raise_for_status(response)
+ except HfHubHTTPError:
+ # Permanent HTTP error (404/403/...). Never retry — fail fast.
+ raise
+ except httpx2.DecodingError:
+ # Response ended prematurely.
+ break
+ except KeyboardInterrupt:
+ break
+ except (httpx2.HTTPError, httpcore2.TimeoutException) as err:
+ is_no_new_line_timeout = isinstance(err, (httpx2.ReadTimeout, httpcore2.ReadTimeout))
+ if is_no_new_line_timeout and not follow:
+ break # no-follow: timeout means the buffer is drained
+ if on_iteration_end is not None:
+ # Authoritative-state mode: ReadTimeouts and tolerated errors
+ # fall through to the post-iteration check without consuming
+ # retries. Note: ReadTimeout is handled here regardless of
+ # `tolerated_exception_types` — entries in that tuple only
+ # fire for non-timeout errors.
+ if is_no_new_line_timeout or type(err) in tolerated_exception_types:
+ pass
+ elif nb_tries >= max_retries:
+ raise
+ else:
+ nb_tries += 1
+ sleep_time = min(max_wait_time, max(min_wait_time, sleep_time * 2))
+ error_to_retry = err
+ else:
+ # Retry-only mode: every error in follow mode burns a retry.
+ if nb_tries >= max_retries:
+ if is_no_new_line_timeout:
+ break # follow mode, silent stream, retries exhausted: give up
+ raise
+ nb_tries += 1
+ sleep_time = min(max_wait_time, max(min_wait_time, sleep_time * 2))
+ error_to_retry = err
+ # Drop params after the first attempt: the start_event_idx dedup
+ # requires a stable replay prefix, which `tail` would break.
+ params = None
+ # no-follow: a single attempt, never loop back
+ if not follow or (on_iteration_end is not None and on_iteration_end()):
+ break
+
+ def _fetch_space_logs_sse(
+ self,
+ *,
+ repo_id: str,
+ build: bool,
+ timeout: int,
+ follow: bool,
+ token: bool | str | None = None,
+ ) -> Iterable[dict[str, Any]]:
+ log_type = "build" if build else "run"
+ yield from self._stream_sse_events(
+ url=f"{self.endpoint}/api/spaces/{repo_id}/logs/{log_type}",
+ log_label=f"spaces /logs/{log_type} for repo_id={repo_id!r}",
+ timeout=timeout,
+ follow=follow,
+ token=token,
+ )
+
+ @validate_hf_hub_args
+ def fetch_space_logs(
+ self,
+ repo_id: str,
+ *,
+ build: bool = False,
+ follow: bool = False,
+ token: bool | str | None = None,
+ ) -> Iterable[str]:
+ """Fetch the run or build logs of a Space on the Hub.
+
+ Useful for debugging a Space that is failing to build or crashing at runtime,
+ especially from a script or agentic workflow where reading logs in a browser
+ is not an option.
+
+ Args:
+ repo_id (`str`):
+ ID of the Space. Example: `"bigcode/in-the-stack"`.
+ build (`bool`, *optional*, defaults to `False`):
+ If `True`, fetch the container build logs (useful when a Space is stuck
+ in `BUILD_ERROR`). If `False` (default), fetch the run logs, i.e. the
+ stdout/stderr of the running application.
+ follow (`bool`, *optional*, defaults to `False`):
+ If `True`, stream logs in real-time (blocking) until the server closes
+ the stream or `KeyboardInterrupt` is raised. If `False` (default), fetch
+ only the currently buffered logs and return immediately (non-blocking,
+ like `docker logs`).
+ token (`bool` or `str`, *optional*):
+ A valid user access token. Defaults to the locally saved token, which is
+ the recommended authentication method. Set to `False` to disable
+ authentication. See
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ `Iterable[str]`: A generator yielding log lines as they become available.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import fetch_space_logs
+ >>> # Non-blocking: print currently available run logs and exit.
+ >>> for line in fetch_space_logs("username/my-space"):
+ ... print(line, end="")
+
+ >>> # Debug a build failure:
+ >>> for line in fetch_space_logs("username/my-space", build=True):
+ ... print(line, end="")
+
+ >>> # Stream run logs until the server closes the stream.
+ >>> for line in fetch_space_logs("username/my-space", follow=True):
+ ... print(line, end="")
+ ```
+ """
+ # - Spaces /logs/{run|build} is SSE with `data: {"data": "...", "timestamp": "..."}` events.
+ # - Keep-alive messages are sent as empty `data:` events (skipped by the `data: {` filter).
+ # - In no-follow mode we use a short read timeout to drain the buffer and return.
+ timeout = 120 if follow else 5
+ for event in self._fetch_space_logs_sse(
+ repo_id=repo_id,
+ build=build,
+ timeout=timeout,
+ follow=follow,
+ token=token,
+ ):
+ yield event["data"]
+
+ @validate_hf_hub_args
+ def wait_for_space(
+ self,
+ repo_id: str,
+ *,
+ timeout: float | None = None,
+ poll_interval: float = 1.0,
+ token: bool | str | None = None,
+ ) -> SpaceRuntime:
+ """Wait until a Space reaches a terminal stage (not building/starting).
+
+ Polls [`get_space_runtime`] every `poll_interval` seconds until the Space's stage
+ is no longer intermediate (`BUILDING`, `RUNNING_BUILDING`, `APP_STARTING`,
+ `RUNNING_APP_STARTING`). Returns the final [`SpaceRuntime`] in all cases — check
+ `runtime.stage` to act on the outcome (e.g. `RUNNING` vs `BUILD_ERROR`).
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to wait for. Example: `"username/my-space"`.
+ timeout (`float`, *optional*):
+ Maximum time to wait in seconds. If `None`, waits indefinitely.
+ poll_interval (`float`, *optional*):
+ Seconds between status checks. Defaults to 1s.
+ token (`bool` or `str`, *optional*):
+ A valid user access token. Defaults to the locally saved token, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ See https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`SpaceRuntime`]: The final runtime information once the Space reaches a terminal stage.
+
+ Raises:
+ `TimeoutError`:
+ If the Space has not reached a terminal stage after `timeout` seconds.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import restart_space, wait_for_space
+ >>> restart_space("username/my-space")
+ >>> runtime = wait_for_space("username/my-space")
+ >>> runtime.stage
+ 'RUNNING'
+ ```
+ """
+ if timeout is not None and timeout < 0:
+ raise ValueError("`timeout` cannot be negative.")
+ if poll_interval <= 0:
+ raise ValueError("`poll_interval` must be positive.")
+
+ deadline = None if timeout is None else time.monotonic() + timeout
+ while True:
+ runtime = self.get_space_runtime(repo_id, token=token)
+ if runtime.stage not in INTERMEDIATE_SPACE_STAGES:
+ return runtime
+ remaining = None if deadline is None else deadline - time.monotonic()
+ if remaining is not None and remaining <= 0:
+ raise TimeoutError(f"Space '{repo_id}' is still in stage '{runtime.stage}' after {timeout} seconds.")
+ time.sleep(poll_interval if remaining is None else min(poll_interval, remaining))
+
+ @validate_hf_hub_args
+ def duplicate_repo(
+ self,
+ from_id: str,
+ to_id: str | None = None,
+ *,
+ repo_type: str | None = None,
+ private: bool | None = None,
+ visibility: RepoVisibility_T | None = None,
+ token: bool | str | None = None,
+ exist_ok: bool = False,
+ resource_group_id: str | None = None,
+ space_hardware: SpaceHardware | None = None,
+ space_sleep_time: int | None = None,
+ space_secrets: list[dict[str, str]] | None = None,
+ space_variables: list[dict[str, str]] | None = None,
+ space_volumes: list[Volume] | None = None,
+ ) -> DuplicatedRepoUrl:
+ """Duplicate a repo on the Hub (model, dataset, or Space).
+
+ This performs a server-side copy that preserves full git history and LFS objects
+ without requiring a local download/upload round-trip.
+
+ The git history is copied right away, but the LFS/Xet files may still be copied in the background once this
+ method returns. In that case, `files_copy_pending` is `True` on the returned value and downloading these files
+ from the new repo may fail until the copy is complete.
+
+ Args:
+ from_id (`str`):
+ ID of the repo to duplicate. Example: `"openai/gdpval"`.
+ to_id (`str`, *optional*):
+ ID of the new repo. Example: `"myorg/my-gdpval"`. If not provided, the new
+ repo will have the same name as the original repo, but in your account.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if duplicating a dataset or Space,
+ `None` or `"model"` if duplicating a model. Default is `None`.
+ private (`bool`, *optional*):
+ Whether the new repo should be private or not. Defaults to the same
+ privacy as the original repo. Cannot be passed together with `visibility`.
+ visibility (`Literal["public", "private", "protected"]`, *optional*):
+ Visibility of the new repo. Can be `"public"` or `"private"`, or `"protected"` for Spaces. Defaults
+ to the same visibility as the original repo.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ exist_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if repo already exists.
+ resource_group_id (`str`, *optional*):
+ Resource group in which to create the new repo. Resource groups is only available for Enterprise Hub organizations and
+ allow to define which members of the organization can access the resource. The ID of a resource group
+ can be found in the URL of the resource's page on the Hub (e.g. `"66670e5163145ca562cb1988"`).
+ To learn more about resource groups, see https://huggingface.co/docs/hub/en/security-resource-groups.
+ space_hardware (`SpaceHardware` or `str`, *optional*):
+ Choice of Hardware if repo_type is "space". Example: `"t4-medium"`. See
+ [`SpaceHardware`] for a complete list.
+ space_sleep_time (`int`, *optional*):
+ Number of seconds of inactivity to wait before a Space is put to sleep.
+ Set to `-1` if you don't want your Space to sleep (default behavior for
+ upgraded hardware). For free hardware, you can't configure the sleep time
+ (value is fixed to 48 hours of inactivity). Only applicable if repo_type is "space".
+ See https://huggingface.co/docs/hub/spaces-gpus#sleep-time for more details.
+ space_secrets (`list[dict[str, str]]`, *optional*):
+ A list of secret keys to set in your Space. Each item is in the form
+ `{"key": ..., "value": ..., "description": ...}` where description is optional.
+ Only applicable if repo_type is "space".
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets.
+ space_variables (`list[dict[str, str]]`, *optional*):
+ A list of public environment variables to set in your Space. Each item is in
+ the form `{"key": ..., "value": ..., "description": ...}` where description
+ is optional. Only applicable if repo_type is "space".
+ For more details, see https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables.
+ space_volumes (`list[Volume]`, *optional*):
+ A list of [`Volume`] objects to mount in the Space at duplication time. Each volume has a `type`
+ (`"bucket"`, `"model"`, `"dataset"`, or `"space"`), a `source` (repo or bucket ID), a `mount_path`
+ (path inside the container), and optional `revision`, `read_only`, and `path` fields.
+ Only applicable if repo_type is "space".
+
+ Returns:
+ [`DuplicatedRepoUrl`]: URL to the newly created repo. Value is a subclass of [`RepoUrl`] (and of `str`)
+ containing attributes like `endpoint`, `repo_type`, `repo_id` and `files_copy_pending`.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ If one of `from_id` or `to_id` cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+ [`HfHubHTTPError`]:
+ If the HuggingFace API returned an error
+
+ Example:
+ ```python
+ >>> from huggingface_hub import duplicate_repo
+
+ # Duplicate a model to your account
+ >>> repo_url = duplicate_repo("google/gemma-7b")
+ >>> repo_url
+ RepoUrl('https://huggingface.co/nateraw/gemma-7b',...)
+ >>> repo_url.files_copy_pending
+ True
+
+ # Duplicate a dataset with a custom name
+ >>> duplicate_repo("openai/gdpval", to_id="myorg/my-gdpval", repo_type="dataset")
+ RepoUrl('https://huggingface.co/datasets/myorg/my-gdpval',...)
+
+ # Duplicate a Space with custom hardware
+ >>> duplicate_repo("multimodalart/dreambooth-training", repo_type="space", space_hardware="t4-medium")
+ RepoUrl('https://huggingface.co/spaces/nateraw/dreambooth-training',...)
+ ```
+ """
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError("Invalid repo type")
+
+ resolved_visibility = _resolve_repo_visibility(private=private, visibility=visibility, repo_type=repo_type)
+
+ # Map repo_type to API path segment
+ api_prefix = {
+ None: "models",
+ constants.REPO_TYPE_MODEL: "models",
+ constants.REPO_TYPE_DATASET: "datasets",
+ constants.REPO_TYPE_SPACE: "spaces",
+ }[repo_type]
+
+ # Resolve the target namespace + name. When 'to_id' is provided we take the name from it,
+ # otherwise we reuse the source name. Both may be a URL, an 'hf://' URI, or a bare id, so we
+ # try 'parse_hf_uri' first and fall back to a plain split ('/', or just
+ # '' for 'to_id'). A missing namespace defaults to the caller's account.
+ to_namespace: str | None = None
+ if to_id is not None:
+ try:
+ to_namespace, to_repo_name = parse_hf_uri(to_id).id.split("/")
+ except HfUriError:
+ namespace_and_name = to_id.rsplit("/", 1)
+ to_namespace = namespace_and_name[0] if len(namespace_and_name) == 2 else None
+ to_repo_name = namespace_and_name[-1]
+ else:
+ try:
+ to_repo_name = parse_hf_uri(from_id).id.split("/")[1]
+ except HfUriError:
+ to_repo_name = from_id.rsplit("/", 1)[-1]
+ if to_namespace is None:
+ to_namespace = self.whoami(token)["name"]
+
+ payload: dict[str, Any] = {"repository": f"{to_namespace}/{to_repo_name}"}
+
+ if resolved_visibility is not None:
+ payload["visibility"] = resolved_visibility
+ if resource_group_id is not None:
+ payload["resourceGroupId"] = resource_group_id
+
+ # Space-specific options
+ space_args: list[tuple[str, str, Any]] = [
+ # input arg, payload key, value
+ ("space_hardware", "hardware", space_hardware),
+ ("space_sleep_time", "sleepTimeSeconds", space_sleep_time),
+ ("space_secrets", "secrets", space_secrets),
+ ("space_variables", "variables", space_variables),
+ ("space_volumes", "volumes", [v.to_dict() for v in space_volumes] if space_volumes else None),
+ ]
+
+ if repo_type == "space":
+ for _, key, value in space_args:
+ if value is not None:
+ payload[key] = value
+ if space_sleep_time is not None and space_hardware == SpaceHardware.CPU_BASIC:
+ warnings.warn(
+ "If your Space runs on the default 'cpu-basic' hardware, it will go to sleep if inactive for more"
+ " than 48 hours. This value is not configurable. If you don't want your Space to deactivate or if"
+ " you want to set a custom sleep time, you need to upgrade to a paid Hardware.",
+ UserWarning,
+ )
+ else:
+ if provided_space_args := [arg for arg, _, value in space_args if value is not None]:
+ warnings.warn(f"Ignoring provided {', '.join(provided_space_args)} because repo_type is not 'space'.")
+
+ r = get_session().post(
+ f"{self.endpoint}/api/{api_prefix}/{from_id}/duplicate",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if exist_ok and err.response.status_code == 409:
+ pass
+ else:
+ raise
+
+ data = r.json()
+ repo_url = DuplicatedRepoUrl(data["url"], endpoint=self.endpoint)
+ repo_url.files_copy_pending = data.get("filesCopyPending", False)
+ return repo_url
+
+ @validate_hf_hub_args
+ def set_space_volumes(
+ self,
+ repo_id: str,
+ volumes: list[Volume],
+ *,
+ token: bool | str | None = None,
+ ) -> None:
+ """Set volumes for a Space.
+
+ Sets (or replaces) the list of volumes mounted in the Space. Each volume gives the Space's container access
+ to a Hub resource (model, dataset, or storage bucket).
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to update. Example: `"username/my-space"`.
+ volumes (`list[Volume]`):
+ List of [`Volume`] objects to mount. Each volume has a `type` (`"bucket"`, `"model"`, `"dataset"`, or
+ `"space"`), a `source` (repo or bucket ID), a `mount_path` (path inside the container), and optional
+ `revision`, `read_only`, and `path` fields.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`BadRequestError`]:
+ If the Space is a static Space (volumes are not supported on static Spaces).
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi, Volume
+ >>> api = HfApi()
+ >>> api.set_space_volumes(
+ ... "username/my-space",
+ ... volumes=[
+ ... Volume(type="model", source="username/my-model", mount_path="/models", read_only=True),
+ ... Volume(type="bucket", source="username/my-bucket", mount_path="/data"),
+ ... ],
+ ... )
+ ```
+ """
+ payload = {"volumes": [vol.to_dict() for vol in volumes]}
+ r = get_session().put(
+ f"{self.endpoint}/api/spaces/{repo_id}/volumes",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(r)
+
+ @validate_hf_hub_args
+ def delete_space_volumes(
+ self,
+ repo_id: str,
+ *,
+ token: bool | str | None = None,
+ ) -> None:
+ """Remove all volumes from a Space.
+
+ Args:
+ repo_id (`str`):
+ ID of the Space to update. Example: `"username/my-space"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`BadRequestError`]:
+ If the Space has no volumes attached.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> api.delete_space_volumes("username/my-space")
+ ```
+ """
+ r = get_session().delete(
+ f"{self.endpoint}/api/spaces/{repo_id}/volumes",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(r)
+
+ #######################
+ # Inference Endpoints #
+ #######################
+
+ def list_inference_endpoints(
+ self, namespace: str | None = None, *, token: bool | str | None = None
+ ) -> list[InferenceEndpoint]:
+ """Lists all inference endpoints for the given namespace.
+
+ Args:
+ namespace (`str`, *optional*):
+ The namespace to list endpoints for. Defaults to the current user. Set to `"*"` to list all endpoints
+ from all namespaces (i.e. personal namespace and all orgs the user belongs to).
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ list[`InferenceEndpoint`]: A list of all inference endpoints for the given namespace.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> api.list_inference_endpoints()
+ [InferenceEndpoint(name='my-endpoint', ...), ...]
+ ```
+ """
+ # Special case: list all endpoints for all namespaces the user has access to
+ if namespace == "*":
+ user = self.whoami(token=token)
+
+ # List personal endpoints first
+ endpoints: list[InferenceEndpoint] = list_inference_endpoints(namespace=self._get_namespace(token=token))
+
+ # Then list endpoints for all orgs the user belongs to and ignore 401 errors (no billing or no access)
+ for org in user.get("orgs", []):
+ try:
+ endpoints += list_inference_endpoints(namespace=org["name"], token=token)
+ except HfHubHTTPError as error:
+ if error.response.status_code == 401: # Either no billing or user don't have access)
+ logger.debug("Cannot list Inference Endpoints for org '%s': %s", org["name"], error)
+ pass
+
+ return endpoints
+
+ # Normal case: list endpoints for a specific namespace
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().get(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ return [
+ InferenceEndpoint.from_raw(endpoint, namespace=namespace, token=token)
+ for endpoint in response.json()["items"]
+ ]
+
+ def create_inference_endpoint(
+ self,
+ name: str,
+ *,
+ repository: str,
+ framework: str,
+ accelerator: str,
+ instance_size: str,
+ instance_type: str,
+ region: str,
+ vendor: str,
+ account_id: str | None = None,
+ private_link_account_id: str | None = None,
+ private_link_region: str | None = None,
+ min_replica: int = 1,
+ max_replica: int = 1,
+ scaling_metric: InferenceEndpointScalingMetric | None = None,
+ scaling_threshold: float | None = None,
+ scale_to_zero_timeout: int | None = None,
+ revision: str | None = None,
+ task: str | None = None,
+ custom_image: dict | None = None,
+ container_registry_username: str | None = None,
+ container_registry_password: str | None = None,
+ container_command: list[str] | None = None,
+ container_args: list[str] | None = None,
+ env: dict[str, str] | None = None,
+ secrets: dict[str, str] | None = None,
+ type: InferenceEndpointType | str = InferenceEndpointType.AUTHENTICATED,
+ domain: str | None = None,
+ path: str | None = None,
+ cache_http_responses: bool | None = None,
+ tags: list[str] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> InferenceEndpoint:
+ """Create a new Inference Endpoint.
+
+ The `accelerator`, `instance_size`, `instance_type`, `region` and `vendor` values depend on each other; use
+ [`list_inference_endpoints_hardware`] to list the valid combinations.
+
+ Args:
+ name (`str`):
+ The unique name for the new Inference Endpoint.
+ repository (`str`):
+ The name of the model repository associated with the Inference Endpoint (e.g. `"gpt2"`).
+ framework (`str`):
+ The machine learning framework used for the model (e.g. `"custom"`).
+ accelerator (`str`):
+ The hardware accelerator to be used for inference (e.g. `"cpu"`).
+ instance_size (`str`):
+ The size or type of the instance to be used for hosting the model (e.g. `"x4"`).
+ instance_type (`str`):
+ The cloud instance type where the Inference Endpoint will be deployed (e.g. `"intel-icl"`).
+ region (`str`):
+ The cloud region in which the Inference Endpoint will be created (e.g. `"us-east-1"`).
+ vendor (`str`):
+ The cloud provider or vendor where the Inference Endpoint will be hosted (e.g. `"aws"`).
+ account_id (`str`, *optional*):
+ Deprecated and ignored. Use `private_link_account_id` and `private_link_region` to configure
+ AWS PrivateLink.
+ private_link_account_id (`str`, *optional*):
+ The AWS account ID allowed to reach the Inference Endpoint through AWS PrivateLink. Requires
+ `private_link_region`.
+ private_link_region (`str`, *optional*):
+ The AWS region of the PrivateLink entry point (`"us-east-1"` or `"eu-west-1"`), where the VPC
+ endpoint of `private_link_account_id` will connect. Independent of the compute `region` and `vendor`.
+ min_replica (`int`, *optional*):
+ The minimum number of replicas (instances) to keep running for the Inference Endpoint. To enable
+ scaling to zero, set this value to 0 and adjust `scale_to_zero_timeout` accordingly. Defaults to 1.
+ max_replica (`int`, *optional*):
+ The maximum number of replicas (instances) to scale to for the Inference Endpoint. Defaults to 1.
+ scaling_metric (`str` or [`InferenceEndpointScalingMetric `], *optional*):
+ The metric reference for scaling. Either "pendingRequests" or "hardwareUsage" when provided. Defaults to
+ None (meaning: let the HF Endpoints service specify the metric).
+ scaling_threshold (`float`, *optional*):
+ The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.
+ Defaults to None (meaning: let the HF Endpoints service specify the threshold).
+ scale_to_zero_timeout (`int`, *optional*):
+ The duration in minutes before an inactive endpoint is scaled to zero, or no scaling to zero if
+ set to None and `min_replica` is not 0. Defaults to None.
+ revision (`str`, *optional*):
+ The specific model revision to deploy on the Inference Endpoint (e.g. `"6c0e6080953db56375760c0471a8c5f2929baf11"`).
+ task (`str`, *optional*):
+ The task on which to deploy the model (e.g. `"text-classification"`).
+ custom_image (`dict`, *optional*):
+ The container image to run. Either a dict keyed by image variant (e.g.
+ `{"vLLM": {"url": "vllm/vllm-openai:v0.23.0", "port": 8000}}`, also `sGLang`, `tgi`, `tei`,
+ `llamacpp`, `hfServe`, ...), which is forwarded as-is, or a flat dict describing a custom
+ container (e.g. `{"url": ..., "port": ...}`), which is sent as `{"custom": ...}` (see examples).
+ Defaults to the Hugging Face managed image.
+ container_registry_username (`str`, *optional*):
+ Username used to authenticate with the registry hosting a custom container image.
+ container_registry_password (`str`, *optional*):
+ Password used to authenticate with the registry hosting a custom container image. Requires
+ `container_registry_username`. Omitted from the API payload when not provided.
+ container_command (`list[str]`, *optional*):
+ Override the container entrypoint command (maps to `model.command` in the API payload). Works with
+ both managed engine images (e.g. vLLM, SGLang) and custom images.
+ container_args (`list[str]`, *optional*):
+ Arguments appended to the container entrypoint (maps to `model.args` in the API payload). Works with
+ both managed engine images (e.g. vLLM, SGLang) and custom images.
+ env (`dict[str, str]`, *optional*):
+ Non-secret environment variables to inject in the container environment.
+ secrets (`dict[str, str]`, *optional*):
+ Secret values to inject in the container environment.
+ type ([`InferenceEndpointType]`, *optional*):
+ The type of the Inference Endpoint, which can be `"authenticated"` (default), `"public"` or
+ `"private"`.
+ domain (`str`, *optional*):
+ The custom domain for the Inference Endpoint deployment, if setup the inference endpoint will be available at this domain (e.g. `"my-new-domain.cool-website.woof"`).
+ path (`str`, *optional*):
+ The custom path to the deployed model, should start with a `/` (e.g. `"/models/google-bert/bert-base-uncased"`).
+ cache_http_responses (`bool`, *optional*):
+ Whether to cache HTTP responses from the Inference Endpoint. Defaults to `False`.
+ tags (`list[str]`, *optional*):
+ A list of tags to associate with the Inference Endpoint.
+ namespace (`str`, *optional*):
+ The namespace where the Inference Endpoint will be created. Defaults to the current user's namespace.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the updated Inference Endpoint.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> endpoint = api.create_inference_endpoint(
+ ... "my-endpoint-name",
+ ... repository="gpt2",
+ ... framework="pytorch",
+ ... task="text-generation",
+ ... accelerator="cpu",
+ ... vendor="aws",
+ ... region="us-east-1",
+ ... type="authenticated",
+ ... instance_size="x2",
+ ... instance_type="intel-icl",
+ ... )
+ >>> endpoint
+ InferenceEndpoint(name='my-endpoint-name', status="pending",...)
+
+ # Run inference on the endpoint
+ >>> endpoint.client.text_generation(...)
+ "..."
+ ```
+
+ ```python
+ # Start an Inference Endpoint running Zephyr-7b-beta on TGI
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> endpoint = api.create_inference_endpoint(
+ ... "aws-zephyr-7b-beta-0486",
+ ... repository="HuggingFaceH4/zephyr-7b-beta",
+ ... framework="pytorch",
+ ... task="text-generation",
+ ... accelerator="gpu",
+ ... vendor="aws",
+ ... region="us-east-1",
+ ... type="authenticated",
+ ... instance_size="x1",
+ ... instance_type="nvidia-a10g",
+ ... env={
+ ... "MAX_BATCH_PREFILL_TOKENS": "2048",
+ ... "MAX_INPUT_LENGTH": "1024",
+ ... "MAX_TOTAL_TOKENS": "1512",
+ ... "MODEL_ID": "/repository"
+ ... },
+ ... custom_image={
+ ... "healthRoute": "/health",
+ ... "url": "ghcr.io/huggingface/text-generation-inference:1.1.0",
+ ... },
+ ... secrets={"MY_SECRET_KEY": "secret_value"},
+ ... tags=["dev", "text-generation"],
+ ... )
+ ```
+
+ ```python
+ # Start an Inference Endpoint running ProsusAI/finbert while scaling to zero in 15 minutes
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> endpoint = api.create_inference_endpoint(
+ ... "finbert-classifier",
+ ... repository="ProsusAI/finbert",
+ ... framework="pytorch",
+ ... task="text-classification",
+ ... min_replica=0,
+ ... scale_to_zero_timeout=15,
+ ... accelerator="cpu",
+ ... vendor="aws",
+ ... region="us-east-1",
+ ... type="authenticated",
+ ... instance_size="x2",
+ ... instance_type="intel-icl",
+ ... )
+ >>> endpoint.wait(timeout=300)
+ # Run inference on the endpoint
+ >>> endpoint.client.text_generation(...)
+ TextClassificationOutputElement(label='positive', score=0.8983615040779114)
+ ```
+
+ """
+ if type == "protected":
+ raise ValueError("`type='protected'` is no longer supported. Use `type='authenticated'` instead.")
+ if account_id is not None:
+ warnings.warn(
+ "`account_id` has been ignored. Use `private_link_account_id` and `private_link_region` to configure"
+ " AWS PrivateLink.",
+ FutureWarning,
+ )
+
+ namespace = namespace or self._get_namespace(token=token)
+
+ image: dict[str, Any]
+ if custom_image is None:
+ if container_registry_password is not None and container_registry_username is None:
+ raise ValueError("`container_registry_password` requires `container_registry_username`.")
+ if container_registry_username is not None:
+ raise ValueError("`custom_image` is required when setting container registry credentials.")
+ image = {"huggingface": {}}
+ else:
+ image = _build_endpoint_image_payload(
+ custom_image,
+ container_registry_username=container_registry_username,
+ container_registry_password=container_registry_password,
+ )
+
+ payload: dict = {
+ "compute": {
+ "accelerator": accelerator,
+ "instanceSize": instance_size,
+ "instanceType": instance_type,
+ "scaling": {
+ "maxReplica": max_replica,
+ "minReplica": min_replica,
+ "scaleToZeroTimeout": scale_to_zero_timeout,
+ },
+ },
+ "model": {
+ "framework": framework,
+ "repository": repository,
+ "revision": revision,
+ "image": image,
+ },
+ "name": name,
+ "provider": {
+ "region": region,
+ "vendor": vendor,
+ },
+ "type": type,
+ }
+ if private_link_account_id is not None:
+ if private_link_region is None:
+ raise ValueError(
+ "`private_link_region` is required with `private_link_account_id`: the AWS region of the"
+ " PrivateLink entry point, 'us-east-1' or 'eu-west-1'."
+ )
+ payload["privateService"] = {"accountId": private_link_account_id, "region": private_link_region}
+ if scaling_metric:
+ payload["compute"]["scaling"]["measure"] = {scaling_metric: scaling_threshold} # type: ignore
+ model_payload: dict[str, Any] = payload["model"]
+ # `model.task` is not nullable server-side, unlike `revision`, so omit it rather than sending null.
+ if task is not None:
+ model_payload["task"] = task
+ if container_command is not None:
+ model_payload["command"] = container_command
+ if container_args is not None:
+ model_payload["args"] = container_args
+ if env:
+ payload["model"]["env"] = env
+ if secrets:
+ payload["model"]["secrets"] = secrets
+ if domain is not None or path is not None:
+ payload["route"] = {}
+ if domain is not None:
+ payload["route"]["domain"] = domain
+ if path is not None:
+ payload["route"]["path"] = path
+ if cache_http_responses is not None:
+ payload["cacheHttpResponses"] = cache_http_responses
+ if tags is not None:
+ payload["tags"] = tags
+
+ response = get_session().post(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(response)
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ @experimental
+ @validate_hf_hub_args
+ def create_inference_endpoint_from_catalog(
+ self,
+ repo_id: str | None = None,
+ *,
+ recipe_id: str | None = None,
+ name: str | None = None,
+ accelerator: Literal["cpu", "gpu", "neuron"] | str | None = None,
+ gguf_file: str | None = None,
+ token: bool | str | None = None,
+ namespace: str | None = None,
+ ) -> InferenceEndpoint:
+ """Create a new Inference Endpoint from the Hugging Face Inference Catalog.
+
+ The goal of the Inference Catalog is to provide a curated list of models that are optimized for inference
+ and for which default configurations have been tested. See https://endpoints.huggingface.co/catalog for a list
+ of available models in the catalog.
+
+ Each catalog model is deployed through a *recipe*: a hardware and engine combination that has been tested for
+ it. Pass `repo_id` to deploy the default recipe of a model, optionally narrowed down with `accelerator` and
+ `gguf_file`, or pass `recipe_id` to deploy an exact recipe listed by [`list_inference_catalog`].
+
+ Args:
+ repo_id (`str`, *optional*):
+ The ID of the model in the catalog to deploy as an Inference Endpoint. Mutually exclusive with
+ `recipe_id`.
+ recipe_id (`str`, *optional*):
+ The ID of the catalog recipe to deploy (see [`InferenceCatalogRecipe`]). Mutually exclusive with
+ `repo_id`.
+ name (`str`, *optional*):
+ The unique name for the new Inference Endpoint. If not provided, a random name will be generated.
+ accelerator (`str`, *optional*):
+ The hardware accelerator to be used for inference. Possible values include `"cpu"`, `"gpu"`, and
+ `"neuron"`. If not provided, the server will use a default appropriate for the model. Only valid
+ with `repo_id`.
+ gguf_file (`str`, *optional*):
+ The GGUF file to deploy, for models that have one recipe per quant (e.g.
+ `"Qwen2.5-Coder-32B-Instruct-Q4_K_M.gguf"`). Only valid with `repo_id`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ namespace (`str`, *optional*):
+ The namespace where the Inference Endpoint will be created. Defaults to the current user's namespace.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the new Inference Endpoint.
+
+ > [!WARNING]
+ > `create_inference_endpoint_from_catalog` is experimental. Its API is subject to change in the future. Please provide feedback
+ > if you have any suggestions or requests.
+ """
+ if token is False:
+ raise ValueError(
+ "Cannot use `token=False` with `create_inference_endpoint_from_catalog` as it requires authentication."
+ )
+ if (repo_id is None) == (recipe_id is None):
+ raise ValueError("Provide exactly one of `repo_id` or `recipe_id`.")
+ if recipe_id is not None and (accelerator is not None or gguf_file is not None):
+ # `accelerator` and `gguf_file` pick a recipe among the ones of a model. A `recipe_id` already is one, so
+ # the server would silently ignore them.
+ raise ValueError(
+ "`accelerator` and `gguf_file` cannot be used with `recipe_id`, which already is a recipe."
+ )
+
+ token = token or self.token or get_token()
+ namespace = namespace or self._get_namespace(token=token)
+ payload: dict = {"namespace": namespace}
+ if name is not None:
+ payload["config"] = {"name": name}
+
+ if recipe_id is not None:
+ url = f"{constants.INFERENCE_CATALOG_ENDPOINT}/recipe/{recipe_id}/deploy"
+ else:
+ url = f"{constants.INFERENCE_CATALOG_ENDPOINT}/model/{repo_id}/deploy"
+ if accelerator is not None:
+ payload["accelerator"] = accelerator
+ if gguf_file is not None:
+ payload["ggufFile"] = gguf_file
+
+ response = get_session().post(url, headers=self._build_hf_headers(token=token), json=payload)
+ hf_raise_for_status(response)
+ return InferenceEndpoint.from_raw(response.json()["endpoint"], namespace=namespace, token=token)
+
+ @experimental
+ @validate_hf_hub_args
+ def list_inference_catalog(
+ self,
+ *,
+ accelerator: Literal["cpu", "gpu", "neuron"] | str | None = None,
+ engine: Literal["llamacpp", "sglang", "tei", "vllm"] | str | None = None,
+ license: str | None = None,
+ task: str | None = None,
+ search: str | None = None,
+ limit: int | None = None,
+ token: bool | str | None = None,
+ ) -> list[InferenceCatalogModel]:
+ """List models available in the Hugging Face Inference Catalog.
+
+ The goal of the Inference Catalog is to provide a curated list of models that are optimized for inference
+ and for which default configurations have been tested. See https://endpoints.huggingface.co/catalog for a list
+ of available models in the catalog.
+
+ Use [`create_inference_endpoint_from_catalog`] to deploy a model or a recipe from the catalog.
+
+ Args:
+ accelerator (`str`, *optional*):
+ Only return models that have a recipe for this accelerator (`"cpu"`, `"gpu"` or `"neuron"`).
+ engine (`str`, *optional*):
+ Only return models that have a recipe for this inference engine (e.g. `"vllm"`, `"llamacpp"`,
+ `"tei"`, `"sglang"`).
+ license (`str`, *optional*):
+ Only return models under this license (e.g. `"Apache 2.0"`).
+ task (`str`, *optional*):
+ Only return models for this task (e.g. `"text-generation"`).
+ search (`str`, *optional*):
+ Only return models matching this search query.
+ limit (`int`, *optional*):
+ The maximum number of models to return.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+
+ Returns:
+ `list[InferenceCatalogModel]`: The models available in the catalog, each with its tested recipes.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> catalog = api.list_inference_catalog(task="text-generation", accelerator="neuron")
+ >>> [(model.repo_id, [recipe.id for recipe in model.recipes]) for model in catalog]
+ [('meta-llama/Llama-3.1-8B-Instruct', ['sizzling-biryani-g4xsi1ac', 'artisanal-quinoa-yz9ynamx']), ...]
+ ```
+
+ > [!WARNING]
+ > `list_inference_catalog` is experimental. Its API is subject to change in the future. Please provide feedback
+ > if you have any suggestions or requests.
+ """
+ params = {
+ "accelerator": accelerator,
+ "engine": engine,
+ "license": license,
+ "task": task,
+ "search": search,
+ "limit": limit,
+ }
+ response = get_session().get(
+ f"{constants.INFERENCE_CATALOG_ENDPOINT}/list",
+ headers=self._build_hf_headers(token=token),
+ params={key: value for key, value in params.items() if value is not None},
+ )
+ hf_raise_for_status(response)
+ return [InferenceCatalogModel.from_raw(item) for item in response.json()["items"]]
+
+ def get_inference_endpoint(
+ self, name: str, *, namespace: str | None = None, token: bool | str | None = None
+ ) -> InferenceEndpoint:
+ """Get information about an Inference Endpoint.
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to retrieve information about.
+ namespace (`str`, *optional*):
+ The namespace in which the Inference Endpoint is located. Defaults to the current user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the requested Inference Endpoint.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> endpoint = api.get_inference_endpoint("my-text-to-image")
+ >>> endpoint
+ InferenceEndpoint(name='my-text-to-image', ...)
+
+ # Get status
+ >>> endpoint.status
+ 'running'
+ >>> endpoint.url
+ 'https://my-text-to-image.region.vendor.endpoints.huggingface.cloud'
+
+ # Run inference
+ >>> endpoint.client.text_to_image(...)
+ ```
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().get(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ def update_inference_endpoint(
+ self,
+ name: str,
+ *,
+ # Compute update
+ accelerator: str | None = None,
+ instance_size: str | None = None,
+ instance_type: str | None = None,
+ min_replica: int | None = None,
+ max_replica: int | None = None,
+ scale_to_zero_timeout: int | None = None,
+ scaling_metric: InferenceEndpointScalingMetric | None = None,
+ scaling_threshold: float | None = None,
+ # Model update
+ repository: str | None = None,
+ framework: str | None = None,
+ revision: str | None = None,
+ task: str | None = None,
+ custom_image: dict | None = None,
+ container_command: list[str] | None = None,
+ container_args: list[str] | None = None,
+ tensor_parallel_size: int | None = None,
+ data_parallel_size: int | None = None,
+ env: dict[str, str] | None = None,
+ secrets: dict[str, str] | None = None,
+ # Route update
+ domain: str | None = None,
+ path: str | None = None,
+ # Other
+ cache_http_responses: bool | None = None,
+ tags: list[str] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> InferenceEndpoint:
+ """Update an Inference Endpoint.
+
+ This method allows the update of either the compute configuration, the deployed model, the route, or any combination.
+ All arguments are optional but at least one must be provided.
+
+ For convenience, you can also update an Inference Endpoint using [`InferenceEndpoint.update`].
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to update.
+
+ accelerator (`str`, *optional*):
+ The hardware accelerator to be used for inference (e.g. `"cpu"`).
+ instance_size (`str`, *optional*):
+ The size or type of the instance to be used for hosting the model (e.g. `"x4"`).
+ instance_type (`str`, *optional*):
+ The cloud instance type where the Inference Endpoint will be deployed (e.g. `"intel-icl"`).
+ min_replica (`int`, *optional*):
+ The minimum number of replicas (instances) to keep running for the Inference Endpoint.
+ max_replica (`int`, *optional*):
+ The maximum number of replicas (instances) to scale to for the Inference Endpoint.
+ scale_to_zero_timeout (`int`, *optional*):
+ The duration in minutes before an inactive endpoint is scaled to zero.
+ scaling_metric (`str` or [`InferenceEndpointScalingMetric `], *optional*):
+ The metric reference for scaling. Either "pendingRequests" or "hardwareUsage" when provided.
+ Defaults to None.
+ scaling_threshold (`float`, *optional*):
+ The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.
+ Defaults to None.
+ repository (`str`, *optional*):
+ The name of the model repository associated with the Inference Endpoint (e.g. `"gpt2"`).
+ framework (`str`, *optional*):
+ The machine learning framework used for the model (e.g. `"custom"`).
+ revision (`str`, *optional*):
+ The specific model revision to deploy on the Inference Endpoint (e.g. `"6c0e6080953db56375760c0471a8c5f2929baf11"`).
+ task (`str`, *optional*):
+ The task on which to deploy the model (e.g. `"text-classification"`).
+ custom_image (`dict`, *optional*):
+ The container image to run. Either a dict keyed by image variant (e.g.
+ `{"vLLM": {"url": "vllm/vllm-openai:v0.23.0", "port": 8000}}`, also `sGLang`, `tgi`, `tei`,
+ `llamacpp`, `hfServe`, ...), which is forwarded as-is, or a flat dict describing a custom
+ container (e.g. `{"url": ..., "port": ...}`), which is sent as `{"custom": ...}`.
+ container_command (`list[str]`, *optional*):
+ Override the container entrypoint command (maps to `model.command` in the API payload). Works with
+ both managed engine images (e.g. vLLM, SGLang) and custom images.
+ container_args (`list[str]`, *optional*):
+ Arguments appended to the container entrypoint (maps to `model.args` in the API payload). Works with
+ both managed engine images (e.g. vLLM, SGLang) and custom images.
+ tensor_parallel_size (`int`, *optional*):
+ Number of accelerators to shard a single model copy across (vLLM and SGLang images). Written inside
+ the engine image config. The API requires `model.image` as a whole, so when `custom_image` is not
+ given the image currently configured on the endpoint is fetched and updated in place.
+ data_parallel_size (`int`, *optional*):
+ Number of model copies to run, one per accelerator (vLLM images). Same handling as
+ `tensor_parallel_size`.
+ env (`dict[str, str]`, *optional*):
+ Non-secret environment variables to inject in the container environment
+ secrets (`dict[str, str]`, *optional*):
+ Secret values to inject in the container environment.
+
+ domain (`str`, *optional*):
+ The custom domain for the Inference Endpoint deployment, if setup the inference endpoint will be available at this domain (e.g. `"my-new-domain.cool-website.woof"`).
+ path (`str`, *optional*):
+ The custom path to the deployed model, should start with a `/` (e.g. `"/models/google-bert/bert-base-uncased"`).
+
+ cache_http_responses (`bool`, *optional*):
+ Whether to cache HTTP responses from the Inference Endpoint.
+ tags (`list[str]`, *optional*):
+ A list of tags to associate with the Inference Endpoint.
+
+ namespace (`str`, *optional*):
+ The namespace where the Inference Endpoint will be updated. Defaults to the current user's namespace.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the updated Inference Endpoint.
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ # Populate only the fields that are not None
+ payload: dict = defaultdict(lambda: defaultdict(dict))
+ if accelerator is not None:
+ payload["compute"]["accelerator"] = accelerator
+ if instance_size is not None:
+ payload["compute"]["instanceSize"] = instance_size
+ if instance_type is not None:
+ payload["compute"]["instanceType"] = instance_type
+ if max_replica is not None:
+ payload["compute"]["scaling"]["maxReplica"] = max_replica
+ if min_replica is not None:
+ payload["compute"]["scaling"]["minReplica"] = min_replica
+ if scale_to_zero_timeout is not None:
+ payload["compute"]["scaling"]["scaleToZeroTimeout"] = scale_to_zero_timeout
+ if scaling_metric:
+ payload["compute"]["scaling"]["measure"] = {scaling_metric: scaling_threshold}
+ if repository is not None:
+ payload["model"]["repository"] = repository
+ if framework is not None:
+ payload["model"]["framework"] = framework
+ if revision is not None:
+ payload["model"]["revision"] = revision
+ if task is not None:
+ payload["model"]["task"] = task
+ if custom_image is not None:
+ payload["model"]["image"] = _build_endpoint_image_payload(custom_image)
+ if tensor_parallel_size is not None or data_parallel_size is not None:
+ # The parallelism sizes cannot be sent on their own: `model.image` is a union and `url` is required
+ # inside the variant. Start from the caller's image, or from the one the endpoint currently runs.
+ image = payload["model"].get("image")
+ if not image:
+ current = self.get_inference_endpoint(name, namespace=namespace, token=token)
+ # `model.image` is required server-side, so an endpoint always has one. If it somehow doesn't,
+ # `_set_parallelism_in_image` rejects the empty payload below.
+ image = (current.raw.get("model") or {}).get("image") or {}
+ if "credentials" in next(iter(image.values()), {}):
+ # The API does not return registry credentials in full, so echoing the fetched image back
+ # would overwrite them with the redacted values.
+ raise ValueError(
+ f"Endpoint '{name}' runs an image with registry credentials, which cannot be safely"
+ " round-tripped. Pass the full image explicitly to set the parallelism sizes."
+ )
+ payload["model"]["image"] = _set_parallelism_in_image(
+ image, tensor_parallel_size=tensor_parallel_size, data_parallel_size=data_parallel_size
+ )
+ if container_command is not None:
+ payload["model"]["command"] = container_command
+ if container_args is not None:
+ payload["model"]["args"] = container_args
+ if env is not None:
+ payload["model"]["env"] = env
+ if secrets is not None:
+ payload["model"]["secrets"] = secrets
+ if domain is not None:
+ payload["route"]["domain"] = domain
+ if path is not None:
+ payload["route"]["path"] = path
+ if cache_http_responses is not None:
+ payload["cacheHttpResponses"] = cache_http_responses
+ if tags is not None:
+ payload["tags"] = tags
+
+ response = get_session().put(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(response)
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ def delete_inference_endpoint(
+ self, name: str, *, namespace: str | None = None, token: bool | str | None = None
+ ) -> None:
+ """Delete an Inference Endpoint.
+
+ This operation is not reversible. If you don't want to be charged for an Inference Endpoint, it is preferable
+ to pause it with [`pause_inference_endpoint`] or scale it to zero with [`scale_to_zero_inference_endpoint`].
+
+ For convenience, you can also delete an Inference Endpoint using [`InferenceEndpoint.delete`].
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to delete.
+ namespace (`str`, *optional*):
+ The namespace in which the Inference Endpoint is located. Defaults to the current user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ """
+ namespace = namespace or self._get_namespace(token=token)
+ response = get_session().delete(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ def pause_inference_endpoint(
+ self, name: str, *, namespace: str | None = None, token: bool | str | None = None
+ ) -> InferenceEndpoint:
+ """Pause an Inference Endpoint.
+
+ A paused Inference Endpoint will not be charged. It can be resumed at any time using [`resume_inference_endpoint`].
+ This is different than scaling the Inference Endpoint to zero with [`scale_to_zero_inference_endpoint`], which
+ would be automatically restarted when a request is made to it.
+
+ For convenience, you can also pause an Inference Endpoint using [`pause_inference_endpoint`].
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to pause.
+ namespace (`str`, *optional*):
+ The namespace in which the Inference Endpoint is located. Defaults to the current user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the paused Inference Endpoint.
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().post(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}/pause",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ def resume_inference_endpoint(
+ self,
+ name: str,
+ *,
+ namespace: str | None = None,
+ running_ok: bool = True,
+ token: bool | str | None = None,
+ ) -> InferenceEndpoint:
+ """Resume an Inference Endpoint.
+
+ For convenience, you can also resume an Inference Endpoint using [`InferenceEndpoint.resume`].
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to resume.
+ namespace (`str`, *optional*):
+ The namespace in which the Inference Endpoint is located. Defaults to the current user.
+ running_ok (`bool`, *optional*):
+ If `True`, the method will not raise an error if the Inference Endpoint is already running. Defaults to
+ `True`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the resumed Inference Endpoint.
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().post(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}/resume",
+ headers=self._build_hf_headers(token=token),
+ )
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as error:
+ # If already running (and it's ok), then fetch current status and return
+ if running_ok and error.response.status_code == 400 and "already running" in error.response.text:
+ return self.get_inference_endpoint(name, namespace=namespace, token=token)
+ # Otherwise, raise the error
+ raise
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ def scale_to_zero_inference_endpoint(
+ self, name: str, *, namespace: str | None = None, token: bool | str | None = None
+ ) -> InferenceEndpoint:
+ """Scale Inference Endpoint to zero.
+
+ An Inference Endpoint scaled to zero will not be charged. It will be resume on the next request to it, with a
+ cold start delay. This is different than pausing the Inference Endpoint with [`pause_inference_endpoint`], which
+ would require a manual resume with [`resume_inference_endpoint`].
+
+ For convenience, you can also scale an Inference Endpoint to zero using [`InferenceEndpoint.scale_to_zero`].
+
+ Args:
+ name (`str`):
+ The name of the Inference Endpoint to scale to zero.
+ namespace (`str`, *optional*):
+ The namespace in which the Inference Endpoint is located. Defaults to the current user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`InferenceEndpoint`]: information about the scaled-to-zero Inference Endpoint.
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().post(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/endpoint/{namespace}/{name}/scale-to-zero",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ return InferenceEndpoint.from_raw(response.json(), namespace=namespace, token=token)
+
+ def list_inference_endpoints_hardware(
+ self, *, namespace: str | None = None, token: bool | str | None = None
+ ) -> list[InferenceEndpointHardware]:
+ """List the hardware available to deploy an Inference Endpoint on.
+
+ Each entry carries the exact `vendor`, `region`, `accelerator`, `instance_type` and `instance_size` values
+ expected by [`create_inference_endpoint`], along with the price and the accelerator quota of the namespace.
+
+ Args:
+ namespace (`str`, *optional*):
+ The namespace whose available hardware and accelerator quota to list. Defaults to the current user.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+
+ Returns:
+ `list[InferenceEndpointHardware]`: The hardware available in every vendor and region, including the
+ hardware that is currently unavailable or deprecated.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> hardware = api.list_inference_endpoints_hardware()
+ >>> [hw.id for hw in hardware if hw.accelerator == "gpu" and hw.status == "available"]
+ ['aws-us-east-1-nvidia-l4-x1', 'aws-us-east-1-nvidia-l4-x4', ...]
+ ```
+ """
+ namespace = namespace or self._get_namespace(token=token)
+
+ response = get_session().get(
+ f"{constants.INFERENCE_ENDPOINTS_ENDPOINT}/provider/{namespace}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ return [
+ InferenceEndpointHardware.from_raw(compute, vendor=vendor["name"], region=region["name"])
+ for vendor in response.json()["vendors"]
+ for region in vendor["regions"]
+ for compute in region["computes"]
+ ]
+
+ def _get_namespace(self, token: bool | str | None = None) -> str:
+ """Get the default namespace for the current user."""
+ me = self.whoami(token=token)
+ if me["type"] == "user":
+ return me["name"]
+ else:
+ raise ValueError(
+ "Cannot determine default namespace. You must provide a 'namespace' as input or be logged in as a"
+ " user."
+ )
+
+ ########################
+ # Collection Endpoints #
+ ########################
+ @validate_hf_hub_args
+ def list_collections(
+ self,
+ *,
+ owner: list[str] | str | None = None,
+ item: list[str] | str | None = None,
+ sort: CollectionSort_T | None = None,
+ limit: int | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[Collection]:
+ """List collections on the Huggingface Hub, given some filters.
+
+ > [!WARNING]
+ > When listing collections, the item list per collection is truncated to 4 items maximum. To retrieve all items
+ > from a collection, you must use [`get_collection`].
+
+ Args:
+ owner (`list[str]` or `str`, *optional*):
+ Filter by owner's username.
+ item (`list[str]` or `str`, *optional*):
+ Filter collections containing a particular items. Example: `"models/teknium/OpenHermes-2.5-Mistral-7B"`, `"datasets/squad"` or `"papers/2311.12983"`.
+ sort (`Literal["lastModified", "trending", "upvotes"]`, *optional*):
+ Sort collections by last modified, trending or upvotes.
+ limit (`int`, *optional*):
+ Maximum number of collections to be returned.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[Collection]`: an iterable of [`Collection`] objects.
+ """
+ # Construct the API endpoint
+ path = f"{self.endpoint}/api/collections"
+ headers = self._build_hf_headers(token=token)
+ params: dict = {}
+ if owner is not None:
+ params.update({"owner": owner})
+ if item is not None:
+ params.update({"item": item})
+ if sort is not None:
+ params.update({"sort": sort})
+ if limit is not None:
+ params.update({"limit": limit})
+
+ # Paginate over the results until limit is reached
+ items = paginate(path, headers=headers, params=params)
+ if limit is not None:
+ items = islice(items, limit) # Do not iterate over all pages
+
+ # Parse as Collection and return
+ for position, collection_data in enumerate(items):
+ yield Collection(position=position, **collection_data)
+
+ def get_collection(self, collection_slug: str, *, token: bool | str | None = None) -> Collection:
+ """Gets information about a Collection on the Hub.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection of the Hub. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns: [`Collection`]
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import get_collection
+ >>> collection = get_collection("TheBloke/recent-models-64f9a55bb3115b4f513ec026")
+ >>> collection.title
+ 'Recent models'
+ >>> len(collection.items)
+ 37
+ >>> collection.items[0]
+ CollectionItem(
+ item_object_id='651446103cd773a050bf64c2',
+ item_id='TheBloke/U-Amethyst-20B-AWQ',
+ item_type='model',
+ position=88,
+ note=None
+ )
+ ```
+ """
+ r = get_session().get(
+ f"{self.endpoint}/api/collections/{collection_slug}", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(r)
+ return Collection(**{**r.json(), "endpoint": self.endpoint})
+
+ def create_collection(
+ self,
+ title: str,
+ *,
+ namespace: str | None = None,
+ description: str | None = None,
+ private: bool = False,
+ resource_group_id: str | None = None,
+ exists_ok: bool = False,
+ token: bool | str | None = None,
+ ) -> Collection:
+ """Create a new Collection on the Hub.
+
+ Args:
+ title (`str`):
+ Title of the collection to create. Example: `"Recent models"`.
+ namespace (`str`, *optional*):
+ Namespace of the collection to create (username or org). Will default to the owner name.
+ description (`str`, *optional*):
+ Description of the collection to create. The maximum size for a description is 150 characters.
+ private (`bool`, *optional*):
+ Whether the collection should be private or not. Defaults to `False` (i.e. public collection).
+ resource_group_id (`str`, *optional*):
+ Assign the collection to a resource group of the owning organization. Only valid for
+ organization-owned collections. The resource group ID is a 24-character hexadecimal string.
+ exists_ok (`bool`, *optional*):
+ If `True`, do not raise an error if collection already exists.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns: [`Collection`]
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import create_collection
+ >>> collection = create_collection(
+ ... title="ICCV 2023",
+ ... description="Portfolio of models, papers and demos I presented at ICCV 2023",
+ ... )
+ >>> collection.slug
+ "username/iccv-2023-64f9a55bb3115b4f513ec026"
+ ```
+ """
+ if namespace is None:
+ namespace = self.whoami(token)["name"]
+
+ payload = {
+ "title": title,
+ "namespace": namespace,
+ "private": private,
+ }
+ if description is not None:
+ payload["description"] = description
+ if resource_group_id is not None:
+ payload["resourceGroupId"] = resource_group_id
+
+ r = get_session().post(
+ f"{self.endpoint}/api/collections", headers=self._build_hf_headers(token=token), json=payload
+ )
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if exists_ok and err.response.status_code == 409:
+ # Collection already exists and `exists_ok=True`
+ slug = r.json()["slug"]
+ return self.get_collection(slug, token=token)
+ else:
+ raise
+ return Collection(**{**r.json(), "endpoint": self.endpoint})
+
+ def update_collection_metadata(
+ self,
+ collection_slug: str,
+ *,
+ title: str | None = None,
+ description: str | None = None,
+ position: int | None = None,
+ private: bool | None = None,
+ theme: str | None = None,
+ token: bool | str | None = None,
+ ) -> Collection:
+ """Update metadata of a collection on the Hub.
+
+ All arguments are optional. Only provided metadata will be updated.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to update. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ title (`str`):
+ Title of the collection to update.
+ description (`str`, *optional*):
+ Description of the collection to update. The maximum size for a description is 150 characters.
+ position (`int`, *optional*):
+ New position of the collection in the list of collections of the user.
+ private (`bool`, *optional*):
+ Whether the collection should be private or not.
+ theme (`str`, *optional*):
+ Theme of the collection on the Hub.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns: [`Collection`]
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import update_collection_metadata
+ >>> collection = update_collection_metadata(
+ ... collection_slug="username/iccv-2023-64f9a55bb3115b4f513ec026",
+ ... title="ICCV Oct. 2023"
+ ... description="Portfolio of models, datasets, papers and demos I presented at ICCV Oct. 2023",
+ ... private=False,
+ ... theme="pink",
+ ... )
+ >>> collection.slug
+ "username/iccv-oct-2023-64f9a55bb3115b4f513ec026"
+ # ^collection slug got updated but not the trailing ID
+ ```
+ """
+ payload = {
+ "position": position,
+ "private": private,
+ "theme": theme,
+ "title": title,
+ "description": description,
+ }
+ r = get_session().patch(
+ f"{self.endpoint}/api/collections/{collection_slug}",
+ headers=self._build_hf_headers(token=token),
+ # Only send not-none values to the API
+ json={key: value for key, value in payload.items() if value is not None},
+ )
+ hf_raise_for_status(r)
+ return Collection(**{**r.json()["data"], "endpoint": self.endpoint})
+
+ def update_collection_resource_group(
+ self,
+ collection_slug: str,
+ resource_group_id: str | None,
+ *,
+ token: bool | str | None = None,
+ ) -> None:
+ """Assign a collection to a resource group, or remove it from any resource group.
+
+ Only valid for organization-owned collections.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to update. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ resource_group_id (`str` or `None`):
+ The resource group to assign the collection to, as a 24-character hexadecimal string. If `None`,
+ the collection is removed from any resource group.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import update_collection_resource_group
+ >>> update_collection_resource_group(
+ ... collection_slug="my-org/iccv-2023-64f9a55bb3115b4f513ec026",
+ ... resource_group_id="66980ecfc1e12a49c8f0e42d",
+ ... )
+ ```
+ """
+ r = get_session().post(
+ f"{self.endpoint}/api/collections/{collection_slug}/resource-group",
+ headers=self._build_hf_headers(token=token),
+ json={"resourceGroupId": resource_group_id},
+ )
+ hf_raise_for_status(r)
+
+ def delete_collection(
+ self, collection_slug: str, *, missing_ok: bool = False, token: bool | str | None = None
+ ) -> None:
+ """Delete a collection on the Hub.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to delete. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ missing_ok (`bool`, *optional*):
+ If `True`, do not raise an error if the collection doesn't exist.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import delete_collection
+ >>> collection = delete_collection("username/useless-collection-64f9a55bb3115b4f513ec026", missing_ok=True)
+ ```
+
+ > [!WARNING]
+ > This is a non-revertible action. A deleted collection cannot be restored.
+ """
+ r = get_session().delete(
+ f"{self.endpoint}/api/collections/{collection_slug}", headers=self._build_hf_headers(token=token)
+ )
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if missing_ok and err.response.status_code == 404:
+ # Collection doesn't exist and `missing_ok=True`
+ return
+ else:
+ raise
+
+ def add_collection_item(
+ self,
+ collection_slug: str,
+ item_id: str,
+ item_type: CollectionItemType_T,
+ *,
+ note: str | None = None,
+ exists_ok: bool = False,
+ token: bool | str | None = None,
+ ) -> Collection:
+ """Add an item to a collection on the Hub.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to update. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ item_id (`str`):
+ Id of the item to add to the collection. Use the repo_id for repos/spaces/datasets,
+ the paper id for papers, the slug of another collection (e.g. `"moonshotai/kimi-k2"`)
+ or a bucket id (e.g. `"namespace/bucket-name"`).
+ item_type (`str`):
+ Type of the item to add. Can be one of `"model"`, `"dataset"`, `"space"`, `"paper"`, `"collection"`
+ or `"bucket"`.
+ note (`str`, *optional*):
+ A note to attach to the item in the collection. The maximum size for a note is 500 characters.
+ exists_ok (`bool`, *optional*):
+ If `True`, do not raise an error if item already exists.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns: [`Collection`]
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the item you try to add to the collection does not exist on the Hub.
+ [`HfHubHTTPError`]:
+ HTTP 409 if the item you try to add to the collection is already in the collection (and exists_ok=False)
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import add_collection_item
+ >>> collection = add_collection_item(
+ ... collection_slug="davanstrien/climate-64f99dc2a5067f6b65531bab",
+ ... item_id="pierre-loic/climate-news-articles",
+ ... item_type="dataset"
+ ... )
+ >>> collection.items[-1].item_id
+ "pierre-loic/climate-news-articles"
+ # ^item got added to the collection on last position
+
+ # Add item with a note
+ >>> add_collection_item(
+ ... collection_slug="davanstrien/climate-64f99dc2a5067f6b65531bab",
+ ... item_id="datasets/climate_fever",
+ ... item_type="dataset"
+ ... note="This dataset adopts the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet."
+ ... )
+ (...)
+ ```
+ """
+ payload: dict[str, Any] = {"item": {"id": item_id, "type": item_type}}
+ if note is not None:
+ payload["note"] = note
+ r = get_session().post(
+ f"{self.endpoint}/api/collections/{collection_slug}/items",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if exists_ok and err.response.status_code == 409:
+ # Item already exists and `exists_ok=True`
+ return self.get_collection(collection_slug, token=token)
+ else:
+ raise
+ return Collection(**{**r.json(), "endpoint": self.endpoint})
+
+ def update_collection_item(
+ self,
+ collection_slug: str,
+ item_object_id: str,
+ *,
+ note: str | None = None,
+ position: int | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """Update an item in a collection.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to update. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ item_object_id (`str`):
+ ID of the item in the collection. This is not the id of the item on the Hub (repo_id or paper id).
+ It must be retrieved from a [`CollectionItem`] object. Example: `collection.items[0].item_object_id`.
+ note (`str`, *optional*):
+ A note to attach to the item in the collection. The maximum size for a note is 500 characters.
+ position (`int`, *optional*):
+ New position of the item in the collection.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import get_collection, update_collection_item
+
+ # Get collection first
+ >>> collection = get_collection("TheBloke/recent-models-64f9a55bb3115b4f513ec026")
+
+ # Update item based on its ID (add note + update position)
+ >>> update_collection_item(
+ ... collection_slug="TheBloke/recent-models-64f9a55bb3115b4f513ec026",
+ ... item_object_id=collection.items[-1].item_object_id,
+ ... note="Newly updated model!"
+ ... position=0,
+ ... )
+ ```
+ """
+ payload = {"position": position, "note": note}
+ r = get_session().patch(
+ f"{self.endpoint}/api/collections/{collection_slug}/items/{item_object_id}",
+ headers=self._build_hf_headers(token=token),
+ # Only send not-none values to the API
+ json={key: value for key, value in payload.items() if value is not None},
+ )
+ hf_raise_for_status(r)
+
+ def delete_collection_item(
+ self,
+ collection_slug: str,
+ item_object_id: str,
+ *,
+ missing_ok: bool = False,
+ token: bool | str | None = None,
+ ) -> None:
+ """Delete an item from a collection.
+
+ Args:
+ collection_slug (`str`):
+ Slug of the collection to update. Example: `"TheBloke/recent-models-64f9a55bb3115b4f513ec026"`.
+ item_object_id (`str`):
+ ID of the item in the collection. This is not the id of the item on the Hub (repo_id or paper id).
+ It must be retrieved from a [`CollectionItem`] object. Example: `collection.items[0].item_object_id`.
+ missing_ok (`bool`, *optional*):
+ If `True`, do not raise an error if the item doesn't exist.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import get_collection, delete_collection_item
+
+ # Get collection first
+ >>> collection = get_collection("TheBloke/recent-models-64f9a55bb3115b4f513ec026")
+
+ # Delete item based on its ID
+ >>> delete_collection_item(
+ ... collection_slug="TheBloke/recent-models-64f9a55bb3115b4f513ec026",
+ ... item_object_id=collection.items[-1].item_object_id,
+ ... )
+ ```
+ """
+ r = get_session().delete(
+ f"{self.endpoint}/api/collections/{collection_slug}/items/{item_object_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ try:
+ hf_raise_for_status(r)
+ except HfHubHTTPError as err:
+ if missing_ok and err.response.status_code == 404:
+ # Item already deleted and `missing_ok=True`
+ return
+ else:
+ raise
+
+ ##########################
+ # Manage access requests #
+ ##########################
+
+ @validate_hf_hub_args
+ def list_pending_access_requests(
+ self, repo_id: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> Iterable[AccessRequest]:
+ """
+ Get pending access requests for a given gated repo.
+
+ A pending request means the user has requested access to the repo but the request has not been processed yet.
+ If the approval mode is automatic, this list should be empty. Pending requests can be accepted or rejected
+ using [`accept_access_request`] and [`reject_access_request`].
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to get access requests for.
+ repo_type (`str`, *optional*):
+ The type of the repo to get access requests for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[AccessRequest]`: An iterable of [`AccessRequest`] objects. Each time contains a `username`, `email`,
+ `status` and `timestamp` attribute. If the gated repo has a custom form, the `fields` attribute will
+ be populated with user's answers.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import list_pending_access_requests, accept_access_request
+
+ # List pending requests
+ >>> requests = list(list_pending_access_requests("meta-llama/Llama-2-7b"))
+ >>> len(requests)
+ 411
+ >>> requests[0]
+ [
+ AccessRequest(
+ username='clem',
+ fullname='Clem 🤗',
+ email='***',
+ timestamp=datetime.datetime(2023, 11, 23, 18, 4, 53, 828000, tzinfo=datetime.timezone.utc),
+ status='pending',
+ fields=None,
+ ),
+ ...
+ ]
+
+ # Accept Clem's request
+ >>> accept_access_request("meta-llama/Llama-2-7b", "clem")
+ ```
+ """
+ yield from self._list_access_requests(repo_id, "pending", repo_type=repo_type, token=token)
+
+ @validate_hf_hub_args
+ def list_accepted_access_requests(
+ self, repo_id: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> Iterable[AccessRequest]:
+ """
+ Get accepted access requests for a given gated repo.
+
+ An accepted request means the user has requested access to the repo and the request has been accepted. The user
+ can download any file of the repo. If the approval mode is automatic, this list should contains by default all
+ requests. Accepted requests can be cancelled or rejected at any time using [`cancel_access_request`] and
+ [`reject_access_request`]. A cancelled request will go back to the pending list while a rejected request will
+ go to the rejected list. In both cases, the user will lose access to the repo.
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to get access requests for.
+ repo_type (`str`, *optional*):
+ The type of the repo to get access requests for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[AccessRequest]`: An iterable of [`AccessRequest`] objects. Each time contains a `username`, `email`,
+ `status` and `timestamp` attribute. If the gated repo has a custom form, the `fields` attribute will
+ be populated with user's answers.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import list_accepted_access_requests
+
+ >>> requests = list(list_accepted_access_requests("meta-llama/Llama-2-7b"))
+ >>> len(requests)
+ 411
+ >>> requests[0]
+ [
+ AccessRequest(
+ username='clem',
+ fullname='Clem 🤗',
+ email='***',
+ timestamp=datetime.datetime(2023, 11, 23, 18, 4, 53, 828000, tzinfo=datetime.timezone.utc),
+ status='accepted',
+ fields=None,
+ ),
+ ...
+ ]
+ ```
+ """
+ yield from self._list_access_requests(repo_id, "accepted", repo_type=repo_type, token=token)
+
+ @validate_hf_hub_args
+ def list_rejected_access_requests(
+ self, repo_id: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> Iterable[AccessRequest]:
+ """
+ Get rejected access requests for a given gated repo.
+
+ A rejected request means the user has requested access to the repo and the request has been explicitly rejected
+ by a repo owner (either you or another user from your organization). The user cannot download any file of the
+ repo. Rejected requests can be accepted or cancelled at any time using [`accept_access_request`] and
+ [`cancel_access_request`]. A cancelled request will go back to the pending list while an accepted request will
+ go to the accepted list.
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to get access requests for.
+ repo_type (`str`, *optional*):
+ The type of the repo to get access requests for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[AccessRequest]`: An iterable of [`AccessRequest`] objects. Each time contains a `username`, `email`,
+ `status` and `timestamp` attribute. If the gated repo has a custom form, the `fields` attribute will
+ be populated with user's answers.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import list_rejected_access_requests
+
+ >>> requests = list(list_rejected_access_requests("meta-llama/Llama-2-7b"))
+ >>> len(requests)
+ 411
+ >>> requests[0]
+ [
+ AccessRequest(
+ username='clem',
+ fullname='Clem 🤗',
+ email='***',
+ timestamp=datetime.datetime(2023, 11, 23, 18, 4, 53, 828000, tzinfo=datetime.timezone.utc),
+ status='rejected',
+ fields=None,
+ ),
+ ...
+ ]
+ ```
+ """
+ yield from self._list_access_requests(repo_id, "rejected", repo_type=repo_type, token=token)
+
+ def _list_access_requests(
+ self,
+ repo_id: str,
+ status: Literal["accepted", "rejected", "pending"],
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[AccessRequest]:
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ for request in paginate(
+ f"{constants.ENDPOINT}/api/{repo_type}s/{repo_id}/user-access-request/{status}",
+ params={},
+ headers=self._build_hf_headers(token=token),
+ ):
+ yield AccessRequest(
+ username=request["user"]["user"],
+ fullname=request["user"]["fullname"],
+ email=request["user"].get("email"),
+ status=request["status"],
+ timestamp=parse_datetime(request["timestamp"]),
+ fields=request.get("fields"), # only if custom fields in form
+ )
+
+ @validate_hf_hub_args
+ def cancel_access_request(
+ self, repo_id: str, user: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> None:
+ """
+ Cancel an access request from a user for a given gated repo.
+
+ A cancelled request will go back to the pending list and the user will lose access to the repo.
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to cancel access request for.
+ user (`str`):
+ The username of the user which access request should be cancelled.
+ repo_type (`str`, *optional*):
+ The type of the repo to cancel access request for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user does not exist on the Hub.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request cannot be found.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request is already in the pending list.
+ """
+ self._handle_access_request(repo_id, user, "pending", repo_type=repo_type, token=token)
+
+ @validate_hf_hub_args
+ def accept_access_request(
+ self, repo_id: str, user: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> None:
+ """
+ Accept an access request from a user for a given gated repo.
+
+ Once the request is accepted, the user will be able to download any file of the repo and access the community
+ tab. If the approval mode is automatic, you don't have to accept requests manually. An accepted request can be
+ cancelled or rejected at any time using [`cancel_access_request`] and [`reject_access_request`].
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to accept access request for.
+ user (`str`):
+ The username of the user which access request should be accepted.
+ repo_type (`str`, *optional*):
+ The type of the repo to accept access request for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user does not exist on the Hub.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request cannot be found.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request is already in the accepted list.
+ """
+ self._handle_access_request(repo_id, user, "accepted", repo_type=repo_type, token=token)
+
+ @validate_hf_hub_args
+ def reject_access_request(
+ self,
+ repo_id: str,
+ user: str,
+ *,
+ repo_type: str | None = None,
+ rejection_reason: str | None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Reject an access request from a user for a given gated repo.
+
+ A rejected request will go to the rejected list. The user cannot download any file of the repo. Rejected
+ requests can be accepted or cancelled at any time using [`accept_access_request`] and [`cancel_access_request`].
+ A cancelled request will go back to the pending list while an accepted request will go to the accepted list.
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to reject access request for.
+ user (`str`):
+ The username of the user which access request should be rejected.
+ repo_type (`str`, *optional*):
+ The type of the repo to reject access request for. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ rejection_reason (`str`, *optional*):
+ Optional rejection reason that will be visible to the user (max 200 characters).
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user does not exist on the Hub.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request cannot be found.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user access request is already in the rejected list.
+ """
+ self._handle_access_request(
+ repo_id, user, "rejected", repo_type=repo_type, rejection_reason=rejection_reason, token=token
+ )
+
+ @validate_hf_hub_args
+ def _handle_access_request(
+ self,
+ repo_id: str,
+ user: str,
+ status: Literal["accepted", "rejected", "pending"],
+ repo_type: str | None = None,
+ rejection_reason: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ payload = {"user": user, "status": status}
+
+ if rejection_reason is not None:
+ if status != "rejected":
+ raise ValueError("`rejection_reason` can only be passed when rejecting an access request.")
+ payload["rejectionReason"] = rejection_reason
+
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/{repo_type}s/{repo_id}/user-access-request/handle",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def grant_access(
+ self, repo_id: str, user: str, *, repo_type: str | None = None, token: bool | str | None = None
+ ) -> None:
+ """
+ Grant access to a user for a given gated repo.
+
+ Granting access don't require for the user to send an access request by themselves. The user is automatically
+ added to the accepted list meaning they can download the files You can revoke the granted access at any time
+ using [`cancel_access_request`] or [`reject_access_request`].
+
+ For more info about gated repos, see https://huggingface.co/docs/hub/models-gated.
+
+ Args:
+ repo_id (`str`):
+ The id of the repo to grant access to.
+ user (`str`):
+ The username of the user to grant access.
+ repo_type (`str`, *optional*):
+ The type of the repo to grant access to. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 400 if the repo is not gated.
+ [`HfHubHTTPError`]:
+ HTTP 400 if the user already has access to the repo.
+ [`HfHubHTTPError`]:
+ HTTP 403 if you only have read-only access to the repo. This can be the case if you don't have `write`
+ or `admin` role in the organization the repo belongs to or if you passed a `read` token.
+ [`HfHubHTTPError`]:
+ HTTP 404 if the user does not exist on the Hub.
+ """
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/{repo_type}s/{repo_id}/user-access-request/grant",
+ headers=self._build_hf_headers(token=token),
+ json={"user": user},
+ )
+ hf_raise_for_status(response)
+ return response.json()
+
+ ###################
+ # Manage webhooks #
+ ###################
+
+ @validate_hf_hub_args
+ def get_webhook(self, webhook_id: str, *, token: bool | str | None = None) -> WebhookInfo:
+ """Get a webhook by its id.
+
+ Args:
+ webhook_id (`str`):
+ The unique identifier of the webhook to get.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`WebhookInfo`]:
+ Info about the webhook.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import get_webhook
+ >>> webhook = get_webhook("654bbbc16f2ec14d77f109cc")
+ >>> print(webhook)
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ job=None,
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ secret="my-secret",
+ domains=["repo", "discussion"],
+ disabled=False,
+ )
+ ```
+ """
+ response = get_session().get(
+ f"{constants.ENDPOINT}/api/settings/webhooks/{webhook_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhook_data = response.json()["webhook"]
+
+ watched_items = [WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook_data["watched"]]
+
+ webhook = WebhookInfo(
+ id=webhook_data["id"],
+ url=webhook_data.get("url"),
+ job=JobSpec(**webhook_data["job"]) if webhook_data.get("job") else None,
+ watched=watched_items,
+ domains=webhook_data["domains"],
+ secret=webhook_data.get("secret"),
+ disabled=webhook_data["disabled"],
+ )
+
+ return webhook
+
+ @validate_hf_hub_args
+ def list_webhooks(self, *, token: bool | str | None = None) -> list[WebhookInfo]:
+ """List all configured webhooks.
+
+ Args:
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `list[WebhookInfo]`:
+ List of webhook info objects.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_webhooks
+ >>> webhooks = list_webhooks()
+ >>> len(webhooks)
+ 2
+ >>> webhooks[0]
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ secret="my-secret",
+ domains=["repo", "discussion"],
+ disabled=False,
+ )
+ ```
+ """
+ response = get_session().get(
+ f"{constants.ENDPOINT}/api/settings/webhooks",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhooks_data = response.json()
+
+ return [
+ WebhookInfo(
+ id=webhook["id"],
+ url=webhook.get("url"),
+ job=JobSpec(**webhook["job"]) if webhook.get("job") else None,
+ watched=[WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook["watched"]],
+ domains=webhook["domains"],
+ secret=webhook.get("secret"),
+ disabled=webhook["disabled"],
+ )
+ for webhook in webhooks_data
+ ]
+
+ @validate_hf_hub_args
+ def create_webhook(
+ self,
+ *,
+ url: str | None = None,
+ job_id: str | None = None,
+ watched: list[dict | WebhookWatchedItem],
+ domains: list[constants.WEBHOOK_DOMAIN_T] | None = None,
+ secret: str | None = None,
+ secrets: dict[str, str] | None = None,
+ token: bool | str | None = None,
+ ) -> WebhookInfo:
+ """Create a new webhook.
+
+ The webhook can either send a payload to a URL, or trigger a Job to run on Hugging Face infrastructure.
+ This function should be called with one of `url` or `job_id`, but not both.
+
+ Args:
+ url (`str`):
+ URL to send the payload to.
+ job_id (`str`):
+ ID of the source Job to trigger with the webhook payload in the environment variable WEBHOOK_PAYLOAD.
+ Additional environment variables are available for convenience: WEBHOOK_REPO_ID, WEBHOOK_REPO_TYPE and WEBHOOK_SECRET.
+ watched (`list[WebhookWatchedItem]`):
+ List of [`WebhookWatchedItem`] to be watched by the webhook. It can be users, orgs, models, datasets, spaces or buckets.
+ Watched items can also be provided as plain dictionaries.
+ domains (`list[Literal["repo", "discussion"]]`, optional):
+ List of domains to watch. It can be "repo", "discussion" or both.
+ secret (`str`, optional):
+ A secret to sign the payload with.
+ secrets (`dict[str, str]`, optional):
+ Secret environment variables for the Job triggered by the webhook (e.g. `{"HF_TOKEN": "hf_***"}`).
+ Only valid with `job_id`. The source Job's secrets are not copied to the webhook, so pass them here.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`WebhookInfo`]:
+ Info about the newly created webhook.
+
+ Example:
+
+ Create a webhook that sends a payload to a URL
+ ```python
+ >>> from huggingface_hub import create_webhook
+ >>> payload = create_webhook(
+ ... watched=[{"type": "user", "name": "julien-c"}, {"type": "org", "name": "HuggingFaceH4"}],
+ ... url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ ... domains=["repo", "discussion"],
+ ... secret="my-secret",
+ ... )
+ >>> print(payload)
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ job=None,
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ domains=["repo", "discussion"],
+ secret="my-secret",
+ disabled=False,
+ )
+ ```
+
+ Run a Job and then create a webhook that triggers this Job
+ ```python
+ >>> from huggingface_hub import create_webhook, run_job
+ >>> job = run_job(
+ ... image="ubuntu",
+ ... command=["bash", "-c", r"echo An event occurred in $WEBHOOK_REPO_ID: $WEBHOOK_PAYLOAD"],
+ ... )
+ >>> payload = create_webhook(
+ ... watched=[{"type": "user", "name": "julien-c"}, {"type": "org", "name": "HuggingFaceH4"}],
+ ... job_id=job.id,
+ ... domains=["repo", "discussion"],
+ ... secret="my-secret",
+ ... )
+ >>> print(payload)
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ url=None,
+ job=JobSpec(
+ docker_image='ubuntu',
+ space_id=None,
+ command=['bash', '-c', 'echo An event occurred in $WEBHOOK_REPO_ID: $WEBHOOK_PAYLOAD'],
+ arguments=[],
+ environment={},
+ secrets=[],
+ flavor='cpu-basic',
+ timeout=None,
+ tags=None,
+ arch=None
+ ),
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ domains=["repo", "discussion"],
+ secret="my-secret",
+ disabled=False,
+ )
+ ```
+ """
+ watched_dicts = [asdict(item) if isinstance(item, WebhookWatchedItem) else item for item in watched]
+
+ post_webhooks_json: dict = {"watched": watched_dicts}
+ if domains is not None:
+ post_webhooks_json["domains"] = domains
+ if secret is not None:
+ post_webhooks_json["secret"] = secret
+ if url is not None and job_id is not None:
+ raise ValueError("Set `url` or `job_id` but not both.")
+ elif url is not None:
+ post_webhooks_json["url"] = url
+ elif job_id is not None:
+ post_webhooks_json["jobSourceId"] = job_id
+ else:
+ raise ValueError("Missing argument for webhook: `url` or `job_id`.")
+ if secrets is not None:
+ if job_id is None:
+ raise ValueError("`secrets` can only be set together with `job_id`.")
+ post_webhooks_json["secrets"] = secrets
+
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/settings/webhooks",
+ json=post_webhooks_json,
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhook_data = response.json()["webhook"]
+ watched_items = [WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook_data["watched"]]
+
+ webhook = WebhookInfo(
+ id=webhook_data["id"],
+ url=webhook_data.get("url"),
+ job=JobSpec(**webhook_data["job"]) if webhook_data.get("job") else None,
+ watched=watched_items,
+ domains=webhook_data["domains"],
+ secret=webhook_data.get("secret"),
+ disabled=webhook_data["disabled"],
+ )
+
+ return webhook
+
+ @validate_hf_hub_args
+ def update_webhook(
+ self,
+ webhook_id: str,
+ *,
+ url: str | None = None,
+ job_id: str | None = None,
+ watched: list[dict | WebhookWatchedItem] | None = None,
+ domains: list[constants.WEBHOOK_DOMAIN_T] | None = None,
+ secret: str | None = None,
+ secrets: dict[str, str] | None = None,
+ token: bool | str | None = None,
+ ) -> WebhookInfo:
+ """Update an existing webhook.
+
+ Args:
+ webhook_id (`str`):
+ The unique identifier of the webhook to be updated.
+ url (`str`, optional):
+ The URL to which the payload will be sent.
+ job_id (`str`, optional):
+ ID of the source Job to trigger with the webhook payload. Required when setting `secrets`.
+ watched (`list[WebhookWatchedItem]`, optional):
+ List of items to watch. It can be users, orgs, models, datasets, or spaces.
+ Refer to [`WebhookWatchedItem`] for more details. Watched items can also be provided as plain dictionaries.
+ domains (`list[Literal["repo", "discussion"]]`, optional):
+ The domains to watch. This can include "repo", "discussion", or both.
+ secret (`str`, optional):
+ A secret to sign the payload with, providing an additional layer of security.
+ secrets (`dict[str, str]`, optional):
+ Secret environment variables for the Job triggered by the webhook. Listed keys replace the stored
+ value, an empty string removes that secret, and omitted keys keep their stored value. Requires `job_id`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`WebhookInfo`]:
+ Info about the updated webhook.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import update_webhook
+ >>> updated_payload = update_webhook(
+ ... webhook_id="654bbbc16f2ec14d77f109cc",
+ ... url="https://new.webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ ... watched=[{"type": "user", "name": "julien-c"}, {"type": "org", "name": "HuggingFaceH4"}],
+ ... domains=["repo"],
+ ... secret="my-secret",
+ ... )
+ >>> print(updated_payload)
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ job=None,
+ url="https://new.webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ domains=["repo"],
+ secret="my-secret",
+ disabled=False,
+ ```
+ """
+ if watched is None:
+ watched = []
+ watched_dicts = [asdict(item) if isinstance(item, WebhookWatchedItem) else item for item in watched]
+
+ update_json: dict = {"watched": watched_dicts}
+ if url is not None and job_id is not None:
+ raise ValueError("Set `url` or `job_id` but not both.")
+ if url is not None:
+ update_json["url"] = url
+ if job_id is not None:
+ update_json["jobSourceId"] = job_id
+ if domains is not None:
+ update_json["domains"] = domains
+ if secret is not None:
+ update_json["secret"] = secret
+ if secrets is not None:
+ if job_id is None:
+ raise ValueError("`secrets` can only be set together with `job_id`.")
+ update_json["secrets"] = secrets
+
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/settings/webhooks/{webhook_id}",
+ json=update_json,
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhook_data = response.json()["webhook"]
+
+ watched_items = [WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook_data["watched"]]
+
+ webhook = WebhookInfo(
+ id=webhook_data["id"],
+ url=webhook_data.get("url"),
+ job=JobSpec(**webhook_data["job"]) if webhook_data.get("job") else None,
+ watched=watched_items,
+ domains=webhook_data["domains"],
+ secret=webhook_data.get("secret"),
+ disabled=webhook_data["disabled"],
+ )
+
+ return webhook
+
+ @validate_hf_hub_args
+ def enable_webhook(self, webhook_id: str, *, token: bool | str | None = None) -> WebhookInfo:
+ """Enable a webhook (makes it "active").
+
+ Args:
+ webhook_id (`str`):
+ The unique identifier of the webhook to enable.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`WebhookInfo`]:
+ Info about the enabled webhook.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import enable_webhook
+ >>> enabled_webhook = enable_webhook("654bbbc16f2ec14d77f109cc")
+ >>> enabled_webhook
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ job=None,
+ url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ domains=["repo", "discussion"],
+ secret="my-secret",
+ disabled=False,
+ )
+ ```
+ """
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/settings/webhooks/{webhook_id}/enable",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhook_data = response.json()["webhook"]
+
+ watched_items = [WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook_data["watched"]]
+
+ webhook = WebhookInfo(
+ id=webhook_data["id"],
+ url=webhook_data.get("url"),
+ job=JobSpec(**webhook_data["job"]) if webhook_data.get("job") else None,
+ watched=watched_items,
+ domains=webhook_data["domains"],
+ secret=webhook_data.get("secret"),
+ disabled=webhook_data["disabled"],
+ )
+
+ return webhook
+
+ @validate_hf_hub_args
+ def disable_webhook(self, webhook_id: str, *, token: bool | str | None = None) -> WebhookInfo:
+ """Disable a webhook (makes it "disabled").
+
+ Args:
+ webhook_id (`str`):
+ The unique identifier of the webhook to disable.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`WebhookInfo`]:
+ Info about the disabled webhook.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import disable_webhook
+ >>> disabled_webhook = disable_webhook("654bbbc16f2ec14d77f109cc")
+ >>> disabled_webhook
+ WebhookInfo(
+ id="654bbbc16f2ec14d77f109cc",
+ url="https://webhook.site/a2176e82-5720-43ee-9e06-f91cb4c91548",
+ jon=None,
+ watched=[WebhookWatchedItem(type="user", name="julien-c"), WebhookWatchedItem(type="org", name="HuggingFaceH4")],
+ domains=["repo", "discussion"],
+ secret="my-secret",
+ disabled=True,
+ )
+ ```
+ """
+ response = get_session().post(
+ f"{constants.ENDPOINT}/api/settings/webhooks/{webhook_id}/disable",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ webhook_data = response.json()["webhook"]
+
+ watched_items = [WebhookWatchedItem(type=item["type"], name=item["name"]) for item in webhook_data["watched"]]
+
+ webhook = WebhookInfo(
+ id=webhook_data["id"],
+ url=webhook_data.get("url"),
+ job=JobSpec(**webhook_data["job"]) if webhook_data.get("job") else None,
+ watched=watched_items,
+ domains=webhook_data["domains"],
+ secret=webhook_data.get("secret"),
+ disabled=webhook_data["disabled"],
+ )
+
+ return webhook
+
+ @validate_hf_hub_args
+ def delete_webhook(self, webhook_id: str, *, token: bool | str | None = None) -> None:
+ """Delete a webhook.
+
+ Args:
+ webhook_id (`str`):
+ The unique identifier of the webhook to delete.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended
+ method for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `None`
+
+ Example:
+ ```python
+ >>> from huggingface_hub import delete_webhook
+ >>> delete_webhook("654bbbc16f2ec14d77f109cc")
+ ```
+ """
+ response = get_session().delete(
+ f"{constants.ENDPOINT}/api/settings/webhooks/{webhook_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ #############
+ # Internals #
+ #############
+
+ def _build_hf_headers(
+ self,
+ token: bool | str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+ ) -> dict[str, str]:
+ """
+ Alias for [`build_hf_headers`] that uses the token from [`HfApi`] client
+ when `token` is not provided.
+ """
+ if token is None:
+ # Cannot do `token = token or self.token` as token can be `False`.
+ token = self.token
+ return build_hf_headers(
+ token=token,
+ library_name=library_name or self.library_name,
+ library_version=library_version or self.library_version,
+ user_agent=user_agent or self.user_agent,
+ headers=self.headers,
+ )
+
+ def _prepare_folder_deletions(
+ self,
+ repo_id: str,
+ repo_type: str | None,
+ revision: str | None,
+ path_in_repo: str,
+ delete_patterns: list[str] | str | None,
+ token: bool | str | None = None,
+ ) -> list[CommitOperationDelete]:
+ """Generate the list of Delete operations for a commit to delete files from a repo.
+
+ List remote files and match them against the `delete_patterns` constraints. Returns a list of [`CommitOperationDelete`]
+ with the matching items.
+
+ Note: `.gitattributes` file is essential to make a repo work properly on the Hub. This file will always be
+ kept even if it matches the `delete_patterns` constraints.
+ """
+
+ from ._commit_api import CommitOperationDelete
+
+ if delete_patterns is None:
+ # If no delete patterns, no need to list and filter remote files
+ return []
+
+ # List remote files
+ filenames = self.list_repo_files(repo_id=repo_id, revision=revision, repo_type=repo_type, token=token)
+
+ # Compute relative path in repo
+ if path_in_repo and path_in_repo not in (".", "./"):
+ path_in_repo = path_in_repo.strip("/") + "/" # harmonize
+ relpath_to_abspath = {
+ file[len(path_in_repo) :]: file for file in filenames if file.startswith(path_in_repo)
+ }
+ else:
+ relpath_to_abspath = {file: file for file in filenames}
+
+ # Apply filter on relative paths and return
+ return [
+ CommitOperationDelete(path_in_repo=relpath_to_abspath[relpath], is_folder=False)
+ for relpath in filter_repo_objects(relpath_to_abspath.keys(), allow_patterns=delete_patterns)
+ if relpath_to_abspath[relpath] != ".gitattributes"
+ ]
+
+ def _prepare_upload_folder_additions(
+ self,
+ folder_path: str | Path,
+ path_in_repo: str,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ ) -> list[CommitOperationAdd]:
+ """Generate the list of Add operations for a commit to upload a folder.
+
+ Files not matching the `allow_patterns` (allowlist) and `ignore_patterns` (denylist)
+ constraints are discarded.
+ """
+
+ from ._commit_api import CommitOperationAdd
+
+ folder_path = Path(folder_path).expanduser().resolve()
+ if not folder_path.is_dir():
+ raise ValueError(f"Provided path: '{folder_path}' is not a directory")
+
+ # List files from folder
+ relpath_to_abspath = {
+ path.relative_to(folder_path).as_posix(): path
+ for path in sorted(folder_path.glob("**/*")) # sorted to be deterministic
+ if path.is_file()
+ }
+
+ # Filter files
+ # Patterns are applied on the path relative to `folder_path`. `path_in_repo` is prefixed after the filtering.
+ filtered_repo_objects = list(
+ filter_repo_objects(
+ relpath_to_abspath.keys(), allow_patterns=allow_patterns, ignore_patterns=ignore_patterns
+ )
+ )
+
+ prefix = f"{path_in_repo.strip('/')}/" if path_in_repo else ""
+
+ # If updating a README.md file, make sure the metadata format is valid
+ # It's better to fail early than to fail after all the files have been hashed.
+ if "README.md" in filtered_repo_objects:
+ self._validate_yaml(
+ content=relpath_to_abspath["README.md"].read_text(encoding="utf8"),
+ repo_type=repo_type,
+ token=token,
+ )
+ return [
+ CommitOperationAdd(
+ path_or_fileobj=relpath_to_abspath[relpath], # absolute path on disk
+ path_in_repo=prefix + relpath, # "absolute" path in repo
+ )
+ for relpath in filtered_repo_objects
+ ]
+
+ def _validate_yaml(self, content: str, *, repo_type: str | None = None, token: bool | str | None = None):
+ """
+ Validate YAML from `README.md`, used before file hashing and upload.
+
+ Args:
+ content (`str`):
+ Content of `README.md` to validate.
+ repo_type (`str`, *optional*):
+ The type of the repo to grant access to. Must be one of `model`, `dataset` or `space`.
+ Defaults to `model`.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ if YAML is invalid
+ """
+ repo_type = repo_type if repo_type is not None else constants.REPO_TYPE_MODEL
+ headers = self._build_hf_headers(token=token)
+
+ response = get_session().post(
+ f"{self.endpoint}/api/validate-yaml",
+ json={"content": content, "repoType": repo_type},
+ headers=headers,
+ )
+ # Handle warnings (example: empty metadata)
+ response_content = response.json()
+ message = "\n".join([f"- {warning.get('message')}" for warning in response_content.get("warnings", [])])
+ if message:
+ warnings.warn(f"Warnings while validating metadata in README.md:\n{message}")
+
+ # Raise on errors
+ try:
+ hf_raise_for_status(response)
+ except BadRequestError as e:
+ errors = response_content.get("errors", [])
+ message = "\n".join([f"- {error.get('message')}" for error in errors])
+ raise ValueError(f"Invalid metadata in README.md.\n{message}") from e
+
+ def get_user_overview(self, username: str, token: bool | str | None = None) -> User:
+ """
+ Get an overview of a user on the Hub.
+
+ Args:
+ username (`str`):
+ Username of the user to get an overview of.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `User`: A [`User`] object with the user's overview.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the user does not exist on the Hub.
+ """
+ r = get_session().get(
+ f"{constants.ENDPOINT}/api/users/{username}/overview", headers=self._build_hf_headers(token=token)
+ )
+ hf_raise_for_status(r)
+ return User(**r.json())
+
+ @validate_hf_hub_args
+ def get_organization_overview(self, organization: str, token: bool | str | None = None) -> Organization:
+ """
+ Get an overview of an organization on the Hub.
+
+ Args:
+ organization (`str`):
+ Name of the organization to get an overview of.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved token, which is the recommended method
+ for authentication (see https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Organization`: An [`Organization`] object with the organization's overview.
+
+ Raises:
+ [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError):
+ HTTP 404 If the organization does not exist on the Hub.
+ """
+ r = get_session().get(
+ f"{constants.ENDPOINT}/api/organizations/{organization}/overview",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(r)
+ return Organization(**r.json())
+
+ @validate_hf_hub_args
+ def list_organization_followers(self, organization: str, token: bool | str | None = None) -> Iterable[User]:
+ """
+ List followers of an organization on the Hub.
+
+ Args:
+ organization (`str`):
+ Name of the organization to get the followers of.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[User]`: A list of [`User`] objects with the followers of the organization.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the organization does not exist on the Hub.
+
+ """
+ for follower in paginate(
+ path=f"{constants.ENDPOINT}/api/organizations/{organization}/followers",
+ params={},
+ headers=self._build_hf_headers(token=token),
+ ):
+ yield User(**follower)
+
+ def list_organization_members(self, organization: str, token: bool | str | None = None) -> Iterable[User]:
+ """
+ List of members of an organization on the Hub.
+
+ Args:
+ organization (`str`):
+ Name of the organization to get the members of.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[User]`: A list of [`User`] objects with the members of the organization.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the organization does not exist on the Hub.
+
+ """
+ for member in paginate(
+ path=f"{constants.ENDPOINT}/api/organizations/{organization}/members",
+ params={},
+ headers=self._build_hf_headers(token=token),
+ ):
+ yield User(**member)
+
+ def list_user_followers(self, username: str, token: bool | str | None = None) -> Iterable[User]:
+ """
+ Get the list of followers of a user on the Hub.
+
+ Args:
+ username (`str`):
+ Username of the user to get the followers of.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[User]`: A list of [`User`] objects with the followers of the user.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the user does not exist on the Hub.
+
+ """
+ for follower in paginate(
+ path=f"{constants.ENDPOINT}/api/users/{username}/followers",
+ params={},
+ headers=self._build_hf_headers(token=token),
+ ):
+ yield User(**follower)
+
+ def list_user_following(self, username: str, token: bool | str | None = None) -> Iterable[User]:
+ """
+ Get the list of users followed by a user on the Hub.
+
+ Args:
+ username (`str`):
+ Username of the user to get the users followed by.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[User]`: A list of [`User`] objects with the users followed by the user.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the user does not exist on the Hub.
+
+ """
+ for followed_user in paginate(
+ path=f"{constants.ENDPOINT}/api/users/{username}/following",
+ params={},
+ headers=self._build_hf_headers(token=token),
+ ):
+ yield User(**followed_user)
+
+ def list_papers(
+ self,
+ *,
+ query: str | None = None,
+ limit: int | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[PaperInfo]:
+ """
+ List daily papers on the Hugging Face Hub given a search query.
+
+ Args:
+ query (`str`, *optional*):
+ A search query string to find papers.
+ If provided, returns papers that match the query.
+ limit (`int`, *optional*):
+ The maximum number of papers to return.
+ token (Union[bool, str, None], *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[PaperInfo]`: an iterable of [`huggingface_hub.hf_api.PaperInfo`] objects.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import HfApi
+
+ >>> api = HfApi()
+
+ # List all papers with "attention" in their title
+ >>> api.list_papers(query="attention")
+ ```
+ """
+ path = f"{self.endpoint}/api/papers/search"
+ params: dict[str, Any] = {}
+ if query:
+ params["q"] = query
+ if limit is not None:
+ params["limit"] = limit
+ r = get_session().get(
+ path,
+ params=params,
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(r)
+ for paper in r.json():
+ yield PaperInfo(**paper)
+
+ def paper_info(self, id: str) -> PaperInfo:
+ """
+ Get information for a paper on the Hub.
+
+ Args:
+ id (`str`, **optional**):
+ ArXiv id of the paper.
+
+ Returns:
+ `PaperInfo`: A `PaperInfo` object.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the paper does not exist on the Hub.
+ """
+ path = f"{self.endpoint}/api/papers/{id}"
+ r = get_session().get(path)
+ hf_raise_for_status(r)
+ return PaperInfo(**r.json())
+
+ def read_paper(self, id: str) -> str:
+ """
+ Get the markdown content of a paper page on the Hub.
+
+ Args:
+ id (`str`):
+ ArXiv id of the paper.
+
+ Returns:
+ `str`: The paper page content as markdown.
+
+ Raises:
+ [`HfHubHTTPError`]:
+ HTTP 404 If the paper does not exist on the Hub.
+ """
+ path = f"{self.endpoint}/papers/{id}.md"
+ r = get_session().get(path)
+ hf_raise_for_status(r)
+ return r.text
+
+ def list_daily_papers(
+ self,
+ *,
+ date: str | None = None,
+ token: bool | str | None = None,
+ week: str | None = None,
+ month: str | None = None,
+ submitter: str | None = None,
+ sort: DailyPapersSort_T | None = None,
+ p: int | None = None,
+ limit: int | None = None,
+ ) -> Iterable[PaperInfo]:
+ """
+ List the daily papers published on a given date on the Hugging Face Hub.
+
+ Args:
+ date (`str`, *optional*):
+ Date in ISO format (YYYY-MM-DD) for which to fetch daily papers.
+ Defaults to most recent ones.
+ token (Union[bool, str, None], *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token. To disable authentication, pass `False`.
+ week (`str`, *optional*):
+ Week in ISO format (YYYY-Www) for which to fetch daily papers. Example, `2025-W09`.
+ month (`str`, *optional*):
+ Month in ISO format (YYYY-MM) for which to fetch daily papers. Example, `2025-02`.
+ submitter (`str`, *optional*):
+ Username of the submitter to filter daily papers.
+ sort (`Literal["publishedAt", "trending"]`, *optional*):
+ Sort order for the daily papers. Can be either by `publishedAt` or by `trending`.
+ Defaults to `"publishedAt"`
+ p (`int`, *optional*):
+ Page number for pagination. Defaults to 0.
+ limit (`int`, *optional*):
+ Limit of papers to fetch. Defaults to 50.
+
+ Returns:
+ `Iterable[PaperInfo]`: an iterable of [`huggingface_hub.hf_api.PaperInfo`] objects.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import HfApi
+
+ >>> api = HfApi()
+ >>> list(api.list_daily_papers(date="2025-10-29"))
+ ```
+ """
+ path = f"{self.endpoint}/api/daily_papers"
+
+ params = {
+ k: v
+ for k, v in {
+ "p": p,
+ "limit": limit,
+ "sort": sort,
+ "date": date,
+ "week": week,
+ "month": month,
+ "submitter": submitter,
+ }.items()
+ if v is not None
+ }
+
+ r = get_session().get(path, params=params, headers=self._build_hf_headers(token=token))
+ hf_raise_for_status(r)
+ for paper in r.json():
+ yield PaperInfo(**paper)
+
+ def auth_check(
+ self,
+ repo_id: str,
+ *,
+ repo_type: str | None = None,
+ token: bool | str | None = None,
+ write: bool = False,
+ ) -> None:
+ """
+ Check if the provided user token has access to a specific repository on the Hugging Face Hub.
+
+ This method verifies whether the user, authenticated via the provided token, has access to the specified
+ repository. If the repository is not found or if the user lacks the required permissions to access it,
+ the method raises an appropriate exception.
+
+ Args:
+ repo_id (`str`):
+ The repository to check for access. Format should be `"user/repo_name"`.
+ Example: `"user/my-cool-model"`.
+
+ repo_type (`str`, *optional*):
+ The type of the repository. Should be one of `"model"`, `"dataset"`, or `"space"`.
+ If not specified, the default is `"model"`.
+
+ token (`Union[bool, str, None]`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ write (`bool`, *optional*):
+ If `True`, checks whether the user has content write permission on the repository.
+ If `False` (default), only checks for read access.
+
+ Raises:
+ [`~utils.RepositoryNotFoundError`]:
+ Raised if the repository does not exist, is private, or the user does not have access. This can
+ occur if the `repo_id` or `repo_type` is incorrect or if the repository is private but the user
+ is not authenticated.
+
+ [`~utils.GatedRepoError`]:
+ Raised if the repository exists but is gated and the user is not authorized to access it.
+
+ Example:
+ Check if the user has access to a repository:
+
+ ```python
+ >>> from huggingface_hub import auth_check
+ >>> from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
+
+ try:
+ auth_check("user/my-cool-model")
+ except GatedRepoError:
+ # Handle gated repository error
+ print("You do not have permission to access this gated repository.")
+ except RepositoryNotFoundError:
+ # Handle repository not found error
+ print("The repository was not found or you do not have access.")
+ ```
+
+ In this example:
+ - If the user has access, the method completes successfully.
+ - If the repository is gated or does not exist, appropriate exceptions are raised, allowing the user
+ to handle them accordingly.
+ """
+ headers = self._build_hf_headers(token=token)
+ if repo_type is None:
+ repo_type = constants.REPO_TYPE_MODEL
+ if repo_type not in constants.REPO_TYPES:
+ raise ValueError(f"Invalid repo type, must be one of {constants.REPO_TYPES}")
+ path = f"{self.endpoint}/api/{repo_type}s/{repo_id}/auth-check"
+ if write:
+ path = f"{path}/write"
+ r = get_session().get(path, headers=headers)
+ hf_raise_for_status(r)
+
+ def run_job(
+ self,
+ *,
+ image: str,
+ command: list[str],
+ env: dict[str, Any] | None = None,
+ secrets: dict[str, Any] | None = None,
+ flavor: JobHardware | str | None = None,
+ timeout: int | float | str | None = None,
+ attempts: int | None = None,
+ name: str | None = None,
+ labels: dict[str, str] | None = None,
+ volumes: list[Volume] | None = None,
+ expose: list[int] | None = None,
+ expose_public: list[int] | None = None,
+ ssh: bool = False,
+ network_group: str | None = None,
+ network_aliases: list[str] | None = None,
+ resource_group_id: str | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """
+ Run compute Jobs on Hugging Face infrastructure.
+
+ Args:
+ image (`str`):
+ The Docker image to use.
+ Examples: `"ubuntu"`, `"python:3.12"`, `"pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel"`.
+ Example with an image from a Space: `"hf.co/spaces/lhoestq/duckdb"`.
+
+ command (`list[str]`):
+ The command to run. Example: `["echo", "hello"]`.
+
+ env (`dict[str, Any]`, *optional*):
+ Defines the environment variables for the Job.
+
+ secrets (`dict[str, Any]`, *optional*):
+ Defines the secret environment variables for the Job.
+
+ flavor (`str`, *optional*):
+ Flavor for the hardware. See [`JobHardware`] for possible values.
+ Defaults to `"cpu-basic"`.
+
+ timeout (`Union[int, float, str]`, *optional*):
+ Max duration for the Job: int with s (seconds, default), m (minutes), h (hours) or d (days).
+ Example: `300` or `"5m"` for 5 minutes.
+
+ attempts (`int`, *optional*):
+ Maximum number of attempts, including the initial run. Defaults to 1. For example, 3 retries a
+ failed Job up to 2 times.
+
+ name (`str`, *optional*):
+ A name for the Job. Stored as the `name` label. Cannot be passed together with a `name` key in
+ `labels`. Names do not have to be unique. Defaults to a name derived from image and command (with a short hash suffix).
+
+ labels (`dict[str, str]`, *optional*):
+ Labels to attach to the job (key-value pairs).
+
+ volumes (`list[Volume]`, *optional*):
+ Hugging Face Buckets or Repos to mount as volumes in the job container.
+ Each volume is a [`Volume`] with `type` (`"bucket"`, `"model"`, `"dataset"`, or `"space"`),
+ `source` (e.g. `"username/my-bucket"`), and `mount_path` (e.g. `"/data"`).
+
+ expose (`list[int]`, *optional*):
+ Container ports to expose through the jobs proxy. Each listed port is reachable
+ on the public jobs domain (e.g. `https://--8000.hf.jobs`). Access always
+ requires an HF token with read access to the job's namespace. Use `expose_public` for
+ unauthenticated access.
+
+ expose_public (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy without authentication. They don't need to be listed in
+ `expose`.
+
+ ssh (`bool`, *optional*):
+ If True, the job's container is reachable over SSH at the URL given by `job.status.ssh_url`
+ (e.g. `ssh @ssh.hf.jobs`, or `hf jobs ssh ` from the CLI). Connecting requires
+ write access to the job's namespace and an SSH public key registered on the Hub
+ (https://huggingface.co/settings/keys). Defaults to False.
+
+ network_group (`str`, *optional*):
+ Name of a network group to join. Jobs in the same namespace and resource group sharing a group are
+ placed together and can reach each other on every port. Inside each member,
+ `HF_NETWORK_GROUP_HOSTNAME` resolves to every member of the group. Lowercase alphanumerics and dashes,
+ 46 characters max.
+
+ network_aliases (`list[str]`, *optional*):
+ Aliases this job claims in its network group. Members reach the jobs claiming an alias at
+ `${HF_NETWORK_GROUP_PREFIX}`. Several jobs may claim the same alias. Lowercase alphanumerics
+ and dashes, 34 characters max, unique within the job. Requires `network_group`.
+
+ resource_group_id (`str`, *optional*):
+ The ID of the resource group to create the Job in. Used to control access to resources within an
+ organization and for cost attribution/spending-limit features. If not provided, the Job is created
+ outside of any resource group.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job will be created. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+ Run your first Job:
+
+ ```python
+ >>> from huggingface_hub import run_job
+ >>> run_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"])
+ ```
+
+ Run a GPU Job:
+
+ ```python
+ >>> from huggingface_hub import run_job
+ >>> image = "pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel"
+ >>> command = ["python", "-c", "import torch; print(f"This code ran with the following GPU: {torch.cuda.get_device_name()}")"]
+ >>> run_job(image=image, command=command, flavor="a10g-small")
+ ```
+
+ Run a Job with volumes:
+
+ ```python
+ >>> from huggingface_hub import Volume, run_job
+ >>> dataset_volume = Volume(type="dataset", source="HuggingFaceFW/fineweb", mount_path="/data")
+ >>> output_bucket_volume = Volume(type="bucket", source="username/my-bucket", mount_path="/output")
+ >>> image = "duckdb/duckdb"
+ >>> command = ["duckdb", "-c", "COPY (SELECT * FROM '/data/**/*.parquet' LIMIT 5) TO '/output/first-rows.parquet'"]
+ >>> run_job(image=image, command=command, volumes=[dataset_volume, output_bucket_volume])
+ ```
+
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ if name is None and not (labels and "name" in labels):
+ name = _default_job_name_from_image(image, command)
+ job_spec = _create_job_spec(
+ image=image,
+ command=command,
+ env=env,
+ secrets=secrets,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ labels=labels,
+ volumes=volumes,
+ expose=expose,
+ expose_public=expose_public,
+ ssh=ssh,
+ network_group=network_group,
+ network_aliases=network_aliases,
+ resource_group_id=resource_group_id,
+ )
+ response = get_session().post(
+ f"{self.endpoint}/api/jobs/{namespace}",
+ json=job_spec,
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ job_info = response.json()
+ return JobInfo(**job_info, endpoint=self.endpoint)
+
+ def _fetch_running_job_sse(
+ self,
+ *,
+ job_id: str,
+ route: str,
+ timeout: int,
+ skip_previous_events_on_retry: bool,
+ tolerated_status_codes: tuple[int, ...] = (),
+ tolerated_exception_types: tuple[type[Exception], ...] = (),
+ follow: bool = True,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ params: dict[str, Any] | None = None,
+ ) -> Iterable[dict[str, Any]]:
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+
+ def has_job_finished() -> bool:
+ # We don't use http_backoff: this is the authoritative check that
+ # decides whether to keep streaming.
+ job_status_response = get_session().get(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(job_status_response)
+ job_status = job_status_response.json()
+ return "status" in job_status and job_status["status"]["stage"] not in ("RUNNING", "UPDATING")
+
+ yield from self._stream_sse_events(
+ url=f"{self.endpoint}/api/jobs/{namespace}/{job_id}/{route}",
+ log_label=f"jobs /{route} for {job_id=}",
+ timeout=timeout,
+ follow=follow,
+ token=token,
+ skip_previous_events_on_retry=skip_previous_events_on_retry,
+ tolerated_status_codes=tolerated_status_codes,
+ tolerated_exception_types=tolerated_exception_types,
+ on_iteration_end=has_job_finished,
+ params=params,
+ )
+
+ def fetch_job_logs(
+ self,
+ *,
+ job_id: str,
+ namespace: str | None = None,
+ follow: bool = False,
+ tail: int | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[str]:
+ """
+ Fetch all the logs from a compute Job on Hugging Face infrastructure.
+
+ Args:
+ job_id (`str`):
+ ID of the Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job is running. Defaults to the current user's namespace.
+
+ follow (`bool`, *optional*):
+ If `True`, stream logs in real-time until the job completes (blocking).
+ If `False` (default), fetch only the currently available logs and return immediately (non-blocking).
+
+ tail (`int`, *optional*):
+ Maximum number of lines to return from the logs. When combined with `follow=True`,
+ starts from the last N lines and continues streaming new logs. When `follow=False`,
+ returns only the last N lines from currently available logs.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import fetch_job_logs, run_job
+ >>> job = run_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"])
+ >>> for log in fetch_job_logs(job_id=job.id):
+ ... print(log)
+ Hello from HF compute!
+
+ >>> # Non-blocking: fetch only currently available logs
+ >>> for log in fetch_job_logs(job_id=job.id, follow=False):
+ ... print(log)
+
+ >>> # Stream logs starting from the last 100 lines
+ >>> for log in fetch_job_logs(job_id=job.id, follow=True, tail=100):
+ ... print(log)
+ ```
+ """
+ # - We need to retry because sometimes the /logs doesn't return logs when the job just started.
+ # (for example it can return only two lines: one for "Job started" and one empty line)
+ # - Timeouts can happen in case of build errors
+ # - ChunkedEncodingError can happen in case of stopped logging in the middle of streaming
+ # - Infinite empty log stream can happen in case of build error
+ # (the logs stream is infinite and empty except for the Job started message)
+ # - there is a ": keep-alive" every 30 seconds
+
+ seconds_between_keep_alive = 30
+ # When not following, use a short timeout: the server replays historical logs
+ # quickly, then pauses waiting for new events (~30s keep-alive). 5 seconds is
+ # enough to receive all buffered logs.
+ timeout = 4 * seconds_between_keep_alive if follow else 5
+ params = {"tail": tail} if tail is not None else None
+ for event in self._fetch_running_job_sse(
+ job_id=job_id,
+ route="logs",
+ timeout=timeout,
+ skip_previous_events_on_retry=True,
+ follow=follow,
+ namespace=namespace,
+ token=token,
+ params=params,
+ ):
+ # timestamp = event["timestamp"]
+ if not event["data"].startswith("===== Job started"):
+ log = event["data"]
+ yield log
+
+ def fetch_job_metrics(
+ self,
+ *,
+ job_id: str,
+ namespace: str | None = None,
+ follow: bool = False,
+ token: bool | str | None = None,
+ ) -> Iterable[dict[str, Any]]:
+ """
+ Fetch the live metrics from a compute Job on Hugging Face infrastructure.
+
+ Args:
+ job_id (`str`):
+ ID of the Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job is running. Defaults to the current user's namespace.
+
+ follow (`bool`, *optional*):
+ If `True`, stream metrics in real-time until the job completes (blocking).
+ If `False` (default), fetch only the current metrics and return (non-blocking).
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import fetch_job_metrics, run_job
+ >>> job = run_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"], flavor="a10g-small")
+ >>> for metrics in fetch_job_metrics(job_id=job.id):
+ ... print(metrics)
+ {
+ "cpu_usage_pct": 0,
+ "cpu_millicores": 3500,
+ "memory_used_bytes": 1306624,
+ "memory_total_bytes": 15032385536,
+ "rx_bps": 0,
+ "tx_bps": 0,
+ "gpus": {
+ "882fa930": {
+ "utilization": 0,
+ "memory_used_bytes": 0,
+ "memory_total_bytes": 22836000000
+ }
+ },
+ "replica": "57vr7"
+ }
+
+ >>> # Stream metrics until the job completes
+ >>> for metrics in fetch_job_metrics(job_id=job.id, follow=True):
+ ... print(metrics)
+ ```
+ """
+ # - there is one "metric" event every second, like this:
+ # event: metric
+ # data: {"cpu_usage_pct":0,"cpu_millicores":3500,"memory_used_bytes":1417216,"memory_total_bytes":15032385536,"rx_bps":0,"tx_bps":0,"gpus":{"d901cd7f":{"utilization":0,"memory_used_bytes":0,"memory_total_bytes":22836000000}},"replica":"j6qz9"}
+ # - the stream doesn't end when the job finishes, so we rely on timeouts (httpx2.NetworkError with Timeout as cause)
+ # - httpx2.ReadTimeout can happen if the job is marked as running but the hardware is not available yet, that we can ignore
+ # - it returns a 406 (previously a 500) if the job has already finished, we simply ignore it
+ # - ChunkedEncodingError can happen in case of stopped logging in the middle of streaming
+ # - there is a ": keep-alive" every 30 seconds
+ seconds_between_events = 1
+ metrics_stream = self._fetch_running_job_sse(
+ job_id=job_id,
+ route="metrics",
+ timeout=10 * seconds_between_events,
+ skip_previous_events_on_retry=False,
+ tolerated_status_codes=(406, 500),
+ namespace=namespace,
+ token=token,
+ follow=follow,
+ )
+ if follow:
+ yield from metrics_stream
+ else: # the stream has no history: its first sample is the current one
+ yield from itertools.islice(metrics_stream, 1)
+
+ def list_jobs(
+ self,
+ *,
+ status: list[JobStage | str] | JobStage | str | None = None,
+ labels: dict[str, str] | None = None,
+ timeout: int | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[JobInfo]:
+ """
+ List compute Jobs on Hugging Face infrastructure.
+
+ Args:
+ status (`JobStage`, `str` or `list`, *optional*):
+ Only return Jobs with the given status(es), e.g. `"RUNNING"` or `[JobStage.RUNNING, JobStage.SCHEDULING]`.
+ See [`JobStage`] for possible values.
+
+ labels (`dict[str, str]`, *optional*):
+ Only return Jobs that have all the given `key=value` labels, e.g. `{"env": "prod", "team": "ml"}`.
+
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+
+ namespace (`str`, *optional*):
+ The namespace from where it lists the jobs. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ `Iterable[JobInfo]`: an iterable of [`JobInfo`] objects.
+ """
+ if namespace is None:
+ namespace = whoami(token=token)["name"]
+ params: list[tuple[str, Any]] = []
+ if status is not None:
+ statuses = [status] if isinstance(status, (str, JobStage)) else status
+ params.extend(("stage", (s.value if isinstance(s, JobStage) else str(s)).upper()) for s in statuses)
+ if labels is not None:
+ params.extend(("label", f"{key}={value}") for key, value in labels.items())
+
+ path = f"{self.endpoint}/api/jobs/{namespace}"
+ headers = self._build_hf_headers(token=token)
+ for job_info in paginate(path, params=params, headers=headers, timeout=timeout):
+ yield JobInfo(**job_info, endpoint=self.endpoint)
+
+ def list_jobs_hardware(self, token: bool | str | None = None) -> list[JobHardwareInfo]:
+ """
+ List available hardware options for Jobs on Hugging Face infrastructure.
+
+ Returns:
+ `list[JobHardwareInfo]`: A list of available hardware configurations.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+ >>> hardware_list = api.list_jobs_hardware()
+ >>> hardware_list[0]
+ JobHardwareInfo(name='cpu-basic', pretty_name='CPU Basic', cpu='2 vCPU', ram='16 GB', ephemeral_storage='20 GB', accelerator=None, unit_cost_micro_usd=167, unit_cost_usd=0.000167, unit_label='minute')
+ >>> hardware_list[0].name
+ 'cpu-basic'
+
+ # Filter GPU options
+ >>> gpu_hardware = [hw for hw in hardware_list if hw.accelerator is not None]
+ >>> gpu_hardware[0].accelerator.model
+ 'T4'
+ ```
+ """
+ response = get_session().get(f"{self.endpoint}/api/jobs/hardware", headers=self._build_hf_headers(token=token))
+ hf_raise_for_status(response)
+ return [JobHardwareInfo(**hardware) for hardware in response.json()]
+
+ def inspect_job(
+ self,
+ *,
+ job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """
+ Inspect a compute Job on Hugging Face infrastructure.
+
+ Args:
+ job_id (`str`):
+ ID of the Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job is running. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import inspect_job, run_job
+ >>> job = run_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"])
+ >>> inspect_job(job.id)
+ JobInfo(
+ id='68780d00bbe36d38803f645f',
+ created_at=datetime.datetime(2025, 7, 16, 20, 35, 12, 808000, tzinfo=datetime.timezone.utc),
+ docker_image='python:3.12',
+ space_id=None,
+ command=['python', '-c', "print('Hello from HF compute!')"],
+ arguments=[],
+ environment={},
+ secrets={},
+ flavor='cpu-basic',
+ status=JobStatus(stage='RUNNING', message=None)
+ )
+ ```
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().get(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return JobInfo(**response.json(), endpoint=self.endpoint)
+
+ @overload
+ def wait_for_job(
+ self,
+ job_id: str,
+ *,
+ timeout: float | None = None,
+ poll_interval: float = 1.0,
+ stages: list[JobStage] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo: ...
+
+ @overload
+ def wait_for_job(
+ self,
+ job_id: list[str],
+ *,
+ timeout: float | None = None,
+ poll_interval: float = 1.0,
+ stages: list[JobStage] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> list[JobInfo]: ...
+
+ def wait_for_job(
+ self,
+ job_id: str | list[str],
+ *,
+ timeout: float | None = None,
+ poll_interval: float = 1.0,
+ stages: list[JobStage] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo | list[JobInfo]:
+ """
+ Wait until one or more compute Jobs on Hugging Face infrastructure reach a given stage.
+
+ Each Job status is polled (with [`inspect_job`]) every `poll_interval` seconds until its stage is one
+ of `stages` (terminal stages by default: `"COMPLETED"`, `"CANCELED"`, `"ERROR"` or `"DELETED"`). The
+ final [`JobInfo`] is returned in all cases: a failed or canceled Job does **not** raise an exception —
+ check `job.status.stage` to act on the outcome.
+
+ Terminal stages always stop the wait, even when not listed in `stages`. This avoids waiting forever for
+ a stage the Job will never reach (e.g. waiting for `"RUNNING"` on a Job that fails during scheduling).
+
+ Args:
+ job_id (`str` or `list[str]`):
+ ID of the Job, or a list of Job IDs to wait for. If a list is passed, a list of [`JobInfo`]
+ is returned (in the same order).
+
+ timeout (`float`, *optional*):
+ The maximum time to wait for the Job(s) to finish, in seconds. If `None`, will wait
+ indefinitely.
+
+ poll_interval (`float`, *optional*):
+ The time to wait between each status check, in seconds. Defaults to 1s.
+
+ stages (`list[JobStage]`, *optional*):
+ The stages to wait for. Defaults to the terminal stages (`"COMPLETED"`, `"CANCELED"`,
+ `"ERROR"`, `"DELETED"`). Pass e.g. `[JobStage.RUNNING]` to wait for the Job to start running.
+ Terminal stages always stop the wait regardless of this value.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job(s) are running. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`JobInfo`] or `list[JobInfo]`: the final Job info(s).
+
+ Raises:
+ `TimeoutError`:
+ If at least one Job has not reached one of the target stages after `timeout` seconds.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import run_job, wait_for_job
+ >>> job = run_job(image="python:3.12", command=["python", "-c", "print('Hello from HF compute!')"])
+ >>> wait_for_job(job_id=job.id).status.stage
+ 'COMPLETED'
+ ```
+ """
+ if timeout is not None and timeout < 0:
+ raise ValueError("`timeout` cannot be negative.")
+ if poll_interval <= 0:
+ raise ValueError("`poll_interval` must be positive.")
+
+ # Terminal stages always stop the wait, so a Job that never reaches the target stage doesn't hang.
+ target_stages = set(stages) | set(TERMINAL_JOB_STAGES) if stages else set(TERMINAL_JOB_STAGES)
+
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ deadline = None if timeout is None else time.monotonic() + timeout
+
+ def _wait_single(single_job_id: str) -> JobInfo:
+ while True:
+ job = self.inspect_job(job_id=single_job_id, namespace=namespace, token=token)
+ if job.status.stage in target_stages:
+ return job
+ remaining = None if deadline is None else deadline - time.monotonic()
+ if remaining is not None and remaining <= 0:
+ raise TimeoutError(
+ f"Job '{single_job_id}' is still in stage '{job.status.stage}' after {timeout} seconds."
+ )
+ time.sleep(poll_interval if remaining is None else min(poll_interval, remaining))
+
+ if isinstance(job_id, str):
+ return _wait_single(job_id)
+ return [_wait_single(single_job_id) for single_job_id in job_id]
+
+ def rerun_job(
+ self,
+ *,
+ job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """Run a new Job using the existing Job's spec, including its retry and resource settings.
+
+ Args:
+ job_id (`str`):
+ ID of the Job to rerun.
+ namespace (`str`, *optional*):
+ Namespace of the Job. Defaults to the current user's namespace.
+ token (`bool` or `str`, *optional*):
+ User access token. Defaults to the locally saved token.
+
+ Returns:
+ [`JobInfo`]: The newly started Job.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}/duplicate",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return JobInfo(**response.json(), endpoint=self.endpoint)
+
+ def cancel_job(
+ self,
+ *,
+ job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Cancel a compute Job on Hugging Face infrastructure.
+
+ Args:
+ job_id (`str`):
+ ID of the Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job is running. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}/cancel",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ def update_job_expose(
+ self,
+ *,
+ job_id: str,
+ expose: list[int] | None = None,
+ expose_public: list[int] | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """Replace a running Job's exposed ports without rerunning it.
+
+ `expose` and `expose_public` together form the complete port configuration: ports that are in neither list
+ are closed. Pass neither to close all exposed ports.
+
+ Args:
+ job_id (`str`):
+ ID of the running Job.
+ expose (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy. Access requires an HF token with read access to the Job's
+ namespace.
+ expose_public (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy without authentication. They don't need to be listed in
+ `expose`.
+ namespace (`str`, *optional*):
+ Namespace of the Job. Defaults to the current user's namespace.
+ token (`bool` or `str`, *optional*):
+ User access token. Defaults to the locally saved token.
+
+ Returns:
+ [`JobInfo`]: The updated Job.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().put(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}/expose",
+ json=_build_expose_payload(expose, expose_public),
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return JobInfo(**response.json(), endpoint=self.endpoint)
+
+ def update_job_labels(
+ self,
+ *,
+ job_id: str,
+ labels: dict[str, str],
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """
+ Update labels of an existing Job.
+
+ Replaces all existing user-provided labels with the new labels.
+
+ Args:
+ job_id (`str`):
+ ID of the Job.
+
+ labels (`dict[str, str]`):
+ New labels to set on the job. Replaces all existing labels.
+ Both keys and values must be max 100 characters and contain only
+ alphanumeric characters, dashes, and underscores.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job is running. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`JobInfo`]: The updated Job info.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().put(
+ f"{self.endpoint}/api/jobs/{namespace}/{job_id}/labels",
+ headers=self._build_hf_headers(token=token),
+ json={"labels": labels},
+ )
+ hf_raise_for_status(response)
+ return JobInfo(**response.json(), endpoint=self.endpoint)
+
+ @experimental
+ def run_uv_job(
+ self,
+ script: str,
+ *,
+ script_args: list[str] | None = None,
+ dependencies: list[str] | None = None,
+ python: str | None = None,
+ image: str | None = None,
+ env: dict[str, Any] | None = None,
+ secrets: dict[str, Any] | None = None,
+ flavor: JobHardware | str | None = None,
+ timeout: int | float | str | None = None,
+ attempts: int | None = None,
+ name: str | None = None,
+ labels: dict[str, str] | None = None,
+ volumes: list[Volume] | None = None,
+ expose: list[int] | None = None,
+ expose_public: list[int] | None = None,
+ ssh: bool = False,
+ network_group: str | None = None,
+ network_aliases: list[str] | None = None,
+ resource_group_id: str | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """
+ Run a UV script Job on Hugging Face infrastructure.
+
+ > [!WARNING]
+ > Unlike `hf jobs uv run`, this method ignores the optional `[tool.hf-jobs]` table a UV script can
+ > carry in its PEP 723 header (see the [Jobs guide](../guides/jobs#ship-the-launch-config-with-the-script)):
+ > the launch configuration has to be passed explicitly here.
+
+ Args:
+ script (`str`):
+ Path or URL of the UV script, or a command.
+
+ script_args (`list[str]`, *optional*):
+ Arguments to pass to the script or command.
+
+ dependencies (`list[str]`, *optional*):
+ Dependencies to use to run the UV script.
+
+ python (`str`, *optional*):
+ Use a specific Python version. Default is 3.12.
+
+ image (`str`, *optional*, defaults to "ghcr.io/astral-sh/uv:python3.12-bookworm"):
+ Use a custom Docker image with `uv` installed.
+
+ env (`dict[str, Any]`, *optional*):
+ Defines the environment variables for the Job.
+
+ secrets (`dict[str, Any]`, *optional*):
+ Defines the secret environment variables for the Job.
+
+ flavor (`str`, *optional*):
+ Flavor for the hardware. See [`JobHardware`] for possible values.
+ Defaults to `"cpu-basic"`.
+
+ timeout (`Union[int, float, str]`, *optional*):
+ Max duration for the Job: int with s (seconds, default), m (minutes), h (hours) or d (days).
+ Example: `300` or `"5m"` for 5 minutes.
+
+ attempts (`int`, *optional*):
+ Maximum number of attempts, including the initial run. Defaults to 1.
+
+ name (`str`, *optional*):
+ A name for the Job. Stored as the `name` label. Cannot be passed together with a `name` key in
+ `labels`. Names do not have to be unique. Defaults to a name derived from script and its arguments (with a short hash suffix).
+
+ labels (`dict[str, str]`, *optional*):
+ Labels to attach to the job (key-value pairs).
+
+ volumes (`list[Volume]`, *optional*):
+ Hugging Face Buckets or Repos to mount as volumes in the job container.
+ Each volume is a [`Volume`] with `type` (`"bucket"`, `"model"`, `"dataset"`, or `"space"`),
+ `source` (e.g. `"username/my-bucket"`), and `mount_path` (e.g. `"/data"`).
+
+ expose (`list[int]`, *optional*):
+ Container ports to expose through the jobs proxy. Each listed port is reachable
+ on the public jobs domain (e.g. `https://--8000.hf.jobs`). Access always
+ requires an HF token with read access to the job's namespace. Use `expose_public` for
+ unauthenticated access.
+
+ expose_public (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy without authentication. They don't need to be listed in
+ `expose`.
+
+ ssh (`bool`, *optional*):
+ If True, the job's container is reachable over SSH at the URL given by `job.status.ssh_url`
+ (e.g. `ssh @ssh.hf.jobs`, or `hf jobs ssh ` from the CLI). Connecting requires
+ write access to the job's namespace and an SSH public key registered on the Hub
+ (https://huggingface.co/settings/keys). Defaults to False.
+
+ network_group (`str`, *optional*):
+ Name of a network group to join. Jobs in the same namespace and resource group sharing a group are
+ placed together and can reach each other on every port. Inside each member,
+ `HF_NETWORK_GROUP_HOSTNAME` resolves to every member of the group. Lowercase alphanumerics and dashes,
+ 46 characters max.
+
+ network_aliases (`list[str]`, *optional*):
+ Aliases this job claims in its network group. Members reach the jobs claiming an alias at
+ `${HF_NETWORK_GROUP_PREFIX}`. Several jobs may claim the same alias. Lowercase alphanumerics
+ and dashes, 34 characters max, unique within the job. Requires `network_group`.
+
+ resource_group_id (`str`, *optional*):
+ The ID of the resource group to create the Job in. Used to control access to resources within an
+ organization and for cost attribution/spending-limit features. If not provided, the Job is created
+ outside of any resource group.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job will be created. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ Run a script from a URL:
+
+ ```python
+ >>> from huggingface_hub import run_uv_job
+ >>> script = "https://raw.githubusercontent.com/huggingface/trl/refs/heads/main/trl/scripts/sft.py"
+ >>> script_args = ["--model_name_or_path", "Qwen/Qwen2-0.5B", "--dataset_name", "trl-lib/Capybara", "--push_to_hub"]
+ >>> run_uv_job(script, script_args=script_args, dependencies=["trl"], flavor="a10g-small")
+ ```
+
+ Run a local script:
+
+ ```python
+ >>> from huggingface_hub import run_uv_job
+ >>> script = "my_sft.py"
+ >>> script_args = ["--model_name_or_path", "Qwen/Qwen2-0.5B", "--dataset_name", "trl-lib/Capybara", "--push_to_hub"]
+ >>> run_uv_job(script, script_args=script_args, dependencies=["trl"], flavor="a10g-small")
+ ```
+
+ Run a command:
+
+ ```python
+ >>> from huggingface_hub import run_uv_job
+ >>> script = "lighteval"
+ >>> script_args= ["endpoint", "inference-providers", "model_name=openai/gpt-oss-20b,provider=auto", "lighteval|gsm8k|0|0"]
+ >>> run_uv_job(script, script_args=script_args, dependencies=["lighteval"], flavor="a10g-small")
+ ```
+
+ Mount volumes, e.g. to save model checkpoints during training:
+
+ ```python
+ >>> from huggingface_hub import Volume, run_uv_job
+ >>> script = "my_sft.py"
+ >>> script_args = ["--output_dir", "/training-outputs/training-v3-final", ...]
+ >>> checkpoints_bucket = Volume(type="bucket", source="username/my-bucket", mount_path="/training-outputs")
+ >>> run_uv_job(script, script_args=script_args, volumes=[checkpoints_bucket])
+ ```
+ """
+ image = image or DEFAULT_UV_IMAGE
+ env = env or {}
+ secrets = secrets or {}
+
+ if name is None and not (labels and "name" in labels):
+ name = _default_job_name_from_script(script, script_args or [])
+
+ # Build command
+ command, env, secrets, extra_volumes = self._create_uv_command_env_and_secrets(
+ script=script,
+ script_args=script_args,
+ dependencies=dependencies,
+ python=python,
+ env=env,
+ secrets=secrets,
+ namespace=namespace,
+ token=token,
+ volumes=volumes,
+ )
+ if extra_volumes:
+ volumes = (volumes or []) + extra_volumes
+ # Create RunCommand args
+ return self.run_job(
+ image=image,
+ command=command,
+ env=env,
+ secrets=secrets,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ labels=labels,
+ volumes=volumes,
+ expose=expose,
+ expose_public=expose_public,
+ ssh=ssh,
+ network_group=network_group,
+ network_aliases=network_aliases,
+ resource_group_id=resource_group_id,
+ namespace=namespace,
+ token=token,
+ )
+
+ def create_scheduled_job(
+ self,
+ *,
+ image: str,
+ command: list[str],
+ schedule: str,
+ suspend: bool | None = None,
+ concurrency: bool | None = None,
+ env: dict[str, Any] | None = None,
+ secrets: dict[str, Any] | None = None,
+ flavor: JobHardware | str | None = None,
+ timeout: int | float | str | None = None,
+ attempts: int | None = None,
+ name: str | None = None,
+ labels: dict[str, str] | None = None,
+ volumes: list[Volume] | None = None,
+ expose: list[int] | None = None,
+ expose_public: list[int] | None = None,
+ resource_group_id: str | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> ScheduledJobInfo:
+ """
+ Create scheduled compute Jobs on Hugging Face infrastructure.
+
+ Args:
+ image (`str`):
+ The Docker image to use.
+ Examples: `"ubuntu"`, `"python:3.12"`, `"pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel"`.
+ Example with an image from a Space: `"hf.co/spaces/lhoestq/duckdb"`.
+
+ command (`list[str]`):
+ The command to run. Example: `["echo", "hello"]`.
+
+ schedule (`str`):
+ One of "@annually", "@yearly", "@monthly", "@weekly", "@daily", "@hourly", or a
+ CRON schedule expression (e.g., '0 9 * * 1' for 9 AM every Monday).
+
+ suspend (`bool`, *optional*):
+ If True, the scheduled Job is suspended (paused). Defaults to False.
+
+ concurrency (`bool`, *optional*):
+ If True, multiple instances of this Job can run concurrently. Defaults to False.
+
+ env (`dict[str, Any]`, *optional*):
+ Defines the environment variables for the Job.
+
+ secrets (`dict[str, Any]`, *optional*):
+ Defines the secret environment variables for the Job.
+
+ flavor (`str`, *optional*):
+ Flavor for the hardware. See [`JobHardware`] for possible values.
+ Defaults to `"cpu-basic"`.
+
+ timeout (`Union[int, float, str]`, *optional*):
+ Max duration for the Job: int with s (seconds, default), m (minutes), h (hours) or d (days).
+ Example: `300` or `"5m"` for 5 minutes.
+
+ attempts (`int`, *optional*):
+ Maximum number of attempts for each run, including the initial attempt. Defaults to 1.
+
+ name (`str`, *optional*):
+ A name for the scheduled Job. Stored as the `name` label. Cannot be passed together with a `name`
+ key in `labels`. Names do not have to be unique. Defaults to a name derived from image and command (with a short hash suffix).
+
+ labels (`dict[str, str]`, *optional*):
+ Labels to attach to the job (key-value pairs).
+
+ volumes (`list[Volume]`, *optional*):
+ Hugging Face Buckets or Repos to mount as volumes in the job container.
+ Each volume is a [`Volume`] with `type` (`"bucket"`, `"model"`, `"dataset"`, or `"space"`),
+ `source` (e.g. `"username/my-bucket"`), and `mount_path` (e.g. `"/data"`).
+
+ expose (`list[int]`, *optional*):
+ Container ports to expose through the jobs proxy. Each listed port is reachable
+ on the public jobs domain (e.g. `https://--8000.hf.jobs`). Access always
+ requires an HF token with read access to the job's namespace. Use `expose_public` for
+ unauthenticated access.
+
+ expose_public (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy without authentication. They don't need to be listed in
+ `expose`.
+
+ resource_group_id (`str`, *optional*):
+ The ID of the resource group to create the scheduled Job in. Used to control access to resources
+ within an organization and for cost attribution/spending-limit features. If not provided, the
+ scheduled Job is created outside of any resource group.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job will be created. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+ Create your first scheduled Job:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_job
+ >>> create_scheduled_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"], schedule="@hourly")
+ ```
+
+ Use a CRON schedule expression:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_job
+ >>> create_scheduled_job(image="python:3.12", command=["python", "-c" ,"print('this runs every 5min')"], schedule="*/5 * * * *")
+ ```
+
+ Create a scheduled GPU Job:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_job
+ >>> image = "pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel"
+ >>> command = ["python", "-c", "import torch; print(f"This code ran with the following GPU: {torch.cuda.get_device_name()}")"]
+ >>> create_scheduled_job(image, command, flavor="a10g-small", schedule="@hourly")
+ ```
+
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ if name is None and not (labels and "name" in labels):
+ name = _default_job_name_from_image(image, command)
+
+ # prepare payload to send to HF Jobs API
+ job_spec = _create_job_spec(
+ image=image,
+ command=command,
+ env=env,
+ secrets=secrets,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ labels=labels,
+ volumes=volumes,
+ expose=expose,
+ expose_public=expose_public,
+ resource_group_id=resource_group_id,
+ )
+ input_json: dict[str, Any] = {
+ "jobSpec": job_spec,
+ "schedule": schedule,
+ }
+ if concurrency is not None:
+ input_json["concurrency"] = concurrency
+ if suspend is not None:
+ input_json["suspend"] = suspend
+ response = get_session().post(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}",
+ json=input_json,
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ scheduled_job_info = response.json()
+ return ScheduledJobInfo(**scheduled_job_info)
+
+ def list_scheduled_jobs(
+ self,
+ *,
+ labels: dict[str, str] | None = None,
+ timeout: int | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> list[ScheduledJobInfo]:
+ """
+ List scheduled compute Jobs on Hugging Face infrastructure.
+
+ Args:
+ labels (`dict[str, str]`, *optional*):
+ Only return scheduled Jobs that have all the given `key=value` labels, e.g.
+ `{"env": "prod", "team": "ml"}`.
+
+ timeout (`float`, *optional*):
+ Whether to set a timeout for the request to the Hub.
+
+ namespace (`str`, *optional*):
+ The namespace from where it lists the jobs. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ `list[ScheduledJobInfo]`: a list of [`ScheduledJobInfo`] objects.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ params: dict[str, Any] = {}
+ if labels:
+ # The endpoint only supports a single `label` filter server-side. Send the first one to keep the
+ # payload small, then filter the remaining ones client-side.
+ key, value = next(iter(labels.items()))
+ params["label"] = f"{key}={value}"
+ response = get_session().get(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}",
+ headers=self._build_hf_headers(token=token),
+ params=params or None,
+ timeout=timeout,
+ )
+ hf_raise_for_status(response)
+ scheduled_jobs = [ScheduledJobInfo(**scheduled_job_info) for scheduled_job_info in response.json()]
+ if labels:
+ scheduled_jobs = [
+ scheduled_job
+ for scheduled_job in scheduled_jobs
+ if all((scheduled_job.job_spec.labels or {}).get(key) == value for key, value in labels.items())
+ ]
+ return scheduled_jobs
+
+ def inspect_scheduled_job(
+ self,
+ *,
+ scheduled_job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> ScheduledJobInfo:
+ """
+ Inspect a scheduled compute Job on Hugging Face infrastructure.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import inspect_job, create_scheduled_job
+ >>> scheduled_job = create_scheduled_job(image="python:3.12", command=["python", "-c" ,"print('Hello from HF compute!')"], schedule="@hourly")
+ >>> inspect_scheduled_job(scheduled_job.id)
+ ```
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().get(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return ScheduledJobInfo(**response.json())
+
+ def delete_scheduled_job(
+ self,
+ *,
+ scheduled_job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Delete a scheduled compute Job on Hugging Face infrastructure.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().delete(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ def suspend_scheduled_job(
+ self,
+ *,
+ scheduled_job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Suspend (pause) a scheduled compute Job on Hugging Face infrastructure.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}/suspend",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ def resume_scheduled_job(
+ self,
+ *,
+ scheduled_job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> None:
+ """
+ Resume (unpause) a scheduled compute Job on Hugging Face infrastructure.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}/resume",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+
+ def update_scheduled_job_schedule(
+ self,
+ *,
+ scheduled_job_id: str,
+ schedule: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> ScheduledJobInfo:
+ """
+ Change when an existing scheduled Job runs.
+
+ Only the schedule is updated: the Job spec, labels and suspended state are kept. To run the scheduled Job once
+ right now without changing its schedule, use [`trigger_scheduled_job`] instead.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ schedule (`str`):
+ One of "@annually", "@yearly", "@monthly", "@weekly", "@daily", "@hourly", or a
+ CRON schedule expression (e.g., '0 9 * * 1' for 9 AM every Monday).
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`ScheduledJobInfo`]: The updated scheduled Job info.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import update_scheduled_job_schedule
+ >>> scheduled_job = update_scheduled_job_schedule(scheduled_job_id="6abb8dc9c617607c354d45f4", schedule="@daily")
+ >>> scheduled_job.schedule
+ '@daily'
+ ```
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}/schedule",
+ json={"schedule": schedule},
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return ScheduledJobInfo(**response.json())
+
+ def trigger_scheduled_job(
+ self,
+ *,
+ scheduled_job_id: str,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> JobInfo:
+ """
+ Trigger a scheduled Job to run immediately.
+
+ This triggers one immediate run of the scheduled job's spec. It does **not** modify the schedule
+ and does **not** affect the next scheduled run. If an instance is already running and the scheduled
+ job does not allow concurrent runs, the request is rejected (HTTP 409).
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`JobInfo`]: Info about the triggered run.
+
+ Raises:
+ [`~utils.HfHubHTTPError`]:
+ HTTP 409 if another instance is already running and `concurrency` is disabled on the scheduled job.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().post(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}/run",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return JobInfo(**response.json(), endpoint=self.endpoint)
+
+ def update_scheduled_job_labels(
+ self,
+ *,
+ scheduled_job_id: str,
+ labels: dict[str, str],
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> ScheduledJobInfo:
+ """
+ Update labels of an existing scheduled Job.
+
+ Replaces all existing user-provided labels with the new labels.
+
+ Args:
+ scheduled_job_id (`str`):
+ ID of the scheduled Job.
+
+ labels (`dict[str, str]`):
+ New labels to set on the scheduled job. Replaces all existing labels.
+ Both keys and values must be max 100 characters and contain only
+ alphanumeric characters, dashes, and underscores.
+
+ namespace (`str`, *optional*):
+ The namespace where the scheduled Job is. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`ScheduledJobInfo`]: The updated scheduled Job info.
+ """
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ response = get_session().put(
+ f"{self.endpoint}/api/scheduled-jobs/{namespace}/{scheduled_job_id}/labels",
+ headers=self._build_hf_headers(token=token),
+ json={"labels": labels},
+ )
+ hf_raise_for_status(response)
+ return ScheduledJobInfo(**response.json())
+
+ @experimental
+ def create_scheduled_uv_job(
+ self,
+ script: str,
+ *,
+ script_args: list[str] | None = None,
+ schedule: str,
+ suspend: bool | None = None,
+ concurrency: bool | None = None,
+ dependencies: list[str] | None = None,
+ python: str | None = None,
+ image: str | None = None,
+ env: dict[str, Any] | None = None,
+ secrets: dict[str, Any] | None = None,
+ flavor: JobHardware | str | None = None,
+ timeout: int | float | str | None = None,
+ attempts: int | None = None,
+ name: str | None = None,
+ labels: dict[str, str] | None = None,
+ volumes: list[Volume] | None = None,
+ expose: list[int] | None = None,
+ expose_public: list[int] | None = None,
+ resource_group_id: str | None = None,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> ScheduledJobInfo:
+ """
+ Run a UV script Job on Hugging Face infrastructure.
+
+ > [!WARNING]
+ > Unlike `hf jobs uv run`, this method ignores the optional `[tool.hf-jobs]` table a UV script can
+ > carry in its PEP 723 header (see the [Jobs guide](../guides/jobs#ship-the-launch-config-with-the-script)):
+ > the launch configuration has to be passed explicitly here.
+
+ Args:
+ script (`str`):
+ Path or URL of the UV script, or a command.
+
+ script_args (`list[str]`, *optional*):
+ Arguments to pass to the script, or a command.
+
+ schedule (`str`):
+ One of "@annually", "@yearly", "@monthly", "@weekly", "@daily", "@hourly", or a
+ CRON schedule expression (e.g., '0 9 * * 1' for 9 AM every Monday).
+
+ suspend (`bool`, *optional*):
+ If True, the scheduled Job is suspended (paused). Defaults to False.
+
+ concurrency (`bool`, *optional*):
+ If True, multiple instances of this Job can run concurrently. Defaults to False.
+
+ dependencies (`list[str]`, *optional*):
+ Dependencies to use to run the UV script.
+
+ python (`str`, *optional*):
+ Use a specific Python version. Default is 3.12.
+
+ image (`str`, *optional*, defaults to "ghcr.io/astral-sh/uv:python3.12-bookworm"):
+ Use a custom Docker image with `uv` installed.
+
+ env (`dict[str, Any]`, *optional*):
+ Defines the environment variables for the Job.
+
+ secrets (`dict[str, Any]`, *optional*):
+ Defines the secret environment variables for the Job.
+
+ flavor (`str`, *optional*):
+ Flavor for the hardware. See [`JobHardware`] for possible values.
+ Defaults to `"cpu-basic"`.
+
+ timeout (`Union[int, float, str]`, *optional*):
+ Max duration for the Job: int with s (seconds, default), m (minutes), h (hours) or d (days).
+ Example: `300` or `"5m"` for 5 minutes.
+
+ attempts (`int`, *optional*):
+ Maximum number of attempts for each run, including the initial attempt. Defaults to 1.
+
+ name (`str`, *optional*):
+ A name for the scheduled Job. Stored as the `name` label. Cannot be passed together with a `name`
+ key in `labels`. Names do not have to be unique. Defaults to a name derived from script and its arguments (with a short hash suffix).
+
+ labels (`dict[str, str]`, *optional*):
+ Labels to attach to the job (key-value pairs).
+
+ volumes (`list[Volume]`, *optional*):
+ Hugging Face Buckets or Repos to mount as volumes in the job container.
+ Each volume is a [`Volume`] with `type` (`"bucket"`, `"model"`, `"dataset"`, or `"space"`),
+ `source` (e.g. `"username/my-bucket"`), and `mount_path` (e.g. `"/data"`).
+
+ expose (`list[int]`, *optional*):
+ Container ports to expose through the jobs proxy. Each listed port is reachable
+ on the public jobs domain (e.g. `https://--8000.hf.jobs`). Access always
+ requires an HF token with read access to the job's namespace. Use `expose_public` for
+ unauthenticated access.
+
+ expose_public (`list[int]`, *optional*):
+ Ports to expose through the Jobs proxy without authentication. They don't need to be listed in
+ `expose`.
+
+ resource_group_id (`str`, *optional*):
+ The ID of the resource group to create the scheduled Job in. Used to control access to resources
+ within an organization and for cost attribution/spending-limit features. If not provided, the
+ scheduled Job is created outside of any resource group.
+
+ namespace (`str`, *optional*):
+ The namespace where the Job will be created. Defaults to the current user's namespace.
+
+ token (`bool` or `str`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Example:
+
+ Schedule a script from a URL:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_uv_job
+ >>> script = "https://raw.githubusercontent.com/huggingface/trl/refs/heads/main/trl/scripts/sft.py"
+ >>> script_args = ["--model_name_or_path", "Qwen/Qwen2-0.5B", "--dataset_name", "trl-lib/Capybara", "--push_to_hub"]
+ >>> create_scheduled_uv_job(script, script_args=script_args, dependencies=["trl"], flavor="a10g-small", schedule="@weekly")
+ ```
+
+ Schedule a local script:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_uv_job
+ >>> script = "my_sft.py"
+ >>> script_args = ["--model_name_or_path", "Qwen/Qwen2-0.5B", "--dataset_name", "trl-lib/Capybara", "--push_to_hub"]
+ >>> create_scheduled_uv_job(script, script_args=script_args, dependencies=["trl"], flavor="a10g-small", schedule="@weekly")
+ ```
+
+ Schedule a command:
+
+ ```python
+ >>> from huggingface_hub import create_scheduled_uv_job
+ >>> script = "lighteval"
+ >>> script_args= ["endpoint", "inference-providers", "model_name=openai/gpt-oss-20b,provider=auto", "lighteval|gsm8k|0|0"]
+ >>> create_scheduled_uv_job(script, script_args=script_args, dependencies=["lighteval"], flavor="a10g-small", schedule="@weekly")
+ ```
+ """
+ image = image or DEFAULT_UV_IMAGE
+ if name is None and not (labels and "name" in labels):
+ name = _default_job_name_from_script(script, script_args or [])
+
+ # Build command
+ command, env, secrets, extra_volumes = self._create_uv_command_env_and_secrets(
+ script=script,
+ script_args=script_args,
+ dependencies=dependencies,
+ python=python,
+ env=env,
+ secrets=secrets,
+ namespace=namespace,
+ token=token,
+ volumes=volumes,
+ )
+ if extra_volumes:
+ volumes = (volumes or []) + extra_volumes
+ # Create RunCommand args
+ return self.create_scheduled_job(
+ image=image,
+ command=command,
+ schedule=schedule,
+ suspend=suspend,
+ concurrency=concurrency,
+ env=env,
+ secrets=secrets,
+ flavor=flavor,
+ timeout=timeout,
+ attempts=attempts,
+ name=name,
+ labels=labels,
+ volumes=volumes,
+ expose=expose,
+ expose_public=expose_public,
+ resource_group_id=resource_group_id,
+ namespace=namespace,
+ token=token,
+ )
+
+ def _create_uv_command_env_and_secrets(
+ self,
+ *,
+ script: str,
+ script_args: list[str] | None,
+ dependencies: list[str] | None,
+ python: str | None,
+ env: dict[str, Any] | None,
+ secrets: dict[str, Any] | None,
+ namespace: str | None,
+ token: bool | str | None,
+ volumes: list[Volume] | None = None,
+ ) -> tuple[list[str], dict[str, Any], dict[str, Any], list[Volume]]:
+ env = env or {}
+ secrets = secrets or {}
+
+ # Build command
+ uv_args = []
+ if dependencies:
+ for dependency in dependencies:
+ uv_args += ["--with", dependency]
+ if python:
+ uv_args += ["--python", python]
+ script_args = script_args or []
+
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+
+ # Find the local files to pass to the job
+ local_files_to_include = {candidate for candidate in [script] + script_args if Path(candidate).is_file()}
+ # Fail early for missing scripts or config files
+ missing_local_files = {
+ candidate
+ for candidate in [script] + script_args
+ if not Path(candidate).is_file()
+ and Path(candidate).suffix in [".py", ".sh", ".yaml", ".yml", ".toml"]
+ and not candidate.startswith("https://")
+ and not candidate.startswith("http://")
+ }
+ if missing_local_files:
+ raise FileNotFoundError(", ".join(missing_local_files))
+
+ if len(local_files_to_include) == 0:
+ # Direct URL execution or command - no upload needed
+ command = ["uv", "run"] + uv_args + [script] + script_args
+ return command, env, secrets, []
+
+ # Find appropriate remote file names
+ remote_to_local_file_names: dict[str, str] = {}
+ for local_file_to_include in local_files_to_include:
+ local_file_path = Path(local_file_to_include)
+ # Sanitize spaces for predictable remote paths
+ remote_file_path = Path(local_file_path.name.replace(" ", "_"))
+ if remote_file_path.name in remote_to_local_file_names:
+ for i in itertools.count():
+ remote_file_name = remote_file_path.with_stem(remote_file_path.stem + f"({i})").name
+ if remote_file_name not in remote_to_local_file_names:
+ remote_to_local_file_names[remote_file_name] = local_file_to_include
+ break
+ else:
+ remote_to_local_file_names[remote_file_path.name] = local_file_to_include
+ local_to_remote_file_names = {
+ local_file_to_include: remote_file_name
+ for remote_file_name, local_file_to_include in remote_to_local_file_names.items()
+ }
+
+ # Local files are shipped to the job via a bucket mounted at /data.
+ existing_mount_paths = {v.mount_path for v in (volumes or [])}
+ if constants.HF_JOBS_ARTIFACTS_MOUNT_PATH in existing_mount_paths:
+ raise ValueError(
+ f"Mount path {constants.HF_JOBS_ARTIFACTS_MOUNT_PATH!r} is reserved for Jobs artifacts when running local scripts. Mount your volume at a different path."
+ )
+
+ extra_volumes = self._upload_scripts_to_bucket(
+ namespace=namespace,
+ remote_to_local_file_names=remote_to_local_file_names,
+ token=token,
+ )
+ # Rewrite script and script_args to reference the mounted path. The bucket
+ # volume is scoped to the per-job subfolder (via `Volume.path`), so the job
+ # container sees the uploaded files directly at the mount root.
+ mount_path = constants.HF_JOBS_ARTIFACTS_MOUNT_PATH
+ if script in local_to_remote_file_names:
+ script = f"{mount_path}/{local_to_remote_file_names[script]}"
+ script_args = [
+ f"{mount_path}/{local_to_remote_file_names[arg]}" if arg in local_to_remote_file_names else arg
+ for arg in script_args
+ ]
+ command = ["uv", "run"] + uv_args + [script] + script_args
+ return command, env, secrets, extra_volumes
+
+ def _upload_scripts_to_bucket(
+ self,
+ *,
+ namespace: str,
+ remote_to_local_file_names: dict[str, str],
+ token: bool | str | None,
+ ) -> list[Volume]:
+ """Upload script files to a per-job subfolder in the artifacts bucket.
+
+ Creates a bucket `/jobs-artifacts` (if it doesn't exist) and uploads
+ each script to `{timestamp}-{random}/{remote_name}` inside it. Returns a
+ [`Volume`] scoped to that bucket subfolder. Volume is in read-write mode so the Job can save data back to this bucket.
+ """
+ bucket_id = f"{namespace}/{constants.HF_JOBS_ARTIFACTS_BUCKET_NAME}"
+ subfolder_id = f"{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S')}-{token_hex(3)}"
+
+ bucket_url = self.create_bucket(bucket_id=bucket_id, exist_ok=True, token=token, private=True)
+
+ add_ops: list[tuple[str | Path | bytes, str]] = [
+ (Path(local_path), f"{subfolder_id}/{remote_name}")
+ for remote_name, local_path in remote_to_local_file_names.items()
+ ]
+ self.batch_bucket_files(bucket_id=bucket_id, add=add_ops, token=token)
+ print(f"Your script and Job artifacts will be saved in this bucket: {bucket_url.url}")
+
+ volume = Volume(
+ type="bucket",
+ source=bucket_id,
+ mount_path=constants.HF_JOBS_ARTIFACTS_MOUNT_PATH,
+ path=subfolder_id,
+ read_only=False,
+ )
+ return [volume]
+
+ def sync_job_volume(
+ self,
+ source: str | Path,
+ mount_path: str,
+ *,
+ remote_name: str | None = None,
+ read_only: bool = True,
+ namespace: str | None = None,
+ token: bool | str | None = None,
+ ) -> Volume:
+ """Sync a local directory to a bucket and return a [`Volume`] ready to mount in a Job.
+
+ Files are uploaded to a subfolder of the `{namespace}/jobs-artifacts` bucket (auto-created as
+ private; a warning is emitted if it already exists and is public) using the same sync logic
+ as [`sync_bucket`]: re-syncing the same directory only
+ uploads new or modified files. By default the subfolder name is derived from the directory
+ path and the machine's hostname, so repeated calls from the same directory reuse the same
+ remote folder. Pass `remote_name` to use a fixed name instead.
+
+ Note that the data is *copied* to the bucket, not mounted live: changes made locally after
+ the sync are not visible to the Job (re-run `sync_job_volume` to update), and the volume is
+ mounted read-only by default. To retrieve data written by a Job to a read-write volume, sync
+ the bucket folder back with [`sync_bucket`]. If the source directory is empty (e.g. an output
+ directory), a placeholder `.keep` file is uploaded so the volume can still be mounted.
+
+ Args:
+ source (`str` or `Path`):
+ Path to a local directory to sync.
+ mount_path (`str`):
+ Mount path inside the Job container, e.g. `"/inputs"`. Must start with `/`.
+ remote_name (`str`, *optional*):
+ Name of the bucket subfolder to sync to. Defaults to a `{dirname}-{hash}` name derived
+ from the source path and the machine's hostname.
+ read_only (`bool`, *optional*, defaults to `True`):
+ Mount the volume read-only in the Job. Pass `False` to let the Job write back to the
+ bucket folder (e.g. to retrieve outputs with [`sync_bucket`] afterwards).
+ namespace (`str`, *optional*):
+ The namespace owning the `jobs-artifacts` bucket. Defaults to the current user's
+ namespace. Use the same namespace as the Job that will mount the volume.
+ token (`Union[bool, str, None]`, *optional*):
+ A valid user access token. If not provided, the locally saved token will be used, which is the
+ recommended authentication method. Set to `False` to disable authentication.
+ Refer to: https://huggingface.co/docs/huggingface_hub/quick-start#authentication.
+
+ Returns:
+ [`Volume`]: A bucket volume scoped to the synced subfolder, to pass in the `volumes` list
+ of [`run_job`], [`run_uv_job`], [`create_scheduled_job`] or [`create_scheduled_uv_job`].
+
+ Example:
+ ```python
+ >>> from huggingface_hub import run_uv_job, sync_job_volume
+
+ # Upload ./training-data once, then run multiple jobs against it
+ >>> volume = sync_job_volume("./training-data", "/data")
+ >>> run_uv_job("train.py", script_args=["--learning-rate", "0.01"], volumes=[volume])
+ >>> run_uv_job("train.py", script_args=["--learning-rate", "0.05"], volumes=[volume])
+
+ # Read-write volume to retrieve outputs after the job completes
+ >>> volume = sync_job_volume("./outputs", "/outputs", read_only=False)
+ >>> job = run_uv_job("process.py", volumes=[volume])
+ ```
+ """
+ if not mount_path.startswith("/"):
+ raise ValueError(
+ f"Mount path must be an absolute path inside the container (e.g. '/data'), got {mount_path!r}."
+ )
+ source_path = Path(source).expanduser()
+ if not source_path.is_dir():
+ raise ValueError(f"Source must be an existing local directory: '{source}'.")
+
+ if namespace is None:
+ namespace = self.whoami(token=token)["name"]
+ bucket_id = f"{namespace}/{constants.HF_JOBS_ARTIFACTS_BUCKET_NAME}"
+ folder = remote_name or _derive_job_volume_name(source_path)
+
+ # The jobs-artifacts bucket holds user scripts and data, so it must be private. A new bucket
+ # is created private here; if it already exists we cannot change its visibility, so warn when
+ # it is public instead of silently uploading the data to a publicly accessible bucket.
+ self.create_bucket(bucket_id=bucket_id, exist_ok=True, private=True, token=token)
+ if not self.bucket_info(bucket_id=bucket_id, token=token).private:
+ warnings.warn(
+ f"Bucket '{bucket_id}' already exists and is public: data synced for the Job will be "
+ f"publicly accessible. Make it private from {self.endpoint}/buckets/{bucket_id}.",
+ UserWarning,
+ )
+ self.sync_bucket(str(source_path), f"hf://buckets/{bucket_id}/{folder}", token=token)
+ if not any(path.is_file() for path in source_path.rglob("*")):
+ # A folder cannot be empty in a bucket, and mounting a non-existent folder fails the Job.
+ # Upload a placeholder file so an empty directory (e.g. an output dir) can still be mounted.
+ self.batch_bucket_files(bucket_id, add=[(b"", f"{folder}/.keep")], token=token)
+
+ return Volume(type="bucket", source=bucket_id, mount_path=mount_path, path=folder, read_only=read_only)
+
+ @validate_hf_hub_args
+ def create_bucket(
+ self,
+ bucket_id: str,
+ *,
+ private: bool | None = None,
+ visibility: Literal["public", "private"] | None = None,
+ resource_group_id: str | None = None,
+ region: REPO_REGIONS | None = None,
+ exist_ok: bool = False,
+ token: bool | str | None = None,
+ ) -> BucketUrl:
+ """Create a bucket on the Hub.
+
+ Args:
+ bucket_id (`str`):
+ A namespace (user or an organization) and a bucket name separated by a `/`.
+ If no namespace is provided, the bucket will be created in the current user's namespace.
+ private (`bool`, *optional*):
+ Whether to make the bucket private. If `None` (default), the bucket will be public unless the
+ organization's default is private. Cannot be passed together with `visibility`.
+ visibility (`Literal["public", "private"]`, *optional*):
+ Visibility of the bucket. Can be `"public"` or `"private"`. If `None` (default), the bucket will be
+ public unless the organization's default is private.
+ resource_group_id (`str`, *optional*):
+ Resource group in which to create the bucket. Resource groups are only available for Enterprise Hub
+ organizations and allow to define which members of the organization can access the resource. The ID
+ of a resource group can be found in the URL of the resource's page on the Hub
+ (e.g. `"66670e5163145ca562cb1988"`). To learn more about resource groups, see
+ https://huggingface.co/docs/hub/en/security-resource-groups.
+ region (`Literal["us", "eu"]`, *optional*):
+ Cloud region in which to create the bucket. Can be one of `"us"` or `"eu"`. If not specified, the bucket will be
+ created in the default region. Requires Team plan or above.
+ exist_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if the bucket already exists.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`BucketUrl`]: URL to the newly created bucket containing
+ attributes like `endpoint`, `namespace`, and `bucket_id`.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import create_bucket
+
+ >>> url = create_bucket(bucket_id="my-bucket")
+ >>> url.bucket_id
+ 'user/my-bucket'
+ >>> url.url
+ 'https://huggingface.co/buckets/user/my-bucket'
+ >>> url.uri.to_uri()
+ 'hf://buckets/user/my-bucket'
+
+ >>> create_bucket(bucket_id="my-bucket", private=True, exist_ok=True)
+ BucketUrl(...)
+
+ >>> create_bucket(bucket_id="my-bucket", region="us")
+ BucketUrl(...)
+ ```
+ """
+ from ._buckets import BucketUrl, _parse_bucket_uri
+
+ resolved_visibility = _resolve_repo_visibility(private=private, visibility=visibility, repo_type="bucket")
+
+ payload: dict[str, Any] = {}
+ if resolved_visibility is not None:
+ payload["visibility"] = resolved_visibility
+ if resource_group_id is not None:
+ payload["resourceGroupId"] = resource_group_id
+ if region is not None:
+ payload["region"] = region
+
+ if "/" not in bucket_id:
+ namespace, name = "me", bucket_id # "me" namespace refers to the current user
+ else:
+ parsed = _parse_bucket_uri(bucket_id)
+ if parsed.path_in_repo:
+ raise ValueError(f"Invalid bucket ID: {bucket_id}")
+ namespace, name = parsed.id.split("/")
+
+ response = get_session().post(
+ f"{self.endpoint}/api/buckets/{namespace}/{name}",
+ headers=self._build_hf_headers(token=token),
+ json=payload,
+ )
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as err:
+ if exist_ok and err.response.status_code == 409:
+ # Bucket already exists and `exist_ok=True`
+ pass
+ elif exist_ok and err.response.status_code in (401, 403):
+ # 401 -> if JWT token without create bucket scope
+ # 403 -> if no write permission on the namespace
+ # In both cases, bucket might already exist
+ try:
+ self.bucket_info(bucket_id=bucket_id, token=token)
+ return BucketUrl(f"{self.endpoint}/buckets/{bucket_id}", endpoint=self.endpoint)
+ except HfHubHTTPError:
+ raise err
+ else:
+ raise
+ return BucketUrl(response.json()["url"], endpoint=self.endpoint)
+
+ @validate_hf_hub_args
+ def bucket_info(
+ self,
+ bucket_id: str,
+ *,
+ token: bool | str | None = None,
+ ) -> BucketInfo:
+ """Get information about a specific bucket on the Hub.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`BucketInfo`]: The bucket information.
+
+ Raises:
+ [`~errors.BucketNotFoundError`]: If the bucket cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import bucket_info
+ >>> info = bucket_info(bucket_id="Wauplin/first-bucket")
+ >>> info.id
+ 'Wauplin/first-bucket'
+ >>> info.private
+ False
+ >>> info.created_at
+ datetime.datetime(2026, 2, 6, 17, 37, 57, tzinfo=datetime.timezone.utc)
+ >>> info.size
+ 551879671
+ >>> info.total_files
+ 12
+ ```
+ """
+ from ._buckets import BucketInfo
+
+ response = get_session().get(
+ f"{self.endpoint}/api/buckets/{bucket_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+ hf_raise_for_status(response)
+ return BucketInfo(**response.json())
+
+ @validate_hf_hub_args
+ def list_buckets(
+ self,
+ namespace: str | None = None,
+ *,
+ search: str | None = None,
+ token: bool | str | None = None,
+ ) -> Iterable[BucketInfo]:
+ """List buckets on the Hub under a certain namespace.
+
+ Args:
+ namespace (`str`, *optional*):
+ List buckets under this namespace (user or organization). Defaults to listing user's buckets.
+ search (`str`, *optional*):
+ A search string to filter bucket names.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[BucketInfo]`: An iterable of [`BucketInfo`] objects.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_buckets
+ >>> for bucket in list_buckets(): # lists buckets in the user's namespace
+ ... print(bucket)
+
+ >>> for bucket in list_buckets(namespace="huggingface"): # lists buckets in the "huggingface" organization
+ ... print(bucket)
+
+ >>> for bucket in list_buckets(search="my-prefix"): # filter buckets by name
+ ... print(bucket)
+ ```
+ """
+ from ._buckets import BucketInfo
+
+ if namespace is None:
+ namespace = "me"
+ params: dict[str, Any] = {}
+ if search is not None:
+ params["search"] = search
+ for item in paginate(
+ f"{self.endpoint}/api/buckets/{namespace}", params=params, headers=self._build_hf_headers(token=token)
+ ):
+ yield BucketInfo(**item)
+
+ @validate_hf_hub_args
+ def delete_bucket(
+ self,
+ bucket_id: str,
+ *,
+ missing_ok: bool = False,
+ token: bool | str | None = None,
+ ) -> None:
+ """Delete a bucket from the Hub.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ missing_ok (`bool`, *optional*, defaults to `False`):
+ If `True`, do not raise an error if the bucket does not exist.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`~errors.BucketNotFoundError`]: If the bucket cannot be found and `missing_ok` is set to `False` (default).
+
+ Example:
+ ```python
+ >>> from huggingface_hub import delete_bucket
+ >>> delete_bucket(bucket_id="Wauplin/first-bucket")
+ >>> delete_bucket(bucket_id="Wauplin/first-bucket", missing_ok=True)
+ ```
+ """
+ response = get_session().delete(
+ f"{self.endpoint}/api/buckets/{bucket_id}",
+ headers=self._build_hf_headers(token=token),
+ )
+
+ try:
+ hf_raise_for_status(response)
+ except HfHubHTTPError as e:
+ if e.response.status_code != 404 or not missing_ok:
+ raise
+
+ @validate_hf_hub_args
+ def move_bucket(
+ self,
+ from_id: str,
+ to_id: str,
+ *,
+ token: bool | str | None = None,
+ ) -> None:
+ """Move a bucket from "namespace1/repo_name1" to "namespace2/repo_name2"
+
+ Note there are certain limitations. For more information about moving
+ repositories, please see
+ https://hf.co/docs/hub/repositories-settings#renaming-or-transferring-a-repo.
+
+ Args:
+ from_id (`str`):
+ A namespace (user or an organization) and a bucket name separated
+ by a `/`. Original bucket identifier (e.g. `"username/my-bucket"`).
+ to_id (`str`):
+ A namespace (user or an organization) and a bucket name separated
+ by a `/`. Final bucket identifier (e.g. `"username/new-bucket-name"`
+ or `"organization/my-bucket"`).
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`~errors.BucketNotFoundError`]:
+ If the source bucket cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import move_bucket
+
+ >>> # Rename a bucket within the same namespace
+ >>> move_bucket(from_id="username/old-name", to_id="username/new-name")
+
+ >>> # Transfer a bucket to an organization
+ >>> move_bucket(from_id="username/my-bucket", to_id="my-org/my-bucket")
+ ```
+ """
+ if len(from_id.split("/")) != 2:
+ raise ValueError(f"Invalid repo_id: {from_id}. It should have a namespace (:namespace:/:repo_name:)")
+
+ if len(to_id.split("/")) != 2:
+ raise ValueError(f"Invalid repo_id: {to_id}. It should have a namespace (:namespace:/:repo_name:)")
+
+ json_payload = {"fromRepo": from_id, "toRepo": to_id, "type": "bucket"}
+
+ path = f"{self.endpoint}/api/repos/move"
+ headers = self._build_hf_headers(token=token)
+ response = get_session().post(path, headers=headers, json=json_payload)
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def update_bucket_settings(
+ self,
+ bucket_id: str,
+ *,
+ private: bool,
+ token: bool | str | None = None,
+ ) -> None:
+ """Update the settings of a bucket on the Hub.
+
+ Currently, the only supported setting is the bucket's visibility.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ private (`bool`):
+ Whether to make the bucket private.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`~errors.BucketNotFoundError`]: If the bucket cannot be found. This may be because it doesn't exist,
+ or because it is set to `private` and you do not have access.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import update_bucket_settings
+
+ >>> # Make a bucket public
+ >>> update_bucket_settings(bucket_id="Wauplin/first-bucket", private=False)
+
+ >>> # Make it private again
+ >>> update_bucket_settings(bucket_id="Wauplin/first-bucket", private=True)
+ ```
+ """
+ response = get_session().put(
+ f"{self.endpoint}/api/buckets/{bucket_id}/settings",
+ headers=self._build_hf_headers(token=token),
+ json={"private": private},
+ )
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def list_bucket_tree(
+ self,
+ bucket_id: str,
+ prefix: str | None = None,
+ *,
+ recursive: bool | None = None,
+ token: str | bool | None = None,
+ ) -> Iterable[BucketFile | BucketFolder]:
+ """List files in a bucket.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ prefix (`str`, *optional*):
+ Filter results to files whose path starts with this prefix.
+ recursive (`bool`, *optional*):
+ If `True`, list files recursively. If `False` (default), list files and directories only at root.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[Union[BucketFile, BucketFolder]]`: An iterable of [`BucketFile`] and [`BucketFolder`] objects
+ containing file and directory information (path, etc.).
+
+ Example:
+ ```python
+ >>> from huggingface_hub import list_bucket_tree
+ >>> for file_info in list_bucket_tree(bucket_id="username/my-bucket"):
+ ... print(file_info.path)
+
+ >>> # Filter by prefix
+ >>> for file_info in list_bucket_tree(bucket_id="username/my-bucket", prefix="models/"):
+ ... print(file_info.path)
+ ```
+ """
+ from ._buckets import BucketFile, BucketFolder
+
+ encoded_prefix = "/" + quote(prefix, safe="") if prefix else ""
+ params = {}
+ if recursive is not None:
+ params["recursive"] = recursive
+ for item in paginate(
+ path=f"{self.endpoint}/api/buckets/{bucket_id}/tree{encoded_prefix}",
+ headers=self._build_hf_headers(token=token),
+ params=params,
+ ):
+ if item["type"] == "file":
+ yield BucketFile(**item)
+ elif item["type"] == "directory":
+ yield BucketFolder(**item)
+
+ @validate_hf_hub_args
+ def get_bucket_paths_info(
+ self,
+ bucket_id: str,
+ paths: Iterable[str],
+ *,
+ token: str | bool | None = None,
+ ) -> Iterable[BucketFile]:
+ """
+ Get information about a bucket's paths.
+
+ Calls are made in batches of 1000 paths. Results are yielded as they are received.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ paths (`Iterable[str]`):
+ The paths to get information about. If a path does not exist, it is ignored without raising an exception.
+ Only file paths are supported.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ `Iterable[BucketFile]`:
+ The information about the paths, as an iterable of [`BucketFile`] objects.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import get_bucket_paths_info
+ >>> paths_info = get_bucket_paths_info("username/my-bucket", ["file.txt", "checkpoints/model.safetensors"])
+ >>> for info in paths_info:
+ ... print(info)
+ BucketFile(type='file', path='file.txt', size=2379, xet_hash='96e637d9665bd35477b1908a23f2e254edfba0618dbd2d62f90a6baee7d139cf', mtime=datetime.datetime(2024, 9, 25, 15, 31, 2, 346000, tzinfo=datetime.timezone.utc))
+ BucketFile(type='file', path='checkpoints/model.safetensors', size=2408828, xet_hash='3ed0e9fefe788ddd61d1e26eba67057e9740a064b009256fbafadf6bb95785ca', mtime=datetime.datetime(2024, 9, 25, 15, 31, 2, 346000, tzinfo=datetime.timezone.utc))
+ ```
+ """
+ from ._buckets import BucketFile
+
+ headers = self._build_hf_headers(token=token)
+
+ for batch in chunk_iterable(paths, chunk_size=_BUCKET_PATHS_INFO_BATCH_SIZE):
+ response = http_backoff(
+ "POST",
+ f"{self.endpoint}/api/buckets/{bucket_id}/paths-info",
+ json={"paths": list(batch)},
+ headers=headers,
+ )
+ hf_raise_for_status(response)
+ for path_info in response.json():
+ yield BucketFile(**path_info)
+
+ @validate_hf_hub_args
+ def copy_files(self, source: str, destination: str, *, token: str | bool | None = None) -> None:
+ """Copy files between locations on the Hub.
+
+ Copy files from a bucket or repository (model, dataset, space) to a bucket or another repository.
+ Both individual files and entire folders are supported.
+
+ When copying folders, a trailing `/` on the source path uses rsync-style semantics: copy the *contents*
+ of the folder into the destination, without nesting the source folder itself. Without a trailing `/`,
+ the source folder is nested inside the destination (like `cp -r`).
+
+ When copying from a repository to a bucket, `.gitattributes` files are automatically excluded since they
+ are git-specific metadata and not relevant in a bucket context.
+
+ Repo-to-repo copies use [`CommitOperationCopy`] under the hood and create a commit on the destination
+ repository. Bucket-to-repo copies are not supported.
+
+ > [!WARNING]
+ > Server-side copies only work within the same [storage region](https://huggingface.co/docs/hub/storage-regions).
+
+ Args:
+ source (`str`):
+ Source location as an `hf://` URI. Can be a bucket path (e.g. `"hf://buckets/my-bucket/path/to/file"`)
+ or a repo path (e.g. `"hf://username/my-model/weights.bin"`, `"hf://datasets/username/my-dataset/data/"`).
+ destination (`str`):
+ Destination location as an `hf://` URI pointing to a bucket (e.g. `"hf://buckets/my-bucket/target/path"`)
+ or a repository (e.g. `"hf://username/my-model/target/path"`).
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+ If source/destination URIs are invalid or if copying from a bucket to a repo.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import copy_files
+
+ # Copy a single file between buckets
+ >>> copy_files("hf://buckets/my-bucket/data.bin", "hf://buckets/other-bucket/data.bin")
+
+ # Copy a folder into another bucket (nests: backup/models/...)
+ >>> copy_files("hf://buckets/my-bucket/models", "hf://buckets/other-bucket/backup/")
+
+ # Copy folder contents (trailing /): files go directly into backup/
+ >>> copy_files("hf://buckets/my-bucket/models/", "hf://buckets/other-bucket/backup/")
+
+ # Copy a file from a model repo to a bucket
+ >>> copy_files("hf://username/my-model/model.safetensors", "hf://buckets/my-bucket/")
+
+ # Copy an entire dataset to a bucket
+ >>> copy_files("hf://datasets/username/my-dataset/", "hf://buckets/my-bucket/datasets/")
+
+ # Copy files between repositories
+ >>> copy_files("hf://username/source-model/", "hf://username/dest-model/")
+
+ # Copy a file from one repo to another
+ >>> copy_files("hf://username/source-model/config.json", "hf://username/dest-model/config.json")
+ ```
+ """
+ source_uri = parse_hf_uri(source)
+ destination_uri = parse_hf_uri(destination)
+
+ # Rsync-style trailing slash on source: "copy contents of" instead of "copy directory into".
+ # Check before parsing strips the slash.
+ merge_contents = source.endswith("/")
+
+ if destination_uri.is_repo:
+ if source_uri.is_bucket:
+ raise ValueError("Bucket-to-repo copy is not supported.")
+ self._copy_to_repo(source_uri, destination_uri, merge_contents, source, destination, token=token)
+ else:
+ self._copy_to_bucket(source_uri, destination_uri, merge_contents, source, destination, token=token)
+
+ def _copy_to_bucket(
+ self,
+ source: HfUri,
+ destination: HfUri,
+ merge_contents: bool,
+ source_str: str,
+ destination_str: str,
+ *,
+ token: str | bool | None = None,
+ ) -> None:
+ from ._buckets import BucketFile
+
+ destination_bucket_id = destination.id
+ destination_path = destination.path_in_repo
+ destination_is_directory = False
+ destination_exists_as_directory = False
+
+ if destination_path == "":
+ destination_is_directory = True
+ destination_exists_as_directory = True
+ else:
+ dest_path_info = list(self.get_bucket_paths_info(destination_bucket_id, [destination_path], token=token))
+ if dest_path_info:
+ destination_is_directory = False
+ else:
+ # The server matches `prefix` lexically (listing "logs" also returns "logs.json" or "logs_backup/")
+ # => only count entries that are `destination_path` itself or below it.
+ destination_exists_as_directory = any(
+ item.path == destination_path or item.path.startswith(f"{destination_path}/")
+ for item in self.list_bucket_tree(
+ destination_bucket_id, prefix=destination_path, recursive=False, token=token
+ )
+ )
+ destination_is_directory = destination_exists_as_directory or destination_str.endswith("/")
+
+ all_adds: list[tuple[str | Path | bytes, str]] = []
+ all_copies: list[tuple[str, str, str, str]] = []
+ pending_downloads: list[tuple[str, str]] = []
+
+ def _resolve_target_path(src_file_path: str, src_root_path: str | None, is_single_file: bool) -> str:
+ return _resolve_copy_target_path(
+ src_file_path,
+ src_root_path,
+ is_single_file,
+ destination_path,
+ destination_is_directory,
+ destination_exists_as_directory,
+ merge_contents,
+ )
+
+ def _add_repo_file(file: RepoFile, target_path: str) -> None:
+ if file.xet_hash is not None:
+ all_copies.append((source.type, source.id, file.xet_hash, target_path))
+ else:
+ pending_downloads.append((file.path, target_path))
+
+ if source.is_bucket:
+ source_path = source.path_in_repo
+ source_path_info = list(self.get_bucket_paths_info(source.id, [source_path], token=token))
+
+ if source_path_info:
+ source_file = source_path_info[0]
+ target_path = _resolve_target_path(source_file.path, None, is_single_file=True)
+ all_copies.append(("bucket", source.id, source_file.xet_hash, target_path))
+ else:
+ for item in self.list_bucket_tree(source.id, prefix=source_path or None, recursive=True, token=token):
+ if not isinstance(item, BucketFile):
+ continue
+ if source_path and not (item.path == source_path or item.path.startswith(source_path + "/")):
+ continue
+ target_path = _resolve_target_path(item.path, source_path or None, is_single_file=False)
+ all_copies.append(("bucket", source.id, item.xet_hash, target_path))
+ else:
+ for file, target_path in self._iter_repo_files_for_copy(
+ source,
+ destination_path,
+ destination_is_directory,
+ destination_exists_as_directory,
+ merge_contents,
+ token=token,
+ ):
+ # Skip .gitattributes files (git-specific metadata, not relevant in a bucket)
+ if file.path.rsplit("/", 1)[-1] == ".gitattributes":
+ continue
+ _add_repo_file(file, target_path)
+
+ if not all_copies and not all_adds and not pending_downloads:
+ if source.is_bucket:
+ raise EntryNotFoundError(f"No files found at '{source_str}' in bucket '{source.id}'.")
+ else:
+ raise EntryNotFoundError(f"No files found at '{source_str}' in {source.type} '{source.id}'.")
+
+ if pending_downloads:
+ from .utils import silent_tqdm
+
+ def _download_and_collect(item: tuple[str, str]) -> None:
+ file_path, target_path = item
+ local_path = self.hf_hub_download(
+ repo_id=source.id,
+ repo_type=source.type,
+ filename=file_path,
+ revision=source.revision,
+ token=token,
+ tqdm_class=silent_tqdm, # type: ignore
+ )
+ all_adds.append((local_path, target_path))
+
+ from .utils import hf_thread_map
+
+ hf_thread_map(_download_and_collect, pending_downloads, desc="Downloading text files for copy")
+
+ self.batch_bucket_files(destination_bucket_id, add=all_adds, copy=all_copies, token=token)
+
+ def _iter_repo_files_for_copy(
+ self,
+ source: HfUri,
+ destination_path: str,
+ destination_is_directory: bool,
+ destination_exists_as_directory: bool,
+ merge_contents: bool,
+ *,
+ token: str | bool | None = None,
+ ) -> Iterable[tuple[RepoFile, str]]:
+ """Yield (file, target_path) pairs from a repo source, with target paths resolved."""
+ source_path = source.path_in_repo
+ source_repo_path_info: list[RepoFile | RepoFolder] = []
+ if source_path != "":
+ source_repo_path_info = self.get_paths_info(
+ repo_id=source.id,
+ paths=[source_path],
+ repo_type=source.type,
+ revision=source.revision,
+ token=token,
+ )
+
+ def _resolve(src_file_path: str, src_root_path: str | None, is_single_file: bool) -> str:
+ return _resolve_copy_target_path(
+ src_file_path,
+ src_root_path,
+ is_single_file,
+ destination_path,
+ destination_is_directory,
+ destination_exists_as_directory,
+ merge_contents,
+ )
+
+ if len(source_repo_path_info) == 1 and isinstance(source_repo_path_info[0], RepoFile):
+ file = source_repo_path_info[0]
+ yield file, _resolve(file.path, None, is_single_file=True)
+ else:
+ for repo_item in self.list_repo_tree(
+ repo_id=source.id,
+ path_in_repo=source_path,
+ recursive=True,
+ repo_type=source.type,
+ revision=source.revision,
+ token=token,
+ ):
+ if not isinstance(repo_item, RepoFile):
+ continue
+ yield repo_item, _resolve(repo_item.path, source_path or None, is_single_file=False)
+
+ def _copy_to_repo(
+ self,
+ source: HfUri,
+ destination: HfUri,
+ merge_contents: bool,
+ source_str: str,
+ destination_str: str,
+ *,
+ token: str | bool | None = None,
+ ) -> None:
+
+ from ._commit_api import CommitOperationCopy
+
+ destination_path = destination.path_in_repo
+ destination_is_directory = False
+ destination_exists_as_directory = False
+
+ if destination_path == "":
+ destination_is_directory = True
+ destination_exists_as_directory = True
+ else:
+ dest_path_info = self.get_paths_info(
+ repo_id=destination.id,
+ paths=[destination_path],
+ repo_type=destination.type,
+ revision=destination.revision,
+ token=token,
+ )
+ if len(dest_path_info) == 1 and isinstance(dest_path_info[0], RepoFile):
+ destination_is_directory = False
+ elif len(dest_path_info) == 1 and isinstance(dest_path_info[0], RepoFolder):
+ destination_is_directory = True
+ destination_exists_as_directory = True
+ else:
+ try:
+ destination_exists_as_directory = any(
+ self.list_repo_tree(
+ repo_id=destination.id,
+ path_in_repo=destination_path,
+ repo_type=destination.type,
+ revision=destination.revision,
+ token=token,
+ )
+ )
+ except RemoteEntryNotFoundError:
+ destination_exists_as_directory = False
+ destination_is_directory = destination_exists_as_directory or destination_str.endswith("/")
+
+ is_same_repo = source.id == destination.id and source.type == destination.type
+
+ commit_ops: list[CommitOperationCopy] = [
+ CommitOperationCopy(
+ src_path_in_repo=file.path,
+ path_in_repo=target,
+ src_revision=source.revision,
+ src_repo_id=None if is_same_repo else source.id,
+ src_repo_type=None if is_same_repo else source.type,
+ )
+ for file, target in self._iter_repo_files_for_copy(
+ source,
+ destination_path,
+ destination_is_directory,
+ destination_exists_as_directory,
+ merge_contents,
+ token=token,
+ )
+ ]
+
+ if not commit_ops:
+ raise EntryNotFoundError(f"No files found at '{source_str}' in {source.type} '{source.id}'.")
+
+ self.create_commit(
+ repo_id=destination.id,
+ repo_type=destination.type,
+ revision=destination.revision,
+ operations=commit_ops,
+ commit_message=f"Copy files from {source.type}s/{source.id}",
+ token=token,
+ )
+
+ @validate_hf_hub_args
+ def batch_bucket_files(
+ self,
+ bucket_id: str,
+ *,
+ add: list[tuple[str | Path | bytes, str]] | None = None,
+ copy: list[tuple[str, str, str, str]] | None = None,
+ delete: list[str] | None = None,
+ token: str | bool | None = None,
+ ):
+ """Add, copy, and/or delete files in a bucket.
+
+ This is a non-transactional operation. If an error occurs in the process, some files may have been uploaded,
+ copied, or deleted while others haven't.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ add (`list` of `tuple`, *optional*):
+ Files to upload. Each element is a `(source, destination)` tuple where `source` is a path to a local
+ file (`str` or `Path`) or raw `bytes` content, and `destination` is the path in the bucket.
+ copy (`list` of `tuple`, *optional*):
+ Files to copy by xet hash. Each element is a `(source_repo_type, source_repo_id, xet_hash,
+ destination)` tuple where:
+ - `source_repo_type` is the type of the source repository: `"model"`, `"dataset"`, `"space"`, or
+ `"bucket"`.
+ - `source_repo_id` is the ID of the source repository or bucket (e.g. `"username/my-model"`).
+ - `xet_hash` is the xet hash of the file to copy.
+ - `destination` is the destination path in the bucket.
+ This is a server-side operation — no data is downloaded or re-uploaded.
+ delete (`list` of `str`, *optional*):
+ Paths of files to delete from the bucket.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Raises:
+ [`~errors.BucketBatchError`]:
+ If the server reports that some operations failed. The error is raised once every operation has
+ been sent, so the other operations of the batch have been applied.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import batch_bucket_files
+
+ # Upload files
+ >>> batch_bucket_files(
+ ... "username/my-bucket",
+ ... add=[
+ ... ("./model.safetensors", "models/model.safetensors"),
+ ... (b'{{"key": "value"}}', "config.json"),
+ ... ],
+ ... )
+
+ # Copy xet files from another bucket or repo (server-side, no data transfer)
+ >>> batch_bucket_files(
+ ... "username/my-bucket",
+ ... copy=[
+ ... ("bucket", "username/source-bucket", "", "models/model.safetensors"),
+ ... ("model", "username/my-model", "", "models/config.safetensors"),
+ ... ],
+ ... )
+
+ # Delete files
+ >>> batch_bucket_files("username/my-bucket", delete=["old-model.bin"])
+
+ # Upload and delete in one batch
+ >>> batch_bucket_files(
+ ... "username/my-bucket",
+ ... add=[("./new.txt", "new.txt")],
+ ... delete=["old.txt"],
+ ... )
+ ```
+ """
+ add = add or []
+ copy = copy or []
+ delete = delete or []
+
+ # Small batch: do everything in one call
+ if len(add) + len(copy) + len(delete) <= _BUCKET_BATCH_ADD_CHUNK_SIZE:
+ self._batch_bucket_files(bucket_id, add=add, copy=copy, delete=delete, token=token) # type: ignore
+ return
+
+ # Large batch: chunk copies first (no upload), then adds, then deletes
+ from .utils import are_progress_bars_disabled
+ from .utils._xet_progress_reporting import XetUploadProgressReporter
+
+ if add and not are_progress_bars_disabled():
+ progress = XetUploadProgressReporter(total_files=len(add))
+ else:
+ progress = None
+
+ # A failing chunk must not stop the others: collect the errors and raise once everything has been sent
+ errors: list[BucketBatchError] = []
+
+ def _send_chunk(**operations: Any) -> None:
+ try:
+ self._batch_bucket_files(bucket_id, token=token, **operations)
+ except BucketBatchError as error:
+ logger.warning(str(error))
+ errors.append(error)
+
+ try:
+ for copy_chunk in chunk_iterable(copy, chunk_size=_BUCKET_BATCH_ADD_CHUNK_SIZE):
+ _send_chunk(copy=list(copy_chunk))
+
+ for add_chunk in chunk_iterable(add, chunk_size=_BUCKET_BATCH_ADD_CHUNK_SIZE):
+ _send_chunk(add=list(add_chunk), _progress=progress)
+
+ for delete_chunk in chunk_iterable(delete, chunk_size=_BUCKET_BATCH_DELETE_CHUNK_SIZE):
+ _send_chunk(delete=list(delete_chunk))
+ finally:
+ if progress is not None:
+ progress.close()
+
+ if errors:
+ raise _bucket_batch_error(
+ bucket_id,
+ [failure for error in errors for failure in error.failures],
+ sent=len(add) + len(copy) + len(delete),
+ response=errors[-1].response,
+ )
+
+ def _batch_bucket_files(
+ self,
+ bucket_id: str,
+ *,
+ add: list[tuple[str | Path | bytes, str] | _BucketAddFile] | None = None,
+ copy: list[tuple[str, str, str, str] | _BucketCopyFile] | None = None,
+ delete: list[str | _BucketDeleteFile] | None = None,
+ token: str | bool | None = None,
+ _progress: XetUploadProgressReporter | None = None,
+ ):
+ """Internal method: process a single batch of bucket file operations (upload to XET + call /batch)."""
+ from ._buckets import _BucketAddFile, _BucketCopyFile, _BucketDeleteFile
+
+ # Convert public API inputs to internal operation objects
+ operations: list[_BucketAddFile | _BucketCopyFile | _BucketDeleteFile] = []
+ if add:
+ for add_item in add:
+ if isinstance(add_item, _BucketAddFile):
+ operations.append(add_item)
+ else:
+ source, destination = add_item
+ operations.append(_BucketAddFile(source=source, destination=destination))
+ if copy:
+ for copy_item in copy:
+ if isinstance(copy_item, _BucketCopyFile):
+ operations.append(copy_item)
+ else:
+ source_repo_type, source_repo_id, xet_hash, destination = copy_item
+ operations.append(
+ _BucketCopyFile(
+ destination=destination,
+ xet_hash=xet_hash,
+ source_repo_type=source_repo_type,
+ source_repo_id=source_repo_id,
+ )
+ )
+ if delete:
+ for delete_item in delete:
+ if isinstance(delete_item, _BucketDeleteFile):
+ operations.append(delete_item)
+ else:
+ operations.append(_BucketDeleteFile(path=delete_item))
+
+ if not operations:
+ return
+
+ from hf_xet import SKIP_SHA256
+
+ from .utils import are_progress_bars_disabled
+ from .utils._xet import (
+ XetTokenType,
+ abort_xet_session,
+ get_xet_session,
+ xet_connection_info_refresh_url,
+ xet_headers_without_auth,
+ )
+ from .utils._xet_progress_reporting import XetUploadProgressReporter
+
+ headers = self._build_hf_headers(token=token)
+
+ add_operations = [op for op in operations if isinstance(op, _BucketAddFile)]
+ add_operations_to_upload = [op for op in add_operations if op.xet_hash is None]
+ add_bytes_operations = [op for op in add_operations if isinstance(op.source, bytes)]
+ add_path_operations = [op for op in add_operations if not isinstance(op.source, bytes)]
+
+ if len(add_operations_to_upload) > 0:
+ refresh_url = xet_connection_info_refresh_url(
+ token_type=XetTokenType.WRITE,
+ repo_id=bucket_id,
+ repo_type="bucket",
+ endpoint=self.endpoint,
+ )
+ xet_headers = xet_headers_without_auth(headers)
+
+ owns_progress = _progress is None
+ if _progress is not None:
+ progress = _progress
+ progress.reset_for_next_commit()
+ progress_callback = progress.update_progress
+ elif not are_progress_bars_disabled():
+ progress = XetUploadProgressReporter()
+ progress_callback = progress.update_progress
+ else:
+ progress, progress_callback = None, None
+
+ session = get_xet_session()
+
+ try:
+ with session.new_upload_commit(
+ token_refresh_url=refresh_url,
+ token_refresh_headers=headers,
+ custom_headers=xet_headers,
+ progress_callback=progress_callback,
+ ) as commit:
+ handles = []
+ for op in add_path_operations:
+ if op.xet_hash is None:
+ handles.append((commit.start_upload_file(str(op.source), sha256=SKIP_SHA256), op))
+ for op in add_bytes_operations:
+ if op.xet_hash is None:
+ handles.append((commit.start_upload_bytes(op.source, sha256=SKIP_SHA256), op))
+ for handle, op in handles:
+ result = handle.result()
+ op.xet_hash = result.xet_info.hash
+ op.size = result.xet_info.file_size
+ except KeyboardInterrupt:
+ abort_xet_session()
+ raise
+ finally:
+ if owns_progress and progress is not None:
+ progress.close()
+
+ def _payload_as_ndjson() -> Iterable[bytes]:
+ for op in operations:
+ if isinstance(op, _BucketAddFile):
+ payload = {
+ "type": "addFile",
+ "path": op.destination,
+ "xetHash": op.xet_hash,
+ "mtime": op.mtime,
+ }
+ if op.content_type is not None:
+ payload["contentType"] = op.content_type
+ elif isinstance(op, _BucketCopyFile):
+ payload = {
+ "type": "copyFile",
+ "path": op.destination,
+ "xetHash": op.xet_hash,
+ "mtime": op.mtime,
+ "sourceRepoType": op.source_repo_type,
+ "sourceRepoId": op.source_repo_id,
+ }
+ else:
+ payload = {
+ "type": "deleteFile",
+ "path": op.path,
+ }
+ yield json.dumps(payload).encode()
+ yield b"\n"
+
+ headers = {
+ "Content-Type": "application/x-ndjson",
+ **headers,
+ }
+ data = b"".join(_payload_as_ndjson())
+
+ response = http_backoff(
+ "POST", f"{self.endpoint}/api/buckets/{bucket_id}/batch", headers=headers, content=data
+ )
+ # Failed operations are listed in the body of a 200 (partial failure) or a 422 (all failed)
+ if response.status_code in (200, 422):
+ failures = response.json().get("failed", [])
+ if failures:
+ raise _bucket_batch_error(bucket_id, failures, sent=len(operations), response=response)
+ hf_raise_for_status(response)
+
+ @validate_hf_hub_args
+ def get_bucket_file_metadata(
+ self,
+ bucket_id: str,
+ remote_path: str,
+ *,
+ token: str | bool | None = None,
+ ) -> BucketFileMetadata:
+ """Fetch metadata of a file in a bucket.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ remote_path (`str`):
+ The path of the file in the bucket.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Returns:
+ [`BucketFileMetadata`]: The file metadata containing size and xet information.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import get_bucket_file_metadata
+ >>> metadata = get_bucket_file_metadata(
+ ... bucket_id="username/my-bucket",
+ ... remote_path="models/model.safetensors",
+ ... )
+ >>> metadata.size
+ 42000
+ ```
+ """
+ from ._buckets import BucketFileMetadata
+
+ headers = self._build_hf_headers(token=token)
+ headers["Accept-Encoding"] = "identity" # prevent compression so the size matches the file
+
+ response = _httpx2_follow_hub_redirects_with_backoff(
+ "HEAD",
+ f"{self.endpoint}/buckets/{bucket_id}/resolve/{quote(remote_path, safe='')}",
+ headers=headers,
+ retry_on_errors=True,
+ )
+
+ xet_file_data = parse_xet_file_data_from_response(response)
+ if xet_file_data is None:
+ raise ValueError(f"Could not parse xet file data for '{remote_path}' in bucket '{bucket_id}'.")
+
+ size = response.headers.get(constants.HUGGINGFACE_HEADER_X_LINKED_SIZE) or (
+ None if response.is_redirect else response.headers.get("Content-Length")
+ )
+ if size is None:
+ raise ValueError(f"Could not get size for '{remote_path}' in bucket '{bucket_id}'.")
+
+ return BucketFileMetadata(size=int(size), xet_file_data=xet_file_data)
+
+ @validate_hf_hub_args
+ def download_bucket_files(
+ self,
+ bucket_id: str,
+ files: list[tuple[str | BucketFile, str | Path]],
+ *,
+ raise_on_missing_files: bool = False,
+ token: str | bool | None = None,
+ ) -> None:
+ """Download files from a bucket.
+
+ Files input is a list of `(remote file, local file)` tuples where `remote file` is either the path of the file
+ in the bucket or a [`BucketFile`] object, and `local file` is the destination path on the local filesystem.
+ When passing a [`BucketFile`] object (obtained from [`list_bucket_tree`]), the method will skip the metadata
+ fetching step and directly download the files.
+
+ Args:
+ bucket_id (`str`):
+ The ID of the bucket (e.g. `"username/my-bucket"`).
+ files (`list[tuple[Union[str, BucketFile], Union[str, Path]]]`):
+ Files to download as a list of tuple (source, destination). See description above for format details.
+ raise_on_missing_files (`bool`, *optional*):
+ If `True`, raise an [`EntryNotFoundError`] when a requested file does not exist in the bucket. If
+ `False` (default), missing files are skipped with a warning.
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import download_bucket_files
+
+ >>> download_bucket_files(
+ ... bucket_id="username/my-bucket",
+ ... files=[
+ ... ("models/model.safetensors", "./local/model.safetensors"),
+ ... ("config.json", "./local/config.json"),
+ ... ],
+ ... )
+ ```
+
+ ```python
+ >>> from huggingface_hub import download_bucket_files
+
+ >>> parquet_files = [file for file in list_bucket_tree(bucket_id="username/my-bucket") if file.path.endswith(".parquet")]
+ >>> download_bucket_files(
+ ... bucket_id="username/my-bucket",
+ ... files=[(file, f"./local/{file.path}") for file in parquet_files],
+ ... )
+ ```
+ """
+ from hf_xet import XetFileInfo # type: ignore[no-redef]
+
+ from ._buckets import BucketFile
+ from .utils._xet import abort_xet_session, get_xet_session, xet_headers_without_auth
+
+ headers = self._build_hf_headers(token=token)
+
+ if len(files) == 0:
+ return
+
+ # Resolve all string paths to BucketFile objects in a single batch request
+ str_paths = [path for path, _ in files if not isinstance(path, BucketFile)]
+ bucket_files_by_path: dict[str, BucketFile] = {}
+ if str_paths:
+ bucket_files_by_path = {
+ info.path: info for info in self.get_bucket_paths_info(bucket_id, str_paths, token=token)
+ }
+
+ # Check for missing files
+ missing_paths = [path for path in str_paths if path not in bucket_files_by_path]
+ if missing_paths:
+ if raise_on_missing_files:
+ raise EntryNotFoundError(
+ f"{len(missing_paths)} file(s) not found in bucket '{bucket_id}': {', '.join(missing_paths)}"
+ )
+ for path in missing_paths:
+ warnings.warn(f"File '{path}' not found in bucket '{bucket_id}'. Skipping.")
+
+ non_zero_download_items: list[tuple[XetFileInfo, str]] = []
+ first_valid_bucket_file: BucketFile | None = None
+ for remote_file, local_path in files:
+ if not isinstance(remote_file, BucketFile):
+ if remote_file not in bucket_files_by_path:
+ continue # skip missing files (already warned above)
+ remote_file = bucket_files_by_path[remote_file]
+ if first_valid_bucket_file is None:
+ first_valid_bucket_file = remote_file
+ dest_path = Path(local_path).absolute()
+ if remote_file.size == 0:
+ # Create empty file without downloading
+ if dest_path.exists():
+ if dest_path.is_dir():
+ raise IsADirectoryError(f"Expected file but found directory at '{dest_path}'")
+ if dest_path.stat().st_size != 0:
+ dest_path.write_bytes(b"")
+ else:
+ dest_path.parent.mkdir(parents=True, exist_ok=True)
+ dest_path.touch()
+ else:
+ non_zero_download_items.append((XetFileInfo(remote_file.xet_hash, remote_file.size), str(dest_path)))
+
+ if len(non_zero_download_items) == 0 or first_valid_bucket_file is None:
+ return
+
+ # Fetch refresh route (same for all files in this bucket)
+ remote_path = first_valid_bucket_file.path
+ metadata = self.get_bucket_file_metadata(bucket_id, remote_path, token=token)
+
+ xet_headers = xet_headers_without_auth(headers)
+
+ # Download files
+ from .utils._xet_progress_reporting import XetDownloadProgressReporter
+
+ session = get_xet_session()
+
+ with XetDownloadProgressReporter(
+ reconstruction_desc="Downloading bucket files",
+ transfer_desc="Downloading bytes",
+ total=sum(xet_info.file_size for xet_info, _ in non_zero_download_items),
+ log_level=logger.getEffectiveLevel(),
+ name="huggingface_hub.download_bucket_files",
+ ) as progress:
+ try:
+ with session.new_file_download_group(
+ token_refresh_url=metadata.xet_file_data.refresh_route,
+ token_refresh_headers=headers,
+ custom_headers=xet_headers,
+ progress_callback=progress.update_progress,
+ ) as group:
+ for xet_info, dest in non_zero_download_items:
+ group.start_download_file(xet_info, dest)
+ except KeyboardInterrupt:
+ abort_xet_session()
+ raise
+
+ @validate_hf_hub_args
+ def sync_bucket(
+ self,
+ source: str | None = None,
+ dest: str | None = None,
+ *,
+ delete: bool = False,
+ ignore_times: bool = False,
+ ignore_sizes: bool = False,
+ existing: bool = False,
+ ignore_existing: bool = False,
+ include: list[str] | None = None,
+ exclude: list[str] | None = None,
+ filter_from: str | None = None,
+ plan: str | None = None,
+ apply: str | None = None,
+ dry_run: bool = False,
+ verbose: bool = False,
+ quiet: bool = False,
+ token: bool | str | None = None,
+ ) -> SyncPlan:
+ """Sync files between a local directory and a bucket.
+
+ This is equivalent to the ``hf buckets sync`` CLI command. One of ``source`` or ``dest`` must be a bucket path
+ (``hf://buckets/...``) and the other must be a local directory path.
+
+ Args:
+ source (`str`, *optional*):
+ Source path: local directory or ``hf://buckets/namespace/bucket_name(/prefix)``.
+ Required unless using ``apply``.
+ dest (`str`, *optional*):
+ Destination path: local directory or ``hf://buckets/namespace/bucket_name(/prefix)``.
+ Required unless using ``apply``.
+ delete (`bool`, *optional*, defaults to `False`):
+ Delete destination files not present in source.
+ ignore_times (`bool`, *optional*, defaults to `False`):
+ Skip files only based on size, ignoring modification times.
+ ignore_sizes (`bool`, *optional*, defaults to `False`):
+ Skip files only based on modification times, ignoring sizes.
+ existing (`bool`, *optional*, defaults to `False`):
+ Skip creating new files on receiver (only update existing files).
+ ignore_existing (`bool`, *optional*, defaults to `False`):
+ Skip updating files that exist on receiver (only create new files).
+ include (`list[str]`, *optional*):
+ Include files matching patterns (fnmatch-style).
+ exclude (`list[str]`, *optional*):
+ Exclude files matching patterns (fnmatch-style).
+ filter_from (`str`, *optional*):
+ Path to a filter file with include/exclude rules.
+ plan (`str`, *optional*):
+ Save sync plan to this JSONL file instead of executing.
+ apply (`str`, *optional*):
+ Apply a previously saved plan file. When set, ``source`` and ``dest`` are not needed.
+ dry_run (`bool`, *optional*, defaults to `False`):
+ Print sync plan to stdout as JSONL without executing.
+ verbose (`bool`, *optional*, defaults to `False`):
+ Show detailed per-file operations.
+ quiet (`bool`, *optional*, defaults to `False`):
+ Suppress all output and progress bars.
+ token (Union[bool, str, None], optional):
+ A valid user access token. If not provided, the locally saved token will be used.
+
+ Returns:
+ [`SyncPlan`]: The computed (or loaded) sync plan.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import HfApi
+ >>> api = HfApi()
+
+ # Upload local directory to bucket
+ >>> api.sync_bucket("./data", "hf://buckets/username/my-bucket")
+
+ # Download bucket to local directory
+ >>> api.sync_bucket("hf://buckets/username/my-bucket", "./data")
+
+ # Sync with delete and filtering
+ >>> api.sync_bucket(
+ ... "./data",
+ ... "hf://buckets/username/my-bucket",
+ ... delete=True,
+ ... include=["*.safetensors"],
+ ... )
+
+ # Dry run: preview what would be synced
+ >>> plan = api.sync_bucket("./data", "hf://buckets/username/my-bucket", dry_run=True)
+ >>> plan.summary()
+ {'uploads': 3, 'downloads': 0, 'deletes': 0, 'skips': 1, 'total_size': 4096}
+
+ # Save plan for review, then apply
+ >>> api.sync_bucket("./data", "hf://buckets/username/my-bucket", plan="sync-plan.jsonl")
+ >>> api.sync_bucket(apply="sync-plan.jsonl")
+ ```
+ """
+ from ._buckets import sync_bucket_internal
+
+ return sync_bucket_internal(
+ source=source,
+ dest=dest,
+ api=self,
+ delete=delete,
+ ignore_times=ignore_times,
+ ignore_sizes=ignore_sizes,
+ existing=existing,
+ ignore_existing=ignore_existing,
+ include=include,
+ exclude=exclude,
+ filter_from=filter_from,
+ plan=plan,
+ apply=apply,
+ dry_run=dry_run,
+ verbose=verbose,
+ quiet=quiet,
+ token=token,
+ )
+
+
+def _bucket_batch_error(
+ bucket_id: str, failures: list[dict[str, str]], *, sent: int, response: httpx2.Response
+) -> BucketBatchError:
+ messages = [f" - {f['path']}: {f['error']}" for f in failures[:_BUCKET_BATCH_MAX_LISTED_FAILURES]]
+ if len(failures) > _BUCKET_BATCH_MAX_LISTED_FAILURES:
+ messages.append(f" - ... and {len(failures) - _BUCKET_BATCH_MAX_LISTED_FAILURES} more")
+ error = BucketBatchError(
+ f"Failed to apply {len(failures)} out of {sent} operation(s) on bucket '{bucket_id}':\n" + "\n".join(messages),
+ response=response,
+ )
+ error.failures = failures
+ return error
+
+
+def _parse_revision_from_pr_url(pr_url: str) -> str:
+ """Safely parse revision number from a PR url.
+
+ Example:
+ ```py
+ >>> _parse_revision_from_pr_url("https://huggingface.co/bigscience/bloom/discussions/2")
+ "refs/pr/2"
+ ```
+ """
+ re_match = _REGEX_DISCUSSION_URL.fullmatch(pr_url)
+ if re_match is None:
+ raise RuntimeError(f"Unexpected response from the hub, expected a Pull Request URL but got: '{pr_url}'")
+ return f"refs/pr/{re_match[1]}"
+
+
+def parse_local_safetensors_file_metadata(path: str | Path) -> SafetensorsFileMetadata:
+ """
+ Parse metadata from a local safetensors file.
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Args:
+ path (`str` or `Path`):
+ Path to the safetensors file.
+
+ Returns:
+ [`SafetensorsFileMetadata`]: information related to the safetensors file.
+
+ Raises:
+ [`SafetensorsParsingError`]:
+ If the safetensors file header couldn't be parsed correctly.
+ `FileNotFoundError`:
+ If the file does not exist.
+
+ Example:
+ ```py
+ >>> metadata = parse_local_safetensors_file_metadata("path/to/model.safetensors")
+ >>> metadata
+ SafetensorsFileMetadata(
+ metadata={'format': 'pt'},
+ tensors={'layer.weight': TensorInfo(dtype='F32', shape=[512, 512], ...}, ...}
+ )
+ >>> metadata.parameter_count
+ {'F32': 262144}
+ ```
+ """
+ path = Path(path)
+ filename = path.name
+ context_msg = f"path '{path}'"
+
+ with open(path, "rb") as f:
+ # 1. Read first 8 bytes and parse/validate metadata size using shared helper
+ size_bytes = f.read(8)
+ metadata_size = _get_safetensors_metadata_size(size_bytes, filename, context_msg)
+
+ # 2. Read metadata bytes
+ metadata_as_bytes = f.read(metadata_size)
+ if len(metadata_as_bytes) < metadata_size:
+ raise SafetensorsParsingError(
+ f"Failed to parse safetensors header for '{filename}' ({context_msg}): file is truncated. Expected "
+ f"{metadata_size} bytes of metadata but got {len(metadata_as_bytes)}."
+ )
+
+ # 3. Parse using shared helper
+ return _parse_safetensors_header(metadata_as_bytes, filename, context_msg)
+
+
+def get_local_safetensors_metadata(path: str | Path) -> SafetensorsRepoMetadata:
+ """
+ Parse metadata for a local safetensors file or folder.
+
+ Supports:
+ - Single safetensors file (e.g., `model.safetensors`)
+ - Directory with non-sharded model (contains `model.safetensors`)
+ - Directory with sharded model (contains `model.safetensors.index.json`)
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Args:
+ path (`str` or `Path`):
+ Path to a safetensors file or directory containing safetensors files.
+
+ Returns:
+ [`SafetensorsRepoMetadata`]: information related to the safetensors repo.
+
+ Raises:
+ [`NotASafetensorsRepoError`]:
+ If the path is not a valid safetensors file or folder (i.e., doesn't have either a
+ `model.safetensors` or a `model.safetensors.index.json` file).
+ [`SafetensorsParsingError`]:
+ If a safetensors file header couldn't be parsed correctly.
+ `FileNotFoundError`:
+ If the path does not exist.
+
+ Example:
+ ```py
+ # Parse single safetensors file
+ >>> metadata = get_local_safetensors_metadata("path/to/model.safetensors")
+ >>> metadata
+ SafetensorsRepoMetadata(metadata=None, sharded=False, weight_map={...}, files_metadata={...})
+
+ # Parse directory with sharded model
+ >>> metadata = get_local_safetensors_metadata("path/to/model_folder")
+ >>> metadata
+ SafetensorsRepoMetadata(metadata={'total_size': ...}, sharded=True, weight_map={...}, files_metadata={...})
+ >>> len(metadata.files_metadata)
+ 3 # Number of safetensors shards
+ ```
+ """
+ path = Path(path)
+
+ # Case 1: Direct path to a safetensors file
+ if path.is_file():
+ file_metadata = parse_local_safetensors_file_metadata(path)
+ return SafetensorsRepoMetadata(
+ metadata=None,
+ sharded=False,
+ weight_map={tensor_name: path.name for tensor_name in file_metadata.tensors.keys()},
+ files_metadata={path.name: file_metadata},
+ )
+
+ # Case 2: Directory
+ if not path.is_dir():
+ raise FileNotFoundError(f"Path '{path}' does not exist.")
+
+ single_file_path = path / constants.SAFETENSORS_SINGLE_FILE
+ index_file_path = path / constants.SAFETENSORS_INDEX_FILE
+
+ # Case 2a: Non-sharded model (single model.safetensors file)
+ if single_file_path.exists():
+ file_metadata = parse_local_safetensors_file_metadata(single_file_path)
+ return SafetensorsRepoMetadata(
+ metadata=None,
+ sharded=False,
+ weight_map={
+ tensor_name: constants.SAFETENSORS_SINGLE_FILE for tensor_name in file_metadata.tensors.keys()
+ },
+ files_metadata={constants.SAFETENSORS_SINGLE_FILE: file_metadata},
+ )
+
+ # Case 2b: Sharded model (model.safetensors.index.json)
+ if index_file_path.exists():
+ with open(index_file_path) as f:
+ index = json.load(f)
+
+ weight_map = index.get("weight_map", {})
+
+ # Parse metadata from each shard
+ files_metadata = {}
+ for shard_filename in set(weight_map.values()):
+ shard_path = path / shard_filename
+ files_metadata[shard_filename] = parse_local_safetensors_file_metadata(shard_path)
+
+ return SafetensorsRepoMetadata(
+ metadata=index.get("metadata", None),
+ sharded=True,
+ weight_map=weight_map,
+ files_metadata=files_metadata,
+ )
+
+ # Not a valid safetensors folder
+ raise NotASafetensorsRepoError(
+ f"'{path}' is not a valid safetensors folder. Couldn't find '{constants.SAFETENSORS_INDEX_FILE}' or "
+ f"'{constants.SAFETENSORS_SINGLE_FILE}' files."
+ )
+
+
+api = HfApi()
+
+whoami = api.whoami
+auth_check = api.auth_check
+
+list_models = api.list_models
+model_info = api.model_info
+
+list_datasets = api.list_datasets
+list_dataset_parquet_files = api.list_dataset_parquet_files
+dataset_info = api.dataset_info
+get_dataset_leaderboard = api.get_dataset_leaderboard
+
+list_spaces = api.list_spaces
+list_space_templates = api.list_space_templates
+search_spaces = api.search_spaces
+space_info = api.space_info
+
+kernel_info = api.kernel_info
+
+list_papers = api.list_papers
+paper_info = api.paper_info
+read_paper = api.read_paper
+list_daily_papers = api.list_daily_papers
+
+repo_exists = api.repo_exists
+revision_exists = api.revision_exists
+file_exists = api.file_exists
+repo_info = api.repo_info
+resolve_revision = api.resolve_revision
+list_repo_files = api.list_repo_files
+list_repo_refs = api.list_repo_refs
+list_repo_commits = api.list_repo_commits
+list_repo_tree = api.list_repo_tree
+get_paths_info = api.get_paths_info
+verify_repo_checksums = api.verify_repo_checksums
+
+get_model_tags = api.get_model_tags
+get_dataset_tags = api.get_dataset_tags
+
+create_commit = api.create_commit
+create_repo = api.create_repo
+delete_repo = api.delete_repo
+update_repo_settings = api.update_repo_settings
+move_repo = api.move_repo
+upload_file = api.upload_file
+upload_folder = api.upload_folder
+delete_file = api.delete_file
+delete_folder = api.delete_folder
+delete_files = api.delete_files
+preupload_lfs_files = api.preupload_lfs_files
+create_branch = api.create_branch
+delete_branch = api.delete_branch
+create_tag = api.create_tag
+delete_tag = api.delete_tag
+get_full_repo_name = api.get_full_repo_name
+
+# Danger-zone API
+super_squash_history = api.super_squash_history
+list_lfs_files = api.list_lfs_files
+permanently_delete_lfs_files = api.permanently_delete_lfs_files
+
+# Safetensors helpers
+get_safetensors_metadata = api.get_safetensors_metadata
+parse_safetensors_file_metadata = api.parse_safetensors_file_metadata
+
+# Background jobs
+run_as_future = api.run_as_future
+
+# Activity API
+list_liked_repos = api.list_liked_repos
+list_repo_likers = api.list_repo_likers
+list_user_repos = api.list_user_repos
+unlike = api.unlike
+
+# Community API
+get_discussion_details = api.get_discussion_details
+get_repo_discussions = api.get_repo_discussions
+create_discussion = api.create_discussion
+create_pull_request = api.create_pull_request
+change_discussion_status = api.change_discussion_status
+comment_discussion = api.comment_discussion
+edit_discussion_comment = api.edit_discussion_comment
+rename_discussion = api.rename_discussion
+merge_pull_request = api.merge_pull_request
+
+# Space API
+get_space_secrets = api.get_space_secrets
+add_space_secret = api.add_space_secret
+delete_space_secret = api.delete_space_secret
+get_space_variables = api.get_space_variables
+add_space_variable = api.add_space_variable
+delete_space_variable = api.delete_space_variable
+get_space_runtime = api.get_space_runtime
+list_spaces_hardware = api.list_spaces_hardware
+get_zero_gpu_quota = api.get_zero_gpu_quota
+request_space_hardware = api.request_space_hardware
+set_space_sleep_time = api.set_space_sleep_time
+pause_space = api.pause_space
+restart_space = api.restart_space
+duplicate_repo = api.duplicate_repo
+set_space_volumes = api.set_space_volumes
+delete_space_volumes = api.delete_space_volumes
+enable_space_dev_mode = api.enable_space_dev_mode
+disable_space_dev_mode = api.disable_space_dev_mode
+fetch_space_logs = api.fetch_space_logs
+wait_for_space = api.wait_for_space
+
+# Inference Endpoint API
+list_inference_endpoints = api.list_inference_endpoints
+create_inference_endpoint = api.create_inference_endpoint
+get_inference_endpoint = api.get_inference_endpoint
+update_inference_endpoint = api.update_inference_endpoint
+delete_inference_endpoint = api.delete_inference_endpoint
+pause_inference_endpoint = api.pause_inference_endpoint
+resume_inference_endpoint = api.resume_inference_endpoint
+scale_to_zero_inference_endpoint = api.scale_to_zero_inference_endpoint
+create_inference_endpoint_from_catalog = api.create_inference_endpoint_from_catalog
+list_inference_catalog = api.list_inference_catalog
+list_inference_endpoints_hardware = api.list_inference_endpoints_hardware
+
+# Collections API
+get_collection = api.get_collection
+list_collections = api.list_collections
+create_collection = api.create_collection
+update_collection_metadata = api.update_collection_metadata
+update_collection_resource_group = api.update_collection_resource_group
+delete_collection = api.delete_collection
+add_collection_item = api.add_collection_item
+update_collection_item = api.update_collection_item
+delete_collection_item = api.delete_collection_item
+delete_collection_item = api.delete_collection_item
+
+# Access requests API
+list_pending_access_requests = api.list_pending_access_requests
+list_accepted_access_requests = api.list_accepted_access_requests
+list_rejected_access_requests = api.list_rejected_access_requests
+cancel_access_request = api.cancel_access_request
+accept_access_request = api.accept_access_request
+reject_access_request = api.reject_access_request
+grant_access = api.grant_access
+
+# Webhooks API
+create_webhook = api.create_webhook
+disable_webhook = api.disable_webhook
+delete_webhook = api.delete_webhook
+enable_webhook = api.enable_webhook
+get_webhook = api.get_webhook
+list_webhooks = api.list_webhooks
+update_webhook = api.update_webhook
+
+
+# User API
+get_user_overview = api.get_user_overview
+get_organization_overview = api.get_organization_overview
+list_organization_followers = api.list_organization_followers
+list_organization_members = api.list_organization_members
+list_user_followers = api.list_user_followers
+list_user_following = api.list_user_following
+
+# Jobs API
+run_job = api.run_job
+fetch_job_logs = api.fetch_job_logs
+fetch_job_metrics = api.fetch_job_metrics
+list_jobs = api.list_jobs
+list_jobs_hardware = api.list_jobs_hardware
+inspect_job = api.inspect_job
+wait_for_job = api.wait_for_job
+rerun_job = api.rerun_job
+cancel_job = api.cancel_job
+update_job_labels = api.update_job_labels
+update_job_expose = api.update_job_expose
+run_uv_job = api.run_uv_job
+create_scheduled_job = api.create_scheduled_job
+list_scheduled_jobs = api.list_scheduled_jobs
+inspect_scheduled_job = api.inspect_scheduled_job
+delete_scheduled_job = api.delete_scheduled_job
+suspend_scheduled_job = api.suspend_scheduled_job
+resume_scheduled_job = api.resume_scheduled_job
+trigger_scheduled_job = api.trigger_scheduled_job
+update_scheduled_job_schedule = api.update_scheduled_job_schedule
+update_scheduled_job_labels = api.update_scheduled_job_labels
+create_scheduled_uv_job = api.create_scheduled_uv_job
+sync_job_volume = api.sync_job_volume
+
+# Buckets API
+create_bucket = api.create_bucket
+bucket_info = api.bucket_info
+list_buckets = api.list_buckets
+delete_bucket = api.delete_bucket
+move_bucket = api.move_bucket
+update_bucket_settings = api.update_bucket_settings
+list_bucket_tree = api.list_bucket_tree
+get_bucket_paths_info = api.get_bucket_paths_info
+copy_files = api.copy_files
+batch_bucket_files = api.batch_bucket_files
+get_bucket_file_metadata = api.get_bucket_file_metadata
+download_bucket_files = api.download_bucket_files
+sync_bucket = api.sync_bucket
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a267ad97ed1573df296e6a83f3a2415c16a1532
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py
@@ -0,0 +1,1482 @@
+import os
+import tempfile
+import threading
+from collections import deque
+from collections.abc import Iterable, Iterator
+from contextlib import ExitStack
+from copy import deepcopy
+from dataclasses import dataclass, field
+from datetime import datetime
+from itertools import chain
+from pathlib import Path, PurePosixPath
+from typing import Any, NoReturn, Union
+from urllib.parse import quote, unquote
+
+import fsspec
+import httpx2
+from fsspec.callbacks import _DEFAULT_CALLBACK, NoOpCallback, TqdmCallback
+from fsspec.config import apply_config
+from fsspec.utils import isfilelike
+
+from . import constants
+from ._buckets import BucketFile, BucketFolder
+from ._commit_api import CommitOperationCopy, CommitOperationDelete
+from ._local_folder import _validate_relative_filename
+from .errors import (
+ BucketNotFoundError,
+ EntryNotFoundError,
+ HfHubHTTPError,
+ RepositoryNotFoundError,
+ RevisionNotFoundError,
+)
+from .file_download import hf_hub_url, http_get, xet_get
+from .hf_api import SPECIAL_REFS_REVISION_REGEX, HfApi, LastCommitInfo, RepoFile, RepoFolder
+from .utils import HFValidationError, XetFileData, hf_raise_for_status, http_backoff, http_stream_backoff, parse_hf_uri
+from .utils._runtime import is_xet_available
+from .utils._xet import XetTokenType, xet_connection_info_refresh_url
+from .utils.insecure_hashlib import md5
+
+
+@dataclass
+class HfFileSystemResolvedPath:
+ """Top level Data structure containing information about a resolved Hugging Face file system path."""
+
+ root: str
+ path: str
+
+ def unresolve(self) -> str:
+ return f"{self.root}/{self.path}".rstrip("/")
+
+
+@dataclass
+class HfFileSystemResolvedRepositoryPath(HfFileSystemResolvedPath):
+ """Data structure containing information about a resolved path in a repository."""
+
+ repo_type: str
+ repo_id: str
+ revision: str
+ path_in_repo: str
+ root: str = field(init=False)
+ path: str = field(init=False)
+ # The part placed after '@' in the initial path. It can be a quoted or unquoted refs revision.
+ # Used to reconstruct the unresolved path to return to the user.
+ _raw_revision: str | None = field(default=None, repr=False)
+
+ def __post_init__(self):
+ repo_path = constants.REPO_TYPES_URL_PREFIXES.get(self.repo_type, "") + self.repo_id
+ if self._raw_revision:
+ self.root = f"{repo_path}@{self._raw_revision}"
+ elif self.revision != constants.DEFAULT_REVISION:
+ self.root = f"{repo_path}@{safe_revision(self.revision)}"
+ else:
+ self.root = repo_path
+ self.path = self.path_in_repo
+
+
+@dataclass
+class HfFileSystemResolvedBucketPath(HfFileSystemResolvedPath):
+ """Data structure containing information about a resolved path in a bucket."""
+
+ bucket_id: str
+ root: str = field(init=False)
+
+ def __post_init__(self):
+ self.root = "buckets/" + self.bucket_id
+
+
+# We need to improve fsspec.spec._Cached which is AbstractFileSystem's metaclass
+_cached_base: Any = type(fsspec.AbstractFileSystem)
+
+
+class _Cached(_cached_base):
+ """
+ Metaclass for caching HfFileSystem instances according to the args.
+
+ This creates an additional reference to the filesystem, which prevents the
+ filesystem from being garbage collected when all *user* references go away.
+ A call to the :meth:`AbstractFileSystem.clear_instance_cache` must *also*
+ be made for a filesystem instance to be garbage collected.
+
+ This is a slightly modified version of `fsspec.spec._Cached` to improve it.
+ In particular in `_tokenize` the pid isn't taken into account for the
+ `fs_token` used to identify cached instances. The `fs_token` logic is also
+ robust to defaults values and the order of the args. Finally new instances
+ reuse the states from sister instances in the main thread.
+ """
+
+ def __init__(cls, *args, **kwargs):
+ # Hack: override https://github.com/fsspec/filesystem_spec/blob/dcb167e8f50e6273d4cfdfc4cab8fc5aa4c958bf/fsspec/spec.py#L53
+ super().__init__(*args, **kwargs)
+ # Note: we intentionally create a reference here, to avoid garbage
+ # collecting instances when all other references are gone. To really
+ # delete a FileSystem, the cache must be cleared.
+ cls._cache = {}
+
+ def __call__(cls, *args, **kwargs):
+ # Hack: override https://github.com/fsspec/filesystem_spec/blob/dcb167e8f50e6273d4cfdfc4cab8fc5aa4c958bf/fsspec/spec.py#L65
+ # Apply fsspec config (env vars / config files) before tokenizing so that
+ # HfFileSystem picks up defaults the same way other fsspec filesystems do.
+ kwargs = apply_config(cls, kwargs)
+ skip = kwargs.pop("skip_instance_cache", False)
+ fs_token = cls._tokenize(cls, threading.get_ident(), *args, **kwargs)
+ fs_token_main_thread = cls._tokenize(cls, threading.main_thread().ident, *args, **kwargs)
+ if not skip and cls.cachable and fs_token in cls._cache:
+ # reuse cached instance
+ cls._latest = fs_token
+ return cls._cache[fs_token]
+ else:
+ # create new instance
+ obj = type.__call__(cls, *args, **kwargs)
+ if not skip and cls.cachable and fs_token_main_thread in cls._cache:
+ # reuse the cache from the main thread instance in the new instance
+ instance_state = cls._cache[fs_token_main_thread]._get_instance_state()
+ for attr, state_value in instance_state.items():
+ setattr(obj, attr, state_value)
+ obj._fs_token_ = fs_token
+ obj.storage_args = args
+ obj.storage_options = kwargs
+ if cls.cachable and not skip:
+ cls._latest = fs_token
+ cls._cache[fs_token] = obj
+ return obj
+
+
+class HfFileSystem(fsspec.AbstractFileSystem, metaclass=_Cached): # ty: ignore[conflicting-metaclass]
+ """
+ Access a remote Hugging Face Hub repository as if were a local file system.
+
+ > [!WARNING]
+ > [`HfFileSystem`] provides fsspec compatibility, which is useful for libraries that require it (e.g., reading
+ > Hugging Face datasets directly with `pandas`). However, it introduces additional overhead due to this compatibility
+ > layer. For better performance and reliability, it's recommended to use `HfApi` methods when possible.
+
+ The file system supports paths for the `hf://` protocol, which follows those URL schemes:
+
+ * Models, Datasets and Spaces repositories:
+
+ ```
+ hf://[@]/
+ hf://datasets/[@]/
+ hf://spaces/[@]/
+ ```
+
+ * Buckets (generic storage):
+
+ ```
+ hf://buckets//
+ ```
+
+ Note: when using the [`HfFileSystem`] directly, passing the `hf://` protocol prefix is optional in paths.
+
+ Args:
+ endpoint (`str`, *optional*):
+ Endpoint of the Hub. Defaults to .
+ token (`bool` or `str`, *optional*):
+ A valid user access token (string). Defaults to the locally saved
+ token, which is the recommended method for authentication (see
+ https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+ To disable authentication, pass `False`.
+ block_size (`int`, *optional*):
+ Block size for reading and writing files.
+ expand_info (`bool`, *optional*):
+ Whether to expand the information of the files.
+ **storage_options (`dict`, *optional*):
+ Additional options for the filesystem. See [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.__init__).
+
+ Usage:
+
+ ```python
+ >>> from huggingface_hub import hffs
+
+ >>> # List files
+ >>> hffs.glob("my-username/my-model/*.bin")
+ ['my-username/my-model/pytorch_model.bin']
+ >>> hffs.ls("datasets/my-username/my-dataset", detail=False)
+ ['datasets/my-username/my-dataset/.gitattributes', 'datasets/my-username/my-dataset/README.md', 'datasets/my-username/my-dataset/data.json']
+
+ >>> # Read/write files
+ >>> with hffs.open("my-username/my-model/pytorch_model.bin") as f:
+ ... data = f.read()
+ >>> with hffs.open("my-username/my-model/pytorch_model.bin", "wb") as f:
+ ... f.write(data)
+ ```
+
+ Specify a token for authentication:
+ ```python
+ >>> from huggingface_hub import HfFileSystem
+ >>> hffs = HfFileSystem(token=token)
+ ```
+ """
+
+ root_marker = ""
+ protocol = "hf"
+
+ def __init__(
+ self,
+ *args,
+ endpoint: str | None = None,
+ token: bool | str | None = None,
+ block_size: int | None = None,
+ expand_info: bool | None = None,
+ **storage_options,
+ ):
+ super().__init__(*args, **storage_options)
+ self.endpoint = endpoint or constants.ENDPOINT
+ self.token = token
+ self._api = HfApi(endpoint=endpoint, token=token)
+ self.block_size = block_size
+ self.expand_info = expand_info
+ # Maps (repo_type, repo_id, revision) to a 2-tuple with:
+ # * the 1st element indicating whether the repository and the revision exist
+ # * the 2nd element being the exception raised if the repository or revision doesn't exist
+ self._repo_and_revision_exists_cache: dict[tuple[str, str, str | None], tuple[bool, Exception | None]] = {}
+ # Same for buckets
+ self._bucket_exists_cache: dict[str, tuple[bool, Exception | None]] = {}
+ # Note: special case for buckets: revision is always None
+ # Maps parent directory path to path infos
+ self.dircache: dict[str, list[dict[str, Any]]] = {}
+
+ @classmethod
+ def _tokenize(cls, threading_ident: int, *args, **kwargs) -> str:
+ """Deterministic token for caching"""
+ # make fs_token robust to default values and to kwargs order
+ kwargs["endpoint"] = kwargs.get("endpoint") or constants.ENDPOINT
+ kwargs["token"] = kwargs.get("token")
+ kwargs = {key: kwargs[key] for key in sorted(kwargs)}
+ # contrary to fsspec, we don't include pid here
+ tokenize_args = (cls, threading_ident, args, kwargs)
+ h = md5(str(tokenize_args).encode())
+ return h.hexdigest()
+
+ def _repo_and_revision_exist(
+ self, repo_type: str, repo_id: str, revision: str | None
+ ) -> tuple[bool, Exception | None]:
+ if (repo_type, repo_id, revision) not in self._repo_and_revision_exists_cache:
+ try:
+ self._api.repo_info(
+ repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
+ )
+ except (RepositoryNotFoundError, HFValidationError) as e:
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = False, e
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = False, e
+ except RevisionNotFoundError as e:
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = False, e
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = True, None
+ else:
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = True, None
+ self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = True, None
+ return self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)]
+
+ def _bucket_exists(self, bucket_id: str) -> tuple[bool, Exception | None]:
+ if bucket_id not in self._bucket_exists_cache:
+ try:
+ self._api.bucket_info(bucket_id)
+ except BucketNotFoundError as e:
+ self._bucket_exists_cache[bucket_id] = False, e
+ else:
+ self._bucket_exists_cache[bucket_id] = True, None
+ return self._bucket_exists_cache[bucket_id]
+
+ def resolve_path(
+ self, path: str, revision: str | None = None
+ ) -> HfFileSystemResolvedRepositoryPath | HfFileSystemResolvedBucketPath:
+ """
+ Resolve a Hugging Face file system path into its components.
+
+ Args:
+ path (`str`):
+ Path to resolve.
+ revision (`str`, *optional*):
+ The revision of the repo to resolve. Defaults to the revision specified in the path.
+
+ Returns:
+ [`HfFileSystemResolvedPath`]: Resolved path information containing `repo_type`, `repo_id`, `revision` and `path_in_repo`.
+
+ Raises:
+ `ValueError`:
+ If path contains conflicting revision information.
+ `NotImplementedError`:
+ If trying to list repositories.
+ """
+ path = self._strip_protocol(path)
+ if not path:
+ raise NotImplementedError("Access to buckets and repositories lists is not implemented.")
+ if path.count("/") == 0:
+ raise ValueError(
+ f"Repository id must be 'namespace/name', got '{path}'. Single-segment ids (e.g. 'gpt2') are no longer supported."
+ )
+
+ parsed = parse_hf_uri(f"{constants.HF_PROTOCOL}{path}")
+
+ # --- Buckets ---
+ if parsed.is_bucket:
+ bucket_exists, err = self._bucket_exists(parsed.id)
+ if not bucket_exists:
+ _raise_file_not_found(path, err)
+ return HfFileSystemResolvedBucketPath(bucket_id=parsed.id, path=parsed.path_in_repo)
+
+ # --- Repositories ---
+ # Align revision from path with explicit revision argument
+ if revision is not None and parsed.revision is not None and parsed.revision != revision:
+ # The caller provided an explicit revision that conflicts with what parse_hf_uri
+ # parsed. This can happen when a user has a branch literally named "refs" and a
+ # file at "pr/10" — parse_hf_uri would greedily match "refs/pr/10" as a special
+ # ref. Fall back to simple '@' splitting so the caller's revision wins.
+ path_without_type = path.split("/", 1)[1] if path.split("/")[0] in constants.HF_URI_TYPE_PREFIXES else path
+ repo_id, after_at = path_without_type.split("@", 1)
+ revision_in_path, path_in_repo = after_at.split("/", 1) if "/" in after_at else (after_at, "")
+ revision_in_path_decoded = unquote(revision_in_path)
+ if revision_in_path_decoded != revision:
+ raise ValueError(
+ f'Revision specified in path ("{revision_in_path_decoded}") and in `revision` argument ("{revision}") are not the same.'
+ )
+ repo_and_revision_exist, err = self._repo_and_revision_exist(parsed.type, repo_id, revision)
+ if not repo_and_revision_exist:
+ _raise_file_not_found(path, err)
+ return HfFileSystemResolvedRepositoryPath(
+ parsed.type, repo_id, revision, path_in_repo, _raw_revision=revision_in_path
+ )
+
+ if parsed.revision is not None and revision is None:
+ revision = parsed.revision
+
+ repo_and_revision_exist, err = self._repo_and_revision_exist(parsed.type, parsed.id, revision)
+ if not repo_and_revision_exist:
+ _raise_file_not_found(path, err)
+
+ # Extract raw revision from original path for unresolve() fidelity
+ raw_revision: str | None = None
+ if "@" in path and parsed.revision is not None:
+ path_without_type = path.split("/", 1)[1] if path.split("/")[0] in constants.HF_URI_TYPE_PREFIXES else path
+ raw_after_at = path_without_type.split("@", 1)[1]
+ raw_revision = raw_after_at[: -(len(parsed.path_in_repo) + 1)] if parsed.path_in_repo else raw_after_at
+
+ revision = revision if revision is not None else constants.DEFAULT_REVISION
+ return HfFileSystemResolvedRepositoryPath(
+ parsed.type, parsed.id, revision, parsed.path_in_repo, _raw_revision=raw_revision
+ )
+
+ def invalidate_cache(self, path: str | None = None) -> None:
+ """
+ Clear the cache for a given path.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.invalidate_cache).
+
+ Args:
+ path (`str`, *optional*):
+ Path to clear from cache. If not provided, clear the entire cache.
+
+ """
+ if not path:
+ self.dircache.clear()
+ self._repo_and_revision_exists_cache.clear()
+ self._bucket_exists_cache.clear()
+ else:
+ try:
+ resolved_path = self.resolve_path(path)
+ except FileNotFoundError:
+ # repo, revision or bucket cached as missing: forget misses so the next lookup hits the Hub
+ self._repo_and_revision_exists_cache = {
+ k: v for k, v in self._repo_and_revision_exists_cache.items() if v[0]
+ }
+ self._bucket_exists_cache = {k: v for k, v in self._bucket_exists_cache.items() if v[0]}
+ return
+ path = resolved_path.unresolve()
+ while path:
+ self.dircache.pop(path, None)
+ path = self._parent(path)
+
+ # Only clear repo cache if path is to repo root
+ if not resolved_path.path:
+ if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+ self._repo_and_revision_exists_cache.pop(
+ (resolved_path.repo_type, resolved_path.repo_id, None), None
+ )
+ self._repo_and_revision_exists_cache.pop(
+ (resolved_path.repo_type, resolved_path.repo_id, resolved_path.revision), None
+ )
+ else:
+ self._bucket_exists_cache.pop(resolved_path.bucket_id, None)
+
+ def _open( # type: ignore
+ self,
+ path: str,
+ mode: str = "rb",
+ block_size: int | None = None,
+ revision: str | None = None,
+ **kwargs,
+ ) -> Union["HfFileSystemFile", "HfFileSystemStreamFile"]:
+ block_size = block_size if block_size is not None else self.block_size
+ if block_size is not None:
+ kwargs["block_size"] = block_size
+ if "a" in mode:
+ raise NotImplementedError("Appending to remote files is not yet supported.")
+ if block_size == 0:
+ return HfFileSystemStreamFile(self, path, mode=mode, revision=revision, **kwargs)
+ else:
+ return HfFileSystemFile(self, path, mode=mode, revision=revision, **kwargs)
+
+ def _rm(self, path: str, revision: str | None = None, **kwargs) -> None:
+ resolved_path = self.resolve_path(path, revision=revision)
+ if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ self._api.batch_bucket_files(resolved_path.bucket_id, delete=[resolved_path.path])
+ else:
+ self._api.delete_file(
+ path_in_repo=resolved_path.path_in_repo,
+ repo_id=resolved_path.repo_id,
+ token=self.token,
+ repo_type=resolved_path.repo_type,
+ revision=resolved_path.revision,
+ commit_message=kwargs.get("commit_message"),
+ commit_description=kwargs.get("commit_description"),
+ )
+ self.invalidate_cache(path=resolved_path.unresolve())
+
+ def rm(
+ self,
+ path: str,
+ recursive: bool = False,
+ maxdepth: int | None = None,
+ revision: str | None = None,
+ **kwargs,
+ ) -> None:
+ """
+ Delete files from a repository.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.rm).
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.delete_file()` for better performance.
+
+ Args:
+ path (`str`):
+ Path to delete.
+ recursive (`bool`, *optional*):
+ If True, delete directory and all its contents. Defaults to False.
+ maxdepth (`int`, *optional*):
+ Maximum number of subdirectories to visit when deleting recursively.
+ revision (`str`, *optional*):
+ The git revision to delete from.
+
+ """
+ resolved_path = self.resolve_path(path, revision=revision)
+ paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth, revision=revision)
+ if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ delete = [self.resolve_path(path).path for path in paths if not self.isdir(path)]
+ self._api.batch_bucket_files(resolved_path.bucket_id, delete=delete)
+ else:
+ paths_in_repo = [self.resolve_path(path).path for path in paths if not self.isdir(path)]
+ operations = [CommitOperationDelete(path_in_repo=path_in_repo) for path_in_repo in paths_in_repo]
+ commit_message = f"Delete {path} "
+ commit_message += "recursively " if recursive else ""
+ commit_message += f"up to depth {maxdepth} " if maxdepth is not None else ""
+ # TODO: use `commit_description` to list all the deleted paths?
+ self._api.create_commit(
+ repo_id=resolved_path.repo_id,
+ repo_type=resolved_path.repo_type,
+ token=self.token,
+ operations=operations,
+ revision=resolved_path.revision,
+ commit_message=kwargs.get("commit_message", commit_message),
+ commit_description=kwargs.get("commit_description"),
+ )
+ self.invalidate_cache(path=resolved_path.unresolve())
+
+ def ls(
+ self, path: str, detail: bool = True, refresh: bool = False, revision: str | None = None, **kwargs
+ ) -> list[str | dict[str, Any]]:
+ """
+ List the contents of a directory.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.ls).
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.list_repo_tree()` for better performance.
+
+ Args:
+ path (`str`):
+ Path to the directory.
+ detail (`bool`, *optional*):
+ If True, returns a list of dictionaries containing file information. If False,
+ returns a list of file paths. Defaults to True.
+ refresh (`bool`, *optional*):
+ If True, bypass the cache and fetch the latest data. Defaults to False.
+ revision (`str`, *optional*):
+ The git revision to list from.
+
+ Returns:
+ `list[Union[str, dict[str, Any]]]`: List of file paths (if detail=False) or list of file information
+ dictionaries (if detail=True).
+ """
+ resolved_path = self.resolve_path(path, revision=revision)
+ path = resolved_path.unresolve()
+ try:
+ out = self._ls_tree(path, refresh=refresh, revision=revision, **kwargs)
+ except EntryNotFoundError:
+ # Path could be a file
+ if not resolved_path.path:
+ _raise_file_not_found(path, None)
+ try:
+ out = self._ls_tree(self._parent(path), refresh=refresh, revision=revision, **kwargs)
+ except EntryNotFoundError:
+ out = []
+ out = [o for o in out if o["name"] == path]
+ if len(out) == 0:
+ _raise_file_not_found(path, None)
+ return out if detail else [o["name"] for o in out]
+
+ def _ls_tree(
+ self,
+ path: str,
+ recursive: bool = False,
+ refresh: bool = False,
+ revision: str | None = None,
+ expand_info: bool | None = None,
+ maxdepth: int | None = None,
+ ):
+ expand_info = (
+ expand_info if expand_info is not None else (self.expand_info if self.expand_info is not None else False)
+ )
+ resolved_path = self.resolve_path(path, revision=revision)
+ path = resolved_path.unresolve()
+ root_path = resolved_path.root
+ maxdepth = maxdepth if recursive else 1
+
+ out = []
+ if path in self.dircache and not refresh:
+ cached_path_infos = self.dircache[path]
+ out.extend(cached_path_infos)
+ dirs_not_in_dircache = []
+ if recursive:
+ # Use BFS to traverse the cache and build the "recursive "output
+ # (The Hub uses a so-called "tree first" strategy for the tree endpoint but we sort the output to follow the spec so the result is (eventually) the same)
+ depth = 2
+ dirs_to_visit = deque(
+ [(depth, path_info) for path_info in cached_path_infos if path_info["type"] == "directory"]
+ )
+ while dirs_to_visit:
+ depth, dir_info = dirs_to_visit.popleft()
+ if maxdepth is None or depth <= maxdepth:
+ if dir_info["name"] not in self.dircache:
+ dirs_not_in_dircache.append(dir_info["name"])
+ else:
+ cached_path_infos = self.dircache[dir_info["name"]]
+ out.extend(cached_path_infos)
+ dirs_to_visit.extend(
+ [
+ (depth + 1, path_info)
+ for path_info in cached_path_infos
+ if path_info["type"] == "directory"
+ ]
+ )
+
+ dirs_not_expanded = []
+ if expand_info and isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+ # Check if there are directories in repos with non-expanded entries
+ dirs_not_expanded = [self._parent(o["name"]) for o in out if o["last_commit"] is None]
+
+ if (recursive and dirs_not_in_dircache) or (expand_info and dirs_not_expanded):
+ # If the dircache is incomplete, find the common path of the missing and non-expanded entries
+ # and extend the output with the result of `_ls_tree(common_path, recursive=True)`
+ common_prefix = os.path.commonprefix(dirs_not_in_dircache + dirs_not_expanded)
+ # Get the parent directory if the common prefix itself is not a directory
+ common_path = (
+ common_prefix.rstrip("/")
+ if common_prefix.endswith("/")
+ or common_prefix == root_path
+ or common_prefix in chain(dirs_not_in_dircache, dirs_not_expanded)
+ else self._parent(common_prefix)
+ )
+ if maxdepth is not None:
+ common_path_depth = common_path[len(path) :].count("/")
+ maxdepth -= common_path_depth
+ out = [o for o in out if not o["name"].startswith(common_path + "/")]
+ for cached_path in list(self.dircache):
+ if cached_path.startswith(common_path + "/"):
+ self.dircache.pop(cached_path, None)
+ self.dircache.pop(common_path, None)
+ out.extend(
+ self._ls_tree(
+ common_path,
+ recursive=recursive,
+ refresh=True,
+ revision=revision,
+ expand_info=expand_info,
+ maxdepth=maxdepth,
+ )
+ )
+ else:
+ tree: Iterable[RepoFile | RepoFolder | BucketFile | BucketFolder]
+ if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ tree = self._list_bucket_tree_with_folders(
+ resolved_path.bucket_id,
+ prefix=resolved_path.path,
+ recursive=recursive,
+ )
+ else:
+ tree = self._api.list_repo_tree(
+ resolved_path.repo_id,
+ resolved_path.path,
+ recursive=recursive,
+ expand=expand_info,
+ revision=resolved_path.revision,
+ repo_type=resolved_path.repo_type,
+ )
+ listings: dict[str, list[dict[str, Any]]] = {}
+ for path_info in tree:
+ cache_path = root_path + "/" + path_info.path
+ if isinstance(path_info, RepoFile):
+ cache_path_info = {
+ "name": cache_path,
+ "size": path_info.size,
+ "type": "file",
+ "blob_id": path_info.blob_id,
+ "lfs": path_info.lfs,
+ "xet_hash": path_info.xet_hash,
+ "last_commit": path_info.last_commit,
+ "security": path_info.security,
+ }
+ elif isinstance(path_info, BucketFile):
+ cache_path_info = {
+ "name": cache_path,
+ "size": path_info.size,
+ "type": "file",
+ "xet_hash": path_info.xet_hash,
+ "mtime": path_info.mtime,
+ "uploaded_at": path_info.uploaded_at,
+ }
+ elif isinstance(path_info, RepoFolder):
+ cache_path_info = {
+ "name": cache_path,
+ "size": 0,
+ "type": "directory",
+ "tree_id": path_info.tree_id,
+ "last_commit": path_info.last_commit,
+ }
+ else:
+ cache_path_info = {
+ "name": cache_path,
+ "size": 0,
+ "type": "directory",
+ "uploaded_at": path_info.uploaded_at,
+ }
+ if cache_path != path: # listing a bucket file returns the file, not its folder's listing
+ listings.setdefault(self._parent(cache_path), []).append(cache_path_info)
+ depth = cache_path[len(path) :].count("/")
+ if maxdepth is None or depth <= maxdepth:
+ out.append(cache_path_info)
+ # replace cached listings, only once the listing is complete
+ self.dircache.update(listings)
+ if not out:
+ # Empty bucket: `info()` on a child path reads `dircache[parent]`
+ self.dircache[path] = []
+ return out
+
+ def _list_bucket_tree_with_folders(
+ self, bucket_id: str, prefix: str, recursive: bool
+ ) -> Iterable[BucketFile | BucketFolder]:
+ """Same as `HfApi.list_bucket_tree` but always includes folders"""
+ bucket_files = self._api.list_bucket_tree(bucket_id, prefix, recursive=recursive)
+ bucket_folders: dict[str, BucketFolder] = {}
+ min_depth = 1 + prefix.count("/") if prefix else 0
+ out: list[BucketFile | BucketFolder] = []
+
+ for bucket_entry in bucket_files:
+ # The server matches `prefix` lexically, so listing "logs" also returns "logs_existing/...".
+ # Filesystem semantics require path components => drop entries that aren't `prefix` itself
+ # or below it.
+ if prefix and bucket_entry.path != prefix and not bucket_entry.path.startswith(f"{prefix}/"):
+ continue
+ out.append(bucket_entry)
+
+ # If recursive=False, both files and folders are returned by the server => nothing to do
+ if not recursive:
+ continue
+
+ # Otherwise, let's rebuild BucketFolders manually
+ for parent_bucket_folder_str in list(PurePosixPath(bucket_entry.path).parents)[: -min_depth - 1]:
+ parent_bucket_folder = BucketFolder(
+ type="directory", path=str(parent_bucket_folder_str), uploaded_at=bucket_entry.uploaded_at
+ )
+
+ # If folder not visited yet, add it
+ if parent_bucket_folder.path not in bucket_folders:
+ out.append(parent_bucket_folder)
+ bucket_folders[parent_bucket_folder.path] = parent_bucket_folder
+ continue
+
+ # Otherwise, get back BucketFolder object and update its 'uploaded_at'
+ if parent_bucket_folder.uploaded_at is not None:
+ bucket_folder = bucket_folders[parent_bucket_folder.path]
+ if bucket_folder.uploaded_at is None or (
+ bucket_folder.uploaded_at < parent_bucket_folder.uploaded_at
+ ):
+ bucket_folder.uploaded_at = parent_bucket_folder.uploaded_at
+
+ # An empty listing is only a "not found" when a specific prefix was requested. Listing the
+ # bucket root itself is valid even with zero objects (e.g. freshly created bucket).
+ if prefix and not out:
+ raise EntryNotFoundError(f"File not found in bucket '{bucket_id}': '{prefix}'")
+ return out
+
+ def walk(self, path: str, *args, **kwargs) -> Iterator[tuple[str, list[str], list[str]]]:
+ """
+ Return all files below the given path.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.walk).
+
+ Args:
+ path (`str`):
+ Root path to list files from.
+
+ Returns:
+ `Iterator[tuple[str, list[str], list[str]]]`: An iterator of (path, list of directory names, list of file names) tuples.
+ """
+ path = self.resolve_path(path, revision=kwargs.get("revision")).unresolve()
+ yield from super().walk(path, *args, **kwargs)
+
+ def glob(self, path: str, maxdepth: int | None = None, **kwargs) -> list[str]:
+ """
+ Find files by glob-matching.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.glob).
+
+ Args:
+ path (`str`):
+ Path pattern to match.
+ maxdepth (`int`, *optional*):
+ Maximum depth to descend into directories. By default, no limit.
+
+ Returns:
+ `list[str]`: List of paths matching the pattern.
+ """
+ path = self.resolve_path(path, revision=kwargs.get("revision")).unresolve()
+ return super().glob(path, maxdepth=maxdepth, **kwargs)
+
+ def find(
+ self,
+ path: str,
+ maxdepth: int | None = None,
+ withdirs: bool = False,
+ detail: bool = False,
+ refresh: bool = False,
+ revision: str | None = None,
+ **kwargs,
+ ) -> list[str] | dict[str, dict[str, Any]]:
+ """
+ List all files below path.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.find).
+
+ Args:
+ path (`str`):
+ Root path to list files from.
+ maxdepth (`int`, *optional*):
+ Maximum depth to descend into subdirectories.
+ withdirs (`bool`, *optional*):
+ Include directory paths in the output. Defaults to False.
+ detail (`bool`, *optional*):
+ If True, returns a dict mapping paths to file information. Defaults to False.
+ refresh (`bool`, *optional*):
+ If True, bypass the cache and fetch the latest data. Defaults to False.
+ revision (`str`, *optional*):
+ The git revision to list from.
+
+ Returns:
+ `Union[list[str], dict[str, dict[str, Any]]]`: List of paths or dict of file information.
+ """
+ if maxdepth is not None and maxdepth < 1:
+ raise ValueError("maxdepth must be at least 1")
+ resolved_path = self.resolve_path(path, revision=revision)
+ path = resolved_path.unresolve()
+ try:
+ out = self._ls_tree(path, recursive=True, refresh=refresh, maxdepth=maxdepth, **kwargs)
+ except EntryNotFoundError:
+ # Path could be a file
+ try:
+ if self.info(path, revision=revision, **kwargs)["type"] == "file":
+ out = {path: {}}
+ else:
+ out = {}
+ except FileNotFoundError:
+ out = {}
+ else:
+ if not withdirs:
+ out = [o for o in out if o["type"] != "directory"]
+ else:
+ # If `withdirs=True`, include the directory itself to be consistent with the spec
+ path_info = self.info(path, **kwargs)
+ out = [path_info] + out if path_info["type"] == "directory" else out
+ out = {o["name"]: o for o in out}
+ names = sorted(out)
+ if not detail:
+ return names
+ else:
+ return {name: out[name] for name in names}
+
+ def cp_file(self, path1: str, path2: str, revision: str | None = None, **kwargs) -> None:
+ """
+ Copy a file within or between repositories.
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.upload_file()` for better performance.
+
+ Args:
+ path1 (`str`):
+ Source path to copy from.
+ path2 (`str`):
+ Destination path to copy to.
+ revision (`str`, *optional*):
+ The git revision to copy from.
+
+ """
+ resolved_path1 = self.resolve_path(path1, revision=revision)
+ resolved_path2 = self.resolve_path(path2, revision=revision)
+ if isinstance(resolved_path1, HfFileSystemResolvedBucketPath) or isinstance(
+ resolved_path2, HfFileSystemResolvedBucketPath
+ ):
+ raise NotImplementedError("Copy from/to buckets is not available yet")
+
+ same_repo = (
+ resolved_path1.repo_type == resolved_path2.repo_type and resolved_path1.repo_id == resolved_path2.repo_id
+ )
+
+ if same_repo:
+ commit_message = f"Copy {path1} to {path2}"
+ self._api.create_commit(
+ repo_id=resolved_path1.repo_id,
+ repo_type=resolved_path1.repo_type,
+ revision=resolved_path2.revision,
+ commit_message=kwargs.get("commit_message", commit_message),
+ commit_description=kwargs.get("commit_description", ""),
+ operations=[
+ CommitOperationCopy(
+ src_path_in_repo=resolved_path1.path_in_repo,
+ path_in_repo=resolved_path2.path_in_repo,
+ src_revision=resolved_path1.revision,
+ )
+ ],
+ )
+ else:
+ with self.open(path1, "rb", revision=resolved_path1.revision) as f:
+ content = f.read()
+ commit_message = f"Copy {path1} to {path2}"
+ self._api.upload_file(
+ path_or_fileobj=content,
+ path_in_repo=resolved_path2.path_in_repo,
+ repo_id=resolved_path2.repo_id,
+ token=self.token,
+ repo_type=resolved_path2.repo_type,
+ revision=resolved_path2.revision,
+ commit_message=kwargs.get("commit_message", commit_message),
+ commit_description=kwargs.get("commit_description"),
+ )
+ self.invalidate_cache(path=resolved_path1.unresolve())
+ self.invalidate_cache(path=resolved_path2.unresolve())
+
+ def modified(self, path: str, **kwargs) -> datetime:
+ """
+ Get the last modified time of a file.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.modified).
+
+ Args:
+ path (`str`):
+ Path to the file.
+
+ Returns:
+ `datetime`: Last modified time of the file.
+ """
+ info = self.info(path, **{**kwargs, "expand_info": True}) # type: ignore
+ if "last_commit" in info:
+ if info["last_commit"] is None:
+ raise NotImplementedError(f"'modified' is not implemented for repository paths like '{path}'")
+ return info["last_commit"].date
+ elif "mtime" in info and info["mtime"]:
+ return info["mtime"]
+ elif "uploaded_at" in info and info["uploaded_at"]:
+ return info["uploaded_at"]
+ else:
+ raise NotImplementedError(f"Cannot determined 'modified' for path '{path}' (info: {info})")
+
+ def info(self, path: str, refresh: bool = False, revision: str | None = None, **kwargs) -> dict[str, Any]:
+ """
+ Get information about a file or directory.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.info).
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.get_paths_info()` or `HfApi.repo_info()` for better performance
+ > (or `HfApi.get_bucket_paths_info()` or `HfApi.bucket_info()` for buckets)
+
+ Args:
+ path (`str`):
+ Path to get info for.
+ refresh (`bool`, *optional*):
+ If True, bypass the cache and fetch the latest data. Defaults to False.
+ revision (`str`, *optional*):
+ The git revision to get info from.
+
+ Returns:
+ `dict[str, Any]`: Dictionary containing file information (type, size, commit info, etc.).
+
+ """
+ resolved_path = self.resolve_path(path, revision=revision)
+ path = resolved_path.unresolve()
+ expand_info = kwargs.get(
+ "expand_info", self.expand_info if self.expand_info is not None else False
+ ) # don't expose it as a parameter in the public API to follow the spec
+ out: dict[str, Any] | None
+ if not resolved_path.path:
+ # Path is the root directory
+ out = {
+ "name": path,
+ "size": 0,
+ "type": "directory",
+ }
+ if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+ out["last_commit"] = None
+ if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath) and expand_info:
+ last_commit = self._api.list_repo_commits(
+ resolved_path.repo_id, repo_type=resolved_path.repo_type, revision=resolved_path.revision
+ )[-1]
+ out = {
+ **out,
+ "tree_id": None, # TODO: tree_id of the root directory?
+ "last_commit": LastCommitInfo(
+ oid=last_commit.commit_id, title=last_commit.title, date=last_commit.created_at
+ ),
+ }
+ elif isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ parent_path = self._parent(path)
+ # Fill the cache with cheap call
+ self.ls(parent_path, refresh=refresh)
+ out1 = [o for o in self.dircache[parent_path] if o["name"] == path]
+ if not out1:
+ _raise_file_not_found(path, None)
+ out = out1[0]
+ else:
+ out = None
+ parent_path = self._parent(path)
+ if not expand_info and parent_path not in self.dircache:
+ # Fill the cache with cheap call
+ self.ls(parent_path)
+ if parent_path in self.dircache:
+ # Check if the path is in the cache
+ out1 = [o for o in self.dircache[parent_path] if o["name"] == path]
+ if not out1:
+ _raise_file_not_found(path, None)
+ out = out1[0]
+ if refresh or out is None or (expand_info and out and out["last_commit"] is None):
+ paths_info = self._api.get_paths_info(
+ resolved_path.repo_id,
+ resolved_path.path_in_repo,
+ expand=expand_info,
+ revision=resolved_path.revision,
+ repo_type=resolved_path.repo_type,
+ )
+ if not paths_info:
+ _raise_file_not_found(path, None)
+ path_info = paths_info[0]
+ root_path = HfFileSystemResolvedRepositoryPath(
+ resolved_path.repo_type,
+ resolved_path.repo_id,
+ resolved_path.revision,
+ path_in_repo="",
+ _raw_revision=resolved_path._raw_revision,
+ ).unresolve()
+ if isinstance(path_info, RepoFile):
+ out = {
+ "name": root_path + "/" + path_info.path,
+ "size": path_info.size,
+ "type": "file",
+ "blob_id": path_info.blob_id,
+ "lfs": path_info.lfs,
+ "xet_hash": path_info.xet_hash,
+ "last_commit": path_info.last_commit,
+ "security": path_info.security,
+ }
+ else:
+ out = {
+ "name": root_path + "/" + path_info.path,
+ "size": 0,
+ "type": "directory",
+ "tree_id": path_info.tree_id,
+ "last_commit": path_info.last_commit,
+ }
+ if not expand_info:
+ out = {k: out[k] for k in ["name", "size", "type"]}
+ assert out is not None
+ return out
+
+ def exists(self, path, **kwargs):
+ """
+ Check if a file exists.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.exists).
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.file_exists()` for better performance.
+
+ Args:
+ path (`str`):
+ Path to check.
+
+ Returns:
+ `bool`: True if file exists, False otherwise.
+ """
+ try:
+ if kwargs.get("refresh", False):
+ self.invalidate_cache(path)
+
+ self.info(path, **kwargs)
+ return True
+ except OSError:
+ return False
+
+ def isdir(self, path):
+ """
+ Check if a path is a directory.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.isdir).
+
+ Args:
+ path (`str`):
+ Path to check.
+
+ Returns:
+ `bool`: True if path is a directory, False otherwise.
+ """
+ try:
+ return self.info(path)["type"] == "directory"
+ except OSError:
+ return False
+
+ def isfile(self, path):
+ """
+ Check if a path is a file.
+
+ For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.isfile).
+
+ Args:
+ path (`str`):
+ Path to check.
+
+ Returns:
+ `bool`: True if path is a file, False otherwise.
+ """
+ try:
+ return self.info(path)["type"] == "file"
+ except OSError:
+ return False
+
+ def url(self, path: str) -> str:
+ """
+ Get the HTTP URL of the given path.
+
+ Args:
+ path (`str`):
+ Path to get URL for.
+
+ Returns:
+ `str`: HTTP URL to access the file or directory on the Hub.
+ """
+ resolved_path = self.resolve_path(path)
+ if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ url = f"{self.endpoint}/buckets/{resolved_path.bucket_id}/resolve/{quote(resolved_path.path)}"
+ else:
+ url = hf_hub_url(
+ resolved_path.repo_id,
+ resolved_path.path_in_repo,
+ repo_type=resolved_path.repo_type,
+ revision=resolved_path.revision,
+ endpoint=self.endpoint,
+ )
+ if self.isdir(path):
+ url = url.replace("/resolve/", "/tree/", 1)
+ return url
+
+ def _get_xet_file_data(
+ self, resolved_path: HfFileSystemResolvedRepositoryPath | HfFileSystemResolvedBucketPath, info: dict[str, Any]
+ ) -> XetFileData | None:
+ """Build the Xet metadata of a file from its `info()` entry, or return None if it can't be downloaded with Xet."""
+ if not is_xet_available() or (xet_hash := info.get("xet_hash")) is None:
+ return None
+ if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+ repo_id, repo_type, revision = resolved_path.bucket_id, "bucket", None
+ else:
+ # Special refs (e.g. "refs/pr/1") must be quoted in the token route
+ repo_id, repo_type = resolved_path.repo_id, resolved_path.repo_type
+ revision = quote(resolved_path.revision, safe="")
+ refresh_route = xet_connection_info_refresh_url(
+ token_type=XetTokenType.READ,
+ repo_id=repo_id,
+ repo_type=repo_type,
+ revision=revision,
+ endpoint=self.endpoint,
+ )
+ return XetFileData(file_hash=xet_hash, refresh_route=refresh_route)
+
+ def get_file(self, rpath, lpath=None, callback=_DEFAULT_CALLBACK, outfile=None, **kwargs) -> None:
+ """
+ Copy single remote file to local.
+
+ > [!WARNING]
+ > Note: When possible, use `HfApi.hf_hub_download()` or `HfApi.download_bucket_files` for better performance.
+
+ Args:
+ rpath (`str`):
+ Remote path to download from.
+ lpath (`str`, *optional*):
+ Local path to download to. Can be omitted if `outfile` is provided.
+ callback (`Callback`, *optional*):
+ Optional callback to track download progress. Defaults to no callback.
+ outfile (`IO`, *optional*):
+ Optional file-like object to write to. If provided, `lpath` is ignored.
+
+ """
+ revision = kwargs.get("revision")
+ resolve_remote_path = self.resolve_path(rpath, revision=revision)
+ # Recursive downloads map remote filenames to local paths, including on Windows.
+ # Validate before creating directories, opening files, or delegating to fsspec.
+ _validate_relative_filename(resolve_remote_path.path)
+ unhandled_kwargs = set(kwargs.keys()) - {"revision"}
+ if not isinstance(callback, (NoOpCallback, TqdmCallback)) or len(unhandled_kwargs) > 0:
+ # for now, let's not handle custom callbacks
+ # and let's not handle custom kwargs
+ return super().get_file(rpath, lpath, callback=callback, outfile=outfile, **kwargs)
+
+ info = self.info(rpath, revision=revision)
+
+ # Adapted from https://github.com/fsspec/filesystem_spec/blob/0f76baaae4a227b085b18e8469e6cc1792a95ade/fsspec/spec.py#L975
+ close_file = False
+ if outfile is None:
+ if lpath is None:
+ raise ValueError("Either `lpath` or `outfile` must be provided.")
+ if isfilelike(lpath):
+ outfile = lpath
+ elif self.isdir(rpath):
+ os.makedirs(lpath, exist_ok=True)
+ return None
+ else:
+ os.makedirs(os.path.dirname(lpath) or os.curdir, exist_ok=True)
+ if (xet_file_data := self._get_xet_file_data(resolve_remote_path, info)) is not None:
+ # hf_xet writes the file to disk directly
+ callback.set_size(info["size"])
+ xet_get(
+ incomplete_path=Path(lpath),
+ xet_file_data=xet_file_data,
+ headers=self._api._build_hf_headers(),
+ expected_size=info["size"],
+ displayed_filename=rpath,
+ _tqdm_bar=callback.tqdm if isinstance(callback, TqdmCallback) else None,
+ )
+ return None
+ outfile = open(lpath, "wb")
+ close_file = True
+ initial_pos = outfile.tell()
+
+ # Custom implementation of `get_file` to use `http_get`.
+ expected_size = info["size"]
+ callback.set_size(expected_size)
+ try:
+ http_get(
+ url=self.url(resolve_remote_path.unresolve()),
+ temp_file=outfile, # type: ignore
+ displayed_filename=rpath,
+ expected_size=expected_size,
+ resume_size=0,
+ headers=self._api._build_hf_headers(),
+ _tqdm_bar=callback.tqdm if isinstance(callback, TqdmCallback) else None,
+ )
+ outfile.seek(initial_pos)
+ finally:
+ # Close file only if we opened it ourselves
+ if close_file:
+ outfile.close()
+
+ @property
+ def transaction(self):
+ """A context within which files are committed together upon exit
+
+ Requires the file class to implement `.commit()` and `.discard()`
+ for the normal and exception cases.
+ """
+ # Taken from https://github.com/fsspec/filesystem_spec/blob/3fbb6fee33b46cccb015607630843dea049d3243/fsspec/spec.py#L231
+ # See https://github.com/huggingface/huggingface_hub/issues/1733
+ raise NotImplementedError("Transactional commits are not supported.")
+
+ def start_transaction(self):
+ """Begin write transaction for deferring files, non-context version"""
+ # Taken from https://github.com/fsspec/filesystem_spec/blob/3fbb6fee33b46cccb015607630843dea049d3243/fsspec/spec.py#L241
+ # See https://github.com/huggingface/huggingface_hub/issues/1733
+ raise NotImplementedError("Transactional commits are not supported.")
+
+ def __reduce__(self):
+ # re-populate the instance cache at HfFileSystem._cache and re-populate the state of every instance
+ return make_instance, (
+ type(self),
+ self.storage_args,
+ self.storage_options,
+ self._get_instance_state(),
+ )
+
+ def _get_instance_state(self):
+ return {
+ "dircache": deepcopy(self.dircache),
+ "_repo_and_revision_exists_cache": deepcopy(self._repo_and_revision_exists_cache),
+ "_bucket_exists_cache": deepcopy(self._bucket_exists_cache),
+ }
+
+
+class HfFileSystemFile(fsspec.spec.AbstractBufferedFile):
+ def __init__(self, fs: HfFileSystem, path: str, revision: str | None = None, **kwargs):
+ try:
+ self.resolved_path = fs.resolve_path(path, revision=revision)
+ except FileNotFoundError as e:
+ if "w" in kwargs.get("mode", ""):
+ raise FileNotFoundError(
+ f"{e}.\nMake sure the repository and revision exist before writing data."
+ ) from e
+ raise
+ super().__init__(fs, self.resolved_path.unresolve(), **kwargs)
+ self.fs: HfFileSystem
+
+ def __del__(self):
+ if not hasattr(self, "resolved_path"):
+ # Means that the constructor failed. Nothing to do.
+ return
+ return super().__del__()
+
+ def _fetch_range(self, start: int, end: int) -> bytes:
+ headers = {
+ "range": f"bytes={start}-{end - 1}",
+ **self.fs._api._build_hf_headers(),
+ }
+ url = self.url()
+ r = http_backoff("GET", url, headers=headers, timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT)
+ hf_raise_for_status(r)
+ return r.content
+
+ def _initiate_upload(self) -> None:
+ self.temp_file = tempfile.NamedTemporaryFile(prefix="hffs-", delete=False)
+
+ def _upload_chunk(self, final: bool = False) -> None:
+ self.buffer.seek(0)
+ block = self.buffer.read()
+ self.temp_file.write(block)
+ if final:
+ self.temp_file.close()
+ if isinstance(self.resolved_path, HfFileSystemResolvedBucketPath):
+ self.fs._api.batch_bucket_files(
+ self.resolved_path.bucket_id, add=[(self.temp_file.name, self.resolved_path.path)]
+ )
+ else:
+ self.fs._api.upload_file(
+ path_or_fileobj=self.temp_file.name,
+ path_in_repo=self.resolved_path.path_in_repo,
+ repo_id=self.resolved_path.repo_id,
+ token=self.fs.token,
+ repo_type=self.resolved_path.repo_type,
+ revision=self.resolved_path.revision,
+ commit_message=self.kwargs.get("commit_message"),
+ commit_description=self.kwargs.get("commit_description"),
+ )
+ os.remove(self.temp_file.name)
+ self.fs.invalidate_cache(
+ path=self.resolved_path.unresolve(),
+ )
+
+ def read(self, length=-1):
+ """Read remote file.
+
+ If `length` is not provided or is -1, the entire file is downloaded and read. On POSIX systems the file is
+ loaded in memory directly. Otherwise, the file is downloaded to a temporary file and read from there.
+ """
+ if self.mode == "rb" and (length is None or length == -1) and self.loc == 0:
+ with self.fs.open(self.path, "rb", block_size=0) as f: # block_size=0 enables fast streaming
+ out = f.read()
+ self.loc += len(out)
+ return out
+ return super().read(length)
+
+ def url(self) -> str:
+ return self.fs.url(self.path)
+
+
+class HfFileSystemStreamFile(fsspec.spec.AbstractBufferedFile):
+ def __init__(
+ self,
+ fs: HfFileSystem,
+ path: str,
+ mode: str = "rb",
+ revision: str | None = None,
+ block_size: int = 0,
+ cache_type: str = "none",
+ **kwargs,
+ ):
+ if block_size != 0:
+ raise ValueError(f"HfFileSystemStreamFile only supports block_size=0 but got {block_size}")
+ if cache_type != "none":
+ raise ValueError(f"HfFileSystemStreamFile only supports cache_type='none' but got {cache_type}")
+ if "w" in mode:
+ raise ValueError(f"HfFileSystemStreamFile only supports reading but got mode='{mode}'")
+ # Let a missing repo/revision/file surface as FileNotFoundError (as the buffered
+ # `HfFileSystemFile` does) instead of an AttributeError on `self.resolved_path` below.
+ self.resolved_path = fs.resolve_path(path, revision=revision)
+ # avoid an unnecessary .info() call to instantiate .details
+ self.details = {"name": self.resolved_path.unresolve(), "size": None}
+ super().__init__(
+ fs, self.resolved_path.unresolve(), mode=mode, block_size=block_size, cache_type=cache_type, **kwargs
+ )
+ self.response: httpx2.Response | None = None
+ self.fs: HfFileSystem
+ self._exit_stack = ExitStack()
+ # streaming state
+ self._stream_iterator: Iterator[bytes] | None = None
+ self._stream_buffer = bytearray()
+
+ def seek(self, loc: int, whence: int = 0):
+ if loc == 0 and whence == 1:
+ return
+ if loc == self.loc and whence == 0:
+ return
+ raise ValueError("Cannot seek streaming HF file")
+
+ def read(self, length: int = -1):
+ """Read the remote file.
+
+ If the file is already open, we reuse the connection.
+ Otherwise, open a new connection and read from it.
+
+ If reading the stream fails, we retry with a new connection.
+ """
+ if self.response is None:
+ self._open_connection()
+
+ retried_once = False
+ while True:
+ try:
+ if self.response is None or self._stream_iterator is None:
+ return b"" # Already read the entire file
+ out = self._read_from_stream(self._stream_iterator, length)
+ self.loc += len(out)
+ return out
+ except Exception:
+ if self.response is not None:
+ self.response.close()
+ if retried_once: # Already retried once, give up
+ raise
+ # First failure, retry with range header
+ self._open_connection()
+ retried_once = True
+
+ def _read_from_stream(self, iterator: Iterator[bytes], length: int = -1) -> bytes:
+ """Read up to `length` bytes from stream buffer and stream.
+
+ If length < 0, read until EOF.
+
+ If EOF is reached before length, fewer bytes may be returned.
+ """
+ if length == 0:
+ return b""
+
+ if length < 0:
+ buf = bytearray(self._stream_buffer)
+ self._stream_buffer.clear()
+ for chunk in iterator:
+ buf.extend(chunk)
+ return bytes(buf)
+
+ if length <= len(self._stream_buffer):
+ result = bytes(self._stream_buffer[:length])
+ del self._stream_buffer[:length]
+ return result
+
+ buf = bytearray(self._stream_buffer)
+ self._stream_buffer.clear()
+ for chunk in iterator:
+ need = length - len(buf)
+ if need > len(chunk):
+ buf.extend(chunk)
+ else:
+ buf.extend(chunk[:need])
+ self._stream_buffer.extend(chunk[need:])
+ break
+ return bytes(buf)
+
+ def url(self) -> str:
+ return self.fs.url(self.path)
+
+ def __del__(self):
+ if not hasattr(self, "resolved_path"):
+ # Means that the constructor failed. Nothing to do.
+ return
+ self._exit_stack.close()
+ return super().__del__()
+
+ def __reduce__(self):
+ return reopen, (self.fs, self.path, self.mode, self.blocksize, self.cache.name)
+
+ def _open_connection(self):
+ """Open a connection to the remote file."""
+ # reset streaming state
+ self._stream_buffer.clear()
+ self._stream_iterator = None
+
+ url = self.url()
+ headers = self.fs._api._build_hf_headers()
+ if self.loc > 0:
+ headers["Range"] = f"bytes={self.loc}-"
+ self.response = self._exit_stack.enter_context(
+ http_stream_backoff(
+ "GET",
+ url,
+ headers=headers,
+ timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ )
+ )
+
+ try:
+ hf_raise_for_status(self.response)
+ except HfHubHTTPError as e:
+ if e.response.status_code == 416:
+ # Range not satisfiable => means that we have already read the entire file
+ self.response = None
+ return
+ raise
+
+ self._stream_iterator = self.response.iter_bytes()
+
+
+def safe_revision(revision: str) -> str:
+ return revision if SPECIAL_REFS_REVISION_REGEX.search(revision) else safe_quote(revision)
+
+
+def safe_quote(s: str) -> str:
+ return quote(s, safe="")
+
+
+def _raise_file_not_found(path: str, err: Exception | None) -> NoReturn:
+ msg = path
+ if isinstance(err, RepositoryNotFoundError):
+ msg = f"{path} (repository not found)"
+ elif isinstance(err, RevisionNotFoundError):
+ msg = f"{path} (revision not found)"
+ elif isinstance(err, HFValidationError):
+ msg = f"{path} (invalid repository id)"
+ raise FileNotFoundError(msg) from err
+
+
+def reopen(fs: HfFileSystem, path: str, mode: str, block_size: int, cache_type: str):
+ return fs.open(path, mode=mode, block_size=block_size, cache_type=cache_type)
+
+
+def make_instance(cls, args, kwargs, instance_state):
+ fs = cls(*args, **kwargs)
+ for attr, state_value in instance_state.items():
+ setattr(fs, attr, state_value)
+ return fs
+
+
+hffs = HfFileSystem()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea0d1539a6da9692a42f6d94405d0f4621d045b7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py
@@ -0,0 +1,830 @@
+import inspect
+import json
+import os
+from collections.abc import Callable
+from dataclasses import Field, asdict, dataclass, is_dataclass
+from pathlib import Path
+from typing import Any, ClassVar, Protocol, TypeVar, cast
+
+import packaging.version
+
+from . import constants
+from .errors import EntryNotFoundError, HfHubHTTPError
+from .file_download import hf_hub_download
+from .hf_api import HfApi
+from .repocard import ModelCard, ModelCardData
+from .utils import (
+ SoftTemporaryDirectory,
+ is_jsonable,
+ is_simple_optional_type,
+ logging,
+ unwrap_simple_optional_type,
+ validate_hf_hub_args,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+# Type alias for dataclass instances, copied from https://github.com/python/typeshed/blob/9f28171658b9ca6c32a7cb93fbb99fc92b17858b/stdlib/_typeshed/__init__.pyi#L349
+class DataclassInstance(Protocol):
+ __dataclass_fields__: ClassVar[dict[str, Field]]
+
+
+# Generic variable that is either ModelHubMixin or a subclass thereof
+T = TypeVar("T", bound="ModelHubMixin")
+# Generic variable to represent an args type
+ARGS_T = TypeVar("ARGS_T")
+ENCODER_T = Callable[[ARGS_T], Any]
+DECODER_T = Callable[[Any], ARGS_T]
+CODER_T = tuple[ENCODER_T, DECODER_T]
+
+
+DEFAULT_MODEL_CARD = """
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{{ card_data }}
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: {{ repo_url | default("[More Information Needed]", true) }}
+- Paper: {{ paper_url | default("[More Information Needed]", true) }}
+- Docs: {{ docs_url | default("[More Information Needed]", true) }}
+"""
+
+
+@dataclass
+class MixinInfo:
+ model_card_template: str
+ model_card_data: ModelCardData
+ docs_url: str | None = None
+ paper_url: str | None = None
+ repo_url: str | None = None
+
+
+class ModelHubMixin:
+ """
+ A generic mixin to integrate ANY machine learning framework with the Hub.
+
+ To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models
+ have to be overwritten in [`_from_pretrained`] and [`_save_pretrained`]. [`PyTorchModelHubMixin`] is a good example
+ of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions.
+
+ When inheriting from [`ModelHubMixin`], you can define class-level attributes. These attributes are not passed to
+ `__init__` but to the class definition itself. This is useful to define metadata about the library integrating
+ [`ModelHubMixin`].
+
+ For more details on how to integrate the mixin with your library, checkout the [integration guide](../guides/integrations).
+
+ Args:
+ repo_url (`str`, *optional*):
+ URL of the library repository. Used to generate model card.
+ paper_url (`str`, *optional*):
+ URL of the library paper. Used to generate model card.
+ docs_url (`str`, *optional*):
+ URL of the library documentation. Used to generate model card.
+ model_card_template (`str`, *optional*):
+ Template of the model card. Used to generate model card. Defaults to a generic template.
+ language (`str` or `list[str]`, *optional*):
+ Language supported by the library. Used to generate model card.
+ library_name (`str`, *optional*):
+ Name of the library integrating ModelHubMixin. Used to generate model card.
+ license (`str`, *optional*):
+ License of the library integrating ModelHubMixin. Used to generate model card.
+ E.g: "apache-2.0"
+ license_name (`str`, *optional*):
+ Name of the library integrating ModelHubMixin. Used to generate model card.
+ Only used if `license` is set to `other`.
+ E.g: "coqui-public-model-license".
+ license_link (`str`, *optional*):
+ URL to the license of the library integrating ModelHubMixin. Used to generate model card.
+ Only used if `license` is set to `other` and `license_name` is set.
+ E.g: "https://coqui.ai/cpml".
+ pipeline_tag (`str`, *optional*):
+ Tag of the pipeline. Used to generate model card. E.g. "text-classification".
+ tags (`list[str]`, *optional*):
+ Tags to be added to the model card. Used to generate model card. E.g. ["computer-vision"]
+ coders (`dict[Type, tuple[Callable, Callable]]`, *optional*):
+ Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not
+ jsonable by default. E.g. dataclasses, argparse.Namespace, OmegaConf, etc.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import ModelHubMixin
+
+ # Inherit from ModelHubMixin
+ >>> class MyCustomModel(
+ ... ModelHubMixin,
+ ... library_name="my-library",
+ ... tags=["computer-vision"],
+ ... repo_url="https://github.com/huggingface/my-cool-library",
+ ... paper_url="https://arxiv.org/abs/2304.12244",
+ ... docs_url="https://huggingface.co/docs/my-cool-library",
+ ... # ^ optional metadata to generate model card
+ ... ):
+ ... def __init__(self, size: int = 512, device: str = "cpu"):
+ ... # define how to initialize your model
+ ... super().__init__()
+ ... ...
+ ...
+ ... def _save_pretrained(self, save_directory: Path) -> None:
+ ... # define how to serialize your model
+ ... ...
+ ...
+ ... @classmethod
+ ... def from_pretrained(
+ ... cls: type[T],
+ ... pretrained_model_name_or_path: Union[str, Path],
+ ... *,
+ ... force_download: bool = False,
+ ... token: Optional[Union[str, bool]] = None,
+ ... cache_dir: Optional[Union[str, Path]] = None,
+ ... local_files_only: bool = False,
+ ... revision: Optional[str] = None,
+ ... **model_kwargs,
+ ... ) -> T:
+ ... # define how to deserialize your model
+ ... ...
+
+ >>> model = MyCustomModel(size=256, device="gpu")
+
+ # Save model weights to local directory
+ >>> model.save_pretrained("my-awesome-model")
+
+ # Push model weights to the Hub
+ >>> model.push_to_hub("my-awesome-model")
+
+ # Download and initialize weights from the Hub
+ >>> reloaded_model = MyCustomModel.from_pretrained("username/my-awesome-model")
+ >>> reloaded_model.size
+ 256
+
+ # Model card has been correctly populated
+ >>> from huggingface_hub import ModelCard
+ >>> card = ModelCard.load("username/my-awesome-model")
+ >>> card.data.tags
+ ["x-custom-tag", "pytorch_model_hub_mixin", "model_hub_mixin"]
+ >>> card.data.library_name
+ "my-library"
+ ```
+ """
+
+ _hub_mixin_config: dict | DataclassInstance | None = None
+ # ^ optional config attribute automatically set in `from_pretrained`
+ _hub_mixin_info: MixinInfo
+ # ^ information about the library integrating ModelHubMixin (used to generate model card)
+ _hub_mixin_inject_config: bool # whether `_from_pretrained` expects `config` or not
+ _hub_mixin_init_parameters: dict[str, inspect.Parameter] # __init__ parameters
+ _hub_mixin_jsonable_default_values: dict[str, Any] # default values for __init__ parameters
+ _hub_mixin_jsonable_custom_types: tuple[type, ...] # custom types that can be encoded/decoded
+ _hub_mixin_coders: dict[type, CODER_T] # encoders/decoders for custom types
+ # ^ internal values to handle config
+
+ def __init_subclass__(
+ cls,
+ *,
+ # Generic info for model card
+ repo_url: str | None = None,
+ paper_url: str | None = None,
+ docs_url: str | None = None,
+ # Model card template
+ model_card_template: str = DEFAULT_MODEL_CARD,
+ # Model card metadata
+ language: list[str] | None = None,
+ library_name: str | None = None,
+ license: str | None = None,
+ license_name: str | None = None,
+ license_link: str | None = None,
+ pipeline_tag: str | None = None,
+ tags: list[str] | None = None,
+ # How to encode/decode arguments with custom type into a JSON config?
+ coders: None
+ | (
+ dict[type, CODER_T]
+ # Key is a type.
+ # Value is a tuple (encoder, decoder).
+ # Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))}
+ ) = None,
+ ) -> None:
+ """Inspect __init__ signature only once when subclassing + handle modelcard."""
+ super().__init_subclass__()
+
+ # Will be reused when creating modelcard
+ tags = tags or []
+ tags.append("model_hub_mixin")
+
+ # Initialize MixinInfo if not existent
+ info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData())
+
+ # If parent class has a MixinInfo, inherit from it as a copy
+ if hasattr(cls, "_hub_mixin_info"):
+ # Inherit model card template from parent class if not explicitly set
+ if model_card_template == DEFAULT_MODEL_CARD:
+ info.model_card_template = cls._hub_mixin_info.model_card_template
+
+ # Inherit from parent model card data
+ info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
+
+ # Inherit other info
+ info.docs_url = cls._hub_mixin_info.docs_url
+ info.paper_url = cls._hub_mixin_info.paper_url
+ info.repo_url = cls._hub_mixin_info.repo_url
+ cls._hub_mixin_info = info
+
+ # Update MixinInfo with metadata
+ if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
+ info.model_card_template = model_card_template
+ if repo_url is not None:
+ info.repo_url = repo_url
+ if paper_url is not None:
+ info.paper_url = paper_url
+ if docs_url is not None:
+ info.docs_url = docs_url
+ if language is not None:
+ info.model_card_data.language = language
+ if library_name is not None:
+ info.model_card_data.library_name = library_name
+ if license is not None:
+ info.model_card_data.license = license
+ if license_name is not None:
+ info.model_card_data.license_name = license_name
+ if license_link is not None:
+ info.model_card_data.license_link = license_link
+ if pipeline_tag is not None:
+ info.model_card_data.pipeline_tag = pipeline_tag
+ if tags is not None:
+ normalized_tags = list(tags)
+ if info.model_card_data.tags is not None:
+ info.model_card_data.tags.extend(normalized_tags)
+ else:
+ info.model_card_data.tags = normalized_tags
+
+ if info.model_card_data.tags is not None:
+ info.model_card_data.tags = sorted(set(info.model_card_data.tags))
+
+ # Handle encoders/decoders for args
+ cls._hub_mixin_coders = coders or {}
+ cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys())
+
+ # Inspect __init__ signature to handle config
+ cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters)
+ cls._hub_mixin_jsonable_default_values = {
+ param.name: cls._encode_arg(param.default)
+ for param in cls._hub_mixin_init_parameters.values()
+ if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default)
+ }
+ cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters
+
+ def __new__(cls: type[T], *args, **kwargs) -> T:
+ """Create a new instance of the class and handle config.
+
+ 3 cases:
+ - If `self._hub_mixin_config` is already set, do nothing.
+ - If `config` is passed as a dataclass, set it as `self._hub_mixin_config`.
+ - Otherwise, build `self._hub_mixin_config` from default values and passed values.
+ """
+ instance = super().__new__(cls)
+
+ # If `config` is already set, return early
+ if instance._hub_mixin_config is not None:
+ return instance
+
+ # Infer passed values
+ passed_values = {
+ **{
+ key: value
+ for key, value in zip(
+ # [1:] to skip `self` parameter
+ list(cls._hub_mixin_init_parameters)[1:],
+ args,
+ )
+ },
+ **kwargs,
+ }
+
+ # If config passed as dataclass => set it and return early
+ if is_dataclass(passed_values.get("config")):
+ instance._hub_mixin_config = passed_values["config"]
+ return instance
+
+ # Otherwise, build config from default + passed values
+ init_config = {
+ # default values
+ **cls._hub_mixin_jsonable_default_values,
+ # passed values
+ **{
+ key: cls._encode_arg(value) # Encode custom types as jsonable value
+ for key, value in passed_values.items()
+ if instance._is_jsonable(value) # Only if jsonable or we have a custom encoder
+ },
+ }
+ passed_config = init_config.pop("config", {})
+
+ # Populate `init_config` with provided config
+ if isinstance(passed_config, dict):
+ init_config.update(passed_config)
+
+ # Set `config` attribute and return
+ if init_config != {}:
+ instance._hub_mixin_config = init_config
+ return instance
+
+ @classmethod
+ def _is_jsonable(cls, value: Any) -> bool:
+ """Check if a value is JSON serializable."""
+ if is_dataclass(value):
+ return True
+ if isinstance(value, cls._hub_mixin_jsonable_custom_types):
+ return True
+ return is_jsonable(value)
+
+ @classmethod
+ def _encode_arg(cls, arg: Any) -> Any:
+ """Encode an argument into a JSON serializable format."""
+ if is_dataclass(arg):
+ return asdict(arg) # type: ignore[arg-type]
+ for type_, (encoder, _) in cls._hub_mixin_coders.items():
+ if isinstance(arg, type_):
+ if arg is None:
+ return None
+ return encoder(arg)
+ return arg
+
+ @classmethod
+ def _decode_arg(cls, expected_type: type[ARGS_T], value: Any) -> ARGS_T | None:
+ """Decode a JSON serializable value into an argument."""
+ if is_simple_optional_type(expected_type):
+ if value is None:
+ return None
+ expected_type = unwrap_simple_optional_type(expected_type) # type: ignore
+ # Dataclass => handle it
+ if is_dataclass(expected_type):
+ return _load_dataclass(expected_type, value) # type: ignore
+ # Otherwise => check custom decoders
+ for type_, (_, decoder) in cls._hub_mixin_coders.items():
+ if inspect.isclass(expected_type) and issubclass(expected_type, type_):
+ return decoder(value)
+ # Otherwise => don't decode
+ return value
+
+ def save_pretrained(
+ self,
+ save_directory: str | Path,
+ *,
+ config: dict | DataclassInstance | None = None,
+ repo_id: str | None = None,
+ push_to_hub: bool = False,
+ model_card_kwargs: dict[str, Any] | None = None,
+ **push_to_hub_kwargs,
+ ) -> str | None:
+ """
+ Save weights in local directory.
+
+ Args:
+ save_directory (`str` or `Path`):
+ Path to directory in which the model weights and configuration will be saved.
+ config (`dict` or `DataclassInstance`, *optional*):
+ Model configuration specified as a key/value dictionary or a dataclass instance.
+ push_to_hub (`bool`, *optional*, defaults to `False`):
+ Whether or not to push your model to the Huggingface Hub after saving it.
+ repo_id (`str`, *optional*):
+ ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if
+ not provided.
+ model_card_kwargs (`dict[str, Any]`, *optional*):
+ Additional arguments passed to the model card template to customize the model card.
+ push_to_hub_kwargs:
+ Additional key word arguments passed along to the [`~ModelHubMixin.push_to_hub`] method.
+ Returns:
+ `str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise.
+ """
+ save_directory = Path(save_directory)
+ save_directory.mkdir(parents=True, exist_ok=True)
+
+ # Remove config.json if already exists. After `_save_pretrained` we don't want to overwrite config.json
+ # as it might have been saved by the custom `_save_pretrained` already. However we do want to overwrite
+ # an existing config.json if it was not saved by `_save_pretrained`.
+ config_path = save_directory / constants.CONFIG_NAME
+ config_path.unlink(missing_ok=True)
+
+ # save model weights/files (framework-specific)
+ self._save_pretrained(save_directory)
+
+ # save config (if provided and if not serialized yet in `_save_pretrained`)
+ if config is None:
+ config = self._hub_mixin_config
+ if config is not None:
+ if is_dataclass(config):
+ config = asdict(config) # type: ignore[arg-type]
+ if not config_path.exists():
+ config_str = json.dumps(config, sort_keys=True, indent=2)
+ config_path.write_text(config_str)
+
+ # save model card
+ model_card_path = save_directory / "README.md"
+ model_card_kwargs = model_card_kwargs if model_card_kwargs is not None else {}
+ if not model_card_path.exists(): # do not overwrite if already exists
+ self.generate_model_card(**model_card_kwargs).save(save_directory / "README.md")
+
+ # push to the Hub if required
+ if push_to_hub:
+ kwargs = push_to_hub_kwargs.copy() # soft-copy to avoid mutating input
+ if config is not None: # kwarg for `push_to_hub`
+ kwargs["config"] = config
+ if repo_id is None:
+ repo_id = save_directory.name # Defaults to `save_directory` name
+ return self.push_to_hub(repo_id=repo_id, model_card_kwargs=model_card_kwargs, **kwargs)
+ return None
+
+ def _save_pretrained(self, save_directory: Path) -> None:
+ """
+ Overwrite this method in subclass to define how to save your model.
+ Check out our [integration guide](../guides/integrations) for instructions.
+
+ Args:
+ save_directory (`str` or `Path`):
+ Path to directory in which the model weights and configuration will be saved.
+ """
+ raise NotImplementedError
+
+ @classmethod
+ @validate_hf_hub_args
+ def from_pretrained(
+ cls: type[T],
+ pretrained_model_name_or_path: str | Path,
+ *,
+ force_download: bool = False,
+ token: str | bool | None = None,
+ cache_dir: str | Path | None = None,
+ local_files_only: bool = False,
+ revision: str | None = None,
+ **model_kwargs,
+ ) -> T:
+ """
+ Download a model from the Huggingface Hub and instantiate it.
+
+ Args:
+ pretrained_model_name_or_path (`str`, `Path`):
+ - Either the `model_id` (string) of a model hosted on the Hub, e.g. `bigscience/bloom`.
+ - Or a path to a `directory` containing model weights saved using
+ [`~transformers.PreTrainedModel.save_pretrained`], e.g., `../path/to/my_model_directory/`.
+ revision (`str`, *optional*):
+ Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
+ Defaults to the latest commit on `main` branch.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+ the existing cache.
+ token (`str` or `bool`, *optional*):
+ The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+ cached when running `hf auth login`.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+ model_kwargs (`dict`, *optional*):
+ Additional kwargs to pass to the model during initialization.
+ """
+ model_id = str(pretrained_model_name_or_path)
+ config_file: str | None = None
+ if os.path.isdir(model_id):
+ if constants.CONFIG_NAME in os.listdir(model_id):
+ config_file = os.path.join(model_id, constants.CONFIG_NAME)
+ else:
+ logger.warning(f"{constants.CONFIG_NAME} not found in {Path(model_id).resolve()}")
+ else:
+ try:
+ config_file = hf_hub_download(
+ repo_id=model_id,
+ filename=constants.CONFIG_NAME,
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ token=token,
+ local_files_only=local_files_only,
+ )
+ except HfHubHTTPError as e:
+ logger.info(f"{constants.CONFIG_NAME} not found on the HuggingFace Hub: {str(e)}")
+
+ # Read config
+ config = None
+ if config_file is not None:
+ with open(config_file, encoding="utf-8") as f:
+ config = json.load(f)
+
+ # Decode custom types in config
+ for key, value in config.items():
+ if key in cls._hub_mixin_init_parameters:
+ expected_type = cls._hub_mixin_init_parameters[key].annotation
+ if expected_type is not inspect.Parameter.empty:
+ config[key] = cls._decode_arg(expected_type, value)
+
+ # Populate model_kwargs from config
+ for param in cls._hub_mixin_init_parameters.values():
+ if param.name not in model_kwargs and param.name in config:
+ model_kwargs[param.name] = config[param.name]
+
+ # Check if `config` argument was passed at init
+ if "config" in cls._hub_mixin_init_parameters and "config" not in model_kwargs:
+ # Decode `config` argument if it was passed
+ config_annotation = cls._hub_mixin_init_parameters["config"].annotation
+ config = cls._decode_arg(config_annotation, config)
+
+ # Forward config to model initialization
+ model_kwargs["config"] = config
+
+ # Inject config if `**kwargs` are expected
+ if is_dataclass(cls):
+ for key in cls.__dataclass_fields__:
+ if key not in model_kwargs and key in config:
+ model_kwargs[key] = config[key]
+ elif any(param.kind == inspect.Parameter.VAR_KEYWORD for param in cls._hub_mixin_init_parameters.values()):
+ for key, value in config.items(): # type: ignore[union-attr]
+ if key not in model_kwargs:
+ model_kwargs[key] = value
+
+ # Finally, also inject if `_from_pretrained` expects it
+ if cls._hub_mixin_inject_config and "config" not in model_kwargs:
+ model_kwargs["config"] = config
+
+ instance = cls._from_pretrained(
+ model_id=str(model_id),
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ local_files_only=local_files_only,
+ token=token,
+ **model_kwargs,
+ )
+
+ # Implicitly set the config as instance attribute if not already set by the class
+ # This way `config` will be available when calling `save_pretrained` or `push_to_hub`.
+ if config is not None and (getattr(instance, "_hub_mixin_config", None) in (None, {})):
+ instance._hub_mixin_config = config
+
+ return instance
+
+ @classmethod
+ def _from_pretrained(
+ cls: type[T],
+ *,
+ model_id: str,
+ revision: str | None,
+ cache_dir: str | Path | None,
+ force_download: bool,
+ local_files_only: bool,
+ token: str | bool | None,
+ **model_kwargs,
+ ) -> T:
+ """Overwrite this method in subclass to define how to load your model from pretrained.
+
+ Use [`hf_hub_download`] or [`snapshot_download`] to download files from the Hub before loading them. Most
+ args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this
+ method using "model_kwargs". For example [`PyTorchModelHubMixin._from_pretrained`] takes as input a `map_location`
+ parameter to set on which device the model should be loaded.
+
+ Check out our [integration guide](../guides/integrations) for more instructions.
+
+ Args:
+ model_id (`str`):
+ ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
+ revision (`str`, *optional*):
+ Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
+ latest commit on `main` branch.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+ the existing cache.
+ token (`str` or `bool`, *optional*):
+ The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+ cached when running `hf auth login`.
+ cache_dir (`str`, `Path`, *optional*):
+ Path to the folder where cached files are stored.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+ model_kwargs:
+ Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
+ """
+ raise NotImplementedError
+
+ @validate_hf_hub_args
+ def push_to_hub(
+ self,
+ repo_id: str,
+ *,
+ config: dict | DataclassInstance | None = None,
+ commit_message: str = "Push model using huggingface_hub.",
+ private: bool | None = None,
+ token: str | None = None,
+ branch: str | None = None,
+ create_pr: bool | None = None,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ delete_patterns: list[str] | str | None = None,
+ model_card_kwargs: dict[str, Any] | None = None,
+ ) -> str:
+ """
+ Upload model checkpoint to the Hub.
+
+ Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+ `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+ details.
+
+ Args:
+ repo_id (`str`):
+ ID of the repository to push to (example: `"username/my-model"`).
+ config (`dict` or `DataclassInstance`, *optional*):
+ Model configuration specified as a key/value dictionary or a dataclass instance.
+ commit_message (`str`, *optional*):
+ Message to commit while pushing.
+ private (`bool`, *optional*):
+ Whether the repository created should be private.
+ If `None` (default), the repo will be public unless the organization's default is private.
+ token (`str`, *optional*):
+ The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+ cached when running `hf auth login`.
+ branch (`str`, *optional*):
+ The git branch on which to push the model. This defaults to `"main"`.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`.
+ allow_patterns (`list[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are pushed.
+ ignore_patterns (`list[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not pushed.
+ delete_patterns (`list[str]` or `str`, *optional*):
+ If provided, remote files matching any of the patterns will be deleted from the repo.
+ model_card_kwargs (`dict[str, Any]`, *optional*):
+ Additional arguments passed to the model card template to customize the model card.
+
+ Returns:
+ The url of the commit of your model in the given repository.
+ """
+ api = HfApi(token=token)
+ repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
+
+ # Push the files to the repo in a single commit
+ with SoftTemporaryDirectory() as tmp:
+ saved_path = Path(tmp) / repo_id
+ self.save_pretrained(saved_path, config=config, model_card_kwargs=model_card_kwargs)
+ return api.upload_folder(
+ repo_id=repo_id,
+ repo_type="model",
+ folder_path=saved_path,
+ commit_message=commit_message,
+ revision=branch,
+ create_pr=create_pr,
+ allow_patterns=allow_patterns,
+ ignore_patterns=ignore_patterns,
+ delete_patterns=delete_patterns,
+ )
+
+ def generate_model_card(self, *args, **kwargs) -> ModelCard:
+ card = ModelCard.from_template(
+ card_data=self._hub_mixin_info.model_card_data,
+ template_str=self._hub_mixin_info.model_card_template,
+ repo_url=self._hub_mixin_info.repo_url,
+ paper_url=self._hub_mixin_info.paper_url,
+ docs_url=self._hub_mixin_info.docs_url,
+ **kwargs,
+ )
+ return card
+
+
+class PyTorchModelHubMixin(ModelHubMixin):
+ """
+ Implementation of [`ModelHubMixin`] to provide model Hub upload/download capabilities to PyTorch models. The model
+ is set in evaluation mode by default using `model.eval()` (dropout modules are deactivated). To train the model,
+ you should first set it back in training mode with `model.train()`.
+
+ See [`ModelHubMixin`] for more details on how to use the mixin.
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> import torch.nn as nn
+ >>> from huggingface_hub import PyTorchModelHubMixin
+
+ >>> class MyModel(
+ ... nn.Module,
+ ... PyTorchModelHubMixin,
+ ... library_name="keras-nlp",
+ ... repo_url="https://github.com/keras-team/keras-nlp",
+ ... paper_url="https://arxiv.org/abs/2304.12244",
+ ... docs_url="https://keras.io/keras_nlp/",
+ ... # ^ optional metadata to generate model card
+ ... ):
+ ... def __init__(self, hidden_size: int = 512, vocab_size: int = 30000, output_size: int = 4):
+ ... super().__init__()
+ ... self.param = nn.Parameter(torch.rand(hidden_size, vocab_size))
+ ... self.linear = nn.Linear(output_size, vocab_size)
+
+ ... def forward(self, x):
+ ... return self.linear(x + self.param)
+ >>> model = MyModel(hidden_size=256)
+
+ # Save model weights to local directory
+ >>> model.save_pretrained("my-awesome-model")
+
+ # Push model weights to the Hub
+ >>> model.push_to_hub("my-awesome-model")
+
+ # Download and initialize weights from the Hub
+ >>> model = MyModel.from_pretrained("username/my-awesome-model")
+ >>> model.hidden_size
+ 256
+ ```
+ """
+
+ def __init_subclass__(cls, *args, tags: list[str] | None = None, **kwargs) -> None:
+ tags = tags or []
+ tags.append("pytorch_model_hub_mixin")
+ kwargs["tags"] = tags
+ return super().__init_subclass__(*args, **kwargs)
+
+ def _save_pretrained(self, save_directory: Path) -> None:
+ """Save weights from a Pytorch model to a local directory."""
+ from safetensors.torch import save_model as save_model_as_safetensor
+
+ model_to_save = self.module if hasattr(self, "module") else self # type: ignore
+ save_model_as_safetensor(cast(Any, model_to_save), str(save_directory / constants.SAFETENSORS_SINGLE_FILE))
+
+ @classmethod
+ def _from_pretrained(
+ cls,
+ *,
+ model_id: str,
+ revision: str | None,
+ cache_dir: str | Path | None,
+ force_download: bool,
+ local_files_only: bool,
+ token: str | bool | None,
+ map_location: str = "cpu",
+ strict: bool = False,
+ **model_kwargs,
+ ):
+ """Load Pytorch pretrained weights and return the loaded model."""
+ model = cls(**model_kwargs)
+ if os.path.isdir(model_id):
+ print("Loading weights from local directory")
+ model_file = os.path.join(model_id, constants.SAFETENSORS_SINGLE_FILE)
+ return cls._load_as_safetensor(model, model_file, map_location, strict)
+ else:
+ try:
+ model_file = hf_hub_download(
+ repo_id=model_id,
+ filename=constants.SAFETENSORS_SINGLE_FILE,
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ token=token,
+ local_files_only=local_files_only,
+ )
+ return cls._load_as_safetensor(model, model_file, map_location, strict)
+ except EntryNotFoundError:
+ model_file = hf_hub_download(
+ repo_id=model_id,
+ filename=constants.PYTORCH_WEIGHTS_NAME,
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ token=token,
+ local_files_only=local_files_only,
+ )
+ return cls._load_as_pickle(model, model_file, map_location, strict)
+
+ @classmethod
+ def _load_as_pickle(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+ import torch
+
+ state_dict = torch.load(model_file, map_location=torch.device(map_location), weights_only=True)
+ model.load_state_dict(state_dict, strict=strict) # type: ignore
+ model.eval() # type: ignore
+ return model
+
+ @classmethod
+ def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+ import safetensors
+ import safetensors.torch
+
+ if packaging.version.parse(safetensors.__version__) < packaging.version.parse("0.4.3"): # type: ignore [attr-defined]
+ safetensors.torch.load_model(cast(Any, model), model_file, strict=strict)
+ if map_location != "cpu":
+ logger.warning(
+ "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
+ " This means that the model is loaded on 'cpu' first and then copied to the device."
+ " This leads to a slower loading time."
+ " Please update safetensors to version 0.4.3 or above for improved performance."
+ )
+ model.to(map_location) # type: ignore [attr-defined]
+ else:
+ safetensors.torch.load_model(cast(Any, model), model_file, strict=strict, device=map_location)
+ model.eval() # type: ignore
+ return model
+
+
+def _load_dataclass(datacls: type[DataclassInstance], data: dict) -> DataclassInstance:
+ """Load a dataclass instance from a dictionary.
+
+ Fields not expected by the dataclass are ignored.
+ """
+ return datacls(**{k: v for k, v in data.items() if k in datacls.__dataclass_fields__})
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/__init__.py
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@@ -0,0 +1,3365 @@
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Related resources:
+# https://huggingface.co/tasks
+# https://huggingface.co/docs/huggingface.js/inference/README
+# https://github.com/huggingface/huggingface.js/tree/main/packages/inference/src
+# https://github.com/huggingface/text-generation-inference/tree/main/clients/python
+# https://github.com/huggingface/text-generation-inference/blob/main/clients/python/text_generation/client.py
+# https://huggingface.slack.com/archives/C03E4DQ9LAJ/p1680169099087869
+# https://github.com/huggingface/unity-api#tasks
+#
+# Some TODO:
+# - add all tasks
+#
+# NOTE: the philosophy of this client is "let's make it as easy as possible to use it, even if less optimized". Some
+# examples of how it translates:
+# - Timeout / Server unavailable is handled by the client in a single "timeout" parameter.
+# - Files can be provided as bytes, file paths, or URLs and the client will try to "guess" the type.
+# - Images are parsed as PIL.Image for easier manipulation.
+# - Provides a "recommended model" for each task => suboptimal but user-wise quicker to get a first script running.
+# - Only the main parameters are publicly exposed. Power users can always read the docs for more options.
+import base64
+import logging
+import os
+import re
+import warnings
+from collections.abc import Iterable
+from contextlib import ExitStack
+from typing import TYPE_CHECKING, Any, Literal, Optional, Union, overload
+
+from huggingface_hub import constants
+from huggingface_hub.errors import BadRequestError, HfHubHTTPError, InferenceTimeoutError
+from huggingface_hub.inference._common import (
+ TASKS_EXPECTING_IMAGES,
+ ContentT,
+ RequestParameters,
+ _b64_encode,
+ _b64_to_image,
+ _bytes_to_dict,
+ _bytes_to_image,
+ _bytes_to_list,
+ _get_unsupported_text_generation_kwargs,
+ _import_numpy,
+ _set_unsupported_text_generation_kwargs,
+ _stream_chat_completion_response,
+ _stream_text_generation_response,
+ raise_text_generation_error,
+)
+from huggingface_hub.inference._generated.types import (
+ AudioClassificationOutputElement,
+ AudioClassificationOutputTransform,
+ AudioToAudioOutputElement,
+ AutomaticSpeechRecognitionOutput,
+ ChatCompletionInputGrammarType,
+ ChatCompletionInputMessage,
+ ChatCompletionInputStreamOptions,
+ ChatCompletionInputTool,
+ ChatCompletionInputToolChoiceClass,
+ ChatCompletionInputToolChoiceEnum,
+ ChatCompletionOutput,
+ ChatCompletionStreamOutput,
+ DocumentQuestionAnsweringOutputElement,
+ FillMaskOutputElement,
+ ImageClassificationOutputElement,
+ ImageClassificationOutputTransform,
+ ImageSegmentationOutputElement,
+ ImageSegmentationSubtask,
+ ImageToImageTargetSize,
+ ImageToTextOutput,
+ ImageToVideoTargetSize,
+ ObjectDetectionOutputElement,
+ Padding,
+ QuestionAnsweringOutputElement,
+ SummarizationOutput,
+ SummarizationTruncationStrategy,
+ TableQuestionAnsweringOutputElement,
+ TextClassificationOutputElement,
+ TextClassificationOutputTransform,
+ TextGenerationInputGrammarType,
+ TextGenerationOutput,
+ TextGenerationStreamOutput,
+ TextToSpeechEarlyStoppingEnum,
+ TokenClassificationAggregationStrategy,
+ TokenClassificationOutputElement,
+ TranslationOutput,
+ TranslationTruncationStrategy,
+ VisualQuestionAnsweringOutputElement,
+ ZeroShotClassificationOutputElement,
+ ZeroShotImageClassificationOutputElement,
+)
+from huggingface_hub.inference._providers import PROVIDER_OR_POLICY_T, get_provider_helper
+from huggingface_hub.utils import (
+ build_hf_headers,
+ get_session,
+ hf_raise_for_status,
+ validate_hf_hub_args,
+)
+
+
+if TYPE_CHECKING:
+ import numpy as np
+ from PIL.Image import Image
+
+logger = logging.getLogger(__name__)
+
+
+MODEL_KWARGS_NOT_USED_REGEX = re.compile(r"The following `model_kwargs` are not used by the model: \[(.*?)\]")
+
+
+class InferenceClient:
+ """
+ Initialize a new Inference Client.
+
+ [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used
+ seamlessly with either the (free) Inference API, self-hosted Inference Endpoints, or third-party Inference Providers.
+
+ Args:
+ model (`str`, `optional`):
+ The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `meta-llama/Meta-Llama-3-8B-Instruct`
+ or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
+ automatically selected for the task.
+ Note: for better compatibility with OpenAI's client, `model` has been aliased as `base_url`. Those 2
+ arguments are mutually exclusive. If a URL is passed as `model` or `base_url` for chat completion, the `(/v1)/chat/completions` suffix path will be appended to the URL.
+ provider (`str`, *optional*):
+ Name of the provider to use for inference. Can be `"baseten"`, `"cerebras"`, `"cohere"`, `"deepinfra"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"novita"`, `"nscale"`, `"openai"`, `"ovhcloud"`, `"publicai"`, `"replicate"`, `"scaleway"`, `"together"`, `"wavespeed"` or `"zai-org"`.
+ Defaults to "auto": automatic routing, which defaults to "fastest" provider; you can
+ switch to "cheapest" or "preferred" provider order at https://hf.co/settings/inference-providers.
+ If model is a URL or `base_url` is passed, then `provider` is not used.
+ token (`str`, *optional*):
+ Hugging Face token. Will default to the locally saved token if not provided.
+ Note: for better compatibility with OpenAI's client, `token` has been aliased as `api_key`. Those 2
+ arguments are mutually exclusive and have the exact same behavior.
+ timeout (`float`, `optional`):
+ The maximum number of seconds to wait for a response from the server. Defaults to None, meaning it will loop until the server is available.
+ headers (`dict[str, str]`, `optional`):
+ Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
+ Values in this dictionary will override the default values.
+ bill_to (`str`, `optional`):
+ The billing account to use for the requests. By default the requests are billed on the user's account.
+ Requests can only be billed to an organization the user is a member of, and which has subscribed to Enterprise Hub.
+ cookies (`dict[str, str]`, `optional`):
+ Additional cookies to send to the server.
+ base_url (`str`, `optional`):
+ Base URL to run inference. This is a duplicated argument from `model` to make [`InferenceClient`]
+ follow the same pattern as `openai.OpenAI` client. Cannot be used if `model` is set. Defaults to None.
+ api_key (`str`, `optional`):
+ Token to use for authentication. This is a duplicated argument from `token` to make [`InferenceClient`]
+ follow the same pattern as `openai.OpenAI` client. Cannot be used if `token` is set. Defaults to None.
+ """
+
+ provider: PROVIDER_OR_POLICY_T | None
+
+ @validate_hf_hub_args
+ def __init__(
+ self,
+ model: str | None = None,
+ *,
+ provider: PROVIDER_OR_POLICY_T | None = None,
+ token: str | None = None,
+ timeout: float | None = None,
+ headers: dict[str, str] | None = None,
+ cookies: dict[str, str] | None = None,
+ bill_to: str | None = None,
+ # OpenAI compatibility
+ base_url: str | None = None,
+ api_key: str | None = None,
+ ) -> None:
+ if model is not None and base_url is not None:
+ raise ValueError(
+ "Received both `model` and `base_url` arguments. Please provide only one of them."
+ " `base_url` is an alias for `model` to make the API compatible with OpenAI's client."
+ " If using `base_url` for chat completion, the `/chat/completions` suffix path will be appended to the base url."
+ " When passing a URL as `model`, the client will not append any suffix path to it."
+ )
+ if token is not None and api_key is not None:
+ raise ValueError(
+ "Received both `token` and `api_key` arguments. Please provide only one of them."
+ " `api_key` is an alias for `token` to make the API compatible with OpenAI's client."
+ " It has the exact same behavior as `token`."
+ )
+ token = token if token is not None else api_key
+ if isinstance(token, bool):
+ raise TypeError("`token` must be a string or `None`.")
+ self.model: str | None = base_url or model
+ self.token: str | None = token
+
+ self.headers = {**headers} if headers is not None else {}
+ if bill_to is not None:
+ if (
+ constants.HUGGINGFACE_HEADER_X_BILL_TO in self.headers
+ and self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO] != bill_to
+ ):
+ warnings.warn(
+ f"Overriding existing '{self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO]}' value in headers with '{bill_to}'.",
+ UserWarning,
+ )
+ self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO] = bill_to
+
+ if token is not None and not token.startswith("hf_"):
+ warnings.warn(
+ "You've provided an external provider's API key, so requests will be billed directly by the provider. "
+ "The `bill_to` parameter is only applicable for Hugging Face billing and will be ignored.",
+ UserWarning,
+ )
+
+ # Configure provider
+ self.provider = provider # type: ignore[assignment]
+
+ self.cookies = cookies
+ self.timeout = timeout
+
+ self.exit_stack = ExitStack()
+
+ def __repr__(self):
+ return f""
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.exit_stack.close()
+
+ def close(self):
+ self.exit_stack.close()
+
+ @overload
+ def _inner_post( # type: ignore[misc]
+ self, request_parameters: RequestParameters, *, stream: Literal[False] = ...
+ ) -> bytes: ...
+
+ @overload
+ def _inner_post( # type: ignore[misc]
+ self, request_parameters: RequestParameters, *, stream: Literal[True] = ...
+ ) -> Iterable[str]: ...
+
+ @overload
+ def _inner_post(self, request_parameters: RequestParameters, *, stream: bool = False) -> bytes | Iterable[str]: ...
+
+ def _inner_post(self, request_parameters: RequestParameters, *, stream: bool = False) -> bytes | Iterable[str]:
+ """Make a request to the inference server."""
+ # TODO: this should be handled in provider helpers directly
+ if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers:
+ request_parameters.headers["Accept"] = "image/png"
+
+ try:
+ response = self.exit_stack.enter_context(
+ get_session().stream(
+ "POST",
+ request_parameters.url,
+ json=request_parameters.json,
+ content=request_parameters.data,
+ headers=request_parameters.headers,
+ cookies=self.cookies,
+ timeout=self.timeout,
+ )
+ )
+ hf_raise_for_status(response)
+ if stream:
+ return response.iter_lines()
+ else:
+ return response.read()
+ except TimeoutError as error:
+ # Convert any `TimeoutError` to a `InferenceTimeoutError`
+ raise InferenceTimeoutError(f"Inference call timed out: {request_parameters.url}") from error # type: ignore
+ except HfHubHTTPError as error:
+ if error.response.status_code == 422 and request_parameters.task != "unknown":
+ msg = str(error.args[0])
+ if len(error.response.text) > 0:
+ msg += f"{os.linesep}{error.response.text}{os.linesep}"
+ error.args = (msg,) + error.args[1:]
+ if error.response.status_code == 504 and not stream and request_parameters.task == "conversational":
+ msg = str(error.args[0])
+ msg += (
+ f"{os.linesep}Note: the request timed out before the model finished generating."
+ " If you are generating long outputs (e.g. long reasoning traces), pass `stream=True`"
+ " to receive tokens as they are generated and avoid hitting this timeout."
+ )
+ error.args = (msg,) + error.args[1:]
+ raise
+
+ def audio_classification(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ function_to_apply: Optional["AudioClassificationOutputTransform"] = None,
+ ) -> list[AudioClassificationOutputElement]:
+ """
+ Perform audio classification on the provided audio content.
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an
+ audio file.
+ model (`str`, *optional*):
+ The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub
+ or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
+ audio classification will be used.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ function_to_apply (`"AudioClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+
+ Returns:
+ `list[AudioClassificationOutputElement]`: List of [`AudioClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.audio_classification("audio.flac")
+ [
+ AudioClassificationOutputElement(score=0.4976358711719513, label='hap'),
+ AudioClassificationOutputElement(score=0.3677836060523987, label='neu'),
+ ...
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="audio-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={"function_to_apply": function_to_apply, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return AudioClassificationOutputElement.parse_obj_as_list(response)
+
+ def audio_to_audio(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ ) -> list[AudioToAudioOutputElement]:
+ """
+ Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation).
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an
+ audio file.
+ model (`str`, *optional*):
+ The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub
+ or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
+ audio_to_audio will be used.
+
+ Returns:
+ `list[AudioToAudioOutputElement]`: A list of [`AudioToAudioOutputElement`] items containing audios label, content-type, and audio content in blob.
+
+ Raises:
+ `InferenceTimeoutError`:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> audio_output = client.audio_to_audio("audio.flac")
+ >>> for i, item in enumerate(audio_output):
+ >>> with open(f"output_{i}.flac", "wb") as f:
+ f.write(item.blob)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="audio-to-audio", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ audio_output = AudioToAudioOutputElement.parse_obj_as_list(response)
+ for item in audio_output:
+ item.blob = base64.b64decode(item.blob)
+ return audio_output
+
+ def automatic_speech_recognition(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ extra_body: dict | None = None,
+ ) -> AutomaticSpeechRecognitionOutput:
+ """
+ Perform automatic speech recognition (ASR or audio-to-text) on the given audio content.
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file.
+ model (`str`, *optional*):
+ The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for ASR will be used.
+ extra_body (`dict`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ [`AutomaticSpeechRecognitionOutput`]: An item containing the transcribed text and optionally the timestamp chunks.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.automatic_speech_recognition("hello_world.flac").text
+ "hello world"
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="automatic-speech-recognition", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={**(extra_body or {})},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_params=request_parameters)
+ return AutomaticSpeechRecognitionOutput.parse_obj_as_instance(response)
+
+ @overload
+ def chat_completion( # type: ignore
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: Literal[False] = False,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput: ...
+
+ @overload
+ def chat_completion( # type: ignore
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: Literal[True] = True,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> Iterable[ChatCompletionStreamOutput]: ...
+
+ @overload
+ def chat_completion(
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: bool = False,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput | Iterable[ChatCompletionStreamOutput]: ...
+
+ def chat_completion(
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: bool = False,
+ # Parameters from ChatCompletionInput (handled manually)
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput | Iterable[ChatCompletionStreamOutput]:
+ """
+ A method for completing conversations using a specified language model.
+
+ > [!TIP]
+ > The `client.chat_completion` method is aliased as `client.chat.completions.create` for compatibility with OpenAI's client.
+ > Inputs and outputs are strictly the same and using either syntax will yield the same results.
+ > Check out the [Inference guide](https://huggingface.co/docs/huggingface_hub/guides/inference#openai-compatibility)
+ > for more details about OpenAI's compatibility.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ messages (List of [`ChatCompletionInputMessage`]):
+ Conversation history consisting of roles and content pairs.
+ model (`str`, *optional*):
+ The model to use for chat-completion. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for chat-based text-generation will be used.
+ See https://huggingface.co/tasks/text-generation for more details.
+ If `model` is a model ID, it is passed to the server as the `model` parameter. If you want to define a
+ custom URL while setting `model` in the request payload, you must set `base_url` when initializing [`InferenceClient`].
+ frequency_penalty (`float`, *optional*):
+ Penalizes new tokens based on their existing frequency
+ in the text so far. Range: [-2.0, 2.0]. Defaults to 0.0.
+ logit_bias (`list[float]`, *optional*):
+ Adjusts the likelihood of specific tokens appearing in the generated output.
+ logprobs (`bool`, *optional*):
+ Whether to return log probabilities of the output tokens or not. If true, returns the log
+ probabilities of each output token returned in the content of message.
+ max_tokens (`int`, *optional*):
+ Maximum number of tokens allowed in the response. Defaults to 100.
+ n (`int`, *optional*):
+ The number of completions to generate for each prompt.
+ presence_penalty (`float`, *optional*):
+ Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the
+ text so far, increasing the model's likelihood to talk about new topics.
+ response_format ([`ChatCompletionInputGrammarType`], *optional*):
+ Grammar constraints. Can be either a JSONSchema or a regex.
+ seed (Optional[`int`], *optional*):
+ Seed for reproducible control flow. Defaults to None.
+ stop (`list[str]`, *optional*):
+ Up to four strings which trigger the end of the response.
+ Defaults to None.
+ stream (`bool`, *optional*):
+ Enable realtime streaming of responses. Defaults to False.
+ stream_options ([`ChatCompletionInputStreamOptions`], *optional*):
+ Options for streaming completions.
+ temperature (`float`, *optional*):
+ Controls randomness of the generations. Lower values ensure
+ less random completions. Range: [0, 2]. Defaults to 1.0.
+ top_logprobs (`int`, *optional*):
+ An integer between 0 and 5 specifying the number of most likely tokens to return at each token
+ position, each with an associated log probability. logprobs must be set to true if this parameter is
+ used.
+ top_p (`float`, *optional*):
+ Fraction of the most likely next words to sample from.
+ Must be between 0 and 1. Defaults to 1.0.
+ tool_choice ([`ChatCompletionInputToolChoiceClass`] or [`ChatCompletionInputToolChoiceEnum`], *optional*):
+ The tool to use for the completion. Defaults to "auto".
+ tool_prompt (`str`, *optional*):
+ A prompt to be appended before the tools.
+ tools (List of [`ChatCompletionInputTool`], *optional*):
+ A list of tools the model may call. Currently, only functions are supported as a tool. Use this to
+ provide a list of functions the model may generate JSON inputs for.
+ extra_body (`dict`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ [`ChatCompletionOutput`] or Iterable of [`ChatCompletionStreamOutput`]:
+ Generated text returned from the server:
+ - if `stream=False`, the generated text is returned as a [`ChatCompletionOutput`] (default).
+ - if `stream=True`, the generated text is returned token by token as a sequence of [`ChatCompletionStreamOutput`].
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> messages = [{"role": "user", "content": "What is the capital of France?"}]
+ >>> client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+ >>> client.chat_completion(messages, max_tokens=100)
+ ChatCompletionOutput(
+ choices=[
+ ChatCompletionOutputComplete(
+ finish_reason='eos_token',
+ index=0,
+ message=ChatCompletionOutputMessage(
+ role='assistant',
+ content='The capital of France is Paris.',
+ name=None,
+ tool_calls=None
+ ),
+ logprobs=None
+ )
+ ],
+ created=1719907176,
+ id='',
+ model='meta-llama/Meta-Llama-3-8B-Instruct',
+ object='text_completion',
+ system_fingerprint='2.0.4-sha-f426a33',
+ usage=ChatCompletionOutputUsage(
+ completion_tokens=8,
+ prompt_tokens=17,
+ total_tokens=25
+ )
+ )
+ ```
+
+ Example using streaming:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> messages = [{"role": "user", "content": "What is the capital of France?"}]
+ >>> client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+ >>> for token in client.chat_completion(messages, max_tokens=10, stream=True):
+ ... print(token)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content='The', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' capital', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ (...)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' may', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ ```
+
+ Example using OpenAI's syntax:
+ ```py
+ # instead of `from openai import OpenAI`
+ from huggingface_hub import InferenceClient
+
+ # instead of `client = OpenAI(...)`
+ client = InferenceClient(
+ base_url=...,
+ api_key=...,
+ )
+
+ output = client.chat.completions.create(
+ model="meta-llama/Meta-Llama-3-8B-Instruct",
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Count to 10"},
+ ],
+ stream=True,
+ max_tokens=1024,
+ )
+
+ for chunk in output:
+ print(chunk.choices[0].delta.content)
+ ```
+
+ Example using a third-party provider directly with extra (provider-specific) parameters. Usage will be billed on your Together AI account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="together", # Use Together AI provider
+ ... api_key="", # Pass your Together API key directly
+ ... )
+ >>> client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-8B-Instruct",
+ ... messages=[{"role": "user", "content": "What is the capital of France?"}],
+ ... extra_body={"safety_model": "Meta-Llama/Llama-Guard-7b"},
+ ... )
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="novita", # Use Novita provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-8B-Instruct",
+ ... messages=[{"role": "user", "content": "What is the capital of France?"}],
+ ... )
+ ```
+
+ Example using Image + Text as input:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+
+ # provide a remote URL
+ >>> image_url ="https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+ # or a base64-encoded image
+ >>> image_path = "/path/to/image.jpeg"
+ >>> with open(image_path, "rb") as f:
+ ... base64_image = base64.b64encode(f.read()).decode("utf-8")
+ >>> image_url = f"data:image/jpeg;base64,{base64_image}"
+
+ >>> client = InferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct")
+ >>> output = client.chat.completions.create(
+ ... messages=[
+ ... {
+ ... "role": "user",
+ ... "content": [
+ ... {
+ ... "type": "image_url",
+ ... "image_url": {"url": image_url},
+ ... },
+ ... {
+ ... "type": "text",
+ ... "text": "Describe this image in one sentence.",
+ ... },
+ ... ],
+ ... },
+ ... ],
+ ... )
+ >>> output
+ The image depicts the iconic Statue of Liberty situated in New York Harbor, New York, on a clear day.
+ ```
+
+ Example using tools:
+ ```py
+ >>> client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> messages = [
+ ... {
+ ... "role": "system",
+ ... "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.",
+ ... },
+ ... {
+ ... "role": "user",
+ ... "content": "What's the weather like the next 3 days in San Francisco, CA?",
+ ... },
+ ... ]
+ >>> tools = [
+ ... {
+ ... "type": "function",
+ ... "function": {
+ ... "name": "get_current_weather",
+ ... "description": "Get the current weather",
+ ... "parameters": {
+ ... "type": "object",
+ ... "properties": {
+ ... "location": {
+ ... "type": "string",
+ ... "description": "The city and state, e.g. San Francisco, CA",
+ ... },
+ ... "format": {
+ ... "type": "string",
+ ... "enum": ["celsius", "fahrenheit"],
+ ... "description": "The temperature unit to use. Infer this from the users location.",
+ ... },
+ ... },
+ ... "required": ["location", "format"],
+ ... },
+ ... },
+ ... },
+ ... {
+ ... "type": "function",
+ ... "function": {
+ ... "name": "get_n_day_weather_forecast",
+ ... "description": "Get an N-day weather forecast",
+ ... "parameters": {
+ ... "type": "object",
+ ... "properties": {
+ ... "location": {
+ ... "type": "string",
+ ... "description": "The city and state, e.g. San Francisco, CA",
+ ... },
+ ... "format": {
+ ... "type": "string",
+ ... "enum": ["celsius", "fahrenheit"],
+ ... "description": "The temperature unit to use. Infer this from the users location.",
+ ... },
+ ... "num_days": {
+ ... "type": "integer",
+ ... "description": "The number of days to forecast",
+ ... },
+ ... },
+ ... "required": ["location", "format", "num_days"],
+ ... },
+ ... },
+ ... },
+ ... ]
+
+ >>> response = client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-70B-Instruct",
+ ... messages=messages,
+ ... tools=tools,
+ ... tool_choice="auto",
+ ... max_tokens=500,
+ ... )
+ >>> response.choices[0].message.tool_calls[0].function
+ ChatCompletionOutputFunctionDefinition(
+ arguments={
+ 'location': 'San Francisco, CA',
+ 'format': 'fahrenheit',
+ 'num_days': 3
+ },
+ name='get_n_day_weather_forecast',
+ description=None
+ )
+ ```
+
+ Example using response_format:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> messages = [
+ ... {
+ ... "role": "user",
+ ... "content": "I saw a puppy a cat and a raccoon during my bike ride in the park. What did I see and when?",
+ ... },
+ ... ]
+ >>> response_format = {
+ ... "type": "json",
+ ... "value": {
+ ... "properties": {
+ ... "location": {"type": "string"},
+ ... "activity": {"type": "string"},
+ ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
+ ... "animals": {"type": "array", "items": {"type": "string"}},
+ ... },
+ ... "required": ["location", "activity", "animals_seen", "animals"],
+ ... },
+ ... }
+ >>> response = client.chat_completion(
+ ... messages=messages,
+ ... response_format=response_format,
+ ... max_tokens=500,
+ ... )
+ >>> response.choices[0].message.content
+ '{\n\n"activity": "bike ride",\n"animals": ["puppy", "cat", "raccoon"],\n"animals_seen": 3,\n"location": "park"}'
+ ```
+ """
+ # Since `chat_completion(..., model=xxx)` is also a payload parameter for the server, we need to handle 'model' differently.
+ # `self.model` takes precedence over 'model' argument for building URL.
+ # `model` takes precedence for payload value.
+ model_id_or_url = self.model or model
+ payload_model = model or self.model
+
+ # Get the provider helper
+ provider_helper = get_provider_helper(
+ self.provider,
+ task="conversational",
+ model=model_id_or_url
+ if model_id_or_url is not None and model_id_or_url.startswith(("http://", "https://"))
+ else payload_model,
+ )
+
+ # Prepare the payload
+ parameters = {
+ "model": payload_model,
+ "frequency_penalty": frequency_penalty,
+ "logit_bias": logit_bias,
+ "logprobs": logprobs,
+ "max_tokens": max_tokens,
+ "n": n,
+ "presence_penalty": presence_penalty,
+ "response_format": response_format,
+ "seed": seed,
+ "stop": stop,
+ "temperature": temperature,
+ "tool_choice": tool_choice,
+ "tool_prompt": tool_prompt,
+ "tools": tools,
+ "top_logprobs": top_logprobs,
+ "top_p": top_p,
+ "stream": stream,
+ "stream_options": stream_options,
+ **(extra_body or {}),
+ }
+ request_parameters = provider_helper.prepare_request(
+ inputs=messages,
+ parameters=parameters,
+ headers=self.headers,
+ model=model_id_or_url,
+ api_key=self.token,
+ )
+ data = self._inner_post(request_parameters, stream=stream)
+
+ if stream:
+ return _stream_chat_completion_response(data) # type: ignore
+
+ return ChatCompletionOutput.parse_obj_as_instance(data) # type: ignore
+
+ def document_question_answering(
+ self,
+ image: ContentT,
+ question: str,
+ *,
+ model: str | None = None,
+ doc_stride: int | None = None,
+ handle_impossible_answer: bool | None = None,
+ lang: str | None = None,
+ max_answer_len: int | None = None,
+ max_question_len: int | None = None,
+ max_seq_len: int | None = None,
+ top_k: int | None = None,
+ word_boxes: list[list[float] | str] | None = None,
+ ) -> list[DocumentQuestionAnsweringOutputElement]:
+ """
+ Answer questions on document images.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO]`):
+ The input image for the context. It can be raw bytes, an image file, or a URL to an online image.
+ question (`str`):
+ Question to be answered.
+ model (`str`, *optional*):
+ The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used.
+ Defaults to None.
+ doc_stride (`int`, *optional*):
+ If the words in the document are too long to fit with the question for the model, it will be split in
+ several chunks with some overlap. This argument controls the size of that overlap.
+ handle_impossible_answer (`bool`, *optional*):
+ Whether to accept impossible as an answer
+ lang (`str`, *optional*):
+ Language to use while running OCR. Defaults to english.
+ max_answer_len (`int`, *optional*):
+ The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
+ max_question_len (`int`, *optional*):
+ The maximum length of the question after tokenization. It will be truncated if needed.
+ max_seq_len (`int`, *optional*):
+ The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
+ model. The context will be split in several chunks (using doc_stride as overlap) if needed.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Can return less than top_k
+ answers if there are not enough options available within the context.
+ word_boxes (`list[Union[list[float], str`, *optional*):
+ A list of words and bounding boxes (normalized 0->1000). If provided, the inference will skip the OCR
+ step and use the provided bounding boxes instead.
+ Returns:
+ `list[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.document_question_answering(image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", question="What is the invoice number?")
+ [DocumentQuestionAnsweringOutputElement(answer='us-001', end=16, score=0.9999666213989258, start=16)]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="document-question-answering", model=model_id)
+ inputs: dict[str, Any] = {"question": question, "image": _b64_encode(image)}
+ request_parameters = provider_helper.prepare_request(
+ inputs=inputs,
+ parameters={
+ "doc_stride": doc_stride,
+ "handle_impossible_answer": handle_impossible_answer,
+ "lang": lang,
+ "max_answer_len": max_answer_len,
+ "max_question_len": max_question_len,
+ "max_seq_len": max_seq_len,
+ "top_k": top_k,
+ "word_boxes": word_boxes,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)
+
+ def feature_extraction(
+ self,
+ text: str | list[str],
+ *,
+ normalize: bool | None = None,
+ prompt_name: str | None = None,
+ truncate: bool | None = None,
+ truncation_direction: Literal["left", "right"] | None = None,
+ dimensions: int | None = None,
+ encoding_format: Literal["float", "base64"] | None = None,
+ model: str | None = None,
+ ) -> "np.ndarray":
+ """
+ Generate embeddings for a given text or batch of texts.
+
+ Args:
+ text (`str` or `list[str]`):
+ The text or list of texts to embed.
+ model (`str`, *optional*):
+ The model to use for the feature extraction task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended feature extraction model will be used.
+ Defaults to None.
+ normalize (`bool`, *optional*):
+ Whether to normalize the embeddings or not.
+ Only available on server powered by Text-Embedding-Inference.
+ prompt_name (`str`, *optional*):
+ The name of the prompt that should be used by for encoding. If not set, no prompt will be applied.
+ Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+ For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",...},
+ then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?"
+ because the prompt text will be prepended before any text to encode.
+ truncate (`bool`, *optional*):
+ Whether to truncate the embeddings or not.
+ Only available on server powered by Text-Embedding-Inference.
+ truncation_direction (`Literal["left", "right"]`, *optional*):
+ Which side of the input should be truncated when `truncate=True` is passed.
+ dimensions (`int`, *optional*):
+ The number of dimensions the resulting output embeddings should have.
+ Only available on OpenAI-compatible embedding endpoints.
+ encoding_format (`Literal["float", "base64"]`, *optional*):
+ The format of the output embeddings. Either "float" or "base64".
+ Only available on OpenAI-compatible embedding endpoints.
+
+ Returns:
+ `np.ndarray`: The embedding representing the input text(s) as a float32 numpy array.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.feature_extraction("Hi, who are you?")
+ array([[ 2.424802 , 2.93384 , 1.1750331 , ..., 1.240499, -0.13776633, -0.7889173 ],
+ [-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ],
+ ...,
+ [ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="feature-extraction", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "normalize": normalize,
+ "prompt_name": prompt_name,
+ "truncate": truncate,
+ "truncation_direction": truncation_direction,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ np = _import_numpy()
+ return np.array(provider_helper.get_response(response), dtype="float32")
+
+ def fill_mask(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ targets: list[str] | None = None,
+ top_k: int | None = None,
+ ) -> list[FillMaskOutputElement]:
+ """
+ Fill in a hole with a missing word (token to be precise).
+
+ Args:
+ text (`str`):
+ a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask).
+ model (`str`, *optional*):
+ The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used.
+ targets (`list[str`, *optional*):
+ When passed, the model will limit the scores to the passed targets instead of looking up in the whole
+ vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first
+ resulting token will be used (with a warning, and that might be slower).
+ top_k (`int`, *optional*):
+ When passed, overrides the number of predictions to return.
+ Returns:
+ `list[FillMaskOutputElement]`: a list of [`FillMaskOutputElement`] items containing the predicted label, associated
+ probability, token reference, and completed text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.fill_mask("The goal of life is .")
+ [
+ FillMaskOutputElement(score=0.06897063553333282, token=11098, token_str=' happiness', sequence='The goal of life is happiness.'),
+ FillMaskOutputElement(score=0.06554922461509705, token=45075, token_str=' immortality', sequence='The goal of life is immortality.')
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="fill-mask", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={"targets": targets, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return FillMaskOutputElement.parse_obj_as_list(response)
+
+ def image_classification(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ function_to_apply: Optional["ImageClassificationOutputTransform"] = None,
+ top_k: int | None = None,
+ ) -> list[ImageClassificationOutputElement]:
+ """
+ Perform image classification on the given image using the specified model.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to classify. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used.
+ function_to_apply (`"ImageClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ Returns:
+ `list[ImageClassificationOutputElement]`: a list of [`ImageClassificationOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.image_classification("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
+ [ImageClassificationOutputElement(label='Blenheim spaniel', score=0.9779096841812134), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"function_to_apply": function_to_apply, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return ImageClassificationOutputElement.parse_obj_as_list(response)
+
+ def image_segmentation(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ mask_threshold: float | None = None,
+ overlap_mask_area_threshold: float | None = None,
+ subtask: Optional["ImageSegmentationSubtask"] = None,
+ threshold: float | None = None,
+ ) -> list[ImageSegmentationOutputElement]:
+ """
+ Perform image segmentation on the given image using the specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to segment. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used.
+ mask_threshold (`float`, *optional*):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*):
+ Mask overlap threshold to eliminate small, disconnected segments.
+ subtask (`"ImageSegmentationSubtask"`, *optional*):
+ Segmentation task to be performed, depending on model capabilities.
+ threshold (`float`, *optional*):
+ Probability threshold to filter out predicted masks.
+ Returns:
+ `list[ImageSegmentationOutputElement]`: A list of [`ImageSegmentationOutputElement`] items containing the segmented masks and associated attributes.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.image_segmentation("cat.jpg")
+ [ImageSegmentationOutputElement(score=0.989008, label='LABEL_184', mask=), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-segmentation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "mask_threshold": mask_threshold,
+ "overlap_mask_area_threshold": overlap_mask_area_threshold,
+ "subtask": subtask,
+ "threshold": threshold,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ output = ImageSegmentationOutputElement.parse_obj_as_list(response)
+ for item in output:
+ item.mask = _b64_to_image(item.mask) # type: ignore
+ return output
+
+ def image_to_image(
+ self,
+ image: ContentT,
+ prompt: str | None = None,
+ *,
+ negative_prompt: str | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ model: str | None = None,
+ target_size: ImageToImageTargetSize | None = None,
+ **kwargs,
+ ) -> "Image":
+ """
+ Perform image-to-image translation using a specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image for translation. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ prompt (`str`, *optional*):
+ The text prompt to guide the image generation.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in image generation.
+ num_inference_steps (`int`, *optional*):
+ For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher
+ quality image at the expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ For diffusion models. A higher guidance scale value encourages the model to generate images closely
+ linked to the text prompt at the expense of lower image quality.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ target_size (`ImageToImageTargetSize`, *optional*):
+ The size in pixels of the output image. This parameter is only supported by some providers and for
+ specific models. It will be ignored when unsupported.
+
+ Returns:
+ `Image`: The translated image.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> image = client.image_to_image("cat.jpg", prompt="turn the cat into a tiger")
+ >>> image.save("tiger.jpg")
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-image", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "prompt": prompt,
+ "negative_prompt": negative_prompt,
+ "target_size": target_size,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ **kwargs,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return _bytes_to_image(response)
+
+ def image_to_video(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ prompt: str | None = None,
+ negative_prompt: str | None = None,
+ num_frames: float | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ seed: int | None = None,
+ target_size: ImageToVideoTargetSize | None = None,
+ **kwargs,
+ ) -> bytes:
+ """
+ Generate a video from an input image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to generate a video from. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ prompt (`str`, *optional*):
+ The text prompt to guide the video generation.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in video generation.
+ num_frames (`float`, *optional*):
+ The num_frames parameter determines how many video frames are generated.
+ num_inference_steps (`int`, *optional*):
+ For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher
+ quality image at the expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ For diffusion models. A higher guidance scale value encourages the model to generate videos closely
+ linked to the text prompt at the expense of lower image quality.
+ seed (`int`, *optional*):
+ The seed to use for the video generation.
+ target_size (`ImageToVideoTargetSize`, *optional*):
+ The size in pixel of the output video frames.
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality video at the
+ expense of slower inference.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+
+ Returns:
+ `bytes`: The generated video.
+
+ Examples:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> video = client.image_to_video("cat.jpg", model="Wan-AI/Wan2.2-I2V-A14B", prompt="turn the cat into a tiger")
+ >>> with open("tiger.mp4", "wb") as f:
+ ... f.write(video)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-video", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "prompt": prompt,
+ "negative_prompt": negative_prompt,
+ "num_frames": num_frames,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ "seed": seed,
+ "target_size": target_size,
+ **kwargs,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return response
+
+ def image_to_text(self, image: ContentT, *, model: str | None = None) -> ImageToTextOutput:
+ """
+ Takes an input image and return text.
+
+ Models can have very different outputs depending on your use case (image captioning, optical character recognition
+ (OCR), Pix2Struct, etc.). Please have a look to the model card to learn more about a model's specificities.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to caption. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ [`ImageToTextOutput`]: The generated text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.image_to_text("cat.jpg")
+ 'a cat standing in a grassy field '
+ >>> client.image_to_text("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
+ 'a dog laying on the grass next to a flower pot '
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-text", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ output_list: list[ImageToTextOutput] = ImageToTextOutput.parse_obj_as_list(response)
+ return output_list[0]
+
+ def object_detection(
+ self, image: ContentT, *, model: str | None = None, threshold: float | None = None
+ ) -> list[ObjectDetectionOutputElement]:
+ """
+ Perform object detection on the given image using the specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to detect objects on. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for object detection. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for object detection (DETR) will be used.
+ threshold (`float`, *optional*):
+ The probability necessary to make a prediction.
+ Returns:
+ `list[ObjectDetectionOutputElement]`: A list of [`ObjectDetectionOutputElement`] items containing the bounding boxes and associated attributes.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+ `ValueError`:
+ If the request output is not a List.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.object_detection("people.jpg")
+ [ObjectDetectionOutputElement(score=0.9486683011054993, label='person', box=ObjectDetectionBoundingBox(xmin=59, ymin=39, xmax=420, ymax=510)), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="object-detection", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"threshold": threshold},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return ObjectDetectionOutputElement.parse_obj_as_list(response)
+
+ def question_answering(
+ self,
+ question: str,
+ context: str,
+ *,
+ model: str | None = None,
+ align_to_words: bool | None = None,
+ doc_stride: int | None = None,
+ handle_impossible_answer: bool | None = None,
+ max_answer_len: int | None = None,
+ max_question_len: int | None = None,
+ max_seq_len: int | None = None,
+ top_k: int | None = None,
+ ) -> QuestionAnsweringOutputElement | list[QuestionAnsweringOutputElement]:
+ """
+ Retrieve the answer to a question from a given text.
+
+ Args:
+ question (`str`):
+ Question to be answered.
+ context (`str`):
+ The context of the question.
+ model (`str`):
+ The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint.
+ align_to_words (`bool`, *optional*):
+ Attempts to align the answer to real words. Improves quality on space separated languages. Might hurt
+ on non-space-separated languages (like Japanese or Chinese)
+ doc_stride (`int`, *optional*):
+ If the context is too long to fit with the question for the model, it will be split in several chunks
+ with some overlap. This argument controls the size of that overlap.
+ handle_impossible_answer (`bool`, *optional*):
+ Whether to accept impossible as an answer.
+ max_answer_len (`int`, *optional*):
+ The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
+ max_question_len (`int`, *optional*):
+ The maximum length of the question after tokenization. It will be truncated if needed.
+ max_seq_len (`int`, *optional*):
+ The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
+ model. The context will be split in several chunks (using docStride as overlap) if needed.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Note that we return less than
+ topk answers if there are not enough options available within the context.
+
+ Returns:
+ Union[`QuestionAnsweringOutputElement`, list[`QuestionAnsweringOutputElement`]]:
+ When top_k is 1 or not provided, it returns a single `QuestionAnsweringOutputElement`.
+ When top_k is greater than 1, it returns a list of `QuestionAnsweringOutputElement`.
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.question_answering(question="What's my name?", context="My name is Clara and I live in Berkeley.")
+ QuestionAnsweringOutputElement(answer='Clara', end=16, score=0.9326565265655518, start=11)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"question": question, "context": context},
+ parameters={
+ "align_to_words": align_to_words,
+ "doc_stride": doc_stride,
+ "handle_impossible_answer": handle_impossible_answer,
+ "max_answer_len": max_answer_len,
+ "max_question_len": max_question_len,
+ "max_seq_len": max_seq_len,
+ "top_k": top_k,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ # Parse the response as a single `QuestionAnsweringOutputElement` when top_k is 1 or not provided, or a list of `QuestionAnsweringOutputElement` to ensure backward compatibility.
+ output = QuestionAnsweringOutputElement.parse_obj(response)
+ return output
+
+ def sentence_similarity(
+ self, sentence: str, other_sentences: list[str], *, model: str | None = None
+ ) -> list[float]:
+ """
+ Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.
+
+ Args:
+ sentence (`str`):
+ The main sentence to compare to others.
+ other_sentences (`list[str]`):
+ The list of sentences to compare to.
+ model (`str`, *optional*):
+ The model to use for the sentence similarity task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended sentence similarity model will be used.
+ Defaults to None.
+
+ Returns:
+ `list[float]`: The embedding representing the input text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.sentence_similarity(
+ ... "Machine learning is so easy.",
+ ... other_sentences=[
+ ... "Deep learning is so straightforward.",
+ ... "This is so difficult, like rocket science.",
+ ... "I can't believe how much I struggled with this.",
+ ... ],
+ ... )
+ [0.7785726189613342, 0.45876261591911316, 0.2906220555305481]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="sentence-similarity", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"source_sentence": sentence, "sentences": other_sentences},
+ parameters={},
+ extra_payload={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ def summarization(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ clean_up_tokenization_spaces: bool | None = None,
+ generate_parameters: dict[str, Any] | None = None,
+ truncation: Optional["SummarizationTruncationStrategy"] = None,
+ ) -> SummarizationOutput:
+ """
+ Generate a summary of a given text using a specified model.
+
+ Args:
+ text (`str`):
+ The input text to summarize.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for summarization will be used.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether to clean up the potential extra spaces in the text output.
+ generate_parameters (`dict[str, Any]`, *optional*):
+ Additional parametrization of the text generation algorithm.
+ truncation (`"SummarizationTruncationStrategy"`, *optional*):
+ The truncation strategy to use.
+ Returns:
+ [`SummarizationOutput`]: The generated summary text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.summarization("The Eiffel tower...")
+ SummarizationOutput(generated_text="The Eiffel tower is one of the most famous landmarks in the world....")
+ ```
+ """
+ parameters = {
+ "clean_up_tokenization_spaces": clean_up_tokenization_spaces,
+ "generate_parameters": generate_parameters,
+ "truncation": truncation,
+ }
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="summarization", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters=parameters,
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return SummarizationOutput.parse_obj_as_list(response)[0]
+
+ def table_question_answering(
+ self,
+ table: dict[str, Any],
+ query: str,
+ *,
+ model: str | None = None,
+ padding: Optional["Padding"] = None,
+ sequential: bool | None = None,
+ truncation: bool | None = None,
+ ) -> TableQuestionAnsweringOutputElement:
+ """
+ Retrieve the answer to a question from information given in a table.
+
+ Args:
+ table (`str`):
+ A table of data represented as a dict of lists where entries are headers and the lists are all the
+ values, all lists must have the same size.
+ query (`str`):
+ The query in plain text that you want to ask the table.
+ model (`str`):
+ The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
+ Hub or a URL to a deployed Inference Endpoint.
+ padding (`"Padding"`, *optional*):
+ Activates and controls padding.
+ sequential (`bool`, *optional*):
+ Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
+ inference to be done sequentially to extract relations within sequences, given their conversational
+ nature.
+ truncation (`bool`, *optional*):
+ Activates and controls truncation.
+
+ Returns:
+ [`TableQuestionAnsweringOutputElement`]: a table question answering output containing the answer, coordinates, cells and the aggregator used.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> query = "How many stars does the transformers repository have?"
+ >>> table = {"Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"]}
+ >>> client.table_question_answering(table, query, model="google/tapas-base-finetuned-wtq")
+ TableQuestionAnsweringOutputElement(answer='36542', coordinates=[[0, 1]], cells=['36542'], aggregator='AVERAGE')
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="table-question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"query": query, "table": table},
+ parameters={"model": model, "padding": padding, "sequential": sequential, "truncation": truncation},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return TableQuestionAnsweringOutputElement.parse_obj_as_instance(response)
+
+ def tabular_classification(self, table: dict[str, Any], *, model: str | None = None) -> list[str]:
+ """
+ Classifying a target category (a group) based on a set of attributes.
+
+ Args:
+ table (`dict[str, Any]`):
+ Set of attributes to classify.
+ model (`str`, *optional*):
+ The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended tabular classification model will be used.
+ Defaults to None.
+
+ Returns:
+ `List`: a list of labels, one per row in the initial table.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> table = {
+ ... "fixed_acidity": ["7.4", "7.8", "10.3"],
+ ... "volatile_acidity": ["0.7", "0.88", "0.32"],
+ ... "citric_acid": ["0", "0", "0.45"],
+ ... "residual_sugar": ["1.9", "2.6", "6.4"],
+ ... "chlorides": ["0.076", "0.098", "0.073"],
+ ... "free_sulfur_dioxide": ["11", "25", "5"],
+ ... "total_sulfur_dioxide": ["34", "67", "13"],
+ ... "density": ["0.9978", "0.9968", "0.9976"],
+ ... "pH": ["3.51", "3.2", "3.23"],
+ ... "sulphates": ["0.56", "0.68", "0.82"],
+ ... "alcohol": ["9.4", "9.8", "12.6"],
+ ... }
+ >>> client.tabular_classification(table=table, model="julien-c/wine-quality")
+ ["5", "5", "5"]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="tabular-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=None,
+ extra_payload={"table": table},
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ def tabular_regression(self, table: dict[str, Any], *, model: str | None = None) -> list[float]:
+ """
+ Predicting a numerical target value given a set of attributes/features in a table.
+
+ Args:
+ table (`dict[str, Any]`):
+ Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.
+ model (`str`, *optional*):
+ The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended tabular regression model will be used.
+ Defaults to None.
+
+ Returns:
+ `List`: a list of predicted numerical target values.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> table = {
+ ... "Height": ["11.52", "12.48", "12.3778"],
+ ... "Length1": ["23.2", "24", "23.9"],
+ ... "Length2": ["25.4", "26.3", "26.5"],
+ ... "Length3": ["30", "31.2", "31.1"],
+ ... "Species": ["Bream", "Bream", "Bream"],
+ ... "Width": ["4.02", "4.3056", "4.6961"],
+ ... }
+ >>> client.tabular_regression(table, model="scikit-learn/Fish-Weight")
+ [110, 120, 130]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="tabular-regression", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=None,
+ parameters={},
+ extra_payload={"table": table},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ def text_classification(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ function_to_apply: Optional["TextClassificationOutputTransform"] = None,
+ ) -> list[TextClassificationOutputElement]:
+ """
+ Perform text classification (e.g. sentiment-analysis) on the given text.
+
+ Args:
+ text (`str`):
+ A string to be classified.
+ model (`str`, *optional*):
+ The model to use for the text classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
+ Defaults to None.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ function_to_apply (`"TextClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+
+ Returns:
+ `list[TextClassificationOutputElement]`: a list of [`TextClassificationOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.text_classification("I like you")
+ [
+ TextClassificationOutputElement(label='POSITIVE', score=0.9998695850372314),
+ TextClassificationOutputElement(label='NEGATIVE', score=0.0001304351753788069),
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "function_to_apply": function_to_apply,
+ "top_k": top_k,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return TextClassificationOutputElement.parse_obj_as_list(response)[0] # type: ignore
+
+ @overload
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[True],
+ stream: Literal[True],
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> Iterable[TextGenerationStreamOutput]: ...
+
+ @overload
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[True],
+ stream: Literal[False] | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> TextGenerationOutput: ...
+
+ @overload
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[False] | None = None,
+ stream: Literal[True],
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None, # Manual default value
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> Iterable[str]: ...
+
+ @overload
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[False] | None = None,
+ stream: Literal[False] | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str: ...
+
+ @overload
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: bool | None = None,
+ stream: bool | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str | TextGenerationOutput | Iterable[str] | Iterable[TextGenerationStreamOutput]: ...
+
+ def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: bool | None = None,
+ stream: bool | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str | TextGenerationOutput | Iterable[str] | Iterable[TextGenerationStreamOutput]:
+ """
+ Given a prompt, generate the following text.
+
+ > [!TIP]
+ > If you want to generate a response from chat messages, you should use the [`InferenceClient.chat_completion`] method.
+ > It accepts a list of messages instead of a single text prompt and handles the chat templating for you.
+
+ Args:
+ prompt (`str`):
+ Input text.
+ details (`bool`, *optional*):
+ By default, text_generation returns a string. Pass `details=True` if you want a detailed output (tokens,
+ probabilities, seed, finish reason, etc.). Only available for models running on with the
+ `text-generation-inference` backend.
+ stream (`bool`, *optional*):
+ By default, text_generation returns the full generated text. Pass `stream=True` if you want a stream of
+ tokens to be returned. Only available for models running on with the `text-generation-inference`
+ backend.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ adapter_id (`str`, *optional*):
+ Lora adapter id.
+ best_of (`int`, *optional*):
+ Generate best_of sequences and return the one if the highest token logprobs.
+ decoder_input_details (`bool`, *optional*):
+ Return the decoder input token logprobs and ids. You must set `details=True` as well for it to be taken
+ into account. Defaults to `False`.
+ do_sample (`bool`, *optional*):
+ Activate logits sampling
+ frequency_penalty (`float`, *optional*):
+ Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in
+ the text so far, decreasing the model's likelihood to repeat the same line verbatim.
+ grammar ([`TextGenerationInputGrammarType`], *optional*):
+ Grammar constraints. Can be either a JSONSchema or a regex.
+ max_new_tokens (`int`, *optional*):
+ Maximum number of generated tokens. Defaults to 100.
+ repetition_penalty (`float`, *optional*):
+ The parameter for repetition penalty. 1.0 means no penalty. See [this
+ paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+ return_full_text (`bool`, *optional*):
+ Whether to prepend the prompt to the generated text
+ seed (`int`, *optional*):
+ Random sampling seed
+ stop (`list[str]`, *optional*):
+ Stop generating tokens if a member of `stop` is generated.
+ temperature (`float`, *optional*):
+ The value used to module the logits distribution.
+ top_n_tokens (`int`, *optional*):
+ Return information about the `top_n_tokens` most likely tokens at each generation step, instead of
+ just the sampled token.
+ top_k (`int`, *optional`):
+ The number of highest probability vocabulary tokens to keep for top-k-filtering.
+ top_p (`float`, *optional`):
+ If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+ higher are kept for generation.
+ truncate (`int`, *optional`):
+ Truncate inputs tokens to the given size.
+ typical_p (`float`, *optional`):
+ Typical Decoding mass
+ See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+ watermark (`bool`, *optional*):
+ Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+ Returns:
+ `Union[str, TextGenerationOutput, Iterable[str], Iterable[TextGenerationStreamOutput]]`:
+ Generated text returned from the server:
+ - if `stream=False` and `details=False`, the generated text is returned as a `str` (default)
+ - if `stream=True` and `details=False`, the generated text is returned token by token as a `Iterable[str]`
+ - if `stream=False` and `details=True`, the generated text is returned with more details as a [`~huggingface_hub.TextGenerationOutput`]
+ - if `details=True` and `stream=True`, the generated text is returned token by token as a iterable of [`~huggingface_hub.TextGenerationStreamOutput`]
+
+ Raises:
+ `ValidationError`:
+ If input values are not valid. No HTTP call is made to the server.
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+
+ # Case 1: generate text
+ >>> client.text_generation("The huggingface_hub library is ", max_new_tokens=12)
+ '100% open source and built to be easy to use.'
+
+ # Case 2: iterate over the generated tokens. Useful for large generation.
+ >>> for token in client.text_generation("The huggingface_hub library is ", max_new_tokens=12, stream=True):
+ ... print(token)
+ 100
+ %
+ open
+ source
+ and
+ built
+ to
+ be
+ easy
+ to
+ use
+ .
+
+ # Case 3: get more details about the generation process.
+ >>> client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True)
+ TextGenerationOutput(
+ generated_text='100% open source and built to be easy to use.',
+ details=TextGenerationDetails(
+ finish_reason='length',
+ generated_tokens=12,
+ seed=None,
+ prefill=[
+ TextGenerationPrefillOutputToken(id=487, text='The', logprob=None),
+ TextGenerationPrefillOutputToken(id=53789, text=' hugging', logprob=-13.171875),
+ (...)
+ TextGenerationPrefillOutputToken(id=204, text=' ', logprob=-7.0390625)
+ ],
+ tokens=[
+ TokenElement(id=1425, text='100', logprob=-1.0175781, special=False),
+ TokenElement(id=16, text='%', logprob=-0.0463562, special=False),
+ (...)
+ TokenElement(id=25, text='.', logprob=-0.5703125, special=False)
+ ],
+ best_of_sequences=None
+ )
+ )
+
+ # Case 4: iterate over the generated tokens with more details.
+ # Last object is more complete, containing the full generated text and the finish reason.
+ >>> for details in client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True, stream=True):
+ ... print(details)
+ ...
+ TextGenerationStreamOutput(token=TokenElement(id=1425, text='100', logprob=-1.0175781, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=16, text='%', logprob=-0.0463562, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=1314, text=' open', logprob=-1.3359375, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=3178, text=' source', logprob=-0.28100586, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=273, text=' and', logprob=-0.5961914, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=3426, text=' built', logprob=-1.9423828, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-1.4121094, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=314, text=' be', logprob=-1.5224609, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=1833, text=' easy', logprob=-2.1132812, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-0.08520508, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=745, text=' use', logprob=-0.39453125, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(
+ id=25,
+ text='.',
+ logprob=-0.5703125,
+ special=False),
+ generated_text='100% open source and built to be easy to use.',
+ details=TextGenerationStreamOutputStreamDetails(finish_reason='length', generated_tokens=12, seed=None)
+ )
+
+ # Case 5: generate constrained output using grammar
+ >>> response = client.text_generation(
+ ... prompt="I saw a puppy a cat and a raccoon during my bike ride in the park",
+ ... model="HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
+ ... max_new_tokens=100,
+ ... repetition_penalty=1.3,
+ ... grammar={
+ ... "type": "json",
+ ... "value": {
+ ... "properties": {
+ ... "location": {"type": "string"},
+ ... "activity": {"type": "string"},
+ ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
+ ... "animals": {"type": "array", "items": {"type": "string"}},
+ ... },
+ ... "required": ["location", "activity", "animals_seen", "animals"],
+ ... },
+ ... },
+ ... )
+ >>> json.loads(response)
+ {
+ "activity": "bike riding",
+ "animals": ["puppy", "cat", "raccoon"],
+ "animals_seen": 3,
+ "location": "park"
+ }
+ ```
+ """
+ if decoder_input_details and not details:
+ warnings.warn(
+ "`decoder_input_details=True` has been passed to the server but `details=False` is set meaning that"
+ " the output from the server will be truncated."
+ )
+ decoder_input_details = False
+
+ # Build payload
+ parameters = {
+ "adapter_id": adapter_id,
+ "best_of": best_of,
+ "decoder_input_details": decoder_input_details,
+ "details": details,
+ "do_sample": do_sample,
+ "frequency_penalty": frequency_penalty,
+ "grammar": grammar,
+ "max_new_tokens": max_new_tokens,
+ "repetition_penalty": repetition_penalty,
+ "return_full_text": return_full_text,
+ "seed": seed,
+ "stop": stop,
+ "temperature": temperature,
+ "top_k": top_k,
+ "top_n_tokens": top_n_tokens,
+ "top_p": top_p,
+ "truncate": truncate,
+ "typical_p": typical_p,
+ "watermark": watermark,
+ }
+
+ # Remove some parameters if not a TGI server
+ unsupported_kwargs = _get_unsupported_text_generation_kwargs(model)
+ if len(unsupported_kwargs) > 0:
+ # The server does not support some parameters
+ # => means it is not a TGI server
+ # => remove unsupported parameters and warn the user
+
+ ignored_parameters = []
+ for key in unsupported_kwargs:
+ if parameters.get(key):
+ ignored_parameters.append(key)
+ parameters.pop(key, None)
+ if len(ignored_parameters) > 0:
+ warnings.warn(
+ "API endpoint/model for text-generation is not served via TGI. Ignoring following parameters:"
+ f" {', '.join(ignored_parameters)}.",
+ UserWarning,
+ )
+ if details:
+ warnings.warn(
+ "API endpoint/model for text-generation is not served via TGI. Parameter `details=True` will"
+ " be ignored meaning only the generated text will be returned.",
+ UserWarning,
+ )
+ details = False
+ if stream:
+ raise ValueError(
+ "API endpoint/model for text-generation is not served via TGI. Cannot return output as a stream."
+ " Please pass `stream=False` as input."
+ )
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-generation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters=parameters,
+ extra_payload={"stream": stream},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+
+ # Handle errors separately for more precise error messages
+ try:
+ bytes_output = self._inner_post(request_parameters, stream=stream or False)
+ except HfHubHTTPError as e:
+ match = MODEL_KWARGS_NOT_USED_REGEX.search(str(e))
+ if isinstance(e, BadRequestError) and match:
+ unused_params = [kwarg.strip("' ") for kwarg in match.group(1).split(",")]
+ _set_unsupported_text_generation_kwargs(model, unused_params)
+ return self.text_generation( # type: ignore
+ prompt=prompt,
+ details=details,
+ stream=stream,
+ model=model_id,
+ adapter_id=adapter_id,
+ best_of=best_of,
+ decoder_input_details=decoder_input_details,
+ do_sample=do_sample,
+ frequency_penalty=frequency_penalty,
+ grammar=grammar,
+ max_new_tokens=max_new_tokens,
+ repetition_penalty=repetition_penalty,
+ return_full_text=return_full_text,
+ seed=seed,
+ stop=stop,
+ temperature=temperature,
+ top_k=top_k,
+ top_n_tokens=top_n_tokens,
+ top_p=top_p,
+ truncate=truncate,
+ typical_p=typical_p,
+ watermark=watermark,
+ )
+ raise_text_generation_error(e)
+
+ # Parse output
+ if stream:
+ return _stream_text_generation_response(bytes_output, details) # type: ignore
+
+ data = _bytes_to_dict(bytes_output) # type: ignore
+
+ # Data can be a single element (dict) or an iterable of dicts where we select the first element of.
+ if isinstance(data, list):
+ data = data[0]
+ response = provider_helper.get_response(data, request_parameters)
+ return TextGenerationOutput.parse_obj_as_instance(response) if details else response["generated_text"]
+
+ def text_to_image(
+ self,
+ prompt: str,
+ *,
+ negative_prompt: str | None = None,
+ height: int | None = None,
+ width: int | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ model: str | None = None,
+ scheduler: str | None = None,
+ seed: int | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> "Image":
+ """
+ Generate an image based on a given text using a specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ prompt (`str`):
+ The prompt to generate an image from.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in image generation.
+ height (`int`, *optional*):
+ The height in pixels of the output image
+ width (`int`, *optional*):
+ The width in pixels of the output image
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ prompt, but values too high may cause saturation and other artifacts.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-image model will be used.
+ Defaults to None.
+ scheduler (`str`, *optional*):
+ Override the scheduler with a compatible one.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+
+ Returns:
+ `Image`: The generated image.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+
+ >>> image = client.text_to_image("An astronaut riding a horse on the moon.")
+ >>> image.save("astronaut.png")
+
+ >>> image = client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... negative_prompt="low resolution, blurry",
+ ... model="stabilityai/stable-diffusion-2-1",
+ ... )
+ >>> image.save("better_astronaut.png")
+ ```
+ Example using a third-party provider directly. Usage will be billed on your fal.ai account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="fal-ai", # Use fal.ai provider
+ ... api_key="fal-ai-api-key", # Pass your fal.ai API key
+ ... )
+ >>> image = client.text_to_image(
+ ... "A majestic lion in a fantasy forest",
+ ... model="black-forest-labs/FLUX.1-schnell",
+ ... )
+ >>> image.save("lion.png")
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> image = client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... model="black-forest-labs/FLUX.1-dev",
+ ... )
+ >>> image.save("astronaut.png")
+ ```
+
+ Example using Replicate provider with extra parameters
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> image = client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... model="black-forest-labs/FLUX.1-schnell",
+ ... extra_body={"output_quality": 100},
+ ... )
+ >>> image.save("astronaut.png")
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-image", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters={
+ "negative_prompt": negative_prompt,
+ "height": height,
+ "width": width,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ "scheduler": scheduler,
+ "seed": seed,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return _bytes_to_image(response)
+
+ def text_to_video(
+ self,
+ prompt: str,
+ *,
+ model: str | None = None,
+ guidance_scale: float | None = None,
+ negative_prompt: list[str] | None = None,
+ num_frames: float | None = None,
+ num_inference_steps: int | None = None,
+ seed: int | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> bytes:
+ """
+ Generate a video based on a given text.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ prompt (`str`):
+ The prompt to generate a video from.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-video model will be used.
+ Defaults to None.
+ guidance_scale (`float`, *optional*):
+ A higher guidance scale value encourages the model to generate videos closely linked to the text
+ prompt, but values too high may cause saturation and other artifacts.
+ negative_prompt (`list[str]`, *optional*):
+ One or several prompt to guide what NOT to include in video generation.
+ num_frames (`float`, *optional*):
+ The num_frames parameter determines how many video frames are generated.
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality video at the
+ expense of slower inference.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+
+ Returns:
+ `bytes`: The generated video.
+
+ Example:
+
+ Example using a third-party provider directly. Usage will be billed on your fal.ai account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="fal-ai", # Using fal.ai provider
+ ... api_key="fal-ai-api-key", # Pass your fal.ai API key
+ ... )
+ >>> video = client.text_to_video(
+ ... "A majestic lion running in a fantasy forest",
+ ... model="tencent/HunyuanVideo",
+ ... )
+ >>> with open("lion.mp4", "wb") as file:
+ ... file.write(video)
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Using replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> video = client.text_to_video(
+ ... "A cat running in a park",
+ ... model="genmo/mochi-1-preview",
+ ... )
+ >>> with open("cat.mp4", "wb") as file:
+ ... file.write(video)
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-video", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters={
+ "guidance_scale": guidance_scale,
+ "negative_prompt": negative_prompt,
+ "num_frames": num_frames,
+ "num_inference_steps": num_inference_steps,
+ "seed": seed,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return response
+
+ def text_to_speech(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ do_sample: bool | None = None,
+ early_stopping: Union[bool, "TextToSpeechEarlyStoppingEnum"] | None = None,
+ epsilon_cutoff: float | None = None,
+ eta_cutoff: float | None = None,
+ max_length: int | None = None,
+ max_new_tokens: int | None = None,
+ min_length: int | None = None,
+ min_new_tokens: int | None = None,
+ num_beam_groups: int | None = None,
+ num_beams: int | None = None,
+ penalty_alpha: float | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_p: float | None = None,
+ typical_p: float | None = None,
+ use_cache: bool | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> bytes:
+ """
+ Synthesize an audio of a voice pronouncing a given text.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ text (`str`):
+ The text to synthesize.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-speech model will be used.
+ Defaults to None.
+ do_sample (`bool`, *optional*):
+ Whether to use sampling instead of greedy decoding when generating new tokens.
+ early_stopping (`Union[bool, "TextToSpeechEarlyStoppingEnum"]`, *optional*):
+ Controls the stopping condition for beam-based methods.
+ epsilon_cutoff (`float`, *optional*):
+ If set to float strictly between 0 and 1, only tokens with a conditional probability greater than
+ epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on
+ the size of the model. See [Truncation Sampling as Language Model
+ Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ eta_cutoff (`float`, *optional*):
+ Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly
+ between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff)
+ * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token
+ probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3,
+ depending on the size of the model. See [Truncation Sampling as Language Model
+ Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ max_length (`int`, *optional*):
+ The maximum length (in tokens) of the generated text, including the input.
+ max_new_tokens (`int`, *optional*):
+ The maximum number of tokens to generate. Takes precedence over max_length.
+ min_length (`int`, *optional*):
+ The minimum length (in tokens) of the generated text, including the input.
+ min_new_tokens (`int`, *optional*):
+ The minimum number of tokens to generate. Takes precedence over min_length.
+ num_beam_groups (`int`, *optional*):
+ Number of groups to divide num_beams into in order to ensure diversity among different groups of beams.
+ See [this paper](https://hf.co/papers/1610.02424) for more details.
+ num_beams (`int`, *optional*):
+ Number of beams to use for beam search.
+ penalty_alpha (`float`, *optional*):
+ The value balances the model confidence and the degeneration penalty in contrastive search decoding.
+ temperature (`float`, *optional*):
+ The value used to modulate the next token probabilities.
+ top_k (`int`, *optional*):
+ The number of highest probability vocabulary tokens to keep for top-k-filtering.
+ top_p (`float`, *optional*):
+ If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
+ top_p or higher are kept for generation.
+ typical_p (`float`, *optional*):
+ Local typicality measures how similar the conditional probability of predicting a target token next is
+ to the expected conditional probability of predicting a random token next, given the partial text
+ already generated. If set to float < 1, the smallest set of the most locally typical tokens with
+ probabilities that add up to typical_p or higher are kept for generation. See [this
+ paper](https://hf.co/papers/2202.00666) for more details.
+ use_cache (`bool`, *optional*):
+ Whether the model should use the past last key/values attentions to speed up decoding
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ `bytes`: The generated audio.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from pathlib import Path
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+
+ >>> audio = client.text_to_speech("Hello world")
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+
+ Example using a third-party provider directly. Usage will be billed on your Replicate account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate",
+ ... api_key="your-replicate-api-key", # Pass your Replicate API key directly
+ ... )
+ >>> audio = client.text_to_speech(
+ ... text="Hello world",
+ ... model="OuteAI/OuteTTS-0.3-500M",
+ ... )
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate",
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> audio =client.text_to_speech(
+ ... text="Hello world",
+ ... model="OuteAI/OuteTTS-0.3-500M",
+ ... )
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+ Example using Replicate provider with extra parameters
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> audio = client.text_to_speech(
+ ... "Hello, my name is Kororo, an awesome text-to-speech model.",
+ ... model="hexgrad/Kokoro-82M",
+ ... extra_body={"voice": "af_nicole"},
+ ... )
+ >>> Path("hello.flac").write_bytes(audio)
+ ```
+
+ Example music-gen using "YuE-s1-7B-anneal-en-cot" on fal.ai
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> lyrics = '''
+ ... [verse]
+ ... In the town where I was born
+ ... Lived a man who sailed to sea
+ ... And he told us of his life
+ ... In the land of submarines
+ ... So we sailed on to the sun
+ ... 'Til we found a sea of green
+ ... And we lived beneath the waves
+ ... In our yellow submarine
+
+ ... [chorus]
+ ... We all live in a yellow submarine
+ ... Yellow submarine, yellow submarine
+ ... We all live in a yellow submarine
+ ... Yellow submarine, yellow submarine
+ ... '''
+ >>> genres = "pavarotti-style tenor voice"
+ >>> client = InferenceClient(
+ ... provider="fal-ai",
+ ... model="m-a-p/YuE-s1-7B-anneal-en-cot",
+ ... api_key=...,
+ ... )
+ >>> audio = client.text_to_speech(lyrics, extra_body={"genres": genres})
+ >>> with open("output.mp3", "wb") as f:
+ ... f.write(audio)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-speech", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "do_sample": do_sample,
+ "early_stopping": early_stopping,
+ "epsilon_cutoff": epsilon_cutoff,
+ "eta_cutoff": eta_cutoff,
+ "max_length": max_length,
+ "max_new_tokens": max_new_tokens,
+ "min_length": min_length,
+ "min_new_tokens": min_new_tokens,
+ "num_beam_groups": num_beam_groups,
+ "num_beams": num_beams,
+ "penalty_alpha": penalty_alpha,
+ "temperature": temperature,
+ "top_k": top_k,
+ "top_p": top_p,
+ "typical_p": typical_p,
+ "use_cache": use_cache,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ response = provider_helper.get_response(response)
+ return response
+
+ def token_classification(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ aggregation_strategy: Optional["TokenClassificationAggregationStrategy"] = None,
+ ignore_labels: list[str] | None = None,
+ stride: int | None = None,
+ ) -> list[TokenClassificationOutputElement]:
+ """
+ Perform token classification on the given text.
+ Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text.
+
+ Args:
+ text (`str`):
+ A string to be classified.
+ model (`str`, *optional*):
+ The model to use for the token classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended token classification model will be used.
+ Defaults to None.
+ aggregation_strategy (`"TokenClassificationAggregationStrategy"`, *optional*):
+ The strategy used to fuse tokens based on model predictions
+ ignore_labels (`list[str`, *optional*):
+ A list of labels to ignore
+ stride (`int`, *optional*):
+ The number of overlapping tokens between chunks when splitting the input text.
+
+ Returns:
+ `list[TokenClassificationOutputElement]`: List of [`TokenClassificationOutputElement`] items containing the entity group, confidence score, word, start and end index.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.token_classification("My name is Sarah Jessica Parker but you can call me Jessica")
+ [
+ TokenClassificationOutputElement(
+ entity_group='PER',
+ score=0.9971321225166321,
+ word='Sarah Jessica Parker',
+ start=11,
+ end=31,
+ ),
+ TokenClassificationOutputElement(
+ entity_group='PER',
+ score=0.9773476123809814,
+ word='Jessica',
+ start=52,
+ end=59,
+ )
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="token-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "aggregation_strategy": aggregation_strategy,
+ "ignore_labels": ignore_labels,
+ "stride": stride,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return TokenClassificationOutputElement.parse_obj_as_list(response)
+
+ def translation(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ src_lang: str | None = None,
+ tgt_lang: str | None = None,
+ clean_up_tokenization_spaces: bool | None = None,
+ truncation: Optional["TranslationTruncationStrategy"] = None,
+ generate_parameters: dict[str, Any] | None = None,
+ ) -> TranslationOutput:
+ """
+ Convert text from one language to another.
+
+ Check out https://huggingface.co/tasks/translation for more information on how to choose the best model for
+ your specific use case. Source and target languages usually depend on the model.
+ However, it is possible to specify source and target languages for certain models. If you are working with one of these models,
+ you can use `src_lang` and `tgt_lang` arguments to pass the relevant information.
+
+ Args:
+ text (`str`):
+ A string to be translated.
+ model (`str`, *optional*):
+ The model to use for the translation task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended translation model will be used.
+ Defaults to None.
+ src_lang (`str`, *optional*):
+ The source language of the text. Required for models that can translate from multiple languages.
+ tgt_lang (`str`, *optional*):
+ Target language to translate to. Required for models that can translate to multiple languages.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether to clean up the potential extra spaces in the text output.
+ truncation (`"TranslationTruncationStrategy"`, *optional*):
+ The truncation strategy to use.
+ generate_parameters (`dict[str, Any]`, *optional*):
+ Additional parametrization of the text generation algorithm.
+
+ Returns:
+ [`TranslationOutput`]: The generated translated text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+ `ValueError`:
+ If only one of the `src_lang` and `tgt_lang` arguments are provided.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.translation("My name is Wolfgang and I live in Berlin")
+ 'Mein Name ist Wolfgang und ich lebe in Berlin.'
+ >>> client.translation("My name is Wolfgang and I live in Berlin", model="Helsinki-NLP/opus-mt-en-fr")
+ TranslationOutput(translation_text='Je m'appelle Wolfgang et je vis à Berlin.')
+ ```
+
+ Specifying languages:
+ ```py
+ >>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="fr_XX")
+ "Mon nom est Sarah Jessica Parker mais vous pouvez m'appeler Jessica"
+ ```
+ """
+ # Throw error if only one of `src_lang` and `tgt_lang` was given
+ if src_lang is not None and tgt_lang is None:
+ raise ValueError("You cannot specify `src_lang` without specifying `tgt_lang`.")
+
+ if src_lang is None and tgt_lang is not None:
+ raise ValueError("You cannot specify `tgt_lang` without specifying `src_lang`.")
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="translation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "src_lang": src_lang,
+ "tgt_lang": tgt_lang,
+ "clean_up_tokenization_spaces": clean_up_tokenization_spaces,
+ "truncation": truncation,
+ "generate_parameters": generate_parameters,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return TranslationOutput.parse_obj_as_list(response)[0]
+
+ def visual_question_answering(
+ self,
+ image: ContentT,
+ question: str,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ ) -> list[VisualQuestionAnsweringOutputElement]:
+ """
+ Answering open-ended questions based on an image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image for the context. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ question (`str`):
+ Question to be answered.
+ model (`str`, *optional*):
+ The model to use for the visual question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended visual question answering model will be used.
+ Defaults to None.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Note that we return less than
+ topk answers if there are not enough options available within the context.
+ Returns:
+ `list[VisualQuestionAnsweringOutputElement]`: a list of [`VisualQuestionAnsweringOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ `InferenceTimeoutError`:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.visual_question_answering(
+ ... image="https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg",
+ ... question="What is the animal doing?"
+ ... )
+ [
+ VisualQuestionAnsweringOutputElement(score=0.778609573841095, answer='laying down'),
+ VisualQuestionAnsweringOutputElement(score=0.6957435607910156, answer='sitting'),
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="visual-question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ extra_payload={"question": question, "image": _b64_encode(image)},
+ )
+ response = self._inner_post(request_parameters)
+ return VisualQuestionAnsweringOutputElement.parse_obj_as_list(response)
+
+ def zero_shot_classification(
+ self,
+ text: str,
+ candidate_labels: list[str],
+ *,
+ multi_label: bool | None = False,
+ hypothesis_template: str | None = None,
+ model: str | None = None,
+ ) -> list[ZeroShotClassificationOutputElement]:
+ """
+ Provide as input a text and a set of candidate labels to classify the input text.
+
+ Args:
+ text (`str`):
+ The input text to classify.
+ candidate_labels (`list[str]`):
+ The set of possible class labels to classify the text into.
+ multi_label (`bool`, *optional*):
+ Whether multiple candidate labels can be true. If false, the scores are normalized such that the sum of
+ the label likelihoods for each sequence is 1. If true, the labels are considered independent and
+ probabilities are normalized for each candidate.
+ hypothesis_template (`str`, *optional*):
+ The sentence used in conjunction with `candidate_labels` to attempt the text classification by
+ replacing the placeholder with the candidate labels.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot classification model will be used.
+
+
+ Returns:
+ `list[ZeroShotClassificationOutputElement]`: List of [`ZeroShotClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example with `multi_label=False`:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> text = (
+ ... "A new model offers an explanation for how the Galilean satellites formed around the solar system's"
+ ... "largest world. Konstantin Batygin did not set out to solve one of the solar system's most puzzling"
+ ... " mysteries when he went for a run up a hill in Nice, France."
+ ... )
+ >>> labels = ["space & cosmos", "scientific discovery", "microbiology", "robots", "archeology"]
+ >>> client.zero_shot_classification(text, labels)
+ [
+ ZeroShotClassificationOutputElement(label='scientific discovery', score=0.7961668968200684),
+ ZeroShotClassificationOutputElement(label='space & cosmos', score=0.18570658564567566),
+ ZeroShotClassificationOutputElement(label='microbiology', score=0.00730885099619627),
+ ZeroShotClassificationOutputElement(label='archeology', score=0.006258360575884581),
+ ZeroShotClassificationOutputElement(label='robots', score=0.004559356719255447),
+ ]
+ >>> client.zero_shot_classification(text, labels, multi_label=True)
+ [
+ ZeroShotClassificationOutputElement(label='scientific discovery', score=0.9829297661781311),
+ ZeroShotClassificationOutputElement(label='space & cosmos', score=0.755190908908844),
+ ZeroShotClassificationOutputElement(label='microbiology', score=0.0005462635890580714),
+ ZeroShotClassificationOutputElement(label='archeology', score=0.00047131875180639327),
+ ZeroShotClassificationOutputElement(label='robots', score=0.00030448526376858354),
+ ]
+ ```
+
+ Example with `multi_label=True` and a custom `hypothesis_template`:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+ >>> client.zero_shot_classification(
+ ... text="I really like our dinner and I'm very happy. I don't like the weather though.",
+ ... candidate_labels=["positive", "negative", "pessimistic", "optimistic"],
+ ... multi_label=True,
+ ... hypothesis_template="This text is {} towards the weather"
+ ... )
+ [
+ ZeroShotClassificationOutputElement(label='negative', score=0.9231801629066467),
+ ZeroShotClassificationOutputElement(label='pessimistic', score=0.8760990500450134),
+ ZeroShotClassificationOutputElement(label='optimistic', score=0.0008674879791215062),
+ ZeroShotClassificationOutputElement(label='positive', score=0.0005250611575320363)
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="zero-shot-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "candidate_labels": candidate_labels,
+ "multi_label": multi_label,
+ "hypothesis_template": hypothesis_template,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ output = _bytes_to_dict(response)
+ return ZeroShotClassificationOutputElement.parse_obj_as_list(output)
+
+ def zero_shot_image_classification(
+ self,
+ image: ContentT,
+ candidate_labels: list[str],
+ *,
+ model: str | None = None,
+ hypothesis_template: str | None = None,
+ ) -> list[ZeroShotImageClassificationOutputElement]:
+ """
+ Provide input image and text labels to predict text labels for the image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to caption. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ candidate_labels (`list[str]`):
+ The candidate labels for this image
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot image classification model will be used.
+ hypothesis_template (`str`, *optional*):
+ The sentence used in conjunction with `candidate_labels` to attempt the image classification by
+ replacing the placeholder with the candidate labels.
+
+ Returns:
+ `list[ZeroShotImageClassificationOutputElement]`: List of [`ZeroShotImageClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient()
+
+ >>> client.zero_shot_image_classification(
+ ... "https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg",
+ ... candidate_labels=["dog", "cat", "horse"],
+ ... )
+ [ZeroShotImageClassificationOutputElement(label='dog', score=0.956),...]
+ ```
+ """
+ # Raise ValueError if input is less than 2 labels
+ if len(candidate_labels) < 2:
+ raise ValueError("You must specify at least 2 classes to compare.")
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="zero-shot-image-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "candidate_labels": candidate_labels,
+ "hypothesis_template": hypothesis_template,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = self._inner_post(request_parameters)
+ return ZeroShotImageClassificationOutputElement.parse_obj_as_list(response)
+
+ def get_endpoint_info(self, *, model: str | None = None) -> dict[str, Any]:
+ """
+ Get information about the deployed endpoint.
+
+ This endpoint is only available on endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI).
+ Endpoints powered by `transformers` return an empty payload.
+
+ Args:
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ `dict[str, Any]`: Information about the endpoint.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> client.get_endpoint_info()
+ {
+ 'model_id': 'meta-llama/Meta-Llama-3-70B-Instruct',
+ 'model_sha': None,
+ 'model_dtype': 'torch.float16',
+ 'model_device_type': 'cuda',
+ 'model_pipeline_tag': None,
+ 'max_concurrent_requests': 128,
+ 'max_best_of': 2,
+ 'max_stop_sequences': 4,
+ 'max_input_length': 8191,
+ 'max_total_tokens': 8192,
+ 'waiting_served_ratio': 0.3,
+ 'max_batch_total_tokens': 1259392,
+ 'max_waiting_tokens': 20,
+ 'max_batch_size': None,
+ 'validation_workers': 32,
+ 'max_client_batch_size': 4,
+ 'version': '2.0.2',
+ 'sha': 'dccab72549635c7eb5ddb17f43f0b7cdff07c214',
+ 'docker_label': 'sha-dccab72'
+ }
+ ```
+ """
+ if self.provider != "hf-inference":
+ raise ValueError(f"Getting endpoint info is not supported on '{self.provider}'.")
+
+ model = model or self.model
+ if model is None:
+ raise ValueError("Model id not provided.")
+ if model.startswith(("http://", "https://")):
+ url = model.rstrip("/") + "/info"
+ else:
+ url = f"{constants.INFERENCE_ENDPOINT}/models/{model}/info"
+
+ response = get_session().get(url, headers=build_hf_headers(token=self.token))
+ hf_raise_for_status(response)
+ return response.json()
+
+ def health_check(self, model: str | None = None) -> bool:
+ """
+ Check the health of the deployed endpoint.
+
+ Health check is only available with Inference Endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI).
+
+ Args:
+ model (`str`, *optional*):
+ URL of the Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ `bool`: True if everything is working fine.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient("https://jzgu0buei5.us-east-1.aws.endpoints.huggingface.cloud")
+ >>> client.health_check()
+ True
+ ```
+ """
+ if self.provider != "hf-inference":
+ raise ValueError(f"Health check is not supported on '{self.provider}'.")
+
+ model = model or self.model
+ if model is None:
+ raise ValueError("Model id not provided.")
+ if not model.startswith(("http://", "https://")):
+ raise ValueError("Model must be an Inference Endpoint URL.")
+ url = model.rstrip("/") + "/health"
+
+ response = get_session().get(url, headers=build_hf_headers(token=self.token))
+ return response.status_code == 200
+
+ @property
+ def chat(self) -> "ProxyClientChat":
+ return ProxyClientChat(self)
+
+
+class _ProxyClient:
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ def __init__(self, client: InferenceClient):
+ self._client = client
+
+
+class ProxyClientChat(_ProxyClient):
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ @property
+ def completions(self) -> "ProxyClientChatCompletions":
+ return ProxyClientChatCompletions(self._client)
+
+
+class ProxyClientChatCompletions(_ProxyClient):
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ @property
+ def create(self):
+ return self._client.chat_completion
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_common.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..9aa3fbecc7cda3571fbb691bb087672e9cd9f871
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_common.py
@@ -0,0 +1,432 @@
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities used by both the sync and async inference clients."""
+
+import base64
+import io
+import json
+import logging
+import mimetypes
+from collections.abc import AsyncIterable, Iterable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, BinaryIO, Literal, NoReturn, Union, overload
+
+import httpx2
+
+from huggingface_hub.errors import (
+ GenerationError,
+ HfHubHTTPError,
+ IncompleteGenerationError,
+ OverloadedError,
+ TextGenerationError,
+ UnknownError,
+ ValidationError,
+)
+
+from ..utils import get_session, is_numpy_available, is_pillow_available
+from ._generated.types import ChatCompletionStreamOutput, TextGenerationStreamOutput
+
+
+if TYPE_CHECKING:
+ from PIL.Image import Image
+
+# TYPES
+UrlT = str
+PathT = Union[str, Path]
+ContentT = Union[bytes, BinaryIO, PathT, UrlT, "Image", bytearray, memoryview]
+
+# Use to set an Accept: image/png header
+TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"}
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RequestParameters:
+ url: str
+ task: str
+ model: str | None
+ json: str | dict | list | None
+ data: bytes | None
+ headers: dict[str, Any]
+
+
+class MimeBytes(bytes):
+ """
+ A bytes object with a mime type.
+ To be returned by `_prepare_payload_open_as_mime_bytes` in subclasses.
+
+ Example:
+ ```python
+ >>> b = MimeBytes(b"hello", "text/plain")
+ >>> isinstance(b, bytes)
+ True
+ >>> b.mime_type
+ 'text/plain'
+ ```
+ """
+
+ mime_type: str | None
+
+ def __new__(cls, data: bytes, mime_type: str | None = None):
+ obj = super().__new__(cls, data)
+ obj.mime_type = mime_type
+ if isinstance(data, MimeBytes) and mime_type is None:
+ obj.mime_type = data.mime_type
+ return obj
+
+
+## IMPORT UTILS
+
+
+def _import_numpy():
+ """Make sure `numpy` is installed on the machine."""
+ if not is_numpy_available():
+ raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).")
+ import numpy
+
+ return numpy
+
+
+def _import_pil_image():
+ """Make sure `PIL` is installed on the machine."""
+ if not is_pillow_available():
+ raise ImportError(
+ "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be"
+ " post-processed, use `client.post(...)` and get the raw response from the server."
+ )
+ from PIL import Image
+
+ return Image
+
+
+## ENCODING / DECODING UTILS
+
+
+@overload
+def _open_as_mime_bytes(content: ContentT) -> MimeBytes: ... # means "if input is not None, output is not None"
+
+
+@overload
+def _open_as_mime_bytes(content: Literal[None]) -> Literal[None]: ... # means "if input is None, output is None"
+
+
+def _open_as_mime_bytes(content: ContentT | None) -> MimeBytes | None:
+ """Open `content` as a binary file, either from a URL, a local path, raw bytes, or a PIL Image.
+
+ Do nothing if `content` is None.
+ """
+ # If content is None, yield None
+ if content is None:
+ return None
+
+ # If content is bytes, return it
+ if isinstance(content, bytes):
+ return MimeBytes(content)
+
+ # If content is raw binary data (bytearray, memoryview)
+ if isinstance(content, (bytearray, memoryview)):
+ return MimeBytes(bytes(content))
+
+ # If content is a binary file-like object
+ if hasattr(content, "read"): # duck-typing instead of isinstance(content, BinaryIO)
+ logger.debug("Reading content from BinaryIO")
+ data = content.read()
+ mime_type = mimetypes.guess_type(str(content.name))[0] if hasattr(content, "name") else None
+ if isinstance(data, str):
+ raise TypeError("Expected binary stream (bytes), but got text stream")
+ return MimeBytes(data, mime_type=mime_type)
+
+ # If content is a string => must be either a URL or a path
+ if isinstance(content, str):
+ if content.startswith("https://") or content.startswith("http://"):
+ logger.debug(f"Downloading content from {content}")
+ response = get_session().get(content)
+ mime_type = response.headers.get("Content-Type")
+ if mime_type is None:
+ mime_type = mimetypes.guess_type(content)[0]
+ return MimeBytes(response.content, mime_type=mime_type)
+
+ content = Path(content)
+ if not content.exists():
+ raise FileNotFoundError(
+ f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local"
+ " file. To pass raw content, please encode it as bytes first."
+ )
+
+ # If content is a Path => open it
+ if isinstance(content, Path):
+ logger.debug(f"Opening content from {content}")
+ return MimeBytes(content.read_bytes(), mime_type=mimetypes.guess_type(content)[0])
+
+ # If content is a PIL Image => convert to bytes
+ if is_pillow_available():
+ from PIL import Image
+
+ if isinstance(content, Image.Image):
+ logger.debug("Converting PIL Image to bytes")
+ buffer = io.BytesIO()
+ format = content.format or "PNG"
+ content.save(buffer, format=format)
+ return MimeBytes(buffer.getvalue(), mime_type=f"image/{format.lower()}")
+
+ # If nothing matched, raise error
+ raise TypeError(
+ f"Unsupported content type: {type(content)}. "
+ "Expected one of: bytes, bytearray, BinaryIO, memoryview, Path, str (URL or file path), or PIL.Image.Image."
+ )
+
+
+def _b64_encode(content: ContentT) -> str:
+ """Encode a raw file (image, audio) into base64. Can be bytes, an opened file, a path or a URL."""
+ raw_bytes = _open_as_mime_bytes(content)
+ return base64.b64encode(raw_bytes).decode()
+
+
+def _as_url(content: ContentT, default_mime_type: str) -> str:
+ if isinstance(content, str) and content.startswith(("http://", "https://", "data:")):
+ return content
+
+ # Convert content to bytes
+ raw_bytes = _open_as_mime_bytes(content)
+
+ # Get MIME type
+ mime_type = raw_bytes.mime_type or default_mime_type
+
+ # Encode content to base64
+ encoded_data = base64.b64encode(raw_bytes).decode()
+
+ # Build data URL
+ return f"data:{mime_type};base64,{encoded_data}"
+
+
+def _b64_to_image(encoded_image: str) -> "Image":
+ """Parse a base64-encoded string into a PIL Image."""
+ Image = _import_pil_image()
+ return Image.open(io.BytesIO(base64.b64decode(encoded_image)))
+
+
+def _bytes_to_list(content: bytes) -> list:
+ """Parse bytes from a Response object into a Python list.
+
+ Expects the response body to be JSON-encoded data.
+
+ NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a
+ dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+ """
+ return json.loads(content.decode())
+
+
+def _bytes_to_dict(content: bytes) -> dict:
+ """Parse bytes from a Response object into a Python dictionary.
+
+ Expects the response body to be JSON-encoded data.
+
+ NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a
+ list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+ """
+ return json.loads(content.decode())
+
+
+def _bytes_to_image(content: bytes) -> "Image":
+ """Parse bytes from a Response object into a PIL Image.
+
+ Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead.
+ """
+ Image = _import_pil_image()
+ return Image.open(io.BytesIO(content))
+
+
+def _as_dict(response: bytes | dict) -> dict:
+ return json.loads(response) if isinstance(response, bytes) else response
+
+
+## STREAMING UTILS
+
+
+def _stream_text_generation_response(
+ output_lines: Iterable[str], details: bool
+) -> Iterable[str] | Iterable[TextGenerationStreamOutput]:
+ """Used in `InferenceClient.text_generation`."""
+ # Parse ServerSentEvents
+ for line in output_lines:
+ try:
+ output = _format_text_generation_stream_output(line, details)
+ except StopIteration:
+ break
+ if output is not None:
+ yield output
+
+
+async def _async_stream_text_generation_response(
+ output_lines: AsyncIterable[str], details: bool
+) -> AsyncIterable[str] | AsyncIterable[TextGenerationStreamOutput]:
+ """Used in `AsyncInferenceClient.text_generation`."""
+ # Parse ServerSentEvents
+ async for line in output_lines:
+ try:
+ output = _format_text_generation_stream_output(line, details)
+ except StopIteration:
+ break
+ if output is not None:
+ yield output
+
+
+def _format_text_generation_stream_output(line: str, details: bool) -> str | TextGenerationStreamOutput | None:
+ if not line.startswith("data:"):
+ return None # empty line
+
+ if line.strip() == "data: [DONE]":
+ raise StopIteration("[DONE] signal received.")
+
+ # Decode payload
+ payload = line.lstrip("data:").rstrip("/n")
+ json_payload = json.loads(payload)
+
+ # Either an error as being returned
+ if json_payload.get("error") is not None:
+ raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+ # Or parse token payload
+ output = TextGenerationStreamOutput.parse_obj_as_instance(json_payload)
+ return output.token.text if not details else output
+
+
+def _stream_chat_completion_response(
+ lines: Iterable[str],
+) -> Iterable[ChatCompletionStreamOutput]:
+ """Used in `InferenceClient.chat_completion` if model is served with TGI."""
+ for line in lines:
+ try:
+ output = _format_chat_completion_stream_output(line)
+ except StopIteration:
+ break
+ if output is not None:
+ yield output
+
+
+async def _async_stream_chat_completion_response(
+ lines: AsyncIterable[str],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+ """Used in `AsyncInferenceClient.chat_completion`."""
+ async for line in lines:
+ try:
+ output = _format_chat_completion_stream_output(line)
+ except StopIteration:
+ break
+ if output is not None:
+ yield output
+
+
+def _format_chat_completion_stream_output(
+ line: str,
+) -> ChatCompletionStreamOutput | None:
+ if not line.startswith("data:"):
+ return None # empty line
+
+ if line.strip() == "data: [DONE]":
+ raise StopIteration("[DONE] signal received.")
+
+ # Decode payload
+ json_payload = json.loads(line.lstrip("data:").strip())
+
+ # Either an error as being returned
+ if json_payload.get("error") is not None:
+ raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+ # Or parse token payload
+ return ChatCompletionStreamOutput.parse_obj_as_instance(json_payload)
+
+
+async def _async_yield_from(client: httpx2.AsyncClient, response: httpx2.Response) -> AsyncIterable[str]:
+ async for line in response.aiter_lines():
+ yield line.strip()
+
+
+# "TGI servers" are servers running with the `text-generation-inference` backend.
+# This backend is the go-to solution to run large language models at scale. However,
+# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference`
+# solution is still in use.
+#
+# Both approaches have very similar APIs, but not exactly the same. What we do first in
+# the `text_generation` method is to assume the model is served via TGI. If we realize
+# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fall back to the
+# default API with a warning message. When that's the case, We remember the unsupported
+# attributes for this model in the `_UNSUPPORTED_TEXT_GENERATION_KWARGS` global variable.
+#
+# In addition, TGI servers have a built-in API route for chat-completion, which is not
+# available on the default API. We use this route to provide a more consistent behavior
+# when available.
+#
+# For more details, see https://github.com/huggingface/text-generation-inference and
+# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task.
+
+_UNSUPPORTED_TEXT_GENERATION_KWARGS: dict[str | None, list[str]] = {}
+
+
+def _set_unsupported_text_generation_kwargs(model: str | None, unsupported_kwargs: list[str]) -> None:
+ _UNSUPPORTED_TEXT_GENERATION_KWARGS.setdefault(model, []).extend(unsupported_kwargs)
+
+
+def _get_unsupported_text_generation_kwargs(model: str | None) -> list[str]:
+ return _UNSUPPORTED_TEXT_GENERATION_KWARGS.get(model, [])
+
+
+# TEXT GENERATION ERRORS
+# ----------------------
+# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
+# inference project (https://github.com/huggingface/text-generation-inference).
+# ----------------------
+
+
+def raise_text_generation_error(http_error: HfHubHTTPError) -> NoReturn:
+ """
+ Try to parse text-generation-inference error message and raise HTTPError in any case.
+
+ Args:
+ http_error (`HfHubHTTPError`):
+ The HTTPError that has been raised.
+ """
+ # Try to parse a Text Generation Inference error
+ if http_error.response is None:
+ raise http_error
+
+ try:
+ # Hacky way to retrieve payload in case of aiohttp error
+ payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
+ error = payload.get("error")
+ error_type = payload.get("error_type")
+ except Exception: # no payload
+ raise http_error
+
+ # If error_type => more information than `hf_raise_for_status`
+ if error_type is not None:
+ exception = _parse_text_generation_error(error, error_type)
+ raise exception from http_error
+
+ # Otherwise, fallback to default error
+ raise http_error
+
+
+def _parse_text_generation_error(error: str | None, error_type: str | None) -> TextGenerationError:
+ if error_type == "generation":
+ return GenerationError(error) # type: ignore
+ if error_type == "incomplete_generation":
+ return IncompleteGenerationError(error) # type: ignore
+ if error_type == "overloaded":
+ return OverloadedError(error) # type: ignore
+ if error_type == "validation":
+ return ValidationError(error) # type: ignore
+ return UnknownError(error) # type: ignore
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/__init__.py
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/_async_client.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/_async_client.py
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@@ -0,0 +1,3423 @@
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# WARNING
+# This entire file has been adapted from the sync-client code in `src/huggingface_hub/inference/_client.py`.
+# Any change in InferenceClient will be automatically reflected in AsyncInferenceClient.
+# To re-generate the code, run `make style` or `python ./utils/generate_async_inference_client.py --update`.
+# WARNING
+import asyncio
+import base64
+import logging
+import os
+import re
+import warnings
+from contextlib import AsyncExitStack
+from typing import TYPE_CHECKING, Any, AsyncIterable, Literal, Optional, Union, overload
+
+import httpx2
+
+from huggingface_hub import constants
+from huggingface_hub.errors import BadRequestError, HfHubHTTPError, InferenceTimeoutError
+from huggingface_hub.inference._common import (
+ TASKS_EXPECTING_IMAGES,
+ ContentT,
+ RequestParameters,
+ _async_stream_chat_completion_response,
+ _async_stream_text_generation_response,
+ _b64_encode,
+ _b64_to_image,
+ _bytes_to_dict,
+ _bytes_to_image,
+ _bytes_to_list,
+ _get_unsupported_text_generation_kwargs,
+ _import_numpy,
+ _set_unsupported_text_generation_kwargs,
+ raise_text_generation_error,
+)
+from huggingface_hub.inference._generated.types import (
+ AudioClassificationOutputElement,
+ AudioClassificationOutputTransform,
+ AudioToAudioOutputElement,
+ AutomaticSpeechRecognitionOutput,
+ ChatCompletionInputGrammarType,
+ ChatCompletionInputMessage,
+ ChatCompletionInputStreamOptions,
+ ChatCompletionInputTool,
+ ChatCompletionInputToolChoiceClass,
+ ChatCompletionInputToolChoiceEnum,
+ ChatCompletionOutput,
+ ChatCompletionStreamOutput,
+ DocumentQuestionAnsweringOutputElement,
+ FillMaskOutputElement,
+ ImageClassificationOutputElement,
+ ImageClassificationOutputTransform,
+ ImageSegmentationOutputElement,
+ ImageSegmentationSubtask,
+ ImageToImageTargetSize,
+ ImageToTextOutput,
+ ImageToVideoTargetSize,
+ ObjectDetectionOutputElement,
+ Padding,
+ QuestionAnsweringOutputElement,
+ SummarizationOutput,
+ SummarizationTruncationStrategy,
+ TableQuestionAnsweringOutputElement,
+ TextClassificationOutputElement,
+ TextClassificationOutputTransform,
+ TextGenerationInputGrammarType,
+ TextGenerationOutput,
+ TextGenerationStreamOutput,
+ TextToSpeechEarlyStoppingEnum,
+ TokenClassificationAggregationStrategy,
+ TokenClassificationOutputElement,
+ TranslationOutput,
+ TranslationTruncationStrategy,
+ VisualQuestionAnsweringOutputElement,
+ ZeroShotClassificationOutputElement,
+ ZeroShotImageClassificationOutputElement,
+)
+from huggingface_hub.inference._providers import PROVIDER_OR_POLICY_T, get_provider_helper
+from huggingface_hub.utils import (
+ build_hf_headers,
+ get_async_session,
+ hf_raise_for_status,
+ validate_hf_hub_args,
+)
+
+from .._common import _async_yield_from
+
+
+if TYPE_CHECKING:
+ import numpy as np
+ from PIL.Image import Image
+
+logger = logging.getLogger(__name__)
+
+
+MODEL_KWARGS_NOT_USED_REGEX = re.compile(r"The following `model_kwargs` are not used by the model: \[(.*?)\]")
+
+
+class AsyncInferenceClient:
+ """
+ Initialize a new Inference Client.
+
+ [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used
+ seamlessly with either the (free) Inference API, self-hosted Inference Endpoints, or third-party Inference Providers.
+
+ Args:
+ model (`str`, `optional`):
+ The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `meta-llama/Meta-Llama-3-8B-Instruct`
+ or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
+ automatically selected for the task.
+ Note: for better compatibility with OpenAI's client, `model` has been aliased as `base_url`. Those 2
+ arguments are mutually exclusive. If a URL is passed as `model` or `base_url` for chat completion, the `(/v1)/chat/completions` suffix path will be appended to the URL.
+ provider (`str`, *optional*):
+ Name of the provider to use for inference. Can be `"baseten"`, `"cerebras"`, `"cohere"`, `"deepinfra"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"novita"`, `"nscale"`, `"openai"`, `"ovhcloud"`, `"publicai"`, `"replicate"`, `"scaleway"`, `"together"`, `"wavespeed"` or `"zai-org"`.
+ Defaults to "auto": automatic routing, which defaults to "fastest" provider; you can
+ switch to "cheapest" or "preferred" provider order at https://hf.co/settings/inference-providers.
+ If model is a URL or `base_url` is passed, then `provider` is not used.
+ token (`str`, *optional*):
+ Hugging Face token. Will default to the locally saved token if not provided.
+ Note: for better compatibility with OpenAI's client, `token` has been aliased as `api_key`. Those 2
+ arguments are mutually exclusive and have the exact same behavior.
+ timeout (`float`, `optional`):
+ The maximum number of seconds to wait for a response from the server. Defaults to None, meaning it will loop until the server is available.
+ headers (`dict[str, str]`, `optional`):
+ Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
+ Values in this dictionary will override the default values.
+ bill_to (`str`, `optional`):
+ The billing account to use for the requests. By default the requests are billed on the user's account.
+ Requests can only be billed to an organization the user is a member of, and which has subscribed to Enterprise Hub.
+ cookies (`dict[str, str]`, `optional`):
+ Additional cookies to send to the server.
+ base_url (`str`, `optional`):
+ Base URL to run inference. This is a duplicated argument from `model` to make [`InferenceClient`]
+ follow the same pattern as `openai.OpenAI` client. Cannot be used if `model` is set. Defaults to None.
+ api_key (`str`, `optional`):
+ Token to use for authentication. This is a duplicated argument from `token` to make [`InferenceClient`]
+ follow the same pattern as `openai.OpenAI` client. Cannot be used if `token` is set. Defaults to None.
+ """
+
+ provider: PROVIDER_OR_POLICY_T | None
+
+ @validate_hf_hub_args
+ def __init__(
+ self,
+ model: str | None = None,
+ *,
+ provider: PROVIDER_OR_POLICY_T | None = None,
+ token: str | None = None,
+ timeout: float | None = None,
+ headers: dict[str, str] | None = None,
+ cookies: dict[str, str] | None = None,
+ bill_to: str | None = None,
+ # OpenAI compatibility
+ base_url: str | None = None,
+ api_key: str | None = None,
+ ) -> None:
+ if model is not None and base_url is not None:
+ raise ValueError(
+ "Received both `model` and `base_url` arguments. Please provide only one of them."
+ " `base_url` is an alias for `model` to make the API compatible with OpenAI's client."
+ " If using `base_url` for chat completion, the `/chat/completions` suffix path will be appended to the base url."
+ " When passing a URL as `model`, the client will not append any suffix path to it."
+ )
+ if token is not None and api_key is not None:
+ raise ValueError(
+ "Received both `token` and `api_key` arguments. Please provide only one of them."
+ " `api_key` is an alias for `token` to make the API compatible with OpenAI's client."
+ " It has the exact same behavior as `token`."
+ )
+ token = token if token is not None else api_key
+ if isinstance(token, bool):
+ raise TypeError("`token` must be a string or `None`.")
+ self.model: str | None = base_url or model
+ self.token: str | None = token
+
+ self.headers = {**headers} if headers is not None else {}
+ if bill_to is not None:
+ if (
+ constants.HUGGINGFACE_HEADER_X_BILL_TO in self.headers
+ and self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO] != bill_to
+ ):
+ warnings.warn(
+ f"Overriding existing '{self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO]}' value in headers with '{bill_to}'.",
+ UserWarning,
+ )
+ self.headers[constants.HUGGINGFACE_HEADER_X_BILL_TO] = bill_to
+
+ if token is not None and not token.startswith("hf_"):
+ warnings.warn(
+ "You've provided an external provider's API key, so requests will be billed directly by the provider. "
+ "The `bill_to` parameter is only applicable for Hugging Face billing and will be ignored.",
+ UserWarning,
+ )
+
+ # Configure provider
+ self.provider = provider # type: ignore[assignment]
+
+ self.cookies = cookies
+ self.timeout = timeout
+
+ self.exit_stack = AsyncExitStack()
+ self._async_client: Optional[httpx2.AsyncClient] = None
+
+ def __repr__(self):
+ return f""
+
+ async def __aenter__(self):
+ return self
+
+ async def __aexit__(self, exc_type, exc_value, traceback):
+ await self.close()
+
+ async def close(self):
+ """Close the client.
+
+ This method is automatically called when using the client as a context manager.
+ """
+ await self.exit_stack.aclose()
+
+ async def _get_async_client(self):
+ """Get a unique async client for this AsyncInferenceClient instance.
+
+ Returns the same client instance on subsequent calls, ensuring proper
+ connection reuse and resource management through the exit stack.
+ """
+ if self._async_client is None:
+ self._async_client = await self.exit_stack.enter_async_context(get_async_session())
+ return self._async_client
+
+ @overload
+ async def _inner_post( # type: ignore[misc]
+ self, request_parameters: RequestParameters, *, stream: Literal[False] = ...
+ ) -> bytes: ...
+
+ @overload
+ async def _inner_post( # type: ignore[misc]
+ self, request_parameters: RequestParameters, *, stream: Literal[True] = ...
+ ) -> AsyncIterable[str]: ...
+
+ @overload
+ async def _inner_post(
+ self, request_parameters: RequestParameters, *, stream: bool = False
+ ) -> bytes | AsyncIterable[str]: ...
+
+ async def _inner_post(
+ self, request_parameters: RequestParameters, *, stream: bool = False
+ ) -> bytes | AsyncIterable[str]:
+ """Make a request to the inference server."""
+
+ # TODO: this should be handled in provider helpers directly
+ if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers:
+ request_parameters.headers["Accept"] = "image/png"
+
+ try:
+ client = await self._get_async_client()
+ if stream:
+ response = await self.exit_stack.enter_async_context(
+ client.stream(
+ "POST",
+ request_parameters.url,
+ json=request_parameters.json,
+ data=request_parameters.data,
+ headers=request_parameters.headers,
+ cookies=self.cookies,
+ timeout=self.timeout,
+ )
+ )
+ hf_raise_for_status(response)
+ return _async_yield_from(client, response)
+ else:
+ response = await client.post(
+ request_parameters.url,
+ json=request_parameters.json,
+ data=request_parameters.data,
+ headers=request_parameters.headers,
+ cookies=self.cookies,
+ timeout=self.timeout,
+ )
+ hf_raise_for_status(response)
+ return response.content
+ except asyncio.TimeoutError as error:
+ # Convert any `TimeoutError` to a `InferenceTimeoutError`
+ raise InferenceTimeoutError(f"Inference call timed out: {request_parameters.url}") from error # type: ignore
+ except HfHubHTTPError as error:
+ if error.response.status_code == 422 and request_parameters.task != "unknown":
+ msg = str(error.args[0])
+ if len(error.response.text) > 0:
+ msg += f"{os.linesep}{error.response.text}{os.linesep}"
+ error.args = (msg,) + error.args[1:]
+ if error.response.status_code == 504 and not stream and request_parameters.task == "conversational":
+ msg = str(error.args[0])
+ msg += (
+ f"{os.linesep}Note: the request timed out before the model finished generating."
+ " If you are generating long outputs (e.g. long reasoning traces), pass `stream=True`"
+ " to receive tokens as they are generated and avoid hitting this timeout."
+ )
+ error.args = (msg,) + error.args[1:]
+ raise
+
+ async def audio_classification(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ function_to_apply: Optional["AudioClassificationOutputTransform"] = None,
+ ) -> list[AudioClassificationOutputElement]:
+ """
+ Perform audio classification on the provided audio content.
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an
+ audio file.
+ model (`str`, *optional*):
+ The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub
+ or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
+ audio classification will be used.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ function_to_apply (`"AudioClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+
+ Returns:
+ `list[AudioClassificationOutputElement]`: List of [`AudioClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.audio_classification("audio.flac")
+ [
+ AudioClassificationOutputElement(score=0.4976358711719513, label='hap'),
+ AudioClassificationOutputElement(score=0.3677836060523987, label='neu'),
+ ...
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="audio-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={"function_to_apply": function_to_apply, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return AudioClassificationOutputElement.parse_obj_as_list(response)
+
+ async def audio_to_audio(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ ) -> list[AudioToAudioOutputElement]:
+ """
+ Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation).
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an
+ audio file.
+ model (`str`, *optional*):
+ The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub
+ or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
+ audio_to_audio will be used.
+
+ Returns:
+ `list[AudioToAudioOutputElement]`: A list of [`AudioToAudioOutputElement`] items containing audios label, content-type, and audio content in blob.
+
+ Raises:
+ `InferenceTimeoutError`:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> audio_output = await client.audio_to_audio("audio.flac")
+ >>> async for i, item in enumerate(audio_output):
+ >>> with open(f"output_{i}.flac", "wb") as f:
+ f.write(item.blob)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="audio-to-audio", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ audio_output = AudioToAudioOutputElement.parse_obj_as_list(response)
+ for item in audio_output:
+ item.blob = base64.b64decode(item.blob)
+ return audio_output
+
+ async def automatic_speech_recognition(
+ self,
+ audio: ContentT,
+ *,
+ model: str | None = None,
+ extra_body: dict | None = None,
+ ) -> AutomaticSpeechRecognitionOutput:
+ """
+ Perform automatic speech recognition (ASR or audio-to-text) on the given audio content.
+
+ Args:
+ audio (Union[str, Path, bytes, BinaryIO]):
+ The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file.
+ model (`str`, *optional*):
+ The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for ASR will be used.
+ extra_body (`dict`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ [`AutomaticSpeechRecognitionOutput`]: An item containing the transcribed text and optionally the timestamp chunks.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.automatic_speech_recognition("hello_world.flac").text
+ "hello world"
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="automatic-speech-recognition", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=audio,
+ parameters={**(extra_body or {})},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_params=request_parameters)
+ return AutomaticSpeechRecognitionOutput.parse_obj_as_instance(response)
+
+ @overload
+ async def chat_completion( # type: ignore
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: Literal[False] = False,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput: ...
+
+ @overload
+ async def chat_completion( # type: ignore
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: Literal[True] = True,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> AsyncIterable[ChatCompletionStreamOutput]: ...
+
+ @overload
+ async def chat_completion(
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: bool = False,
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput | AsyncIterable[ChatCompletionStreamOutput]: ...
+
+ async def chat_completion(
+ self,
+ messages: list[dict | ChatCompletionInputMessage],
+ *,
+ model: str | None = None,
+ stream: bool = False,
+ # Parameters from ChatCompletionInput (handled manually)
+ frequency_penalty: float | None = None,
+ logit_bias: list[float] | None = None,
+ logprobs: bool | None = None,
+ max_tokens: int | None = None,
+ n: int | None = None,
+ presence_penalty: float | None = None,
+ response_format: ChatCompletionInputGrammarType | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ stream_options: ChatCompletionInputStreamOptions | None = None,
+ temperature: float | None = None,
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None,
+ tool_prompt: str | None = None,
+ tools: list[ChatCompletionInputTool] | None = None,
+ top_logprobs: int | None = None,
+ top_p: float | None = None,
+ extra_body: dict | None = None,
+ ) -> ChatCompletionOutput | AsyncIterable[ChatCompletionStreamOutput]:
+ """
+ A method for completing conversations using a specified language model.
+
+ > [!TIP]
+ > The `client.chat_completion` method is aliased as `client.chat.completions.create` for compatibility with OpenAI's client.
+ > Inputs and outputs are strictly the same and using either syntax will yield the same results.
+ > Check out the [Inference guide](https://huggingface.co/docs/huggingface_hub/guides/inference#openai-compatibility)
+ > for more details about OpenAI's compatibility.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ messages (List of [`ChatCompletionInputMessage`]):
+ Conversation history consisting of roles and content pairs.
+ model (`str`, *optional*):
+ The model to use for chat-completion. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for chat-based text-generation will be used.
+ See https://huggingface.co/tasks/text-generation for more details.
+ If `model` is a model ID, it is passed to the server as the `model` parameter. If you want to define a
+ custom URL while setting `model` in the request payload, you must set `base_url` when initializing [`InferenceClient`].
+ frequency_penalty (`float`, *optional*):
+ Penalizes new tokens based on their existing frequency
+ in the text so far. Range: [-2.0, 2.0]. Defaults to 0.0.
+ logit_bias (`list[float]`, *optional*):
+ Adjusts the likelihood of specific tokens appearing in the generated output.
+ logprobs (`bool`, *optional*):
+ Whether to return log probabilities of the output tokens or not. If true, returns the log
+ probabilities of each output token returned in the content of message.
+ max_tokens (`int`, *optional*):
+ Maximum number of tokens allowed in the response. Defaults to 100.
+ n (`int`, *optional*):
+ The number of completions to generate for each prompt.
+ presence_penalty (`float`, *optional*):
+ Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the
+ text so far, increasing the model's likelihood to talk about new topics.
+ response_format ([`ChatCompletionInputGrammarType`], *optional*):
+ Grammar constraints. Can be either a JSONSchema or a regex.
+ seed (Optional[`int`], *optional*):
+ Seed for reproducible control flow. Defaults to None.
+ stop (`list[str]`, *optional*):
+ Up to four strings which trigger the end of the response.
+ Defaults to None.
+ stream (`bool`, *optional*):
+ Enable realtime streaming of responses. Defaults to False.
+ stream_options ([`ChatCompletionInputStreamOptions`], *optional*):
+ Options for streaming completions.
+ temperature (`float`, *optional*):
+ Controls randomness of the generations. Lower values ensure
+ less random completions. Range: [0, 2]. Defaults to 1.0.
+ top_logprobs (`int`, *optional*):
+ An integer between 0 and 5 specifying the number of most likely tokens to return at each token
+ position, each with an associated log probability. logprobs must be set to true if this parameter is
+ used.
+ top_p (`float`, *optional*):
+ Fraction of the most likely next words to sample from.
+ Must be between 0 and 1. Defaults to 1.0.
+ tool_choice ([`ChatCompletionInputToolChoiceClass`] or [`ChatCompletionInputToolChoiceEnum`], *optional*):
+ The tool to use for the completion. Defaults to "auto".
+ tool_prompt (`str`, *optional*):
+ A prompt to be appended before the tools.
+ tools (List of [`ChatCompletionInputTool`], *optional*):
+ A list of tools the model may call. Currently, only functions are supported as a tool. Use this to
+ provide a list of functions the model may generate JSON inputs for.
+ extra_body (`dict`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ [`ChatCompletionOutput`] or Iterable of [`ChatCompletionStreamOutput`]:
+ Generated text returned from the server:
+ - if `stream=False`, the generated text is returned as a [`ChatCompletionOutput`] (default).
+ - if `stream=True`, the generated text is returned token by token as a sequence of [`ChatCompletionStreamOutput`].
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> messages = [{"role": "user", "content": "What is the capital of France?"}]
+ >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+ >>> await client.chat_completion(messages, max_tokens=100)
+ ChatCompletionOutput(
+ choices=[
+ ChatCompletionOutputComplete(
+ finish_reason='eos_token',
+ index=0,
+ message=ChatCompletionOutputMessage(
+ role='assistant',
+ content='The capital of France is Paris.',
+ name=None,
+ tool_calls=None
+ ),
+ logprobs=None
+ )
+ ],
+ created=1719907176,
+ id='',
+ model='meta-llama/Meta-Llama-3-8B-Instruct',
+ object='text_completion',
+ system_fingerprint='2.0.4-sha-f426a33',
+ usage=ChatCompletionOutputUsage(
+ completion_tokens=8,
+ prompt_tokens=17,
+ total_tokens=25
+ )
+ )
+ ```
+
+ Example using streaming:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> messages = [{"role": "user", "content": "What is the capital of France?"}]
+ >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+ >>> async for token in await client.chat_completion(messages, max_tokens=10, stream=True):
+ ... print(token)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content='The', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' capital', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ (...)
+ ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' may', role='assistant'), index=0, finish_reason=None)], created=1710498504)
+ ```
+
+ Example using OpenAI's syntax:
+ ```py
+ # Must be run in an async context
+ # instead of `from openai import OpenAI`
+ from huggingface_hub import AsyncInferenceClient
+
+ # instead of `client = OpenAI(...)`
+ client = AsyncInferenceClient(
+ base_url=...,
+ api_key=...,
+ )
+
+ output = await client.chat.completions.create(
+ model="meta-llama/Meta-Llama-3-8B-Instruct",
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Count to 10"},
+ ],
+ stream=True,
+ max_tokens=1024,
+ )
+
+ for chunk in output:
+ print(chunk.choices[0].delta.content)
+ ```
+
+ Example using a third-party provider directly with extra (provider-specific) parameters. Usage will be billed on your Together AI account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="together", # Use Together AI provider
+ ... api_key="", # Pass your Together API key directly
+ ... )
+ >>> client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-8B-Instruct",
+ ... messages=[{"role": "user", "content": "What is the capital of France?"}],
+ ... extra_body={"safety_model": "Meta-Llama/Llama-Guard-7b"},
+ ... )
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="novita", # Use Novita provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-8B-Instruct",
+ ... messages=[{"role": "user", "content": "What is the capital of France?"}],
+ ... )
+ ```
+
+ Example using Image + Text as input:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+
+ # provide a remote URL
+ >>> image_url ="https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+ # or a base64-encoded image
+ >>> image_path = "/path/to/image.jpeg"
+ >>> with open(image_path, "rb") as f:
+ ... base64_image = base64.b64encode(f.read()).decode("utf-8")
+ >>> image_url = f"data:image/jpeg;base64,{base64_image}"
+
+ >>> client = AsyncInferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct")
+ >>> output = await client.chat.completions.create(
+ ... messages=[
+ ... {
+ ... "role": "user",
+ ... "content": [
+ ... {
+ ... "type": "image_url",
+ ... "image_url": {"url": image_url},
+ ... },
+ ... {
+ ... "type": "text",
+ ... "text": "Describe this image in one sentence.",
+ ... },
+ ... ],
+ ... },
+ ... ],
+ ... )
+ >>> output
+ The image depicts the iconic Statue of Liberty situated in New York Harbor, New York, on a clear day.
+ ```
+
+ Example using tools:
+ ```py
+ # Must be run in an async context
+ >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> messages = [
+ ... {
+ ... "role": "system",
+ ... "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.",
+ ... },
+ ... {
+ ... "role": "user",
+ ... "content": "What's the weather like the next 3 days in San Francisco, CA?",
+ ... },
+ ... ]
+ >>> tools = [
+ ... {
+ ... "type": "function",
+ ... "function": {
+ ... "name": "get_current_weather",
+ ... "description": "Get the current weather",
+ ... "parameters": {
+ ... "type": "object",
+ ... "properties": {
+ ... "location": {
+ ... "type": "string",
+ ... "description": "The city and state, e.g. San Francisco, CA",
+ ... },
+ ... "format": {
+ ... "type": "string",
+ ... "enum": ["celsius", "fahrenheit"],
+ ... "description": "The temperature unit to use. Infer this from the users location.",
+ ... },
+ ... },
+ ... "required": ["location", "format"],
+ ... },
+ ... },
+ ... },
+ ... {
+ ... "type": "function",
+ ... "function": {
+ ... "name": "get_n_day_weather_forecast",
+ ... "description": "Get an N-day weather forecast",
+ ... "parameters": {
+ ... "type": "object",
+ ... "properties": {
+ ... "location": {
+ ... "type": "string",
+ ... "description": "The city and state, e.g. San Francisco, CA",
+ ... },
+ ... "format": {
+ ... "type": "string",
+ ... "enum": ["celsius", "fahrenheit"],
+ ... "description": "The temperature unit to use. Infer this from the users location.",
+ ... },
+ ... "num_days": {
+ ... "type": "integer",
+ ... "description": "The number of days to forecast",
+ ... },
+ ... },
+ ... "required": ["location", "format", "num_days"],
+ ... },
+ ... },
+ ... },
+ ... ]
+
+ >>> response = await client.chat_completion(
+ ... model="meta-llama/Meta-Llama-3-70B-Instruct",
+ ... messages=messages,
+ ... tools=tools,
+ ... tool_choice="auto",
+ ... max_tokens=500,
+ ... )
+ >>> response.choices[0].message.tool_calls[0].function
+ ChatCompletionOutputFunctionDefinition(
+ arguments={
+ 'location': 'San Francisco, CA',
+ 'format': 'fahrenheit',
+ 'num_days': 3
+ },
+ name='get_n_day_weather_forecast',
+ description=None
+ )
+ ```
+
+ Example using response_format:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> messages = [
+ ... {
+ ... "role": "user",
+ ... "content": "I saw a puppy a cat and a raccoon during my bike ride in the park. What did I see and when?",
+ ... },
+ ... ]
+ >>> response_format = {
+ ... "type": "json",
+ ... "value": {
+ ... "properties": {
+ ... "location": {"type": "string"},
+ ... "activity": {"type": "string"},
+ ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
+ ... "animals": {"type": "array", "items": {"type": "string"}},
+ ... },
+ ... "required": ["location", "activity", "animals_seen", "animals"],
+ ... },
+ ... }
+ >>> response = await client.chat_completion(
+ ... messages=messages,
+ ... response_format=response_format,
+ ... max_tokens=500,
+ ... )
+ >>> response.choices[0].message.content
+ '{\n\n"activity": "bike ride",\n"animals": ["puppy", "cat", "raccoon"],\n"animals_seen": 3,\n"location": "park"}'
+ ```
+ """
+ # Since `chat_completion(..., model=xxx)` is also a payload parameter for the server, we need to handle 'model' differently.
+ # `self.model` takes precedence over 'model' argument for building URL.
+ # `model` takes precedence for payload value.
+ model_id_or_url = self.model or model
+ payload_model = model or self.model
+
+ # Get the provider helper
+ provider_helper = get_provider_helper(
+ self.provider,
+ task="conversational",
+ model=model_id_or_url
+ if model_id_or_url is not None and model_id_or_url.startswith(("http://", "https://"))
+ else payload_model,
+ )
+
+ # Prepare the payload
+ parameters = {
+ "model": payload_model,
+ "frequency_penalty": frequency_penalty,
+ "logit_bias": logit_bias,
+ "logprobs": logprobs,
+ "max_tokens": max_tokens,
+ "n": n,
+ "presence_penalty": presence_penalty,
+ "response_format": response_format,
+ "seed": seed,
+ "stop": stop,
+ "temperature": temperature,
+ "tool_choice": tool_choice,
+ "tool_prompt": tool_prompt,
+ "tools": tools,
+ "top_logprobs": top_logprobs,
+ "top_p": top_p,
+ "stream": stream,
+ "stream_options": stream_options,
+ **(extra_body or {}),
+ }
+ request_parameters = provider_helper.prepare_request(
+ inputs=messages,
+ parameters=parameters,
+ headers=self.headers,
+ model=model_id_or_url,
+ api_key=self.token,
+ )
+ data = await self._inner_post(request_parameters, stream=stream)
+
+ if stream:
+ return _async_stream_chat_completion_response(data) # type: ignore
+
+ return ChatCompletionOutput.parse_obj_as_instance(data) # type: ignore
+
+ async def document_question_answering(
+ self,
+ image: ContentT,
+ question: str,
+ *,
+ model: str | None = None,
+ doc_stride: int | None = None,
+ handle_impossible_answer: bool | None = None,
+ lang: str | None = None,
+ max_answer_len: int | None = None,
+ max_question_len: int | None = None,
+ max_seq_len: int | None = None,
+ top_k: int | None = None,
+ word_boxes: list[list[float] | str] | None = None,
+ ) -> list[DocumentQuestionAnsweringOutputElement]:
+ """
+ Answer questions on document images.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO]`):
+ The input image for the context. It can be raw bytes, an image file, or a URL to an online image.
+ question (`str`):
+ Question to be answered.
+ model (`str`, *optional*):
+ The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used.
+ Defaults to None.
+ doc_stride (`int`, *optional*):
+ If the words in the document are too long to fit with the question for the model, it will be split in
+ several chunks with some overlap. This argument controls the size of that overlap.
+ handle_impossible_answer (`bool`, *optional*):
+ Whether to accept impossible as an answer
+ lang (`str`, *optional*):
+ Language to use while running OCR. Defaults to english.
+ max_answer_len (`int`, *optional*):
+ The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
+ max_question_len (`int`, *optional*):
+ The maximum length of the question after tokenization. It will be truncated if needed.
+ max_seq_len (`int`, *optional*):
+ The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
+ model. The context will be split in several chunks (using doc_stride as overlap) if needed.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Can return less than top_k
+ answers if there are not enough options available within the context.
+ word_boxes (`list[Union[list[float], str`, *optional*):
+ A list of words and bounding boxes (normalized 0->1000). If provided, the inference will skip the OCR
+ step and use the provided bounding boxes instead.
+ Returns:
+ `list[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.document_question_answering(image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", question="What is the invoice number?")
+ [DocumentQuestionAnsweringOutputElement(answer='us-001', end=16, score=0.9999666213989258, start=16)]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="document-question-answering", model=model_id)
+ inputs: dict[str, Any] = {"question": question, "image": _b64_encode(image)}
+ request_parameters = provider_helper.prepare_request(
+ inputs=inputs,
+ parameters={
+ "doc_stride": doc_stride,
+ "handle_impossible_answer": handle_impossible_answer,
+ "lang": lang,
+ "max_answer_len": max_answer_len,
+ "max_question_len": max_question_len,
+ "max_seq_len": max_seq_len,
+ "top_k": top_k,
+ "word_boxes": word_boxes,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)
+
+ async def feature_extraction(
+ self,
+ text: str | list[str],
+ *,
+ normalize: bool | None = None,
+ prompt_name: str | None = None,
+ truncate: bool | None = None,
+ truncation_direction: Literal["left", "right"] | None = None,
+ dimensions: int | None = None,
+ encoding_format: Literal["float", "base64"] | None = None,
+ model: str | None = None,
+ ) -> "np.ndarray":
+ """
+ Generate embeddings for a given text or batch of texts.
+
+ Args:
+ text (`str` or `list[str]`):
+ The text or list of texts to embed.
+ model (`str`, *optional*):
+ The model to use for the feature extraction task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended feature extraction model will be used.
+ Defaults to None.
+ normalize (`bool`, *optional*):
+ Whether to normalize the embeddings or not.
+ Only available on server powered by Text-Embedding-Inference.
+ prompt_name (`str`, *optional*):
+ The name of the prompt that should be used by for encoding. If not set, no prompt will be applied.
+ Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+ For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",...},
+ then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?"
+ because the prompt text will be prepended before any text to encode.
+ truncate (`bool`, *optional*):
+ Whether to truncate the embeddings or not.
+ Only available on server powered by Text-Embedding-Inference.
+ truncation_direction (`Literal["left", "right"]`, *optional*):
+ Which side of the input should be truncated when `truncate=True` is passed.
+ dimensions (`int`, *optional*):
+ The number of dimensions the resulting output embeddings should have.
+ Only available on OpenAI-compatible embedding endpoints.
+ encoding_format (`Literal["float", "base64"]`, *optional*):
+ The format of the output embeddings. Either "float" or "base64".
+ Only available on OpenAI-compatible embedding endpoints.
+
+ Returns:
+ `np.ndarray`: The embedding representing the input text(s) as a float32 numpy array.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.feature_extraction("Hi, who are you?")
+ array([[ 2.424802 , 2.93384 , 1.1750331 , ..., 1.240499, -0.13776633, -0.7889173 ],
+ [-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ],
+ ...,
+ [ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="feature-extraction", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "normalize": normalize,
+ "prompt_name": prompt_name,
+ "truncate": truncate,
+ "truncation_direction": truncation_direction,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ np = _import_numpy()
+ return np.array(provider_helper.get_response(response), dtype="float32")
+
+ async def fill_mask(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ targets: list[str] | None = None,
+ top_k: int | None = None,
+ ) -> list[FillMaskOutputElement]:
+ """
+ Fill in a hole with a missing word (token to be precise).
+
+ Args:
+ text (`str`):
+ a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask).
+ model (`str`, *optional*):
+ The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used.
+ targets (`list[str`, *optional*):
+ When passed, the model will limit the scores to the passed targets instead of looking up in the whole
+ vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first
+ resulting token will be used (with a warning, and that might be slower).
+ top_k (`int`, *optional*):
+ When passed, overrides the number of predictions to return.
+ Returns:
+ `list[FillMaskOutputElement]`: a list of [`FillMaskOutputElement`] items containing the predicted label, associated
+ probability, token reference, and completed text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.fill_mask("The goal of life is .")
+ [
+ FillMaskOutputElement(score=0.06897063553333282, token=11098, token_str=' happiness', sequence='The goal of life is happiness.'),
+ FillMaskOutputElement(score=0.06554922461509705, token=45075, token_str=' immortality', sequence='The goal of life is immortality.')
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="fill-mask", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={"targets": targets, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return FillMaskOutputElement.parse_obj_as_list(response)
+
+ async def image_classification(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ function_to_apply: Optional["ImageClassificationOutputTransform"] = None,
+ top_k: int | None = None,
+ ) -> list[ImageClassificationOutputElement]:
+ """
+ Perform image classification on the given image using the specified model.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to classify. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used.
+ function_to_apply (`"ImageClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ Returns:
+ `list[ImageClassificationOutputElement]`: a list of [`ImageClassificationOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.image_classification("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
+ [ImageClassificationOutputElement(label='Blenheim spaniel', score=0.9779096841812134), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"function_to_apply": function_to_apply, "top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return ImageClassificationOutputElement.parse_obj_as_list(response)
+
+ async def image_segmentation(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ mask_threshold: float | None = None,
+ overlap_mask_area_threshold: float | None = None,
+ subtask: Optional["ImageSegmentationSubtask"] = None,
+ threshold: float | None = None,
+ ) -> list[ImageSegmentationOutputElement]:
+ """
+ Perform image segmentation on the given image using the specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to segment. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used.
+ mask_threshold (`float`, *optional*):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*):
+ Mask overlap threshold to eliminate small, disconnected segments.
+ subtask (`"ImageSegmentationSubtask"`, *optional*):
+ Segmentation task to be performed, depending on model capabilities.
+ threshold (`float`, *optional*):
+ Probability threshold to filter out predicted masks.
+ Returns:
+ `list[ImageSegmentationOutputElement]`: A list of [`ImageSegmentationOutputElement`] items containing the segmented masks and associated attributes.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.image_segmentation("cat.jpg")
+ [ImageSegmentationOutputElement(score=0.989008, label='LABEL_184', mask=), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-segmentation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "mask_threshold": mask_threshold,
+ "overlap_mask_area_threshold": overlap_mask_area_threshold,
+ "subtask": subtask,
+ "threshold": threshold,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ output = ImageSegmentationOutputElement.parse_obj_as_list(response)
+ for item in output:
+ item.mask = _b64_to_image(item.mask) # type: ignore
+ return output
+
+ async def image_to_image(
+ self,
+ image: ContentT,
+ prompt: str | None = None,
+ *,
+ negative_prompt: str | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ model: str | None = None,
+ target_size: ImageToImageTargetSize | None = None,
+ **kwargs,
+ ) -> "Image":
+ """
+ Perform image-to-image translation using a specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image for translation. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ prompt (`str`, *optional*):
+ The text prompt to guide the image generation.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in image generation.
+ num_inference_steps (`int`, *optional*):
+ For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher
+ quality image at the expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ For diffusion models. A higher guidance scale value encourages the model to generate images closely
+ linked to the text prompt at the expense of lower image quality.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ target_size (`ImageToImageTargetSize`, *optional*):
+ The size in pixels of the output image. This parameter is only supported by some providers and for
+ specific models. It will be ignored when unsupported.
+
+ Returns:
+ `Image`: The translated image.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> image = await client.image_to_image("cat.jpg", prompt="turn the cat into a tiger")
+ >>> image.save("tiger.jpg")
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-image", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "prompt": prompt,
+ "negative_prompt": negative_prompt,
+ "target_size": target_size,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ **kwargs,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return _bytes_to_image(response)
+
+ async def image_to_video(
+ self,
+ image: ContentT,
+ *,
+ model: str | None = None,
+ prompt: str | None = None,
+ negative_prompt: str | None = None,
+ num_frames: float | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ seed: int | None = None,
+ target_size: ImageToVideoTargetSize | None = None,
+ **kwargs,
+ ) -> bytes:
+ """
+ Generate a video from an input image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to generate a video from. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ prompt (`str`, *optional*):
+ The text prompt to guide the video generation.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in video generation.
+ num_frames (`float`, *optional*):
+ The num_frames parameter determines how many video frames are generated.
+ num_inference_steps (`int`, *optional*):
+ For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher
+ quality image at the expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ For diffusion models. A higher guidance scale value encourages the model to generate videos closely
+ linked to the text prompt at the expense of lower image quality.
+ seed (`int`, *optional*):
+ The seed to use for the video generation.
+ target_size (`ImageToVideoTargetSize`, *optional*):
+ The size in pixel of the output video frames.
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality video at the
+ expense of slower inference.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+
+ Returns:
+ `bytes`: The generated video.
+
+ Examples:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> video = await client.image_to_video("cat.jpg", model="Wan-AI/Wan2.2-I2V-A14B", prompt="turn the cat into a tiger")
+ >>> with open("tiger.mp4", "wb") as f:
+ ... f.write(video)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-video", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "prompt": prompt,
+ "negative_prompt": negative_prompt,
+ "num_frames": num_frames,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ "seed": seed,
+ "target_size": target_size,
+ **kwargs,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return response
+
+ async def image_to_text(self, image: ContentT, *, model: str | None = None) -> ImageToTextOutput:
+ """
+ Takes an input image and return text.
+
+ Models can have very different outputs depending on your use case (image captioning, optical character recognition
+ (OCR), Pix2Struct, etc.). Please have a look to the model card to learn more about a model's specificities.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to caption. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ [`ImageToTextOutput`]: The generated text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.image_to_text("cat.jpg")
+ 'a cat standing in a grassy field '
+ >>> await client.image_to_text("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
+ 'a dog laying on the grass next to a flower pot '
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="image-to-text", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ output_list: list[ImageToTextOutput] = ImageToTextOutput.parse_obj_as_list(response)
+ return output_list[0]
+
+ async def object_detection(
+ self, image: ContentT, *, model: str | None = None, threshold: float | None = None
+ ) -> list[ObjectDetectionOutputElement]:
+ """
+ Perform object detection on the given image using the specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The image to detect objects on. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ model (`str`, *optional*):
+ The model to use for object detection. Can be a model ID hosted on the Hugging Face Hub or a URL to a
+ deployed Inference Endpoint. If not provided, the default recommended model for object detection (DETR) will be used.
+ threshold (`float`, *optional*):
+ The probability necessary to make a prediction.
+ Returns:
+ `list[ObjectDetectionOutputElement]`: A list of [`ObjectDetectionOutputElement`] items containing the bounding boxes and associated attributes.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+ `ValueError`:
+ If the request output is not a List.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.object_detection("people.jpg")
+ [ObjectDetectionOutputElement(score=0.9486683011054993, label='person', box=ObjectDetectionBoundingBox(xmin=59, ymin=39, xmax=420, ymax=510)), ...]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="object-detection", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"threshold": threshold},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return ObjectDetectionOutputElement.parse_obj_as_list(response)
+
+ async def question_answering(
+ self,
+ question: str,
+ context: str,
+ *,
+ model: str | None = None,
+ align_to_words: bool | None = None,
+ doc_stride: int | None = None,
+ handle_impossible_answer: bool | None = None,
+ max_answer_len: int | None = None,
+ max_question_len: int | None = None,
+ max_seq_len: int | None = None,
+ top_k: int | None = None,
+ ) -> QuestionAnsweringOutputElement | list[QuestionAnsweringOutputElement]:
+ """
+ Retrieve the answer to a question from a given text.
+
+ Args:
+ question (`str`):
+ Question to be answered.
+ context (`str`):
+ The context of the question.
+ model (`str`):
+ The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint.
+ align_to_words (`bool`, *optional*):
+ Attempts to align the answer to real words. Improves quality on space separated languages. Might hurt
+ on non-space-separated languages (like Japanese or Chinese)
+ doc_stride (`int`, *optional*):
+ If the context is too long to fit with the question for the model, it will be split in several chunks
+ with some overlap. This argument controls the size of that overlap.
+ handle_impossible_answer (`bool`, *optional*):
+ Whether to accept impossible as an answer.
+ max_answer_len (`int`, *optional*):
+ The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
+ max_question_len (`int`, *optional*):
+ The maximum length of the question after tokenization. It will be truncated if needed.
+ max_seq_len (`int`, *optional*):
+ The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
+ model. The context will be split in several chunks (using docStride as overlap) if needed.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Note that we return less than
+ topk answers if there are not enough options available within the context.
+
+ Returns:
+ Union[`QuestionAnsweringOutputElement`, list[`QuestionAnsweringOutputElement`]]:
+ When top_k is 1 or not provided, it returns a single `QuestionAnsweringOutputElement`.
+ When top_k is greater than 1, it returns a list of `QuestionAnsweringOutputElement`.
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.question_answering(question="What's my name?", context="My name is Clara and I live in Berkeley.")
+ QuestionAnsweringOutputElement(answer='Clara', end=16, score=0.9326565265655518, start=11)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"question": question, "context": context},
+ parameters={
+ "align_to_words": align_to_words,
+ "doc_stride": doc_stride,
+ "handle_impossible_answer": handle_impossible_answer,
+ "max_answer_len": max_answer_len,
+ "max_question_len": max_question_len,
+ "max_seq_len": max_seq_len,
+ "top_k": top_k,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ # Parse the response as a single `QuestionAnsweringOutputElement` when top_k is 1 or not provided, or a list of `QuestionAnsweringOutputElement` to ensure backward compatibility.
+ output = QuestionAnsweringOutputElement.parse_obj(response)
+ return output
+
+ async def sentence_similarity(
+ self, sentence: str, other_sentences: list[str], *, model: str | None = None
+ ) -> list[float]:
+ """
+ Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.
+
+ Args:
+ sentence (`str`):
+ The main sentence to compare to others.
+ other_sentences (`list[str]`):
+ The list of sentences to compare to.
+ model (`str`, *optional*):
+ The model to use for the sentence similarity task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended sentence similarity model will be used.
+ Defaults to None.
+
+ Returns:
+ `list[float]`: The embedding representing the input text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.sentence_similarity(
+ ... "Machine learning is so easy.",
+ ... other_sentences=[
+ ... "Deep learning is so straightforward.",
+ ... "This is so difficult, like rocket science.",
+ ... "I can't believe how much I struggled with this.",
+ ... ],
+ ... )
+ [0.7785726189613342, 0.45876261591911316, 0.2906220555305481]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="sentence-similarity", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"source_sentence": sentence, "sentences": other_sentences},
+ parameters={},
+ extra_payload={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ async def summarization(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ clean_up_tokenization_spaces: bool | None = None,
+ generate_parameters: dict[str, Any] | None = None,
+ truncation: Optional["SummarizationTruncationStrategy"] = None,
+ ) -> SummarizationOutput:
+ """
+ Generate a summary of a given text using a specified model.
+
+ Args:
+ text (`str`):
+ The input text to summarize.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended model for summarization will be used.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether to clean up the potential extra spaces in the text output.
+ generate_parameters (`dict[str, Any]`, *optional*):
+ Additional parametrization of the text generation algorithm.
+ truncation (`"SummarizationTruncationStrategy"`, *optional*):
+ The truncation strategy to use.
+ Returns:
+ [`SummarizationOutput`]: The generated summary text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.summarization("The Eiffel tower...")
+ SummarizationOutput(generated_text="The Eiffel tower is one of the most famous landmarks in the world....")
+ ```
+ """
+ parameters = {
+ "clean_up_tokenization_spaces": clean_up_tokenization_spaces,
+ "generate_parameters": generate_parameters,
+ "truncation": truncation,
+ }
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="summarization", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters=parameters,
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return SummarizationOutput.parse_obj_as_list(response)[0]
+
+ async def table_question_answering(
+ self,
+ table: dict[str, Any],
+ query: str,
+ *,
+ model: str | None = None,
+ padding: Optional["Padding"] = None,
+ sequential: bool | None = None,
+ truncation: bool | None = None,
+ ) -> TableQuestionAnsweringOutputElement:
+ """
+ Retrieve the answer to a question from information given in a table.
+
+ Args:
+ table (`str`):
+ A table of data represented as a dict of lists where entries are headers and the lists are all the
+ values, all lists must have the same size.
+ query (`str`):
+ The query in plain text that you want to ask the table.
+ model (`str`):
+ The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
+ Hub or a URL to a deployed Inference Endpoint.
+ padding (`"Padding"`, *optional*):
+ Activates and controls padding.
+ sequential (`bool`, *optional*):
+ Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
+ inference to be done sequentially to extract relations within sequences, given their conversational
+ nature.
+ truncation (`bool`, *optional*):
+ Activates and controls truncation.
+
+ Returns:
+ [`TableQuestionAnsweringOutputElement`]: a table question answering output containing the answer, coordinates, cells and the aggregator used.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> query = "How many stars does the transformers repository have?"
+ >>> table = {"Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"]}
+ >>> await client.table_question_answering(table, query, model="google/tapas-base-finetuned-wtq")
+ TableQuestionAnsweringOutputElement(answer='36542', coordinates=[[0, 1]], cells=['36542'], aggregator='AVERAGE')
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="table-question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs={"query": query, "table": table},
+ parameters={"model": model, "padding": padding, "sequential": sequential, "truncation": truncation},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return TableQuestionAnsweringOutputElement.parse_obj_as_instance(response)
+
+ async def tabular_classification(self, table: dict[str, Any], *, model: str | None = None) -> list[str]:
+ """
+ Classifying a target category (a group) based on a set of attributes.
+
+ Args:
+ table (`dict[str, Any]`):
+ Set of attributes to classify.
+ model (`str`, *optional*):
+ The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended tabular classification model will be used.
+ Defaults to None.
+
+ Returns:
+ `List`: a list of labels, one per row in the initial table.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> table = {
+ ... "fixed_acidity": ["7.4", "7.8", "10.3"],
+ ... "volatile_acidity": ["0.7", "0.88", "0.32"],
+ ... "citric_acid": ["0", "0", "0.45"],
+ ... "residual_sugar": ["1.9", "2.6", "6.4"],
+ ... "chlorides": ["0.076", "0.098", "0.073"],
+ ... "free_sulfur_dioxide": ["11", "25", "5"],
+ ... "total_sulfur_dioxide": ["34", "67", "13"],
+ ... "density": ["0.9978", "0.9968", "0.9976"],
+ ... "pH": ["3.51", "3.2", "3.23"],
+ ... "sulphates": ["0.56", "0.68", "0.82"],
+ ... "alcohol": ["9.4", "9.8", "12.6"],
+ ... }
+ >>> await client.tabular_classification(table=table, model="julien-c/wine-quality")
+ ["5", "5", "5"]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="tabular-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=None,
+ extra_payload={"table": table},
+ parameters={},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ async def tabular_regression(self, table: dict[str, Any], *, model: str | None = None) -> list[float]:
+ """
+ Predicting a numerical target value given a set of attributes/features in a table.
+
+ Args:
+ table (`dict[str, Any]`):
+ Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.
+ model (`str`, *optional*):
+ The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended tabular regression model will be used.
+ Defaults to None.
+
+ Returns:
+ `List`: a list of predicted numerical target values.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> table = {
+ ... "Height": ["11.52", "12.48", "12.3778"],
+ ... "Length1": ["23.2", "24", "23.9"],
+ ... "Length2": ["25.4", "26.3", "26.5"],
+ ... "Length3": ["30", "31.2", "31.1"],
+ ... "Species": ["Bream", "Bream", "Bream"],
+ ... "Width": ["4.02", "4.3056", "4.6961"],
+ ... }
+ >>> await client.tabular_regression(table, model="scikit-learn/Fish-Weight")
+ [110, 120, 130]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="tabular-regression", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=None,
+ parameters={},
+ extra_payload={"table": table},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return _bytes_to_list(response)
+
+ async def text_classification(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ function_to_apply: Optional["TextClassificationOutputTransform"] = None,
+ ) -> list[TextClassificationOutputElement]:
+ """
+ Perform text classification (e.g. sentiment-analysis) on the given text.
+
+ Args:
+ text (`str`):
+ A string to be classified.
+ model (`str`, *optional*):
+ The model to use for the text classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
+ Defaults to None.
+ top_k (`int`, *optional*):
+ When specified, limits the output to the top K most probable classes.
+ function_to_apply (`"TextClassificationOutputTransform"`, *optional*):
+ The function to apply to the model outputs in order to retrieve the scores.
+
+ Returns:
+ `list[TextClassificationOutputElement]`: a list of [`TextClassificationOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.text_classification("I like you")
+ [
+ TextClassificationOutputElement(label='POSITIVE', score=0.9998695850372314),
+ TextClassificationOutputElement(label='NEGATIVE', score=0.0001304351753788069),
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "function_to_apply": function_to_apply,
+ "top_k": top_k,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return TextClassificationOutputElement.parse_obj_as_list(response)[0] # type: ignore
+
+ @overload
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[True],
+ stream: Literal[True],
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> AsyncIterable[TextGenerationStreamOutput]: ...
+
+ @overload
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[True],
+ stream: Literal[False] | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> TextGenerationOutput: ...
+
+ @overload
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[False] | None = None,
+ stream: Literal[True],
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None, # Manual default value
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> AsyncIterable[str]: ...
+
+ @overload
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: Literal[False] | None = None,
+ stream: Literal[False] | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str: ...
+
+ @overload
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: bool | None = None,
+ stream: bool | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str | TextGenerationOutput | AsyncIterable[str] | AsyncIterable[TextGenerationStreamOutput]: ...
+
+ async def text_generation(
+ self,
+ prompt: str,
+ *,
+ details: bool | None = None,
+ stream: bool | None = None,
+ model: str | None = None,
+ # Parameters from `TextGenerationInputGenerateParameters` (maintained manually)
+ adapter_id: str | None = None,
+ best_of: int | None = None,
+ decoder_input_details: bool | None = None,
+ do_sample: bool | None = None,
+ frequency_penalty: float | None = None,
+ grammar: TextGenerationInputGrammarType | None = None,
+ max_new_tokens: int | None = None,
+ repetition_penalty: float | None = None,
+ return_full_text: bool | None = None,
+ seed: int | None = None,
+ stop: list[str] | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_n_tokens: int | None = None,
+ top_p: float | None = None,
+ truncate: int | None = None,
+ typical_p: float | None = None,
+ watermark: bool | None = None,
+ ) -> str | TextGenerationOutput | AsyncIterable[str] | AsyncIterable[TextGenerationStreamOutput]:
+ """
+ Given a prompt, generate the following text.
+
+ > [!TIP]
+ > If you want to generate a response from chat messages, you should use the [`InferenceClient.chat_completion`] method.
+ > It accepts a list of messages instead of a single text prompt and handles the chat templating for you.
+
+ Args:
+ prompt (`str`):
+ Input text.
+ details (`bool`, *optional*):
+ By default, text_generation returns a string. Pass `details=True` if you want a detailed output (tokens,
+ probabilities, seed, finish reason, etc.). Only available for models running on with the
+ `text-generation-inference` backend.
+ stream (`bool`, *optional*):
+ By default, text_generation returns the full generated text. Pass `stream=True` if you want a stream of
+ tokens to be returned. Only available for models running on with the `text-generation-inference`
+ backend.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+ adapter_id (`str`, *optional*):
+ Lora adapter id.
+ best_of (`int`, *optional*):
+ Generate best_of sequences and return the one if the highest token logprobs.
+ decoder_input_details (`bool`, *optional*):
+ Return the decoder input token logprobs and ids. You must set `details=True` as well for it to be taken
+ into account. Defaults to `False`.
+ do_sample (`bool`, *optional*):
+ Activate logits sampling
+ frequency_penalty (`float`, *optional*):
+ Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in
+ the text so far, decreasing the model's likelihood to repeat the same line verbatim.
+ grammar ([`TextGenerationInputGrammarType`], *optional*):
+ Grammar constraints. Can be either a JSONSchema or a regex.
+ max_new_tokens (`int`, *optional*):
+ Maximum number of generated tokens. Defaults to 100.
+ repetition_penalty (`float`, *optional*):
+ The parameter for repetition penalty. 1.0 means no penalty. See [this
+ paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+ return_full_text (`bool`, *optional*):
+ Whether to prepend the prompt to the generated text
+ seed (`int`, *optional*):
+ Random sampling seed
+ stop (`list[str]`, *optional*):
+ Stop generating tokens if a member of `stop` is generated.
+ temperature (`float`, *optional*):
+ The value used to module the logits distribution.
+ top_n_tokens (`int`, *optional*):
+ Return information about the `top_n_tokens` most likely tokens at each generation step, instead of
+ just the sampled token.
+ top_k (`int`, *optional`):
+ The number of highest probability vocabulary tokens to keep for top-k-filtering.
+ top_p (`float`, *optional`):
+ If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
+ higher are kept for generation.
+ truncate (`int`, *optional`):
+ Truncate inputs tokens to the given size.
+ typical_p (`float`, *optional`):
+ Typical Decoding mass
+ See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
+ watermark (`bool`, *optional*):
+ Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
+
+ Returns:
+ `Union[str, TextGenerationOutput, AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]`:
+ Generated text returned from the server:
+ - if `stream=False` and `details=False`, the generated text is returned as a `str` (default)
+ - if `stream=True` and `details=False`, the generated text is returned token by token as a `AsyncIterable[str]`
+ - if `stream=False` and `details=True`, the generated text is returned with more details as a [`~huggingface_hub.TextGenerationOutput`]
+ - if `details=True` and `stream=True`, the generated text is returned token by token as a iterable of [`~huggingface_hub.TextGenerationStreamOutput`]
+
+ Raises:
+ `ValidationError`:
+ If input values are not valid. No HTTP call is made to the server.
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+
+ # Case 1: generate text
+ >>> await client.text_generation("The huggingface_hub library is ", max_new_tokens=12)
+ '100% open source and built to be easy to use.'
+
+ # Case 2: iterate over the generated tokens. Useful for large generation.
+ >>> async for token in await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, stream=True):
+ ... print(token)
+ 100
+ %
+ open
+ source
+ and
+ built
+ to
+ be
+ easy
+ to
+ use
+ .
+
+ # Case 3: get more details about the generation process.
+ >>> await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True)
+ TextGenerationOutput(
+ generated_text='100% open source and built to be easy to use.',
+ details=TextGenerationDetails(
+ finish_reason='length',
+ generated_tokens=12,
+ seed=None,
+ prefill=[
+ TextGenerationPrefillOutputToken(id=487, text='The', logprob=None),
+ TextGenerationPrefillOutputToken(id=53789, text=' hugging', logprob=-13.171875),
+ (...)
+ TextGenerationPrefillOutputToken(id=204, text=' ', logprob=-7.0390625)
+ ],
+ tokens=[
+ TokenElement(id=1425, text='100', logprob=-1.0175781, special=False),
+ TokenElement(id=16, text='%', logprob=-0.0463562, special=False),
+ (...)
+ TokenElement(id=25, text='.', logprob=-0.5703125, special=False)
+ ],
+ best_of_sequences=None
+ )
+ )
+
+ # Case 4: iterate over the generated tokens with more details.
+ # Last object is more complete, containing the full generated text and the finish reason.
+ >>> async for details in await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True, stream=True):
+ ... print(details)
+ ...
+ TextGenerationStreamOutput(token=TokenElement(id=1425, text='100', logprob=-1.0175781, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=16, text='%', logprob=-0.0463562, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=1314, text=' open', logprob=-1.3359375, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=3178, text=' source', logprob=-0.28100586, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=273, text=' and', logprob=-0.5961914, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=3426, text=' built', logprob=-1.9423828, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-1.4121094, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=314, text=' be', logprob=-1.5224609, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=1833, text=' easy', logprob=-2.1132812, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-0.08520508, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(id=745, text=' use', logprob=-0.39453125, special=False), generated_text=None, details=None)
+ TextGenerationStreamOutput(token=TokenElement(
+ id=25,
+ text='.',
+ logprob=-0.5703125,
+ special=False),
+ generated_text='100% open source and built to be easy to use.',
+ details=TextGenerationStreamOutputStreamDetails(finish_reason='length', generated_tokens=12, seed=None)
+ )
+
+ # Case 5: generate constrained output using grammar
+ >>> response = await client.text_generation(
+ ... prompt="I saw a puppy a cat and a raccoon during my bike ride in the park",
+ ... model="HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
+ ... max_new_tokens=100,
+ ... repetition_penalty=1.3,
+ ... grammar={
+ ... "type": "json",
+ ... "value": {
+ ... "properties": {
+ ... "location": {"type": "string"},
+ ... "activity": {"type": "string"},
+ ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
+ ... "animals": {"type": "array", "items": {"type": "string"}},
+ ... },
+ ... "required": ["location", "activity", "animals_seen", "animals"],
+ ... },
+ ... },
+ ... )
+ >>> json.loads(response)
+ {
+ "activity": "bike riding",
+ "animals": ["puppy", "cat", "raccoon"],
+ "animals_seen": 3,
+ "location": "park"
+ }
+ ```
+ """
+ if decoder_input_details and not details:
+ warnings.warn(
+ "`decoder_input_details=True` has been passed to the server but `details=False` is set meaning that"
+ " the output from the server will be truncated."
+ )
+ decoder_input_details = False
+
+ # Build payload
+ parameters = {
+ "adapter_id": adapter_id,
+ "best_of": best_of,
+ "decoder_input_details": decoder_input_details,
+ "details": details,
+ "do_sample": do_sample,
+ "frequency_penalty": frequency_penalty,
+ "grammar": grammar,
+ "max_new_tokens": max_new_tokens,
+ "repetition_penalty": repetition_penalty,
+ "return_full_text": return_full_text,
+ "seed": seed,
+ "stop": stop,
+ "temperature": temperature,
+ "top_k": top_k,
+ "top_n_tokens": top_n_tokens,
+ "top_p": top_p,
+ "truncate": truncate,
+ "typical_p": typical_p,
+ "watermark": watermark,
+ }
+
+ # Remove some parameters if not a TGI server
+ unsupported_kwargs = _get_unsupported_text_generation_kwargs(model)
+ if len(unsupported_kwargs) > 0:
+ # The server does not support some parameters
+ # => means it is not a TGI server
+ # => remove unsupported parameters and warn the user
+
+ ignored_parameters = []
+ for key in unsupported_kwargs:
+ if parameters.get(key):
+ ignored_parameters.append(key)
+ parameters.pop(key, None)
+ if len(ignored_parameters) > 0:
+ warnings.warn(
+ "API endpoint/model for text-generation is not served via TGI. Ignoring following parameters:"
+ f" {', '.join(ignored_parameters)}.",
+ UserWarning,
+ )
+ if details:
+ warnings.warn(
+ "API endpoint/model for text-generation is not served via TGI. Parameter `details=True` will"
+ " be ignored meaning only the generated text will be returned.",
+ UserWarning,
+ )
+ details = False
+ if stream:
+ raise ValueError(
+ "API endpoint/model for text-generation is not served via TGI. Cannot return output as a stream."
+ " Please pass `stream=False` as input."
+ )
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-generation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters=parameters,
+ extra_payload={"stream": stream},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+
+ # Handle errors separately for more precise error messages
+ try:
+ bytes_output = await self._inner_post(request_parameters, stream=stream or False)
+ except HfHubHTTPError as e:
+ match = MODEL_KWARGS_NOT_USED_REGEX.search(str(e))
+ if isinstance(e, BadRequestError) and match:
+ unused_params = [kwarg.strip("' ") for kwarg in match.group(1).split(",")]
+ _set_unsupported_text_generation_kwargs(model, unused_params)
+ return await self.text_generation( # type: ignore
+ prompt=prompt,
+ details=details,
+ stream=stream,
+ model=model_id,
+ adapter_id=adapter_id,
+ best_of=best_of,
+ decoder_input_details=decoder_input_details,
+ do_sample=do_sample,
+ frequency_penalty=frequency_penalty,
+ grammar=grammar,
+ max_new_tokens=max_new_tokens,
+ repetition_penalty=repetition_penalty,
+ return_full_text=return_full_text,
+ seed=seed,
+ stop=stop,
+ temperature=temperature,
+ top_k=top_k,
+ top_n_tokens=top_n_tokens,
+ top_p=top_p,
+ truncate=truncate,
+ typical_p=typical_p,
+ watermark=watermark,
+ )
+ raise_text_generation_error(e)
+
+ # Parse output
+ if stream:
+ return _async_stream_text_generation_response(bytes_output, details) # type: ignore
+
+ data = _bytes_to_dict(bytes_output) # type: ignore
+
+ # Data can be a single element (dict) or an iterable of dicts where we select the first element of.
+ if isinstance(data, list):
+ data = data[0]
+ response = provider_helper.get_response(data, request_parameters)
+ return TextGenerationOutput.parse_obj_as_instance(response) if details else response["generated_text"]
+
+ async def text_to_image(
+ self,
+ prompt: str,
+ *,
+ negative_prompt: str | None = None,
+ height: int | None = None,
+ width: int | None = None,
+ num_inference_steps: int | None = None,
+ guidance_scale: float | None = None,
+ model: str | None = None,
+ scheduler: str | None = None,
+ seed: int | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> "Image":
+ """
+ Generate an image based on a given text using a specified model.
+
+ > [!WARNING]
+ > You must have `PIL` installed if you want to work with images (`pip install Pillow`).
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ prompt (`str`):
+ The prompt to generate an image from.
+ negative_prompt (`str`, *optional*):
+ One prompt to guide what NOT to include in image generation.
+ height (`int`, *optional*):
+ The height in pixels of the output image
+ width (`int`, *optional*):
+ The width in pixels of the output image
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ prompt, but values too high may cause saturation and other artifacts.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-image model will be used.
+ Defaults to None.
+ scheduler (`str`, *optional*):
+ Override the scheduler with a compatible one.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+
+ Returns:
+ `Image`: The generated image.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+
+ >>> image = await client.text_to_image("An astronaut riding a horse on the moon.")
+ >>> image.save("astronaut.png")
+
+ >>> image = await client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... negative_prompt="low resolution, blurry",
+ ... model="stabilityai/stable-diffusion-2-1",
+ ... )
+ >>> image.save("better_astronaut.png")
+ ```
+ Example using a third-party provider directly. Usage will be billed on your fal.ai account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="fal-ai", # Use fal.ai provider
+ ... api_key="fal-ai-api-key", # Pass your fal.ai API key
+ ... )
+ >>> image = client.text_to_image(
+ ... "A majestic lion in a fantasy forest",
+ ... model="black-forest-labs/FLUX.1-schnell",
+ ... )
+ >>> image.save("lion.png")
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> image = client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... model="black-forest-labs/FLUX.1-dev",
+ ... )
+ >>> image.save("astronaut.png")
+ ```
+
+ Example using Replicate provider with extra parameters
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> image = client.text_to_image(
+ ... "An astronaut riding a horse on the moon.",
+ ... model="black-forest-labs/FLUX.1-schnell",
+ ... extra_body={"output_quality": 100},
+ ... )
+ >>> image.save("astronaut.png")
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-image", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters={
+ "negative_prompt": negative_prompt,
+ "height": height,
+ "width": width,
+ "num_inference_steps": num_inference_steps,
+ "guidance_scale": guidance_scale,
+ "scheduler": scheduler,
+ "seed": seed,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return _bytes_to_image(response)
+
+ async def text_to_video(
+ self,
+ prompt: str,
+ *,
+ model: str | None = None,
+ guidance_scale: float | None = None,
+ negative_prompt: list[str] | None = None,
+ num_frames: float | None = None,
+ num_inference_steps: int | None = None,
+ seed: int | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> bytes:
+ """
+ Generate a video based on a given text.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ prompt (`str`):
+ The prompt to generate a video from.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-video model will be used.
+ Defaults to None.
+ guidance_scale (`float`, *optional*):
+ A higher guidance scale value encourages the model to generate videos closely linked to the text
+ prompt, but values too high may cause saturation and other artifacts.
+ negative_prompt (`list[str]`, *optional*):
+ One or several prompt to guide what NOT to include in video generation.
+ num_frames (`float`, *optional*):
+ The num_frames parameter determines how many video frames are generated.
+ num_inference_steps (`int`, *optional*):
+ The number of denoising steps. More denoising steps usually lead to a higher quality video at the
+ expense of slower inference.
+ seed (`int`, *optional*):
+ Seed for the random number generator.
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+
+ Returns:
+ `bytes`: The generated video.
+
+ Example:
+
+ Example using a third-party provider directly. Usage will be billed on your fal.ai account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="fal-ai", # Using fal.ai provider
+ ... api_key="fal-ai-api-key", # Pass your fal.ai API key
+ ... )
+ >>> video = client.text_to_video(
+ ... "A majestic lion running in a fantasy forest",
+ ... model="tencent/HunyuanVideo",
+ ... )
+ >>> with open("lion.mp4", "wb") as file:
+ ... file.write(video)
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Using replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> video = client.text_to_video(
+ ... "A cat running in a park",
+ ... model="genmo/mochi-1-preview",
+ ... )
+ >>> with open("cat.mp4", "wb") as file:
+ ... file.write(video)
+ ```
+
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-video", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=prompt,
+ parameters={
+ "guidance_scale": guidance_scale,
+ "negative_prompt": negative_prompt,
+ "num_frames": num_frames,
+ "num_inference_steps": num_inference_steps,
+ "seed": seed,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response, request_parameters)
+ return response
+
+ async def text_to_speech(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ do_sample: bool | None = None,
+ early_stopping: Union[bool, "TextToSpeechEarlyStoppingEnum"] | None = None,
+ epsilon_cutoff: float | None = None,
+ eta_cutoff: float | None = None,
+ max_length: int | None = None,
+ max_new_tokens: int | None = None,
+ min_length: int | None = None,
+ min_new_tokens: int | None = None,
+ num_beam_groups: int | None = None,
+ num_beams: int | None = None,
+ penalty_alpha: float | None = None,
+ temperature: float | None = None,
+ top_k: int | None = None,
+ top_p: float | None = None,
+ typical_p: float | None = None,
+ use_cache: bool | None = None,
+ extra_body: dict[str, Any] | None = None,
+ ) -> bytes:
+ """
+ Synthesize an audio of a voice pronouncing a given text.
+
+ > [!TIP]
+ > You can pass provider-specific parameters to the model by using the `extra_body` argument.
+
+ Args:
+ text (`str`):
+ The text to synthesize.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. If not provided, the default recommended text-to-speech model will be used.
+ Defaults to None.
+ do_sample (`bool`, *optional*):
+ Whether to use sampling instead of greedy decoding when generating new tokens.
+ early_stopping (`Union[bool, "TextToSpeechEarlyStoppingEnum"]`, *optional*):
+ Controls the stopping condition for beam-based methods.
+ epsilon_cutoff (`float`, *optional*):
+ If set to float strictly between 0 and 1, only tokens with a conditional probability greater than
+ epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on
+ the size of the model. See [Truncation Sampling as Language Model
+ Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ eta_cutoff (`float`, *optional*):
+ Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly
+ between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff)
+ * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token
+ probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3,
+ depending on the size of the model. See [Truncation Sampling as Language Model
+ Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ max_length (`int`, *optional*):
+ The maximum length (in tokens) of the generated text, including the input.
+ max_new_tokens (`int`, *optional*):
+ The maximum number of tokens to generate. Takes precedence over max_length.
+ min_length (`int`, *optional*):
+ The minimum length (in tokens) of the generated text, including the input.
+ min_new_tokens (`int`, *optional*):
+ The minimum number of tokens to generate. Takes precedence over min_length.
+ num_beam_groups (`int`, *optional*):
+ Number of groups to divide num_beams into in order to ensure diversity among different groups of beams.
+ See [this paper](https://hf.co/papers/1610.02424) for more details.
+ num_beams (`int`, *optional*):
+ Number of beams to use for beam search.
+ penalty_alpha (`float`, *optional*):
+ The value balances the model confidence and the degeneration penalty in contrastive search decoding.
+ temperature (`float`, *optional*):
+ The value used to modulate the next token probabilities.
+ top_k (`int`, *optional*):
+ The number of highest probability vocabulary tokens to keep for top-k-filtering.
+ top_p (`float`, *optional*):
+ If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
+ top_p or higher are kept for generation.
+ typical_p (`float`, *optional*):
+ Local typicality measures how similar the conditional probability of predicting a target token next is
+ to the expected conditional probability of predicting a random token next, given the partial text
+ already generated. If set to float < 1, the smallest set of the most locally typical tokens with
+ probabilities that add up to typical_p or higher are kept for generation. See [this
+ paper](https://hf.co/papers/2202.00666) for more details.
+ use_cache (`bool`, *optional*):
+ Whether the model should use the past last key/values attentions to speed up decoding
+ extra_body (`dict[str, Any]`, *optional*):
+ Additional provider-specific parameters to pass to the model. Refer to the provider's documentation
+ for supported parameters.
+ Returns:
+ `bytes`: The generated audio.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from pathlib import Path
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+
+ >>> audio = await client.text_to_speech("Hello world")
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+
+ Example using a third-party provider directly. Usage will be billed on your Replicate account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate",
+ ... api_key="your-replicate-api-key", # Pass your Replicate API key directly
+ ... )
+ >>> audio = client.text_to_speech(
+ ... text="Hello world",
+ ... model="OuteAI/OuteTTS-0.3-500M",
+ ... )
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+
+ Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account.
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate",
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> audio =client.text_to_speech(
+ ... text="Hello world",
+ ... model="OuteAI/OuteTTS-0.3-500M",
+ ... )
+ >>> Path("hello_world.flac").write_bytes(audio)
+ ```
+ Example using Replicate provider with extra parameters
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> client = InferenceClient(
+ ... provider="replicate", # Use replicate provider
+ ... api_key="hf_...", # Pass your HF token
+ ... )
+ >>> audio = client.text_to_speech(
+ ... "Hello, my name is Kororo, an awesome text-to-speech model.",
+ ... model="hexgrad/Kokoro-82M",
+ ... extra_body={"voice": "af_nicole"},
+ ... )
+ >>> Path("hello.flac").write_bytes(audio)
+ ```
+
+ Example music-gen using "YuE-s1-7B-anneal-en-cot" on fal.ai
+ ```py
+ >>> from huggingface_hub import InferenceClient
+ >>> lyrics = '''
+ ... [verse]
+ ... In the town where I was born
+ ... Lived a man who sailed to sea
+ ... And he told us of his life
+ ... In the land of submarines
+ ... So we sailed on to the sun
+ ... 'Til we found a sea of green
+ ... And we lived beneath the waves
+ ... In our yellow submarine
+
+ ... [chorus]
+ ... We all live in a yellow submarine
+ ... Yellow submarine, yellow submarine
+ ... We all live in a yellow submarine
+ ... Yellow submarine, yellow submarine
+ ... '''
+ >>> genres = "pavarotti-style tenor voice"
+ >>> client = InferenceClient(
+ ... provider="fal-ai",
+ ... model="m-a-p/YuE-s1-7B-anneal-en-cot",
+ ... api_key=...,
+ ... )
+ >>> audio = client.text_to_speech(lyrics, extra_body={"genres": genres})
+ >>> with open("output.mp3", "wb") as f:
+ ... f.write(audio)
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="text-to-speech", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "do_sample": do_sample,
+ "early_stopping": early_stopping,
+ "epsilon_cutoff": epsilon_cutoff,
+ "eta_cutoff": eta_cutoff,
+ "max_length": max_length,
+ "max_new_tokens": max_new_tokens,
+ "min_length": min_length,
+ "min_new_tokens": min_new_tokens,
+ "num_beam_groups": num_beam_groups,
+ "num_beams": num_beams,
+ "penalty_alpha": penalty_alpha,
+ "temperature": temperature,
+ "top_k": top_k,
+ "top_p": top_p,
+ "typical_p": typical_p,
+ "use_cache": use_cache,
+ **(extra_body or {}),
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ response = provider_helper.get_response(response)
+ return response
+
+ async def token_classification(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ aggregation_strategy: Optional["TokenClassificationAggregationStrategy"] = None,
+ ignore_labels: list[str] | None = None,
+ stride: int | None = None,
+ ) -> list[TokenClassificationOutputElement]:
+ """
+ Perform token classification on the given text.
+ Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text.
+
+ Args:
+ text (`str`):
+ A string to be classified.
+ model (`str`, *optional*):
+ The model to use for the token classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended token classification model will be used.
+ Defaults to None.
+ aggregation_strategy (`"TokenClassificationAggregationStrategy"`, *optional*):
+ The strategy used to fuse tokens based on model predictions
+ ignore_labels (`list[str`, *optional*):
+ A list of labels to ignore
+ stride (`int`, *optional*):
+ The number of overlapping tokens between chunks when splitting the input text.
+
+ Returns:
+ `list[TokenClassificationOutputElement]`: List of [`TokenClassificationOutputElement`] items containing the entity group, confidence score, word, start and end index.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.token_classification("My name is Sarah Jessica Parker but you can call me Jessica")
+ [
+ TokenClassificationOutputElement(
+ entity_group='PER',
+ score=0.9971321225166321,
+ word='Sarah Jessica Parker',
+ start=11,
+ end=31,
+ ),
+ TokenClassificationOutputElement(
+ entity_group='PER',
+ score=0.9773476123809814,
+ word='Jessica',
+ start=52,
+ end=59,
+ )
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="token-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "aggregation_strategy": aggregation_strategy,
+ "ignore_labels": ignore_labels,
+ "stride": stride,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return TokenClassificationOutputElement.parse_obj_as_list(response)
+
+ async def translation(
+ self,
+ text: str,
+ *,
+ model: str | None = None,
+ src_lang: str | None = None,
+ tgt_lang: str | None = None,
+ clean_up_tokenization_spaces: bool | None = None,
+ truncation: Optional["TranslationTruncationStrategy"] = None,
+ generate_parameters: dict[str, Any] | None = None,
+ ) -> TranslationOutput:
+ """
+ Convert text from one language to another.
+
+ Check out https://huggingface.co/tasks/translation for more information on how to choose the best model for
+ your specific use case. Source and target languages usually depend on the model.
+ However, it is possible to specify source and target languages for certain models. If you are working with one of these models,
+ you can use `src_lang` and `tgt_lang` arguments to pass the relevant information.
+
+ Args:
+ text (`str`):
+ A string to be translated.
+ model (`str`, *optional*):
+ The model to use for the translation task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended translation model will be used.
+ Defaults to None.
+ src_lang (`str`, *optional*):
+ The source language of the text. Required for models that can translate from multiple languages.
+ tgt_lang (`str`, *optional*):
+ Target language to translate to. Required for models that can translate to multiple languages.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether to clean up the potential extra spaces in the text output.
+ truncation (`"TranslationTruncationStrategy"`, *optional*):
+ The truncation strategy to use.
+ generate_parameters (`dict[str, Any]`, *optional*):
+ Additional parametrization of the text generation algorithm.
+
+ Returns:
+ [`TranslationOutput`]: The generated translated text.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+ `ValueError`:
+ If only one of the `src_lang` and `tgt_lang` arguments are provided.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.translation("My name is Wolfgang and I live in Berlin")
+ 'Mein Name ist Wolfgang und ich lebe in Berlin.'
+ >>> await client.translation("My name is Wolfgang and I live in Berlin", model="Helsinki-NLP/opus-mt-en-fr")
+ TranslationOutput(translation_text='Je m'appelle Wolfgang et je vis à Berlin.')
+ ```
+
+ Specifying languages:
+ ```py
+ >>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="fr_XX")
+ "Mon nom est Sarah Jessica Parker mais vous pouvez m'appeler Jessica"
+ ```
+ """
+ # Throw error if only one of `src_lang` and `tgt_lang` was given
+ if src_lang is not None and tgt_lang is None:
+ raise ValueError("You cannot specify `src_lang` without specifying `tgt_lang`.")
+
+ if src_lang is None and tgt_lang is not None:
+ raise ValueError("You cannot specify `tgt_lang` without specifying `src_lang`.")
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="translation", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "src_lang": src_lang,
+ "tgt_lang": tgt_lang,
+ "clean_up_tokenization_spaces": clean_up_tokenization_spaces,
+ "truncation": truncation,
+ "generate_parameters": generate_parameters,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return TranslationOutput.parse_obj_as_list(response)[0]
+
+ async def visual_question_answering(
+ self,
+ image: ContentT,
+ question: str,
+ *,
+ model: str | None = None,
+ top_k: int | None = None,
+ ) -> list[VisualQuestionAnsweringOutputElement]:
+ """
+ Answering open-ended questions based on an image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image for the context. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ question (`str`):
+ Question to be answered.
+ model (`str`, *optional*):
+ The model to use for the visual question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
+ a deployed Inference Endpoint. If not provided, the default recommended visual question answering model will be used.
+ Defaults to None.
+ top_k (`int`, *optional*):
+ The number of answers to return (will be chosen by order of likelihood). Note that we return less than
+ topk answers if there are not enough options available within the context.
+ Returns:
+ `list[VisualQuestionAnsweringOutputElement]`: a list of [`VisualQuestionAnsweringOutputElement`] items containing the predicted label and associated probability.
+
+ Raises:
+ `InferenceTimeoutError`:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.visual_question_answering(
+ ... image="https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg",
+ ... question="What is the animal doing?"
+ ... )
+ [
+ VisualQuestionAnsweringOutputElement(score=0.778609573841095, answer='laying down'),
+ VisualQuestionAnsweringOutputElement(score=0.6957435607910156, answer='sitting'),
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="visual-question-answering", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={"top_k": top_k},
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ extra_payload={"question": question, "image": _b64_encode(image)},
+ )
+ response = await self._inner_post(request_parameters)
+ return VisualQuestionAnsweringOutputElement.parse_obj_as_list(response)
+
+ async def zero_shot_classification(
+ self,
+ text: str,
+ candidate_labels: list[str],
+ *,
+ multi_label: bool | None = False,
+ hypothesis_template: str | None = None,
+ model: str | None = None,
+ ) -> list[ZeroShotClassificationOutputElement]:
+ """
+ Provide as input a text and a set of candidate labels to classify the input text.
+
+ Args:
+ text (`str`):
+ The input text to classify.
+ candidate_labels (`list[str]`):
+ The set of possible class labels to classify the text into.
+ multi_label (`bool`, *optional*):
+ Whether multiple candidate labels can be true. If false, the scores are normalized such that the sum of
+ the label likelihoods for each sequence is 1. If true, the labels are considered independent and
+ probabilities are normalized for each candidate.
+ hypothesis_template (`str`, *optional*):
+ The sentence used in conjunction with `candidate_labels` to attempt the text classification by
+ replacing the placeholder with the candidate labels.
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot classification model will be used.
+
+
+ Returns:
+ `list[ZeroShotClassificationOutputElement]`: List of [`ZeroShotClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example with `multi_label=False`:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> text = (
+ ... "A new model offers an explanation for how the Galilean satellites formed around the solar system's"
+ ... "largest world. Konstantin Batygin did not set out to solve one of the solar system's most puzzling"
+ ... " mysteries when he went for a run up a hill in Nice, France."
+ ... )
+ >>> labels = ["space & cosmos", "scientific discovery", "microbiology", "robots", "archeology"]
+ >>> await client.zero_shot_classification(text, labels)
+ [
+ ZeroShotClassificationOutputElement(label='scientific discovery', score=0.7961668968200684),
+ ZeroShotClassificationOutputElement(label='space & cosmos', score=0.18570658564567566),
+ ZeroShotClassificationOutputElement(label='microbiology', score=0.00730885099619627),
+ ZeroShotClassificationOutputElement(label='archeology', score=0.006258360575884581),
+ ZeroShotClassificationOutputElement(label='robots', score=0.004559356719255447),
+ ]
+ >>> await client.zero_shot_classification(text, labels, multi_label=True)
+ [
+ ZeroShotClassificationOutputElement(label='scientific discovery', score=0.9829297661781311),
+ ZeroShotClassificationOutputElement(label='space & cosmos', score=0.755190908908844),
+ ZeroShotClassificationOutputElement(label='microbiology', score=0.0005462635890580714),
+ ZeroShotClassificationOutputElement(label='archeology', score=0.00047131875180639327),
+ ZeroShotClassificationOutputElement(label='robots', score=0.00030448526376858354),
+ ]
+ ```
+
+ Example with `multi_label=True` and a custom `hypothesis_template`:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+ >>> await client.zero_shot_classification(
+ ... text="I really like our dinner and I'm very happy. I don't like the weather though.",
+ ... candidate_labels=["positive", "negative", "pessimistic", "optimistic"],
+ ... multi_label=True,
+ ... hypothesis_template="This text is {} towards the weather"
+ ... )
+ [
+ ZeroShotClassificationOutputElement(label='negative', score=0.9231801629066467),
+ ZeroShotClassificationOutputElement(label='pessimistic', score=0.8760990500450134),
+ ZeroShotClassificationOutputElement(label='optimistic', score=0.0008674879791215062),
+ ZeroShotClassificationOutputElement(label='positive', score=0.0005250611575320363)
+ ]
+ ```
+ """
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="zero-shot-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=text,
+ parameters={
+ "candidate_labels": candidate_labels,
+ "multi_label": multi_label,
+ "hypothesis_template": hypothesis_template,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ output = _bytes_to_dict(response)
+ return ZeroShotClassificationOutputElement.parse_obj_as_list(output)
+
+ async def zero_shot_image_classification(
+ self,
+ image: ContentT,
+ candidate_labels: list[str],
+ *,
+ model: str | None = None,
+ hypothesis_template: str | None = None,
+ ) -> list[ZeroShotImageClassificationOutputElement]:
+ """
+ Provide input image and text labels to predict text labels for the image.
+
+ Args:
+ image (`Union[str, Path, bytes, BinaryIO, PIL.Image.Image]`):
+ The input image to caption. It can be raw bytes, an image file, a URL to an online image, or a PIL Image.
+ candidate_labels (`list[str]`):
+ The candidate labels for this image
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot image classification model will be used.
+ hypothesis_template (`str`, *optional*):
+ The sentence used in conjunction with `candidate_labels` to attempt the image classification by
+ replacing the placeholder with the candidate labels.
+
+ Returns:
+ `list[ZeroShotImageClassificationOutputElement]`: List of [`ZeroShotImageClassificationOutputElement`] items containing the predicted labels and their confidence.
+
+ Raises:
+ [`InferenceTimeoutError`]:
+ If the model is unavailable or the request times out.
+ [`HfHubHTTPError`]:
+ If the request fails with an HTTP error status code other than HTTP 503.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient()
+
+ >>> await client.zero_shot_image_classification(
+ ... "https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg",
+ ... candidate_labels=["dog", "cat", "horse"],
+ ... )
+ [ZeroShotImageClassificationOutputElement(label='dog', score=0.956),...]
+ ```
+ """
+ # Raise ValueError if input is less than 2 labels
+ if len(candidate_labels) < 2:
+ raise ValueError("You must specify at least 2 classes to compare.")
+
+ model_id = model or self.model
+ provider_helper = get_provider_helper(self.provider, task="zero-shot-image-classification", model=model_id)
+ request_parameters = provider_helper.prepare_request(
+ inputs=image,
+ parameters={
+ "candidate_labels": candidate_labels,
+ "hypothesis_template": hypothesis_template,
+ },
+ headers=self.headers,
+ model=model_id,
+ api_key=self.token,
+ )
+ response = await self._inner_post(request_parameters)
+ return ZeroShotImageClassificationOutputElement.parse_obj_as_list(response)
+
+ async def get_endpoint_info(self, *, model: str | None = None) -> dict[str, Any]:
+ """
+ Get information about the deployed endpoint.
+
+ This endpoint is only available on endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI).
+ Endpoints powered by `transformers` return an empty payload.
+
+ Args:
+ model (`str`, *optional*):
+ The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
+ Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ `dict[str, Any]`: Information about the endpoint.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
+ >>> await client.get_endpoint_info()
+ {
+ 'model_id': 'meta-llama/Meta-Llama-3-70B-Instruct',
+ 'model_sha': None,
+ 'model_dtype': 'torch.float16',
+ 'model_device_type': 'cuda',
+ 'model_pipeline_tag': None,
+ 'max_concurrent_requests': 128,
+ 'max_best_of': 2,
+ 'max_stop_sequences': 4,
+ 'max_input_length': 8191,
+ 'max_total_tokens': 8192,
+ 'waiting_served_ratio': 0.3,
+ 'max_batch_total_tokens': 1259392,
+ 'max_waiting_tokens': 20,
+ 'max_batch_size': None,
+ 'validation_workers': 32,
+ 'max_client_batch_size': 4,
+ 'version': '2.0.2',
+ 'sha': 'dccab72549635c7eb5ddb17f43f0b7cdff07c214',
+ 'docker_label': 'sha-dccab72'
+ }
+ ```
+ """
+ if self.provider != "hf-inference":
+ raise ValueError(f"Getting endpoint info is not supported on '{self.provider}'.")
+
+ model = model or self.model
+ if model is None:
+ raise ValueError("Model id not provided.")
+ if model.startswith(("http://", "https://")):
+ url = model.rstrip("/") + "/info"
+ else:
+ url = f"{constants.INFERENCE_ENDPOINT}/models/{model}/info"
+
+ client = await self._get_async_client()
+ response = await client.get(url, headers=build_hf_headers(token=self.token))
+ hf_raise_for_status(response)
+ return response.json()
+
+ async def health_check(self, model: str | None = None) -> bool:
+ """
+ Check the health of the deployed endpoint.
+
+ Health check is only available with Inference Endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI).
+
+ Args:
+ model (`str`, *optional*):
+ URL of the Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.
+
+ Returns:
+ `bool`: True if everything is working fine.
+
+ Example:
+ ```py
+ # Must be run in an async context
+ >>> from huggingface_hub import AsyncInferenceClient
+ >>> client = AsyncInferenceClient("https://jzgu0buei5.us-east-1.aws.endpoints.huggingface.cloud")
+ >>> await client.health_check()
+ True
+ ```
+ """
+ if self.provider != "hf-inference":
+ raise ValueError(f"Health check is not supported on '{self.provider}'.")
+
+ model = model or self.model
+ if model is None:
+ raise ValueError("Model id not provided.")
+ if not model.startswith(("http://", "https://")):
+ raise ValueError("Model must be an Inference Endpoint URL.")
+ url = model.rstrip("/") + "/health"
+
+ client = await self._get_async_client()
+ response = await client.get(url, headers=build_hf_headers(token=self.token))
+ return response.status_code == 200
+
+ @property
+ def chat(self) -> "ProxyClientChat":
+ return ProxyClientChat(self)
+
+
+class _ProxyClient:
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ def __init__(self, client: AsyncInferenceClient):
+ self._client = client
+
+
+class ProxyClientChat(_ProxyClient):
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ @property
+ def completions(self) -> "ProxyClientChatCompletions":
+ return ProxyClientChatCompletions(self._client)
+
+
+class ProxyClientChatCompletions(_ProxyClient):
+ """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client."""
+
+ @property
+ def create(self):
+ return self._client.chat_completion
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f95dca555d80774ec863bf26d147dd43f15aeb6
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/__init__.py
@@ -0,0 +1,204 @@
+# This file is auto-generated by `utils/generate_inference_types.py`.
+# Do not modify it manually.
+#
+# ruff: noqa: F401
+
+from .audio_classification import (
+ AudioClassificationInput,
+ AudioClassificationOutputElement,
+ AudioClassificationOutputTransform,
+ AudioClassificationParameters,
+)
+from .audio_to_audio import AudioToAudioInput, AudioToAudioOutputElement
+from .automatic_speech_recognition import (
+ AutomaticSpeechRecognitionEarlyStoppingEnum,
+ AutomaticSpeechRecognitionGenerationParameters,
+ AutomaticSpeechRecognitionInput,
+ AutomaticSpeechRecognitionOutput,
+ AutomaticSpeechRecognitionOutputChunk,
+ AutomaticSpeechRecognitionParameters,
+)
+from .base import BaseInferenceType
+from .chat_completion import (
+ ChatCompletionInput,
+ ChatCompletionInputFunctionDefinition,
+ ChatCompletionInputFunctionName,
+ ChatCompletionInputGrammarType,
+ ChatCompletionInputJSONSchema,
+ ChatCompletionInputMessage,
+ ChatCompletionInputMessageChunk,
+ ChatCompletionInputMessageChunkType,
+ ChatCompletionInputResponseFormatJSONObject,
+ ChatCompletionInputResponseFormatJSONSchema,
+ ChatCompletionInputResponseFormatText,
+ ChatCompletionInputStreamOptions,
+ ChatCompletionInputTool,
+ ChatCompletionInputToolCall,
+ ChatCompletionInputToolChoiceClass,
+ ChatCompletionInputToolChoiceEnum,
+ ChatCompletionInputURL,
+ ChatCompletionOutput,
+ ChatCompletionOutputComplete,
+ ChatCompletionOutputFunctionDefinition,
+ ChatCompletionOutputLogprob,
+ ChatCompletionOutputLogprobs,
+ ChatCompletionOutputMessage,
+ ChatCompletionOutputToolCall,
+ ChatCompletionOutputTopLogprob,
+ ChatCompletionOutputUsage,
+ ChatCompletionStreamOutput,
+ ChatCompletionStreamOutputChoice,
+ ChatCompletionStreamOutputDelta,
+ ChatCompletionStreamOutputDeltaToolCall,
+ ChatCompletionStreamOutputFunction,
+ ChatCompletionStreamOutputLogprob,
+ ChatCompletionStreamOutputLogprobs,
+ ChatCompletionStreamOutputTopLogprob,
+ ChatCompletionStreamOutputUsage,
+)
+from .depth_estimation import DepthEstimationInput, DepthEstimationOutput
+from .document_question_answering import (
+ DocumentQuestionAnsweringInput,
+ DocumentQuestionAnsweringInputData,
+ DocumentQuestionAnsweringOutputElement,
+ DocumentQuestionAnsweringParameters,
+)
+from .feature_extraction import FeatureExtractionInput, FeatureExtractionInputTruncationDirection
+from .fill_mask import FillMaskInput, FillMaskOutputElement, FillMaskParameters
+from .image_classification import (
+ ImageClassificationInput,
+ ImageClassificationOutputElement,
+ ImageClassificationOutputTransform,
+ ImageClassificationParameters,
+)
+from .image_segmentation import (
+ ImageSegmentationInput,
+ ImageSegmentationOutputElement,
+ ImageSegmentationParameters,
+ ImageSegmentationSubtask,
+)
+from .image_text_to_image import (
+ ImageTextToImageInput,
+ ImageTextToImageOutput,
+ ImageTextToImageParameters,
+ ImageTextToImageTargetSize,
+)
+from .image_text_to_video import (
+ ImageTextToVideoInput,
+ ImageTextToVideoOutput,
+ ImageTextToVideoParameters,
+ ImageTextToVideoTargetSize,
+)
+from .image_to_image import ImageToImageInput, ImageToImageOutput, ImageToImageParameters, ImageToImageTargetSize
+from .image_to_text import (
+ ImageToTextEarlyStoppingEnum,
+ ImageToTextGenerationParameters,
+ ImageToTextInput,
+ ImageToTextOutput,
+ ImageToTextParameters,
+)
+from .image_to_video import ImageToVideoInput, ImageToVideoOutput, ImageToVideoParameters, ImageToVideoTargetSize
+from .object_detection import (
+ ObjectDetectionBoundingBox,
+ ObjectDetectionInput,
+ ObjectDetectionOutputElement,
+ ObjectDetectionParameters,
+)
+from .question_answering import (
+ QuestionAnsweringInput,
+ QuestionAnsweringInputData,
+ QuestionAnsweringOutputElement,
+ QuestionAnsweringParameters,
+)
+from .sentence_similarity import SentenceSimilarityInput, SentenceSimilarityInputData
+from .summarization import (
+ SummarizationInput,
+ SummarizationOutput,
+ SummarizationParameters,
+ SummarizationTruncationStrategy,
+)
+from .table_question_answering import (
+ Padding,
+ TableQuestionAnsweringInput,
+ TableQuestionAnsweringInputData,
+ TableQuestionAnsweringOutputElement,
+ TableQuestionAnsweringParameters,
+)
+from .text2text_generation import (
+ Text2TextGenerationInput,
+ Text2TextGenerationOutput,
+ Text2TextGenerationParameters,
+ Text2TextGenerationTruncationStrategy,
+)
+from .text_classification import (
+ TextClassificationInput,
+ TextClassificationOutputElement,
+ TextClassificationOutputTransform,
+ TextClassificationParameters,
+)
+from .text_generation import (
+ TextGenerationInput,
+ TextGenerationInputGenerateParameters,
+ TextGenerationInputGrammarType,
+ TextGenerationOutput,
+ TextGenerationOutputBestOfSequence,
+ TextGenerationOutputDetails,
+ TextGenerationOutputFinishReason,
+ TextGenerationOutputPrefillToken,
+ TextGenerationOutputToken,
+ TextGenerationStreamOutput,
+ TextGenerationStreamOutputStreamDetails,
+ TextGenerationStreamOutputToken,
+ TypeEnum,
+)
+from .text_to_audio import (
+ TextToAudioEarlyStoppingEnum,
+ TextToAudioGenerationParameters,
+ TextToAudioInput,
+ TextToAudioOutput,
+ TextToAudioParameters,
+)
+from .text_to_image import TextToImageInput, TextToImageOutput, TextToImageParameters
+from .text_to_speech import (
+ TextToSpeechEarlyStoppingEnum,
+ TextToSpeechGenerationParameters,
+ TextToSpeechInput,
+ TextToSpeechOutput,
+ TextToSpeechParameters,
+)
+from .text_to_video import TextToVideoInput, TextToVideoOutput, TextToVideoParameters
+from .token_classification import (
+ TokenClassificationAggregationStrategy,
+ TokenClassificationInput,
+ TokenClassificationOutputElement,
+ TokenClassificationParameters,
+)
+from .translation import TranslationInput, TranslationOutput, TranslationParameters, TranslationTruncationStrategy
+from .video_classification import (
+ VideoClassificationInput,
+ VideoClassificationOutputElement,
+ VideoClassificationOutputTransform,
+ VideoClassificationParameters,
+)
+from .visual_question_answering import (
+ VisualQuestionAnsweringInput,
+ VisualQuestionAnsweringInputData,
+ VisualQuestionAnsweringOutputElement,
+ VisualQuestionAnsweringParameters,
+)
+from .zero_shot_classification import (
+ ZeroShotClassificationInput,
+ ZeroShotClassificationOutputElement,
+ ZeroShotClassificationParameters,
+)
+from .zero_shot_image_classification import (
+ ZeroShotImageClassificationInput,
+ ZeroShotImageClassificationOutputElement,
+ ZeroShotImageClassificationParameters,
+)
+from .zero_shot_object_detection import (
+ ZeroShotObjectDetectionBoundingBox,
+ ZeroShotObjectDetectionInput,
+ ZeroShotObjectDetectionOutputElement,
+ ZeroShotObjectDetectionParameters,
+)
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..b99507c4a7caaeaa306a5b20348c36eb2099b2e2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py
@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AudioClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class AudioClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Audio Classification"""
+
+ function_to_apply: Optional["AudioClassificationOutputTransform"] = None
+ """The function to apply to the model outputs in order to retrieve the scores."""
+ top_k: int | None = None
+ """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class AudioClassificationInput(BaseInferenceType):
+ """Inputs for Audio Classification inference"""
+
+ inputs: str
+ """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the audio data as a raw bytes payload.
+ """
+ parameters: AudioClassificationParameters | None = None
+ """Additional inference parameters for Audio Classification"""
+
+
+@dataclass_with_extra
+class AudioClassificationOutputElement(BaseInferenceType):
+ """Outputs for Audio Classification inference"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..43f376b5345fab6b854b028d1c17416c020d7bc1
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py
@@ -0,0 +1,30 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class AudioToAudioInput(BaseInferenceType):
+ """Inputs for Audio to Audio inference"""
+
+ inputs: Any
+ """The input audio data"""
+
+
+@dataclass_with_extra
+class AudioToAudioOutputElement(BaseInferenceType):
+ """Outputs of inference for the Audio To Audio task
+ A generated audio file with its label.
+ """
+
+ blob: Any
+ """The generated audio file."""
+ content_type: str
+ """The content type of audio file."""
+ label: str
+ """The label of the audio file."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d728bfdb83377c6726fce804013a33fdb2fbdbd
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py
@@ -0,0 +1,113 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AutomaticSpeechRecognitionEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionGenerationParameters(BaseInferenceType):
+ """Parametrization of the text generation process"""
+
+ do_sample: bool | None = None
+ """Whether to use sampling instead of greedy decoding when generating new tokens."""
+ early_stopping: Union[bool, "AutomaticSpeechRecognitionEarlyStoppingEnum"] | None = None
+ """Controls the stopping condition for beam-based methods."""
+ epsilon_cutoff: float | None = None
+ """If set to float strictly between 0 and 1, only tokens with a conditional probability
+ greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+ 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+ Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ """
+ eta_cutoff: float | None = None
+ """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+ float strictly between 0 and 1, a token is only considered if it is greater than either
+ eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+ term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+ the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+ See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+ for more details.
+ """
+ max_length: int | None = None
+ """The maximum length (in tokens) of the generated text, including the input."""
+ max_new_tokens: int | None = None
+ """The maximum number of tokens to generate. Takes precedence over max_length."""
+ min_length: int | None = None
+ """The minimum length (in tokens) of the generated text, including the input."""
+ min_new_tokens: int | None = None
+ """The minimum number of tokens to generate. Takes precedence over min_length."""
+ num_beam_groups: int | None = None
+ """Number of groups to divide num_beams into in order to ensure diversity among different
+ groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+ """
+ num_beams: int | None = None
+ """Number of beams to use for beam search."""
+ penalty_alpha: float | None = None
+ """The value balances the model confidence and the degeneration penalty in contrastive
+ search decoding.
+ """
+ temperature: float | None = None
+ """The value used to modulate the next token probabilities."""
+ top_k: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+ top_p: float | None = None
+ """If set to float < 1, only the smallest set of most probable tokens with probabilities
+ that add up to top_p or higher are kept for generation.
+ """
+ typical_p: float | None = None
+ """Local typicality measures how similar the conditional probability of predicting a target
+ token next is to the expected conditional probability of predicting a random token next,
+ given the partial text already generated. If set to float < 1, the smallest set of the
+ most locally typical tokens with probabilities that add up to typical_p or higher are
+ kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+ """
+ use_cache: bool | None = None
+ """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionParameters(BaseInferenceType):
+ """Additional inference parameters for Automatic Speech Recognition"""
+
+ generation_parameters: AutomaticSpeechRecognitionGenerationParameters | None = None
+ """Parametrization of the text generation process"""
+ return_timestamps: bool | None = None
+ """Whether to output corresponding timestamps with the generated text"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionInput(BaseInferenceType):
+ """Inputs for Automatic Speech Recognition inference"""
+
+ inputs: str
+ """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the audio data as a raw bytes payload.
+ """
+ parameters: AutomaticSpeechRecognitionParameters | None = None
+ """Additional inference parameters for Automatic Speech Recognition"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutputChunk(BaseInferenceType):
+ text: str
+ """A chunk of text identified by the model"""
+ timestamp: list[float]
+ """The start and end timestamps corresponding with the text"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutput(BaseInferenceType):
+ """Outputs of inference for the Automatic Speech Recognition task"""
+
+ text: str
+ """The recognized text."""
+ chunks: list[AutomaticSpeechRecognitionOutputChunk] | None = None
+ """When returnTimestamps is enabled, chunks contains a list of audio chunks identified by
+ the model.
+ """
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/base.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad4016f2d57d11e13dad44c161d3bce3ef741d50
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/base.py
@@ -0,0 +1,167 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a base class for all inference types."""
+
+import inspect
+import json
+import types
+from dataclasses import asdict, dataclass
+from typing import Any, TypeVar, get_args
+
+from typing_extensions import dataclass_transform
+
+
+T = TypeVar("T", bound="BaseInferenceType")
+
+
+def _repr_with_extra(self):
+ fields = list(self.__dataclass_fields__.keys())
+ other_fields = list(k for k in self.__dict__ if k not in fields)
+ return f"{self.__class__.__name__}({', '.join(f'{k}={self.__dict__[k]!r}' for k in fields + other_fields)})"
+
+
+@dataclass_transform()
+def dataclass_with_extra(cls: type[T]) -> type[T]:
+ """Decorator to add a custom __repr__ method to a dataclass, showing all fields, including extra ones.
+
+ This decorator only works with dataclasses that inherit from `BaseInferenceType`.
+ """
+ cls = dataclass(cls)
+ cls.__repr__ = _repr_with_extra # type: ignore[method-assign]
+ return cls
+
+
+@dataclass
+class BaseInferenceType(dict):
+ """Base class for all inference types.
+
+ Object is a dataclass and a dict for backward compatibility but plan is to remove the dict part in the future.
+
+ Handle parsing from dict, list and json strings in a permissive way to ensure future-compatibility (e.g. all fields
+ are made optional, and non-expected fields are added as dict attributes).
+ """
+
+ @classmethod
+ def parse_obj_as_list(cls: type[T], data: bytes | str | list | dict) -> list[T]:
+ """Alias to parse server response and return a single instance.
+
+ See `parse_obj` for more details.
+ """
+ output = cls.parse_obj(data)
+ if not isinstance(output, list):
+ raise ValueError(f"Invalid input data for {cls}. Expected a list, but got {type(output)}.")
+ return output
+
+ @classmethod
+ def parse_obj_as_instance(cls: type[T], data: bytes | str | list | dict) -> T:
+ """Alias to parse server response and return a single instance.
+
+ See `parse_obj` for more details.
+ """
+ output = cls.parse_obj(data)
+ if isinstance(output, list):
+ raise ValueError(f"Invalid input data for {cls}. Expected a single instance, but got a list.")
+ return output
+
+ @classmethod
+ def parse_obj(cls: type[T], data: bytes | str | list | dict) -> list[T] | T:
+ """Parse server response as a dataclass or list of dataclasses.
+
+ To enable future-compatibility, we want to handle cases where the server return more fields than expected.
+ In such cases, we don't want to raise an error but still create the dataclass object. Remaining fields are
+ added as dict attributes.
+ """
+ # Parse server response (from bytes)
+ if isinstance(data, bytes):
+ data = data.decode()
+ if isinstance(data, str):
+ data = json.loads(data)
+
+ # If a list, parse each item individually
+ if isinstance(data, list):
+ return [cls.parse_obj(d) for d in data] # type: ignore
+
+ # At this point, we expect a dict
+ if not isinstance(data, dict):
+ raise ValueError(f"Invalid data type: {type(data)}")
+
+ init_values = {}
+ other_values = {}
+ for key, value in data.items():
+ key = normalize_key(key)
+ if key in cls.__dataclass_fields__ and cls.__dataclass_fields__[key].init:
+ if isinstance(value, dict) or isinstance(value, list):
+ field_type = cls.__dataclass_fields__[key].type
+
+ # if `field_type` is a `BaseInferenceType`, parse it
+ if inspect.isclass(field_type) and issubclass(field_type, BaseInferenceType):
+ value = field_type.parse_obj(value)
+
+ # otherwise, recursively parse nested dataclasses (if possible)
+ # `get_args` returns handle Union and Optional for us
+ else:
+ expected_types = get_args(field_type)
+ for expected_type in expected_types:
+ if (
+ isinstance(expected_type, types.GenericAlias) and expected_type.__origin__ is list
+ ) or getattr(expected_type, "_name", None) == "List":
+ expected_type = get_args(expected_type)[
+ 0
+ ] # assume same type for all items in the list
+ if inspect.isclass(expected_type) and issubclass(expected_type, BaseInferenceType):
+ value = expected_type.parse_obj(value)
+ break
+ init_values[key] = value
+ else:
+ other_values[key] = value
+
+ # Make all missing fields default to None
+ # => ensure that dataclass initialization will never fail even if the server does not return all fields.
+ for key in cls.__dataclass_fields__:
+ if key not in init_values:
+ init_values[key] = None
+
+ # Initialize dataclass with expected values
+ item = cls(**init_values)
+
+ # Add remaining fields as dict attributes
+ item.update(other_values)
+
+ # Add remaining fields as extra dataclass fields.
+ # They won't be part of the dataclass fields but will be accessible as attributes.
+ # Use @dataclass_with_extra to show them in __repr__.
+ item.__dict__.update(other_values)
+ return item
+
+ def __post_init__(self):
+ self.update(asdict(self))
+
+ def __setitem__(self, __key: Any, __value: Any) -> None:
+ # Hacky way to keep dataclass values in sync when dict is updated
+ super().__setitem__(__key, __value)
+ if __key in self.__dataclass_fields__ and getattr(self, __key, None) != __value:
+ self.__setattr__(__key, __value)
+ return
+
+ def __setattr__(self, __name: str, __value: Any) -> None:
+ # Hacky way to keep dict values is sync when dataclass is updated
+ super().__setattr__(__name, __value)
+ if self.get(__name) != __value:
+ self[__name] = __value
+ return
+
+
+def normalize_key(key: str) -> str:
+ # e.g "content-type" -> "content_type", "Accept" -> "accept"
+ return key.replace("-", "_").replace(" ", "_").lower()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/chat_completion.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/chat_completion.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a5d69ef70ed25d0ed0731c6a6b536e3130cdd73
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/chat_completion.py
@@ -0,0 +1,347 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ChatCompletionInputURL(BaseInferenceType):
+ url: str
+
+
+ChatCompletionInputMessageChunkType = Literal["text", "image_url"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessageChunk(BaseInferenceType):
+ type: "ChatCompletionInputMessageChunkType"
+ image_url: ChatCompletionInputURL | None = None
+ text: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionDefinition(BaseInferenceType):
+ name: str
+ parameters: Any
+ description: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolCall(BaseInferenceType):
+ function: ChatCompletionInputFunctionDefinition
+ id: str
+ type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessage(BaseInferenceType):
+ role: str
+ content: list[ChatCompletionInputMessageChunk] | str | None = None
+ name: str | None = None
+ tool_calls: list[ChatCompletionInputToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputJSONSchema(BaseInferenceType):
+ name: str
+ """
+ The name of the response format.
+ """
+ description: str | None = None
+ """
+ A description of what the response format is for, used by the model to determine
+ how to respond in the format.
+ """
+ schema: dict[str, object] | None = None
+ """
+ The schema for the response format, described as a JSON Schema object. Learn how
+ to build JSON schemas [here](https://json-schema.org/).
+ """
+ strict: bool | None = None
+ """
+ Whether to enable strict schema adherence when generating the output. If set to
+ true, the model will always follow the exact schema defined in the `schema`
+ field.
+ """
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatText(BaseInferenceType):
+ type: Literal["text"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatJSONSchema(BaseInferenceType):
+ type: Literal["json_schema"]
+ json_schema: ChatCompletionInputJSONSchema
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatJSONObject(BaseInferenceType):
+ type: Literal["json_object"]
+
+
+ChatCompletionInputGrammarType = Union[
+ ChatCompletionInputResponseFormatText,
+ ChatCompletionInputResponseFormatJSONSchema,
+ ChatCompletionInputResponseFormatJSONObject,
+]
+
+
+@dataclass_with_extra
+class ChatCompletionInputStreamOptions(BaseInferenceType):
+ include_usage: bool | None = None
+ """If set, an additional chunk will be streamed before the data: [DONE] message. The usage
+ field on this chunk shows the token usage statistics for the entire request, and the
+ choices field will always be an empty array. All other chunks will also include a usage
+ field, but with a null value.
+ """
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionName(BaseInferenceType):
+ name: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolChoiceClass(BaseInferenceType):
+ function: ChatCompletionInputFunctionName
+
+
+ChatCompletionInputToolChoiceEnum = Literal["auto", "none", "required"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputTool(BaseInferenceType):
+ function: ChatCompletionInputFunctionDefinition
+ type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInput(BaseInferenceType):
+ """Chat Completion Input.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ messages: list[ChatCompletionInputMessage]
+ """A list of messages comprising the conversation so far."""
+ frequency_penalty: float | None = None
+ """Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing
+ frequency in the text so far,
+ decreasing the model's likelihood to repeat the same line verbatim.
+ """
+ logit_bias: list[float] | None = None
+ """UNUSED
+ Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON
+ object that maps tokens
+ (specified by their token ID in the tokenizer) to an associated bias value from -100 to
+ 100. Mathematically,
+ the bias is added to the logits generated by the model prior to sampling. The exact
+ effect will vary per model,
+ but values between -1 and 1 should decrease or increase likelihood of selection; values
+ like -100 or 100 should
+ result in a ban or exclusive selection of the relevant token.
+ """
+ logprobs: bool | None = None
+ """Whether to return log probabilities of the output tokens or not. If true, returns the log
+ probabilities of each
+ output token returned in the content of message.
+ """
+ max_tokens: int | None = None
+ """The maximum number of tokens that can be generated in the chat completion."""
+ model: str | None = None
+ """[UNUSED] ID of the model to use. See the model endpoint compatibility table for details
+ on which models work with the Chat API.
+ """
+ n: int | None = None
+ """UNUSED
+ How many chat completion choices to generate for each input message. Note that you will
+ be charged based on the
+ number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
+ """
+ presence_penalty: float | None = None
+ """Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they
+ appear in the text so far,
+ increasing the model's likelihood to talk about new topics
+ """
+ response_format: ChatCompletionInputGrammarType | None = None
+ seed: int | None = None
+ stop: list[str] | None = None
+ """Up to 4 sequences where the API will stop generating further tokens."""
+ stream: bool | None = None
+ stream_options: ChatCompletionInputStreamOptions | None = None
+ temperature: float | None = None
+ """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the
+ output more random, while
+ lower values like 0.2 will make it more focused and deterministic.
+ We generally recommend altering this or `top_p` but not both.
+ """
+ tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None
+ tool_prompt: str | None = None
+ """A prompt to be appended before the tools"""
+ tools: list[ChatCompletionInputTool] | None = None
+ """A list of tools the model may call. Currently, only functions are supported as a tool.
+ Use this to provide a list of
+ functions the model may generate JSON inputs for.
+ """
+ top_logprobs: int | None = None
+ """An integer between 0 and 5 specifying the number of most likely tokens to return at each
+ token position, each with
+ an associated log probability. logprobs must be set to true if this parameter is used.
+ """
+ top_p: float | None = None
+ """An alternative to sampling with temperature, called nucleus sampling, where the model
+ considers the results of the
+ tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%
+ probability mass are considered.
+ """
+
+
+@dataclass_with_extra
+class ChatCompletionOutputTopLogprob(BaseInferenceType):
+ logprob: float
+ token: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprob(BaseInferenceType):
+ logprob: float
+ token: str
+ top_logprobs: list[ChatCompletionOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprobs(BaseInferenceType):
+ content: list[ChatCompletionOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputFunctionDefinition(BaseInferenceType):
+ arguments: str
+ name: str
+ description: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputToolCall(BaseInferenceType):
+ function: ChatCompletionOutputFunctionDefinition
+ id: str
+ type: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputMessage(BaseInferenceType):
+ role: str
+ content: str | None = None
+ reasoning: str | None = None
+ tool_call_id: str | None = None
+ tool_calls: list[ChatCompletionOutputToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputComplete(BaseInferenceType):
+ finish_reason: str
+ index: int
+ message: ChatCompletionOutputMessage
+ logprobs: ChatCompletionOutputLogprobs | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputUsage(BaseInferenceType):
+ completion_tokens: int
+ prompt_tokens: int
+ total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionOutput(BaseInferenceType):
+ """Chat Completion Output.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ choices: list[ChatCompletionOutputComplete]
+ created: int
+ id: str
+ model: str
+ system_fingerprint: str
+ usage: ChatCompletionOutputUsage
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputFunction(BaseInferenceType):
+ arguments: str
+ name: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDeltaToolCall(BaseInferenceType):
+ function: ChatCompletionStreamOutputFunction
+ id: str
+ index: int
+ type: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDelta(BaseInferenceType):
+ role: str
+ content: str | None = None
+ reasoning: str | None = None
+ tool_call_id: str | None = None
+ tool_calls: list[ChatCompletionStreamOutputDeltaToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputTopLogprob(BaseInferenceType):
+ logprob: float
+ token: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprob(BaseInferenceType):
+ logprob: float
+ token: str
+ top_logprobs: list[ChatCompletionStreamOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprobs(BaseInferenceType):
+ content: list[ChatCompletionStreamOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputChoice(BaseInferenceType):
+ delta: ChatCompletionStreamOutputDelta
+ index: int
+ finish_reason: str | None = None
+ logprobs: ChatCompletionStreamOutputLogprobs | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputUsage(BaseInferenceType):
+ completion_tokens: int
+ prompt_tokens: int
+ total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutput(BaseInferenceType):
+ """Chat Completion Stream Output.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ choices: list[ChatCompletionStreamOutputChoice]
+ created: int
+ id: str
+ model: str
+ system_fingerprint: str
+ usage: ChatCompletionStreamOutputUsage | None = None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/depth_estimation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/depth_estimation.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf26998ed518acd9e0f16d2f0f0de264a941840c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/depth_estimation.py
@@ -0,0 +1,28 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DepthEstimationInput(BaseInferenceType):
+ """Inputs for Depth Estimation inference"""
+
+ inputs: Any
+ """The input image data"""
+ parameters: dict[str, Any] | None = None
+ """Additional inference parameters for Depth Estimation"""
+
+
+@dataclass_with_extra
+class DepthEstimationOutput(BaseInferenceType):
+ """Outputs of inference for the Depth Estimation task"""
+
+ depth: Any
+ """The predicted depth as an image"""
+ predicted_depth: Any
+ """The predicted depth as a tensor"""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ec9c29e306671a389321e332b9bc197e77e3c65
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py
@@ -0,0 +1,80 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInputData(BaseInferenceType):
+ """One (document, question) pair to answer"""
+
+ image: Any
+ """The image on which the question is asked"""
+ question: str
+ """A question to ask of the document"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+ """Additional inference parameters for Document Question Answering"""
+
+ doc_stride: int | None = None
+ """If the words in the document are too long to fit with the question for the model, it will
+ be split in several chunks with some overlap. This argument controls the size of that
+ overlap.
+ """
+ handle_impossible_answer: bool | None = None
+ """Whether to accept impossible as an answer"""
+ lang: str | None = None
+ """Language to use while running OCR. Defaults to english."""
+ max_answer_len: int | None = None
+ """The maximum length of predicted answers (e.g., only answers with a shorter length are
+ considered).
+ """
+ max_question_len: int | None = None
+ """The maximum length of the question after tokenization. It will be truncated if needed."""
+ max_seq_len: int | None = None
+ """The maximum length of the total sentence (context + question) in tokens of each chunk
+ passed to the model. The context will be split in several chunks (using doc_stride as
+ overlap) if needed.
+ """
+ top_k: int | None = None
+ """The number of answers to return (will be chosen by order of likelihood). Can return less
+ than top_k answers if there are not enough options available within the context.
+ """
+ word_boxes: list[list[float] | str] | None = None
+ """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will
+ skip the OCR step and use the provided bounding boxes instead.
+ """
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+ """Inputs for Document Question Answering inference"""
+
+ inputs: DocumentQuestionAnsweringInputData
+ """One (document, question) pair to answer"""
+ parameters: DocumentQuestionAnsweringParameters | None = None
+ """Additional inference parameters for Document Question Answering"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringOutputElement(BaseInferenceType):
+ """Outputs of inference for the Document Question Answering task"""
+
+ answer: str
+ """The answer to the question."""
+ end: int
+ """The end word index of the answer (in the OCR’d version of the input or provided word
+ boxes).
+ """
+ score: float
+ """The probability associated to the answer."""
+ start: int
+ """The start word index of the answer (in the OCR’d version of the input or provided word
+ boxes).
+ """
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2868432b8dc937582b467ba14bcb12c3148eaf7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py
@@ -0,0 +1,36 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+FeatureExtractionInputTruncationDirection = Literal["left", "right"]
+
+
+@dataclass_with_extra
+class FeatureExtractionInput(BaseInferenceType):
+ """Feature Extraction Input.
+ Auto-generated from TEI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.
+ """
+
+ inputs: list[str] | str
+ """The text or list of texts to embed."""
+ normalize: bool | None = None
+ prompt_name: str | None = None
+ """The name of the prompt that should be used by for encoding. If not set, no prompt
+ will be applied.
+ Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+ For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",
+ ...},
+ then the sentence "What is the capital of France?" will be encoded as
+ "query: What is the capital of France?" because the prompt text will be prepended before
+ any text to encode.
+ """
+ truncate: bool | None = None
+ truncation_direction: Optional["FeatureExtractionInputTruncationDirection"] = None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..84fcac730ee43763150068ddeca63a1ed127d59c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py
@@ -0,0 +1,47 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class FillMaskParameters(BaseInferenceType):
+ """Additional inference parameters for Fill Mask"""
+
+ targets: list[str] | None = None
+ """When passed, the model will limit the scores to the passed targets instead of looking up
+ in the whole vocabulary. If the provided targets are not in the model vocab, they will be
+ tokenized and the first resulting token will be used (with a warning, and that might be
+ slower).
+ """
+ top_k: int | None = None
+ """When passed, overrides the number of predictions to return."""
+
+
+@dataclass_with_extra
+class FillMaskInput(BaseInferenceType):
+ """Inputs for Fill Mask inference"""
+
+ inputs: str
+ """The text with masked tokens"""
+ parameters: FillMaskParameters | None = None
+ """Additional inference parameters for Fill Mask"""
+
+
+@dataclass_with_extra
+class FillMaskOutputElement(BaseInferenceType):
+ """Outputs of inference for the Fill Mask task"""
+
+ score: float
+ """The corresponding probability"""
+ sequence: str
+ """The corresponding input with the mask token prediction."""
+ token: int
+ """The predicted token id (to replace the masked one)."""
+ token_str: Any
+ fill_mask_output_token_str: str | None = None
+ """The predicted token (to replace the masked one)."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d2d564b7d573cea0847308bbb723d519a5cede
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_classification.py
@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class ImageClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Image Classification"""
+
+ function_to_apply: Optional["ImageClassificationOutputTransform"] = None
+ """The function to apply to the model outputs in order to retrieve the scores."""
+ top_k: int | None = None
+ """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class ImageClassificationInput(BaseInferenceType):
+ """Inputs for Image Classification inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload.
+ """
+ parameters: ImageClassificationParameters | None = None
+ """Additional inference parameters for Image Classification"""
+
+
+@dataclass_with_extra
+class ImageClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Image Classification task"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2938d89cab79329d1aeac3b16d85c8b76e49cdc
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py
@@ -0,0 +1,51 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass_with_extra
+class ImageSegmentationParameters(BaseInferenceType):
+ """Additional inference parameters for Image Segmentation"""
+
+ mask_threshold: float | None = None
+ """Threshold to use when turning the predicted masks into binary values."""
+ overlap_mask_area_threshold: float | None = None
+ """Mask overlap threshold to eliminate small, disconnected segments."""
+ subtask: Optional["ImageSegmentationSubtask"] = None
+ """Segmentation task to be performed, depending on model capabilities."""
+ threshold: float | None = None
+ """Probability threshold to filter out predicted masks."""
+
+
+@dataclass_with_extra
+class ImageSegmentationInput(BaseInferenceType):
+ """Inputs for Image Segmentation inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload.
+ """
+ parameters: ImageSegmentationParameters | None = None
+ """Additional inference parameters for Image Segmentation"""
+
+
+@dataclass_with_extra
+class ImageSegmentationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Image Segmentation task
+ A predicted mask / segment
+ """
+
+ label: str
+ """The label of the predicted segment."""
+ mask: str
+ """The corresponding mask as a black-and-white image (base64-encoded)."""
+ score: float | None = None
+ """The score or confidence degree the model has."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_image.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..d711f40ca021e5dfa3c072d19b5730d615dec6eb
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_image.py
@@ -0,0 +1,67 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageTextToImageTargetSize(BaseInferenceType):
+ """The size in pixels of the output image. This parameter is only supported by some
+ providers and for specific models. It will be ignored when unsupported.
+ """
+
+ height: int
+ width: int
+
+
+@dataclass_with_extra
+class ImageTextToImageParameters(BaseInferenceType):
+ """Additional inference parameters for Image Text To Image"""
+
+ guidance_scale: float | None = None
+ """For diffusion models. A higher guidance scale value encourages the model to generate
+ images closely linked to the text prompt at the expense of lower image quality.
+ """
+ negative_prompt: str | None = None
+ """One prompt to guide what NOT to include in image generation."""
+ num_inference_steps: int | None = None
+ """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+ a higher quality image at the expense of slower inference.
+ """
+ prompt: str | None = None
+ """The text prompt to guide the image generation. Either this or inputs (image) must be
+ provided.
+ """
+ seed: int | None = None
+ """Seed for the random number generator."""
+ target_size: ImageTextToImageTargetSize | None = None
+ """The size in pixels of the output image. This parameter is only supported by some
+ providers and for specific models. It will be ignored when unsupported.
+ """
+
+
+@dataclass_with_extra
+class ImageTextToImageInput(BaseInferenceType):
+ """Inputs for Image Text To Image inference. Either inputs (image) or prompt (in parameters)
+ must be provided, or both.
+ """
+
+ inputs: str | None = None
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload. Either this or prompt must be
+ provided.
+ """
+ parameters: ImageTextToImageParameters | None = None
+ """Additional inference parameters for Image Text To Image"""
+
+
+@dataclass_with_extra
+class ImageTextToImageOutput(BaseInferenceType):
+ """Outputs of inference for the Image Text To Image task"""
+
+ image: Any
+ """The generated image returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_video.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..870bb16c04add8cfb7909da74cef902b98486f85
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_text_to_video.py
@@ -0,0 +1,65 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageTextToVideoTargetSize(BaseInferenceType):
+ """The size in pixel of the output video frames."""
+
+ height: int
+ width: int
+
+
+@dataclass_with_extra
+class ImageTextToVideoParameters(BaseInferenceType):
+ """Additional inference parameters for Image Text To Video"""
+
+ guidance_scale: float | None = None
+ """For diffusion models. A higher guidance scale value encourages the model to generate
+ videos closely linked to the text prompt at the expense of lower image quality.
+ """
+ negative_prompt: str | None = None
+ """One prompt to guide what NOT to include in video generation."""
+ num_frames: float | None = None
+ """The num_frames parameter determines how many video frames are generated."""
+ num_inference_steps: int | None = None
+ """The number of denoising steps. More denoising steps usually lead to a higher quality
+ video at the expense of slower inference.
+ """
+ prompt: str | None = None
+ """The text prompt to guide the video generation. Either this or inputs (image) must be
+ provided.
+ """
+ seed: int | None = None
+ """Seed for the random number generator."""
+ target_size: ImageTextToVideoTargetSize | None = None
+ """The size in pixel of the output video frames."""
+
+
+@dataclass_with_extra
+class ImageTextToVideoInput(BaseInferenceType):
+ """Inputs for Image Text To Video inference. Either inputs (image) or prompt (in parameters)
+ must be provided, or both.
+ """
+
+ inputs: str | None = None
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload. Either this or prompt must be
+ provided.
+ """
+ parameters: ImageTextToVideoParameters | None = None
+ """Additional inference parameters for Image Text To Video"""
+
+
+@dataclass_with_extra
+class ImageTextToVideoOutput(BaseInferenceType):
+ """Outputs of inference for the Image Text To Video task"""
+
+ video: Any
+ """The generated video returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e943d73915803c05211e0d8b1a0e64fc4fdba3a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py
@@ -0,0 +1,60 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageToImageTargetSize(BaseInferenceType):
+ """The size in pixels of the output image. This parameter is only supported by some
+ providers and for specific models. It will be ignored when unsupported.
+ """
+
+ height: int
+ width: int
+
+
+@dataclass_with_extra
+class ImageToImageParameters(BaseInferenceType):
+ """Additional inference parameters for Image To Image"""
+
+ guidance_scale: float | None = None
+ """For diffusion models. A higher guidance scale value encourages the model to generate
+ images closely linked to the text prompt at the expense of lower image quality.
+ """
+ negative_prompt: str | None = None
+ """One prompt to guide what NOT to include in image generation."""
+ num_inference_steps: int | None = None
+ """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+ a higher quality image at the expense of slower inference.
+ """
+ prompt: str | None = None
+ """The text prompt to guide the image generation."""
+ target_size: ImageToImageTargetSize | None = None
+ """The size in pixels of the output image. This parameter is only supported by some
+ providers and for specific models. It will be ignored when unsupported.
+ """
+
+
+@dataclass_with_extra
+class ImageToImageInput(BaseInferenceType):
+ """Inputs for Image To Image inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload.
+ """
+ parameters: ImageToImageParameters | None = None
+ """Additional inference parameters for Image To Image"""
+
+
+@dataclass_with_extra
+class ImageToImageOutput(BaseInferenceType):
+ """Outputs of inference for the Image To Image task"""
+
+ image: Any
+ """The output image returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_text.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..3924a6612c8b246069215e2ad581dc058f54ac41
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_text.py
@@ -0,0 +1,100 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageToTextEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class ImageToTextGenerationParameters(BaseInferenceType):
+ """Parametrization of the text generation process"""
+
+ do_sample: bool | None = None
+ """Whether to use sampling instead of greedy decoding when generating new tokens."""
+ early_stopping: Union[bool, "ImageToTextEarlyStoppingEnum"] | None = None
+ """Controls the stopping condition for beam-based methods."""
+ epsilon_cutoff: float | None = None
+ """If set to float strictly between 0 and 1, only tokens with a conditional probability
+ greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+ 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+ Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ """
+ eta_cutoff: float | None = None
+ """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+ float strictly between 0 and 1, a token is only considered if it is greater than either
+ eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+ term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+ the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+ See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+ for more details.
+ """
+ max_length: int | None = None
+ """The maximum length (in tokens) of the generated text, including the input."""
+ max_new_tokens: int | None = None
+ """The maximum number of tokens to generate. Takes precedence over max_length."""
+ min_length: int | None = None
+ """The minimum length (in tokens) of the generated text, including the input."""
+ min_new_tokens: int | None = None
+ """The minimum number of tokens to generate. Takes precedence over min_length."""
+ num_beam_groups: int | None = None
+ """Number of groups to divide num_beams into in order to ensure diversity among different
+ groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+ """
+ num_beams: int | None = None
+ """Number of beams to use for beam search."""
+ penalty_alpha: float | None = None
+ """The value balances the model confidence and the degeneration penalty in contrastive
+ search decoding.
+ """
+ temperature: float | None = None
+ """The value used to modulate the next token probabilities."""
+ top_k: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+ top_p: float | None = None
+ """If set to float < 1, only the smallest set of most probable tokens with probabilities
+ that add up to top_p or higher are kept for generation.
+ """
+ typical_p: float | None = None
+ """Local typicality measures how similar the conditional probability of predicting a target
+ token next is to the expected conditional probability of predicting a random token next,
+ given the partial text already generated. If set to float < 1, the smallest set of the
+ most locally typical tokens with probabilities that add up to typical_p or higher are
+ kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+ """
+ use_cache: bool | None = None
+ """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class ImageToTextParameters(BaseInferenceType):
+ """Additional inference parameters for Image To Text"""
+
+ generation_parameters: ImageToTextGenerationParameters | None = None
+ """Parametrization of the text generation process"""
+ max_new_tokens: int | None = None
+ """The amount of maximum tokens to generate."""
+
+
+@dataclass_with_extra
+class ImageToTextInput(BaseInferenceType):
+ """Inputs for Image To Text inference"""
+
+ inputs: Any
+ """The input image data"""
+ parameters: ImageToTextParameters | None = None
+ """Additional inference parameters for Image To Text"""
+
+
+@dataclass_with_extra
+class ImageToTextOutput(BaseInferenceType):
+ """Outputs of inference for the Image To Text task"""
+
+ generated_text: Any
+ image_to_text_output_generated_text: str | None = None
+ """The generated text."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_video.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..b14883f044c13c935f7b3d41f23a50f0b7c29850
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/image_to_video.py
@@ -0,0 +1,60 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageToVideoTargetSize(BaseInferenceType):
+ """The size in pixel of the output video frames."""
+
+ height: int
+ width: int
+
+
+@dataclass_with_extra
+class ImageToVideoParameters(BaseInferenceType):
+ """Additional inference parameters for Image To Video"""
+
+ guidance_scale: float | None = None
+ """For diffusion models. A higher guidance scale value encourages the model to generate
+ videos closely linked to the text prompt at the expense of lower image quality.
+ """
+ negative_prompt: str | None = None
+ """One prompt to guide what NOT to include in video generation."""
+ num_frames: float | None = None
+ """The num_frames parameter determines how many video frames are generated."""
+ num_inference_steps: int | None = None
+ """The number of denoising steps. More denoising steps usually lead to a higher quality
+ video at the expense of slower inference.
+ """
+ prompt: str | None = None
+ """The text prompt to guide the video generation."""
+ seed: int | None = None
+ """Seed for the random number generator."""
+ target_size: ImageToVideoTargetSize | None = None
+ """The size in pixel of the output video frames."""
+
+
+@dataclass_with_extra
+class ImageToVideoInput(BaseInferenceType):
+ """Inputs for Image To Video inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload.
+ """
+ parameters: ImageToVideoParameters | None = None
+ """Additional inference parameters for Image To Video"""
+
+
+@dataclass_with_extra
+class ImageToVideoOutput(BaseInferenceType):
+ """Outputs of inference for the Image To Video task"""
+
+ video: Any
+ """The generated video returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/object_detection.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/object_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c7ef7843424c75e0af2d7363a8561bec57b81f3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/object_detection.py
@@ -0,0 +1,56 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ObjectDetectionParameters(BaseInferenceType):
+ """Additional inference parameters for Object Detection"""
+
+ threshold: float | None = None
+ """The probability necessary to make a prediction."""
+
+
+@dataclass_with_extra
+class ObjectDetectionInput(BaseInferenceType):
+ """Inputs for Object Detection inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+ also provide the image data as a raw bytes payload.
+ """
+ parameters: ObjectDetectionParameters | None = None
+ """Additional inference parameters for Object Detection"""
+
+
+@dataclass_with_extra
+class ObjectDetectionBoundingBox(BaseInferenceType):
+ """The predicted bounding box. Coordinates are relative to the top left corner of the input
+ image.
+ """
+
+ xmax: int
+ """The x-coordinate of the bottom-right corner of the bounding box."""
+ xmin: int
+ """The x-coordinate of the top-left corner of the bounding box."""
+ ymax: int
+ """The y-coordinate of the bottom-right corner of the bounding box."""
+ ymin: int
+ """The y-coordinate of the top-left corner of the bounding box."""
+
+
+@dataclass_with_extra
+class ObjectDetectionOutputElement(BaseInferenceType):
+ """Outputs of inference for the Object Detection task"""
+
+ box: ObjectDetectionBoundingBox
+ """The predicted bounding box. Coordinates are relative to the top left corner of the input
+ image.
+ """
+ label: str
+ """The predicted label for the bounding box."""
+ score: float
+ """The associated score / probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/question_answering.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee97c638d68c3bedf4fe8e8f9a8f996d4955e30a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/question_answering.py
@@ -0,0 +1,72 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class QuestionAnsweringInputData(BaseInferenceType):
+ """One (context, question) pair to answer"""
+
+ context: str
+ """The context to be used for answering the question"""
+ question: str
+ """The question to be answered"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringParameters(BaseInferenceType):
+ """Additional inference parameters for Question Answering"""
+
+ align_to_words: bool | None = None
+ """Attempts to align the answer to real words. Improves quality on space separated
+ languages. Might hurt on non-space-separated languages (like Japanese or Chinese)
+ """
+ doc_stride: int | None = None
+ """If the context is too long to fit with the question for the model, it will be split in
+ several chunks with some overlap. This argument controls the size of that overlap.
+ """
+ handle_impossible_answer: bool | None = None
+ """Whether to accept impossible as an answer."""
+ max_answer_len: int | None = None
+ """The maximum length of predicted answers (e.g., only answers with a shorter length are
+ considered).
+ """
+ max_question_len: int | None = None
+ """The maximum length of the question after tokenization. It will be truncated if needed."""
+ max_seq_len: int | None = None
+ """The maximum length of the total sentence (context + question) in tokens of each chunk
+ passed to the model. The context will be split in several chunks (using docStride as
+ overlap) if needed.
+ """
+ top_k: int | None = None
+ """The number of answers to return (will be chosen by order of likelihood). Note that we
+ return less than topk answers if there are not enough options available within the
+ context.
+ """
+
+
+@dataclass_with_extra
+class QuestionAnsweringInput(BaseInferenceType):
+ """Inputs for Question Answering inference"""
+
+ inputs: QuestionAnsweringInputData
+ """One (context, question) pair to answer"""
+ parameters: QuestionAnsweringParameters | None = None
+ """Additional inference parameters for Question Answering"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringOutputElement(BaseInferenceType):
+ """Outputs of inference for the Question Answering task"""
+
+ answer: str
+ """The answer to the question."""
+ end: int
+ """The character position in the input where the answer ends."""
+ score: float
+ """The probability associated to the answer."""
+ start: int
+ """The character position in the input where the answer begins."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/sentence_similarity.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/sentence_similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..a06c32d395fce557784f95ca77c0a62f04b255db
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/sentence_similarity.py
@@ -0,0 +1,27 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class SentenceSimilarityInputData(BaseInferenceType):
+ sentences: list[str]
+ """A list of strings which will be compared against the source_sentence."""
+ source_sentence: str
+ """The string that you wish to compare the other strings with. This can be a phrase,
+ sentence, or longer passage, depending on the model being used.
+ """
+
+
+@dataclass_with_extra
+class SentenceSimilarityInput(BaseInferenceType):
+ """Inputs for Sentence similarity inference"""
+
+ inputs: SentenceSimilarityInputData
+ parameters: dict[str, Any] | None = None
+ """Additional inference parameters for Sentence Similarity"""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/summarization.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/summarization.py
new file mode 100644
index 0000000000000000000000000000000000000000..35f2a86f308f9d4839f46ac6496097961a2df723
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/summarization.py
@@ -0,0 +1,41 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+SummarizationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class SummarizationParameters(BaseInferenceType):
+ """Additional inference parameters for summarization."""
+
+ clean_up_tokenization_spaces: bool | None = None
+ """Whether to clean up the potential extra spaces in the text output."""
+ generate_parameters: dict[str, Any] | None = None
+ """Additional parametrization of the text generation algorithm."""
+ truncation: Optional["SummarizationTruncationStrategy"] = None
+ """The truncation strategy to use."""
+
+
+@dataclass_with_extra
+class SummarizationInput(BaseInferenceType):
+ """Inputs for Summarization inference"""
+
+ inputs: str
+ """The input text to summarize."""
+ parameters: SummarizationParameters | None = None
+ """Additional inference parameters for summarization."""
+
+
+@dataclass_with_extra
+class SummarizationOutput(BaseInferenceType):
+ """Outputs of inference for the Summarization task"""
+
+ summary_text: str
+ """The summarized text."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..1909f0d2b77551ff8b7b4cf0df199a46e5c72197
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py
@@ -0,0 +1,62 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInputData(BaseInferenceType):
+ """One (table, question) pair to answer"""
+
+ question: str
+ """The question to be answered about the table"""
+ table: dict[str, list[str]]
+ """The table to serve as context for the questions"""
+
+
+Padding = Literal["do_not_pad", "longest", "max_length"]
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringParameters(BaseInferenceType):
+ """Additional inference parameters for Table Question Answering"""
+
+ padding: Optional["Padding"] = None
+ """Activates and controls padding."""
+ sequential: bool | None = None
+ """Whether to do inference sequentially or as a batch. Batching is faster, but models like
+ SQA require the inference to be done sequentially to extract relations within sequences,
+ given their conversational nature.
+ """
+ truncation: bool | None = None
+ """Activates and controls truncation."""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInput(BaseInferenceType):
+ """Inputs for Table Question Answering inference"""
+
+ inputs: TableQuestionAnsweringInputData
+ """One (table, question) pair to answer"""
+ parameters: TableQuestionAnsweringParameters | None = None
+ """Additional inference parameters for Table Question Answering"""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringOutputElement(BaseInferenceType):
+ """Outputs of inference for the Table Question Answering task"""
+
+ answer: str
+ """The answer of the question given the table. If there is an aggregator, the answer will be
+ preceded by `AGGREGATOR >`.
+ """
+ cells: list[str]
+ """list of strings made up of the answer cell values."""
+ coordinates: list[list[int]]
+ """Coordinates of the cells of the answers."""
+ aggregator: str | None = None
+ """If the model has an aggregator, this returns the aggregator."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4508823749a52d3238fc288e62e479e126e1827
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py
@@ -0,0 +1,42 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class Text2TextGenerationParameters(BaseInferenceType):
+ """Additional inference parameters for Text2text Generation"""
+
+ clean_up_tokenization_spaces: bool | None = None
+ """Whether to clean up the potential extra spaces in the text output."""
+ generate_parameters: dict[str, Any] | None = None
+ """Additional parametrization of the text generation algorithm"""
+ truncation: Optional["Text2TextGenerationTruncationStrategy"] = None
+ """The truncation strategy to use"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationInput(BaseInferenceType):
+ """Inputs for Text2text Generation inference"""
+
+ inputs: str
+ """The input text data"""
+ parameters: Text2TextGenerationParameters | None = None
+ """Additional inference parameters for Text2text Generation"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationOutput(BaseInferenceType):
+ """Outputs of inference for the Text2text Generation task"""
+
+ generated_text: Any
+ text2_text_generation_output_generated_text: str | None = None
+ """The generated text."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..9df576b4de724a73be32ff5ecbf81dd5ea576f91
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_classification.py
@@ -0,0 +1,41 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class TextClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Text Classification"""
+
+ function_to_apply: Optional["TextClassificationOutputTransform"] = None
+ """The function to apply to the model outputs in order to retrieve the scores."""
+ top_k: int | None = None
+ """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class TextClassificationInput(BaseInferenceType):
+ """Inputs for Text Classification inference"""
+
+ inputs: str
+ """The text to classify"""
+ parameters: TextClassificationParameters | None = None
+ """Additional inference parameters for Text Classification"""
+
+
+@dataclass_with_extra
+class TextClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Text Classification task"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_generation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b2269955bcaef7f23972b00ca8991b6854f8181
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_generation.py
@@ -0,0 +1,168 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TypeEnum = Literal["json", "regex", "json_schema"]
+
+
+@dataclass_with_extra
+class TextGenerationInputGrammarType(BaseInferenceType):
+ type: "TypeEnum"
+ value: Any
+ """A string that represents a [JSON Schema](https://json-schema.org/).
+ JSON Schema is a declarative language that allows to annotate JSON documents
+ with types and descriptions.
+ """
+
+
+@dataclass_with_extra
+class TextGenerationInputGenerateParameters(BaseInferenceType):
+ adapter_id: str | None = None
+ """Lora adapter id"""
+ best_of: int | None = None
+ """Generate best_of sequences and return the one if the highest token logprobs."""
+ decoder_input_details: bool | None = None
+ """Whether to return decoder input token logprobs and ids."""
+ details: bool | None = None
+ """Whether to return generation details."""
+ do_sample: bool | None = None
+ """Activate logits sampling."""
+ frequency_penalty: float | None = None
+ """The parameter for frequency penalty. 1.0 means no penalty
+ Penalize new tokens based on their existing frequency in the text so far,
+ decreasing the model's likelihood to repeat the same line verbatim.
+ """
+ grammar: TextGenerationInputGrammarType | None = None
+ max_new_tokens: int | None = None
+ """Maximum number of tokens to generate."""
+ repetition_penalty: float | None = None
+ """The parameter for repetition penalty. 1.0 means no penalty.
+ See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+ """
+ return_full_text: bool | None = None
+ """Whether to prepend the prompt to the generated text"""
+ seed: int | None = None
+ """Random sampling seed."""
+ stop: list[str] | None = None
+ """Stop generating tokens if a member of `stop` is generated."""
+ temperature: float | None = None
+ """The value used to module the logits distribution."""
+ top_k: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+ top_n_tokens: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-n-filtering."""
+ top_p: float | None = None
+ """Top-p value for nucleus sampling."""
+ truncate: int | None = None
+ """Truncate inputs tokens to the given size."""
+ typical_p: float | None = None
+ """Typical Decoding mass
+ See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
+ for more information.
+ """
+ watermark: bool | None = None
+ """Watermarking with [A Watermark for Large Language
+ Models](https://arxiv.org/abs/2301.10226).
+ """
+
+
+@dataclass_with_extra
+class TextGenerationInput(BaseInferenceType):
+ """Text Generation Input.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ inputs: str
+ parameters: TextGenerationInputGenerateParameters | None = None
+ stream: bool | None = None
+
+
+TextGenerationOutputFinishReason = Literal["length", "eos_token", "stop_sequence"]
+
+
+@dataclass_with_extra
+class TextGenerationOutputPrefillToken(BaseInferenceType):
+ id: int
+ logprob: float
+ text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputToken(BaseInferenceType):
+ id: int
+ logprob: float
+ special: bool
+ text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputBestOfSequence(BaseInferenceType):
+ finish_reason: "TextGenerationOutputFinishReason"
+ generated_text: str
+ generated_tokens: int
+ prefill: list[TextGenerationOutputPrefillToken]
+ tokens: list[TextGenerationOutputToken]
+ seed: int | None = None
+ top_tokens: list[list[TextGenerationOutputToken]] | None = None
+
+
+@dataclass_with_extra
+class TextGenerationOutputDetails(BaseInferenceType):
+ finish_reason: "TextGenerationOutputFinishReason"
+ generated_tokens: int
+ prefill: list[TextGenerationOutputPrefillToken]
+ tokens: list[TextGenerationOutputToken]
+ best_of_sequences: list[TextGenerationOutputBestOfSequence] | None = None
+ seed: int | None = None
+ top_tokens: list[list[TextGenerationOutputToken]] | None = None
+
+
+@dataclass_with_extra
+class TextGenerationOutput(BaseInferenceType):
+ """Text Generation Output.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ generated_text: str
+ details: TextGenerationOutputDetails | None = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputStreamDetails(BaseInferenceType):
+ finish_reason: "TextGenerationOutputFinishReason"
+ generated_tokens: int
+ input_length: int
+ seed: int | None = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputToken(BaseInferenceType):
+ id: int
+ logprob: float
+ special: bool
+ text: str
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutput(BaseInferenceType):
+ """Text Generation Stream Output.
+ Auto-generated from TGI specs.
+ For more details, check out
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+ """
+
+ index: int
+ token: TextGenerationStreamOutputToken
+ details: TextGenerationStreamOutputStreamDetails | None = None
+ generated_text: str | None = None
+ top_tokens: list[TextGenerationStreamOutputToken] | None = None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..35033f5129cdfb3d3e7512727bdfc248c882d35e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py
@@ -0,0 +1,99 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextToAudioEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class TextToAudioGenerationParameters(BaseInferenceType):
+ """Parametrization of the text generation process"""
+
+ do_sample: bool | None = None
+ """Whether to use sampling instead of greedy decoding when generating new tokens."""
+ early_stopping: Union[bool, "TextToAudioEarlyStoppingEnum"] | None = None
+ """Controls the stopping condition for beam-based methods."""
+ epsilon_cutoff: float | None = None
+ """If set to float strictly between 0 and 1, only tokens with a conditional probability
+ greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+ 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+ Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ """
+ eta_cutoff: float | None = None
+ """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+ float strictly between 0 and 1, a token is only considered if it is greater than either
+ eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+ term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+ the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+ See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+ for more details.
+ """
+ max_length: int | None = None
+ """The maximum length (in tokens) of the generated text, including the input."""
+ max_new_tokens: int | None = None
+ """The maximum number of tokens to generate. Takes precedence over max_length."""
+ min_length: int | None = None
+ """The minimum length (in tokens) of the generated text, including the input."""
+ min_new_tokens: int | None = None
+ """The minimum number of tokens to generate. Takes precedence over min_length."""
+ num_beam_groups: int | None = None
+ """Number of groups to divide num_beams into in order to ensure diversity among different
+ groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+ """
+ num_beams: int | None = None
+ """Number of beams to use for beam search."""
+ penalty_alpha: float | None = None
+ """The value balances the model confidence and the degeneration penalty in contrastive
+ search decoding.
+ """
+ temperature: float | None = None
+ """The value used to modulate the next token probabilities."""
+ top_k: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+ top_p: float | None = None
+ """If set to float < 1, only the smallest set of most probable tokens with probabilities
+ that add up to top_p or higher are kept for generation.
+ """
+ typical_p: float | None = None
+ """Local typicality measures how similar the conditional probability of predicting a target
+ token next is to the expected conditional probability of predicting a random token next,
+ given the partial text already generated. If set to float < 1, the smallest set of the
+ most locally typical tokens with probabilities that add up to typical_p or higher are
+ kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+ """
+ use_cache: bool | None = None
+ """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class TextToAudioParameters(BaseInferenceType):
+ """Additional inference parameters for Text To Audio"""
+
+ generation_parameters: TextToAudioGenerationParameters | None = None
+ """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class TextToAudioInput(BaseInferenceType):
+ """Inputs for Text To Audio inference"""
+
+ inputs: str
+ """The input text data"""
+ parameters: TextToAudioParameters | None = None
+ """Additional inference parameters for Text To Audio"""
+
+
+@dataclass_with_extra
+class TextToAudioOutput(BaseInferenceType):
+ """Outputs of inference for the Text To Audio task"""
+
+ audio: Any
+ """The generated audio waveform."""
+ sampling_rate: float
+ """The sampling rate of the generated audio waveform."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..716f240385fba6116b8b19b88e177536a3d18ec0
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py
@@ -0,0 +1,50 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TextToImageParameters(BaseInferenceType):
+ """Additional inference parameters for Text To Image"""
+
+ guidance_scale: float | None = None
+ """A higher guidance scale value encourages the model to generate images closely linked to
+ the text prompt, but values too high may cause saturation and other artifacts.
+ """
+ height: int | None = None
+ """The height in pixels of the output image"""
+ negative_prompt: str | None = None
+ """One prompt to guide what NOT to include in image generation."""
+ num_inference_steps: int | None = None
+ """The number of denoising steps. More denoising steps usually lead to a higher quality
+ image at the expense of slower inference.
+ """
+ scheduler: str | None = None
+ """Override the scheduler with a compatible one."""
+ seed: int | None = None
+ """Seed for the random number generator."""
+ width: int | None = None
+ """The width in pixels of the output image"""
+
+
+@dataclass_with_extra
+class TextToImageInput(BaseInferenceType):
+ """Inputs for Text To Image inference"""
+
+ inputs: str
+ """The input text data (sometimes called "prompt")"""
+ parameters: TextToImageParameters | None = None
+ """Additional inference parameters for Text To Image"""
+
+
+@dataclass_with_extra
+class TextToImageOutput(BaseInferenceType):
+ """Outputs of inference for the Text To Image task"""
+
+ image: Any
+ """The generated image returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py
new file mode 100644
index 0000000000000000000000000000000000000000..588e0d1a566bde3bf66a3a07271638c6f42ecb95
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py
@@ -0,0 +1,99 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextToSpeechEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class TextToSpeechGenerationParameters(BaseInferenceType):
+ """Parametrization of the text generation process"""
+
+ do_sample: bool | None = None
+ """Whether to use sampling instead of greedy decoding when generating new tokens."""
+ early_stopping: Union[bool, "TextToSpeechEarlyStoppingEnum"] | None = None
+ """Controls the stopping condition for beam-based methods."""
+ epsilon_cutoff: float | None = None
+ """If set to float strictly between 0 and 1, only tokens with a conditional probability
+ greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+ 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+ Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+ """
+ eta_cutoff: float | None = None
+ """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+ float strictly between 0 and 1, a token is only considered if it is greater than either
+ eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+ term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+ the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+ See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+ for more details.
+ """
+ max_length: int | None = None
+ """The maximum length (in tokens) of the generated text, including the input."""
+ max_new_tokens: int | None = None
+ """The maximum number of tokens to generate. Takes precedence over max_length."""
+ min_length: int | None = None
+ """The minimum length (in tokens) of the generated text, including the input."""
+ min_new_tokens: int | None = None
+ """The minimum number of tokens to generate. Takes precedence over min_length."""
+ num_beam_groups: int | None = None
+ """Number of groups to divide num_beams into in order to ensure diversity among different
+ groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+ """
+ num_beams: int | None = None
+ """Number of beams to use for beam search."""
+ penalty_alpha: float | None = None
+ """The value balances the model confidence and the degeneration penalty in contrastive
+ search decoding.
+ """
+ temperature: float | None = None
+ """The value used to modulate the next token probabilities."""
+ top_k: int | None = None
+ """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+ top_p: float | None = None
+ """If set to float < 1, only the smallest set of most probable tokens with probabilities
+ that add up to top_p or higher are kept for generation.
+ """
+ typical_p: float | None = None
+ """Local typicality measures how similar the conditional probability of predicting a target
+ token next is to the expected conditional probability of predicting a random token next,
+ given the partial text already generated. If set to float < 1, the smallest set of the
+ most locally typical tokens with probabilities that add up to typical_p or higher are
+ kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+ """
+ use_cache: bool | None = None
+ """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class TextToSpeechParameters(BaseInferenceType):
+ """Additional inference parameters for Text To Speech"""
+
+ generation_parameters: TextToSpeechGenerationParameters | None = None
+ """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class TextToSpeechInput(BaseInferenceType):
+ """Inputs for Text To Speech inference"""
+
+ inputs: str
+ """The input text data"""
+ parameters: TextToSpeechParameters | None = None
+ """Additional inference parameters for Text To Speech"""
+
+
+@dataclass_with_extra
+class TextToSpeechOutput(BaseInferenceType):
+ """Outputs of inference for the Text To Speech task"""
+
+ audio: Any
+ """The generated audio"""
+ sampling_rate: float | None = None
+ """The sampling rate of the generated audio waveform."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e357113cecccce1653c8db977ecd2ca392a79b1
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py
@@ -0,0 +1,46 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TextToVideoParameters(BaseInferenceType):
+ """Additional inference parameters for Text To Video"""
+
+ guidance_scale: float | None = None
+ """A higher guidance scale value encourages the model to generate videos closely linked to
+ the text prompt, but values too high may cause saturation and other artifacts.
+ """
+ negative_prompt: list[str] | None = None
+ """One or several prompt to guide what NOT to include in video generation."""
+ num_frames: float | None = None
+ """The num_frames parameter determines how many video frames are generated."""
+ num_inference_steps: int | None = None
+ """The number of denoising steps. More denoising steps usually lead to a higher quality
+ video at the expense of slower inference.
+ """
+ seed: int | None = None
+ """Seed for the random number generator."""
+
+
+@dataclass_with_extra
+class TextToVideoInput(BaseInferenceType):
+ """Inputs for Text To Video inference"""
+
+ inputs: str
+ """The input text data (sometimes called "prompt")"""
+ parameters: TextToVideoParameters | None = None
+ """Additional inference parameters for Text To Video"""
+
+
+@dataclass_with_extra
+class TextToVideoOutput(BaseInferenceType):
+ """Outputs of inference for the Text To Video task"""
+
+ video: Any
+ """The generated video returned as raw bytes in the payload."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/token_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/token_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf21d19a60b4b90b87309dba367e2ea578f1fea
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/token_classification.py
@@ -0,0 +1,51 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TokenClassificationAggregationStrategy = Literal["none", "simple", "first", "average", "max"]
+
+
+@dataclass_with_extra
+class TokenClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Token Classification"""
+
+ aggregation_strategy: Optional["TokenClassificationAggregationStrategy"] = None
+ """The strategy used to fuse tokens based on model predictions"""
+ ignore_labels: list[str] | None = None
+ """A list of labels to ignore"""
+ stride: int | None = None
+ """The number of overlapping tokens between chunks when splitting the input text."""
+
+
+@dataclass_with_extra
+class TokenClassificationInput(BaseInferenceType):
+ """Inputs for Token Classification inference"""
+
+ inputs: str
+ """The input text data"""
+ parameters: TokenClassificationParameters | None = None
+ """Additional inference parameters for Token Classification"""
+
+
+@dataclass_with_extra
+class TokenClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Token Classification task"""
+
+ end: int
+ """The character position in the input where this group ends."""
+ score: float
+ """The associated score / probability"""
+ start: int
+ """The character position in the input where this group begins."""
+ word: str
+ """The corresponding text"""
+ entity: str | None = None
+ """The predicted label for a single token"""
+ entity_group: str | None = None
+ """The predicted label for a group of one or more tokens"""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/translation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e0b9de2921843846ac0d3cc65faf7d7089e59f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/translation.py
@@ -0,0 +1,49 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TranslationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class TranslationParameters(BaseInferenceType):
+ """Additional inference parameters for Translation"""
+
+ clean_up_tokenization_spaces: bool | None = None
+ """Whether to clean up the potential extra spaces in the text output."""
+ generate_parameters: dict[str, Any] | None = None
+ """Additional parametrization of the text generation algorithm."""
+ src_lang: str | None = None
+ """The source language of the text. Required for models that can translate from multiple
+ languages.
+ """
+ tgt_lang: str | None = None
+ """Target language to translate to. Required for models that can translate to multiple
+ languages.
+ """
+ truncation: Optional["TranslationTruncationStrategy"] = None
+ """The truncation strategy to use."""
+
+
+@dataclass_with_extra
+class TranslationInput(BaseInferenceType):
+ """Inputs for Translation inference"""
+
+ inputs: str
+ """The text to translate."""
+ parameters: TranslationParameters | None = None
+ """Additional inference parameters for Translation"""
+
+
+@dataclass_with_extra
+class TranslationOutput(BaseInferenceType):
+ """Outputs of inference for the Translation task"""
+
+ translation_text: str
+ """The translated text."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/video_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/video_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..489a602c171e0b86fd5404d805c5773adea3b0ee
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/video_classification.py
@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+VideoClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class VideoClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Video Classification"""
+
+ frame_sampling_rate: int | None = None
+ """The sampling rate used to select frames from the video."""
+ function_to_apply: Optional["VideoClassificationOutputTransform"] = None
+ """The function to apply to the model outputs in order to retrieve the scores."""
+ num_frames: int | None = None
+ """The number of sampled frames to consider for classification."""
+ top_k: int | None = None
+ """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class VideoClassificationInput(BaseInferenceType):
+ """Inputs for Video Classification inference"""
+
+ inputs: Any
+ """The input video data"""
+ parameters: VideoClassificationParameters | None = None
+ """Additional inference parameters for Video Classification"""
+
+
+@dataclass_with_extra
+class VideoClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Video Classification task"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..73f532aa06981778515a82fa5e151a1cff6b687d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py
@@ -0,0 +1,49 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInputData(BaseInferenceType):
+ """One (image, question) pair to answer"""
+
+ image: Any
+ """The image."""
+ question: str
+ """The question to answer based on the image."""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringParameters(BaseInferenceType):
+ """Additional inference parameters for Visual Question Answering"""
+
+ top_k: int | None = None
+ """The number of answers to return (will be chosen by order of likelihood). Note that we
+ return less than topk answers if there are not enough options available within the
+ context.
+ """
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInput(BaseInferenceType):
+ """Inputs for Visual Question Answering inference"""
+
+ inputs: VisualQuestionAnsweringInputData
+ """One (image, question) pair to answer"""
+ parameters: VisualQuestionAnsweringParameters | None = None
+ """Additional inference parameters for Visual Question Answering"""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringOutputElement(BaseInferenceType):
+ """Outputs of inference for the Visual Question Answering task"""
+
+ score: float
+ """The associated score / probability"""
+ answer: str | None = None
+ """The answer to the question"""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..a04f1a59353f97b9840d994e9eaed636479189a7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py
@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Zero Shot Classification"""
+
+ candidate_labels: list[str]
+ """The set of possible class labels to classify the text into."""
+ hypothesis_template: str | None = None
+ """The sentence used in conjunction with `candidate_labels` to attempt the text
+ classification by replacing the placeholder with the candidate labels.
+ """
+ multi_label: bool | None = None
+ """Whether multiple candidate labels can be true. If false, the scores are normalized such
+ that the sum of the label likelihoods for each sequence is 1. If true, the labels are
+ considered independent and probabilities are normalized for each candidate.
+ """
+
+
+@dataclass_with_extra
+class ZeroShotClassificationInput(BaseInferenceType):
+ """Inputs for Zero Shot Classification inference"""
+
+ inputs: str
+ """The text to classify"""
+ parameters: ZeroShotClassificationParameters
+ """Additional inference parameters for Zero Shot Classification"""
+
+
+@dataclass_with_extra
+class ZeroShotClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Zero Shot Classification task"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..65c5cd2530665787e1e7d1fc49a6826646b141c8
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py
@@ -0,0 +1,38 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationParameters(BaseInferenceType):
+ """Additional inference parameters for Zero Shot Image Classification"""
+
+ candidate_labels: list[str]
+ """The candidate labels for this image"""
+ hypothesis_template: str | None = None
+ """The sentence used in conjunction with `candidate_labels` to attempt the image
+ classification by replacing the placeholder with the candidate labels.
+ """
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationInput(BaseInferenceType):
+ """Inputs for Zero Shot Image Classification inference"""
+
+ inputs: str
+ """The input image data to classify as a base64-encoded string."""
+ parameters: ZeroShotImageClassificationParameters
+ """Additional inference parameters for Zero Shot Image Classification"""
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationOutputElement(BaseInferenceType):
+ """Outputs of inference for the Zero Shot Image Classification task"""
+
+ label: str
+ """The predicted class label."""
+ score: float
+ """The corresponding probability."""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..e981463b253f61aa0f4c71636813e5fb65d48717
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py
@@ -0,0 +1,50 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionParameters(BaseInferenceType):
+ """Additional inference parameters for Zero Shot Object Detection"""
+
+ candidate_labels: list[str]
+ """The candidate labels for this image"""
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionInput(BaseInferenceType):
+ """Inputs for Zero Shot Object Detection inference"""
+
+ inputs: str
+ """The input image data as a base64-encoded string."""
+ parameters: ZeroShotObjectDetectionParameters
+ """Additional inference parameters for Zero Shot Object Detection"""
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionBoundingBox(BaseInferenceType):
+ """The predicted bounding box. Coordinates are relative to the top left corner of the input
+ image.
+ """
+
+ xmax: int
+ xmin: int
+ ymax: int
+ ymin: int
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionOutputElement(BaseInferenceType):
+ """Outputs of inference for the Zero Shot Object Detection task"""
+
+ box: ZeroShotObjectDetectionBoundingBox
+ """The predicted bounding box. Coordinates are relative to the top left corner of the input
+ image.
+ """
+ label: str
+ """A candidate label"""
+ score: float
+ """The associated score / probability"""
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/__init__.py
new file mode 100644
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/_cli_hacks.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/_cli_hacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf3c41dd687cdaab2969cc5a28455c804105856
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/_cli_hacks.py
@@ -0,0 +1,88 @@
+import asyncio
+import sys
+from functools import partial
+
+import click
+
+
+def _patch_anyio_open_process():
+ """
+ Patch anyio.open_process to allow detached processes on Windows and Unix-like systems.
+
+ This is necessary to prevent the MCP client from being interrupted by Ctrl+C when running in the CLI.
+ """
+ import subprocess
+
+ import anyio
+
+ if getattr(anyio, "_tiny_agents_patched", False):
+ return
+ anyio._tiny_agents_patched = True # ty: ignore[invalid-assignment]
+
+ original_open_process = anyio.open_process
+
+ if sys.platform == "win32":
+ # On Windows, we need to set the creation flags to create a new process group
+
+ async def open_process_in_new_group(*args, **kwargs):
+ """
+ Wrapper for open_process to handle Windows-specific process creation flags.
+ """
+ # Ensure we pass the creation flags for Windows
+ kwargs.setdefault("creationflags", subprocess.CREATE_NEW_PROCESS_GROUP)
+ return await original_open_process(*args, **kwargs)
+
+ anyio.open_process = open_process_in_new_group # ty: ignore[invalid-assignment]
+ else:
+ # For Unix-like systems, we can use setsid to create a new session
+ async def open_process_in_new_group(*args, **kwargs):
+ """
+ Wrapper for open_process to handle Unix-like systems with start_new_session=True.
+ """
+ kwargs.setdefault("start_new_session", True)
+ return await original_open_process(*args, **kwargs)
+
+ anyio.open_process = open_process_in_new_group # ty: ignore[invalid-assignment]
+
+
+async def _async_prompt(exit_event: asyncio.Event, prompt: str = "» ") -> str:
+ """
+ Asynchronous prompt function that reads input from stdin without blocking.
+
+ This function is designed to work in an asynchronous context, allowing the event loop to gracefully stop it (e.g. on Ctrl+C).
+
+ Alternatively, we could use https://github.com/vxgmichel/aioconsole but that would be an additional dependency.
+ """
+ loop = asyncio.get_event_loop()
+
+ if sys.platform == "win32":
+ # Windows: Use run_in_executor to avoid blocking the event loop
+ # Degraded solution: this is not ideal as user will have to CTRL+C once more to stop the prompt (and it'll not be graceful)
+ return await loop.run_in_executor(None, partial(click.prompt, prompt, prompt_suffix=" "))
+ else:
+ # UNIX-like: Use loop.add_reader for non-blocking stdin read
+ future = loop.create_future()
+
+ def on_input():
+ line = sys.stdin.readline()
+ loop.remove_reader(sys.stdin)
+ future.set_result(line)
+
+ print(prompt, end=" ", flush=True)
+ loop.add_reader(sys.stdin, on_input) # not supported on Windows
+
+ # Wait for user input or exit event
+ # Wait until either the user hits enter or exit_event is set
+ exit_task = asyncio.create_task(exit_event.wait())
+ await asyncio.wait(
+ [future, exit_task],
+ return_when=asyncio.FIRST_COMPLETED,
+ )
+
+ # Check which one has been triggered
+ if exit_event.is_set():
+ future.cancel()
+ return ""
+
+ line = await future
+ return line.strip()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/agent.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d867032fa53f721928ef182329d0aa694b83885
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/agent.py
@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+import asyncio
+from typing import AsyncGenerator, Iterable, Optional, Union
+
+from huggingface_hub import ChatCompletionInputMessage, ChatCompletionStreamOutput, MCPClient
+
+from .._providers import PROVIDER_OR_POLICY_T
+from .constants import DEFAULT_SYSTEM_PROMPT, EXIT_LOOP_TOOLS, MAX_NUM_TURNS
+from .types import ServerConfig
+
+
+class Agent(MCPClient):
+ """
+ Implementation of a Simple Agent, which is a simple while loop built right on top of an [`MCPClient`].
+
+ > [!WARNING]
+ > This class is experimental and might be subject to breaking changes in the future without prior notice.
+
+ Args:
+ model (`str`, *optional*):
+ The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `meta-llama/Meta-Llama-3-8B-Instruct`
+ or a URL to a deployed Inference Endpoint or other local or remote endpoint.
+ servers (`Iterable[dict]`):
+ MCP servers to connect to. Each server is a dictionary containing a `type` key and a `config` key. The `type` key can be `"stdio"` or `"sse"`, and the `config` key is a dictionary of arguments for the server.
+ provider (`str`, *optional*):
+ Name of the provider to use for inference. Defaults to "auto" i.e. the first of the providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
+ If model is a URL or `base_url` is passed, then `provider` is not used.
+ base_url (`str`, *optional*):
+ The base URL to run inference. Defaults to None.
+ api_key (`str`, *optional*):
+ Token to use for authentication. Will default to the locally Hugging Face saved token if not provided. You can also use your own provider API key to interact directly with the provider's service.
+ prompt (`str`, *optional*):
+ The system prompt to use for the agent. Defaults to the default system prompt in `constants.py`.
+ """
+
+ def __init__(
+ self,
+ *,
+ model: Optional[str] = None,
+ servers: Iterable[ServerConfig],
+ provider: Optional[PROVIDER_OR_POLICY_T] = None,
+ base_url: Optional[str] = None,
+ api_key: Optional[str] = None,
+ prompt: Optional[str] = None,
+ ):
+ super().__init__(model=model, provider=provider, base_url=base_url, api_key=api_key)
+ self._servers_cfg = list(servers)
+ self.messages: list[Union[dict, ChatCompletionInputMessage]] = [
+ {"role": "system", "content": prompt or DEFAULT_SYSTEM_PROMPT}
+ ]
+
+ async def load_tools(self) -> None:
+ for cfg in self._servers_cfg:
+ await self.add_mcp_server(**cfg)
+
+ async def run(
+ self,
+ user_input: str,
+ *,
+ abort_event: Optional[asyncio.Event] = None,
+ ) -> AsyncGenerator[Union[ChatCompletionStreamOutput, ChatCompletionInputMessage], None]:
+ """
+ Run the agent with the given user input.
+
+ Args:
+ user_input (`str`):
+ The user input to run the agent with.
+ abort_event (`asyncio.Event`, *optional*):
+ An event that can be used to abort the agent. If the event is set, the agent will stop running.
+ """
+ self.messages.append({"role": "user", "content": user_input})
+
+ num_turns: int = 0
+ next_turn_should_call_tools = True
+
+ while True:
+ if abort_event and abort_event.is_set():
+ return
+
+ async for item in self.process_single_turn_with_tools(
+ self.messages,
+ exit_loop_tools=EXIT_LOOP_TOOLS,
+ exit_if_first_chunk_no_tool=(num_turns > 0 and next_turn_should_call_tools),
+ ):
+ yield item
+
+ num_turns += 1
+ last = self.messages[-1]
+
+ if last.get("role") == "tool" and last.get("name") in {t.function.name for t in EXIT_LOOP_TOOLS}:
+ return
+
+ if last.get("role") != "tool" and num_turns > MAX_NUM_TURNS:
+ return
+
+ if last.get("role") != "tool" and next_turn_should_call_tools:
+ return
+
+ next_turn_should_call_tools = last.get("role") != "tool"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/cli.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d1d14d478838893e03a953e96771f4048a6b713
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/cli.py
@@ -0,0 +1,245 @@
+import asyncio
+import os
+import signal
+import traceback
+from typing import Optional
+
+import click
+
+from ...utils import ANSI
+from ._cli_hacks import _async_prompt, _patch_anyio_open_process
+from .agent import Agent
+from .utils import _load_agent_config
+
+
+@click.group(
+ help="A squad of lightweight composable AI applications built on Hugging Face's Inference Client and MCP stack."
+)
+def app() -> None:
+ pass
+
+
+async def run_agent(
+ agent_path: Optional[str],
+) -> None:
+ """
+ Tiny Agent loop.
+
+ Args:
+ agent_path (`str`, *optional*):
+ Path to a local folder containing an `agent.json` and optionally a custom `PROMPT.md` or `AGENTS.md` file or a built-in agent stored in a Hugging Face dataset.
+
+ """
+ _patch_anyio_open_process() # Hacky way to prevent stdio connections to be stopped by Ctrl+C
+
+ config, prompt = _load_agent_config(agent_path)
+
+ inputs = config.get("inputs", [])
+ servers = config.get("servers", [])
+
+ abort_event = asyncio.Event()
+ exit_event = asyncio.Event()
+ first_sigint = True
+
+ loop = asyncio.get_running_loop()
+ original_sigint_handler = signal.getsignal(signal.SIGINT)
+
+ def _sigint_handler() -> None:
+ nonlocal first_sigint
+ if first_sigint:
+ first_sigint = False
+ abort_event.set()
+ print(ANSI.red("\nInterrupted. Press Ctrl+C again to quit."), flush=True)
+ return
+
+ print(ANSI.red("\nExiting..."), flush=True)
+ exit_event.set()
+
+ try:
+ sigint_registered_in_loop = False
+ try:
+ loop.add_signal_handler(signal.SIGINT, _sigint_handler)
+ sigint_registered_in_loop = True
+ except (AttributeError, NotImplementedError):
+ # Windows (or any loop that doesn't support it) : fall back to sync
+ signal.signal(signal.SIGINT, lambda *_: _sigint_handler())
+
+ # Handle inputs (i.e. env variables injection)
+ resolved_inputs: dict[str, str] = {}
+
+ if len(inputs) > 0:
+ print(
+ ANSI.bold(
+ ANSI.blue(
+ "Some initial inputs are required by the agent. "
+ "Please provide a value or leave empty to load from env."
+ )
+ )
+ )
+ for input_item in inputs:
+ input_id = input_item["id"]
+ description = input_item["description"]
+ env_special_value = f"${{input:{input_id}}}"
+
+ # Check if the input is used by any server or as an apiKey
+ input_usages = set()
+ for server in servers:
+ # Check stdio's "env" and http/sse's "headers" mappings
+ env_or_headers = server.get("env", {}) if server["type"] == "stdio" else server.get("headers", {})
+ for key, value in env_or_headers.items():
+ if env_special_value in value:
+ input_usages.add(key)
+
+ raw_api_key = config.get("apiKey")
+ if isinstance(raw_api_key, str) and env_special_value in raw_api_key:
+ input_usages.add("apiKey")
+
+ if not input_usages:
+ print(
+ ANSI.yellow(
+ f"Input '{input_id}' defined in config but not used by any server or as an API key."
+ " Skipping."
+ )
+ )
+ continue
+
+ # Prompt user for input
+ env_variable_key = input_id.replace("-", "_").upper()
+ print(
+ ANSI.blue(f" • {input_id}") + f": {description}. (default: load from {env_variable_key}).",
+ end=" ",
+ )
+ user_input = (await _async_prompt(exit_event=exit_event)).strip()
+ if exit_event.is_set():
+ return
+
+ # Fallback to environment variable when user left blank
+ final_value = user_input
+ if not final_value:
+ final_value = os.getenv(env_variable_key, "")
+ if final_value:
+ print(ANSI.green(f"Value successfully loaded from '{env_variable_key}'"))
+ else:
+ print(
+ ANSI.yellow(
+ f"No value found for '{env_variable_key}' in environment variables. Continuing."
+ )
+ )
+ resolved_inputs[input_id] = final_value
+
+ # Inject resolved value (can be empty) into stdio's env or http/sse's headers
+ for server in servers:
+ env_or_headers = server.get("env", {}) if server["type"] == "stdio" else server.get("headers", {})
+ for key, value in env_or_headers.items():
+ if env_special_value in value:
+ env_or_headers[key] = env_or_headers[key].replace(env_special_value, final_value)
+
+ print()
+
+ raw_api_key = config.get("apiKey")
+ if isinstance(raw_api_key, str):
+ substituted_api_key = raw_api_key
+ for input_id, val in resolved_inputs.items():
+ substituted_api_key = substituted_api_key.replace(f"${{input:{input_id}}}", val)
+ config["apiKey"] = substituted_api_key
+ # Main agent loop
+ async with Agent(
+ provider=config.get("provider"), # type: ignore
+ model=config.get("model"),
+ base_url=config.get("endpointUrl"), # type: ignore[arg-type]
+ api_key=config.get("apiKey"),
+ servers=servers, # type: ignore[arg-type]
+ prompt=prompt,
+ ) as agent:
+ await agent.load_tools()
+ print(ANSI.bold(ANSI.blue("Agent loaded with {} tools:".format(len(agent.available_tools)))))
+ for t in agent.available_tools:
+ print(ANSI.blue(f" • {t.function.name}"))
+
+ while True:
+ abort_event.clear()
+
+ # Check if we should exit
+ if exit_event.is_set():
+ return
+
+ try:
+ user_input = await _async_prompt(exit_event=exit_event)
+ first_sigint = True
+ except EOFError:
+ print(ANSI.red("\nEOF received, exiting."), flush=True)
+ break
+ except KeyboardInterrupt:
+ if not first_sigint and abort_event.is_set():
+ continue
+ else:
+ print(ANSI.red("\nKeyboard interrupt during input processing."), flush=True)
+ break
+
+ try:
+ async for chunk in agent.run(user_input, abort_event=abort_event):
+ if abort_event.is_set() and not first_sigint:
+ break
+ if exit_event.is_set():
+ return
+
+ if hasattr(chunk, "choices"):
+ delta = chunk.choices[0].delta
+ if delta.content:
+ print(delta.content, end="", flush=True)
+ if delta.tool_calls:
+ for call in delta.tool_calls:
+ if call.id:
+ print(f"", end="")
+ if call.function.name:
+ print(f"{call.function.name}", end=" ")
+ if call.function.arguments:
+ print(f"{call.function.arguments}", end="")
+ else:
+ print(
+ ANSI.green(f"\n\nTool[{chunk.name}] {chunk.tool_call_id}\n{chunk.content}\n"),
+ flush=True,
+ )
+
+ print()
+
+ except Exception as e:
+ tb_str = traceback.format_exc()
+ print(ANSI.red(f"\nError during agent run: {e}\n{tb_str}"), flush=True)
+ first_sigint = True # Allow graceful interrupt for the next command
+
+ except Exception as e:
+ tb_str = traceback.format_exc()
+ print(ANSI.red(f"\nAn unexpected error occurred: {e}\n{tb_str}"), flush=True)
+ raise e
+
+ finally:
+ if sigint_registered_in_loop:
+ try:
+ loop.remove_signal_handler(signal.SIGINT)
+ except (AttributeError, NotImplementedError):
+ pass
+ else:
+ signal.signal(signal.SIGINT, original_sigint_handler)
+
+
+@app.command("run", help="Run the Agent in the CLI")
+@click.argument("path", required=False)
+def run(path: Optional[str]) -> None:
+ """
+ Run the agent from PATH: a local folder containing an agent.json file or a built-in agent
+ stored in the 'tiny-agents/tiny-agents' Hugging Face dataset
+ (https://huggingface.co/datasets/tiny-agents/tiny-agents).
+ """
+ try:
+ asyncio.run(run_agent(path))
+ except KeyboardInterrupt:
+ print(ANSI.red("\nApplication terminated by KeyboardInterrupt."), flush=True)
+ raise click.exceptions.Exit(code=130)
+ except Exception as e:
+ print(ANSI.red(f"\nAn unexpected error occurred: {e}"), flush=True)
+ raise e
+
+
+if __name__ == "__main__":
+ app()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/constants.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fb7b562c4ff2df3c0fadb72dfd64cfe01144f36
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/constants.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+from huggingface_hub import ChatCompletionInputTool
+
+
+FILENAME_CONFIG = "agent.json"
+PROMPT_FILENAMES = ("PROMPT.md", "AGENTS.md")
+
+DEFAULT_AGENT = {
+ "model": "Qwen/Qwen2.5-72B-Instruct",
+ "provider": "novita",
+ "servers": [
+ {
+ "type": "stdio",
+ "command": "npx",
+ "args": [
+ "-y",
+ "@modelcontextprotocol/server-filesystem",
+ str(Path.home() / ("Desktop" if sys.platform == "darwin" else "")),
+ ],
+ },
+ {
+ "type": "stdio",
+ "command": "npx",
+ "args": ["@playwright/mcp@latest"],
+ },
+ ],
+}
+
+
+DEFAULT_SYSTEM_PROMPT = """
+You are an agent - please keep going until the user’s query is completely
+resolved, before ending your turn and yielding back to the user. Only terminate
+your turn when you are sure that the problem is solved, or if you need more
+info from the user to solve the problem.
+If you are not sure about anything pertaining to the user’s request, use your
+tools to read files and gather the relevant information: do NOT guess or make
+up an answer.
+You MUST plan extensively before each function call, and reflect extensively
+on the outcomes of the previous function calls. DO NOT do this entire process
+by making function calls only, as this can impair your ability to solve the
+problem and think insightfully.
+""".strip()
+
+MAX_NUM_TURNS = 10
+
+TASK_COMPLETE_TOOL: ChatCompletionInputTool = ChatCompletionInputTool.parse_obj( # type: ignore
+ {
+ "type": "function",
+ "function": {
+ "name": "task_complete",
+ "description": "Call this tool when the task given by the user is complete",
+ "parameters": {
+ "type": "object",
+ "properties": {},
+ },
+ },
+ }
+)
+
+ASK_QUESTION_TOOL: ChatCompletionInputTool = ChatCompletionInputTool.parse_obj( # type: ignore
+ {
+ "type": "function",
+ "function": {
+ "name": "ask_question",
+ "description": "Ask the user for more info required to solve or clarify their problem.",
+ "parameters": {
+ "type": "object",
+ "properties": {},
+ },
+ },
+ }
+)
+
+EXIT_LOOP_TOOLS: list[ChatCompletionInputTool] = [TASK_COMPLETE_TOOL, ASK_QUESTION_TOOL]
+
+
+DEFAULT_REPO_ID = "tiny-agents/tiny-agents"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/mcp_client.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/mcp_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..00c0b572fd947b40d448582e4709d197274fbbf2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/mcp_client.py
@@ -0,0 +1,399 @@
+import json
+import logging
+from contextlib import AsyncExitStack
+from datetime import timedelta
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, AsyncIterable, Literal, Optional, TypedDict, Union, overload
+
+from typing_extensions import NotRequired, TypeAlias, Unpack
+
+from ...utils._runtime import get_hf_hub_version
+from .._generated._async_client import AsyncInferenceClient
+from .._generated.types import (
+ ChatCompletionInputMessage,
+ ChatCompletionInputTool,
+ ChatCompletionStreamOutput,
+ ChatCompletionStreamOutputDeltaToolCall,
+)
+from .._providers import PROVIDER_OR_POLICY_T
+from .utils import format_result
+
+
+if TYPE_CHECKING:
+ from mcp import ClientSession
+
+logger = logging.getLogger(__name__)
+
+# Type alias for tool names
+ToolName: TypeAlias = str
+
+ServerType: TypeAlias = Literal["stdio", "sse", "http"]
+
+
+class StdioServerParameters_T(TypedDict):
+ command: str
+ args: NotRequired[list[str]]
+ env: NotRequired[dict[str, str]]
+ cwd: NotRequired[Union[str, Path, None]]
+
+
+class SSEServerParameters_T(TypedDict):
+ url: str
+ headers: NotRequired[dict[str, Any]]
+ timeout: NotRequired[float]
+ sse_read_timeout: NotRequired[float]
+
+
+class StreamableHTTPParameters_T(TypedDict):
+ url: str
+ headers: NotRequired[dict[str, Any]]
+ timeout: NotRequired[timedelta]
+ sse_read_timeout: NotRequired[timedelta]
+ terminate_on_close: NotRequired[bool]
+
+
+class MCPClient:
+ """
+ Client for connecting to one or more MCP servers and processing chat completions with tools.
+
+ > [!WARNING]
+ > This class is experimental and might be subject to breaking changes in the future without prior notice.
+
+ Args:
+ model (`str`, `optional`):
+ The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `meta-llama/Meta-Llama-3-8B-Instruct`
+ or a URL to a deployed Inference Endpoint or other local or remote endpoint.
+ provider (`str`, *optional*):
+ Name of the provider to use for inference. Defaults to "auto" i.e. the first of the providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
+ If model is a URL or `base_url` is passed, then `provider` is not used.
+ base_url (`str`, *optional*):
+ The base URL to run inference. Defaults to None.
+ api_key (`str`, `optional`):
+ Token to use for authentication. Will default to the locally Hugging Face saved token if not provided. You can also use your own provider API key to interact directly with the provider's service.
+ """
+
+ def __init__(
+ self,
+ *,
+ model: Optional[str] = None,
+ provider: Optional[PROVIDER_OR_POLICY_T] = None,
+ base_url: Optional[str] = None,
+ api_key: Optional[str] = None,
+ ):
+ # Initialize MCP sessions as a dictionary of ClientSession objects
+ self.sessions: dict[ToolName, "ClientSession"] = {}
+ self.exit_stack = AsyncExitStack()
+ self.available_tools: list[ChatCompletionInputTool] = []
+ # To be able to send the model in the payload if `base_url` is provided
+ if model is None and base_url is None:
+ raise ValueError("At least one of `model` or `base_url` should be set in `MCPClient`.")
+ self.payload_model = model
+ self.client = AsyncInferenceClient(
+ model=None if base_url is not None else model,
+ provider=provider,
+ api_key=api_key,
+ base_url=base_url,
+ )
+
+ async def __aenter__(self):
+ """Enter the context manager"""
+ await self.client.__aenter__()
+ await self.exit_stack.__aenter__()
+ return self
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ """Exit the context manager"""
+ await self.client.__aexit__(exc_type, exc_val, exc_tb)
+ await self.cleanup()
+
+ async def cleanup(self):
+ """Clean up resources"""
+ await self.client.close()
+ await self.exit_stack.aclose()
+
+ @overload
+ async def add_mcp_server(self, type: Literal["stdio"], **params: Unpack[StdioServerParameters_T]): ...
+
+ @overload
+ async def add_mcp_server(self, type: Literal["sse"], **params: Unpack[SSEServerParameters_T]): ...
+
+ @overload
+ async def add_mcp_server(self, type: Literal["http"], **params: Unpack[StreamableHTTPParameters_T]): ...
+
+ async def add_mcp_server(self, type: ServerType, **params: Any):
+ """Connect to an MCP server
+
+ Args:
+ type (`str`):
+ Type of the server to connect to. Can be one of:
+ - "stdio": Standard input/output server (local)
+ - "sse": Server-sent events (SSE) server
+ - "http": StreamableHTTP server
+ **params (`dict[str, Any]`):
+ Server parameters that can be either:
+ - For stdio servers:
+ - command (str): The command to run the MCP server
+ - args (list[str], optional): Arguments for the command
+ - env (dict[str, str], optional): Environment variables for the command
+ - cwd (Union[str, Path, None], optional): Working directory for the command
+ - allowed_tools (list[str], optional): List of tool names to allow from this server
+ - For SSE servers:
+ - url (str): The URL of the SSE server
+ - headers (dict[str, Any], optional): Headers for the SSE connection
+ - timeout (float, optional): Connection timeout
+ - sse_read_timeout (float, optional): SSE read timeout
+ - allowed_tools (list[str], optional): List of tool names to allow from this server
+ - For StreamableHTTP servers:
+ - url (str): The URL of the StreamableHTTP server
+ - headers (dict[str, Any], optional): Headers for the StreamableHTTP connection
+ - timeout (timedelta, optional): Connection timeout
+ - sse_read_timeout (timedelta, optional): SSE read timeout
+ - terminate_on_close (bool, optional): Whether to terminate on close
+ - allowed_tools (list[str], optional): List of tool names to allow from this server
+ """
+ from mcp import ClientSession, StdioServerParameters
+ from mcp import types as mcp_types
+
+ # Extract allowed_tools configuration if provided
+ allowed_tools = params.pop("allowed_tools", None)
+
+ # Determine server type and create appropriate parameters
+ if type == "stdio":
+ # Handle stdio server
+ from mcp.client.stdio import stdio_client
+
+ logger.info(f"Connecting to stdio MCP server with command: {params['command']} {params.get('args', [])}")
+
+ client_kwargs = {"command": params["command"]}
+ for key in ["args", "env", "cwd"]:
+ if params.get(key) is not None:
+ client_kwargs[key] = params[key]
+ server_params = StdioServerParameters(**client_kwargs)
+ read, write = await self.exit_stack.enter_async_context(stdio_client(server_params))
+ elif type == "sse":
+ # Handle SSE server
+ from mcp.client.sse import sse_client
+
+ logger.info(f"Connecting to SSE MCP server at: {params['url']}")
+
+ client_kwargs = {"url": params["url"]}
+ for key in ["headers", "timeout", "sse_read_timeout"]:
+ if params.get(key) is not None:
+ client_kwargs[key] = params[key]
+ read, write = await self.exit_stack.enter_async_context(sse_client(**client_kwargs))
+ elif type == "http":
+ # Handle StreamableHTTP server
+ from mcp.client.streamable_http import streamablehttp_client
+
+ logger.info(f"Connecting to StreamableHTTP MCP server at: {params['url']}")
+
+ client_kwargs = {"url": params["url"]}
+ for key in ["headers", "timeout", "sse_read_timeout", "terminate_on_close"]:
+ if params.get(key) is not None:
+ client_kwargs[key] = params[key]
+ read, write, _ = await self.exit_stack.enter_async_context(streamablehttp_client(**client_kwargs))
+ # ^ TODO: should be handle `get_session_id_callback`? (function to retrieve the current session ID)
+ else:
+ raise ValueError(f"Unsupported server type: {type}")
+
+ session = await self.exit_stack.enter_async_context(
+ ClientSession(
+ read_stream=read,
+ write_stream=write,
+ client_info=mcp_types.Implementation(
+ name="huggingface_hub.MCPClient",
+ version=get_hf_hub_version(),
+ ),
+ )
+ )
+
+ logger.debug("Initializing session...")
+ await session.initialize()
+
+ # List available tools, following `nextCursor` since servers may paginate tools/list
+ response = await session.list_tools()
+ tools: list[mcp_types.Tool] = list(response.tools)
+ while response.nextCursor:
+ response = await session.list_tools(cursor=response.nextCursor)
+ tools.extend(response.tools)
+ logger.debug("Connected to server with tools:", [tool.name for tool in tools])
+
+ # Filter tools based on allowed_tools configuration
+ filtered_tools = tools
+
+ if allowed_tools is not None:
+ filtered_tools = [tool for tool in tools if tool.name in allowed_tools]
+ logger.debug(
+ f"Tool filtering applied. Using {len(filtered_tools)} of {len(tools)} available tools: {[tool.name for tool in filtered_tools]}"
+ )
+
+ for tool in filtered_tools:
+ if tool.name in self.sessions:
+ logger.warning(f"Tool '{tool.name}' already defined by another server. Skipping.")
+ continue
+
+ # Map tool names to their server for later lookup
+ self.sessions[tool.name] = session
+
+ # Add tool to the list of available tools (for use in chat completions)
+ self.available_tools.append(
+ ChatCompletionInputTool.parse_obj_as_instance(
+ {
+ "type": "function",
+ "function": {
+ "name": tool.name,
+ "description": tool.description,
+ "parameters": tool.inputSchema,
+ },
+ }
+ )
+ )
+
+ async def process_single_turn_with_tools(
+ self,
+ messages: list[Union[dict, ChatCompletionInputMessage]],
+ exit_loop_tools: Optional[list[ChatCompletionInputTool]] = None,
+ exit_if_first_chunk_no_tool: bool = False,
+ ) -> AsyncIterable[Union[ChatCompletionStreamOutput, ChatCompletionInputMessage]]:
+ """Process a query using `self.model` and available tools, yielding chunks and tool outputs.
+
+ Args:
+ messages (`list[dict]`):
+ List of message objects representing the conversation history
+ exit_loop_tools (`list[ChatCompletionInputTool]`, *optional*):
+ List of tools that should exit the generator when called
+ exit_if_first_chunk_no_tool (`bool`, *optional*):
+ Exit if no tool is present in the first chunks. Default to False.
+
+ Yields:
+ [`ChatCompletionStreamOutput`] chunks or [`ChatCompletionInputMessage`] objects
+ """
+ # Prepare tools list based on options
+ tools = self.available_tools
+ if exit_loop_tools is not None:
+ tools = [*exit_loop_tools, *self.available_tools]
+
+ # Create the streaming request
+ response = await self.client.chat.completions.create(
+ model=self.payload_model,
+ messages=messages,
+ tools=tools,
+ tool_choice="auto",
+ stream=True,
+ )
+
+ message: dict[str, Any] = {"role": "unknown", "content": ""}
+ final_tool_calls: dict[int, ChatCompletionStreamOutputDeltaToolCall] = {}
+ num_of_chunks = 0
+
+ # Read from stream
+ async for chunk in response:
+ num_of_chunks += 1
+ delta = chunk.choices[0].delta if chunk.choices and len(chunk.choices) > 0 else None
+ if not delta:
+ continue
+
+ # Process message
+ if delta.role:
+ message["role"] = delta.role
+ if delta.content:
+ message["content"] += delta.content
+
+ # Process tool calls
+ if delta.tool_calls:
+ for tool_call in delta.tool_calls:
+ idx = tool_call.index
+ # first chunk for this tool call
+ if idx not in final_tool_calls:
+ final_tool_calls[idx] = tool_call
+ if final_tool_calls[idx].function.arguments is None:
+ final_tool_calls[idx].function.arguments = ""
+ continue
+ # safety before concatenating text to .function.arguments
+ if final_tool_calls[idx].function.arguments is None:
+ final_tool_calls[idx].function.arguments = ""
+
+ if tool_call.function.arguments:
+ final_tool_calls[idx].function.arguments += tool_call.function.arguments
+
+ # Optionally exit early if no tools in first chunks
+ if exit_if_first_chunk_no_tool and num_of_chunks <= 2 and len(final_tool_calls) == 0:
+ return
+
+ # Yield each chunk to caller
+ yield chunk
+
+ # Add the assistant message with tool calls (if any) to messages
+ if message["content"] or final_tool_calls:
+ # if the role is unknown, set it to assistant
+ if message.get("role") == "unknown":
+ message["role"] = "assistant"
+ # Convert final_tool_calls to the format expected by OpenAI
+ if final_tool_calls:
+ tool_calls_list: list[dict[str, Any]] = []
+ for tc in final_tool_calls.values():
+ tool_calls_list.append(
+ {
+ "id": tc.id,
+ "type": "function",
+ "function": {
+ "name": tc.function.name,
+ "arguments": tc.function.arguments or "{}",
+ },
+ }
+ )
+ message["tool_calls"] = tool_calls_list
+ messages.append(message)
+
+ # Process tool calls one by one
+ for tool_call in final_tool_calls.values():
+ function_name = tool_call.function.name
+ if function_name is None:
+ message = ChatCompletionInputMessage.parse_obj_as_instance(
+ {
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": "Invalid tool call with no function name.",
+ }
+ )
+ messages.append(message)
+ yield message
+ continue # move to next tool call
+ try:
+ function_args = json.loads(tool_call.function.arguments or "{}")
+ except json.JSONDecodeError as err:
+ tool_message = {
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "name": function_name,
+ "content": f"Invalid JSON generated by the model: {err}",
+ }
+ tool_message_as_obj = ChatCompletionInputMessage.parse_obj_as_instance(tool_message)
+ messages.append(tool_message_as_obj)
+ yield tool_message_as_obj
+ continue # move to next tool call
+
+ tool_message = {"role": "tool", "tool_call_id": tool_call.id, "content": "", "name": function_name}
+
+ # Check if this is an exit loop tool
+ if exit_loop_tools and function_name in [t.function.name for t in exit_loop_tools]:
+ tool_message_as_obj = ChatCompletionInputMessage.parse_obj_as_instance(tool_message)
+ messages.append(tool_message_as_obj)
+ yield tool_message_as_obj
+ return
+
+ # Execute tool call with the appropriate session
+ session = self.sessions.get(function_name)
+ if session is not None:
+ try:
+ result = await session.call_tool(function_name, function_args)
+ tool_message["content"] = format_result(result)
+ except Exception as err:
+ tool_message["content"] = f"Error: MCP tool call failed with error message: {err}"
+ else:
+ tool_message["content"] = f"Error: No session found for tool: {function_name}"
+
+ # Yield tool message
+ tool_message_as_obj = ChatCompletionInputMessage.parse_obj_as_instance(tool_message)
+ messages.append(tool_message_as_obj)
+ yield tool_message_as_obj
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/types.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a531929a8e574e52075b59cec88e24d93cb1dbd7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/types.py
@@ -0,0 +1,45 @@
+from typing import Literal, TypedDict, Union
+
+from typing_extensions import NotRequired
+
+
+class InputConfig(TypedDict, total=False):
+ id: str
+ description: str
+ type: str
+ password: bool
+
+
+class StdioServerConfig(TypedDict):
+ type: Literal["stdio"]
+ command: str
+ args: list[str]
+ env: dict[str, str]
+ cwd: str
+ allowed_tools: NotRequired[list[str]]
+
+
+class HTTPServerConfig(TypedDict):
+ type: Literal["http"]
+ url: str
+ headers: dict[str, str]
+ allowed_tools: NotRequired[list[str]]
+
+
+class SSEServerConfig(TypedDict):
+ type: Literal["sse"]
+ url: str
+ headers: dict[str, str]
+ allowed_tools: NotRequired[list[str]]
+
+
+ServerConfig = Union[StdioServerConfig, HTTPServerConfig, SSEServerConfig]
+
+
+# AgentConfig root object
+class AgentConfig(TypedDict):
+ model: str
+ provider: str
+ apiKey: NotRequired[str]
+ inputs: list[InputConfig]
+ servers: list[ServerConfig]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/utils.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5df9d5183df75f11ad902f514b9f2b6cf5041d6f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_mcp/utils.py
@@ -0,0 +1,130 @@
+"""
+Utility functions for MCPClient and Tiny Agents.
+
+Formatting utilities taken from the JS SDK: https://github.com/huggingface/huggingface.js/blob/main/packages/mcp-client/src/ResultFormatter.ts.
+"""
+
+import json
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional
+
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import EntryNotFoundError
+
+from .constants import DEFAULT_AGENT, DEFAULT_REPO_ID, FILENAME_CONFIG, PROMPT_FILENAMES
+from .types import AgentConfig
+
+
+if TYPE_CHECKING:
+ from mcp import types as mcp_types
+
+
+def format_result(result: "mcp_types.CallToolResult") -> str:
+ """
+ Formats a mcp.types.CallToolResult content into a human-readable string.
+
+ Args:
+ result (CallToolResult)
+ Object returned by mcp.ClientSession.call_tool.
+
+ Returns:
+ str
+ A formatted string representing the content of the result.
+ """
+ content = result.content
+
+ if len(content) == 0:
+ return "[No content]"
+
+ formatted_parts: list[str] = []
+
+ for item in content:
+ match item.type:
+ case "text":
+ formatted_parts.append(item.text)
+
+ case "image":
+ formatted_parts.append(
+ f"[Binary Content: Image {item.mimeType}, {_get_base64_size(item.data)} bytes]\n"
+ f"The task is complete and the content accessible to the User"
+ )
+
+ case "audio":
+ formatted_parts.append(
+ f"[Binary Content: Audio {item.mimeType}, {_get_base64_size(item.data)} bytes]\n"
+ f"The task is complete and the content accessible to the User"
+ )
+
+ case "resource":
+ resource = item.resource
+
+ if hasattr(resource, "text") and isinstance(resource.text, str):
+ formatted_parts.append(resource.text)
+
+ elif hasattr(resource, "blob") and isinstance(resource.blob, str):
+ formatted_parts.append(
+ f"[Binary Content ({resource.uri}): {resource.mimeType},"
+ f" {_get_base64_size(resource.blob)} bytes]\n"
+ f"The task is complete and the content accessible to the User"
+ )
+
+ return "\n".join(formatted_parts)
+
+
+def _get_base64_size(base64_str: str) -> int:
+ """Estimate the byte size of a base64-encoded string."""
+ # Remove any prefix like "data:image/png;base64,"
+ if "," in base64_str:
+ base64_str = base64_str.split(",")[1]
+
+ padding = 0
+ if base64_str.endswith("=="):
+ padding = 2
+ elif base64_str.endswith("="):
+ padding = 1
+
+ return (len(base64_str) * 3) // 4 - padding
+
+
+def _load_agent_config(agent_path: Optional[str]) -> tuple[AgentConfig, Optional[str]]:
+ """Load server config and prompt."""
+
+ def _read_dir(directory: Path) -> tuple[AgentConfig, Optional[str]]:
+ cfg_file = directory / FILENAME_CONFIG
+ if not cfg_file.exists():
+ raise FileNotFoundError(f" Config file not found in {directory}! Please make sure it exists locally")
+
+ config: AgentConfig = json.loads(cfg_file.read_text(encoding="utf-8"))
+ prompt: Optional[str] = None
+ for filename in PROMPT_FILENAMES:
+ prompt_file = directory / filename
+ if prompt_file.exists():
+ prompt = prompt_file.read_text(encoding="utf-8")
+ break
+ return config, prompt
+
+ if agent_path is None:
+ return DEFAULT_AGENT, None # type: ignore
+
+ path = Path(agent_path).expanduser()
+
+ if path.is_file():
+ return json.loads(path.read_text(encoding="utf-8")), None
+
+ if path.is_dir():
+ return _read_dir(path)
+
+ # fetch from the Hub
+ try:
+ repo_dir = Path(
+ snapshot_download(
+ repo_id=DEFAULT_REPO_ID,
+ allow_patterns=f"{agent_path}/*",
+ repo_type="dataset",
+ )
+ )
+ return _read_dir(repo_dir / agent_path)
+ except Exception as err:
+ raise EntryNotFoundError(
+ f" Agent {agent_path} not found in tiny-agents/tiny-agents! Please make sure it exists in https://huggingface.co/datasets/tiny-agents/tiny-agents."
+ ) from err
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5305d8734605f63d44cf0433c6c3a31acb2151c0
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/__init__.py
@@ -0,0 +1,263 @@
+from typing import Literal, Union
+
+from huggingface_hub.inference._providers.featherless_ai import (
+ FeatherlessConversationalTask,
+ FeatherlessTextGenerationTask,
+)
+from huggingface_hub.utils import logging
+
+from ._common import AutoRouterConversationalTask, TaskProviderHelper, _fetch_inference_provider_mapping
+from .baseten import BasetenConversationalTask
+from .cerebras import CerebrasConversationalTask
+from .cohere import CohereConversationalTask
+from .deepinfra import (
+ DeepInfraAutomaticSpeechRecognitionTask,
+ DeepInfraConversationalTask,
+ DeepInfraFeatureExtractionTask,
+ DeepInfraTextGenerationTask,
+ DeepInfraTextToSpeechTask,
+)
+from .fal_ai import (
+ FalAIAutomaticSpeechRecognitionTask,
+ FalAIImageSegmentationTask,
+ FalAIImageToImageTask,
+ FalAIImageToVideoTask,
+ FalAITextToImageTask,
+ FalAITextToSpeechTask,
+ FalAITextToVideoTask,
+)
+from .fireworks_ai import FireworksAIConversationalTask
+from .groq import GroqConversationalTask
+from .hf_inference import (
+ HFInferenceBinaryInputTask,
+ HFInferenceConversational,
+ HFInferenceFeatureExtractionTask,
+ HFInferenceTask,
+)
+from .novita import NovitaConversationalTask, NovitaTextGenerationTask, NovitaTextToVideoTask
+from .nscale import NscaleConversationalTask, NscaleTextToImageTask
+from .openai import OpenAIConversationalTask
+from .ovhcloud import OVHcloudConversationalTask
+from .publicai import PublicAIConversationalTask
+from .replicate import (
+ ReplicateAutomaticSpeechRecognitionTask,
+ ReplicateImageToImageTask,
+ ReplicateTask,
+ ReplicateTextToImageTask,
+ ReplicateTextToSpeechTask,
+)
+from .scaleway import ScalewayConversationalTask, ScalewayFeatureExtractionTask
+from .together import (
+ TogetherConversationalTask,
+ TogetherFeatureExtractionTask,
+ TogetherImageToImageTask,
+ TogetherImageToVideoTask,
+ TogetherTextGenerationTask,
+ TogetherTextToImageTask,
+ TogetherTextToSpeechTask,
+ TogetherTextToVideoTask,
+)
+from .wavespeed import (
+ WavespeedAIImageToImageTask,
+ WavespeedAIImageToVideoTask,
+ WavespeedAITextToImageTask,
+ WavespeedAITextToVideoTask,
+)
+from .zai_org import ZaiConversationalTask, ZaiTextToImageTask
+
+
+logger = logging.get_logger(__name__)
+
+
+PROVIDER_T = Literal[
+ "baseten",
+ "cerebras",
+ "cohere",
+ "deepinfra",
+ "fal-ai",
+ "featherless-ai",
+ "fireworks-ai",
+ "groq",
+ "hf-inference",
+ "novita",
+ "nscale",
+ "openai",
+ "ovhcloud",
+ "publicai",
+ "replicate",
+ "scaleway",
+ "together",
+ "wavespeed",
+ "zai-org",
+]
+
+PROVIDER_OR_POLICY_T = Union[PROVIDER_T, Literal["auto"]]
+
+CONVERSATIONAL_AUTO_ROUTER = AutoRouterConversationalTask()
+
+PROVIDERS: dict[PROVIDER_T, dict[str, TaskProviderHelper]] = {
+ "baseten": {
+ "conversational": BasetenConversationalTask(),
+ },
+ "cerebras": {
+ "conversational": CerebrasConversationalTask(),
+ },
+ "cohere": {
+ "conversational": CohereConversationalTask(),
+ },
+ "deepinfra": {
+ "automatic-speech-recognition": DeepInfraAutomaticSpeechRecognitionTask(),
+ "conversational": DeepInfraConversationalTask(),
+ "feature-extraction": DeepInfraFeatureExtractionTask(),
+ "text-generation": DeepInfraTextGenerationTask(),
+ "text-to-speech": DeepInfraTextToSpeechTask(),
+ },
+ "fal-ai": {
+ "automatic-speech-recognition": FalAIAutomaticSpeechRecognitionTask(),
+ "text-to-image": FalAITextToImageTask(),
+ "text-to-speech": FalAITextToSpeechTask(),
+ "text-to-video": FalAITextToVideoTask(),
+ "image-to-video": FalAIImageToVideoTask(),
+ "image-to-image": FalAIImageToImageTask(),
+ "image-segmentation": FalAIImageSegmentationTask(),
+ },
+ "featherless-ai": {
+ "conversational": FeatherlessConversationalTask(),
+ "text-generation": FeatherlessTextGenerationTask(),
+ },
+ "fireworks-ai": {
+ "conversational": FireworksAIConversationalTask(),
+ },
+ "groq": {
+ "conversational": GroqConversationalTask(),
+ },
+ "hf-inference": {
+ "text-to-image": HFInferenceTask("text-to-image"),
+ "conversational": HFInferenceConversational(),
+ "text-generation": HFInferenceTask("text-generation"),
+ "text-classification": HFInferenceTask("text-classification"),
+ "question-answering": HFInferenceTask("question-answering"),
+ "audio-classification": HFInferenceBinaryInputTask("audio-classification"),
+ "automatic-speech-recognition": HFInferenceBinaryInputTask("automatic-speech-recognition"),
+ "fill-mask": HFInferenceTask("fill-mask"),
+ "feature-extraction": HFInferenceFeatureExtractionTask(),
+ "image-classification": HFInferenceBinaryInputTask("image-classification"),
+ "image-segmentation": HFInferenceBinaryInputTask("image-segmentation"),
+ "document-question-answering": HFInferenceTask("document-question-answering"),
+ "image-to-text": HFInferenceBinaryInputTask("image-to-text"),
+ "object-detection": HFInferenceBinaryInputTask("object-detection"),
+ "audio-to-audio": HFInferenceBinaryInputTask("audio-to-audio"),
+ "zero-shot-image-classification": HFInferenceBinaryInputTask("zero-shot-image-classification"),
+ "zero-shot-classification": HFInferenceTask("zero-shot-classification"),
+ "image-to-image": HFInferenceBinaryInputTask("image-to-image"),
+ "sentence-similarity": HFInferenceTask("sentence-similarity"),
+ "table-question-answering": HFInferenceTask("table-question-answering"),
+ "tabular-classification": HFInferenceTask("tabular-classification"),
+ "text-to-speech": HFInferenceTask("text-to-speech"),
+ "token-classification": HFInferenceTask("token-classification"),
+ "translation": HFInferenceTask("translation"),
+ "summarization": HFInferenceTask("summarization"),
+ "visual-question-answering": HFInferenceBinaryInputTask("visual-question-answering"),
+ },
+ "novita": {
+ "text-generation": NovitaTextGenerationTask(),
+ "conversational": NovitaConversationalTask(),
+ "text-to-video": NovitaTextToVideoTask(),
+ },
+ "nscale": {
+ "conversational": NscaleConversationalTask(),
+ "text-to-image": NscaleTextToImageTask(),
+ },
+ "openai": {
+ "conversational": OpenAIConversationalTask(),
+ },
+ "ovhcloud": {
+ "conversational": OVHcloudConversationalTask(),
+ },
+ "publicai": {
+ "conversational": PublicAIConversationalTask(),
+ },
+ "replicate": {
+ "automatic-speech-recognition": ReplicateAutomaticSpeechRecognitionTask(),
+ "image-to-image": ReplicateImageToImageTask(),
+ "text-to-image": ReplicateTextToImageTask(),
+ "text-to-speech": ReplicateTextToSpeechTask(),
+ "text-to-video": ReplicateTask("text-to-video"),
+ },
+ "scaleway": {
+ "conversational": ScalewayConversationalTask(),
+ "feature-extraction": ScalewayFeatureExtractionTask(),
+ },
+ "together": {
+ "conversational": TogetherConversationalTask(),
+ "feature-extraction": TogetherFeatureExtractionTask(),
+ "image-to-image": TogetherImageToImageTask(),
+ "image-to-video": TogetherImageToVideoTask(),
+ "text-generation": TogetherTextGenerationTask(),
+ "text-to-image": TogetherTextToImageTask(),
+ "text-to-speech": TogetherTextToSpeechTask(),
+ "text-to-video": TogetherTextToVideoTask(),
+ },
+ "wavespeed": {
+ "text-to-image": WavespeedAITextToImageTask(),
+ "text-to-video": WavespeedAITextToVideoTask(),
+ "image-to-image": WavespeedAIImageToImageTask(),
+ "image-to-video": WavespeedAIImageToVideoTask(),
+ },
+ "zai-org": {
+ "conversational": ZaiConversationalTask(),
+ "text-to-image": ZaiTextToImageTask(),
+ },
+}
+
+
+def get_provider_helper(provider: PROVIDER_OR_POLICY_T | None, task: str, model: str | None) -> TaskProviderHelper:
+ """Get provider helper instance by name and task.
+
+ Args:
+ provider (`str`, *optional*): name of the provider, or "auto" to automatically select the provider for the model.
+ task (`str`): Name of the task
+ model (`str`, *optional*): Name of the model
+ Returns:
+ TaskProviderHelper: Helper instance for the specified provider and task
+
+ Raises:
+ ValueError: If provider or task is not supported
+ """
+
+ if (model is None and provider in (None, "auto")) or (
+ model is not None and model.startswith(("http://", "https://"))
+ ):
+ provider = "hf-inference"
+
+ if provider is None:
+ logger.info(
+ "No provider specified for task `conversational`. Defaulting to server-side auto routing."
+ if task == "conversational"
+ else "Defaulting to 'auto' which will select the first provider available for the model, sorted by the user's order in https://hf.co/settings/inference-providers."
+ )
+ provider = "auto"
+
+ if provider == "auto":
+ if model is None:
+ raise ValueError("Specifying a model is required when provider is 'auto'")
+ if task == "conversational":
+ # Special case: we have a dedicated auto-router for conversational models. No need to fetch provider mapping.
+ return CONVERSATIONAL_AUTO_ROUTER
+
+ provider_mapping = _fetch_inference_provider_mapping(model)
+ provider = next(iter(provider_mapping)).provider
+
+ provider_tasks = PROVIDERS.get(provider) # type: ignore
+ if provider_tasks is None:
+ raise ValueError(
+ f"Provider '{provider}' not supported. Available values: 'auto' or any provider from {list(PROVIDERS.keys())}."
+ "Passing 'auto' (default value) will automatically select the first provider available for the model, sorted "
+ "by the user's order in https://hf.co/settings/inference-providers."
+ )
+
+ if task not in provider_tasks:
+ raise ValueError(
+ f"Task '{task}' not supported for provider '{provider}'. Available tasks: {list(provider_tasks.keys())}"
+ )
+ return provider_tasks[task]
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/_common.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..30dba3b2724b81cd32f6bd342e0d8d7a7f1074a3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/_common.py
@@ -0,0 +1,371 @@
+from functools import lru_cache
+from typing import Any, overload
+
+from huggingface_hub import constants
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import MimeBytes, RequestParameters
+from huggingface_hub.inference._generated.types.chat_completion import ChatCompletionInputMessage
+from huggingface_hub.utils import build_hf_headers, get_token, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+# Dev purposes only.
+# If you want to try to run inference for a new model locally before it's registered on huggingface.co
+# for a given Inference Provider, you can add it to the following dictionary.
+HARDCODED_MODEL_INFERENCE_MAPPING: dict[str, dict[str, InferenceProviderMapping]] = {
+ # "HF model ID" => InferenceProviderMapping object initialized with "Model ID on Inference Provider's side"
+ #
+ # Example:
+ # "Qwen/Qwen2.5-Coder-32B-Instruct": InferenceProviderMapping(hf_model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+ # provider_id="Qwen2.5-Coder-32B-Instruct",
+ # task="conversational",
+ # status="live")
+ "cerebras": {},
+ "cohere": {},
+ "deepinfra": {},
+ "fal-ai": {},
+ "fireworks-ai": {},
+ "groq": {},
+ "hf-inference": {},
+ "nscale": {},
+ "ovhcloud": {},
+ "replicate": {},
+ "scaleway": {},
+ "together": {},
+ "wavespeed": {},
+ "zai-org": {},
+}
+
+
+@overload
+def filter_none(obj: dict[str, Any]) -> dict[str, Any]: ...
+@overload
+def filter_none(obj: list[Any]) -> list[Any]: ...
+
+
+def filter_none(obj: dict[str, Any] | list[Any]) -> dict[str, Any] | list[Any]:
+ if isinstance(obj, dict):
+ cleaned: dict[str, Any] = {}
+ for k, v in obj.items():
+ if v is None:
+ continue
+ if isinstance(v, (dict, list)):
+ v = filter_none(v)
+ cleaned[k] = v
+ return cleaned
+
+ if isinstance(obj, list):
+ return [filter_none(v) if isinstance(v, (dict, list)) else v for v in obj]
+
+ raise ValueError(f"Expected dict or list, got {type(obj)}")
+
+
+class TaskProviderHelper:
+ """Base class for task-specific provider helpers."""
+
+ def __init__(self, provider: str, base_url: str, task: str) -> None:
+ self.provider = provider
+ self.task = task
+ self.base_url = base_url
+
+ def prepare_request(
+ self,
+ *,
+ inputs: Any,
+ parameters: dict[str, Any],
+ headers: dict,
+ model: str | None,
+ api_key: str | None,
+ extra_payload: dict[str, Any] | None = None,
+ ) -> RequestParameters:
+ """
+ Prepare the request to be sent to the provider.
+
+ Each step (api_key, model, headers, url, payload) can be customized in subclasses.
+ """
+ # api_key from user, or local token, or raise error
+ api_key = self._prepare_api_key(api_key)
+
+ # mapped model from HF model ID
+ provider_mapping_info = self._prepare_mapping_info(model)
+
+ # default HF headers + user headers (to customize in subclasses)
+ headers = self._prepare_headers(headers, api_key)
+
+ # routed URL if HF token, or direct URL (to customize in '_prepare_route' in subclasses)
+ url = self._prepare_url(api_key, provider_mapping_info.provider_id)
+
+ # prepare payload (to customize in subclasses)
+ payload = self._prepare_payload_as_dict(inputs, parameters, provider_mapping_info=provider_mapping_info)
+ if payload is not None:
+ payload = recursive_merge(payload, filter_none(extra_payload or {}))
+
+ # body data (to customize in subclasses)
+ data = self._prepare_payload_as_bytes(inputs, parameters, provider_mapping_info, extra_payload)
+
+ # check if both payload and data are set and return
+ if payload is not None and data is not None:
+ raise ValueError("Both payload and data cannot be set in the same request.")
+ if payload is None and data is None:
+ raise ValueError("Either payload or data must be set in the request.")
+
+ # normalize headers to lowercase and add content-type if not present
+ normalized_headers = self._normalize_headers(headers, payload, data)
+
+ return RequestParameters(
+ url=url,
+ task=self.task,
+ model=provider_mapping_info.provider_id,
+ json=payload,
+ data=data,
+ headers=normalized_headers,
+ )
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ """
+ Return the response in the expected format.
+
+ Override this method in subclasses for customized response handling."""
+ return response
+
+ def _prepare_api_key(self, api_key: str | None) -> str:
+ """Return the API key to use for the request.
+
+ Usually not overwritten in subclasses."""
+ if api_key is None:
+ api_key = get_token()
+ if api_key is None:
+ raise ValueError(
+ f"You must provide an api_key to work with {self.provider} API or log in with `hf auth login`."
+ )
+ return api_key
+
+ def _prepare_mapping_info(self, model: str | None) -> InferenceProviderMapping:
+ """Return the mapped model ID to use for the request.
+
+ Usually not overwritten in subclasses."""
+ if model is None:
+ raise ValueError(f"Please provide an HF model ID supported by {self.provider}.")
+
+ # hardcoded mapping for local testing
+ if HARDCODED_MODEL_INFERENCE_MAPPING.get(self.provider, {}).get(model):
+ return HARDCODED_MODEL_INFERENCE_MAPPING[self.provider][model]
+
+ provider_mapping = None
+ for mapping in _fetch_inference_provider_mapping(model):
+ if mapping.provider == self.provider:
+ provider_mapping = mapping
+ break
+
+ if provider_mapping is None:
+ raise ValueError(f"Model {model} is not supported by provider {self.provider}.")
+
+ if provider_mapping.task != self.task:
+ raise ValueError(
+ f"Model {model} is not supported for task {self.task} and provider {self.provider}. "
+ f"Supported task: {provider_mapping.task}."
+ )
+
+ if provider_mapping.status == "staging":
+ logger.warning(
+ f"Model {model} is in staging mode for provider {self.provider}. Meant for test purposes only."
+ )
+ if provider_mapping.status == "error":
+ logger.warning(
+ f"Our latest automated health check on model '{model}' for provider '{self.provider}' did not complete successfully. "
+ "Inference call might fail."
+ )
+ return provider_mapping
+
+ def _normalize_headers(
+ self, headers: dict[str, Any], payload: dict[str, Any] | None, data: MimeBytes | None
+ ) -> dict[str, Any]:
+ """Normalize the headers to use for the request.
+
+ Override this method in subclasses for customized headers.
+ """
+ normalized_headers = {key.lower(): value for key, value in headers.items() if value is not None}
+ if normalized_headers.get("content-type") is None:
+ if data is not None and data.mime_type is not None:
+ normalized_headers["content-type"] = data.mime_type
+ elif payload is not None:
+ normalized_headers["content-type"] = "application/json"
+ return normalized_headers
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ """Return the headers to use for the request.
+
+ Override this method in subclasses for customized headers.
+ """
+ return {**build_hf_headers(token=api_key), **headers}
+
+ def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+ """Return the URL to use for the request.
+
+ Usually not overwritten in subclasses."""
+ base_url = self._prepare_base_url(api_key)
+ route = self._prepare_route(mapped_model, api_key)
+ return f"{base_url.rstrip('/')}/{route.lstrip('/')}"
+
+ def _prepare_base_url(self, api_key: str) -> str:
+ """Return the base URL to use for the request.
+
+ Usually not overwritten in subclasses."""
+ # Route to the proxy if the api_key is a HF TOKEN
+ if api_key.startswith("hf_"):
+ logger.info(f"Calling '{self.provider}' provider through Hugging Face router.")
+ return constants.INFERENCE_PROXY_TEMPLATE.format(provider=self.provider)
+ else:
+ logger.info(f"Calling '{self.provider}' provider directly.")
+ return self.base_url
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ """Return the route to use for the request.
+
+ Override this method in subclasses for customized routes.
+ """
+ return ""
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ """Return the payload to use for the request, as a dict.
+
+ Override this method in subclasses for customized payloads.
+ Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
+ """
+ return None
+
+ def _prepare_payload_as_bytes(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ extra_payload: dict | None,
+ ) -> MimeBytes | None:
+ """Return the body to use for the request, as bytes.
+
+ Override this method in subclasses for customized body data.
+ Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
+ """
+ return None
+
+
+class BaseConversationalTask(TaskProviderHelper):
+ """
+ Base class for conversational (chat completion) tasks.
+ The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/chat
+ """
+
+ def __init__(self, provider: str, base_url: str):
+ super().__init__(provider=provider, base_url=base_url, task="conversational")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/chat/completions"
+
+ def _prepare_payload_as_dict(
+ self,
+ inputs: list[dict | ChatCompletionInputMessage],
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ ) -> dict | None:
+ # `model` is serialized first so that a router/proxy can resolve the target provider from a
+ # small prefix of the request body instead of buffering the whole payload — `messages` can
+ # hold megabytes of base64-encoded images.
+ # `parameters` also carries a caller-supplied "model" (see `InferenceClient.chat_completion`),
+ # which must not take precedence over the provider-mapped id: drop it from the spread.
+ return filter_none(
+ {
+ "model": provider_mapping_info.provider_id,
+ "messages": inputs,
+ **{key: value for key, value in parameters.items() if key != "model"},
+ }
+ )
+
+
+class AutoRouterConversationalTask(BaseConversationalTask):
+ """
+ Auto-router for conversational tasks.
+
+ We let the Hugging Face router select the best provider for the model, based on availability and user preferences.
+ This is a special case since the selection is done server-side (avoid 1 API call to fetch provider mapping).
+ """
+
+ def __init__(self):
+ super().__init__(provider="auto", base_url="https://router.huggingface.co")
+
+ def _prepare_base_url(self, api_key: str) -> str:
+ """Return the base URL to use for the request.
+
+ Usually not overwritten in subclasses."""
+ # Route to the proxy if the api_key is a HF TOKEN
+ if not api_key.startswith("hf_"):
+ raise ValueError("Cannot select auto-router when using non-Hugging Face API key.")
+ else:
+ return self.base_url # No `/auto` suffix in the URL
+
+ def _prepare_mapping_info(self, model: str | None) -> InferenceProviderMapping:
+ """
+ In auto-router, we don't need to fetch provider mapping info.
+ We just return a dummy mapping info with provider_id set to the HF model ID.
+ """
+ if model is None:
+ raise ValueError("Please provide an HF model ID.")
+
+ return InferenceProviderMapping(
+ provider="auto",
+ hf_model_id=model,
+ providerId=model,
+ status="live",
+ task="conversational",
+ )
+
+
+class BaseTextGenerationTask(TaskProviderHelper):
+ """
+ Base class for text-generation (completion) tasks.
+ The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/completions
+ """
+
+ def __init__(self, provider: str, base_url: str):
+ super().__init__(provider=provider, base_url=base_url, task="text-generation")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/completions"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return filter_none({"prompt": inputs, **parameters, "model": provider_mapping_info.provider_id})
+
+
+@lru_cache(maxsize=None)
+def _fetch_inference_provider_mapping(model: str) -> list["InferenceProviderMapping"]:
+ """
+ Fetch provider mappings for a model from the Hub.
+ """
+ from huggingface_hub.hf_api import HfApi
+
+ info = HfApi().model_info(model, expand=["inferenceProviderMapping"])
+ provider_mapping = info.inference_provider_mapping
+ if provider_mapping is None:
+ raise ValueError(f"No provider mapping found for model {model}")
+ return provider_mapping
+
+
+def recursive_merge(dict1: dict, dict2: dict) -> dict:
+ return {
+ **dict1,
+ **{
+ key: recursive_merge(dict1[key], value)
+ if (key in dict1 and isinstance(dict1[key], dict) and isinstance(value, dict))
+ else value
+ for key, value in dict2.items()
+ },
+ }
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/baseten.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/baseten.py
new file mode 100644
index 0000000000000000000000000000000000000000..268710e1c22418fbb1e2197dc106f493adc30a46
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/baseten.py
@@ -0,0 +1,6 @@
+from ._common import BaseConversationalTask
+
+
+class BasetenConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="baseten", base_url="https://inference.baseten.co")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cerebras.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cerebras.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b9c3aacb3e134a8e755297c15ece198ffe633d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cerebras.py
@@ -0,0 +1,6 @@
+from ._common import BaseConversationalTask
+
+
+class CerebrasConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="cerebras", base_url="https://api.cerebras.ai")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cohere.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cohere.py
new file mode 100644
index 0000000000000000000000000000000000000000..57ddfc8246bcd2f3d63c74f78ded130dcca53992
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/cohere.py
@@ -0,0 +1,32 @@
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+
+from ._common import BaseConversationalTask
+
+
+_PROVIDER = "cohere"
+_BASE_URL = "https://api.cohere.com"
+
+
+class CohereConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/compatibility/v1/chat/completions"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)
+ response_format = parameters.get("response_format")
+ if isinstance(response_format, dict) and response_format.get("type") == "json_schema":
+ json_schema_details = response_format.get("json_schema")
+ if isinstance(json_schema_details, dict) and "schema" in json_schema_details:
+ payload["response_format"] = { # type: ignore
+ "type": "json_object",
+ "schema": json_schema_details["schema"],
+ }
+
+ return payload
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/deepinfra.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/deepinfra.py
new file mode 100644
index 0000000000000000000000000000000000000000..216c1363bdcd76a3427a1ea2477729186981a877
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/deepinfra.py
@@ -0,0 +1,166 @@
+import json
+import mimetypes
+import uuid
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import MimeBytes, RequestParameters, _as_dict, _open_as_mime_bytes
+
+from ._common import BaseConversationalTask, BaseTextGenerationTask, TaskProviderHelper, filter_none
+
+
+_PROVIDER = "deepinfra"
+_BASE_URL = "https://api.deepinfra.com"
+
+
+def _form_field_value(value: Any) -> str:
+ if isinstance(value, str):
+ return value
+ if isinstance(value, bool): # bool before int: bool is an int subclass
+ return "true" if value else "false"
+ if isinstance(value, (int, float)):
+ return str(value)
+ return json.dumps(value)
+
+
+def _encode_multipart(audio: MimeBytes, fields: dict[str, Any]) -> tuple[bytes, str]:
+ boundary = uuid.uuid4().hex
+ # Fall back to .wav when the MIME type is unknown: transcription servers sniff the format from the filename.
+ filename = "audio" + (mimetypes.guess_extension(audio.mime_type or "") or ".wav")
+ lines: list[bytes] = [
+ f"--{boundary}".encode(),
+ f'Content-Disposition: form-data; name="file"; filename="{filename}"'.encode(),
+ f"Content-Type: {audio.mime_type or 'application/octet-stream'}".encode(),
+ b"",
+ bytes(audio),
+ ]
+ for key, value in fields.items():
+ lines += [
+ f"--{boundary}".encode(),
+ f'Content-Disposition: form-data; name="{key}"'.encode(),
+ b"",
+ _form_field_value(value).encode(),
+ ]
+ lines += [f"--{boundary}--".encode(), b""]
+ return b"\r\n".join(lines), f"multipart/form-data; boundary={boundary}"
+
+
+class DeepInfraTextGenerationTask(BaseTextGenerationTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/openai/completions"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ params = filter_none(parameters.copy())
+ params["max_tokens"] = params.pop("max_new_tokens", None)
+
+ return {"prompt": inputs, **params, "model": provider_mapping_info.provider_id}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ output = _as_dict(response)["choices"][0]
+ return {
+ "generated_text": output["text"],
+ "details": {
+ "finish_reason": output.get("finish_reason"),
+ "seed": output.get("seed"),
+ },
+ }
+
+
+class DeepInfraConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/openai/chat/completions"
+
+
+class DeepInfraAutomaticSpeechRecognitionTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task="automatic-speech-recognition")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/openai/audio/transcriptions"
+
+ def _prepare_payload_as_bytes(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ extra_payload: dict | None,
+ ) -> MimeBytes | None:
+ # OpenAI-compatible transcription endpoint expects a multipart/form-data body, not JSON.
+ audio = _open_as_mime_bytes(inputs)
+ # `model` is applied last so parameters cannot override the mapped provider model.
+ fields: dict[str, Any] = {
+ **filter_none(parameters),
+ **filter_none(extra_payload or {}),
+ "model": provider_mapping_info.provider_id,
+ }
+ body, content_type = _encode_multipart(audio, fields)
+ return MimeBytes(body, mime_type=content_type)
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ output = _as_dict(response)
+ text = output["text"]
+ if not isinstance(text, str):
+ raise ValueError(f"Unexpected output format from DeepInfra API. Expected string, got {type(text)}.")
+ result: dict[str, Any] = {"text": text}
+ segments = output.get("segments")
+ if isinstance(segments, list):
+ result["chunks"] = [
+ {"text": segment.get("text"), "timestamp": [segment.get("start"), segment.get("end")]}
+ for segment in segments
+ if isinstance(segment, dict)
+ ]
+ return result
+
+
+class DeepInfraTextToSpeechTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task="text-to-speech")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/openai/audio/speech"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ # `voice` is model-specific and optional; we pass it through and let the API surface a
+ # clear error when a model requires one. `model` is applied last so parameters cannot
+ # override the mapped provider model.
+ return {
+ "input": inputs,
+ **filter_none(parameters),
+ "model": provider_mapping_info.provider_id,
+ }
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ if isinstance(response, bytes):
+ return response
+ raise ValueError(f"Expected raw audio bytes for text-to-speech, got {type(response).__name__}.")
+
+
+class DeepInfraFeatureExtractionTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task="feature-extraction")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/openai/embeddings"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ # `model` is applied last so parameters cannot override the mapped provider model.
+ return {
+ "input": inputs,
+ **filter_none(parameters),
+ "model": provider_mapping_info.provider_id,
+ }
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ return [item["embedding"] for item in _as_dict(response)["data"]]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fal_ai.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fal_ai.py
new file mode 100644
index 0000000000000000000000000000000000000000..4973960f4323be5e83fa699af107cb23d280b999
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fal_ai.py
@@ -0,0 +1,308 @@
+import base64
+import time
+from abc import ABC
+from typing import Any
+from urllib.parse import urlparse
+
+from huggingface_hub import constants
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict, _as_url
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import get_session, hf_raise_for_status
+from huggingface_hub.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+# Arbitrary polling interval
+_POLLING_INTERVAL = 0.5
+
+
+class FalAITask(TaskProviderHelper, ABC):
+ def __init__(self, task: str):
+ super().__init__(provider="fal-ai", base_url="https://fal.run", task=task)
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ headers = super()._prepare_headers(headers, api_key)
+ if not api_key.startswith("hf_"):
+ headers["authorization"] = f"Key {api_key}"
+ return headers
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return f"/{mapped_model}"
+
+
+class FalAIQueueTask(TaskProviderHelper, ABC):
+ def __init__(self, task: str):
+ super().__init__(provider="fal-ai", base_url="https://queue.fal.run", task=task)
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ headers = super()._prepare_headers(headers, api_key)
+ if not api_key.startswith("hf_"):
+ headers["authorization"] = f"Key {api_key}"
+ return headers
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ if api_key.startswith("hf_"):
+ # Use the queue subdomain for HF routing
+ return f"/{mapped_model}?_subdomain=queue"
+ return f"/{mapped_model}"
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ response_dict = _as_dict(response)
+
+ request_id = response_dict.get("request_id")
+ if not request_id:
+ raise ValueError("No request ID found in the response")
+ if request_params is None:
+ raise ValueError(
+ f"A `RequestParameters` object should be provided to get {self.task} responses with Fal AI."
+ )
+
+ # extract the base url and query params
+ parsed_url = urlparse(request_params.url)
+ # a bit hacky way to concatenate the provider name without parsing `parsed_url.path`
+ base_url = f"{parsed_url.scheme}://{parsed_url.netloc}{'/fal-ai' if parsed_url.netloc == 'router.huggingface.co' else ''}"
+ query_param = f"?{parsed_url.query}" if parsed_url.query else ""
+
+ # extracting the provider model id for status and result urls
+ # from the response as it might be different from the mapped model in `request_params.url`
+ model_id = urlparse(response_dict.get("response_url")).path
+ status_url = f"{base_url}{str(model_id)}/status{query_param}"
+ result_url = f"{base_url}{str(model_id)}{query_param}"
+
+ status = response_dict.get("status")
+ logger.info("Generating the output.. this can take several minutes.")
+ while status != "COMPLETED":
+ time.sleep(_POLLING_INTERVAL)
+ status_response = get_session().get(status_url, headers=request_params.headers)
+ hf_raise_for_status(status_response)
+ status = status_response.json().get("status")
+
+ return get_session().get(result_url, headers=request_params.headers).json()
+
+
+class FalAIAutomaticSpeechRecognitionTask(FalAITask):
+ def __init__(self):
+ super().__init__("automatic-speech-recognition")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ if isinstance(inputs, str) and inputs.startswith(("http://", "https://")):
+ # If input is a URL, pass it directly
+ audio_url = inputs
+ else:
+ # If input is a file path, read it first
+ if isinstance(inputs, str):
+ with open(inputs, "rb") as f:
+ inputs = f.read()
+
+ audio_b64 = base64.b64encode(inputs).decode()
+ content_type = "audio/mpeg"
+ audio_url = f"data:{content_type};base64,{audio_b64}"
+
+ return {"audio_url": audio_url, **filter_none(parameters)}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ text = _as_dict(response)["text"]
+ if not isinstance(text, str):
+ raise ValueError(f"Unexpected output format from FalAI API. Expected string, got {type(text)}.")
+ return {"text": text}
+
+
+class FalAITextToImageTask(FalAITask):
+ def __init__(self):
+ super().__init__("text-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload: dict[str, Any] = {
+ "prompt": inputs,
+ **filter_none(parameters),
+ }
+ if "width" in payload and "height" in payload:
+ payload["image_size"] = {
+ "width": payload.pop("width"),
+ "height": payload.pop("height"),
+ }
+ if provider_mapping_info.adapter_weights_path is not None:
+ lora_path = constants.HUGGINGFACE_CO_URL_TEMPLATE.format(
+ repo_id=provider_mapping_info.hf_model_id,
+ revision="main",
+ filename=provider_mapping_info.adapter_weights_path,
+ )
+ payload["loras"] = [{"path": lora_path, "scale": 1}]
+ if provider_mapping_info.provider_id == "fal-ai/lora":
+ # little hack: fal requires the base model for stable-diffusion-based loras but not for flux-based
+ # See payloads in https://fal.ai/models/fal-ai/lora/api vs https://fal.ai/models/fal-ai/flux-lora/api
+ payload["model_name"] = "stabilityai/stable-diffusion-xl-base-1.0"
+
+ return payload
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ url = _as_dict(response)["images"][0]["url"]
+ return get_session().get(url).content
+
+
+class FalAITextToSpeechTask(FalAITask):
+ def __init__(self):
+ super().__init__("text-to-speech")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return {"text": inputs, **filter_none(parameters)}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ url = _as_dict(response)["audio"]["url"]
+ return get_session().get(url).content
+
+
+class FalAITextToVideoTask(FalAIQueueTask):
+ def __init__(self):
+ super().__init__("text-to-video")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload: dict[str, Any] = {"prompt": inputs, **filter_none(parameters)}
+ if provider_mapping_info.adapter_weights_path is not None:
+ lora_path = constants.HUGGINGFACE_CO_URL_TEMPLATE.format(
+ repo_id=provider_mapping_info.hf_model_id,
+ revision="main",
+ filename=provider_mapping_info.adapter_weights_path,
+ )
+ payload["loras"] = [{"path": lora_path, "scale": 1}]
+ return payload
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ output = super().get_response(response, request_params)
+ url = _as_dict(output)["video"]["url"]
+ return get_session().get(url).content
+
+
+class FalAIImageToImageTask(FalAIQueueTask):
+ def __init__(self):
+ super().__init__("image-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ image_url = _as_url(inputs, default_mime_type="image/jpeg")
+ if "target_size" in parameters:
+ parameters["image_size"] = parameters.pop("target_size")
+ payload: dict[str, Any] = {
+ "image_url": image_url,
+ "image_urls": [image_url],
+ **filter_none(parameters),
+ }
+ if provider_mapping_info.adapter_weights_path is not None:
+ lora_path = constants.HUGGINGFACE_CO_URL_TEMPLATE.format(
+ repo_id=provider_mapping_info.hf_model_id,
+ revision="main",
+ filename=provider_mapping_info.adapter_weights_path,
+ )
+ payload["loras"] = [{"path": lora_path, "scale": 1}]
+
+ return payload
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ output = super().get_response(response, request_params)
+ url = _as_dict(output)["images"][0]["url"]
+ return get_session().get(url).content
+
+
+class FalAIImageToVideoTask(FalAIQueueTask):
+ def __init__(self):
+ super().__init__("image-to-video")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ image_url = _as_url(inputs, default_mime_type="image/jpeg")
+ payload: dict[str, Any] = {
+ "image_url": image_url,
+ **filter_none(parameters),
+ }
+ if provider_mapping_info.adapter_weights_path is not None:
+ lora_path = constants.HUGGINGFACE_CO_URL_TEMPLATE.format(
+ repo_id=provider_mapping_info.hf_model_id,
+ revision="main",
+ filename=provider_mapping_info.adapter_weights_path,
+ )
+ payload["loras"] = [{"path": lora_path, "scale": 1}]
+ return payload
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ output = super().get_response(response, request_params)
+ url = _as_dict(output)["video"]["url"]
+ return get_session().get(url).content
+
+
+class FalAIImageSegmentationTask(FalAIQueueTask):
+ def __init__(self):
+ super().__init__("image-segmentation")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ image_url = _as_url(inputs, default_mime_type="image/png")
+ payload: dict[str, Any] = {
+ "image_url": image_url,
+ **filter_none(parameters),
+ "sync_mode": True,
+ }
+ return payload
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ result = super().get_response(response, request_params)
+ result_dict = _as_dict(result)
+
+ if "image" not in result_dict:
+ raise ValueError(f"Response from fal ai image-segmentation API does not contain an image: {result_dict}")
+
+ image_data = result_dict["image"]
+ if "url" not in image_data:
+ raise ValueError(f"Image data from fal ai image-segmentation API does not contain a URL: {image_data}")
+
+ image_url = image_data["url"]
+
+ if isinstance(image_url, str) and image_url.startswith("data:"):
+ if "," in image_url:
+ mask_base64 = image_url.split(",", 1)[1]
+ else:
+ raise ValueError(f"Invalid data URL format: {image_url}")
+ else:
+ # or it's a regular URL, fetch it
+ mask_response = get_session().get(image_url)
+ hf_raise_for_status(mask_response)
+ mask_base64 = base64.b64encode(mask_response.content).decode()
+
+ return [
+ {
+ "label": "mask",
+ "mask": mask_base64,
+ }
+ ]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/featherless_ai.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/featherless_ai.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a90b332a5050340fb4e472040e05beb24e747b6
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/featherless_ai.py
@@ -0,0 +1,38 @@
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+
+from ._common import BaseConversationalTask, BaseTextGenerationTask, filter_none
+
+
+_PROVIDER = "featherless-ai"
+_BASE_URL = "https://api.featherless.ai"
+
+
+class FeatherlessTextGenerationTask(BaseTextGenerationTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ params = filter_none(parameters.copy())
+ params["max_tokens"] = params.pop("max_new_tokens", None)
+
+ return {"prompt": inputs, **params, "model": provider_mapping_info.provider_id}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ output = _as_dict(response)["choices"][0]
+ return {
+ "generated_text": output["text"],
+ "details": {
+ "finish_reason": output.get("finish_reason"),
+ "seed": output.get("seed"),
+ },
+ }
+
+
+class FeatherlessConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fireworks_ai.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fireworks_ai.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ae57662cf888c258c9b310156c8b986b8643923
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/fireworks_ai.py
@@ -0,0 +1,27 @@
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+
+from ._common import BaseConversationalTask
+
+
+class FireworksAIConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="fireworks-ai", base_url="https://api.fireworks.ai")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/inference/v1/chat/completions"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)
+ response_format = parameters.get("response_format")
+ if isinstance(response_format, dict) and response_format.get("type") == "json_schema":
+ json_schema_details = response_format.get("json_schema")
+ if isinstance(json_schema_details, dict) and "schema" in json_schema_details:
+ payload["response_format"] = { # type: ignore
+ "type": "json_object",
+ "schema": json_schema_details["schema"],
+ }
+ return payload
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/groq.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/groq.py
new file mode 100644
index 0000000000000000000000000000000000000000..11e677504e89bc02b966e7d37d9e11f1b94b297f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/groq.py
@@ -0,0 +1,9 @@
+from ._common import BaseConversationalTask
+
+
+class GroqConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="groq", base_url="https://api.groq.com")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/openai/v1/chat/completions"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/hf_inference.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/hf_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..46fbd94a60aa7073257a6eb14b306f80f9a89b4f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/hf_inference.py
@@ -0,0 +1,228 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse, urlunparse
+
+from huggingface_hub import constants
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import (
+ MimeBytes,
+ RequestParameters,
+ _b64_encode,
+ _bytes_to_dict,
+ _open_as_mime_bytes,
+)
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import build_hf_headers, get_session, get_token, hf_raise_for_status
+
+
+class HFInferenceTask(TaskProviderHelper):
+ """Base class for HF Inference API tasks."""
+
+ def __init__(self, task: str):
+ super().__init__(
+ provider="hf-inference",
+ base_url=constants.INFERENCE_PROXY_TEMPLATE.format(provider="hf-inference"),
+ task=task,
+ )
+
+ def _prepare_api_key(self, api_key: str | None) -> str:
+ # special case: for HF Inference we allow not providing an API key
+ return api_key or get_token() # type: ignore
+
+ def _prepare_mapping_info(self, model: str | None) -> InferenceProviderMapping:
+ if model is not None and model.startswith(("http://", "https://")):
+ return InferenceProviderMapping(
+ provider="hf-inference", providerId=model, hf_model_id=model, task=self.task, status="live"
+ )
+ model_id = model if model is not None else _fetch_recommended_models().get(self.task)
+ if model_id is None:
+ raise ValueError(
+ f"Task {self.task} has no recommended model for HF Inference. Please specify a model"
+ " explicitly. Visit https://huggingface.co/tasks for more info."
+ )
+ _check_supported_task(model_id, self.task)
+ return InferenceProviderMapping(
+ provider="hf-inference", providerId=model_id, hf_model_id=model_id, task=self.task, status="live"
+ )
+
+ def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+ # hf-inference provider can handle URLs (e.g. Inference Endpoints or TGI deployment)
+ if mapped_model.startswith(("http://", "https://")):
+ return mapped_model
+ return (
+ # Feature-extraction and sentence-similarity are the only cases where we handle models with several tasks.
+ f"{self.base_url}/models/{mapped_model}/pipeline/{self.task}"
+ if self.task in ("feature-extraction", "sentence-similarity")
+ # Otherwise, we use the default endpoint
+ else f"{self.base_url}/models/{mapped_model}"
+ )
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ if isinstance(inputs, bytes):
+ raise ValueError(f"Unexpected binary input for task {self.task}.")
+ if isinstance(inputs, Path):
+ raise ValueError(f"Unexpected path input for task {self.task} (got {inputs})")
+ return filter_none({"inputs": inputs, "parameters": parameters})
+
+
+class HFInferenceBinaryInputTask(HFInferenceTask):
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return None
+
+ def _prepare_payload_as_bytes(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ extra_payload: dict | None,
+ ) -> MimeBytes | None:
+ parameters = filter_none(parameters)
+ extra_payload = extra_payload or {}
+ has_parameters = len(parameters) > 0 or len(extra_payload) > 0
+
+ # Raise if not a binary object or a local path or a URL.
+ if not isinstance(inputs, (bytes, Path)) and not isinstance(inputs, str):
+ raise ValueError(f"Expected binary inputs or a local path or a URL. Got {inputs}")
+
+ # Send inputs as raw content when no parameters are provided
+ if not has_parameters:
+ return _open_as_mime_bytes(inputs)
+
+ # Otherwise encode as b64
+ return MimeBytes(
+ json.dumps({"inputs": _b64_encode(inputs), "parameters": parameters, **extra_payload}).encode("utf-8"),
+ mime_type="application/json",
+ )
+
+
+class HFInferenceConversational(HFInferenceTask):
+ def __init__(self):
+ super().__init__("conversational")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload = filter_none(parameters)
+ mapped_model = provider_mapping_info.provider_id
+ payload_model = parameters.get("model") or mapped_model
+
+ if payload_model is None or payload_model.startswith(("http://", "https://")):
+ payload_model = "dummy"
+
+ response_format = parameters.get("response_format")
+ if isinstance(response_format, dict) and response_format.get("type") == "json_schema":
+ payload["response_format"] = {
+ "type": "json_object",
+ "value": response_format["json_schema"]["schema"],
+ }
+ # Same ordering rationale as `BaseConversationalTask`: `model` first so it can be read from a
+ # small prefix of the body, and dropped from the `payload` spread so the already-resolved
+ # `payload_model` wins.
+ return {
+ "model": payload_model,
+ "messages": inputs,
+ **{key: value for key, value in payload.items() if key != "model"},
+ }
+
+ def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+ base_url = (
+ mapped_model
+ if mapped_model.startswith(("http://", "https://"))
+ else f"{constants.INFERENCE_PROXY_TEMPLATE.format(provider='hf-inference')}/models/{mapped_model}"
+ )
+ return _build_chat_completion_url(base_url)
+
+
+def _build_chat_completion_url(model_url: str) -> str:
+ parsed = urlparse(model_url)
+ path = parsed.path.rstrip("/")
+
+ # If the path already ends with /chat/completions, we're done!
+ if path.endswith("/chat/completions"):
+ return model_url
+
+ # Append /chat/completions if not already present
+ if path.endswith("/v1"):
+ new_path = path + "/chat/completions"
+ # If path was empty or just "/", set the full path
+ elif not path:
+ new_path = "/v1/chat/completions"
+ # Append /v1/chat/completions if not already present
+ else:
+ new_path = path + "/v1/chat/completions"
+
+ # Reconstruct the URL with the new path and original query parameters.
+ new_parsed = parsed._replace(path=new_path)
+ return str(urlunparse(new_parsed))
+
+
+@lru_cache(maxsize=1)
+def _fetch_recommended_models() -> dict[str, str | None]:
+ response = get_session().get(f"{constants.ENDPOINT}/api/tasks", headers=build_hf_headers())
+ hf_raise_for_status(response)
+ return {task: next(iter(details["widgetModels"]), None) for task, details in response.json().items()}
+
+
+@lru_cache(maxsize=None)
+def _check_supported_task(model: str, task: str) -> None:
+ from huggingface_hub.hf_api import HfApi
+
+ model_info = HfApi().model_info(model)
+ pipeline_tag = model_info.pipeline_tag
+ tags = model_info.tags or []
+ if task in ("text-generation", "conversational"):
+ if pipeline_tag == "text-generation":
+ return
+
+ if pipeline_tag == "text2text-generation":
+ if task == "text-generation":
+ return
+ raise ValueError(f"Model '{model}' doesn't support task '{task}'.")
+
+ if pipeline_tag == "image-text-to-text":
+ if task == "conversational":
+ return
+ raise ValueError("Non-conversational image-text-to-text task is not supported.")
+
+ if (
+ task in ("feature-extraction", "sentence-similarity")
+ and pipeline_tag in ("feature-extraction", "sentence-similarity")
+ and task in tags
+ ):
+ # feature-extraction and sentence-similarity are interchangeable for HF Inference
+ return
+
+ # For all other tasks, just check pipeline tag
+ if pipeline_tag != task:
+ raise ValueError(
+ f"Model '{model}' doesn't support task '{task}'. Supported tasks: '{pipeline_tag}', got: '{task}'"
+ )
+ return
+
+
+class HFInferenceFeatureExtractionTask(HFInferenceTask):
+ def __init__(self):
+ super().__init__("feature-extraction")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ if isinstance(inputs, bytes):
+ raise ValueError(f"Unexpected binary input for task {self.task}.")
+ if isinstance(inputs, Path):
+ raise ValueError(f"Unexpected path input for task {self.task} (got {inputs})")
+
+ # Parameters are sent at root-level for feature-extraction task
+ # See specs: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/tasks/feature-extraction/spec/input.json
+ return {"inputs": inputs, **filter_none(parameters)}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ if isinstance(response, bytes):
+ return _bytes_to_dict(response)
+ return response
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/novita.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/novita.py
new file mode 100644
index 0000000000000000000000000000000000000000..af29e72032a7eb82f1ec20bc3df529612ab8730c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/novita.py
@@ -0,0 +1,69 @@
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import (
+ BaseConversationalTask,
+ BaseTextGenerationTask,
+ TaskProviderHelper,
+ filter_none,
+)
+from huggingface_hub.utils import get_session
+
+
+_PROVIDER = "novita"
+_BASE_URL = "https://api.novita.ai"
+
+
+class NovitaTextGenerationTask(BaseTextGenerationTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ # there is no v1/ route for novita
+ return "/v3/openai/completions"
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ output = _as_dict(response)["choices"][0]
+ return {
+ "generated_text": output["text"],
+ "details": {
+ "finish_reason": output.get("finish_reason"),
+ "seed": output.get("seed"),
+ },
+ }
+
+
+class NovitaConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ # there is no v1/ route for novita
+ return "/v3/openai/chat/completions"
+
+
+class NovitaTextToVideoTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task="text-to-video")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return f"/v3/hf/{mapped_model}"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return {"prompt": inputs, **filter_none(parameters)}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ if not (
+ isinstance(response_dict, dict)
+ and "video" in response_dict
+ and isinstance(response_dict["video"], dict)
+ and "video_url" in response_dict["video"]
+ ):
+ raise ValueError("Expected response format: { 'video': { 'video_url': string } }")
+
+ video_url = response_dict["video"]["video_url"]
+ return get_session().get(video_url).content
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/nscale.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/nscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a5a4c38e9f0f8ede459d36c584813cf5ef7dbac
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/nscale.py
@@ -0,0 +1,44 @@
+import base64
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+
+from ._common import BaseConversationalTask, TaskProviderHelper, filter_none
+
+
+class NscaleConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="nscale", base_url="https://inference.api.nscale.com")
+
+
+class NscaleTextToImageTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider="nscale", base_url="https://inference.api.nscale.com", task="text-to-image")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/images/generations"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ mapped_model = provider_mapping_info.provider_id
+ # Combine all parameters except inputs and parameters
+ parameters = filter_none(parameters)
+ if "width" in parameters and "height" in parameters:
+ parameters["size"] = f"{parameters.pop('width')}x{parameters.pop('height')}"
+ if "num_inference_steps" in parameters:
+ parameters.pop("num_inference_steps")
+ if "cfg_scale" in parameters:
+ parameters.pop("cfg_scale")
+ payload = {
+ "response_format": "b64_json",
+ "prompt": inputs,
+ "model": mapped_model,
+ **parameters,
+ }
+ return payload
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ return base64.b64decode(response_dict["data"][0]["b64_json"])
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/openai.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0f56002a6493c852836a1e5539900204fc0bb3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/openai.py
@@ -0,0 +1,23 @@
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._providers._common import BaseConversationalTask
+
+
+class OpenAIConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="openai", base_url="https://api.openai.com")
+
+ def _prepare_api_key(self, api_key: str | None) -> str:
+ if api_key is None:
+ raise ValueError("You must provide an api_key to work with OpenAI API.")
+ if api_key.startswith("hf_"):
+ raise ValueError(
+ "OpenAI provider is not available through Hugging Face routing, please use your own OpenAI API key."
+ )
+ return api_key
+
+ def _prepare_mapping_info(self, model: str | None) -> InferenceProviderMapping:
+ if model is None:
+ raise ValueError("Please provide an OpenAI model ID, e.g. `gpt-4o` or `o1`.")
+ return InferenceProviderMapping(
+ provider="openai", providerId=model, task="conversational", status="live", hf_model_id=model
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/ovhcloud.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/ovhcloud.py
new file mode 100644
index 0000000000000000000000000000000000000000..79be8d55089975b1ad9a0282d503cd2fc012127c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/ovhcloud.py
@@ -0,0 +1,10 @@
+from huggingface_hub.inference._providers._common import BaseConversationalTask
+
+
+_PROVIDER = "ovhcloud"
+_BASE_URL = "https://oai.endpoints.kepler.ai.cloud.ovh.net"
+
+
+class OVHcloudConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/publicai.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/publicai.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c88528e4f1e2eefaf6be9315c490db19ff5ca1e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/publicai.py
@@ -0,0 +1,6 @@
+from ._common import BaseConversationalTask
+
+
+class PublicAIConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="publicai", base_url="https://api.publicai.co")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/replicate.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/replicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1aff75ec56460791b35b3acde7208d8c4a03bdb
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/replicate.py
@@ -0,0 +1,160 @@
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict, _as_url
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import get_session
+
+
+_PROVIDER = "replicate"
+_BASE_URL = "https://api.replicate.com"
+
+
+class ReplicateTask(TaskProviderHelper):
+ def __init__(self, task: str):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task=task)
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ headers = super()._prepare_headers(headers, api_key)
+ headers["Prefer"] = "wait"
+ return headers
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ if ":" in mapped_model:
+ return "/v1/predictions"
+ return f"/v1/models/{mapped_model}/predictions"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ mapped_model = provider_mapping_info.provider_id
+ payload: dict[str, Any] = {"input": {"prompt": inputs, **filter_none(parameters)}}
+ if ":" in mapped_model:
+ version = mapped_model.split(":", 1)[1]
+ payload["version"] = version
+ return payload
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ if response_dict.get("output") is None:
+ raise TimeoutError(
+ f"Inference request timed out after 60 seconds. No output generated for model {response_dict.get('model')}"
+ "The model might be in cold state or starting up. Please try again later."
+ )
+ output_url = (
+ response_dict["output"] if isinstance(response_dict["output"], str) else response_dict["output"][0]
+ )
+ return get_session().get(output_url).content
+
+
+class ReplicateTextToImageTask(ReplicateTask):
+ def __init__(self):
+ super().__init__("text-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload: dict = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info) # type: ignore
+ if provider_mapping_info.adapter_weights_path is not None:
+ payload["input"]["lora_weights"] = f"https://huggingface.co/{provider_mapping_info.hf_model_id}"
+ return payload
+
+
+class ReplicateTextToSpeechTask(ReplicateTask):
+ def __init__(self):
+ super().__init__("text-to-speech")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload: dict = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info) # type: ignore
+ payload["input"]["text"] = payload["input"].pop("prompt") # rename "prompt" to "text" for TTS
+ return payload
+
+
+class ReplicateAutomaticSpeechRecognitionTask(ReplicateTask):
+ def __init__(self) -> None:
+ super().__init__("automatic-speech-recognition")
+
+ def _prepare_payload_as_dict(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ ) -> dict | None:
+ mapped_model = provider_mapping_info.provider_id
+ audio_url = _as_url(inputs, default_mime_type="audio/wav")
+
+ payload: dict[str, Any] = {
+ "input": {
+ **{"audio": audio_url},
+ **filter_none(parameters),
+ }
+ }
+
+ if ":" in mapped_model:
+ payload["version"] = mapped_model.split(":", 1)[1]
+
+ return payload
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ output = response_dict.get("output")
+
+ if isinstance(output, str):
+ return {"text": output}
+
+ if isinstance(output, list) and output:
+ first_item = output[0]
+ if isinstance(first_item, str):
+ return {"text": first_item}
+ if isinstance(first_item, dict):
+ output = first_item
+
+ text: str | None = None
+ if isinstance(output, dict):
+ transcription = output.get("transcription")
+ if isinstance(transcription, str):
+ text = transcription
+
+ translation = output.get("translation")
+ if isinstance(translation, str):
+ text = translation
+
+ txt_file = output.get("txt_file")
+ if isinstance(txt_file, str):
+ text_response = get_session().get(txt_file)
+ text_response.raise_for_status()
+ text = text_response.text
+
+ if text is not None:
+ return {"text": text}
+
+ raise ValueError("Received malformed response from Replicate automatic-speech-recognition API")
+
+
+class ReplicateImageToImageTask(ReplicateTask):
+ def __init__(self):
+ super().__init__("image-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ image_url = _as_url(inputs, default_mime_type="image/jpeg")
+
+ # Different Replicate models expect the image in different keys
+ payload: dict[str, Any] = {
+ "input": {
+ "image": image_url,
+ "images": [image_url],
+ "input_image": image_url,
+ "input_images": [image_url],
+ **filter_none(parameters),
+ }
+ }
+
+ mapped_model = provider_mapping_info.provider_id
+ if ":" in mapped_model:
+ version = mapped_model.split(":", 1)[1]
+ payload["version"] = version
+ return payload
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/scaleway.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/scaleway.py
new file mode 100644
index 0000000000000000000000000000000000000000..a64462075a091f4a9d9a664e09be229e0fb7bdf1
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/scaleway.py
@@ -0,0 +1,28 @@
+from typing import Any
+
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+
+from ._common import BaseConversationalTask, InferenceProviderMapping, TaskProviderHelper, filter_none
+
+
+class ScalewayConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider="scaleway", base_url="https://api.scaleway.ai")
+
+
+class ScalewayFeatureExtractionTask(TaskProviderHelper):
+ def __init__(self):
+ super().__init__(provider="scaleway", base_url="https://api.scaleway.ai", task="feature-extraction")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/v1/embeddings"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ parameters = filter_none(parameters)
+ return {"input": inputs, "model": provider_mapping_info.provider_id, **parameters}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ embeddings = _as_dict(response)["data"]
+ return [embedding["embedding"] for embedding in embeddings]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/together.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/together.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f26595af3f919f675f14b5c4d10baa130e647d8
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/together.py
@@ -0,0 +1,291 @@
+import base64
+import time
+from abc import ABC
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import (
+ RequestParameters,
+ _as_dict,
+ _as_url,
+)
+from huggingface_hub.inference._providers._common import (
+ BaseConversationalTask,
+ BaseTextGenerationTask,
+ TaskProviderHelper,
+ filter_none,
+)
+from huggingface_hub.utils import get_session, hf_raise_for_status, logging
+
+
+logger = logging.get_logger(__name__)
+
+_PROVIDER = "together"
+_BASE_URL = "https://api.together.xyz"
+
+# Polling interval for async video generation (in seconds).
+_VIDEO_POLLING_INTERVAL = 2.0
+
+# Upper bound on status polls (initial response may already be terminal; each further poll is one attempt).
+_VIDEO_MAX_POLL_ATTEMPTS = 150 # ~5 minutes at _VIDEO_POLLING_INTERVAL
+
+# Job statuses that mean "keep polling". Together returns "queued" before transitioning to
+# "in_progress", so we must treat both as pending.
+_VIDEO_PENDING_STATUSES = {"queued", "in_progress"}
+
+
+class TogetherTask(TaskProviderHelper, ABC):
+ """Base class for Together API tasks."""
+
+ def __init__(self, task: str):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task=task)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ match self.task:
+ case "text-to-image" | "image-to-image":
+ return "/v1/images/generations"
+ case "text-to-speech":
+ return "/v1/audio/speech"
+ case "feature-extraction":
+ return "/v1/embeddings"
+ case "text-to-video" | "image-to-video":
+ # Video creation lives under /v2 (see https://docs.together.ai/reference/create-videos).
+ return "/v2/videos"
+ raise ValueError(f"Unsupported task '{self.task}' for Together API.")
+
+
+class TogetherTextGenerationTask(BaseTextGenerationTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ output = _as_dict(response)["choices"][0]
+ return {
+ "generated_text": output["text"],
+ "details": {
+ "finish_reason": output.get("finish_reason"),
+ "seed": output.get("seed"),
+ },
+ }
+
+
+class TogetherConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ payload = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)
+ if payload is None:
+ return None
+ # Together accepts response_format `{type: "json_schema", schema: }` (flattened),
+ # so unwrap the OpenAI-style `{type: "json_schema", json_schema: {schema}}` envelope.
+ response_format = payload.get("response_format")
+ if (
+ isinstance(response_format, dict)
+ and response_format.get("type") == "json_schema"
+ and isinstance(response_format.get("json_schema"), dict)
+ and "schema" in response_format["json_schema"]
+ ):
+ payload["response_format"] = {
+ "type": "json_schema",
+ "schema": response_format["json_schema"]["schema"],
+ }
+ return payload
+
+
+class TogetherTextToImageTask(TogetherTask):
+ def __init__(self):
+ super().__init__("text-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ mapped_model = provider_mapping_info.provider_id
+ parameters = filter_none(parameters)
+ if "num_inference_steps" in parameters:
+ parameters["steps"] = parameters.pop("num_inference_steps")
+
+ return {"prompt": inputs, "response_format": "base64", **parameters, "model": mapped_model}
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ return base64.b64decode(response_dict["data"][0]["b64_json"])
+
+
+class TogetherImageToImageTask(TogetherTask):
+ def __init__(self):
+ super().__init__("image-to-image")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ mapped_model = provider_mapping_info.provider_id
+ image_url = _as_url(inputs, default_mime_type="image/jpeg")
+
+ # Filter `None` values first: the client always passes `"prompt": None` when the user
+ # omits the argument, so popping before filtering would yield `None` instead of the
+ # `""` default and send `"prompt": null` to Together (rejected by Flux Kontext).
+ parameters = filter_none(parameters)
+ prompt = parameters.pop("prompt", "")
+ if "num_inference_steps" in parameters:
+ parameters["steps"] = parameters.pop("num_inference_steps")
+
+ # Together exposes two mutually-exclusive image inputs (see
+ # https://docs.together.ai/docs/image-to-image): FLUX.1 Kontext only accepts
+ # `image_url`; FLUX.2 [dev] and Google models (Gemini 3 Pro Image, Flash Image
+ # 2.5) only accept `reference_images`. FLUX.2 [pro]/[flex] accept either but
+ # `reference_images` is the documented default. Use `image_url` only for
+ # FLUX.1 Kontext models and `reference_images` for everything else.
+ lowered = mapped_model.lower()
+ use_image_url = "kontext" in lowered and "flux.1" in lowered
+ image_field: dict[str, Any] = {"image_url": image_url} if use_image_url else {"reference_images": [image_url]}
+ return {
+ "prompt": prompt,
+ **image_field,
+ "response_format": "base64",
+ **parameters,
+ "model": mapped_model,
+ }
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ response_dict = _as_dict(response)
+ return base64.b64decode(response_dict["data"][0]["b64_json"])
+
+
+class TogetherFeatureExtractionTask(TogetherTask):
+ def __init__(self):
+ super().__init__("feature-extraction")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return {
+ "input": inputs,
+ "model": provider_mapping_info.provider_id,
+ **filter_none(parameters),
+ }
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ return [item["embedding"] for item in _as_dict(response)["data"]]
+
+
+class TogetherTextToSpeechTask(TogetherTask):
+ def __init__(self):
+ super().__init__("text-to-speech")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ # `voice` is required by the Together API and is model-specific
+ # (see https://docs.together.ai/docs/text-to-speech#supported-voices),
+ # so we don't set a default and let the API surface a clear error if missing.
+ return {
+ "input": inputs,
+ "model": provider_mapping_info.provider_id,
+ **filter_none(parameters),
+ }
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ if isinstance(response, bytes):
+ return response
+ raise ValueError(f"Expected raw audio bytes for text-to-speech, got {type(response).__name__}.")
+
+
+def _normalize_video_parameters(parameters: dict) -> dict:
+ """Map HF inference-client conventions onto Together's video API parameter names."""
+ parameters = filter_none(parameters)
+ if "num_inference_steps" in parameters:
+ parameters["steps"] = parameters.pop("num_inference_steps")
+ if "target_size" in parameters:
+ target_size = parameters.pop("target_size")
+ if "width" in target_size:
+ parameters["width"] = target_size["width"]
+ if "height" in target_size:
+ parameters["height"] = target_size["height"]
+ return parameters
+
+
+class TogetherVideoTask(TogetherTask, ABC):
+ """Base class for Together's asynchronous video generation tasks."""
+
+ def get_response(self, response: bytes | dict, request_params: RequestParameters | None = None) -> Any:
+ if request_params is None:
+ raise ValueError("A `RequestParameters` object is required to poll Together video jobs.")
+
+ job = _as_dict(response)
+ job_id = job.get("id")
+ if not job_id:
+ raise ValueError("No job ID found in Together video generation response.")
+
+ # Status polling lives at the same /v2/videos URL with the job ID appended.
+ status_url = f"{request_params.url}/{job_id}"
+
+ logger.info("Generating video, polling for completion...")
+ # Together usually returns `status: "queued"` on the initial POST, but the field is
+ # optional per the spec — treat a missing status as "still pending" and poll, rather
+ # than falling through to the "unexpected status" error below.
+ status = job.get("status")
+ for _ in range(_VIDEO_MAX_POLL_ATTEMPTS):
+ if status is not None and status not in _VIDEO_PENDING_STATUSES:
+ break
+ time.sleep(_VIDEO_POLLING_INTERVAL)
+ status_response = get_session().get(status_url, headers=request_params.headers)
+ hf_raise_for_status(status_response)
+ job = status_response.json()
+ status = job.get("status")
+ if status is not None and status not in _VIDEO_PENDING_STATUSES:
+ break
+ else:
+ raise ValueError(
+ "Timed out while waiting for Together video generation "
+ f"— aborting after {_VIDEO_MAX_POLL_ATTEMPTS} status polls"
+ )
+
+ if status == "failed":
+ error = job.get("error") or {}
+ raise RuntimeError(f"Together video generation failed: {error.get('message') or 'Unknown error'}")
+ if status != "completed":
+ raise RuntimeError(f"Unexpected Together video job status: {status!r}")
+
+ video_url = (job.get("outputs") or {}).get("video_url")
+ if not video_url:
+ raise ValueError("No video URL found in completed Together video job.")
+
+ video_response = get_session().get(video_url)
+ hf_raise_for_status(video_response)
+ return video_response.content
+
+
+class TogetherTextToVideoTask(TogetherVideoTask):
+ def __init__(self):
+ super().__init__("text-to-video")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ return {
+ "prompt": inputs,
+ "model": provider_mapping_info.provider_id,
+ **_normalize_video_parameters(parameters),
+ }
+
+
+class TogetherImageToVideoTask(TogetherVideoTask):
+ def __init__(self):
+ super().__init__("image-to-video")
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ # Together expects each keyframe as `{input_image, frame: "first" | "last"}`
+ # for i2v models. See https://docs.together.ai/docs/inference/videos/reference-and-keyframes.
+ # Note: `input_image` accepts a data URL or an HTTP(S) URL but the field is capped
+ # at ~60KB — users with larger inputs should host the image and pass `frame_images`
+ # directly via `extra_body`.
+ return {
+ "model": provider_mapping_info.provider_id,
+ "frame_images": [{"input_image": _as_url(inputs, default_mime_type="image/png"), "frame": "first"}],
+ **_normalize_video_parameters(parameters),
+ }
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/wavespeed.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/wavespeed.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa38eb9da2d47c491e746af6495f669147fe181e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/wavespeed.py
@@ -0,0 +1,138 @@
+import base64
+import time
+from abc import ABC
+from typing import Any
+from urllib.parse import urlparse
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import get_session, hf_raise_for_status
+from huggingface_hub.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+# Polling interval (in seconds)
+_POLLING_INTERVAL = 0.5
+
+
+class WavespeedAITask(TaskProviderHelper, ABC):
+ def __init__(self, task: str):
+ super().__init__(provider="wavespeed", base_url="https://api.wavespeed.ai", task=task)
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return f"/api/v3/{mapped_model}"
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ response_dict = _as_dict(response)
+ data = response_dict.get("data", {})
+ result_path = data.get("urls", {}).get("get")
+
+ if not result_path:
+ raise ValueError("No result URL found in the response")
+ if request_params is None:
+ raise ValueError("A `RequestParameters` object should be provided to get responses with WaveSpeed AI.")
+
+ # Parse the request URL to determine base URL
+ parsed_url = urlparse(request_params.url)
+ # Add /wavespeed to base URL if going through HF router
+ if parsed_url.netloc == "router.huggingface.co":
+ base_url = f"{parsed_url.scheme}://{parsed_url.netloc}/wavespeed"
+ else:
+ base_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
+
+ # Extract path from result_path URL
+ if isinstance(result_path, str):
+ result_url_path = urlparse(result_path).path
+ else:
+ result_url_path = result_path
+
+ result_url = f"{base_url}{result_url_path}"
+
+ logger.info("Processing request, polling for results...")
+
+ # Poll until task is completed
+ while True:
+ time.sleep(_POLLING_INTERVAL)
+ result_response = get_session().get(result_url, headers=request_params.headers)
+ hf_raise_for_status(result_response)
+
+ result = result_response.json()
+ task_result = result.get("data", {})
+ status = task_result.get("status")
+
+ if status == "completed":
+ # Get content from the first output URL
+ if not task_result.get("outputs") or len(task_result["outputs"]) == 0:
+ raise ValueError("No output URL in completed response")
+
+ output_url = task_result["outputs"][0]
+ return get_session().get(output_url).content
+ elif status == "failed":
+ error_msg = task_result.get("error", "Task failed with no specific error message")
+ raise ValueError(f"WaveSpeed AI task failed: {error_msg}")
+ elif status in ["processing", "created"]:
+ continue
+ else:
+ raise ValueError(f"Unknown status: {status}")
+
+
+class WavespeedAITextToImageTask(WavespeedAITask):
+ def __init__(self):
+ super().__init__("text-to-image")
+
+ def _prepare_payload_as_dict(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ ) -> dict | None:
+ return {"prompt": inputs, **filter_none(parameters)}
+
+
+class WavespeedAITextToVideoTask(WavespeedAITextToImageTask):
+ def __init__(self):
+ WavespeedAITask.__init__(self, "text-to-video")
+
+
+class WavespeedAIImageToImageTask(WavespeedAITask):
+ def __init__(self):
+ super().__init__("image-to-image")
+
+ def _prepare_payload_as_dict(
+ self,
+ inputs: Any,
+ parameters: dict,
+ provider_mapping_info: InferenceProviderMapping,
+ ) -> dict | None:
+ # Convert inputs to image (URL or base64)
+ if isinstance(inputs, str) and inputs.startswith(("http://", "https://")):
+ image = inputs
+ elif isinstance(inputs, str):
+ # If input is a file path, read it first
+ with open(inputs, "rb") as f:
+ file_content = f.read()
+ image_b64 = base64.b64encode(file_content).decode("utf-8")
+ image = f"data:image/jpeg;base64,{image_b64}"
+ else:
+ # If input is binary data
+ image_b64 = base64.b64encode(inputs).decode("utf-8")
+ image = f"data:image/jpeg;base64,{image_b64}"
+
+ # Extract prompt from parameters if present
+ prompt = parameters.pop("prompt", None)
+ payload = {"image": image, **filter_none(parameters)}
+ if prompt is not None:
+ payload["prompt"] = prompt
+
+ return payload
+
+
+class WavespeedAIImageToVideoTask(WavespeedAIImageToImageTask):
+ def __init__(self):
+ WavespeedAITask.__init__(self, "image-to-video")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/zai_org.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/zai_org.py
new file mode 100644
index 0000000000000000000000000000000000000000..97225435084ab61860ef838747b5e0e7517f7bd1
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/inference/_providers/zai_org.py
@@ -0,0 +1,127 @@
+import time
+from abc import ABC
+from typing import Any
+
+from huggingface_hub.hf_api import InferenceProviderMapping
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import BaseConversationalTask, TaskProviderHelper, filter_none
+from huggingface_hub.utils import get_session
+
+
+_PROVIDER = "zai-org"
+_BASE_URL = "https://api.z.ai"
+_POLLING_INTERVAL = 5 # seconds
+_MAX_POLL_ATTEMPTS = 60
+
+
+class ZaiTask(TaskProviderHelper, ABC):
+ def __init__(self, task: str):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task=task)
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ headers = super()._prepare_headers(headers, api_key)
+ headers["Accept-Language"] = "en-US,en"
+ headers["x-source-channel"] = "hugging_face"
+ return headers
+
+
+class ZaiConversationalTask(BaseConversationalTask):
+ def __init__(self):
+ super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+ def _prepare_headers(self, headers: dict, api_key: str) -> dict[str, Any]:
+ headers = super()._prepare_headers(headers, api_key)
+ headers["Accept-Language"] = "en-US,en"
+ headers["x-source-channel"] = "hugging_face"
+ return headers
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/api/paas/v4/chat/completions"
+
+
+class ZaiTextToImageTask(ZaiTask):
+ """Text-to-image task for ZAI provider using async API."""
+
+ def __init__(self):
+ super().__init__("text-to-image")
+
+ def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+ return "/api/paas/v4/async/images/generations"
+
+ def _prepare_payload_as_dict(
+ self, inputs: Any, parameters: dict, provider_mapping_info: InferenceProviderMapping
+ ) -> dict | None:
+ width = parameters.pop("width", None)
+ height = parameters.pop("height", None)
+ size = None
+ if width is not None and height is not None:
+ size = f"{width}x{height}"
+
+ payload: dict[str, Any] = {
+ "model": provider_mapping_info.provider_id,
+ "prompt": inputs,
+ }
+ if size is not None:
+ payload["size"] = size
+
+ payload.update(filter_none(parameters))
+ return payload
+
+ def get_response(
+ self,
+ response: bytes | dict,
+ request_params: RequestParameters | None = None,
+ ) -> Any:
+ """Handle async response by polling for results."""
+ response_dict = _as_dict(response)
+
+ task_id = response_dict.get("id")
+ if task_id is None:
+ raise ValueError("No task_id in response from ZAI API")
+
+ task_status = response_dict.get("task_status")
+ if task_status == "FAIL":
+ raise ValueError(f"ZAI image generation failed for request {task_id}")
+
+ if task_status == "PROCESSING" and request_params is not None:
+ return self._poll_for_result(task_id, request_params)
+
+ return self._extract_image(response_dict)
+
+ def _poll_for_result(self, task_id: str, request_params: RequestParameters) -> bytes:
+ """Poll the async-result endpoint until completion."""
+ session = get_session()
+ base_url = request_params.url.rsplit("/api/paas/v4/async/images/generations", 1)[0]
+ poll_url = f"{base_url}/api/paas/v4/async-result/{task_id}"
+
+ for _ in range(_MAX_POLL_ATTEMPTS):
+ poll_response = session.get(poll_url, headers=request_params.headers)
+ poll_response.raise_for_status()
+ result = poll_response.json()
+
+ task_status = result.get("task_status")
+ if task_status == "SUCCESS":
+ return self._extract_image(result)
+ elif task_status == "FAIL":
+ raise ValueError(f"Zai text-to-image generation failed for request {task_id}")
+
+ time.sleep(_POLLING_INTERVAL)
+
+ raise ValueError(
+ f"Timed out while waiting for the result from Zai API - aborting after {_MAX_POLL_ATTEMPTS} attempts"
+ )
+
+ def _extract_image(self, result: dict) -> bytes:
+ """Extract and download the image from the result."""
+ image_result = result.get("image_result")
+ if not image_result or not isinstance(image_result, list) or len(image_result) == 0:
+ raise ValueError("No image_result in response from ZAI API")
+
+ image_url = image_result[0].get("url")
+ if not image_url:
+ raise ValueError("No image URL in response from ZAI API")
+
+ session = get_session()
+ image_response = session.get(image_url)
+ image_response.raise_for_status()
+ return image_response.content
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/lfs.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/lfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..9958a1c332f0ab938cb9769a6384e178fb5e7a96
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/lfs.py
@@ -0,0 +1,442 @@
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Git LFS related type definitions and utilities"""
+
+import io
+import re
+from collections.abc import Iterable
+from math import ceil
+from os.path import getsize
+from typing import TYPE_CHECKING, BinaryIO, TypedDict
+from urllib.parse import unquote
+
+from huggingface_hub import constants
+
+from .utils import (
+ build_hf_headers,
+ fix_hf_endpoint_in_url,
+ hf_raise_for_status,
+ http_backoff,
+ logging,
+ validate_hf_hub_args,
+)
+from .utils._lfs import SliceFileObj
+from .utils.sha import sha256, sha_fileobj
+
+
+if TYPE_CHECKING:
+ from ._commit_api import CommitOperationAdd
+
+logger = logging.get_logger(__name__)
+
+OID_REGEX = re.compile(r"^[0-9a-f]{40}$")
+
+LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload"
+
+LFS_HEADERS = {
+ "Accept": "application/vnd.git-lfs+json",
+ "Content-Type": "application/vnd.git-lfs+json",
+}
+
+
+class UploadInfo:
+ """
+ Data structure holding required information to determine whether a blob
+ should be uploaded to the hub using the LFS protocol or the regular protocol.
+
+ The SHA256 of the blob is computed lazily: creating an `UploadInfo` from a local path only reads
+ the first 512 bytes of the file. The full file is read (and hashed) only if `sha256` is accessed
+ before it has been set. When a file is uploaded through the Xet protocol, the SHA256 is computed
+ during upload (single read pass) and set afterwards.
+
+ Args:
+ size (`int`):
+ Size in bytes of the blob
+ sample (`bytes`):
+ First 512 bytes of the blob
+ sha256 (`bytes`, *optional*):
+ SHA256 hash of the blob, if already known. Otherwise computed lazily from `source_path`.
+ source_path (`str`, *optional*):
+ Path to the local file the blob comes from. Required to lazily compute `sha256` if not provided.
+ """
+
+ def __init__(
+ self,
+ size: int,
+ sample: bytes,
+ sha256: bytes | None = None,
+ source_path: str | None = None,
+ ):
+ if sha256 is None and source_path is None:
+ raise ValueError("Either `sha256` or `source_path` must be provided.")
+ self.size = size
+ self.sample = sample
+ self._sha256 = sha256
+ self._source_path = source_path
+
+ @property
+ def sha256(self) -> bytes:
+ """SHA256 of the blob. If not set yet, reads the whole file from `source_path` to compute it."""
+ if self._sha256 is None:
+ assert self._source_path is not None # guaranteed by __init__
+ with open(self._source_path, "rb") as file:
+ self._sha256 = sha_fileobj(file)
+ return self._sha256
+
+ @sha256.setter
+ def sha256(self, value: bytes) -> None:
+ self._sha256 = value
+
+ @property
+ def is_hashed(self) -> bool:
+ """Whether the SHA256 is already known (accessing `sha256` will not trigger a file read)."""
+ return self._sha256 is not None
+
+ def __repr__(self) -> str:
+ sha = self._sha256.hex() if self._sha256 is not None else ""
+ return f"UploadInfo(size={self.size}, sha256={sha})"
+
+ @classmethod
+ def from_path(cls, path: str):
+ size = getsize(path)
+ with open(path, "rb") as file:
+ sample = file.peek(512)[:512]
+ return cls(size=size, sample=sample, source_path=path)
+
+ @classmethod
+ def from_bytes(cls, data: bytes):
+ sha = sha256(data).digest()
+ return cls(size=len(data), sample=data[:512], sha256=sha)
+
+ @classmethod
+ def from_fileobj(cls, fileobj: BinaryIO):
+ sample = fileobj.read(512)
+ fileobj.seek(0, io.SEEK_SET)
+ sha = sha_fileobj(fileobj)
+ size = fileobj.tell()
+ fileobj.seek(0, io.SEEK_SET)
+ return cls(size=size, sha256=sha, sample=sample)
+
+
+@validate_hf_hub_args
+def post_lfs_batch_info(
+ upload_infos: Iterable[UploadInfo],
+ token: str | None,
+ repo_type: str,
+ repo_id: str,
+ revision: str | None = None,
+ endpoint: str | None = None,
+ headers: dict[str, str] | None = None,
+ transfers: list[str] | None = None,
+) -> tuple[list[dict], list[dict], str | None]:
+ """
+ Requests the LFS batch endpoint to retrieve upload instructions
+
+ Learn more: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
+
+ Args:
+ upload_infos (`Iterable` of `UploadInfo`):
+ `UploadInfo` for the files that are being uploaded, typically obtained
+ from `CommitOperationAdd.upload_info`
+ token (`str` or `None`):
+ An authentication token (see https://huggingface.co/settings/token).
+ Pass `None` to fall back to the local cached token (or no token if unauthenticated).
+ repo_type (`str`):
+ Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated
+ by a `/`.
+ revision (`str`, *optional*):
+ The git revision to upload to.
+ endpoint (`str`, *optional*):
+ The Hub endpoint to send the request to. Defaults to the value of `HF_ENDPOINT`.
+ headers (`dict`, *optional*):
+ Additional headers to include in the request
+ transfers (`list`, *optional*):
+ List of transfer methods to use. Defaults to ["basic", "multipart"].
+
+ Returns:
+ `LfsBatchInfo`: 3-tuple:
+ - First element is the list of upload instructions from the server
+ - Second element is a list of errors, if any
+ - Third element is the chosen transfer adapter if provided by the server (e.g. "basic", "multipart", "xet")
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If an argument is invalid or the server response is malformed.
+ [`HfHubHTTPError`]
+ If the server returned an error.
+ """
+ endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+ url_prefix = ""
+ if repo_type in constants.REPO_TYPES_URL_PREFIXES:
+ url_prefix = constants.REPO_TYPES_URL_PREFIXES[repo_type]
+ batch_url = f"{endpoint}/{url_prefix}{repo_id}.git/info/lfs/objects/batch"
+ payload: dict = {
+ "operation": "upload",
+ "transfers": transfers if transfers is not None else ["basic", "multipart"],
+ "objects": [
+ {
+ "oid": upload.sha256.hex(),
+ "size": upload.size,
+ }
+ for upload in upload_infos
+ ],
+ "hash_algo": "sha256",
+ }
+ if revision is not None:
+ payload["ref"] = {"name": unquote(revision)} # revision has been previously 'quoted'
+
+ headers = {
+ **LFS_HEADERS,
+ **build_hf_headers(token=token),
+ **(headers or {}),
+ }
+ resp = http_backoff("POST", batch_url, headers=headers, json=payload)
+ hf_raise_for_status(resp)
+ batch_info = resp.json()
+
+ objects = batch_info.get("objects", None)
+ if not isinstance(objects, list):
+ raise ValueError("Malformed response from server")
+
+ chosen_transfer = batch_info.get("transfer")
+ chosen_transfer = chosen_transfer if isinstance(chosen_transfer, str) else None
+
+ return (
+ [_validate_batch_actions(obj) for obj in objects if "error" not in obj],
+ [_validate_batch_error(obj) for obj in objects if "error" in obj],
+ chosen_transfer,
+ )
+
+
+class PayloadPartT(TypedDict):
+ partNumber: int
+ etag: str
+
+
+class CompletionPayloadT(TypedDict):
+ """Payload that will be sent to the Hub when uploading multi-part."""
+
+ oid: str
+ parts: list[PayloadPartT]
+
+
+def lfs_upload(
+ operation: "CommitOperationAdd",
+ lfs_batch_action: dict,
+ token: str | None = None,
+ headers: dict[str, str] | None = None,
+ endpoint: str | None = None,
+) -> None:
+ """
+ Handles uploading a given object to the Hub with the LFS protocol.
+
+ Can be a No-op if the content of the file is already present on the hub large file storage.
+
+ Args:
+ operation (`CommitOperationAdd`):
+ The add operation triggering this upload.
+ lfs_batch_action (`dict`):
+ Upload instructions from the LFS batch endpoint for this object. See [`~utils.lfs.post_lfs_batch_info`] for
+ more details.
+ token (`str`, *optional*):
+ An authentication token (see https://huggingface.co/settings/token). Used to call the
+ optional LFS verify step at the end of the upload. If `None`, falls back to the local
+ cached token.
+ headers (`dict`, *optional*):
+ Headers to include in the request, including authentication and user agent headers.
+ endpoint (`str`, *optional*):
+ The Hub endpoint to send the request to. Defaults to the value of `HF_ENDPOINT`.
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If `lfs_batch_action` is improperly formatted
+ [`HfHubHTTPError`]
+ If the upload resulted in an error
+ """
+ # 0. If LFS file is already present, skip upload
+ _validate_batch_actions(lfs_batch_action)
+ actions = lfs_batch_action.get("actions")
+ if actions is None:
+ # The file was already uploaded
+ logger.debug(f"Content of file {operation.path_in_repo} is already present upstream - skipping upload")
+ return
+
+ # 1. Validate server response (check required keys in dict)
+ upload_action = lfs_batch_action["actions"]["upload"]
+ _validate_lfs_action(upload_action)
+ verify_action = lfs_batch_action["actions"].get("verify")
+ if verify_action is not None:
+ _validate_lfs_action(verify_action)
+
+ # 2. Upload file (either single part or multi-part)
+ header = upload_action.get("header", {})
+ chunk_size = header.get("chunk_size")
+ upload_url = fix_hf_endpoint_in_url(upload_action["href"], endpoint=endpoint)
+ if chunk_size is not None:
+ try:
+ chunk_size = int(chunk_size)
+ except (ValueError, TypeError):
+ raise ValueError(
+ f"Malformed response from LFS batch endpoint: `chunk_size` should be an integer. Got '{chunk_size}'."
+ )
+ _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_url)
+ else:
+ _upload_single_part(operation=operation, upload_url=upload_url)
+
+ # 3. Verify upload went well
+ if verify_action is not None:
+ _validate_lfs_action(verify_action)
+ verify_url = fix_hf_endpoint_in_url(verify_action["href"], endpoint)
+ verify_resp = http_backoff(
+ "POST",
+ verify_url,
+ headers=build_hf_headers(token=token, headers=headers),
+ json={"oid": operation.upload_info.sha256.hex(), "size": operation.upload_info.size},
+ )
+ hf_raise_for_status(verify_resp)
+ logger.debug(f"{operation.path_in_repo}: Upload successful")
+
+
+def _validate_lfs_action(lfs_action: dict):
+ """validates response from the LFS batch endpoint"""
+ if not (
+ isinstance(lfs_action.get("href"), str)
+ and (lfs_action.get("header") is None or isinstance(lfs_action.get("header"), dict))
+ ):
+ raise ValueError("lfs_action is improperly formatted")
+ return lfs_action
+
+
+def _validate_batch_actions(lfs_batch_actions: dict):
+ """validates response from the LFS batch endpoint"""
+ if not (isinstance(lfs_batch_actions.get("oid"), str) and isinstance(lfs_batch_actions.get("size"), int)):
+ raise ValueError("lfs_batch_actions is improperly formatted")
+
+ upload_action = lfs_batch_actions.get("actions", {}).get("upload")
+ verify_action = lfs_batch_actions.get("actions", {}).get("verify")
+ if upload_action is not None:
+ _validate_lfs_action(upload_action)
+ if verify_action is not None:
+ _validate_lfs_action(verify_action)
+ return lfs_batch_actions
+
+
+def _validate_batch_error(lfs_batch_error: dict):
+ """validates response from the LFS batch endpoint"""
+ if not (isinstance(lfs_batch_error.get("oid"), str) and isinstance(lfs_batch_error.get("size"), int)):
+ raise ValueError("lfs_batch_error is improperly formatted")
+ error_info = lfs_batch_error.get("error")
+ if not (
+ isinstance(error_info, dict)
+ and isinstance(error_info.get("message"), str)
+ and isinstance(error_info.get("code"), int)
+ ):
+ raise ValueError("lfs_batch_error is improperly formatted")
+ return lfs_batch_error
+
+
+def _upload_single_part(operation: "CommitOperationAdd", upload_url: str) -> None:
+ """
+ Uploads `fileobj` as a single PUT HTTP request (basic LFS transfer protocol)
+
+ Args:
+ upload_url (`str`):
+ The URL to PUT the file to.
+ fileobj:
+ The file-like object holding the data to upload.
+
+ Raises:
+ [`HfHubHTTPError`]
+ If the upload resulted in an error.
+ """
+ with operation.as_file(with_tqdm=True) as fileobj:
+ # S3 might raise a transient 500 error -> let's retry if that happens
+ response = http_backoff("PUT", upload_url, data=fileobj)
+ hf_raise_for_status(response)
+
+
+def _upload_multi_part(operation: "CommitOperationAdd", header: dict, chunk_size: int, upload_url: str) -> None:
+ """
+ Uploads file using HF multipart LFS transfer protocol.
+ """
+ # 1. Get upload URLs for each part
+ sorted_parts_urls = _get_sorted_parts_urls(header=header, upload_info=operation.upload_info, chunk_size=chunk_size)
+
+ # 2. Upload parts (pure Python)
+ response_headers = _upload_parts_iteratively(
+ operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size
+ )
+
+ # 3. Send completion request
+ # NOTE: `upload_url` is the Hub completion endpoint (not the S3 upload URLs).
+ completion_res = http_backoff(
+ "POST",
+ upload_url,
+ json=_get_completion_payload(response_headers, operation.upload_info.sha256.hex()),
+ headers=LFS_HEADERS,
+ )
+ hf_raise_for_status(completion_res)
+
+
+def _get_sorted_parts_urls(header: dict, upload_info: UploadInfo, chunk_size: int) -> list[str]:
+ sorted_part_upload_urls = [
+ upload_url
+ for _, upload_url in sorted(
+ [
+ (int(part_num, 10), upload_url)
+ for part_num, upload_url in header.items()
+ if part_num.isdigit() and len(part_num) > 0
+ ],
+ key=lambda t: t[0],
+ )
+ ]
+ num_parts = len(sorted_part_upload_urls)
+ if num_parts != ceil(upload_info.size / chunk_size):
+ raise ValueError("Invalid server response to upload large LFS file")
+ return sorted_part_upload_urls
+
+
+def _get_completion_payload(response_headers: list[dict], oid: str) -> CompletionPayloadT:
+ parts: list[PayloadPartT] = []
+ for part_number, header in enumerate(response_headers):
+ etag = header.get("etag")
+ if etag is None or etag == "":
+ raise ValueError(f"Invalid etag (`{etag}`) returned for part {part_number + 1}")
+ parts.append(
+ {
+ "partNumber": part_number + 1,
+ "etag": etag,
+ }
+ )
+ return {"oid": oid, "parts": parts}
+
+
+def _upload_parts_iteratively(
+ operation: "CommitOperationAdd", sorted_parts_urls: list[str], chunk_size: int
+) -> list[dict]:
+ headers = []
+ with operation.as_file(with_tqdm=True) as fileobj:
+ for part_idx, part_upload_url in enumerate(sorted_parts_urls):
+ with SliceFileObj(
+ fileobj,
+ seek_from=chunk_size * part_idx,
+ read_limit=chunk_size,
+ ) as fileobj_slice:
+ # S3 might raise a transient 500 error -> let's retry if that happens
+ part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice)
+ hf_raise_for_status(part_upload_res)
+ headers.append(part_upload_res.headers)
+ return headers # type: ignore
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/py.typed b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cffa22e98ef9d91d7eed91664169c0d7ddef663
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard.py
@@ -0,0 +1,835 @@
+import os
+import re
+from pathlib import Path
+from typing import Any, Literal
+
+import yaml
+
+from huggingface_hub.file_download import hf_hub_download
+from huggingface_hub.hf_api import upload_file
+from huggingface_hub.repocard_data import (
+ CardData,
+ DatasetCardData,
+ EvalResult,
+ ModelCardData,
+ SpaceCardData,
+ eval_results_to_model_index,
+ model_index_to_eval_results,
+)
+from huggingface_hub.utils import HfHubHTTPError, get_session, hf_raise_for_status, is_jinja_available, yaml_dump
+
+from . import constants
+from .errors import EntryNotFoundError
+from .utils import SoftTemporaryDirectory, logging, validate_hf_hub_args
+
+
+logger = logging.get_logger(__name__)
+
+
+TEMPLATE_MODELCARD_PATH = Path(__file__).parent / "templates" / "modelcard_template.md"
+TEMPLATE_DATASETCARD_PATH = Path(__file__).parent / "templates" / "datasetcard_template.md"
+
+# exact same regex as in the Hub server. Please keep in sync.
+# See https://github.com/huggingface/moon-landing/blob/main/server/lib/ViewMarkdown.ts#L18
+REGEX_YAML_BLOCK = re.compile(r"^(\s*---(?:\r\n|\r|\n))([\S\s]*?)((?:\r\n|\r|\n)---[ \t]*(\r\n|\n|$))")
+
+
+class RepoCard:
+ card_data_class = CardData
+ default_template_path = TEMPLATE_MODELCARD_PATH
+ repo_type = "model"
+
+ def __init__(self, content: str, ignore_metadata_errors: bool = False):
+ """Initialize a RepoCard from string content. The content should be a
+ Markdown file with a YAML block at the beginning and a Markdown body.
+
+ Args:
+ content (`str`): The content of the Markdown file.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.repocard import RepoCard
+ >>> text = '''
+ ... ---
+ ... language: en
+ ... license: mit
+ ... ---
+ ...
+ ... # My repo
+ ... '''
+ >>> card = RepoCard(text)
+ >>> card.data.to_dict()
+ {'language': 'en', 'license': 'mit'}
+ >>> card.text
+ '\\n# My repo\\n'
+
+ ```
+ > [!TIP]
+ > Raises the following error:
+ >
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > when the content of the repo card metadata is not a dictionary.
+ """
+
+ # Set the content of the RepoCard, as well as underlying .data and .text attributes.
+ # See the `content` property setter for more details.
+ self.ignore_metadata_errors = ignore_metadata_errors
+ self.content = content
+
+ @property
+ def content(self):
+ """The content of the RepoCard, including the YAML block and the Markdown body."""
+ line_break = _detect_line_ending(self._content) or "\n"
+ return f"---{line_break}{self.data.to_yaml(line_break=line_break, original_order=self._original_order)}{line_break}---{line_break}{self.text}"
+
+ @content.setter
+ def content(self, content: str):
+ """Set the content of the RepoCard."""
+ self._content = content
+
+ match = REGEX_YAML_BLOCK.search(content)
+ if match:
+ # Metadata found in the YAML block
+ yaml_block = match.group(2)
+ self.text = content[match.end() :]
+ data_dict = yaml.safe_load(yaml_block)
+
+ if data_dict is None:
+ data_dict = {}
+
+ # The YAML block's data should be a dictionary
+ if not isinstance(data_dict, dict):
+ raise ValueError("repo card metadata block should be a dict")
+ else:
+ # Model card without metadata... create empty metadata
+ logger.warning("Repo card metadata block was not found. Setting CardData to empty.")
+ data_dict = {}
+ self.text = content
+
+ self.data = self.card_data_class(**data_dict, ignore_metadata_errors=self.ignore_metadata_errors)
+ self._original_order = list(data_dict.keys())
+
+ def __str__(self):
+ return self.content
+
+ def save(self, filepath: Path | str):
+ r"""Save a RepoCard to a file.
+
+ Args:
+ filepath (`Union[Path, str]`): Filepath to the markdown file to save.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.repocard import RepoCard
+ >>> card = RepoCard("---\nlanguage: en\n---\n# This is a test repo card")
+ >>> card.save("/tmp/test.md")
+
+ ```
+ """
+ filepath = Path(filepath)
+ filepath.parent.mkdir(parents=True, exist_ok=True)
+ # Preserve newlines as in the existing file.
+ with open(filepath, mode="w", newline="", encoding="utf-8") as f:
+ f.write(str(self))
+
+ @classmethod
+ def load(
+ cls,
+ repo_id_or_path: str | Path,
+ repo_type: str | None = None,
+ token: str | None = None,
+ ignore_metadata_errors: bool = False,
+ ):
+ """Initialize a RepoCard from a Hugging Face Hub repo's README.md or a local filepath.
+
+ Args:
+ repo_id_or_path (`Union[str, Path]`):
+ The repo ID associated with a Hugging Face Hub repo or a local filepath.
+ repo_type (`str`, *optional*):
+ The type of Hugging Face repo to push to. Defaults to None, which will use "model". Other options
+ are "dataset" and "space". Not used when loading from a local filepath. If this is called from a child
+ class, the default value will be the child class's `repo_type`.
+ token (`str`, *optional*):
+ Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to the stored token.
+ ignore_metadata_errors (`str`):
+ If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+ the process. Use it at your own risk.
+
+ Returns:
+ [`huggingface_hub.repocard.RepoCard`]: The RepoCard (or subclass) initialized from the repo's
+ README.md file or filepath.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.repocard import RepoCard
+ >>> card = RepoCard.load("nateraw/food")
+ >>> assert card.data.tags == ["generated_from_trainer", "image-classification", "pytorch"]
+
+ ```
+ """
+
+ if Path(repo_id_or_path).is_file():
+ card_path = Path(repo_id_or_path)
+ elif isinstance(repo_id_or_path, str):
+ card_path = Path(
+ hf_hub_download(
+ repo_id_or_path,
+ constants.REPOCARD_NAME,
+ repo_type=repo_type or cls.repo_type,
+ token=token,
+ )
+ )
+ else:
+ raise ValueError(f"Cannot load RepoCard: path not found on disk ({repo_id_or_path}).")
+
+ # Preserve newlines in the existing file.
+ with card_path.open(mode="r", newline="", encoding="utf-8") as f:
+ return cls(f.read(), ignore_metadata_errors=ignore_metadata_errors)
+
+ def validate(self, repo_type: str | None = None):
+ """Validates card against Hugging Face Hub's card validation logic.
+ Using this function requires access to the internet, so it is only called
+ internally by [`huggingface_hub.repocard.RepoCard.push_to_hub`].
+
+ Args:
+ repo_type (`str`, *optional*, defaults to "model"):
+ The type of Hugging Face repo to push to. Options are "model", "dataset", and "space".
+ If this function is called from a child class, the default will be the child class's `repo_type`.
+
+ > [!TIP]
+ > Raises the following errors:
+ >
+ > - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > if the card fails validation checks.
+ > - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+ > if the request to the Hub API fails for any other reason.
+ """
+
+ # If repo type is provided, otherwise, use the repo type of the card.
+ repo_type = repo_type or self.repo_type
+
+ body = {
+ "repoType": repo_type,
+ "content": str(self),
+ }
+ headers = {"Accept": "text/plain"}
+
+ try:
+ response = get_session().post(f"{constants.ENDPOINT}/api/validate-yaml", json=body, headers=headers)
+ hf_raise_for_status(response)
+ except HfHubHTTPError as exc:
+ if response.status_code == 400:
+ raise ValueError(response.text)
+ else:
+ raise exc
+
+ def push_to_hub(
+ self,
+ repo_id: str,
+ token: str | None = None,
+ repo_type: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ revision: str | None = None,
+ create_pr: bool | None = None,
+ parent_commit: str | None = None,
+ ):
+ """Push a RepoCard to a Hugging Face Hub repo.
+
+ Args:
+ repo_id (`str`):
+ The repo ID of the Hugging Face Hub repo to push to. Example: "nateraw/food".
+ token (`str`, *optional*):
+ Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to
+ the stored token.
+ repo_type (`str`, *optional*, defaults to "model"):
+ The type of Hugging Face repo to push to. Options are "model", "dataset", and "space". If this
+ function is called by a child class, it will default to the child class's `repo_type`.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit.
+ commit_description (`str`, *optional*):
+ The description of the generated commit.
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the `"main"` branch.
+ create_pr (`bool`, *optional*):
+ Whether or not to create a Pull Request with this commit. Defaults to `False`.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed too concurrently.
+ Returns:
+ `str`: URL of the commit which updated the card metadata.
+ """
+
+ # If repo type is provided, otherwise, use the repo type of the card.
+ repo_type = repo_type or self.repo_type
+
+ # Validate card before pushing to hub
+ self.validate(repo_type=repo_type)
+
+ with SoftTemporaryDirectory() as tmpdir:
+ tmp_path = Path(tmpdir) / constants.REPOCARD_NAME
+ tmp_path.write_text(str(self), encoding="utf-8")
+ url = upload_file(
+ path_or_fileobj=str(tmp_path),
+ path_in_repo=constants.REPOCARD_NAME,
+ repo_id=repo_id,
+ token=token,
+ repo_type=repo_type,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ revision=revision,
+ parent_commit=parent_commit,
+ )
+ return url
+
+ @classmethod
+ def from_template(
+ cls,
+ card_data: CardData,
+ template_path: str | None = None,
+ template_str: str | None = None,
+ **template_kwargs,
+ ):
+ """Initialize a RepoCard from a template. By default, it uses the default template.
+
+ Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+ Args:
+ card_data (`huggingface_hub.CardData`):
+ A huggingface_hub.CardData instance containing the metadata you want to include in the YAML
+ header of the repo card on the Hugging Face Hub.
+ template_path (`str`, *optional*):
+ A path to a markdown file with optional Jinja template variables that can be filled
+ in with `template_kwargs`. Defaults to the default template.
+ template_str (`str`, *optional*):
+ A raw Jinja template string with optional variables. Used when neither `template_path`
+ nor the default template is appropriate. Ignored if `template_path` is also provided.
+
+ Returns:
+ [`huggingface_hub.repocard.RepoCard`]: A RepoCard instance with the specified card data and content from the
+ template.
+ """
+ if is_jinja_available():
+ import jinja2
+ else:
+ raise ImportError(
+ "Using RepoCard.from_template requires Jinja2 to be installed. Please"
+ " install it with `pip install Jinja2`."
+ )
+
+ kwargs = card_data.to_dict().copy()
+ kwargs.update(template_kwargs) # Template_kwargs have priority
+
+ if template_path is not None:
+ template_str = Path(template_path).read_text()
+ if template_str is None:
+ template_str = Path(cls.default_template_path).read_text()
+ template = jinja2.Template(template_str)
+ content = template.render(card_data=card_data.to_yaml(), **kwargs)
+ return cls(content)
+
+
+class ModelCard(RepoCard):
+ card_data_class = ModelCardData # type: ignore[assignment]
+ default_template_path = TEMPLATE_MODELCARD_PATH
+ repo_type = "model"
+
+ @classmethod
+ def from_template( # type: ignore # violates Liskov property but easier to use
+ cls,
+ card_data: ModelCardData,
+ template_path: str | None = None,
+ template_str: str | None = None,
+ **template_kwargs,
+ ):
+ """Initialize a ModelCard from a template. By default, it uses the default template, which can be found here:
+ https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md
+
+ Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+ Args:
+ card_data (`huggingface_hub.ModelCardData`):
+ A huggingface_hub.ModelCardData instance containing the metadata you want to include in the YAML
+ header of the model card on the Hugging Face Hub.
+ template_path (`str`, *optional*):
+ A path to a markdown file with optional Jinja template variables that can be filled
+ in with `template_kwargs`. Defaults to the default template.
+ template_str (`str`, *optional*):
+ A raw Jinja template string with optional variables. Used when neither `template_path`
+ nor the default template is appropriate. Ignored if `template_path` is also provided.
+
+ Returns:
+ [`huggingface_hub.ModelCard`]: A ModelCard instance with the specified card data and content from the
+ template.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import ModelCard, ModelCardData, EvalResult
+
+ >>> # Using the Default Template
+ >>> card_data = ModelCardData(
+ ... language='en',
+ ... license='mit',
+ ... library_name='timm',
+ ... tags=['image-classification', 'resnet'],
+ ... datasets=['beans'],
+ ... metrics=['accuracy'],
+ ... )
+ >>> card = ModelCard.from_template(
+ ... card_data,
+ ... model_description='This model does x + y...'
+ ... )
+
+ >>> # Including Evaluation Results
+ >>> card_data = ModelCardData(
+ ... language='en',
+ ... tags=['image-classification', 'resnet'],
+ ... eval_results=[
+ ... EvalResult(
+ ... task_type='image-classification',
+ ... dataset_type='beans',
+ ... dataset_name='Beans',
+ ... metric_type='accuracy',
+ ... metric_value=0.9,
+ ... ),
+ ... ],
+ ... model_name='my-cool-model',
+ ... )
+ >>> card = ModelCard.from_template(card_data)
+
+ >>> # Using a Custom Template
+ >>> card_data = ModelCardData(
+ ... language='en',
+ ... tags=['image-classification', 'resnet']
+ ... )
+ >>> card = ModelCard.from_template(
+ ... card_data=card_data,
+ ... template_path='./src/huggingface_hub/templates/modelcard_template.md',
+ ... custom_template_var='custom value', # will be replaced in template if it exists
+ ... )
+
+ ```
+ """
+ return super().from_template(card_data, template_path, template_str, **template_kwargs)
+
+
+class DatasetCard(RepoCard):
+ card_data_class = DatasetCardData # type: ignore[assignment]
+ default_template_path = TEMPLATE_DATASETCARD_PATH
+ repo_type = "dataset"
+
+ @classmethod
+ def from_template( # type: ignore # violates Liskov property but easier to use
+ cls,
+ card_data: DatasetCardData,
+ template_path: str | None = None,
+ template_str: str | None = None,
+ **template_kwargs,
+ ):
+ """Initialize a DatasetCard from a template. By default, it uses the default template, which can be found here:
+ https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
+
+ Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+ Args:
+ card_data (`huggingface_hub.DatasetCardData`):
+ A huggingface_hub.DatasetCardData instance containing the metadata you want to include in the YAML
+ header of the dataset card on the Hugging Face Hub.
+ template_path (`str`, *optional*):
+ A path to a markdown file with optional Jinja template variables that can be filled
+ in with `template_kwargs`. Defaults to the default template.
+ template_str (`str`, *optional*):
+ A raw Jinja template string with optional variables. Used when neither `template_path`
+ nor the default template is appropriate. Ignored if `template_path` is also provided.
+
+ Returns:
+ [`huggingface_hub.DatasetCard`]: A DatasetCard instance with the specified card data and content from the
+ template.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import DatasetCard, DatasetCardData
+
+ >>> # Using the Default Template
+ >>> card_data = DatasetCardData(
+ ... language='en',
+ ... license='mit',
+ ... annotations_creators='crowdsourced',
+ ... task_categories=['text-classification'],
+ ... task_ids=['sentiment-classification', 'text-scoring'],
+ ... multilinguality='monolingual',
+ ... pretty_name='My Text Classification Dataset',
+ ... )
+ >>> card = DatasetCard.from_template(
+ ... card_data,
+ ... pretty_name=card_data.pretty_name,
+ ... )
+
+ >>> # Using a Custom Template
+ >>> card_data = DatasetCardData(
+ ... language='en',
+ ... license='mit',
+ ... )
+ >>> card = DatasetCard.from_template(
+ ... card_data=card_data,
+ ... template_path='./src/huggingface_hub/templates/datasetcard_template.md',
+ ... custom_template_var='custom value', # will be replaced in template if it exists
+ ... )
+
+ ```
+ """
+ return super().from_template(card_data, template_path, template_str, **template_kwargs)
+
+
+class SpaceCard(RepoCard):
+ card_data_class = SpaceCardData # type: ignore[assignment]
+ default_template_path = TEMPLATE_MODELCARD_PATH
+ repo_type = "space"
+
+
+def _detect_line_ending(content: str) -> Literal["\r", "\n", "\r\n", None]: # noqa: F722
+ """Detect the line ending of a string. Used by RepoCard to avoid making huge diff on newlines.
+
+ Uses same implementation as in Hub server, keep it in sync.
+
+ Returns:
+ str: The detected line ending of the string.
+ """
+ cr = content.count("\r")
+ lf = content.count("\n")
+ crlf = content.count("\r\n")
+ if cr + lf == 0:
+ return None
+ if crlf == cr and crlf == lf:
+ return "\r\n"
+ if cr > lf:
+ return "\r"
+ else:
+ return "\n"
+
+
+def metadata_load(local_path: str | Path) -> dict | None:
+ content = Path(local_path).read_text()
+ match = REGEX_YAML_BLOCK.search(content)
+ if match:
+ yaml_block = match.group(2)
+ data = yaml.safe_load(yaml_block)
+ if data is None or isinstance(data, dict):
+ return data
+ raise ValueError("repo card metadata block should be a dict")
+ else:
+ return None
+
+
+def metadata_save(local_path: str | Path, data: dict) -> None:
+ """
+ Save the metadata dict in the upper YAML part Trying to preserve newlines as
+ in the existing file. Docs about open() with newline="" parameter:
+ https://docs.python.org/3/library/functions.html?highlight=open#open Does
+ not work with "^M" linebreaks, which are replaced by \n
+ """
+ line_break = "\n"
+ content = ""
+ # try to detect existing newline character
+ if os.path.exists(local_path):
+ with open(local_path, newline="", encoding="utf8") as readme:
+ content = readme.read()
+ if isinstance(readme.newlines, tuple):
+ line_break = readme.newlines[0]
+ elif isinstance(readme.newlines, str):
+ line_break = readme.newlines
+
+ # creates a new file if it not
+ with open(local_path, "w", newline="", encoding="utf8") as readme:
+ data_yaml = yaml_dump(data, sort_keys=False, line_break=line_break)
+ # sort_keys: keep dict order
+ match = REGEX_YAML_BLOCK.search(content)
+ if match:
+ output = content[: match.start()] + f"---{line_break}{data_yaml}---{line_break}" + content[match.end() :]
+ else:
+ output = f"---{line_break}{data_yaml}---{line_break}{content}"
+
+ readme.write(output)
+ readme.close()
+
+
+def metadata_eval_result(
+ *,
+ model_pretty_name: str,
+ task_pretty_name: str,
+ task_id: str,
+ metrics_pretty_name: str,
+ metrics_id: str,
+ metrics_value: Any,
+ dataset_pretty_name: str,
+ dataset_id: str,
+ metrics_config: str | None = None,
+ metrics_verified: bool = False,
+ dataset_config: str | None = None,
+ dataset_split: str | None = None,
+ dataset_revision: str | None = None,
+ metrics_verification_token: str | None = None,
+) -> dict:
+ """
+ Creates a metadata dict with the result from a model evaluated on a dataset.
+
+ Args:
+ model_pretty_name (`str`):
+ The name of the model in natural language.
+ task_pretty_name (`str`):
+ The name of a task in natural language.
+ task_id (`str`):
+ Example: automatic-speech-recognition. A task id.
+ metrics_pretty_name (`str`):
+ A name for the metric in natural language. Example: Test WER.
+ metrics_id (`str`):
+ Example: wer. A metric id from https://hf.co/metrics.
+ metrics_value (`Any`):
+ The value from the metric. Example: 20.0 or "20.0 ± 1.2".
+ dataset_pretty_name (`str`):
+ The name of the dataset in natural language.
+ dataset_id (`str`):
+ Example: common_voice. A dataset id from https://hf.co/datasets.
+ metrics_config (`str`, *optional*):
+ The name of the metric configuration used in `load_metric()`.
+ Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+ metrics_verified (`bool`, *optional*, defaults to `False`):
+ Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+ dataset_config (`str`, *optional*):
+ Example: fr. The name of the dataset configuration used in `load_dataset()`.
+ dataset_split (`str`, *optional*):
+ Example: test. The name of the dataset split used in `load_dataset()`.
+ dataset_revision (`str`, *optional*):
+ Example: 5503434ddd753f426f4b38109466949a1217c2bb. The name of the dataset revision
+ used in `load_dataset()`.
+ metrics_verification_token (`bool`, *optional*):
+ A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+
+ Returns:
+ `dict`: a metadata dict with the result from a model evaluated on a dataset.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import metadata_eval_result
+ >>> results = metadata_eval_result(
+ ... model_pretty_name="RoBERTa fine-tuned on ReactionGIF",
+ ... task_pretty_name="Text Classification",
+ ... task_id="text-classification",
+ ... metrics_pretty_name="Accuracy",
+ ... metrics_id="accuracy",
+ ... metrics_value=0.2662102282047272,
+ ... dataset_pretty_name="ReactionJPEG",
+ ... dataset_id="julien-c/reactionjpeg",
+ ... dataset_config="default",
+ ... dataset_split="test",
+ ... )
+ >>> results == {
+ ... 'model-index': [
+ ... {
+ ... 'name': 'RoBERTa fine-tuned on ReactionGIF',
+ ... 'results': [
+ ... {
+ ... 'task': {
+ ... 'type': 'text-classification',
+ ... 'name': 'Text Classification'
+ ... },
+ ... 'dataset': {
+ ... 'name': 'ReactionJPEG',
+ ... 'type': 'julien-c/reactionjpeg',
+ ... 'config': 'default',
+ ... 'split': 'test'
+ ... },
+ ... 'metrics': [
+ ... {
+ ... 'type': 'accuracy',
+ ... 'value': 0.2662102282047272,
+ ... 'name': 'Accuracy',
+ ... 'verified': False
+ ... }
+ ... ]
+ ... }
+ ... ]
+ ... }
+ ... ]
+ ... }
+ True
+
+ ```
+ """
+
+ return {
+ "model-index": eval_results_to_model_index(
+ model_name=model_pretty_name,
+ eval_results=[
+ EvalResult(
+ task_name=task_pretty_name,
+ task_type=task_id,
+ metric_name=metrics_pretty_name,
+ metric_type=metrics_id,
+ metric_value=metrics_value,
+ dataset_name=dataset_pretty_name,
+ dataset_type=dataset_id,
+ metric_config=metrics_config,
+ verified=metrics_verified,
+ verify_token=metrics_verification_token,
+ dataset_config=dataset_config,
+ dataset_split=dataset_split,
+ dataset_revision=dataset_revision,
+ )
+ ],
+ )
+ }
+
+
+@validate_hf_hub_args
+def metadata_update(
+ repo_id: str,
+ metadata: dict,
+ *,
+ repo_type: str | None = None,
+ overwrite: bool = False,
+ token: str | None = None,
+ commit_message: str | None = None,
+ commit_description: str | None = None,
+ revision: str | None = None,
+ create_pr: bool = False,
+ parent_commit: str | None = None,
+) -> str:
+ """
+ Updates the metadata in the README.md of a repository on the Hugging Face Hub.
+ If the README.md file doesn't exist yet, a new one is created with metadata and
+ the default ModelCard or DatasetCard template. For `space` repo, an error is thrown
+ as a Space cannot exist without a `README.md` file.
+
+ Args:
+ repo_id (`str`):
+ The name of the repository.
+ metadata (`dict`):
+ A dictionary containing the metadata to be updated.
+ repo_type (`str`, *optional*):
+ Set to `"dataset"` or `"space"` if updating to a dataset or space,
+ `None` or `"model"` if updating to a model. Default is `None`.
+ overwrite (`bool`, *optional*, defaults to `False`):
+ If set to `True` an existing field can be overwritten, otherwise
+ attempting to overwrite an existing field will cause an error.
+ token (`str`, *optional*):
+ The Hugging Face authentication token.
+ commit_message (`str`, *optional*):
+ The summary / title / first line of the generated commit. Defaults to
+ `f"Update metadata with huggingface_hub"`
+ commit_description (`str`, *optional*):
+ The description of the generated commit
+ revision (`str`, *optional*):
+ The git revision to commit from. Defaults to the head of the
+ `"main"` branch.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request from `revision` with that commit.
+ Defaults to `False`.
+ parent_commit (`str`, *optional*):
+ The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+ If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+ If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+ Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+ especially useful if the repo is updated / committed too concurrently.
+ Returns:
+ `str`: URL of the commit which updated the card metadata.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import metadata_update
+ >>> metadata = {'model-index': [{'name': 'RoBERTa fine-tuned on ReactionGIF',
+ ... 'results': [{'dataset': {'name': 'ReactionGIF',
+ ... 'type': 'julien-c/reactiongif'},
+ ... 'metrics': [{'name': 'Recall',
+ ... 'type': 'recall',
+ ... 'value': 0.7762102282047272}],
+ ... 'task': {'name': 'Text Classification',
+ ... 'type': 'text-classification'}}]}]}
+ >>> url = metadata_update("hf-internal-testing/reactiongif-roberta-card", metadata)
+
+ ```
+ """
+ commit_message = commit_message if commit_message is not None else "Update metadata with huggingface_hub"
+
+ # Card class given repo_type
+ card_class: type[RepoCard]
+ if repo_type is None or repo_type == "model":
+ card_class = ModelCard
+ elif repo_type == "dataset":
+ card_class = DatasetCard
+ elif repo_type == "space":
+ card_class = RepoCard
+ else:
+ raise ValueError(f"Unknown repo_type: {repo_type}")
+
+ # Either load repo_card from the Hub or create an empty one.
+ # NOTE: Will not create the repo if it doesn't exist.
+ try:
+ card = card_class.load(repo_id, token=token, repo_type=repo_type)
+ except EntryNotFoundError:
+ if repo_type == "space":
+ raise ValueError("Cannot update metadata on a Space that doesn't contain a `README.md` file.")
+
+ # Initialize a ModelCard or DatasetCard from default template and no data.
+ # Cast to the concrete expected card type to satisfy type checkers.
+ card = card_class.from_template(CardData()) # type: ignore
+
+ for key, value in metadata.items():
+ if key == "model-index":
+ # if the new metadata doesn't include a name, either use existing one or repo name
+ if "name" not in value[0]:
+ value[0]["name"] = getattr(card, "model_name", repo_id)
+ model_name, new_results = model_index_to_eval_results(value)
+ if card.data.eval_results is None:
+ card.data.eval_results = new_results
+ card.data.model_name = model_name
+ else:
+ existing_results = card.data.eval_results
+
+ # Iterate over new results
+ # Iterate over existing results
+ # If both results describe the same metric but value is different:
+ # If overwrite=True: overwrite the metric value
+ # Else: raise ValueError
+ # Else: append new result to existing ones.
+ for new_result in new_results:
+ result_found = False
+ for existing_result in existing_results:
+ if new_result.is_equal_except_value(existing_result):
+ if new_result != existing_result and not overwrite:
+ raise ValueError(
+ "You passed a new value for the existing metric"
+ f" 'name: {new_result.metric_name}, type: "
+ f"{new_result.metric_type}'. Set `overwrite=True`"
+ " to overwrite existing metrics."
+ )
+ result_found = True
+ existing_result.metric_value = new_result.metric_value
+ if existing_result.verified is True:
+ existing_result.verify_token = new_result.verify_token
+ if not result_found:
+ card.data.eval_results.append(new_result)
+ else:
+ # Any metadata that is not a result metric
+ if card.data.get(key) is not None and not overwrite and card.data.get(key) != value:
+ raise ValueError(
+ f"You passed a new value for the existing meta data field '{key}'."
+ " Set `overwrite=True` to overwrite existing metadata."
+ )
+ else:
+ card.data[key] = value
+
+ return card.push_to_hub(
+ repo_id,
+ token=token,
+ repo_type=repo_type,
+ commit_message=commit_message,
+ commit_description=commit_description,
+ create_pr=create_pr,
+ revision=revision,
+ parent_commit=parent_commit,
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard_data.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8ec8d33bb47e5fabe037ba2a86ddd15ef3ace6d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/repocard_data.py
@@ -0,0 +1,782 @@
+import copy
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any
+
+from huggingface_hub.utils import logging, yaml_dump
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class EvalResult:
+ """
+ Flattened representation of individual evaluation results found in model-index of Model Cards.
+
+ For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1.
+
+ Args:
+ task_type (`str`):
+ The task identifier. Example: "image-classification".
+ dataset_type (`str`):
+ The dataset identifier. Example: "common_voice". Use dataset id from https://hf.co/datasets.
+ dataset_name (`str`):
+ A pretty name for the dataset. Example: "Common Voice (French)".
+ metric_type (`str`):
+ The metric identifier. Example: "wer". Use metric id from https://hf.co/metrics.
+ metric_value (`Any`):
+ The metric value. Example: 0.9 or "20.0 ± 1.2".
+ task_name (`str`, *optional*):
+ A pretty name for the task. Example: "Speech Recognition".
+ dataset_config (`str`, *optional*):
+ The name of the dataset configuration used in `load_dataset()`.
+ Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info:
+ https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+ dataset_split (`str`, *optional*):
+ The split used in `load_dataset()`. Example: "test".
+ dataset_revision (`str`, *optional*):
+ The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
+ Example: 5503434ddd753f426f4b38109466949a1217c2bb
+ dataset_args (`dict[str, Any]`, *optional*):
+ The arguments passed during `Metric.compute()`. Example for `bleu`: `{"max_order": 4}`
+ metric_name (`str`, *optional*):
+ A pretty name for the metric. Example: "Test WER".
+ metric_config (`str`, *optional*):
+ The name of the metric configuration used in `load_metric()`.
+ Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+ See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
+ metric_args (`dict[str, Any]`, *optional*):
+ The arguments passed during `Metric.compute()`. Example for `bleu`: max_order: 4
+ verified (`bool`, *optional*):
+ Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+ verify_token (`str`, *optional*):
+ A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+ source_name (`str`, *optional*):
+ The name of the source of the evaluation result. Example: "Open LLM Leaderboard".
+ source_url (`str`, *optional*):
+ The URL of the source of the evaluation result. Example: "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard".
+ """
+
+ # Required
+
+ # The task identifier
+ # Example: automatic-speech-recognition
+ task_type: str
+
+ # The dataset identifier
+ # Example: common_voice. Use dataset id from https://hf.co/datasets
+ dataset_type: str
+
+ # A pretty name for the dataset.
+ # Example: Common Voice (French)
+ dataset_name: str
+
+ # The metric identifier
+ # Example: wer. Use metric id from https://hf.co/metrics
+ metric_type: str
+
+ # Value of the metric.
+ # Example: 20.0 or "20.0 ± 1.2"
+ metric_value: Any
+
+ # Optional
+
+ # A pretty name for the task.
+ # Example: Speech Recognition
+ task_name: str | None = None
+
+ # The name of the dataset configuration used in `load_dataset()`.
+ # Example: fr in `load_dataset("common_voice", "fr")`.
+ # See the `datasets` docs for more info:
+ # https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+ dataset_config: str | None = None
+
+ # The split used in `load_dataset()`.
+ # Example: test
+ dataset_split: str | None = None
+
+ # The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
+ # Example: 5503434ddd753f426f4b38109466949a1217c2bb
+ dataset_revision: str | None = None
+
+ # The arguments passed during `Metric.compute()`.
+ # Example for `bleu`: max_order: 4
+ dataset_args: dict[str, Any] | None = None
+
+ # A pretty name for the metric.
+ # Example: Test WER
+ metric_name: str | None = None
+
+ # The name of the metric configuration used in `load_metric()`.
+ # Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+ # See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
+ metric_config: str | None = None
+
+ # The arguments passed during `Metric.compute()`.
+ # Example for `bleu`: max_order: 4
+ metric_args: dict[str, Any] | None = None
+
+ # Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+ verified: bool | None = None
+
+ # A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+ verify_token: str | None = None
+
+ # The name of the source of the evaluation result.
+ # Example: Open LLM Leaderboard
+ source_name: str | None = None
+
+ # The URL of the source of the evaluation result.
+ # Example: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
+ source_url: str | None = None
+
+ @property
+ def unique_identifier(self) -> tuple:
+ """Returns a tuple that uniquely identifies this evaluation."""
+ return (
+ self.task_type,
+ self.dataset_type,
+ self.dataset_config,
+ self.dataset_split,
+ self.dataset_revision,
+ )
+
+ def is_equal_except_value(self, other: "EvalResult") -> bool:
+ """
+ Return True if `self` and `other` describe exactly the same metric but with a
+ different value.
+ """
+ for key, _ in self.__dict__.items():
+ if key == "metric_value":
+ continue
+ # For metrics computed by Hugging Face's evaluation service, `verify_token` is derived from `metric_value`,
+ # so we exclude it here in the comparison.
+ if key != "verify_token" and getattr(self, key) != getattr(other, key):
+ return False
+ return True
+
+ def __post_init__(self) -> None:
+ if self.source_name is not None and self.source_url is None:
+ raise ValueError("If `source_name` is provided, `source_url` must also be provided.")
+
+
+@dataclass
+class CardData:
+ """Structure containing metadata from a RepoCard.
+
+ [`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`].
+
+ Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data
+ (example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not
+ inherit from `dict` to allow this export step.
+ """
+
+ def __init__(self, ignore_metadata_errors: bool = False, **kwargs):
+ self.__dict__.update(kwargs)
+
+ def to_dict(self):
+ """Converts CardData to a dict.
+
+ Returns:
+ `dict`: CardData represented as a dictionary ready to be dumped to a YAML
+ block for inclusion in a README.md file.
+ """
+
+ data_dict = copy.deepcopy(self.__dict__)
+ self._to_dict(data_dict)
+ return {key: value for key, value in data_dict.items() if value is not None}
+
+ def _to_dict(self, data_dict):
+ """Use this method in child classes to alter the dict representation of the data. Alter the dict in-place.
+
+ Args:
+ data_dict (`dict`): The raw dict representation of the card data.
+ """
+ pass
+
+ def to_yaml(self, line_break=None, original_order: list[str] | None = None) -> str:
+ """Dumps CardData to a YAML block for inclusion in a README.md file.
+
+ Args:
+ line_break (str, *optional*):
+ The line break to use when dumping to yaml.
+ original_order (`list[str]`, *optional*):
+ If provided, reorder the metadata fields to match this list before dumping.
+ Any keys not in `original_order` are appended after the listed keys, preserving
+ their existing relative order. Useful for round-tripping a YAML block without
+ shuffling its keys.
+
+ Returns:
+ `str`: CardData represented as a YAML block.
+ """
+ if original_order:
+ original_order_set = set(original_order)
+ self.__dict__ = {
+ k: self.__dict__[k]
+ for k in original_order + [k for k in self.__dict__ if k not in original_order_set]
+ if k in self.__dict__
+ }
+ return yaml_dump(self.to_dict(), sort_keys=False, line_break=line_break).strip()
+
+ def __repr__(self):
+ return repr(self.__dict__)
+
+ def __str__(self):
+ return self.to_yaml()
+
+ def get(self, key: str, default: Any = None) -> Any:
+ """Get value for a given metadata key."""
+ value = self.__dict__.get(key)
+ return default if value is None else value
+
+ def pop(self, key: str, default: Any = None) -> Any:
+ """Pop value for a given metadata key."""
+ return self.__dict__.pop(key, default)
+
+ def __getitem__(self, key: str) -> Any:
+ """Get value for a given metadata key."""
+ return self.__dict__[key]
+
+ def __setitem__(self, key: str, value: Any) -> None:
+ """Set value for a given metadata key."""
+ self.__dict__[key] = value
+
+ def __contains__(self, key: str) -> bool:
+ """Check if a given metadata key is set."""
+ return key in self.__dict__
+
+ def __len__(self) -> int:
+ """Return the number of metadata keys set."""
+ return len(self.__dict__)
+
+
+def _validate_eval_results(
+ eval_results: EvalResult | list[EvalResult] | None,
+ model_name: str | None,
+) -> list[EvalResult]:
+ if eval_results is None:
+ return []
+ if isinstance(eval_results, EvalResult):
+ eval_results = [eval_results]
+ if not isinstance(eval_results, list) or not all(isinstance(r, EvalResult) for r in eval_results):
+ raise ValueError(
+ f"`eval_results` should be of type `EvalResult` or a list of `EvalResult`, got {type(eval_results)}."
+ )
+ if model_name is None:
+ raise ValueError("Passing `eval_results` requires `model_name` to be set.")
+ return eval_results
+
+
+class ModelCardData(CardData):
+ """Model Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+ Args:
+ base_model (`str` or `list[str]`, *optional*):
+ The identifier of the base model from which the model derives. This is applicable for example if your model is a
+ fine-tune or adapter of an existing model. The value must be the ID of a model on the Hub (or a list of IDs
+ if your model derives from multiple models). Defaults to None.
+ datasets (`Union[str, list[str]]`, *optional*):
+ Dataset or list of datasets that were used to train this model. Should be a dataset ID
+ found on https://hf.co/datasets. Defaults to None.
+ eval_results (`Union[list[EvalResult], EvalResult]`, *optional*):
+ List of `huggingface_hub.EvalResult` that define evaluation results of the model. If provided,
+ `model_name` is used to as a name on PapersWithCode's leaderboards. Defaults to `None`.
+ language (`Union[str, list[str]]`, *optional*):
+ Language of model's training data or metadata. It must be an ISO 639-1, 639-2 or
+ 639-3 code (two/three letters), or a special value like "code", "multilingual". Defaults to `None`.
+ library_name (`str`, *optional*):
+ Name of library used by this model. Example: keras or any library from
+ https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts.
+ Defaults to None.
+ license (`str`, *optional*):
+ License of this model. Example: apache-2.0 or any license from
+ https://huggingface.co/docs/hub/repositories-licenses. Defaults to None.
+ license_name (`str`, *optional*):
+ Name of the license of this model. Defaults to None. To be used in conjunction with `license_link`.
+ Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a name. In that case, use `license` instead.
+ license_link (`str`, *optional*):
+ Link to the license of this model. Defaults to None. To be used in conjunction with `license_name`.
+ Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a link. In that case, use `license` instead.
+ metrics (`list[str]`, *optional*):
+ List of metrics used to evaluate this model. Should be a metric name that can be found
+ at https://hf.co/metrics. Example: 'accuracy'. Defaults to None.
+ model_name (`str`, *optional*):
+ A name for this model. It is used along with
+ `eval_results` to construct the `model-index` within the card's metadata. The name
+ you supply here is what will be used on PapersWithCode's leaderboards. If None is provided
+ then the repo name is used as a default. Defaults to None.
+ pipeline_tag (`str`, *optional*):
+ The pipeline tag associated with the model. Example: "text-classification".
+ tags (`list[str]`, *optional*):
+ List of tags to add to your model that can be used when filtering on the Hugging
+ Face Hub. Defaults to None.
+ ignore_metadata_errors (`str`):
+ If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+ the process. Use it at your own risk.
+ kwargs (`dict`, *optional*):
+ Additional metadata that will be added to the model card. Defaults to None.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import ModelCardData
+ >>> card_data = ModelCardData(
+ ... language="en",
+ ... license="mit",
+ ... library_name="timm",
+ ... tags=['image-classification', 'resnet'],
+ ... )
+ >>> card_data.to_dict()
+ {'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']}
+
+ ```
+ """
+
+ def __init__(
+ self,
+ *,
+ base_model: str | list[str] | None = None,
+ datasets: str | list[str] | None = None,
+ eval_results: list[EvalResult] | None = None,
+ language: str | list[str] | None = None,
+ library_name: str | None = None,
+ license: str | None = None,
+ license_name: str | None = None,
+ license_link: str | None = None,
+ metrics: list[str] | None = None,
+ model_name: str | None = None,
+ pipeline_tag: str | None = None,
+ tags: list[str] | None = None,
+ ignore_metadata_errors: bool = False,
+ **kwargs,
+ ):
+ self.base_model = base_model
+ self.datasets = datasets
+ self.eval_results = eval_results
+ self.language = language
+ self.library_name = library_name
+ self.license = license
+ self.license_name = license_name
+ self.license_link = license_link
+ self.metrics = metrics
+ self.model_name = model_name
+ self.pipeline_tag = pipeline_tag
+ self.tags = _to_unique_list(tags)
+
+ model_index = kwargs.pop("model-index", None)
+ if model_index:
+ try:
+ model_name, eval_results = model_index_to_eval_results(model_index)
+ self.model_name = model_name
+ self.eval_results = eval_results
+ except (KeyError, TypeError) as error:
+ if ignore_metadata_errors:
+ logger.warning("Invalid model-index. Not loading eval results into CardData.")
+ else:
+ raise ValueError(
+ f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
+ " `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
+ " some information will be lost. Use it at your own risk."
+ )
+
+ super().__init__(**kwargs)
+
+ if self.eval_results:
+ try:
+ self.eval_results = _validate_eval_results(self.eval_results, self.model_name)
+ except Exception as e:
+ if ignore_metadata_errors:
+ logger.warning(f"Failed to validate eval_results: {e}. Not loading eval results into CardData.")
+ else:
+ raise ValueError(f"Failed to validate eval_results: {e}") from e
+
+ def _to_dict(self, data_dict):
+ """Format the internal data dict. In this case, we convert eval results to a valid model index"""
+ if self.eval_results is not None:
+ data_dict["model-index"] = eval_results_to_model_index(self.model_name, self.eval_results) # type: ignore
+ del data_dict["eval_results"], data_dict["model_name"]
+
+
+class DatasetCardData(CardData):
+ """Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+ Args:
+ language (`list[str]`, *optional*):
+ Language of dataset's data or metadata. It must be an ISO 639-1, 639-2 or
+ 639-3 code (two/three letters), or a special value like "code", "multilingual".
+ license (`Union[str, list[str]]`, *optional*):
+ License(s) of this dataset. Example: apache-2.0 or any license from
+ https://huggingface.co/docs/hub/repositories-licenses.
+ annotations_creators (`Union[str, list[str]]`, *optional*):
+ How the annotations for the dataset were created.
+ Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'no-annotation', 'other'.
+ language_creators (`Union[str, list[str]]`, *optional*):
+ How the text-based data in the dataset was created.
+ Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'other'
+ multilinguality (`Union[str, list[str]]`, *optional*):
+ Whether the dataset is multilingual.
+ Options are: 'monolingual', 'multilingual', 'translation', 'other'.
+ size_categories (`Union[str, list[str]]`, *optional*):
+ The number of examples in the dataset. Options are: 'n<1K', '1K1T', and 'other'.
+ source_datasets (`list[str]]`, *optional*):
+ Indicates whether the dataset is an original dataset or extended from another existing dataset.
+ Options are: 'original' and 'extended'.
+ task_categories (`Union[str, list[str]]`, *optional*):
+ What categories of task does the dataset support?
+ task_ids (`Union[str, list[str]]`, *optional*):
+ What specific tasks does the dataset support?
+ paperswithcode_id (`str`, *optional*):
+ ID of the dataset on PapersWithCode.
+ pretty_name (`str`, *optional*):
+ A more human-readable name for the dataset. (ex. "Cats vs. Dogs")
+ train_eval_index (`dict`, *optional*):
+ A dictionary that describes the necessary spec for doing evaluation on the Hub.
+ If not provided, it will be gathered from the 'train-eval-index' key of the kwargs.
+ config_names (`Union[str, list[str]]`, *optional*):
+ A list of the available dataset configs for the dataset.
+ """
+
+ def __init__(
+ self,
+ *,
+ language: str | list[str] | None = None,
+ license: str | list[str] | None = None,
+ annotations_creators: str | list[str] | None = None,
+ language_creators: str | list[str] | None = None,
+ multilinguality: str | list[str] | None = None,
+ size_categories: str | list[str] | None = None,
+ source_datasets: list[str] | None = None,
+ task_categories: str | list[str] | None = None,
+ task_ids: str | list[str] | None = None,
+ paperswithcode_id: str | None = None,
+ pretty_name: str | None = None,
+ train_eval_index: dict | None = None,
+ config_names: str | list[str] | None = None,
+ ignore_metadata_errors: bool = False,
+ **kwargs,
+ ):
+ self.annotations_creators = annotations_creators
+ self.language_creators = language_creators
+ self.language = language
+ self.license = license
+ self.multilinguality = multilinguality
+ self.size_categories = size_categories
+ self.source_datasets = source_datasets
+ self.task_categories = task_categories
+ self.task_ids = task_ids
+ self.paperswithcode_id = paperswithcode_id
+ self.pretty_name = pretty_name
+ self.config_names = config_names
+
+ # TODO - maybe handle this similarly to EvalResult?
+ self.train_eval_index = train_eval_index or kwargs.pop("train-eval-index", None)
+ super().__init__(**kwargs)
+
+ def _to_dict(self, data_dict):
+ data_dict["train-eval-index"] = data_dict.pop("train_eval_index")
+
+
+class SpaceCardData(CardData):
+ """Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+ To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference.
+
+ Args:
+ title (`str`, *optional*):
+ Title of the Space.
+ sdk (`str`, *optional*):
+ SDK of the Space (one of `gradio`, `streamlit`, `docker`, or `static`).
+ sdk_version (`str`, *optional*):
+ Version of the used SDK (if Gradio/Streamlit sdk).
+ python_version (`str`, *optional*):
+ Python version used in the Space (if Gradio/Streamlit sdk).
+ app_file (`str`, *optional*):
+ Path to your main application file (which contains either gradio or streamlit Python code, or static html code).
+ Path is relative to the root of the repository.
+ app_port (`str`, *optional*):
+ Port on which your application is running. Used only if sdk is `docker`.
+ license (`str`, *optional*):
+ License of this model. Example: apache-2.0 or any license from
+ https://huggingface.co/docs/hub/repositories-licenses.
+ duplicated_from (`str`, *optional*):
+ ID of the original Space if this is a duplicated Space.
+ models (`list[str]`, *optional*):
+ List of models related to this Space. Should be a dataset ID found on https://hf.co/models.
+ datasets (`list[str]`, *optional*):
+ List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets.
+ tags (`list[str]`, *optional*):
+ List of tags to add to your Space that can be used when filtering on the Hub.
+ ignore_metadata_errors (`str`):
+ If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+ the process. Use it at your own risk.
+ kwargs (`dict`, *optional*):
+ Additional metadata that will be added to the space card.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import SpaceCardData
+ >>> card_data = SpaceCardData(
+ ... title="Dreambooth Training",
+ ... license="mit",
+ ... sdk="gradio",
+ ... duplicated_from="multimodalart/dreambooth-training"
+ ... )
+ >>> card_data.to_dict()
+ {'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'}
+ ```
+ """
+
+ def __init__(
+ self,
+ *,
+ title: str | None = None,
+ sdk: str | None = None,
+ sdk_version: str | None = None,
+ python_version: str | None = None,
+ app_file: str | None = None,
+ app_port: int | None = None,
+ license: str | None = None,
+ duplicated_from: str | None = None,
+ models: list[str] | None = None,
+ datasets: list[str] | None = None,
+ tags: list[str] | None = None,
+ ignore_metadata_errors: bool = False,
+ **kwargs,
+ ):
+ self.title = title
+ self.sdk = sdk
+ self.sdk_version = sdk_version
+ self.python_version = python_version
+ self.app_file = app_file
+ self.app_port = app_port
+ self.license = license
+ self.duplicated_from = duplicated_from
+ self.models = models
+ self.datasets = datasets
+ self.tags = _to_unique_list(tags)
+ super().__init__(**kwargs)
+
+
+def model_index_to_eval_results(model_index: list[dict[str, Any]]) -> tuple[str, list[EvalResult]]:
+ """Takes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects.
+
+ A detailed spec of the model index can be found here:
+ https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+
+ Args:
+ model_index (`list[dict[str, Any]]`):
+ A model index data structure, likely coming from a README.md file on the
+ Hugging Face Hub.
+
+ Returns:
+ model_name (`str`):
+ The name of the model as found in the model index. This is used as the
+ identifier for the model on leaderboards like PapersWithCode.
+ eval_results (`list[EvalResult]`):
+ A list of `huggingface_hub.EvalResult` objects containing the metrics
+ reported in the provided model_index.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.repocard_data import model_index_to_eval_results
+ >>> # Define a minimal model index
+ >>> model_index = [
+ ... {
+ ... "name": "my-cool-model",
+ ... "results": [
+ ... {
+ ... "task": {
+ ... "type": "image-classification"
+ ... },
+ ... "dataset": {
+ ... "type": "beans",
+ ... "name": "Beans"
+ ... },
+ ... "metrics": [
+ ... {
+ ... "type": "accuracy",
+ ... "value": 0.9
+ ... }
+ ... ]
+ ... }
+ ... ]
+ ... }
+ ... ]
+ >>> model_name, eval_results = model_index_to_eval_results(model_index)
+ >>> model_name
+ 'my-cool-model'
+ >>> eval_results[0].task_type
+ 'image-classification'
+ >>> eval_results[0].metric_type
+ 'accuracy'
+
+ ```
+ """
+
+ if not model_index:
+ raise ValueError("`model_index` must be a non-empty list.")
+
+ eval_results = []
+ for elem in model_index:
+ name = elem["name"]
+ results = elem["results"]
+ for result in results:
+ task_type = result["task"]["type"]
+ task_name = result["task"].get("name")
+ dataset_type = result["dataset"]["type"]
+ dataset_name = result["dataset"]["name"]
+ dataset_config = result["dataset"].get("config")
+ dataset_split = result["dataset"].get("split")
+ dataset_revision = result["dataset"].get("revision")
+ dataset_args = result["dataset"].get("args")
+ source = result.get("source") or {}
+ source_name = source.get("name")
+ source_url = source.get("url")
+
+ for metric in result["metrics"]:
+ metric_type = metric["type"]
+ metric_value = metric["value"]
+ metric_name = metric.get("name")
+ metric_args = metric.get("args")
+ metric_config = metric.get("config")
+ verified = metric.get("verified")
+ verify_token = metric.get("verifyToken")
+
+ eval_result = EvalResult(
+ task_type=task_type, # Required
+ dataset_type=dataset_type, # Required
+ dataset_name=dataset_name, # Required
+ metric_type=metric_type, # Required
+ metric_value=metric_value, # Required
+ task_name=task_name,
+ dataset_config=dataset_config,
+ dataset_split=dataset_split,
+ dataset_revision=dataset_revision,
+ dataset_args=dataset_args,
+ metric_name=metric_name,
+ metric_args=metric_args,
+ metric_config=metric_config,
+ verified=verified,
+ verify_token=verify_token,
+ source_name=source_name,
+ source_url=source_url,
+ )
+ eval_results.append(eval_result)
+ return name, eval_results
+
+
+def _remove_none(obj):
+ """
+ Recursively remove `None` values from a dict. Borrowed from: https://stackoverflow.com/a/20558778
+ """
+ if isinstance(obj, (list, tuple, set)):
+ return type(obj)(_remove_none(x) for x in obj if x is not None)
+ elif isinstance(obj, dict):
+ return type(obj)((_remove_none(k), _remove_none(v)) for k, v in obj.items() if k is not None and v is not None)
+ else:
+ return obj
+
+
+def eval_results_to_model_index(model_name: str, eval_results: list[EvalResult]) -> list[dict[str, Any]]:
+ """Takes in given model name and list of `huggingface_hub.EvalResult` and returns a
+ valid model-index that will be compatible with the format expected by the
+ Hugging Face Hub.
+
+ Args:
+ model_name (`str`):
+ Name of the model (ex. "my-cool-model"). This is used as the identifier
+ for the model on leaderboards like PapersWithCode.
+ eval_results (`list[EvalResult]`):
+ List of `huggingface_hub.EvalResult` objects containing the metrics to be
+ reported in the model-index.
+
+ Returns:
+ model_index (`list[dict[str, Any]]`): The eval_results converted to a model-index.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult
+ >>> # Define minimal eval_results
+ >>> eval_results = [
+ ... EvalResult(
+ ... task_type="image-classification", # Required
+ ... dataset_type="beans", # Required
+ ... dataset_name="Beans", # Required
+ ... metric_type="accuracy", # Required
+ ... metric_value=0.9, # Required
+ ... )
+ ... ]
+ >>> eval_results_to_model_index("my-cool-model", eval_results)
+ [{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}]
+
+ ```
+ """
+
+ # Metrics are reported on a unique task-and-dataset basis.
+ # Here, we make a map of those pairs and the associated EvalResults.
+ task_and_ds_types_map: dict[Any, list[EvalResult]] = defaultdict(list)
+ for eval_result in eval_results:
+ task_and_ds_types_map[eval_result.unique_identifier].append(eval_result)
+
+ # Use the map from above to generate the model index data.
+ model_index_data: list[dict[str, Any]] = []
+ for results in task_and_ds_types_map.values():
+ # All items from `results` share same metadata
+ sample_result = results[0]
+ data: dict[str, Any] = {
+ "task": {
+ "type": sample_result.task_type,
+ "name": sample_result.task_name,
+ },
+ "dataset": {
+ "name": sample_result.dataset_name,
+ "type": sample_result.dataset_type,
+ "config": sample_result.dataset_config,
+ "split": sample_result.dataset_split,
+ "revision": sample_result.dataset_revision,
+ "args": sample_result.dataset_args,
+ },
+ "metrics": [
+ {
+ "type": result.metric_type,
+ "value": result.metric_value,
+ "name": result.metric_name,
+ "config": result.metric_config,
+ "args": result.metric_args,
+ "verified": result.verified,
+ "verifyToken": result.verify_token,
+ }
+ for result in results
+ ],
+ }
+ if sample_result.source_url is not None:
+ source: dict[str, str] = {
+ "url": sample_result.source_url,
+ }
+ if sample_result.source_name is not None:
+ source["name"] = sample_result.source_name
+ data["source"] = source
+ model_index_data.append(data)
+
+ # TODO - Check if there cases where this list is longer than one?
+ # Finally, the model index itself is list of dicts.
+ model_index = [
+ {
+ "name": model_name,
+ "results": model_index_data,
+ }
+ ]
+ return _remove_none(model_index)
+
+
+def _to_unique_list(tags: list[str] | None) -> list[str] | None:
+ if tags is None:
+ return tags
+ if isinstance(tags, str):
+ tags = [tags]
+ unique_tags = [] # make tags unique + keep order explicitly
+ for tag in tags:
+ if tag not in unique_tags:
+ unique_tags.append(tag)
+ return unique_tags
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e624a754176079eb63540513297099025a0685c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ruff: noqa: F401
+"""Contains helpers to serialize tensors."""
+
+from ._base import StateDictSplit, split_state_dict_into_shards_factory
+from ._torch import (
+ get_torch_storage_id,
+ get_torch_storage_size,
+ load_state_dict_from_file,
+ load_torch_model,
+ save_torch_model,
+ save_torch_state_dict,
+ split_torch_state_dict_into_shards,
+)
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_base.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd4f890d9a14d2a75fbd3ed0619a23cc3a74ec3e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_base.py
@@ -0,0 +1,212 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains helpers to split tensors into shards."""
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any, TypeVar
+
+from .. import logging
+
+
+TensorT = TypeVar("TensorT")
+TensorSizeFn_T = Callable[[TensorT], int]
+StorageIDFn_T = Callable[[TensorT], Any | None]
+
+MAX_SHARD_SIZE = "5GB"
+SIZE_UNITS = {
+ "TB": 10**12,
+ "GB": 10**9,
+ "MB": 10**6,
+ "KB": 10**3,
+}
+
+
+logger = logging.get_logger(__file__)
+
+
+@dataclass
+class StateDictSplit:
+ is_sharded: bool = field(init=False)
+ metadata: dict[str, Any]
+ filename_to_tensors: dict[str, list[str]]
+ tensor_to_filename: dict[str, str]
+
+ def __post_init__(self):
+ self.is_sharded = len(self.filename_to_tensors) > 1
+
+
+def split_state_dict_into_shards_factory(
+ state_dict: dict[str, TensorT],
+ *,
+ get_storage_size: TensorSizeFn_T,
+ filename_pattern: str,
+ get_storage_id: StorageIDFn_T = lambda tensor: None,
+ max_shard_size: int | str = MAX_SHARD_SIZE,
+) -> StateDictSplit:
+ """
+ Split a model state dictionary in shards so that each shard is smaller than a given size.
+
+ The shards are determined by iterating through the `state_dict` in the order of its keys. There is no optimization
+ made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
+ have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
+ [6+2+2GB], [6+2GB], [6GB].
+
+ > [!WARNING]
+ > If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+ > size greater than `max_shard_size`.
+
+ Args:
+ state_dict (`dict[str, Tensor]`):
+ The state dictionary to save.
+ get_storage_size (`Callable[[Tensor], int]`):
+ A function that returns the size of a tensor when saved on disk in bytes.
+ get_storage_id (`Callable[[Tensor], Optional[Any]]`, *optional*):
+ A function that returns a unique identifier to a tensor storage. Multiple different tensors can share the
+ same underlying storage. This identifier is guaranteed to be unique and constant for this tensor's storage
+ during its lifetime. Two tensor storages with non-overlapping lifetimes may have the same id.
+ filename_pattern (`str`, *optional*):
+ The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ max_shard_size (`int` or `str`, *optional*):
+ The maximum size of each shard, in bytes. Defaults to 5GB.
+
+ Returns:
+ [`StateDictSplit`]: A `StateDictSplit` object containing the shards and the index to retrieve them.
+ """
+ storage_id_to_tensors: dict[Any, list[str]] = {}
+
+ shard_list: list[dict[str, TensorT]] = []
+ current_shard: dict[str, TensorT] = {}
+ current_shard_size = 0
+ total_size = 0
+
+ if isinstance(max_shard_size, str):
+ max_shard_size = parse_size_to_int(max_shard_size)
+
+ for key, tensor in state_dict.items():
+ # when bnb serialization is used the weights in the state dict can be strings
+ # check: https://github.com/huggingface/transformers/pull/24416 for more details
+ if isinstance(tensor, str):
+ logger.info("Skipping tensor %s as it is a string (bnb serialization)", key)
+ continue
+
+ # If a `tensor` shares the same underlying storage as another tensor, we put `tensor` in the same `block`
+ storage_id = get_storage_id(tensor) # type: ignore[invalid-argument-type]
+ if storage_id is not None:
+ if storage_id in storage_id_to_tensors:
+ # We skip this tensor for now and will reassign to correct shard later
+ storage_id_to_tensors[storage_id].append(key)
+ continue
+ else:
+ # This is the first tensor with this storage_id, we create a new entry
+ # in the storage_id_to_tensors dict => we will assign the shard id later
+ storage_id_to_tensors[storage_id] = [key]
+
+ # Compute tensor size
+ tensor_size = get_storage_size(tensor) # type: ignore[invalid-argument-type]
+
+ # If this tensor is bigger than the maximal size, we put it in its own shard
+ if tensor_size > max_shard_size:
+ total_size += tensor_size
+ shard_list.append({key: tensor})
+ continue
+
+ # If this tensor is going to tip up over the maximal size, we split.
+ # Current shard already has some tensors, we add it to the list of shards and create a new one.
+ if current_shard_size + tensor_size > max_shard_size:
+ shard_list.append(current_shard)
+ current_shard = {}
+ current_shard_size = 0
+
+ # Add the tensor to the current shard
+ current_shard[key] = tensor
+ current_shard_size += tensor_size
+ total_size += tensor_size
+
+ # Add the last shard
+ if len(current_shard) > 0:
+ shard_list.append(current_shard)
+ nb_shards = len(shard_list)
+
+ # Loop over the tensors that share the same storage and assign them together
+ for storage_id, keys in storage_id_to_tensors.items():
+ # Let's try to find the shard where the first tensor of this storage is and put all tensors in the same shard
+ for shard in shard_list:
+ if keys[0] in shard:
+ for key in keys:
+ shard[key] = state_dict[key]
+ break
+
+ # If we only have one shard, we return it => no need to build the index
+ if nb_shards == 1:
+ filename = filename_pattern.format(suffix="")
+ # Use the keys from the shard itself rather than `state_dict` directly, so that tensors
+ # skipped above (e.g. string tensors from bnb serialization) are excluded from the index,
+ # consistently with the multi-shard path below.
+ keys = list(shard_list[0].keys())
+ return StateDictSplit(
+ metadata={"total_size": total_size},
+ filename_to_tensors={filename: keys},
+ tensor_to_filename={key: filename for key in keys},
+ )
+
+ # Now that each tensor is assigned to a shard, let's assign a filename to each shard
+ tensor_name_to_filename = {}
+ filename_to_tensors = {}
+ for idx, shard in enumerate(shard_list):
+ filename = filename_pattern.format(suffix=f"-{idx + 1:05d}-of-{nb_shards:05d}")
+ for key in shard:
+ tensor_name_to_filename[key] = filename
+ filename_to_tensors[filename] = list(shard.keys())
+
+ # Build the index and return
+ return StateDictSplit(
+ metadata={"total_size": total_size},
+ filename_to_tensors=filename_to_tensors,
+ tensor_to_filename=tensor_name_to_filename,
+ )
+
+
+def parse_size_to_int(size_as_str: str) -> int:
+ """
+ Parse a size expressed as a string with digits and unit (like `"5MB"`) to an integer (in bytes).
+
+ Supported units are "TB", "GB", "MB", "KB".
+
+ Args:
+ size_as_str (`str`): The size to convert.
+
+ Example:
+
+ ```py
+ >>> parse_size_to_int("5MB")
+ 5000000
+ ```
+ """
+ size_as_str = size_as_str.strip()
+
+ # Parse unit
+ unit = size_as_str[-2:].upper()
+ if unit not in SIZE_UNITS:
+ raise ValueError(f"Unit '{unit}' not supported. Supported units are TB, GB, MB, KB. Got '{size_as_str}'.")
+ multiplier = SIZE_UNITS[unit]
+
+ # Parse value
+ try:
+ value = float(size_as_str[:-2].strip())
+ except ValueError as e:
+ raise ValueError(f"Could not parse the size value from '{size_as_str}': {e}") from e
+
+ return int(value * multiplier)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_dduf.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_dduf.py
new file mode 100644
index 0000000000000000000000000000000000000000..37e916d419d5a5934525bcb169fd5dae48d6389d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_dduf.py
@@ -0,0 +1,386 @@
+import json
+import logging
+import mmap
+import os
+import shutil
+import zipfile
+from collections.abc import Generator, Iterable
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from ..errors import DDUFCorruptedFileError, DDUFExportError, DDUFInvalidEntryNameError
+
+
+logger = logging.getLogger(__name__)
+
+DDUF_ALLOWED_ENTRIES = {
+ # Allowed file extensions in a DDUF file
+ ".json",
+ ".model",
+ ".safetensors",
+ ".txt",
+}
+
+DDUF_FOLDER_REQUIRED_ENTRIES = {
+ # Each folder must contain at least one of these entries
+ "config.json",
+ "tokenizer_config.json",
+ "preprocessor_config.json",
+ "scheduler_config.json",
+}
+
+
+@dataclass
+class DDUFEntry:
+ """Object representing a file entry in a DDUF file.
+
+ See [`read_dduf_file`] for how to read a DDUF file.
+
+ Attributes:
+ filename (str):
+ The name of the file in the DDUF archive.
+ offset (int):
+ The offset of the file in the DDUF archive.
+ length (int):
+ The length of the file in the DDUF archive.
+ dduf_path (str):
+ The path to the DDUF archive (for internal use).
+ """
+
+ filename: str
+ length: int
+ offset: int
+
+ dduf_path: Path = field(repr=False)
+
+ @contextmanager
+ def as_mmap(self) -> Generator[bytes, None, None]:
+ """Open the file as a memory-mapped file.
+
+ Useful to load safetensors directly from the file.
+
+ Example:
+ ```py
+ >>> import safetensors.torch
+ >>> with entry.as_mmap() as mm:
+ ... tensors = safetensors.torch.load(mm)
+ ```
+ """
+ with self.dduf_path.open("rb") as f:
+ with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
+ yield mm[self.offset : self.offset + self.length]
+
+ def read_text(self, encoding: str = "utf-8") -> str:
+ """Read the file as text.
+
+ Useful for '.txt' and '.json' entries.
+
+ Example:
+ ```py
+ >>> import json
+ >>> index = json.loads(entry.read_text())
+ ```
+ """
+ with self.dduf_path.open("rb") as f:
+ f.seek(self.offset)
+ return f.read(self.length).decode(encoding=encoding)
+
+
+def read_dduf_file(dduf_path: os.PathLike | str) -> dict[str, DDUFEntry]:
+ """
+ Read a DDUF file and return a dictionary of entries.
+
+ Only the metadata is read, the data is not loaded in memory.
+
+ Args:
+ dduf_path (`str` or `os.PathLike`):
+ The path to the DDUF file to read.
+
+ Returns:
+ `dict[str, DDUFEntry]`:
+ A dictionary of [`DDUFEntry`] indexed by filename.
+
+ Raises:
+ - [`DDUFCorruptedFileError`]: If the DDUF file is corrupted (i.e. doesn't follow the DDUF format).
+
+ Example:
+ ```python
+ >>> import json
+ >>> import safetensors.torch
+ >>> from huggingface_hub import read_dduf_file
+
+ # Read DDUF metadata
+ >>> dduf_entries = read_dduf_file("FLUX.1-dev.dduf")
+
+ # Returns a mapping filename <> DDUFEntry
+ >>> dduf_entries["model_index.json"]
+ DDUFEntry(filename='model_index.json', offset=66, length=587)
+
+ # Load model index as JSON
+ >>> json.loads(dduf_entries["model_index.json"].read_text())
+ {'_class_name': 'FluxPipeline', '_diffusers_version': '0.32.0.dev0', '_name_or_path': 'black-forest-labs/FLUX.1-dev', ...
+
+ # Load VAE weights using safetensors
+ >>> with dduf_entries["vae/diffusion_pytorch_model.safetensors"].as_mmap() as mm:
+ ... state_dict = safetensors.torch.load(mm)
+ ```
+ """
+ entries = {}
+ dduf_path = Path(dduf_path)
+ logger.info(f"Reading DDUF file {dduf_path}")
+ with zipfile.ZipFile(str(dduf_path), "r") as zf:
+ for info in zf.infolist():
+ logger.debug(f"Reading entry {info.filename}")
+ if info.compress_type != zipfile.ZIP_STORED:
+ raise DDUFCorruptedFileError("Data must not be compressed in DDUF file.")
+
+ try:
+ _validate_dduf_entry_name(info.filename)
+ except DDUFInvalidEntryNameError as e:
+ raise DDUFCorruptedFileError(f"Invalid entry name in DDUF file: {info.filename}") from e
+
+ offset = _get_data_offset(zf, info)
+
+ entries[info.filename] = DDUFEntry(
+ filename=info.filename, offset=offset, length=info.file_size, dduf_path=dduf_path
+ )
+
+ # Consistency checks on the DDUF file
+ if "model_index.json" not in entries:
+ raise DDUFCorruptedFileError("Missing required 'model_index.json' entry in DDUF file.")
+ index = json.loads(entries["model_index.json"].read_text())
+ _validate_dduf_structure(index, entries.keys())
+
+ logger.info(f"Done reading DDUF file {dduf_path}. Found {len(entries)} entries")
+ return entries
+
+
+def export_entries_as_dduf(dduf_path: str | os.PathLike, entries: Iterable[tuple[str, str | Path | bytes]]) -> None:
+ """Write a DDUF file from an iterable of entries.
+
+ This is a lower-level helper than [`export_folder_as_dduf`] that allows more flexibility when serializing data.
+ In particular, you don't need to save the data on disk before exporting it in the DDUF file.
+
+ Args:
+ dduf_path (`str` or `os.PathLike`):
+ The path to the DDUF file to write.
+ entries (`Iterable[tuple[str, Union[str, Path, bytes]]]`):
+ An iterable of entries to write in the DDUF file. Each entry is a tuple with the filename and the content.
+ The filename should be the path to the file in the DDUF archive.
+ The content can be a string or a pathlib.Path representing a path to a file on the local disk or directly the content as bytes.
+
+ Raises:
+ - [`DDUFExportError`]: If anything goes wrong during the export (e.g. invalid entry name, missing 'model_index.json', etc.).
+
+ Example:
+ ```python
+ # Export specific files from the local disk.
+ >>> from huggingface_hub import export_entries_as_dduf
+ >>> export_entries_as_dduf(
+ ... dduf_path="stable-diffusion-v1-4-FP16.dduf",
+ ... entries=[ # List entries to add to the DDUF file (here, only FP16 weights)
+ ... ("model_index.json", "path/to/model_index.json"),
+ ... ("vae/config.json", "path/to/vae/config.json"),
+ ... ("vae/diffusion_pytorch_model.fp16.safetensors", "path/to/vae/diffusion_pytorch_model.fp16.safetensors"),
+ ... ("text_encoder/config.json", "path/to/text_encoder/config.json"),
+ ... ("text_encoder/model.fp16.safetensors", "path/to/text_encoder/model.fp16.safetensors"),
+ ... # ... add more entries here
+ ... ]
+ ... )
+ ```
+
+ ```python
+ # Export state_dicts one by one from a loaded pipeline
+ >>> from diffusers import DiffusionPipeline
+ >>> from typing import Generator, Tuple
+ >>> import safetensors.torch
+ >>> from huggingface_hub import export_entries_as_dduf
+ >>> pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+ ... # ... do some work with the pipeline
+
+ >>> def as_entries(pipe: DiffusionPipeline) -> Generator[tuple[str, bytes], None, None]:
+ ... # Build a generator that yields the entries to add to the DDUF file.
+ ... # The first element of the tuple is the filename in the DDUF archive (must use UNIX separator!). The second element is the content of the file.
+ ... # Entries will be evaluated lazily when the DDUF file is created (only 1 entry is loaded in memory at a time)
+ ... yield "vae/config.json", pipe.vae.to_json_string().encode()
+ ... yield "vae/diffusion_pytorch_model.safetensors", safetensors.torch.save(pipe.vae.state_dict())
+ ... yield "text_encoder/config.json", pipe.text_encoder.config.to_json_string().encode()
+ ... yield "text_encoder/model.safetensors", safetensors.torch.save(pipe.text_encoder.state_dict())
+ ... # ... add more entries here
+
+ >>> export_entries_as_dduf(dduf_path="stable-diffusion-v1-4.dduf", entries=as_entries(pipe))
+ ```
+ """
+ logger.info(f"Exporting DDUF file '{dduf_path}'")
+ filenames = set()
+ index = None
+ with zipfile.ZipFile(str(dduf_path), "w", zipfile.ZIP_STORED) as archive:
+ for filename, content in entries:
+ if filename in filenames:
+ raise DDUFExportError(f"Can't add duplicate entry: {filename}")
+ filenames.add(filename)
+
+ if filename == "model_index.json":
+ try:
+ index = json.loads(_load_content(content).decode())
+ except json.JSONDecodeError as e:
+ raise DDUFExportError("Failed to parse 'model_index.json'.") from e
+
+ try:
+ filename = _validate_dduf_entry_name(filename)
+ except DDUFInvalidEntryNameError as e:
+ raise DDUFExportError(f"Invalid entry name: {filename}") from e
+ logger.debug(f"Adding entry '{filename}' to DDUF file")
+ _dump_content_in_archive(archive, filename, content)
+
+ # Consistency checks on the DDUF file
+ if index is None:
+ raise DDUFExportError("Missing required 'model_index.json' entry in DDUF file.")
+ try:
+ _validate_dduf_structure(index, filenames)
+ except DDUFCorruptedFileError as e:
+ raise DDUFExportError("Invalid DDUF file structure.") from e
+
+ logger.info(f"Done writing DDUF file {dduf_path}")
+
+
+def export_folder_as_dduf(dduf_path: str | os.PathLike, folder_path: str | os.PathLike) -> None:
+ """
+ Export a folder as a DDUF file.
+
+ AUses [`export_entries_as_dduf`] under the hood.
+
+ Args:
+ dduf_path (`str` or `os.PathLike`):
+ The path to the DDUF file to write.
+ folder_path (`str` or `os.PathLike`):
+ The path to the folder containing the diffusion model.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import export_folder_as_dduf
+ >>> export_folder_as_dduf(dduf_path="FLUX.1-dev.dduf", folder_path="path/to/FLUX.1-dev")
+ ```
+ """
+ folder_path = Path(folder_path)
+
+ def _iterate_over_folder() -> Iterable[tuple[str, Path]]:
+ for path in Path(folder_path).glob("**/*"):
+ if not path.is_file():
+ continue
+ if path.suffix not in DDUF_ALLOWED_ENTRIES:
+ logger.debug(f"Skipping file '{path}' (file type not allowed)")
+ continue
+ path_in_archive = path.relative_to(folder_path)
+ if len(path_in_archive.parts) >= 3:
+ logger.debug(f"Skipping file '{path}' (nested directories not allowed)")
+ continue
+ yield path_in_archive.as_posix(), path
+
+ export_entries_as_dduf(dduf_path, _iterate_over_folder())
+
+
+def _dump_content_in_archive(archive: zipfile.ZipFile, filename: str, content: str | os.PathLike | bytes) -> None:
+ with archive.open(filename, "w", force_zip64=True) as archive_fh:
+ if isinstance(content, (str, Path)):
+ content_path = Path(content)
+ with content_path.open("rb") as content_fh:
+ shutil.copyfileobj(content_fh, archive_fh, 1024 * 1024 * 8) # type: ignore[misc]
+ elif isinstance(content, bytes):
+ archive_fh.write(content)
+ else:
+ raise DDUFExportError(f"Invalid content type for {filename}. Must be str, Path or bytes.")
+
+
+def _load_content(content: str | Path | bytes) -> bytes:
+ """Load the content of an entry as bytes.
+
+ Used only for small checks (not to dump content into archive).
+ """
+ if isinstance(content, (str, Path)):
+ return Path(content).read_bytes()
+ elif isinstance(content, bytes):
+ return content
+ else:
+ raise DDUFExportError(f"Invalid content type. Must be str, Path or bytes. Got {type(content)}.")
+
+
+def _validate_dduf_entry_name(entry_name: str) -> str:
+ if "." + entry_name.split(".")[-1] not in DDUF_ALLOWED_ENTRIES:
+ raise DDUFInvalidEntryNameError(f"File type not allowed: {entry_name}")
+ if "\\" in entry_name:
+ raise DDUFInvalidEntryNameError(f"Entry names must use UNIX separators ('/'). Got {entry_name}.")
+ entry_name = entry_name.strip("/")
+ if entry_name.count("/") > 1:
+ raise DDUFInvalidEntryNameError(f"DDUF only supports 1 level of directory. Got {entry_name}.")
+ return entry_name
+
+
+def _validate_dduf_structure(index: Any, entry_names: Iterable[str]) -> None:
+ """
+ Consistency checks on the DDUF file structure.
+
+ Rules:
+ - The 'model_index.json' entry is required and must contain a dictionary.
+ - Each folder name must correspond to an entry in 'model_index.json'.
+ - Each folder must contain at least a config file ('config.json', 'tokenizer_config.json', 'preprocessor_config.json', 'scheduler_config.json').
+
+ Args:
+ index (Any):
+ The content of the 'model_index.json' entry.
+ entry_names (Iterable[str]):
+ The list of entry names in the DDUF file.
+
+ Raises:
+ - [`DDUFCorruptedFileError`]: If the DDUF file is corrupted (i.e. doesn't follow the DDUF format).
+ """
+ if not isinstance(index, dict):
+ raise DDUFCorruptedFileError(f"Invalid 'model_index.json' content. Must be a dictionary. Got {type(index)}.")
+
+ dduf_folders = {entry.split("/")[0] for entry in entry_names if "/" in entry}
+ for folder in dduf_folders:
+ if folder not in index:
+ raise DDUFCorruptedFileError(f"Missing required entry '{folder}' in 'model_index.json'.")
+ if not any(f"{folder}/{required_entry}" in entry_names for required_entry in DDUF_FOLDER_REQUIRED_ENTRIES):
+ raise DDUFCorruptedFileError(
+ f"Missing required file in folder '{folder}'. Must contains at least one of {DDUF_FOLDER_REQUIRED_ENTRIES}."
+ )
+
+
+def _get_data_offset(zf: zipfile.ZipFile, info: zipfile.ZipInfo) -> int:
+ """
+ Calculate the data offset for a file in a ZIP archive.
+
+ Args:
+ zf (`zipfile.ZipFile`):
+ The opened ZIP file. Must be opened in read mode.
+ info (`zipfile.ZipInfo`):
+ The file info.
+
+ Returns:
+ int: The offset of the file data in the ZIP archive.
+ """
+ if zf.fp is None:
+ raise DDUFCorruptedFileError("ZipFile object must be opened in read mode.")
+
+ # Step 1: Get the local file header offset
+ header_offset = info.header_offset
+
+ # Step 2: Read the local file header
+ zf.fp.seek(header_offset)
+ local_file_header = zf.fp.read(30) # Fixed-size part of the local header
+
+ if len(local_file_header) < 30:
+ raise DDUFCorruptedFileError("Incomplete local file header.")
+
+ # Step 3: Parse the header fields to calculate the start of file data
+ # Local file header: https://en.wikipedia.org/wiki/ZIP_(file_format)#File_headers
+ filename_len = int.from_bytes(local_file_header[26:28], "little")
+ extra_field_len = int.from_bytes(local_file_header[28:30], "little")
+
+ # Data offset is after the fixed header, filename, and extra fields
+ data_offset = header_offset + 30 + filename_len + extra_field_len
+
+ return data_offset
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_torch.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..d77754a36f7acd4dcf74bb42008f83d44a44e92e
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/serialization/_torch.py
@@ -0,0 +1,1186 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains pytorch-specific helpers."""
+
+import importlib
+import importlib.util
+import json
+import os
+import re
+from collections import defaultdict, namedtuple
+from collections.abc import Iterable
+from functools import lru_cache
+from pathlib import Path, PureWindowsPath
+from typing import TYPE_CHECKING, Any, NamedTuple, Union
+
+from packaging import version
+
+from .. import constants, logging
+from ._base import MAX_SHARD_SIZE, StateDictSplit, split_state_dict_into_shards_factory
+
+
+logger = logging.get_logger(__file__)
+
+SAFETENSORS_EXTENSION = ".safetensors"
+
+# A checkpoint index is plain metadata (a tensor name -> shard file mapping). 10 MB is already far beyond
+# anything a real checkpoint produces, so anything bigger is not worth parsing into memory.
+MAX_INDEX_FILE_SIZE = 10 * 1024 * 1024
+
+if TYPE_CHECKING:
+ import torch
+
+# SAVING
+
+
+def save_torch_model(
+ model: "torch.nn.Module",
+ save_directory: str | Path,
+ *,
+ filename_pattern: str | None = None,
+ force_contiguous: bool = True,
+ max_shard_size: int | str = MAX_SHARD_SIZE,
+ metadata: dict[str, str] | None = None,
+ safe_serialization: bool = True,
+ is_main_process: bool = True,
+ shared_tensors_to_discard: list[str] | None = None,
+):
+ """
+ Saves a given torch model to disk, handling sharding and shared tensors issues.
+
+ See also [`save_torch_state_dict`] to save a state dict with more flexibility.
+
+ For more information about tensor sharing, check out [this guide](https://huggingface.co/docs/safetensors/torch_shared_tensors).
+
+ The model state dictionary is split into shards so that each shard is smaller than a given size. The shards are
+ saved in the `save_directory` with the given `filename_pattern`. If the model is too big to fit in a single shard,
+ an index file is saved in the `save_directory` to indicate where each tensor is saved. This helper uses
+ [`split_torch_state_dict_into_shards`] under the hood. If `safe_serialization` is `True`, the shards are saved as
+ safetensors (the default). Otherwise, the shards are saved as pickle.
+
+ Before saving the model, the `save_directory` is cleaned from any previous shard files.
+
+ > [!WARNING]
+ > If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+ > size greater than `max_shard_size`.
+
+ > [!WARNING]
+ > If your model is a `transformers.PreTrainedModel`, you should pass `model._tied_weights_keys` as `shared_tensors_to_discard` to properly handle shared tensors saving. This ensures the correct duplicate tensors are discarded during saving.
+
+ Args:
+ model (`torch.nn.Module`):
+ The model to save on disk.
+ save_directory (`str` or `Path`):
+ The directory in which the model will be saved.
+ filename_pattern (`str`, *optional*):
+ The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ Defaults to `"model{suffix}.safetensors"` or `pytorch_model{suffix}.bin` depending on `safe_serialization`
+ parameter.
+ force_contiguous (`boolean`, *optional*):
+ Forcing the state_dict to be saved as contiguous tensors. This has no effect on the correctness of the
+ model, but it could potentially change performance if the layout of the tensor was chosen specifically for
+ that reason. Defaults to `True`.
+ max_shard_size (`int` or `str`, *optional*):
+ The maximum size of each shard, in bytes. Defaults to 5GB.
+ metadata (`dict[str, str]`, *optional*):
+ Extra information to save along with the model. Some metadata will be added for each dropped tensors.
+ This information will not be enough to recover the entire shared structure but might help understanding
+ things.
+ safe_serialization (`bool`, *optional*):
+ Whether to save as safetensors, which is the default behavior. If `False`, the shards are saved as pickle.
+ Safe serialization is recommended for security reasons. Saving as pickle is deprecated and will be removed
+ in a future version.
+ is_main_process (`bool`, *optional*):
+ Whether the process calling this is the main process or not. Useful when in distributed training like
+ TPUs and need to call this function from all processes. In this case, set `is_main_process=True` only on
+ the main process to avoid race conditions. Defaults to True.
+ shared_tensors_to_discard (`list[str]`, *optional*):
+ List of tensor names to drop when saving shared tensors. If not provided and shared tensors are
+ detected, it will drop the first name alphabetically.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import save_torch_model
+ >>> model = ... # A PyTorch model
+
+ # Save state dict to "path/to/folder". The model will be split into shards of 5GB each and saved as safetensors.
+ >>> save_torch_model(model, "path/to/folder")
+
+ # Load model back
+ >>> from huggingface_hub import load_torch_model # TODO
+ >>> load_torch_model(model, "path/to/folder")
+ >>>
+ ```
+ """
+ save_torch_state_dict(
+ state_dict=model.state_dict(),
+ filename_pattern=filename_pattern,
+ force_contiguous=force_contiguous,
+ max_shard_size=max_shard_size,
+ metadata=metadata,
+ safe_serialization=safe_serialization,
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ shared_tensors_to_discard=shared_tensors_to_discard,
+ )
+
+
+def save_torch_state_dict(
+ state_dict: dict[str, "torch.Tensor"],
+ save_directory: str | Path,
+ *,
+ filename_pattern: str | None = None,
+ force_contiguous: bool = True,
+ max_shard_size: int | str = MAX_SHARD_SIZE,
+ metadata: dict[str, str] | None = None,
+ safe_serialization: bool = True,
+ is_main_process: bool = True,
+ shared_tensors_to_discard: list[str] | None = None,
+) -> None:
+ """
+ Save a model state dictionary to the disk, handling sharding and shared tensors issues.
+
+ See also [`save_torch_model`] to directly save a PyTorch model.
+
+ For more information about tensor sharing, check out [this guide](https://huggingface.co/docs/safetensors/torch_shared_tensors).
+
+ The model state dictionary is split into shards so that each shard is smaller than a given size. The shards are
+ saved in the `save_directory` with the given `filename_pattern`. If the model is too big to fit in a single shard,
+ an index file is saved in the `save_directory` to indicate where each tensor is saved. This helper uses
+ [`split_torch_state_dict_into_shards`] under the hood. If `safe_serialization` is `True`, the shards are saved as
+ safetensors (the default). Otherwise, the shards are saved as pickle.
+
+ Before saving the model, the `save_directory` is cleaned from any previous shard files.
+
+ > [!WARNING]
+ > If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+ > size greater than `max_shard_size`.
+
+ > [!WARNING]
+ > If your model is a `transformers.PreTrainedModel`, you should pass `model._tied_weights_keys` as `shared_tensors_to_discard` to properly handle shared tensors saving. This ensures the correct duplicate tensors are discarded during saving.
+
+ Args:
+ state_dict (`dict[str, torch.Tensor]`):
+ The state dictionary to save.
+ save_directory (`str` or `Path`):
+ The directory in which the model will be saved.
+ filename_pattern (`str`, *optional*):
+ The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ Defaults to `"model{suffix}.safetensors"` or `pytorch_model{suffix}.bin` depending on `safe_serialization`
+ parameter.
+ force_contiguous (`boolean`, *optional*):
+ Forcing the state_dict to be saved as contiguous tensors. This has no effect on the correctness of the
+ model, but it could potentially change performance if the layout of the tensor was chosen specifically for
+ that reason. Defaults to `True`.
+ max_shard_size (`int` or `str`, *optional*):
+ The maximum size of each shard, in bytes. Defaults to 5GB.
+ metadata (`dict[str, str]`, *optional*):
+ Extra information to save along with the model. Some metadata will be added for each dropped tensors.
+ This information will not be enough to recover the entire shared structure but might help understanding
+ things.
+ safe_serialization (`bool`, *optional*):
+ Whether to save as safetensors, which is the default behavior. If `False`, the shards are saved as pickle.
+ Safe serialization is recommended for security reasons. Saving as pickle is deprecated and will be removed
+ in a future version.
+ is_main_process (`bool`, *optional*):
+ Whether the process calling this is the main process or not. Useful when in distributed training like
+ TPUs and need to call this function from all processes. In this case, set `is_main_process=True` only on
+ the main process to avoid race conditions. Defaults to True.
+ shared_tensors_to_discard (`list[str]`, *optional*):
+ List of tensor names to drop when saving shared tensors. If not provided and shared tensors are
+ detected, it will drop the first name alphabetically.
+
+ Example:
+
+ ```py
+ >>> from huggingface_hub import save_torch_state_dict
+ >>> model = ... # A PyTorch model
+
+ # Save state dict to "path/to/folder". The model will be split into shards of 5GB each and saved as safetensors.
+ >>> state_dict = model_to_save.state_dict()
+ >>> save_torch_state_dict(state_dict, "path/to/folder")
+ ```
+ """
+ save_directory = str(save_directory)
+
+ if filename_pattern is None:
+ filename_pattern = (
+ constants.SAFETENSORS_WEIGHTS_FILE_PATTERN
+ if safe_serialization
+ else constants.PYTORCH_WEIGHTS_FILE_PATTERN
+ )
+
+ if metadata is None:
+ metadata = {}
+ if safe_serialization:
+ try:
+ from safetensors.torch import save_file as save_file_fn
+ except ImportError as e:
+ raise ImportError(
+ "Please install `safetensors` to use safe serialization. "
+ "You can install it with `pip install safetensors`."
+ ) from e
+ # Clean state dict for safetensors
+ state_dict = _clean_state_dict_for_safetensors(
+ state_dict,
+ metadata,
+ force_contiguous=force_contiguous,
+ shared_tensors_to_discard=shared_tensors_to_discard,
+ )
+ else:
+ from torch import save as save_file_fn # type: ignore[assignment, no-redef]
+
+ logger.warning(
+ "You are using unsafe serialization. Due to security reasons, it is recommended not to load "
+ "pickled models from untrusted sources. If you intend to share your model, we strongly recommend "
+ "using safe serialization by installing `safetensors` with `pip install safetensors`."
+ )
+ # Split dict
+ state_dict_split = split_torch_state_dict_into_shards(
+ state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
+ )
+
+ # Only main process should clean up existing files to avoid race conditions in distributed environment
+ if is_main_process:
+ # Escape literal parts and use `fullmatch` so that e.g. "model.safetensors.backup" is not deleted.
+ prefix, _, suffix = filename_pattern.partition("{suffix}")
+ existing_files_regex = re.compile(
+ re.escape(prefix) + r"(-\d{5}-of-\d{5})?" + re.escape(suffix) + r"(\.index\.json)?"
+ )
+ for filename in os.listdir(save_directory):
+ if existing_files_regex.fullmatch(filename):
+ try:
+ logger.debug(f"Removing existing file '{filename}' from folder.")
+ os.remove(os.path.join(save_directory, filename))
+ except Exception as e:
+ logger.warning(
+ f"Error when trying to remove existing '{filename}' from folder: {e}. Continuing..."
+ )
+
+ # Save each shard
+ per_file_metadata = {"format": "pt"}
+ if not state_dict_split.is_sharded:
+ per_file_metadata.update(metadata)
+ safe_file_kwargs = {"metadata": per_file_metadata} if safe_serialization else {}
+ for filename, tensors in state_dict_split.filename_to_tensors.items():
+ shard = {tensor: state_dict[tensor] for tensor in tensors}
+ save_file_fn(shard, os.path.join(save_directory, filename), **safe_file_kwargs) # ty: ignore[invalid-argument-type]
+ logger.debug(f"Shard saved to {filename}")
+
+ # Save the index (if any)
+ if state_dict_split.is_sharded:
+ index_path = filename_pattern.format(suffix="") + ".index.json"
+ index = {
+ "metadata": {**state_dict_split.metadata, **metadata},
+ "weight_map": state_dict_split.tensor_to_filename,
+ }
+ with open(os.path.join(save_directory, index_path), "w") as f:
+ json.dump(index, f, indent=2)
+ logger.info(
+ f"The model is bigger than the maximum size per checkpoint ({max_shard_size}). "
+ f"Model weighs have been saved in {len(state_dict_split.filename_to_tensors)} checkpoint shards. "
+ f"You can find where each parameters has been saved in the index located at {index_path}."
+ )
+
+ logger.info(f"Model weights successfully saved to {save_directory}!")
+
+
+def split_torch_state_dict_into_shards(
+ state_dict: dict[str, "torch.Tensor"],
+ *,
+ filename_pattern: str = constants.SAFETENSORS_WEIGHTS_FILE_PATTERN,
+ max_shard_size: int | str = MAX_SHARD_SIZE,
+) -> StateDictSplit:
+ """
+ Split a model state dictionary in shards so that each shard is smaller than a given size.
+
+ The shards are determined by iterating through the `state_dict` in the order of its keys. There is no optimization
+ made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
+ have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
+ [6+2+2GB], [6+2GB], [6GB].
+
+
+ > [!TIP]
+ > To save a model state dictionary to the disk, see [`save_torch_state_dict`]. This helper uses
+ > `split_torch_state_dict_into_shards` under the hood.
+
+ > [!WARNING]
+ > If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+ > size greater than `max_shard_size`.
+
+ Args:
+ state_dict (`dict[str, torch.Tensor]`):
+ The state dictionary to save.
+ filename_pattern (`str`, *optional*):
+ The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ Defaults to `"model{suffix}.safetensors"`.
+ max_shard_size (`int` or `str`, *optional*):
+ The maximum size of each shard, in bytes. Defaults to 5GB.
+
+ Returns:
+ [`StateDictSplit`]: A `StateDictSplit` object containing the shards and the index to retrieve them.
+
+ Example:
+ ```py
+ >>> import json
+ >>> import os
+ >>> from safetensors.torch import save_file as safe_save_file
+ >>> from huggingface_hub import split_torch_state_dict_into_shards
+
+ >>> def save_state_dict(state_dict: dict[str, torch.Tensor], save_directory: str):
+ ... state_dict_split = split_torch_state_dict_into_shards(state_dict)
+ ... for filename, tensors in state_dict_split.filename_to_tensors.items():
+ ... shard = {tensor: state_dict[tensor] for tensor in tensors}
+ ... safe_save_file(
+ ... shard,
+ ... os.path.join(save_directory, filename),
+ ... metadata={"format": "pt"},
+ ... )
+ ... if state_dict_split.is_sharded:
+ ... index = {
+ ... "metadata": state_dict_split.metadata,
+ ... "weight_map": state_dict_split.tensor_to_filename,
+ ... }
+ ... with open(os.path.join(save_directory, "model.safetensors.index.json"), "w") as f:
+ ... f.write(json.dumps(index, indent=2))
+ ```
+ """
+ return split_state_dict_into_shards_factory(
+ state_dict,
+ max_shard_size=max_shard_size,
+ filename_pattern=filename_pattern,
+ get_storage_size=get_torch_storage_size,
+ get_storage_id=get_torch_storage_id,
+ )
+
+
+# LOADING
+
+
+def load_torch_model(
+ model: "torch.nn.Module",
+ checkpoint_path: str | os.PathLike,
+ *,
+ strict: bool = False,
+ safe: bool = True,
+ weights_only: bool = True,
+ map_location: Union[str, "torch.device"] | None = None,
+ mmap: bool = False,
+ filename_pattern: str | None = None,
+) -> NamedTuple:
+ """
+ Load a checkpoint into a model, handling both sharded and non-sharded checkpoints.
+
+ Args:
+ model (`torch.nn.Module`):
+ The model in which to load the checkpoint.
+ checkpoint_path (`str` or `os.PathLike`):
+ Path to either the checkpoint file or directory containing the checkpoint(s).
+ strict (`bool`, *optional*, defaults to `False`):
+ Whether to strictly enforce that the keys in the model state dict match the keys in the checkpoint.
+ As with [`torch.nn.Module.load_state_dict`], the check happens once the checkpoint has been loaded, so
+ the model may have been partially updated when the error is raised.
+ safe (`bool`, *optional*, defaults to `True`):
+ If `safe` is True, the safetensors files will be loaded. If `safe` is False, the function
+ will first attempt to load safetensors files if they are available, otherwise it will fall back to loading
+ pickle files. A `filename_pattern` that does not describe safetensors files is rejected when `safe=True`.
+ weights_only (`bool`, *optional*, defaults to `True`):
+ If True, only loads the model weights without optimizer states and other metadata, using torch's
+ restricted unpickler. Set to False to allow arbitrary Python objects in a pickle checkpoint (this
+ executes arbitrary code at load time). Has no effect on PyTorch < 1.13, which has no restricted
+ unpickler and always unpickles without restriction.
+ map_location (`str` or `torch.device`, *optional*):
+ A `torch.device` object, string or a dict specifying how to remap storage locations. It
+ indicates the location where all tensors should be loaded.
+ mmap (`bool`, *optional*, defaults to `False`):
+ Whether to use memory-mapped file loading. Memory mapping can improve loading performance
+ for large models in PyTorch >= 2.1.0 with zipfile-based checkpoints.
+ filename_pattern (`str`, *optional*):
+ The pattern to look for the index file. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ Defaults to `"model{suffix}.safetensors"`.
+ Returns:
+ `NamedTuple`: A named tuple with `missing_keys` and `unexpected_keys` fields.
+ - `missing_keys` is a list of str containing the missing keys, i.e. keys that are in the model but not in the checkpoint.
+ - `unexpected_keys` is a list of str containing the unexpected keys, i.e. keys that are in the checkpoint but not in the model.
+
+ Raises:
+ [`FileNotFoundError`](https://docs.python.org/3/library/exceptions.html#FileNotFoundError)
+ If the checkpoint file or directory does not exist.
+ [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+ If safetensors or torch is not installed when trying to load a .safetensors file or a PyTorch checkpoint respectively.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If the checkpoint format cannot be determined.
+
+ Example:
+ ```python
+ >>> from huggingface_hub import load_torch_model
+ >>> model = ... # A PyTorch model
+ >>> load_torch_model(model, "path/to/checkpoint")
+ ```
+ """
+ checkpoint_path = Path(checkpoint_path)
+
+ if not checkpoint_path.exists():
+ raise FileNotFoundError(f"Checkpoint path {checkpoint_path} does not exist")
+ # 1. Check if checkpoint is a single file
+ if checkpoint_path.is_file():
+ state_dict = load_state_dict_from_file(
+ checkpoint_file=checkpoint_path,
+ map_location=map_location,
+ weights_only=weights_only,
+ mmap=mmap,
+ safe=safe,
+ )
+ return model.load_state_dict(state_dict, strict=strict)
+
+ # 2. If not, checkpoint_path is a directory
+ if filename_pattern is None:
+ filename_pattern = constants.SAFETENSORS_WEIGHTS_FILE_PATTERN
+ index_path = checkpoint_path / (filename_pattern.format(suffix="") + ".index.json")
+ # Only fallback to pickle format if safetensors index is not found and safe is False.
+ if not index_path.is_file() and not safe:
+ filename_pattern = constants.PYTORCH_WEIGHTS_FILE_PATTERN
+ elif safe and not _is_safetensors(filename_pattern.format(suffix="")):
+ # A pickle `filename_pattern` combined with `safe=True` is a conflict: loading those shards would execute
+ # arbitrary code at load time. Refuse it instead of silently letting the pattern win over `safe`.
+ raise ValueError(
+ f"`filename_pattern={filename_pattern!r}` does not describe safetensors files but `safe=True`. "
+ "Pass `safe=False` to explicitly allow loading pickled weights (this executes arbitrary code at load time)."
+ )
+
+ index_path = checkpoint_path / (filename_pattern.format(suffix="") + ".index.json")
+
+ if index_path.is_file():
+ return _load_sharded_checkpoint(
+ model=model,
+ save_directory=checkpoint_path,
+ strict=strict,
+ weights_only=weights_only,
+ safe=safe,
+ filename_pattern=filename_pattern,
+ )
+
+ # Look for single model file.
+ # Safetensors is tried first even when `safe=False`: the docstring promises a *fallback* to pickle files for
+ # that case, not a pickle-first preference. A directory holding only `model.safetensors` used to raise
+ # `ValueError` under `safe=False`.
+ model_files = list(checkpoint_path.glob("*" + SAFETENSORS_EXTENSION))
+ if len(model_files) != 1 and not safe:
+ # Fallback only helps if it yields exactly one file, so it must also run when several safetensors files
+ # were found (e.g. `model.safetensors` next to `model.fp16.safetensors` and a single `pytorch_model.bin`).
+ model_files = list(checkpoint_path.glob("*.bin"))
+ if len(model_files) == 1:
+ state_dict = load_state_dict_from_file(
+ checkpoint_file=model_files[0],
+ map_location=map_location,
+ weights_only=weights_only,
+ mmap=mmap,
+ safe=safe,
+ )
+ return model.load_state_dict(state_dict, strict=strict)
+
+ raise ValueError(
+ f"Directory '{checkpoint_path}' does not contain a valid checkpoint. "
+ "Expected either a sharded checkpoint with an index file, or a single model file."
+ )
+
+
+def _load_sharded_checkpoint(
+ model: "torch.nn.Module",
+ save_directory: os.PathLike,
+ *,
+ strict: bool = False,
+ weights_only: bool = True,
+ safe: bool = True,
+ filename_pattern: str = constants.SAFETENSORS_WEIGHTS_FILE_PATTERN,
+) -> NamedTuple:
+ """
+ Loads a sharded checkpoint into a model. This is the same as
+ [`torch.nn.Module.load_state_dict`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.load_state_dict)
+ but for a sharded checkpoint. Each shard is loaded one by one and removed from memory after being loaded into the model.
+
+ Args:
+ model (`torch.nn.Module`):
+ The model in which to load the checkpoint.
+ save_directory (`str` or `os.PathLike`):
+ A path to a folder containing the sharded checkpoint.
+ strict (`bool`, *optional*, defaults to `False`):
+ Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.
+ As with [`torch.nn.Module.load_state_dict`], the check happens once every shard has been loaded, so the
+ model may have been partially updated when the error is raised.
+ weights_only (`bool`, *optional*, defaults to `True`):
+ If True, only loads the model weights without optimizer states and other metadata, using torch's
+ restricted unpickler. Set to False to allow arbitrary Python objects in a pickle checkpoint (this
+ executes arbitrary code at load time). Has no effect on PyTorch < 1.13, which has no restricted
+ unpickler and always unpickles without restriction.
+ safe (`bool`, *optional*, defaults to `True`):
+ If True, every shard is loaded with the safetensors loader. If False, shards are loaded as safetensors
+ when their name says so and with `torch.load` otherwise.
+ filename_pattern (`str`, *optional*, defaults to `"model{suffix}.safetensors"`):
+ The pattern to look for the index file. Pattern must be a string that
+ can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+ Defaults to `"model{suffix}.safetensors"`.
+
+ Returns:
+ `NamedTuple`: A named tuple with `missing_keys` and `unexpected_keys` fields,
+ - `missing_keys` is a list of str containing the missing keys
+ - `unexpected_keys` is a list of str containing the unexpected keys
+ """
+
+ # 1. Load and validate index file
+ # The index file contains mapping of parameter names to shard files
+ index_path = filename_pattern.format(suffix="") + ".index.json"
+ index_file = os.path.join(save_directory, index_path)
+ # Refuse oversized index files before parsing them: the index is metadata, never a multi-GB payload.
+ if os.path.getsize(index_file) > MAX_INDEX_FILE_SIZE:
+ raise ValueError(
+ f"Invalid index file '{index_file}': larger than {MAX_INDEX_FILE_SIZE} bytes, which is not a valid "
+ "checkpoint index."
+ )
+ with open(index_file, encoding="utf-8") as f:
+ index = json.load(f)
+ # Validate the structure before touching it: a malformed index would otherwise surface as a raw `KeyError` /
+ # `AttributeError` / `TypeError` traceback from attacker-controlled input.
+ if not isinstance(index, dict) or not isinstance(index.get("weight_map"), dict):
+ raise ValueError(f"Invalid index file '{index_file}': expected a JSON object with a 'weight_map' object.")
+ if not all(isinstance(shard_file, str) for shard_file in index["weight_map"].values()):
+ raise ValueError(f"Invalid index file '{index_file}': all 'weight_map' values must be strings.")
+
+ # 2. Validate shard filenames from the index
+ # This prevents path traversal attacks and extension confusion attacks
+ # (e.g. a safetensors index referencing .bin pickle files)
+ expected_extension = Path(filename_pattern.format(suffix="")).suffix # e.g. ".safetensors"
+ shard_files = list(set(index["weight_map"].values()))
+ for shard_file in shard_files:
+ # Reject anything that could escape `save_directory` on any host OS:
+ # POSIX absolute ("/tmp/x"), Windows drive ("C:x", "C:\\x"), UNC
+ # ("\\\\server\\share\\x"), rooted-without-drive ("\\x", "/x"), or
+ # ".." traversal — including "..\\x" which `os.path.isabs` never caught on POSIX.
+ #
+ # We parse with `PureWindowsPath` *regardless of host OS*: it treats both "/" and
+ # "\\" as separators and exposes `drive` / `root`, so a single check rejects a
+ # malicious index file on Linux too (e.g. if it's later opened on Windows). The
+ # only over-strict case is a POSIX filename like "a:foo" which would be parsed as
+ # drive "a:" — such names are never produced for safetensors shards and would
+ # break on Windows anyway, so rejecting them is fine.
+ win_path = PureWindowsPath(shard_file)
+ if win_path.drive or win_path.root or ".." in win_path.parts:
+ raise ValueError(
+ f"Invalid shard filename '{shard_file}' in index file '{index_file}'. "
+ "Shard filenames must be relative paths without '..' components."
+ )
+ # Reject extension mismatch (e.g. .bin shard in a .safetensors index). Note this check is deliberately
+ # case-*sensitive* while `_is_safetensors` (the loader routing hint) is not: a legitimate index produced by
+ # `save_torch_state_dict` is always lowercase, and there is no reason to accept anything else from an index
+ # file. An index entry spelled `.safetensors` stays routed to the safetensors loader even on a
+ # case-insensitive filesystem resolving it to an uppercase file on disk, so it fails instead of executing.
+ if not shard_file.endswith(expected_extension):
+ raise ValueError(
+ f"Invalid shard filename '{shard_file}' in index file '{index_file}'. "
+ f"Expected '{expected_extension}' extension to match the index format."
+ )
+
+ # 3. Load each shard using `load_state_dict`
+ # Get unique shard files (multiple parameters can be in same shard)
+ loaded_keys: set[str] = set()
+ for shard_file in shard_files:
+ # Load shard into memory
+ shard_path = os.path.join(save_directory, shard_file)
+ state_dict = load_state_dict_from_file(
+ shard_path,
+ map_location="cpu",
+ weights_only=weights_only,
+ safe=safe,
+ )
+ loaded_keys.update(state_dict.keys())
+ # Update model with parameters from this shard. `strict=False` here: a single shard never holds all the
+ # model's keys, so per-shard strict loading would always raise. Strictness is enforced against the real
+ # loaded keys below.
+ model.load_state_dict(state_dict, strict=False)
+ # Explicitly remove the state dict from memory
+ del state_dict
+
+ # 4. Validate keys and return compatibility info.
+ # Both are computed from what the shards actually contained, never from the index file: the index is
+ # attacker-controlled metadata and must not be able to lie about what was loaded into the model — neither by
+ # hiding a tensor it did declare, nor by failing `strict=True` over a tensor no shard ever held.
+ if unexpected := loaded_keys - set(index["weight_map"]):
+ logger.warning(f"Shard files contain tensors absent from the index: {sorted(unexpected)}")
+ if strict:
+ _validate_keys_for_strict_loading(model, loaded_keys)
+ model_keys = set(model.state_dict().keys())
+ return _IncompatibleKeys(
+ missing_keys=list(model_keys - loaded_keys), unexpected_keys=list(loaded_keys - model_keys)
+ )
+
+
+def load_state_dict_from_file(
+ checkpoint_file: str | os.PathLike,
+ map_location: Union[str, "torch.device"] | None = None,
+ weights_only: bool = True,
+ mmap: bool = False,
+ *,
+ safe: bool = True,
+) -> dict[str, "torch.Tensor"] | Any:
+ """
+ Loads a checkpoint file, handling both safetensors and pickle checkpoint formats.
+
+ Args:
+ checkpoint_file (`str` or `os.PathLike`):
+ Path to the checkpoint file to load. Can be either a safetensors or pickle (`.bin`) checkpoint.
+ map_location (`str` or `torch.device`, *optional*):
+ A `torch.device` object, string or a dict specifying how to remap storage locations. It
+ indicates the location where all tensors should be loaded.
+ weights_only (`bool`, *optional*, defaults to `True`):
+ If True, only loads the model weights without optimizer states and other metadata, using torch's
+ restricted unpickler. Set to False to allow arbitrary Python objects in a pickle checkpoint (this
+ executes arbitrary code at load time). Has no effect when loading safetensors files, nor on
+ PyTorch < 1.13 which has no restricted unpickler — those versions always unpickle without restriction
+ and a warning is logged.
+ mmap (`bool`, *optional*, defaults to `False`):
+ Whether to use memory-mapped file loading. Memory mapping can improve loading performance
+ for large models in PyTorch >= 2.1.0 with zipfile-based checkpoints. Has no effect when
+ loading safetensors files, as the `safetensors` library uses memory mapping by default.
+ safe (`bool`, *optional*, defaults to `True`):
+ If True, the checkpoint is always loaded as safetensors, whatever its name. Any other format
+ (e.g. a pickle `.bin` file) raises a `ValueError` instead of being deserialized: pickle checkpoints can
+ execute arbitrary code at load time. If False, the file is loaded as safetensors when its name says so,
+ and with `torch.load` otherwise.
+
+ Returns:
+ `Union[dict[str, "torch.Tensor"], Any]`: The loaded checkpoint.
+ - For safetensors files: always returns a dictionary mapping parameter names to tensors.
+ - For pickle files: returns any Python object that was pickled (commonly a state dict, but could be
+ an entire model, optimizer state, or any other Python object).
+
+ Raises:
+ [`FileNotFoundError`](https://docs.python.org/3/library/exceptions.html#FileNotFoundError)
+ If the checkpoint file does not exist.
+ [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+ If safetensors or torch is not installed when trying to load a .safetensors file or a PyTorch checkpoint respectively.
+ [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError)
+ If the checkpoint file format is invalid or if git-lfs files are not properly downloaded.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If the checkpoint file path is empty or invalid, or if it cannot be deserialized as safetensors while
+ `safe=True` (pass `safe=False` to allow pickle checkpoints).
+
+ Example:
+ ```python
+ >>> from huggingface_hub import load_state_dict_from_file
+
+ # Load a safetensors checkpoint (safe by default)
+ >>> state_dict = load_state_dict_from_file("path/to/model.safetensors", safe=True)
+ >>> model.load_state_dict(state_dict)
+
+ # Load a pickle checkpoint. `safe=False` is required: pickle files can execute arbitrary code.
+ >>> state_dict = load_state_dict_from_file("path/to/model.bin", safe=False, map_location="cpu")
+ >>> model.load_state_dict(state_dict)
+ ```
+ """
+ checkpoint_path = Path(checkpoint_file)
+
+ # Check if file exists and is a regular file (not a directory)
+ if not checkpoint_path.is_file():
+ raise FileNotFoundError(
+ f"No checkpoint file found at '{checkpoint_path}'. Please verify the path is correct and "
+ "the file has been properly downloaded."
+ )
+
+ # Load safetensors checkpoint
+ if safe or _is_safetensors(checkpoint_path):
+ # `safe=True` is a hard guarantee: the safetensors loader is used whatever the file is called. A pickle file
+ # pretending to be safetensors — or any other name the extension check failed to recognize — fails to
+ # deserialize instead of being executed. The filename is only a hint, used in the already-unsafe `safe=False`
+ # branch below.
+ try:
+ return _load_safetensors_file(checkpoint_path, map_location=map_location)
+ except ImportError:
+ raise
+ except OSError:
+ raise # invalid safetensors metadata: the error message is already actionable
+ except Exception as e:
+ if safe:
+ raise ValueError(
+ f"Cannot load '{checkpoint_path}' as safetensors. If this is a pickle checkpoint, pass "
+ "`safe=False` to allow it (this executes arbitrary code at load time)."
+ ) from e
+ raise
+
+ # Otherwise, load from pickle
+ try:
+ import torch
+ from torch import load
+ except ImportError as e:
+ raise ImportError(
+ "Please install `torch` to load torch tensors. You can install it with `pip install torch`."
+ ) from e
+
+ # Add additional kwargs, mmap is only supported in torch >= 2.1.0
+ additional_kwargs = {}
+ if version.parse(torch.__version__) >= version.parse("2.1.0"):
+ additional_kwargs["mmap"] = mmap
+
+ # weights_only is only supported in torch >= 1.13.0
+ if version.parse(torch.__version__) >= version.parse("1.13.0"):
+ additional_kwargs["weights_only"] = weights_only
+
+ if not additional_kwargs.get("weights_only", False):
+ # Warn only when the unrestricted unpickler is what actually runs: either the caller asked for it, or torch
+ # is too old to have a restricted one and `weights_only=True` could not be honored. With the restricted
+ # unpickler in play the warning would be both untrue and noisy (it is emitted once per shard).
+ logger.warning(
+ f"Loading '{checkpoint_path}' with `torch.load` and no restricted unpickler (`weights_only=False`, or "
+ "torch < 1.13 which does not support it). Pickle checkpoints can execute arbitrary code at load time; "
+ "only load files from sources you trust."
+ )
+
+ return load(
+ checkpoint_file,
+ map_location=map_location,
+ **additional_kwargs,
+ )
+
+
+def _load_safetensors_file(
+ checkpoint_file: str | os.PathLike,
+ map_location: Union[str, "torch.device"] | None = None,
+) -> dict[str, "torch.Tensor"]:
+ """Load a safetensors checkpoint. Raises `safetensors.SafetensorError` if the file is not a valid safetensors."""
+ try:
+ from safetensors import safe_open
+ from safetensors.torch import load_file
+ except ImportError as e:
+ raise ImportError(
+ "Please install `safetensors` to load safetensors checkpoint. "
+ "You can install it with `pip install safetensors`."
+ ) from e
+
+ # Check format of the archive
+ with safe_open(checkpoint_file, framework="pt") as f: # type: ignore[attr-defined]
+ metadata = f.metadata()
+ # see comment: https://github.com/huggingface/transformers/blob/3d213b57fe74302e5902d68ed9478c3ad1aaa713/src/transformers/modeling_utils.py#L3966
+ if metadata is not None and metadata.get("format") not in ["pt", "mlx"]:
+ raise OSError(
+ f"The safetensors archive passed at {checkpoint_file} does not contain the valid metadata. Make sure "
+ "you save your model with the `save_torch_model` method."
+ )
+ if map_location is None:
+ device = "cpu" # `load_file`'s own default, spelled out so `device` is always a `str`
+ else:
+ device = str(map_location.type) if hasattr(map_location, "type") else str(map_location)
+ # meta device is not supported with safetensors, falling back to CPU
+ if device == "meta":
+ logger.warning("Meta device is not supported with safetensors. Falling back to CPU device.")
+ device = "cpu"
+ return load_file(checkpoint_file, device=device)
+
+
+# HELPERS
+
+
+def _is_safetensors(filename: Union[str, os.PathLike]) -> bool:
+ """Whether `filename` must be loaded with the safetensors loader.
+
+ The comparison is case-insensitive on purpose: Windows and macOS resolve filenames case-insensitively, so a file
+ named `model.SAFETENSORS` can be picked up by a case-insensitive glob. This is only a *hint* used when `safe=False`;
+ it is never the security boundary (see `load_state_dict_from_file`).
+ """
+ return str(filename).lower().endswith(SAFETENSORS_EXTENSION)
+
+
+def _validate_keys_for_strict_loading(
+ model: "torch.nn.Module",
+ loaded_keys: Iterable[str],
+) -> None:
+ """
+ Validate that model keys match loaded keys when strict loading is enabled.
+
+ Args:
+ model: The PyTorch model being loaded
+ loaded_keys: The keys present in the checkpoint
+
+ Raises:
+ RuntimeError: If there are missing or unexpected keys in strict mode
+ """
+ loaded_keys_set = set(loaded_keys)
+ model_keys = set(model.state_dict().keys())
+ missing_keys = model_keys - loaded_keys_set # Keys in model but not in checkpoint
+ unexpected_keys = loaded_keys_set - model_keys # Keys in checkpoint but not in model
+
+ if missing_keys or unexpected_keys:
+ error_message = f"Error(s) in loading state_dict for {model.__class__.__name__}"
+ if missing_keys:
+ str_missing_keys = ",".join([f'"{k}"' for k in sorted(missing_keys)])
+ error_message += f"\nMissing key(s): {str_missing_keys}."
+ if unexpected_keys:
+ str_unexpected_keys = ",".join([f'"{k}"' for k in sorted(unexpected_keys)])
+ error_message += f"\nUnexpected key(s): {str_unexpected_keys}."
+ raise RuntimeError(error_message)
+
+
+def _get_unique_id(tensor: "torch.Tensor") -> int | tuple[Any, ...]:
+ """Returns a unique id for plain tensor
+ or a (potentially nested) Tuple of unique id for the flattened Tensor
+ if the input is a wrapper tensor subclass Tensor
+ """
+
+ try:
+ from torch.distributed.tensor import DTensor
+
+ if isinstance(tensor, DTensor):
+ local_tensor = tensor.to_local()
+ return local_tensor.storage().data_ptr()
+ except ImportError:
+ pass
+
+ try:
+ # for torch 2.1 and above we can also handle tensor subclasses
+ from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+ if is_traceable_wrapper_subclass(tensor):
+ attrs, _ = tensor.__tensor_flatten__() # type: ignore[attr-defined]
+ return tuple(_get_unique_id(getattr(tensor, attr)) for attr in attrs)
+
+ except ImportError:
+ # for torch version less than 2.1, we can fall back to original implementation
+ pass
+
+ if tensor.device.type == "xla" and is_torch_tpu_available():
+ # NOTE: xla tensors don't have storage
+ # use some other unique id to distinguish.
+ # this is a XLA tensor, it must be created using torch_xla's
+ # device. So the following import is safe:
+ import torch_xla # type: ignore[import]
+
+ unique_id = torch_xla._XLAC._xla_get_tensor_id(tensor)
+ else:
+ unique_id = storage_ptr(tensor)
+
+ return unique_id
+
+
+def get_torch_storage_id(tensor: "torch.Tensor") -> tuple["torch.device", int | tuple[Any, ...], int] | None:
+ """
+ Return unique identifier to a tensor storage.
+
+ Multiple different tensors can share the same underlying storage. This identifier is
+ guaranteed to be unique and constant for this tensor's storage during its lifetime. Two tensor storages with
+ non-overlapping lifetimes may have the same id.
+ In the case of meta tensors, we return None since we can't tell if they share the same storage.
+
+ Taken from https://github.com/huggingface/transformers/blob/1ecf5f7c982d761b4daaa96719d162c324187c64/src/transformers/pytorch_utils.py#L278.
+ """
+ if tensor.device.type == "meta":
+ return None
+ else:
+ return tensor.device, _get_unique_id(tensor), get_torch_storage_size(tensor)
+
+
+def get_torch_storage_size(tensor: "torch.Tensor") -> int:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/08db34094e9e59e2f9218f2df133b7b4aaff5a99/bindings/python/py_src/safetensors/torch.py#L31C1-L41C59
+ """
+ try:
+ from torch.distributed.tensor import DTensor
+
+ if isinstance(tensor, DTensor):
+ # this returns the size of the FULL tensor in bytes
+ return tensor.nbytes
+ except ImportError:
+ pass
+
+ try:
+ # for torch 2.1 and above we can also handle tensor subclasses
+ from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+ if is_traceable_wrapper_subclass(tensor):
+ attrs, _ = tensor.__tensor_flatten__() # type: ignore[attr-defined]
+ return sum(get_torch_storage_size(getattr(tensor, attr)) for attr in attrs)
+ except ImportError:
+ # for torch version less than 2.1, we can fall back to original implementation
+ pass
+
+ try:
+ return tensor.untyped_storage().nbytes()
+ except AttributeError:
+ # Fallback for torch==1.10
+ try:
+ return tensor.storage().size() * _get_dtype_size(tensor.dtype)
+ except NotImplementedError:
+ # Fallback for meta storage
+ # On torch >=2.0 this is the tensor size
+ return tensor.nelement() * _get_dtype_size(tensor.dtype)
+
+
+@lru_cache
+def is_torch_tpu_available(check_device=True):
+ """
+ Checks if `torch_xla` is installed and potentially if a TPU is in the environment
+
+ Taken from https://github.com/huggingface/transformers/blob/1ecf5f7c982d761b4daaa96719d162c324187c64/src/transformers/utils/import_utils.py#L463.
+ """
+ if importlib.util.find_spec("torch_xla") is not None:
+ if check_device:
+ # We need to check if `xla_device` can be found, will raise a RuntimeError if not
+ try:
+ import torch_xla.core.xla_model as xm # type: ignore[import]
+
+ _ = xm.xla_device()
+ return True
+ except RuntimeError:
+ return False
+ return True
+ return False
+
+
+def storage_ptr(tensor: "torch.Tensor") -> int | tuple[Any, ...]:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L11.
+ """
+ try:
+ # for torch 2.1 and above we can also handle tensor subclasses
+ from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+ if is_traceable_wrapper_subclass(tensor):
+ return _get_unique_id(tensor) # type: ignore
+ except ImportError:
+ # for torch version less than 2.1, we can fall back to original implementation
+ pass
+
+ try:
+ return tensor.untyped_storage().data_ptr()
+ except Exception:
+ # Fallback for torch==1.10
+ try:
+ return tensor.storage().data_ptr()
+ except NotImplementedError:
+ # Fallback for meta storage
+ return 0
+
+
+def _clean_state_dict_for_safetensors(
+ state_dict: dict[str, "torch.Tensor"],
+ metadata: dict[str, str],
+ force_contiguous: bool = True,
+ shared_tensors_to_discard: list[str] | None = None,
+):
+ """Remove shared tensors from state_dict and update metadata accordingly (for reloading).
+
+ Warning: `state_dict` and `metadata` are mutated in-place!
+
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L155.
+ """
+ to_removes = _remove_duplicate_names(state_dict, discard_names=shared_tensors_to_discard)
+ for kept_name, to_remove_group in to_removes.items():
+ for to_remove in to_remove_group:
+ if metadata is None:
+ metadata = {}
+
+ if to_remove not in metadata:
+ # Do not override user data
+ metadata[to_remove] = kept_name
+ del state_dict[to_remove]
+ if force_contiguous:
+ state_dict = {k: v.contiguous() for k, v in state_dict.items()}
+ return state_dict
+
+
+def _end_ptr(tensor: "torch.Tensor") -> int:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L23.
+ """
+ if tensor.nelement():
+ stop = tensor.view(-1)[-1].data_ptr() + _get_dtype_size(tensor.dtype)
+ else:
+ stop = tensor.data_ptr()
+ return stop
+
+
+def _filter_shared_not_shared(tensors: list[set[str]], state_dict: dict[str, "torch.Tensor"]) -> list[set[str]]:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L44
+ """
+ filtered_tensors = []
+ for shared in tensors:
+ if len(shared) < 2:
+ filtered_tensors.append(shared)
+ continue
+
+ areas = []
+ for name in shared:
+ tensor = state_dict[name]
+ areas.append((tensor.data_ptr(), _end_ptr(tensor), name))
+ areas.sort()
+
+ _, last_stop, last_name = areas[0]
+ filtered_tensors.append({last_name})
+ for start, stop, name in areas[1:]:
+ if start >= last_stop:
+ filtered_tensors.append({name})
+ else:
+ filtered_tensors[-1].add(name)
+ last_stop = stop
+
+ return filtered_tensors
+
+
+def _find_shared_tensors(state_dict: dict[str, "torch.Tensor"]) -> list[set[str]]:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L69.
+ """
+ import torch
+
+ tensors_dict = defaultdict(set)
+ for k, v in state_dict.items():
+ if v.device != torch.device("meta") and storage_ptr(v) != 0 and get_torch_storage_size(v) != 0:
+ # Need to add device as key because of multiple GPU.
+ tensors_dict[(v.device, storage_ptr(v), get_torch_storage_size(v))].add(k)
+ tensors = list(sorted(tensors_dict.values()))
+ tensors = _filter_shared_not_shared(tensors, state_dict)
+ return tensors
+
+
+def _is_complete(tensor: "torch.Tensor") -> bool:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L80
+ """
+ try:
+ # for torch 2.1 and above we can also handle tensor subclasses
+ from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+ if is_traceable_wrapper_subclass(tensor):
+ attrs, _ = tensor.__tensor_flatten__() # type: ignore[attr-defined]
+ return all(_is_complete(getattr(tensor, attr)) for attr in attrs)
+ except ImportError:
+ # for torch version less than 2.1, we can fall back to original implementation
+ pass
+
+ return tensor.data_ptr() == storage_ptr(tensor) and tensor.nelement() * _get_dtype_size(
+ tensor.dtype
+ ) == get_torch_storage_size(tensor)
+
+
+def _remove_duplicate_names(
+ state_dict: dict[str, "torch.Tensor"],
+ *,
+ preferred_names: list[str] | None = None,
+ discard_names: list[str] | None = None,
+) -> dict[str, list[str]]:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L80
+ """
+ if preferred_names is None:
+ preferred_names = []
+ unique_preferred_names = set(preferred_names)
+ if discard_names is None:
+ discard_names = []
+ unique_discard_names = set(discard_names)
+
+ shareds = _find_shared_tensors(state_dict)
+ to_remove = defaultdict(list)
+ for shared in shareds:
+ complete_names = {name for name in shared if _is_complete(state_dict[name])}
+ if not complete_names:
+ raise RuntimeError(
+ "Error while trying to find names to remove to save state dict, but found no suitable name to keep"
+ f" for saving amongst: {shared}. None is covering the entire storage. Refusing to save/load the model"
+ " since you could be storing much more memory than needed. Please refer to"
+ " https://huggingface.co/docs/safetensors/torch_shared_tensors for more information. Or open an"
+ " issue."
+ )
+
+ keep_name = sorted(list(complete_names))[0]
+
+ # Mechanism to preferentially select keys to keep
+ # coming from the on-disk file to allow
+ # loading models saved with a different choice
+ # of keep_name
+ preferred = complete_names.difference(unique_discard_names)
+ if preferred:
+ keep_name = sorted(list(preferred))[0]
+
+ if unique_preferred_names:
+ preferred = unique_preferred_names.intersection(complete_names)
+ if preferred:
+ keep_name = sorted(list(preferred))[0]
+ for name in sorted(shared):
+ if name != keep_name:
+ to_remove[keep_name].append(name)
+ return to_remove
+
+
+@lru_cache
+def _get_dtype_size(dtype: "torch.dtype") -> int:
+ """
+ Taken from https://github.com/huggingface/safetensors/blob/b7c0f38b6ae072c3cc6208933df0c81fbd2ef837/bindings/python/py_src/safetensors/torch.py#L398
+ """
+ import torch
+
+ # torch.float8 formats require 2.1; we do not support these dtypes on earlier versions
+ _float8_e4m3fn = getattr(torch, "float8_e4m3fn", None)
+ _float8_e4m3fnuz = getattr(torch, "float8_e4m3fnuz", None)
+ _float8_e5m2 = getattr(torch, "float8_e5m2", None)
+ _float8_e5m2fnuz = getattr(torch, "float8_e5m2fnuz", None)
+ _float8_e8m0 = getattr(torch, "float8_e8m0fnu", None)
+ _float4_e2m1_x2 = getattr(torch, "float4_e2m1fn_x2", None)
+ _SIZE = {
+ torch.int64: 8,
+ torch.float32: 4,
+ torch.int32: 4,
+ torch.bfloat16: 2,
+ torch.float16: 2,
+ torch.int16: 2,
+ torch.uint8: 1,
+ torch.int8: 1,
+ torch.bool: 1,
+ torch.float64: 8,
+ torch.complex64: 8,
+ _float8_e4m3fn: 1,
+ _float8_e4m3fnuz: 1,
+ _float8_e5m2: 1,
+ _float8_e5m2fnuz: 1,
+ _float8_e8m0: 1,
+ _float4_e2m1_x2: 1,
+ }
+
+ if hasattr(torch, "uint64"): # Torch 2.3.0+
+ _SIZE.update(
+ {
+ torch.uint64: 8,
+ torch.uint32: 4,
+ torch.uint16: 2,
+ }
+ )
+
+ return _SIZE[dtype]
+
+
+class _IncompatibleKeys(namedtuple("IncompatibleKeys", ["missing_keys", "unexpected_keys"])):
+ """
+ This is used to report missing and unexpected keys in the state dict.
+ Taken from https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/module.py#L52.
+
+ """
+
+ def __repr__(self) -> str:
+ if not self.missing_keys and not self.unexpected_keys:
+ return ""
+ return super().__repr__()
+
+ __str__ = __repr__
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/datasetcard_template.md b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/datasetcard_template.md
new file mode 100644
index 0000000000000000000000000000000000000000..9af29ebbed93653ec74a8952e314e7554323ef15
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/datasetcard_template.md
@@ -0,0 +1,143 @@
+---
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{{ card_data }}
+---
+
+# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
+
+
+
+{{ dataset_summary | default("", true) }}
+
+## Dataset Details
+
+### Dataset Description
+
+
+
+{{ dataset_description | default("", true) }}
+
+- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
+- **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
+- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
+- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
+- **License:** {{ license | default("[More Information Needed]", true)}}
+
+### Dataset Sources [optional]
+
+
+
+- **Repository:** {{ repo | default("[More Information Needed]", true)}}
+- **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
+- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
+
+## Uses
+
+
+
+### Direct Use
+
+
+
+{{ direct_use | default("[More Information Needed]", true)}}
+
+### Out-of-Scope Use
+
+
+
+{{ out_of_scope_use | default("[More Information Needed]", true)}}
+
+## Dataset Structure
+
+
+
+{{ dataset_structure | default("[More Information Needed]", true)}}
+
+## Dataset Creation
+
+### Curation Rationale
+
+
+
+{{ curation_rationale_section | default("[More Information Needed]", true)}}
+
+### Source Data
+
+
+
+#### Data Collection and Processing
+
+
+
+{{ data_collection_and_processing_section | default("[More Information Needed]", true)}}
+
+#### Who are the source data producers?
+
+
+
+{{ source_data_producers_section | default("[More Information Needed]", true)}}
+
+### Annotations [optional]
+
+
+
+#### Annotation process
+
+
+
+{{ annotation_process_section | default("[More Information Needed]", true)}}
+
+#### Who are the annotators?
+
+
+
+{{ who_are_annotators_section | default("[More Information Needed]", true)}}
+
+#### Personal and Sensitive Information
+
+
+
+{{ personal_and_sensitive_information | default("[More Information Needed]", true)}}
+
+## Bias, Risks, and Limitations
+
+
+
+{{ bias_risks_limitations | default("[More Information Needed]", true)}}
+
+### Recommendations
+
+
+
+{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
+
+## Citation [optional]
+
+
+
+**BibTeX:**
+
+{{ citation_bibtex | default("[More Information Needed]", true)}}
+
+**APA:**
+
+{{ citation_apa | default("[More Information Needed]", true)}}
+
+## Glossary [optional]
+
+
+
+{{ glossary | default("[More Information Needed]", true)}}
+
+## More Information [optional]
+
+{{ more_information | default("[More Information Needed]", true)}}
+
+## Dataset Card Authors [optional]
+
+{{ dataset_card_authors | default("[More Information Needed]", true)}}
+
+## Dataset Card Contact
+
+{{ dataset_card_contact | default("[More Information Needed]", true)}}
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/modelcard_template.md b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/modelcard_template.md
new file mode 100644
index 0000000000000000000000000000000000000000..79ca15e4547debac763b390ef8e4b715e6f6403f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/templates/modelcard_template.md
@@ -0,0 +1,200 @@
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{{ card_data }}
+---
+
+# Model Card for {{ model_id | default("Model ID", true) }}
+
+
+
+{{ model_summary | default("", true) }}
+
+## Model Details
+
+### Model Description
+
+
+
+{{ model_description | default("", true) }}
+
+- **Developed by:** {{ developers | default("[More Information Needed]", true)}}
+- **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
+- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
+- **Model type:** {{ model_type | default("[More Information Needed]", true)}}
+- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
+- **License:** {{ license | default("[More Information Needed]", true)}}
+- **Finetuned from model [optional]:** {{ base_model | default("[More Information Needed]", true)}}
+
+### Model Sources [optional]
+
+
+
+- **Repository:** {{ repo | default("[More Information Needed]", true)}}
+- **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
+- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
+
+## Uses
+
+
+
+### Direct Use
+
+
+
+{{ direct_use | default("[More Information Needed]", true)}}
+
+### Downstream Use [optional]
+
+
+
+{{ downstream_use | default("[More Information Needed]", true)}}
+
+### Out-of-Scope Use
+
+
+
+{{ out_of_scope_use | default("[More Information Needed]", true)}}
+
+## Bias, Risks, and Limitations
+
+
+
+{{ bias_risks_limitations | default("[More Information Needed]", true)}}
+
+### Recommendations
+
+
+
+{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+{{ get_started_code | default("[More Information Needed]", true)}}
+
+## Training Details
+
+### Training Data
+
+
+
+{{ training_data | default("[More Information Needed]", true)}}
+
+### Training Procedure
+
+
+
+#### Preprocessing [optional]
+
+{{ preprocessing | default("[More Information Needed]", true)}}
+
+
+#### Training Hyperparameters
+
+- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}}
+
+#### Speeds, Sizes, Times [optional]
+
+
+
+{{ speeds_sizes_times | default("[More Information Needed]", true)}}
+
+## Evaluation
+
+
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+
+
+{{ testing_data | default("[More Information Needed]", true)}}
+
+#### Factors
+
+
+
+{{ testing_factors | default("[More Information Needed]", true)}}
+
+#### Metrics
+
+
+
+{{ testing_metrics | default("[More Information Needed]", true)}}
+
+### Results
+
+{{ results | default("[More Information Needed]", true)}}
+
+#### Summary
+
+{{ results_summary | default("", true) }}
+
+## Model Examination [optional]
+
+
+
+{{ model_examination | default("[More Information Needed]", true)}}
+
+## Environmental Impact
+
+
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** {{ hardware_type | default("[More Information Needed]", true)}}
+- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
+- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
+- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
+- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+{{ model_specs | default("[More Information Needed]", true)}}
+
+### Compute Infrastructure
+
+{{ compute_infrastructure | default("[More Information Needed]", true)}}
+
+#### Hardware
+
+{{ hardware_requirements | default("[More Information Needed]", true)}}
+
+#### Software
+
+{{ software | default("[More Information Needed]", true)}}
+
+## Citation [optional]
+
+
+
+**BibTeX:**
+
+{{ citation_bibtex | default("[More Information Needed]", true)}}
+
+**APA:**
+
+{{ citation_apa | default("[More Information Needed]", true)}}
+
+## Glossary [optional]
+
+
+
+{{ glossary | default("[More Information Needed]", true)}}
+
+## More Information [optional]
+
+{{ more_information | default("[More Information Needed]", true)}}
+
+## Model Card Authors [optional]
+
+{{ model_card_authors | default("[More Information Needed]", true)}}
+
+## Model Card Contact
+
+{{ model_card_contact | default("[More Information Needed]", true)}}
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e0c2a6234958f8d7c0a3aad2395e6a277b79809
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/__init__.py
@@ -0,0 +1,425 @@
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Public utility helpers, loaded on first use.
+
+Most library modules import a small subset of this package. Keeping the facade lazy
+prevents every such import from also loading unrelated cache, terminal, subprocess,
+and optional-dependency helpers.
+"""
+
+# ruff: noqa: F401, F403
+
+import importlib
+import sys
+import types
+from typing import TYPE_CHECKING
+
+
+_SUBMODULES = {
+ "_auth",
+ "_detect_agent",
+ "_http",
+ "_tqdm",
+ "logging",
+}
+
+_SUBMOD_ATTRS = {
+ "_auth": [
+ "get_stored_tokens",
+ "get_token",
+ ],
+ "_cache_assets": [
+ "cached_assets_path",
+ ],
+ "_cache_manager": [
+ "CachedFileInfo",
+ "CachedIncompleteFileInfo",
+ "CachedRepoInfo",
+ "CachedRevisionInfo",
+ "DeleteCacheStrategy",
+ "HFCacheInfo",
+ "_format_size",
+ "scan_cache_dir",
+ ],
+ "_chunk_utils": [
+ "chunk_iterable",
+ ],
+ "_datetime": [
+ "parse_datetime",
+ ],
+ "_detect_agent": [
+ "detect_agent",
+ "is_agent",
+ ],
+ "_experimental": [
+ "experimental",
+ ],
+ "_fixes": [
+ "SoftTemporaryDirectory",
+ "WeakFileLock",
+ "yaml_dump",
+ ],
+ "_git_credential": [
+ "list_credential_helpers",
+ "set_git_credential",
+ "unset_git_credential",
+ ],
+ "_headers": [
+ "build_hf_headers",
+ "get_token_to_send",
+ ],
+ "_hf_uris": [
+ "HfMount",
+ "HfUri",
+ "is_hf_uri",
+ "parse_hf_mount",
+ "parse_hf_uri",
+ ],
+ "_http": [
+ "ASYNC_CLIENT_FACTORY_T",
+ "CLIENT_FACTORY_T",
+ "RateLimitInfo",
+ "close_session",
+ "fix_hf_endpoint_in_url",
+ "get_async_session",
+ "get_session",
+ "hf_raise_for_status",
+ "http_backoff",
+ "http_stream_backoff",
+ "parse_ratelimit_headers",
+ "set_async_client_factory",
+ "set_client_factory",
+ ],
+ "_pagination": [
+ "paginate",
+ ],
+ "_paths": [
+ "DEFAULT_IGNORE_PATTERNS",
+ "FORBIDDEN_FOLDERS",
+ "filter_repo_objects",
+ ],
+ "_runtime": [
+ "dump_environment_info",
+ "get_aiohttp_version",
+ "get_fastai_version",
+ "get_fastapi_version",
+ "get_fastcore_version",
+ "get_gradio_version",
+ "get_graphviz_version",
+ "get_hf_hub_version",
+ "get_jinja_version",
+ "get_numpy_version",
+ "get_pillow_version",
+ "get_pydantic_version",
+ "get_pydot_version",
+ "get_python_version",
+ "get_tensorboard_version",
+ "get_tf_version",
+ "get_torch_version",
+ "installation_method",
+ "is_aiohttp_available",
+ "is_colab_enterprise",
+ "is_fastai_available",
+ "is_fastapi_available",
+ "is_fastcore_available",
+ "is_google_colab",
+ "is_gradio_available",
+ "is_graphviz_available",
+ "is_jinja_available",
+ "is_notebook",
+ "is_numpy_available",
+ "is_package_available",
+ "is_pillow_available",
+ "is_pydantic_available",
+ "is_pydot_available",
+ "is_safetensors_available",
+ "is_tensorboard_available",
+ "is_tf_available",
+ "is_torch_available",
+ ],
+ "_safetensors": [
+ "SafetensorsFileMetadata",
+ "SafetensorsRepoMetadata",
+ "TensorInfo",
+ ],
+ "_subprocess": [
+ "capture_output",
+ "run_interactive_subprocess",
+ "run_subprocess",
+ ],
+ "_telemetry": [
+ "send_telemetry",
+ ],
+ "_terminal": [
+ "ANSI",
+ "StatusLine",
+ "select_choice",
+ "tabulate",
+ ],
+ "_typing": [
+ "is_jsonable",
+ "is_simple_optional_type",
+ "unwrap_simple_optional_type",
+ ],
+ "_validators": [
+ "validate_hf_hub_args",
+ "validate_repo_id",
+ ],
+ "_xet": [
+ "XetFileData",
+ "XetTokenType",
+ "parse_xet_file_data_from_response",
+ ],
+ "huggingface_hub.errors": [
+ "BadRequestError",
+ "BucketNotFoundError",
+ "CacheNotFound",
+ "CorruptedCacheException",
+ "DisabledRepoError",
+ "EntryNotFoundError",
+ "FileMetadataError",
+ "GatedRepoError",
+ "HFValidationError",
+ "HfHubHTTPError",
+ "JobNotFoundError",
+ "LocalEntryNotFoundError",
+ "LocalTokenNotFoundError",
+ "NotASafetensorsRepoError",
+ "OfflineModeIsEnabled",
+ "RepositoryNotFoundError",
+ "RevisionNotFoundError",
+ "SafetensorsParsingError",
+ ],
+ "tqdm": [
+ "are_progress_bars_disabled",
+ "disable_progress_bars",
+ "enable_progress_bars",
+ "hf_thread_map",
+ "is_tqdm_disabled",
+ "silent_tqdm",
+ "tqdm",
+ "tqdm_stream_file",
+ ],
+}
+
+__all__ = sorted(["httpx", *(attr for attrs in _SUBMOD_ATTRS.values() for attr in attrs if not attr.startswith("_"))])
+
+_ATTR_TO_MODULE = {attr: module for module, attrs in _SUBMOD_ATTRS.items() for attr in attrs}
+
+
+def __getattr__(name: str):
+ if name == "httpx": # Keep the same export for downstream libraries across major versions.
+ return importlib.import_module("httpx2")
+ if name in _SUBMODULES:
+ module_name = "tqdm" if name == "_tqdm" else name
+ return importlib.import_module(f"{__name__}.{module_name}")
+ if attr_module_name := _ATTR_TO_MODULE.get(name):
+ if attr_module_name.startswith("huggingface_hub."):
+ module = importlib.import_module(attr_module_name)
+ else:
+ module = importlib.import_module(f"{__name__}.{attr_module_name}")
+ return getattr(module, name)
+ raise AttributeError(f"No {__name__} attribute {name}")
+
+
+def __dir__() -> list[str]:
+ return [*__all__, "httpx"]
+
+
+class _LazyUtilsModule(types.ModuleType):
+ def __getattribute__(self, name: str):
+ # Import machinery stores the `tqdm` submodule on this package. The public
+ # `utils.tqdm` attribute has historically referred to the class instead.
+ if name == "tqdm":
+ return importlib.import_module(f"{__name__}.tqdm").tqdm
+ return super().__getattribute__(name)
+
+
+sys.modules[__name__].__class__ = _LazyUtilsModule
+
+
+if TYPE_CHECKING: # pragma: no cover
+ import httpx2 as httpx # noqa: F401
+
+ from huggingface_hub.errors import (
+ BadRequestError, # noqa: F401
+ BucketNotFoundError, # noqa: F401
+ CacheNotFound, # noqa: F401
+ CorruptedCacheException, # noqa: F401
+ DisabledRepoError, # noqa: F401
+ EntryNotFoundError, # noqa: F401
+ FileMetadataError, # noqa: F401
+ GatedRepoError, # noqa: F401
+ HfHubHTTPError, # noqa: F401
+ HFValidationError, # noqa: F401
+ JobNotFoundError, # noqa: F401
+ LocalEntryNotFoundError, # noqa: F401
+ LocalTokenNotFoundError, # noqa: F401
+ NotASafetensorsRepoError, # noqa: F401
+ OfflineModeIsEnabled, # noqa: F401
+ RepositoryNotFoundError, # noqa: F401
+ RevisionNotFoundError, # noqa: F401
+ SafetensorsParsingError, # noqa: F401
+ )
+
+ from . import _auth as _auth # noqa: F401
+ from . import _detect_agent as _detect_agent # noqa: F401
+ from . import _http as _http # noqa: F401
+ from . import logging as logging # noqa: F401
+ from . import tqdm as _tqdm # noqa: F401
+ from ._auth import (
+ get_stored_tokens, # noqa: F401
+ get_token, # noqa: F401
+ )
+ from ._cache_assets import cached_assets_path # noqa: F401
+ from ._cache_manager import (
+ CachedFileInfo, # noqa: F401
+ CachedIncompleteFileInfo, # noqa: F401
+ CachedRepoInfo, # noqa: F401
+ CachedRevisionInfo, # noqa: F401
+ DeleteCacheStrategy, # noqa: F401
+ HFCacheInfo, # noqa: F401
+ _format_size, # noqa: F401
+ scan_cache_dir, # noqa: F401
+ )
+ from ._chunk_utils import chunk_iterable # noqa: F401
+ from ._datetime import parse_datetime # noqa: F401
+ from ._detect_agent import (
+ detect_agent, # noqa: F401
+ is_agent, # noqa: F401
+ )
+ from ._experimental import experimental # noqa: F401
+ from ._fixes import (
+ SoftTemporaryDirectory, # noqa: F401
+ WeakFileLock, # noqa: F401
+ yaml_dump, # noqa: F401
+ )
+ from ._git_credential import (
+ list_credential_helpers, # noqa: F401
+ set_git_credential, # noqa: F401
+ unset_git_credential, # noqa: F401
+ )
+ from ._headers import (
+ build_hf_headers, # noqa: F401
+ get_token_to_send, # noqa: F401
+ )
+ from ._hf_uris import (
+ HfMount, # noqa: F401
+ HfUri, # noqa: F401
+ is_hf_uri, # noqa: F401
+ parse_hf_mount, # noqa: F401
+ parse_hf_uri, # noqa: F401
+ )
+ from ._http import (
+ ASYNC_CLIENT_FACTORY_T, # noqa: F401
+ CLIENT_FACTORY_T, # noqa: F401
+ RateLimitInfo, # noqa: F401
+ close_session, # noqa: F401
+ fix_hf_endpoint_in_url, # noqa: F401
+ get_async_session, # noqa: F401
+ get_session, # noqa: F401
+ hf_raise_for_status, # noqa: F401
+ http_backoff, # noqa: F401
+ http_stream_backoff, # noqa: F401
+ parse_ratelimit_headers, # noqa: F401
+ set_async_client_factory, # noqa: F401
+ set_client_factory, # noqa: F401
+ )
+ from ._pagination import paginate # noqa: F401
+ from ._paths import (
+ DEFAULT_IGNORE_PATTERNS, # noqa: F401
+ FORBIDDEN_FOLDERS, # noqa: F401
+ filter_repo_objects, # noqa: F401
+ )
+ from ._runtime import (
+ dump_environment_info, # noqa: F401
+ get_aiohttp_version, # noqa: F401
+ get_fastai_version, # noqa: F401
+ get_fastapi_version, # noqa: F401
+ get_fastcore_version, # noqa: F401
+ get_gradio_version, # noqa: F401
+ get_graphviz_version, # noqa: F401
+ get_hf_hub_version, # noqa: F401
+ get_jinja_version, # noqa: F401
+ get_numpy_version, # noqa: F401
+ get_pillow_version, # noqa: F401
+ get_pydantic_version, # noqa: F401
+ get_pydot_version, # noqa: F401
+ get_python_version, # noqa: F401
+ get_tensorboard_version, # noqa: F401
+ get_tf_version, # noqa: F401
+ get_torch_version, # noqa: F401
+ installation_method, # noqa: F401
+ is_aiohttp_available, # noqa: F401
+ is_colab_enterprise, # noqa: F401
+ is_fastai_available, # noqa: F401
+ is_fastapi_available, # noqa: F401
+ is_fastcore_available, # noqa: F401
+ is_google_colab, # noqa: F401
+ is_gradio_available, # noqa: F401
+ is_graphviz_available, # noqa: F401
+ is_jinja_available, # noqa: F401
+ is_notebook, # noqa: F401
+ is_numpy_available, # noqa: F401
+ is_package_available, # noqa: F401
+ is_pillow_available, # noqa: F401
+ is_pydantic_available, # noqa: F401
+ is_pydot_available, # noqa: F401
+ is_safetensors_available, # noqa: F401
+ is_tensorboard_available, # noqa: F401
+ is_tf_available, # noqa: F401
+ is_torch_available, # noqa: F401
+ )
+ from ._safetensors import (
+ SafetensorsFileMetadata, # noqa: F401
+ SafetensorsRepoMetadata, # noqa: F401
+ TensorInfo, # noqa: F401
+ )
+ from ._subprocess import (
+ capture_output, # noqa: F401
+ run_interactive_subprocess, # noqa: F401
+ run_subprocess, # noqa: F401
+ )
+ from ._telemetry import send_telemetry # noqa: F401
+ from ._terminal import (
+ ANSI, # noqa: F401
+ StatusLine, # noqa: F401
+ select_choice, # noqa: F401
+ tabulate, # noqa: F401
+ )
+ from ._typing import (
+ is_jsonable, # noqa: F401
+ is_simple_optional_type, # noqa: F401
+ unwrap_simple_optional_type, # noqa: F401
+ )
+ from ._validators import (
+ validate_hf_hub_args, # noqa: F401
+ validate_repo_id, # noqa: F401
+ )
+ from ._xet import (
+ XetFileData, # noqa: F401
+ XetTokenType, # noqa: F401
+ parse_xet_file_data_from_response, # noqa: F401
+ )
+ from .tqdm import (
+ are_progress_bars_disabled, # noqa: F401
+ disable_progress_bars, # noqa: F401
+ enable_progress_bars, # noqa: F401
+ hf_thread_map, # noqa: F401
+ is_tqdm_disabled, # noqa: F401
+ silent_tqdm, # noqa: F401
+ tqdm, # noqa: F401
+ tqdm_stream_file, # noqa: F401
+ )
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--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_auth.py
@@ -0,0 +1,460 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a helper to get the token from machine (env variable, secret or config file)."""
+
+import configparser
+import io
+import logging
+import os
+import time
+import warnings
+from pathlib import Path
+from threading import Lock
+from typing import TypedDict
+
+from .. import constants
+from ..errors import DeviceCodeError, OAuthErrorCode, OIDCError
+from ._fixes import WeakFileLock
+from ._oauth_device import refresh_access_token
+from ._runtime import is_colab_enterprise, is_google_colab
+
+
+_SECRET_FILE_MODE = 0o600
+_SECRET_DIR_MODE = 0o700
+
+
+def _write_secret(path: Path, content: str) -> None:
+ """Write content to file, restricting both the file and its parent directory to owner-only on POSIX systems."""
+ path.parent.mkdir(parents=True, exist_ok=True, mode=_SECRET_DIR_MODE)
+ fd = os.open(str(path), os.O_WRONLY | os.O_CREAT | os.O_TRUNC, _SECRET_FILE_MODE)
+ # Token names are arbitrary Unicode (a user-set `displayName`), so write UTF-8 rather
+ # than the locale encoding, which cannot represent them on e.g. Windows.
+ with os.fdopen(fd, "w", encoding="utf-8") as f:
+ f.write(content)
+ try:
+ path.chmod(_SECRET_FILE_MODE)
+ path.parent.chmod(_SECRET_DIR_MODE)
+ except (OSError, NotImplementedError):
+ # Windows does not support POSIX modes; chmod() will raise. Best-effort.
+ pass
+
+
+_IS_GOOGLE_COLAB_CHECKED = False
+_GOOGLE_COLAB_SECRET_LOCK = Lock()
+_GOOGLE_COLAB_SECRET: str | None = None
+
+logger = logging.getLogger(__name__)
+
+
+def get_token() -> str | None:
+ """
+ Get token if user is logged in.
+
+ Note: in most cases, you should use [`huggingface_hub.utils.build_hf_headers`] instead. This method is only useful
+ if you want to retrieve the token for other purposes than sending an HTTP request.
+
+ If `HF_OIDC_RESOURCE` is set (Trusted Publishers, typically in CI), a short-lived token obtained via OIDC token
+ exchange takes precedence. Otherwise the token is retrieved from the `HF_TOKEN` environment variable, then from the
+ token file in the Hugging Face home folder. Returns None if user is not logged in. To log in, use [`login`] or
+ `hf auth login`.
+
+ OAuth tokens obtained with the browser-based login come with a refresh token: when such a token is close to
+ expiry, it is transparently refreshed and persisted before being returned.
+
+ Note: if `HF_OIDC_RESOURCE` is set but the OIDC token exchange fails, this raises instead of returning `None`,
+ opting into OIDC is explicit, so a failure surfaces as a clear error rather than a silent fallback.
+
+ Returns:
+ `str` or `None`: The token, `None` if it doesn't exist.
+ """
+ return (
+ _get_token_from_oidc()
+ or _get_token_from_environment()
+ or _get_token_from_file_refreshed()
+ or _get_token_from_google_colab()
+ )
+
+
+def _get_token_from_google_colab() -> str | None:
+ """Get token from Google Colab secrets vault using `google.colab.userdata.get(...)`.
+
+ Token is read from the vault only once per session and then stored in a global variable to avoid re-requesting
+ access to the vault.
+ """
+ # If it's not a Google Colab or it's Colab Enterprise, fallback to environment variable or token file authentication
+ if not is_google_colab() or is_colab_enterprise():
+ return None
+
+ # `google.colab.userdata` is not thread-safe
+ # This can lead to a deadlock if multiple threads try to access it at the same time
+ # (typically when using `snapshot_download`)
+ # => use a lock
+ # See https://github.com/huggingface/huggingface_hub/issues/1952 for more details.
+ with _GOOGLE_COLAB_SECRET_LOCK:
+ global _GOOGLE_COLAB_SECRET
+ global _IS_GOOGLE_COLAB_CHECKED
+
+ if _IS_GOOGLE_COLAB_CHECKED: # request access only once
+ return _GOOGLE_COLAB_SECRET
+
+ try:
+ from google.colab import userdata # type: ignore
+ from google.colab.errors import Error as ColabError # type: ignore
+ except ImportError:
+ return None
+
+ try:
+ token = userdata.get("HF_TOKEN")
+ _GOOGLE_COLAB_SECRET = _clean_token(token)
+ except userdata.NotebookAccessError:
+ # Means the user has a secret call `HF_TOKEN` and got a popup "please grand access to HF_TOKEN" and refused it
+ # => warn user but ignore error => do not re-request access to user
+ warnings.warn(
+ "\nAccess to the secret `HF_TOKEN` has not been granted on this notebook."
+ "\nYou will not be requested again."
+ "\nPlease restart the session if you want to be prompted again."
+ )
+ _GOOGLE_COLAB_SECRET = None
+ except userdata.SecretNotFoundError:
+ # No `HF_TOKEN` secret defined: simply not logged in via the Colab vault. Not worth a
+ # warning now that `login()` is the primary flow (it would even fire during `login()`
+ # itself, telling the user to set up a secret while they are busy authenticating).
+ logger.info(
+ "The secret `HF_TOKEN` does not exist in your Colab secrets. Run `huggingface_hub.login()` to"
+ " authenticate (recommended but still optional to access public models or datasets)."
+ )
+ _GOOGLE_COLAB_SECRET = None
+ except ColabError as e:
+ # Something happen but we don't know what => recommend to open a GitHub issue
+ warnings.warn(f"\nError while fetching `HF_TOKEN` secret value from your vault: '{str(e)}'.")
+ _GOOGLE_COLAB_SECRET = None
+
+ _IS_GOOGLE_COLAB_CHECKED = True
+ return _GOOGLE_COLAB_SECRET
+
+
+def _get_token_from_environment() -> str | None:
+ # `HF_TOKEN` has priority (keep `HUGGING_FACE_HUB_TOKEN` for backward compatibility)
+ return _clean_token(os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN"))
+
+
+def _get_token_from_file() -> str | None:
+ try:
+ return _clean_token(Path(constants.HF_TOKEN_PATH).read_text())
+ except FileNotFoundError:
+ return None
+
+
+class _OidcTokenCache(TypedDict):
+ resource: str
+ token: str
+ expires_at: float # monotonic clock value after which the cached token must be re-exchanged
+
+
+# Cache for the OIDC-exchanged token: re-exchanging on every `get_token()` call would be wasteful,
+# and re-exchanging shortly before expiry transparently keeps long-running jobs authenticated.
+_OIDC_TOKEN_LOCK = Lock()
+_OIDC_TOKEN_CACHE: _OidcTokenCache | None = None
+_OIDC_REFRESH_MARGIN = 300 # re-exchange this many seconds before the token actually expires
+
+
+def _get_token_from_oidc() -> str | None:
+ """Get a short-lived OIDC token in CI (Trusted Publishers).
+
+ Enabled by setting `HF_OIDC_RESOURCE`, which scopes the token to a repo or user.
+ The ID token is read from `HF_OIDC_ID_TOKEN` if available, or minted from a supported CI provider (e.g. GitHub Actions).
+
+ Returns `None` when OIDC is not enabled.
+ If enabled, any failure is raised explicitly rather than falling back silently.
+
+ See `huggingface_hub._oidc` and https://huggingface.co/docs/hub/trusted-publishers.
+ """
+ resource = os.environ.get("HF_OIDC_RESOURCE")
+ if not resource:
+ return None
+
+ from .._oidc import detect_provider, oidc_login
+
+ global _OIDC_TOKEN_CACHE
+ with _OIDC_TOKEN_LOCK:
+ now = time.monotonic()
+ if (
+ _OIDC_TOKEN_CACHE is not None
+ and _OIDC_TOKEN_CACHE["resource"] == resource
+ and now < _OIDC_TOKEN_CACHE["expires_at"]
+ ):
+ return _OIDC_TOKEN_CACHE["token"]
+
+ # An explicit id token (any provider) takes precedence; otherwise mint from a detected one.
+ subject_token = os.environ.get("HF_OIDC_ID_TOKEN") or None
+ if subject_token is None and detect_provider() is None:
+ raise OIDCError(
+ "HF_OIDC_RESOURCE is set but no OIDC id token is available: not running in a supported "
+ "CI provider (github) and HF_OIDC_ID_TOKEN is not set. Set HF_OIDC_ID_TOKEN to the id "
+ "token minted by your CI provider, or unset HF_OIDC_RESOURCE."
+ )
+
+ result = oidc_login(resource=resource, subject_token=subject_token)
+ token = result["access_token"]
+ expires_in = int(result.get("expires_in", 3600))
+ # A pre-supplied HF_OIDC_ID_TOKEN can't be re-minted, so refreshing early is pointless (the id
+ # token is likely already expired by then): cache for the full lifetime. Only the auto-minted
+ # path can refresh, so only it gets the safety margin.
+ margin = 0 if subject_token is not None else _OIDC_REFRESH_MARGIN
+ _OIDC_TOKEN_CACHE = {
+ "resource": resource,
+ "token": token,
+ "expires_at": now + max(expires_in - margin, 0),
+ }
+ return token
+
+
+class _OAuthRefreshCache(TypedDict):
+ file_token: str # token as read from HF_TOKEN_PATH (cache key)
+ resolved_token: str # token to return (refreshed, or identical if no refresh was needed)
+ recheck_at: float # wall-clock timestamp after which the expiry must be re-evaluated
+
+
+# Cache the refresh decision in-process: `get_token()` is called on every HTTP request and must not
+# re-read the stored tokens file (let alone hit the network) each time.
+_OAUTH_REFRESH_LOCK = Lock()
+_OAUTH_REFRESH_CACHE: _OAuthRefreshCache | None = None
+_OAUTH_REFRESH_MARGIN = 24 * 3600 # refresh when less than 1 day of validity remains
+_OAUTH_RECHECK_INTERVAL = 300 # re-check interval when there is no metadata or the refresh failed
+_OAUTH_REFRESH_WARNED = False # warn at most once per process on refresh failure
+
+
+def _get_token_from_file_refreshed() -> str | None:
+ """Get the token from `HF_TOKEN_PATH`, transparently refreshing it if close to expiry."""
+ token = _get_token_from_file()
+ if token is None:
+ return None
+ return _refresh_oauth_token_if_needed(token)
+
+
+def _refresh_oauth_token_if_needed(token: str) -> str:
+ """Refresh an OAuth access token if it is close to expiry. Best-effort: never raises.
+
+ OAuth tokens obtained with the browser-based login are stored with a `refresh_token` and an
+ `expires_at` timestamp (see `_save_token`). When the active token is one of them and about to
+ expire, exchange the refresh token for a new access token and persist it. Any other token is
+ returned unchanged.
+ """
+ global _OAUTH_REFRESH_CACHE
+ with _OAUTH_REFRESH_LOCK:
+ now = time.time()
+ cache = _OAUTH_REFRESH_CACHE
+ if cache is not None and cache["file_token"] == token and now < cache["recheck_at"]:
+ return cache["resolved_token"]
+
+ token_name, fields = next(
+ ((name, fields) for name, fields in _read_stored_tokens_full().items() if fields.get("hf_token") == token),
+ (None, {}),
+ )
+ refresh_token = fields.get("refresh_token")
+ expires_at = _parse_expires_at(fields)
+ if token_name is None or refresh_token is None or expires_at is None:
+ # `token` may have just been replaced by a concurrent refresh (in which case it no
+ # longer appears in the stored tokens): serve the fresh file token without caching.
+ current_file_token = _get_token_from_file()
+ if current_file_token is not None and current_file_token != token:
+ return current_file_token
+ # Not a refreshable OAuth token (or its metadata was lost): nothing to do.
+ _OAUTH_REFRESH_CACHE = {
+ "file_token": token,
+ "resolved_token": token,
+ "recheck_at": now + _OAUTH_RECHECK_INTERVAL,
+ }
+ return token
+
+ if expires_at - _OAUTH_REFRESH_MARGIN > now:
+ _OAUTH_REFRESH_CACHE = {
+ "file_token": token,
+ "resolved_token": token,
+ "recheck_at": expires_at - _OAUTH_REFRESH_MARGIN,
+ }
+ return token
+
+ try:
+ # Cross-process file lock: if the server rotates refresh tokens, two processes
+ # refreshing concurrently would invalidate each other's refresh token.
+ with WeakFileLock(constants.HF_STORED_TOKENS_PATH + ".lock", timeout=30):
+ # Re-read under the lock: another process may have refreshed in the meantime.
+ fields = _read_stored_tokens_full().get(token_name, {})
+ if fields.get("hf_token") != token:
+ # Another process already refreshed this token: adopt its result.
+ new_token = fields.get("hf_token") or token
+ new_expires_at = _parse_expires_at(fields)
+ else:
+ response = refresh_access_token(refresh_token)
+ new_token = response["access_token"]
+ new_expires_at = int(now) + int(response["expires_in"]) if "expires_in" in response else None
+ _save_token(
+ token=new_token,
+ token_name=token_name,
+ # The server may rotate the refresh token; keep the old one if it doesn't.
+ refresh_token=response.get("refresh_token") or refresh_token,
+ expires_at=new_expires_at,
+ )
+ # Update the active token file, unless another process switched to a different token meanwhile.
+ if _get_token_from_file() == token:
+ _write_secret(Path(constants.HF_TOKEN_PATH), new_token)
+ logger.info(f"Access token `{token_name}` has been refreshed.")
+ except Exception as e:
+ if isinstance(e, DeviceCodeError) and e.error_code == OAuthErrorCode.INVALID_GRANT:
+ # Refresh token expired or revoked: retrying is pointless, a re-login is required.
+ # Warned unconditionally (the inf recheck guarantees it fires at most once): an
+ # earlier transient warning must not suppress this actionable message.
+ logger.warning(
+ "Your Hugging Face access token has expired and could not be refreshed "
+ f"(session expired or revoked). Run `hf auth login` to re-authenticate. ({e})"
+ )
+ recheck_at = float("inf")
+ else:
+ # Transient failure (offline, server error, ...): retry later.
+ _warn_refresh_failure_once(f"Could not refresh your Hugging Face access token: {e}. Will retry later.")
+ recheck_at = now + _OAUTH_RECHECK_INTERVAL
+ # Return the existing token: if it's truly expired, the API will reject it with a clear error.
+ _OAUTH_REFRESH_CACHE = {"file_token": token, "resolved_token": token, "recheck_at": recheck_at}
+ return token
+
+ _OAUTH_REFRESH_CACHE = {
+ "file_token": new_token,
+ "resolved_token": new_token,
+ # The floor guards against a token lifetime shorter than the refresh margin, which
+ # would otherwise put `recheck_at` in the past and trigger a refresh on every call.
+ "recheck_at": max(
+ now + _OAUTH_RECHECK_INTERVAL,
+ new_expires_at - _OAUTH_REFRESH_MARGIN if new_expires_at else 0,
+ ),
+ }
+ return new_token
+
+
+def _warn_refresh_failure_once(message: str) -> None:
+ global _OAUTH_REFRESH_WARNED
+ if not _OAUTH_REFRESH_WARNED:
+ logger.warning(message)
+ _OAUTH_REFRESH_WARNED = True
+
+
+def _parse_expires_at(fields: dict[str, str]) -> int | None:
+ """Parse the `expires_at` field of a stored-tokens section, `None` if missing or corrupt."""
+ try:
+ return int(fields["expires_at"])
+ except (KeyError, ValueError):
+ return None
+
+
+def get_stored_tokens() -> dict[str, str]:
+ """
+ Returns the parsed INI file containing the access tokens.
+ The file is located at `HF_STORED_TOKENS_PATH`, defaulting to `~/.cache/huggingface/stored_tokens`.
+ If the file does not exist, an empty dictionary is returned.
+
+ Returns: `dict[str, str]`
+ Key is the token name and value is the token.
+ """
+ return {token_name: fields.get("hf_token", "") for token_name, fields in _read_stored_tokens_full().items()}
+
+
+def _read_stored_tokens_full() -> dict[str, dict[str, str]]:
+ """Read all sections of the stored tokens INI file, with all their fields.
+
+ Beside `hf_token`, sections for OAuth tokens also carry `refresh_token` and `expires_at`
+ (unix timestamp), used by [`get_token`] to transparently refresh them.
+ """
+ tokens_path = Path(constants.HF_STORED_TOKENS_PATH)
+ if not tokens_path.exists():
+ return {}
+ # interpolation=None: token values are opaque strings, a `%` must not be interpreted.
+ # Read as UTF-8 (token names are arbitrary Unicode). A file that is not valid UTF-8 is treated
+ # as unparseable, so that the user can log in again to overwrite it.
+ config = configparser.ConfigParser(interpolation=None)
+ try:
+ config.read(tokens_path, encoding="utf-8")
+ return {token_name: dict(config.items(token_name)) for token_name in config.sections()}
+ except (configparser.Error, UnicodeDecodeError) as e:
+ logger.error(f"Error parsing stored tokens file: {e}")
+ return {}
+
+
+def _save_stored_tokens_full(stored_tokens: dict[str, dict[str, str]]) -> None:
+ """Write all sections and their fields to the stored tokens INI file."""
+ config = configparser.ConfigParser(interpolation=None)
+ for token_name in sorted(stored_tokens.keys()):
+ config.add_section(token_name)
+ for key, value in stored_tokens[token_name].items():
+ config.set(token_name, key, value)
+
+ buf = io.StringIO()
+ config.write(buf)
+ _write_secret(Path(constants.HF_STORED_TOKENS_PATH), buf.getvalue())
+
+
+def _get_token_by_name(token_name: str) -> str | None:
+ """
+ Get the token by name.
+
+ Args:
+ token_name (`str`):
+ The name of the token to get.
+
+ Returns:
+ `str` or `None`: The token, `None` if it doesn't exist.
+
+ """
+ stored_tokens = get_stored_tokens()
+ if token_name not in stored_tokens:
+ return None
+ return _clean_token(stored_tokens[token_name])
+
+
+def _save_token(
+ token: str, token_name: str, *, refresh_token: str | None = None, expires_at: int | None = None
+) -> None:
+ """
+ Save the given token.
+
+ If the stored tokens file does not exist, it will be created.
+ Args:
+ token (`str`):
+ The token to save.
+ token_name (`str`):
+ The name of the token.
+ refresh_token (`str`, *optional*):
+ OAuth refresh token used to renew the access token when it expires.
+ expires_at (`int`, *optional*):
+ Unix timestamp at which the access token expires.
+ """
+ stored_tokens = _read_stored_tokens_full()
+ fields = {"hf_token": token}
+ if refresh_token is not None:
+ fields["refresh_token"] = refresh_token
+ if expires_at is not None:
+ fields["expires_at"] = str(expires_at)
+ # Replace the whole section: re-logging in under the same name must drop stale metadata.
+ stored_tokens[token_name] = fields
+ _save_stored_tokens_full(stored_tokens)
+ logger.info(f"The token `{token_name}` has been saved to {constants.HF_STORED_TOKENS_PATH}")
+
+
+def _clean_token(token: str | None) -> str | None:
+ """Clean token by removing trailing and leading spaces and newlines.
+
+ If token is an empty string, return None.
+ """
+ if token is None:
+ return None
+ return token.replace("\r", "").replace("\n", "").strip() or None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py
new file mode 100644
index 0000000000000000000000000000000000000000..01263873715c56d8331200b47a4608a1b1135ab7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py
@@ -0,0 +1,133 @@
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pathlib import Path
+
+from ..constants import HF_ASSETS_CACHE
+
+
+def cached_assets_path(
+ library_name: str,
+ namespace: str = "default",
+ subfolder: str = "default",
+ *,
+ assets_dir: str | Path | None = None,
+) -> Path:
+ """Return a folder path to cache arbitrary files.
+
+ `huggingface_hub` provides a canonical folder path to store assets. This is the
+ recommended way to integrate cache in a downstream library as it will benefit from
+ the builtins tools to scan and delete the cache properly.
+
+ The distinction is made between files cached from the Hub and assets. Files from the
+ Hub are cached in a git-aware manner and entirely managed by `huggingface_hub`. See
+ [related documentation](https://huggingface.co/docs/huggingface_hub/how-to-cache).
+ All other files that a downstream library caches are considered to be "assets"
+ (files downloaded from external sources, extracted from a .tar archive, preprocessed
+ for training,...).
+
+ Once the folder path is generated, it is guaranteed to exist and to be a directory.
+ The path is based on 3 levels of depth: the library name, a namespace and a
+ subfolder. Those 3 levels grants flexibility while allowing `huggingface_hub` to
+ expect folders when scanning/deleting parts of the assets cache. Within a library,
+ it is expected that all namespaces share the same subset of subfolder names but this
+ is not a mandatory rule. The downstream library has then full control on which file
+ structure to adopt within its cache. Namespace and subfolder are optional (would
+ default to a `"default/"` subfolder) but library name is mandatory as we want every
+ downstream library to manage its own cache.
+
+ Expected tree:
+ ```text
+ assets/
+ └── datasets/
+ │ ├── SQuAD/
+ │ │ ├── downloaded/
+ │ │ ├── extracted/
+ │ │ └── processed/
+ │ ├── Helsinki-NLP--tatoeba_mt/
+ │ ├── downloaded/
+ │ ├── extracted/
+ │ └── processed/
+ └── transformers/
+ ├── default/
+ │ ├── something/
+ ├── bert-base-cased/
+ │ ├── default/
+ │ └── training/
+ hub/
+ └── models--julien-c--EsperBERTo-small/
+ ├── blobs/
+ │ ├── (...)
+ │ ├── (...)
+ ├── refs/
+ │ └── (...)
+ └── [ 128] snapshots/
+ ├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/
+ │ ├── (...)
+ └── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/
+ └── (...)
+ ```
+
+
+ Args:
+ library_name (`str`):
+ Name of the library that will manage the cache folder. Example: `"dataset"`.
+ namespace (`str`, *optional*, defaults to "default"):
+ Namespace to which the data belongs. Example: `"SQuAD"`.
+ subfolder (`str`, *optional*, defaults to "default"):
+ Subfolder in which the data will be stored. Example: `extracted`.
+ assets_dir (`str`, `Path`, *optional*):
+ Path to the folder where assets are cached. This must not be the same folder
+ where Hub files are cached. Defaults to `HF_HOME / "assets"` if not provided.
+ Can also be set with `HF_ASSETS_CACHE` environment variable.
+
+ Returns:
+ Path to the cache folder (`Path`).
+
+ Example:
+ ```py
+ >>> from huggingface_hub import cached_assets_path
+
+ >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download")
+ PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download')
+
+ >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted")
+ PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted')
+
+ >>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt")
+ PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default')
+
+ >>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456")
+ PosixPath('/tmp/tmp123456/datasets/default/default')
+ ```
+ """
+ # Resolve assets_dir
+ if assets_dir is None:
+ assets_dir = HF_ASSETS_CACHE
+ assets_dir = Path(assets_dir).expanduser().resolve()
+
+ # Avoid names that could create path issues
+ for part in (" ", "/", "\\"):
+ library_name = library_name.replace(part, "--")
+ namespace = namespace.replace(part, "--")
+ subfolder = subfolder.replace(part, "--")
+
+ # Path to subfolder is created
+ path = assets_dir / library_name / namespace / subfolder
+ try:
+ path.mkdir(exist_ok=True, parents=True)
+ except (FileExistsError, NotADirectoryError):
+ raise ValueError(f"Corrupted assets folder: cannot create directory because of an existing file ({path}).")
+
+ # Return
+ return path
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_manager.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2da8b51c029fc0c7db0deb0dd3791f4680866e9
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_cache_manager.py
@@ -0,0 +1,1037 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to manage the HF cache directory."""
+
+import os
+import shutil
+from collections import defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Literal
+
+from huggingface_hub.errors import CacheNotFound, CorruptedCacheException
+
+from ..constants import HF_HUB_CACHE, REPO_TYPES_MAPPING
+from . import _shared_blobs, logging
+from ._parsing import format_timesince
+from ._terminal import tabulate
+
+
+logger = logging.get_logger(__name__)
+
+REPO_TYPE_T = Literal["model", "dataset", "space", "kernel"]
+
+# List of OS-created helper files that need to be ignored
+FILES_TO_IGNORE = [".DS_Store", "Thumbs.db", "desktop.ini"]
+
+
+@dataclass(frozen=True)
+class CachedFileInfo:
+ """Frozen data structure holding information about a single cached file.
+
+ Args:
+ file_name (`str`):
+ Name of the file. Example: `config.json`.
+ file_path (`Path`):
+ Path of the file in the `snapshots` directory. The file path is a symlink
+ referring to a blob in the `blobs` folder.
+ blob_path (`Path`):
+ Path of the repo-local blob entry referenced by `file_path`. The blob entry
+ can itself be a symlink to the cache-wide shared blob store.
+ size_on_disk (`int`):
+ Size of the blob file in bytes.
+ blob_last_accessed (`float`):
+ Timestamp of the last time the blob file has been accessed (from any
+ revision).
+ blob_last_modified (`float`):
+ Timestamp of the last time the blob file has been modified/created.
+
+ > [!WARNING]
+ > `blob_last_accessed` and `blob_last_modified` reliability can depend on the OS you
+ > are using. See [python documentation](https://docs.python.org/3/library/os.html#os.stat_result)
+ > for more details.
+ """
+
+ file_name: str
+ file_path: Path
+ blob_path: Path
+ size_on_disk: int
+
+ blob_last_accessed: float
+ blob_last_modified: float
+
+ @property
+ def blob_last_accessed_str(self) -> str:
+ """
+ (property) Timestamp of the last time the blob file has been accessed (from any
+ revision), returned as a human-readable string.
+
+ Example: "2 weeks ago".
+ """
+ return format_timesince(self.blob_last_accessed)
+
+ @property
+ def blob_last_modified_str(self) -> str:
+ """
+ (property) Timestamp of the last time the blob file has been modified, returned
+ as a human-readable string.
+
+ Example: "2 weeks ago".
+ """
+ return format_timesince(self.blob_last_modified)
+
+ @property
+ def size_on_disk_str(self) -> str:
+ """
+ (property) Size of the blob file as a human-readable string.
+
+ Example: "42.2K".
+ """
+ return _format_size(self.size_on_disk)
+
+
+@dataclass(frozen=True)
+class CachedRevisionInfo:
+ """Frozen data structure holding information about a revision.
+
+ A revision correspond to a folder in the `snapshots` folder and is populated with
+ the exact tree structure as the repo on the Hub but contains only symlinks. A
+ revision can be either referenced by 1 or more `refs` or be "detached" (no refs).
+
+ Args:
+ commit_hash (`str`):
+ Hash of the revision (unique).
+ Example: `"9338f7b671827df886678df2bdd7cc7b4f36dffd"`.
+ snapshot_path (`Path`):
+ Path to the revision directory in the `snapshots` folder. It contains the
+ exact tree structure as the repo on the Hub.
+ files: (`frozenset[CachedFileInfo]`):
+ Set of [`~CachedFileInfo`] describing all files contained in the snapshot.
+ refs (`frozenset[str]`):
+ Set of `refs` pointing to this revision. If the revision has no `refs`, it
+ is considered detached.
+ Example: `{"main", "2.4.0"}` or `{"refs/pr/1"}`.
+ size_on_disk (`int`):
+ Sum of the blob file sizes that are symlink-ed by the revision.
+ last_modified (`float`):
+ Timestamp of the last time the revision has been created/modified.
+
+ > [!WARNING]
+ > `last_accessed` cannot be determined correctly on a single revision as blob files
+ > are shared across revisions.
+
+ > [!WARNING]
+ > `size_on_disk` is not necessarily the sum of all file sizes because of possible
+ > duplicated files. Besides, only blobs are taken into account, not the (negligible)
+ > size of folders and symlinks.
+ """
+
+ commit_hash: str
+ snapshot_path: Path
+ size_on_disk: int
+ files: frozenset[CachedFileInfo]
+ refs: frozenset[str]
+
+ last_modified: float
+
+ @property
+ def last_modified_str(self) -> str:
+ """
+ (property) Timestamp of the last time the revision has been modified, returned
+ as a human-readable string.
+
+ Example: "2 weeks ago".
+ """
+ return format_timesince(self.last_modified)
+
+ @property
+ def size_on_disk_str(self) -> str:
+ """
+ (property) Sum of the blob file sizes as a human-readable string.
+
+ Example: "42.2K".
+ """
+ return _format_size(self.size_on_disk)
+
+ @property
+ def nb_files(self) -> int:
+ """
+ (property) Total number of files in the revision.
+ """
+ return len(self.files)
+
+
+@dataclass(frozen=True)
+class CachedRepoInfo:
+ """Frozen data structure holding information about a cached repository.
+
+ Args:
+ repo_id (`str`):
+ Repo id of the repo on the Hub. Example: `"google/fleurs"`.
+ repo_type (`Literal["dataset", "model", "space", "kernel"]`):
+ Type of the cached repo.
+ repo_path (`Path`):
+ Local path to the cached repo.
+ size_on_disk (`int`):
+ Sum of the blob file sizes in the cached repo.
+ nb_files (`int`):
+ Total number of blob files in the cached repo.
+ revisions (`frozenset[CachedRevisionInfo]`):
+ Set of [`~CachedRevisionInfo`] describing all revisions cached in the repo.
+ last_accessed (`float`):
+ Timestamp of the last time a blob file of the repo has been accessed.
+ last_modified (`float`):
+ Timestamp of the last time a blob file of the repo has been modified/created.
+
+ > [!WARNING]
+ > `size_on_disk` is not necessarily the sum of all revisions sizes because of
+ > duplicated files. Besides, only blobs are taken into account, not the (negligible)
+ > size of folders and symlinks.
+
+ > [!WARNING]
+ > `last_accessed` and `last_modified` reliability can depend on the OS you are using.
+ > See [python documentation](https://docs.python.org/3/library/os.html#os.stat_result)
+ > for more details.
+ """
+
+ repo_id: str
+ repo_type: REPO_TYPE_T
+ repo_path: Path
+ size_on_disk: int
+ nb_files: int
+ revisions: frozenset[CachedRevisionInfo]
+
+ last_accessed: float
+ last_modified: float
+
+ @property
+ def last_accessed_str(self) -> str:
+ """
+ (property) Last time a blob file of the repo has been accessed, returned as a
+ human-readable string.
+
+ Example: "2 weeks ago".
+ """
+ return format_timesince(self.last_accessed)
+
+ @property
+ def last_modified_str(self) -> str:
+ """
+ (property) Last time a blob file of the repo has been modified, returned as a
+ human-readable string.
+
+ Example: "2 weeks ago".
+ """
+ return format_timesince(self.last_modified)
+
+ @property
+ def size_on_disk_str(self) -> str:
+ """
+ (property) Sum of the blob file sizes as a human-readable string.
+
+ Example: "42.2K".
+ """
+ return _format_size(self.size_on_disk)
+
+ @property
+ def cache_id(self) -> str:
+ """Canonical `type/id` identifier used across cache tooling."""
+ return f"{self.repo_type}/{self.repo_id}"
+
+ @property
+ def refs(self) -> dict[str, CachedRevisionInfo]:
+ """
+ (property) Mapping between `refs` and revision data structures.
+ """
+ return {ref: revision for revision in self.revisions for ref in revision.refs}
+
+
+@dataclass(frozen=True)
+class DeleteCacheStrategy:
+ """Frozen data structure holding the strategy to delete cached revisions or files.
+
+ This object is not meant to be instantiated programmatically but to be returned by
+ [`~utils.HFCacheInfo.delete_revisions`] or [`~utils.HFCacheInfo.delete_files`]. See
+ documentation for usage example.
+
+ Args:
+ expected_freed_size (`float`):
+ Expected freed size once strategy is executed.
+ blobs (`frozenset[Path]`):
+ Set of blob file paths to be deleted.
+ refs (`frozenset[Path]`):
+ Set of reference file paths to be deleted.
+ repos (`frozenset[Path]`):
+ Set of entire repo paths to be deleted.
+ snapshots (`frozenset[Path]`):
+ Set of snapshots to be deleted (directory of symlinks).
+ files (`frozenset[Path]`, *optional*):
+ Set of individual snapshot entries to be deleted. Their blobs are deleted only if
+ listed in `blobs`.
+ cache_dir (`Path` or `None`):
+ Cache directory the strategy was computed from. Used to collect shared
+ blobs referenced by repo-local symlinks removed by the deletion.
+ """
+
+ expected_freed_size: int
+ blobs: frozenset[Path]
+ refs: frozenset[Path]
+ repos: frozenset[Path]
+ snapshots: frozenset[Path]
+ files: frozenset[Path] = frozenset()
+ cache_dir: Path | None = None
+
+ @property
+ def expected_freed_size_str(self) -> str:
+ """
+ (property) Expected size that will be freed as a human-readable string.
+
+ Example: "42.2K".
+ """
+ return _format_size(self.expected_freed_size)
+
+ def execute(self) -> None:
+ """Execute the defined strategy.
+
+ > [!WARNING]
+ > If this method is interrupted, the cache might get corrupted. Deletion order is
+ > implemented so that references and symlinks are deleted before the actual blob
+ > files.
+
+ > [!WARNING]
+ > This method is irreversible. If executed, cached files are erased and must be
+ > downloaded again.
+ """
+ # Record shared targets before their repo symlinks are deleted; only those manifests are swept below.
+ shared_store_paths: set[Path] = set()
+ if self.cache_dir is not None and _shared_blobs.is_shared_blobs_dir(
+ _shared_blobs.shared_blobs_dir(self.cache_dir)
+ ):
+ blob_paths = set(self.blobs)
+ for repo_path in self.repos:
+ blobs_dir = repo_path / "blobs"
+ try:
+ blob_paths.update(blobs_dir.iterdir())
+ except OSError:
+ pass
+ for blob_path in blob_paths:
+ if store_path := _shared_blobs.shared_blob_target(blob_path, self.cache_dir):
+ shared_store_paths.add(store_path)
+
+ # Deletion order matters. Blobs are deleted in last so that the user can't end
+ # up in a state where a `ref`` refers to a missing snapshot or a snapshot
+ # symlink refers to a deleted blob.
+
+ # Unlink snapshot entries first: if it fails, their blobs must not be deleted.
+ for path in self.files:
+ path.unlink(missing_ok=True)
+
+ # Delete entire repos
+ for path in self.repos:
+ _try_delete_path(path, path_type="repo")
+
+ # Delete snapshot directories
+ for path in self.snapshots:
+ _try_delete_path(path, path_type="snapshot")
+
+ # Delete refs files
+ for path in self.refs:
+ _try_delete_path(path, path_type="ref")
+
+ # Delete blob files
+ for path in self.blobs:
+ _try_delete_path(path, path_type="blob")
+
+ # Manifest entries are validated against the filesystem; failures leak data rather than delete it.
+ if self.cache_dir is not None:
+ for store_path in shared_store_paths:
+ _shared_blobs.sweep_shared_blob(store_path, cache_dir=self.cache_dir)
+
+ logger.info(f"Cache deletion done. Saved {self.expected_freed_size_str}.")
+
+
+@dataclass(frozen=True)
+class CachedIncompleteFileInfo:
+ """Frozen data structure holding information about a single incomplete download.
+
+ Interrupted downloads leave `//blobs/.incomplete` files behind.
+ These are not part of any committed revision, so they are surfaced separately by
+ [`scan_cache_dir`].
+
+ Args:
+ file_path (`Path`):
+ Path of the `.incomplete` file in the `blobs` folder.
+ size_on_disk (`int`):
+ Size of the partially-downloaded file in bytes.
+ """
+
+ file_path: Path
+ size_on_disk: int
+
+
+@dataclass(frozen=True)
+class HFCacheInfo:
+ """Frozen data structure holding information about the entire cache-system.
+
+ This data structure is returned by [`scan_cache_dir`] and is immutable.
+
+ Args:
+ size_on_disk (`int`):
+ Physical size of the cache-system in bytes. A blob shared across repos is
+ counted once, and store-only shared blobs no longer referenced by any repo are
+ included. Can be smaller than the sum of the per-repo sizes.
+ repos (`frozenset[CachedRepoInfo]`):
+ Set of [`~CachedRepoInfo`] describing all valid cached repos found on the
+ cache-system while scanning.
+ incomplete_files (`frozenset[CachedIncompleteFileInfo]`):
+ Set of [`~CachedIncompleteFileInfo`] describing orphaned `*.incomplete`
+ files left behind by interrupted downloads.
+ warnings (`list[CorruptedCacheException]`):
+ List of [`~CorruptedCacheException`] that occurred while scanning the cache.
+ Those exceptions are captured so that the scan can continue. Corrupted repos
+ are skipped from the scan.
+ cache_dir (`Path` or `None`):
+ Cache directory that was scanned.
+
+ > [!WARNING]
+ > `size_on_disk` only accounts for blobs, and corrupted repos are skipped. It is a
+ > physical size: unlike `CachedRepoInfo.size_on_disk`, which attributes a shared blob
+ > to every repo referencing it, a shared blob is counted here only once.
+ """
+
+ size_on_disk: int
+ repos: frozenset[CachedRepoInfo]
+ incomplete_files: frozenset[CachedIncompleteFileInfo]
+ warnings: list[CorruptedCacheException]
+ cache_dir: Path | None = None
+
+ @property
+ def size_on_disk_str(self) -> str:
+ """
+ (property) Physical size of the cache-system as a human-readable string.
+
+ Example: "42.2K".
+ """
+ return _format_size(self.size_on_disk)
+
+ @property
+ def incomplete_size_on_disk(self) -> int:
+ """(property) Sum of all incomplete download sizes in bytes."""
+ return sum(file.size_on_disk for file in self.incomplete_files)
+
+ def delete_revisions(self, *revisions: str) -> DeleteCacheStrategy:
+ """Prepare the strategy to delete one or more revisions cached locally.
+
+ Input revisions can be any revision hash. If a revision hash is not found in the
+ local cache, a warning is thrown but no error is raised. Revisions can be from
+ different cached repos since hashes are unique across repos,
+
+ Examples:
+ ```py
+ >>> from huggingface_hub import scan_cache_dir
+ >>> cache_info = scan_cache_dir()
+ >>> delete_strategy = cache_info.delete_revisions(
+ ... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa"
+ ... )
+ >>> print(f"Will free {delete_strategy.expected_freed_size_str}.")
+ Will free 7.9K.
+ >>> delete_strategy.execute()
+ Cache deletion done. Saved 7.9K.
+ ```
+
+ ```py
+ >>> from huggingface_hub import scan_cache_dir
+ >>> scan_cache_dir().delete_revisions(
+ ... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa",
+ ... "e2983b237dccf3ab4937c97fa717319a9ca1a96d",
+ ... "6c0e6080953db56375760c0471a8c5f2929baf11",
+ ... ).execute()
+ Cache deletion done. Saved 8.6G.
+ ```
+
+ > [!WARNING]
+ > `delete_revisions` returns a [`~utils.DeleteCacheStrategy`] object that needs to
+ > be executed. The [`~utils.DeleteCacheStrategy`] is not meant to be modified but
+ > allows having a dry run before actually executing the deletion.
+ """
+ hashes_to_delete: set[str] = set(revisions)
+
+ repos_with_revisions: dict[CachedRepoInfo, set[CachedRevisionInfo]] = defaultdict(set)
+
+ for repo in self.repos:
+ for revision in repo.revisions:
+ if revision.commit_hash in hashes_to_delete:
+ repos_with_revisions[repo].add(revision)
+ hashes_to_delete.remove(revision.commit_hash)
+
+ if len(hashes_to_delete) > 0:
+ logger.warning(f"Revision(s) not found - cannot delete them: {', '.join(hashes_to_delete)}")
+
+ delete_strategy_blobs: set[Path] = set()
+ delete_strategy_refs: set[Path] = set()
+ delete_strategy_repos: set[Path] = set()
+ delete_strategy_snapshots: set[Path] = set()
+
+ # Blob entries the strategy will unlink (path -> scanned size).
+ blobs_to_unlink: dict[Path, int] = {}
+
+ for affected_repo, revisions_to_delete in repos_with_revisions.items():
+ other_revisions = affected_repo.revisions - revisions_to_delete
+
+ # If no other revisions, it means all revisions are deleted
+ # -> delete the entire cached repo
+ if len(other_revisions) == 0:
+ delete_strategy_repos.add(affected_repo.repo_path)
+ for revision in affected_repo.revisions:
+ for file in revision.files:
+ blobs_to_unlink[file.blob_path] = file.size_on_disk
+ continue
+
+ # Some revisions of the repo will be deleted but not all. We need to filter
+ # which blob files will not be linked anymore.
+ other_blob_paths = {rev_file.blob_path for revision in other_revisions for rev_file in revision.files}
+ for revision_to_delete in revisions_to_delete:
+ # Snapshot dir
+ delete_strategy_snapshots.add(revision_to_delete.snapshot_path)
+
+ # Refs dir
+ for ref in revision_to_delete.refs:
+ delete_strategy_refs.add(affected_repo.repo_path / "refs" / ref)
+
+ # Blobs dir
+ for file in revision_to_delete.files:
+ # Some files are not symlinks to the blobs dir: files copied instead of symlinked
+ # (Windows, filesystems without symlink support) or files created by the user
+ # inside the snapshot dir (e.g. build artifacts from a `pip install`). They are
+ # already removed when deleting the snapshot dir -> count them but don't delete
+ # them a second time.
+ if revision_to_delete.snapshot_path in file.blob_path.parents:
+ blobs_to_unlink[file.blob_path] = file.size_on_disk
+ continue
+
+ # Blob file not referenced by remaining revisions -> delete
+ if file.blob_path not in other_blob_paths:
+ delete_strategy_blobs.add(file.blob_path)
+ blobs_to_unlink[file.blob_path] = file.size_on_disk
+
+ # Return the strategy instead of executing it.
+ return DeleteCacheStrategy(
+ blobs=frozenset(delete_strategy_blobs),
+ refs=frozenset(delete_strategy_refs),
+ repos=frozenset(delete_strategy_repos),
+ snapshots=frozenset(delete_strategy_snapshots),
+ expected_freed_size=_expected_freed_size(blobs_to_unlink, self.cache_dir),
+ cache_dir=self.cache_dir,
+ )
+
+ def delete_files(self, *files: CachedFileInfo) -> DeleteCacheStrategy:
+ """Prepare the strategy to delete individual cached files.
+
+ Only the snapshot entries are removed. A blob is deleted only if no other cached file
+ references it, so blobs shared with other revisions are kept. Refs and snapshot
+ directories are kept as well, even if a snapshot ends up empty. To delete whole
+ revisions or repos, use [`~utils.HFCacheInfo.delete_revisions`].
+
+ Example:
+ ```py
+ >>> from huggingface_hub import scan_cache_dir
+ >>> cache_info = scan_cache_dir()
+ >>> files = [
+ ... file
+ ... for repo in cache_info.repos
+ ... if repo.repo_id == "org/model"
+ ... for revision in repo.revisions
+ ... for file in revision.files
+ ... if file.file_name == "model-Q4_K_M.gguf"
+ ... ]
+ >>> delete_strategy = cache_info.delete_files(*files)
+ >>> print(f"Will free {delete_strategy.expected_freed_size_str}.")
+ Will free 4.9G.
+ >>> delete_strategy.execute()
+ Cache deletion done. Saved 4.9G.
+ ```
+ """
+ selected = set(files)
+ retained_blobs = {
+ file.blob_path
+ for repo in self.repos
+ for revision in repo.revisions
+ for file in revision.files
+ if file not in selected
+ }
+ blobs_to_unlink = {
+ file.blob_path: file.size_on_disk for file in selected if file.blob_path not in retained_blobs
+ }
+ file_paths = frozenset(file.file_path for file in selected)
+ return DeleteCacheStrategy(
+ expected_freed_size=_expected_freed_size(blobs_to_unlink, self.cache_dir),
+ # Files stored directly in the snapshot (no symlink) are already removed with the entry.
+ blobs=frozenset(blobs_to_unlink.keys() - file_paths),
+ refs=frozenset(),
+ repos=frozenset(),
+ snapshots=frozenset(),
+ files=file_paths,
+ cache_dir=self.cache_dir,
+ )
+
+ def export_as_table(self, *, verbosity: int = 0) -> str:
+ """Generate a table from the [`HFCacheInfo`] object.
+
+ Pass `verbosity=0` to get a table with a single row per repo, with columns
+ "repo_id", "repo_type", "size_on_disk", "nb_files", "last_accessed", "last_modified", "refs", "local_path".
+
+ Pass `verbosity=1` to get a table with a row per repo and revision (thus multiple rows can appear for a single repo), with columns
+ "repo_id", "repo_type", "revision", "size_on_disk", "nb_files", "last_modified", "refs", "local_path".
+
+ Example:
+ ```py
+ >>> from huggingface_hub.utils import scan_cache_dir
+
+ >>> hf_cache_info = scan_cache_dir()
+ HFCacheInfo(...)
+
+ >>> print(hf_cache_info.export_as_table())
+ REPO ID REPO TYPE SIZE ON DISK NB FILES LAST_ACCESSED LAST_MODIFIED REFS LOCAL PATH
+ --------------------------------------------------- --------- ------------ -------- ------------- ------------- ---- --------------------------------------------------------------------------------------------------
+ roberta-base model 2.7M 5 1 day ago 1 week ago main ~/.cache/huggingface/hub/models--roberta-base
+ suno/bark model 8.8K 1 1 week ago 1 week ago main ~/.cache/huggingface/hub/models--suno--bark
+ t5-base model 893.8M 4 4 days ago 7 months ago main ~/.cache/huggingface/hub/models--t5-base
+ t5-large model 3.0G 4 5 weeks ago 5 months ago main ~/.cache/huggingface/hub/models--t5-large
+
+ >>> print(hf_cache_info.export_as_table(verbosity=1))
+ REPO ID REPO TYPE REVISION SIZE ON DISK NB FILES LAST_MODIFIED REFS LOCAL PATH
+ --------------------------------------------------- --------- ---------------------------------------- ------------ -------- ------------- ---- -----------------------------------------------------------------------------------------------------------------------------------------------------
+ roberta-base model e2da8e2f811d1448a5b465c236feacd80ffbac7b 2.7M 5 1 week ago main ~/.cache/huggingface/hub/models--roberta-base/snapshots/e2da8e2f811d1448a5b465c236feacd80ffbac7b
+ suno/bark model 70a8a7d34168586dc5d028fa9666aceade177992 8.8K 1 1 week ago main ~/.cache/huggingface/hub/models--suno--bark/snapshots/70a8a7d34168586dc5d028fa9666aceade177992
+ t5-base model a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 893.8M 4 7 months ago main ~/.cache/huggingface/hub/models--t5-base/snapshots/a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1
+ t5-large model 150ebc2c4b72291e770f58e6057481c8d2ed331a 3.0G 4 5 months ago main ~/.cache/huggingface/hub/models--t5-large/snapshots/150ebc2c4b72291e770f58e6057481c8d2ed331a
+ ```
+
+ Args:
+ verbosity (`int`, *optional*):
+ The verbosity level. Defaults to 0.
+
+ Returns:
+ `str`: The table as a string.
+ """
+ if verbosity == 0:
+ return tabulate(
+ rows=[
+ [
+ repo.repo_id,
+ repo.repo_type,
+ f"{repo.size_on_disk_str:>12}",
+ repo.nb_files,
+ repo.last_accessed_str,
+ repo.last_modified_str,
+ ", ".join(sorted(repo.refs)),
+ str(repo.repo_path),
+ ]
+ for repo in sorted(self.repos, key=lambda repo: repo.repo_path)
+ ],
+ headers=[
+ "REPO ID",
+ "REPO TYPE",
+ "SIZE ON DISK",
+ "NB FILES",
+ "LAST_ACCESSED",
+ "LAST_MODIFIED",
+ "REFS",
+ "LOCAL PATH",
+ ],
+ )
+ else:
+ return tabulate(
+ rows=[
+ [
+ repo.repo_id,
+ repo.repo_type,
+ revision.commit_hash,
+ f"{revision.size_on_disk_str:>12}",
+ revision.nb_files,
+ revision.last_modified_str,
+ ", ".join(sorted(revision.refs)),
+ str(revision.snapshot_path),
+ ]
+ for repo in sorted(self.repos, key=lambda repo: repo.repo_path)
+ for revision in sorted(repo.revisions, key=lambda revision: revision.commit_hash)
+ ],
+ headers=[
+ "REPO ID",
+ "REPO TYPE",
+ "REVISION",
+ "SIZE ON DISK",
+ "NB FILES",
+ "LAST_MODIFIED",
+ "REFS",
+ "LOCAL PATH",
+ ],
+ )
+
+
+def scan_cache_dir(cache_dir: str | Path | None = None) -> HFCacheInfo:
+ """Scan the entire HF cache-system and return a [`~HFCacheInfo`] structure.
+
+ Use `scan_cache_dir` in order to programmatically scan your cache-system. The cache
+ will be scanned repo by repo. If a repo is corrupted, a [`~CorruptedCacheException`]
+ will be thrown internally but captured and returned in the [`~HFCacheInfo`]
+ structure. Only valid repos get a proper report.
+
+ ```py
+ >>> from huggingface_hub import scan_cache_dir
+
+ >>> hf_cache_info = scan_cache_dir()
+ HFCacheInfo(
+ size_on_disk=3398085269,
+ repos=frozenset({
+ CachedRepoInfo(
+ repo_id='t5-small',
+ repo_type='model',
+ repo_path=PosixPath(...),
+ size_on_disk=970726914,
+ nb_files=11,
+ revisions=frozenset({
+ CachedRevisionInfo(
+ commit_hash='d78aea13fa7ecd06c29e3e46195d6341255065d5',
+ size_on_disk=970726339,
+ snapshot_path=PosixPath(...),
+ files=frozenset({
+ CachedFileInfo(
+ file_name='config.json',
+ size_on_disk=1197
+ file_path=PosixPath(...),
+ blob_path=PosixPath(...),
+ ),
+ CachedFileInfo(...),
+ ...
+ }),
+ ),
+ CachedRevisionInfo(...),
+ ...
+ }),
+ ),
+ CachedRepoInfo(...),
+ ...
+ }),
+ warnings=[
+ CorruptedCacheException("Snapshots dir doesn't exist in cached repo: ..."),
+ CorruptedCacheException(...),
+ ...
+ ],
+ )
+ ```
+
+ You can also print a detailed report directly from the `hf` command line using:
+ ```text
+ > hf cache ls
+ ID SIZE LAST_ACCESSED LAST_MODIFIED REFS
+ --------------------------- -------- ------------- ------------- -----------
+ dataset/nyu-mll/glue 157.4M 2 days ago 2 days ago main script
+ model/LiquidAI/LFM2-VL-1.6B 3.2G 4 days ago 4 days ago main
+ model/microsoft/UserLM-8b 32.1G 4 days ago 4 days ago main
+
+ Done in 0.0s. Scanned 6 repo(s) for a total of 3.4G.
+ Got 1 warning(s) while scanning. Use -vvv to print details.
+ ```
+
+ Args:
+ cache_dir (`str` or `Path`, `optional`):
+ Cache directory to cache. Defaults to the default HF cache directory.
+
+ > [!WARNING]
+ > Raises:
+ >
+ > `CacheNotFound`
+ > If the cache directory does not exist.
+ >
+ > [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ > If the cache directory is a file, instead of a directory.
+
+ Returns: a [`~HFCacheInfo`] object.
+ """
+ if cache_dir is None:
+ cache_dir = HF_HUB_CACHE
+
+ cache_dir = Path(cache_dir).expanduser().resolve()
+ if not cache_dir.exists():
+ raise CacheNotFound(
+ f"Cache directory not found: {cache_dir}. Please use `cache_dir` argument or set `HF_HUB_CACHE` environment variable.",
+ cache_dir=cache_dir,
+ )
+
+ if cache_dir.is_file():
+ raise ValueError(
+ f"Scan cache expects a directory but found a file: {cache_dir}. Please use `cache_dir` argument or set `HF_HUB_CACHE` environment variable."
+ )
+
+ repos: set[CachedRepoInfo] = set()
+ warnings: list[CorruptedCacheException] = []
+ for repo_path in cache_dir.iterdir():
+ if repo_path.name in FILES_TO_IGNORE:
+ continue
+ if repo_path.name == ".locks": # skip './.locks/' folder
+ continue
+ if repo_path.name == "CACHEDIR.TAG": # skip CACHEDIR.TAG file
+ continue
+ if repo_path.name == _shared_blobs.SHARED_BLOBS_DIR_NAME and _shared_blobs.is_shared_blobs_dir(repo_path):
+ continue
+ try:
+ repos.add(_scan_cached_repo(repo_path))
+ except CorruptedCacheException as e:
+ warnings.append(e)
+
+ # Per-repo sizes stay logical. The cache-wide total counts each shared payload once, including
+ # store-only payloads no repo references anymore.
+ store_marked = _shared_blobs.is_shared_blobs_dir(_shared_blobs.shared_blobs_dir(cache_dir))
+ repo_blob_paths = {
+ file.blob_path
+ for repo in repos
+ for revision in repo.revisions
+ for file in revision.files
+ if not store_marked or _shared_blobs.shared_blob_target(file.blob_path, cache_dir) is None
+ }
+ physical_blob_paths = repo_blob_paths | _shared_blobs.shared_store_blob_paths(cache_dir)
+ physical_size = 0
+ for blob_path in physical_blob_paths:
+ try:
+ physical_size += blob_path.stat().st_size
+ except OSError:
+ pass
+
+ return HFCacheInfo(
+ repos=frozenset(repos),
+ size_on_disk=physical_size,
+ incomplete_files=_scan_incomplete_files(cache_dir),
+ warnings=warnings,
+ cache_dir=cache_dir,
+ )
+
+
+def _scan_incomplete_files(cache_dir: Path) -> frozenset[CachedIncompleteFileInfo]:
+ """Find orphaned `*.incomplete` partial-download files in the cache.
+
+ Interrupted downloads leave `//blobs/.incomplete` files behind.
+ These are not part of any committed revision, so they are reported separately in the
+ [`~HFCacheInfo`] returned by [`scan_cache_dir`].
+ """
+ files: set[CachedIncompleteFileInfo] = set()
+ for path in cache_dir.glob("*/blobs/*.incomplete"):
+ try:
+ size_on_disk = path.stat().st_size
+ except OSError:
+ continue
+ files.add(CachedIncompleteFileInfo(file_path=path, size_on_disk=size_on_disk))
+ return frozenset(files)
+
+
+def _scan_cached_repo(repo_path: Path) -> CachedRepoInfo:
+ """Scan a single cache repo and return information about it.
+
+ Any unexpected behavior will raise a [`~CorruptedCacheException`].
+ """
+ if not repo_path.is_dir():
+ raise CorruptedCacheException(f"Repo path is not a directory: {repo_path}")
+
+ if "--" not in repo_path.name:
+ raise CorruptedCacheException(f"Repo path is not a valid HuggingFace cache directory: {repo_path}")
+
+ repo_type_prefix, repo_id = repo_path.name.split("--", maxsplit=1)
+ repo_id = repo_id.replace("--", "/") # google/fleurs -> "google/fleurs"
+
+ if repo_type_prefix not in REPO_TYPES_MAPPING:
+ raise CorruptedCacheException(
+ f"Repo type must be one of {sorted(REPO_TYPES_MAPPING)}, found `{repo_type_prefix}` ({repo_path})."
+ )
+ repo_type = REPO_TYPES_MAPPING[repo_type_prefix] # "models" -> "model"
+
+ blob_stats: dict[Path, os.stat_result] = {} # Key is blob_path, value is blob stats
+
+ snapshots_path = repo_path / "snapshots"
+ refs_path = repo_path / "refs"
+
+ if not snapshots_path.exists() or not snapshots_path.is_dir():
+ raise CorruptedCacheException(f"Snapshots dir doesn't exist in cached repo: {snapshots_path}")
+
+ # Scan over `refs` directory
+
+ # key is revision hash, value is set of refs
+ refs_by_hash: dict[str, set[str]] = defaultdict(set)
+ if refs_path.exists():
+ # Example of `refs` directory
+ # ── refs
+ # ├── main
+ # └── refs
+ # └── pr
+ # └── 1
+ if refs_path.is_file():
+ raise CorruptedCacheException(f"Refs directory cannot be a file: {refs_path}")
+
+ for ref_path in refs_path.glob("**/*"):
+ # glob("**/*") iterates over all files and directories -> skip directories
+ if ref_path.is_dir() or ref_path.name in FILES_TO_IGNORE:
+ continue
+
+ ref_name = ref_path.relative_to(refs_path).as_posix()
+ with ref_path.open() as f:
+ commit_hash = f.read()
+
+ refs_by_hash[commit_hash].add(ref_name)
+
+ # Scan snapshots directory
+ cached_revisions: set[CachedRevisionInfo] = set()
+ for revision_path in snapshots_path.iterdir():
+ # Ignore OS-created helper files
+ if revision_path.name in FILES_TO_IGNORE:
+ continue
+ if revision_path.is_file():
+ raise CorruptedCacheException(f"Snapshots folder corrupted. Found a file: {revision_path}")
+
+ cached_files = set()
+ for file_path in revision_path.glob("**/*"):
+ # glob("**/*") iterates over all files and directories -> skip directories
+ if file_path.is_dir():
+ continue
+
+ # Keep the first symlink hop: a repo blob may itself point into the shared store, and
+ # deletion must unlink the repo reference, not the shared payload.
+ if file_path.is_symlink():
+ blob_path = Path(os.path.abspath(file_path.parent / os.readlink(file_path)))
+ else:
+ blob_path = file_path
+ if not blob_path.exists():
+ raise CorruptedCacheException(f"Blob missing (broken symlink): {blob_path}")
+
+ if blob_path not in blob_stats:
+ blob_stats[blob_path] = blob_path.stat()
+
+ cached_files.add(
+ CachedFileInfo(
+ file_name=file_path.name,
+ file_path=file_path,
+ size_on_disk=blob_stats[blob_path].st_size,
+ blob_path=blob_path,
+ blob_last_accessed=blob_stats[blob_path].st_atime,
+ blob_last_modified=blob_stats[blob_path].st_mtime,
+ )
+ )
+
+ # Last modified is either the last modified blob file or the revision folder
+ # itself if it is empty
+ if len(cached_files) > 0:
+ revision_last_modified = max(blob_stats[file.blob_path].st_mtime for file in cached_files)
+ else:
+ revision_last_modified = revision_path.stat().st_mtime
+
+ cached_revisions.add(
+ CachedRevisionInfo(
+ commit_hash=revision_path.name,
+ files=frozenset(cached_files),
+ refs=frozenset(refs_by_hash.pop(revision_path.name, set())),
+ size_on_disk=sum(
+ blob_stats[blob_path].st_size for blob_path in {file.blob_path for file in cached_files}
+ ),
+ snapshot_path=revision_path,
+ last_modified=revision_last_modified,
+ )
+ )
+
+ # Check that all refs referred to an existing revision
+ if len(refs_by_hash) > 0:
+ raise CorruptedCacheException(
+ f"Reference(s) refer to missing commit hashes: {dict(refs_by_hash)} ({repo_path})."
+ )
+
+ # Last modified is either the last modified blob file or the repo folder itself if
+ # no blob files has been found. Same for last accessed.
+ if len(blob_stats) > 0:
+ repo_last_accessed = max(stat.st_atime for stat in blob_stats.values())
+ repo_last_modified = max(stat.st_mtime for stat in blob_stats.values())
+ else:
+ repo_stats = repo_path.stat()
+ repo_last_accessed = repo_stats.st_atime
+ repo_last_modified = repo_stats.st_mtime
+
+ # Build and return frozen structure
+ return CachedRepoInfo(
+ nb_files=len(blob_stats),
+ repo_id=repo_id,
+ repo_path=repo_path,
+ repo_type=repo_type, # type: ignore
+ revisions=frozenset(cached_revisions),
+ size_on_disk=sum(stat.st_size for stat in blob_stats.values()),
+ last_accessed=repo_last_accessed,
+ last_modified=repo_last_modified,
+ )
+
+
+def _format_size(num: int) -> str:
+ """Format size in bytes into a human-readable string.
+
+ Taken from https://stackoverflow.com/a/1094933
+ """
+ num_f = float(num)
+ for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
+ if abs(num_f) < 1000.0:
+ return f"{num_f:3.1f}{unit}"
+ num_f /= 1000.0
+ return f"{num_f:.1f}Y"
+
+
+def _expected_freed_size(blobs_to_unlink: dict[Path, int], cache_dir: Path | None) -> int:
+ """Expected number of bytes freed by unlinking a set of blob files.
+
+ Regular repo blobs retain the historical logical accounting. Shared symlinks count
+ their canonical payload once only when every currently valid manifest reference is
+ included in the deletion plan.
+ """
+ if cache_dir is None or not _shared_blobs.is_shared_blobs_dir(_shared_blobs.shared_blobs_dir(cache_dir)):
+ return sum(blobs_to_unlink.values())
+ expected_freed_size = 0
+ shared_store_paths: set[Path] = set()
+ for blob_path, size_on_disk in blobs_to_unlink.items():
+ if store_path := _shared_blobs.shared_blob_target(blob_path, cache_dir):
+ shared_store_paths.add(store_path)
+ else:
+ expected_freed_size += size_on_disk
+ blob_paths_to_delete = set(blobs_to_unlink)
+ for store_path in shared_store_paths:
+ expected_freed_size += _shared_blobs.expected_shared_blob_freed_size(
+ store_path, blob_paths_to_delete=blob_paths_to_delete, cache_dir=cache_dir
+ )
+ return expected_freed_size
+
+
+def _try_delete_path(path: Path, path_type: str) -> None:
+ """Try to delete a local file or folder.
+
+ If the path does not exist, error is logged as a warning and then ignored.
+
+ Args:
+ path (`Path`):
+ Path to delete. Can be a file or a folder.
+ path_type (`str`):
+ What path are we deleting ? Only for logging purposes. Example: "snapshot".
+ """
+ logger.debug(f"Delete {path_type}: {path}")
+ try:
+ if path.is_file() or path.is_symlink(): # `is_file()` is False for a broken symlink
+ os.remove(path)
+ else:
+ shutil.rmtree(path)
+ except FileNotFoundError:
+ # Nothing to delete: the path is already gone. Log without traceback to avoid flooding the
+ # terminal when deleting many paths at once.
+ logger.warning(f"Couldn't delete {path_type}: file not found ({path})")
+ except PermissionError:
+ logger.warning(f"Couldn't delete {path_type}: permission denied ({path})", exc_info=True)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_chunk_utils.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_chunk_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..081d6e32b0ccc1e123b8f0719ae307eeb7b46133
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_chunk_utils.py
@@ -0,0 +1,64 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a utility to iterate by chunks over an iterator."""
+
+import itertools
+from collections.abc import Iterable
+from typing import TypeVar
+
+
+T = TypeVar("T")
+
+
+def chunk_iterable(iterable: Iterable[T], chunk_size: int) -> Iterable[Iterable[T]]:
+ """Iterates over an iterator chunk by chunk.
+
+ Taken from https://stackoverflow.com/a/8998040.
+ See also https://github.com/huggingface/huggingface_hub/pull/920#discussion_r938793088.
+
+ Args:
+ iterable (`Iterable`):
+ The iterable on which we want to iterate.
+ chunk_size (`int`):
+ Size of the chunks. Must be a strictly positive integer (e.g. >0).
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub.utils import chunk_iterable
+
+ >>> for items in chunk_iterable(range(17), chunk_size=8):
+ ... print(items)
+ # [0, 1, 2, 3, 4, 5, 6, 7]
+ # [8, 9, 10, 11, 12, 13, 14, 15]
+ # [16] # smaller last chunk
+ ```
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If `chunk_size` <= 0.
+
+ > [!WARNING]
+ > The last chunk can be smaller than `chunk_size`.
+ """
+ if not isinstance(chunk_size, int) or chunk_size <= 0:
+ raise ValueError("`chunk_size` must be a strictly positive integer (>0).")
+
+ iterator = iter(iterable)
+ while True:
+ try:
+ next_item = next(iterator)
+ except StopIteration:
+ return
+ yield itertools.chain((next_item,), itertools.islice(iterator, chunk_size - 1))
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py
new file mode 100644
index 0000000000000000000000000000000000000000..44961712a959d8be370b7e3adcd2ef298f27c108
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_datetime.py
@@ -0,0 +1,66 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle datetimes in Huggingface Hub."""
+
+from datetime import datetime, timezone
+
+
+def parse_datetime(date_string: str) -> datetime:
+ """
+ Parses a date_string returned from the server to a datetime object.
+
+ This parser is a weak-parser is the sense that it handles only a single format of
+ date_string. It is expected that the server format will never change. The
+ implementation depends only on the standard lib to avoid an external dependency
+ (python-dateutil). See full discussion about this decision on PR:
+ https://github.com/huggingface/huggingface_hub/pull/999.
+
+ Example:
+ ```py
+ > parse_datetime('2022-08-19T07:19:38.123Z')
+ datetime.datetime(2022, 8, 19, 7, 19, 38, 123000, tzinfo=timezone.utc)
+ ```
+
+ Args:
+ date_string (`str`):
+ A string representing a datetime returned by the Hub server.
+ String is expected to follow '%Y-%m-%dT%H:%M:%S.%fZ' pattern.
+
+ Returns:
+ A python datetime object.
+
+ Raises:
+ :class:`ValueError`:
+ If `date_string` cannot be parsed.
+ """
+ try:
+ # Normalize the string to always have 6 digits of fractional seconds
+ if date_string.endswith("Z"):
+ # Case 1: No decimal point (e.g., "2024-11-16T00:27:02Z")
+ if "." not in date_string:
+ # No fractional seconds - insert .000000
+ date_string = date_string[:-1] + ".000000Z"
+ # Case 2: Has decimal point (e.g., "2022-08-19T07:19:38.123456789Z")
+ else:
+ # Get the fractional and base parts
+ base, fraction = date_string[:-1].split(".")
+ # fraction[:6] takes first 6 digits and :0<6 pads with zeros if less than 6 digits
+ date_string = f"{base}.{fraction[:6]:0<6}Z"
+
+ return datetime.strptime(date_string, "%Y-%m-%dT%H:%M:%S.%fZ").replace(tzinfo=timezone.utc)
+ except ValueError as e:
+ raise ValueError(
+ f"Cannot parse '{date_string}' as a datetime. Date string is expected to"
+ " follow '%Y-%m-%dT%H:%M:%S.%fZ' pattern."
+ ) from e
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_deprecation.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_deprecation.py
new file mode 100644
index 0000000000000000000000000000000000000000..99df8405958f565e6bdf8aaa61e0042d685c4e77
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_deprecation.py
@@ -0,0 +1,136 @@
+import warnings
+from collections.abc import Iterable
+from functools import wraps
+from inspect import Parameter, signature
+
+
+def _deprecate_positional_args(*, version: str):
+ """Decorator for methods that issues warnings for positional arguments.
+ Using the keyword-only argument syntax in pep 3102, arguments after the
+ * will issue a warning when passed as a positional argument.
+
+ Args:
+ version (`str`):
+ The version when positional arguments will result in error.
+ """
+
+ def _inner_deprecate_positional_args(f):
+ sig = signature(f)
+ kwonly_args = []
+ all_args = []
+ for name, param in sig.parameters.items():
+ if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
+ all_args.append(name)
+ elif param.kind == Parameter.KEYWORD_ONLY:
+ kwonly_args.append(name)
+
+ @wraps(f)
+ def inner_f(*args, **kwargs):
+ extra_args = len(args) - len(all_args)
+ if extra_args <= 0:
+ return f(*args, **kwargs)
+ # extra_args > 0
+ args_msg = [
+ f"{name}='{arg}'" if isinstance(arg, str) else f"{name}={arg}"
+ for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
+ ]
+ args_msg = ", ".join(args_msg)
+ warnings.warn(
+ f"Deprecated positional argument(s) used in '{f.__name__}': pass"
+ f" {args_msg} as keyword args. From version {version} passing these"
+ " as positional arguments will result in an error,",
+ FutureWarning,
+ )
+ kwargs.update(zip(sig.parameters, args))
+ return f(**kwargs)
+
+ return inner_f
+
+ return _inner_deprecate_positional_args
+
+
+def _deprecate_arguments(
+ *,
+ version: str,
+ deprecated_args: Iterable[str],
+ custom_message: str | None = None,
+):
+ """Decorator to issue warnings when using deprecated arguments.
+
+ TODO: could be useful to be able to set a custom error message.
+
+ Args:
+ version (`str`):
+ The version when deprecated arguments will result in error.
+ deprecated_args (`list[str]`):
+ List of the arguments to be deprecated.
+ custom_message (`str`, *optional*):
+ Warning message that is raised. If not passed, a default warning message
+ will be created.
+ """
+
+ def _inner_deprecate_positional_args(f):
+ sig = signature(f)
+
+ @wraps(f)
+ def inner_f(*args, **kwargs):
+ # Check for used deprecated arguments
+ used_deprecated_args = []
+ for _, parameter in zip(args, sig.parameters.values()):
+ if parameter.name in deprecated_args:
+ used_deprecated_args.append(parameter.name)
+ for kwarg_name, kwarg_value in kwargs.items():
+ if (
+ # If argument is deprecated but still used
+ kwarg_name in deprecated_args
+ # And then the value is not the default value
+ and kwarg_value != sig.parameters[kwarg_name].default
+ ):
+ used_deprecated_args.append(kwarg_name)
+
+ # Warn and proceed
+ if len(used_deprecated_args) > 0:
+ message = (
+ f"Deprecated argument(s) used in '{f.__name__}':"
+ f" {', '.join(used_deprecated_args)}. Will not be supported from"
+ f" version '{version}'."
+ )
+ if custom_message is not None:
+ message += "\n\n" + custom_message
+ warnings.warn(message, FutureWarning)
+ return f(*args, **kwargs)
+
+ return inner_f
+
+ return _inner_deprecate_positional_args
+
+
+def _deprecate_method(*, version: str, message: str | None = None):
+ """Decorator to issue warnings when using a deprecated method.
+
+ Args:
+ version (`str`):
+ The version when deprecated arguments will result in error.
+ message (`str`, *optional*):
+ Warning message that is raised. If not passed, a default warning message
+ will be created.
+ """
+
+ def _inner_deprecate_method(f):
+ name = f.__name__
+ if name == "__init__":
+ name = f.__qualname__.split(".")[0] # class name instead of method name
+
+ @wraps(f)
+ def inner_f(*args, **kwargs):
+ warning_message = (
+ f"'{name}' (from '{f.__module__}') is deprecated and will be removed from version '{version}'."
+ )
+ if message is not None:
+ warning_message += " " + message
+ warnings.warn(warning_message, FutureWarning)
+ return f(*args, **kwargs)
+
+ return inner_f
+
+ return _inner_deprecate_method
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_detect_agent.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_detect_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb2661bc2288fdbcea44de7023b2dd8268c6d215
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_detect_agent.py
@@ -0,0 +1,204 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Detect whether the process is being invoked by an AI coding agent.
+
+Detection is based on environment variables that AI agents set in their shell
+sessions. `AI_AGENT` and `AGENT` are treated as a universal standard (any
+tool can set its harness id there); the remaining checks are tool-specific and
+ordered by priority (first match wins).
+
+The list of known harnesses is maintained on the Hub and exposed at
+`{ENDPOINT}/api/agent-harnesses`. We fetch it at most once a day and cache it
+locally so the list can be updated without requiring a new client release.
+
+Detection is entirely best-effort: there is no hardcoded list of harnesses. When
+the registry cannot be fetched (and no cached copy is available), detection simply
+reports "no agent". Any error while fetching/reading the registry is swallowed —
+detection must never make a process fail.
+
+More details: https://huggingface.co/docs/hub/agents-overview#register-your-agent-harness
+"""
+
+import json
+import os
+import time
+from pathlib import Path
+from typing import Optional, TypedDict
+
+from .. import constants
+from . import logging
+
+
+logger = logging.get_logger(__name__)
+
+# Refresh the cached registry at most once every 24 hours.
+_REGISTRY_TTL_SECONDS = 24 * 3600
+
+# Short timeout: fetching the registry is best-effort telemetry, never block the caller for long.
+_REGISTRY_FETCH_TIMEOUT = 3
+
+
+class HarnessInfo(TypedDict, total=False):
+ """A single harness entry. `envVars` maps an env var name to a match pattern (see `_env_vars_match`)."""
+
+ envVars: dict[str, str]
+
+
+class Registry(TypedDict):
+ """The agent harness registry, as served by `{ENDPOINT}/api/agent-harnesses`."""
+
+ standardEnvVars: list[str]
+ harnesses: dict[str, HarnessInfo]
+
+
+# Empty registry: detection is disabled (no agent ever detected). Used when the
+# Hub is unreachable and no cached copy is available.
+_EMPTY_REGISTRY: Registry = {"standardEnvVars": [], "harnesses": {}}
+
+# In-process cache of the resolved registry. Populated lazily on first detection.
+_registry: Registry | None = None
+
+
+def detect_agent() -> Optional[str]:
+ """Return the id of the detected AI agent harness or `None`.
+
+ Harnesses are checked in registry order; for each one we match its env var
+ pattern(s) and, failing that, the standard `AI_AGENT` / `AGENT` vars
+ against the harness id. The first match wins. When a standard var is set to
+ an unrecognized value, `"unknown"` is returned.
+ """
+ registry = _get_registry()
+ standard_vars = registry.get("standardEnvVars") or []
+ harnesses = registry.get("harnesses") or {}
+
+ for harness_id, info in harnesses.items():
+ env_vars = (info or {}).get("envVars")
+ if env_vars and _env_vars_match(env_vars):
+ return harness_id
+ for var in standard_vars:
+ if os.environ.get(var, "").strip() == harness_id:
+ return harness_id
+
+ # No harness matched but a standard var is set => unrecognized agent.
+ lowercased_harnesses = {k.lower() for k in harnesses.keys()}
+ for var in standard_vars:
+ if value := os.environ.get(var, "").strip().lower():
+ if value in lowercased_harnesses:
+ return value
+ return "unknown"
+
+ return None
+
+
+def is_agent() -> bool:
+ """Return `True` if the process is being invoked by an AI coding agent."""
+ return detect_agent() is not None
+
+
+def _env_vars_match(env_vars: dict[str, str]) -> bool:
+ """Return `True` if any `(var, pattern)` from the harness matches the environment.
+
+ Supported patterns:
+ - `"*"`: the variable is set to any non-empty value
+ - `""`: the variable equals this exact value
+ """
+ for var, pattern in env_vars.items():
+ value = os.environ.get(var)
+ if not value:
+ continue
+ if pattern == "*":
+ return True
+ if value == pattern:
+ return True
+ return False
+
+
+def _get_registry() -> Registry:
+ """Return the harness registry, loading (and caching in-process) on first call.
+
+ Best-effort: any unexpected error degrades to an empty registry so detection
+ never raises.
+ """
+ global _registry
+ if _registry is None:
+ try:
+ _registry = _load_registry()
+ except Exception:
+ logger.debug("Could not resolve agent harnesses registry.", exc_info=True)
+ _registry = _EMPTY_REGISTRY
+ return _registry
+
+
+def _load_registry() -> Registry:
+ """Resolve the registry from the local cache or the Hub.
+
+ No hardcoded list: if the Hub is unreachable and no cached copy exists, an
+ empty registry is returned (i.e. no agent is detected).
+ """
+ path = constants.AGENT_HARNESSES_PATH
+
+ # 1. Use the cached file if it was refreshed within the last 24 hours.
+ if cached := _read_cached_registry(path, max_age=_REGISTRY_TTL_SECONDS):
+ return cached
+
+ # 2. Otherwise refresh it from the Hub and persist it for next time.
+ if (fetched := _fetch_registry()) is not None:
+ _write_cached_registry(path, fetched)
+ return fetched
+
+ # 3. Fetch failed: reuse a stale cache if available, else give up (no detection).
+ if stale := _read_cached_registry(path, max_age=None):
+ return stale
+ return _EMPTY_REGISTRY
+
+
+def _read_cached_registry(path: str, max_age: int | None) -> Registry | None:
+ """Return the cached registry, or `None` if missing/stale/unreadable."""
+ try:
+ if not os.path.exists(path):
+ return None
+ if max_age is not None and (time.time() - os.path.getmtime(path)) >= max_age:
+ return None
+ with open(path, encoding="utf-8") as f:
+ return json.load(f)
+ except Exception:
+ logger.debug("Could not read cached agent harnesses registry.", exc_info=True)
+ return None
+
+
+def _write_cached_registry(path: str, registry: Registry) -> None:
+ try:
+ Path(path).parent.mkdir(parents=True, exist_ok=True)
+ with open(path, "w", encoding="utf-8") as f:
+ json.dump(registry, f)
+ except Exception:
+ logger.debug("Could not cache agent harnesses registry.", exc_info=True)
+
+
+def _fetch_registry() -> Registry | None:
+ """Fetch the registry from the Hub. Returns `None` when offline or on any error."""
+ if constants.HF_HUB_OFFLINE:
+ return None
+ try:
+ from ._http import get_session
+
+ response = get_session().get(
+ f"{constants.ENDPOINT}/api/agent-harnesses",
+ timeout=_REGISTRY_FETCH_TIMEOUT,
+ )
+ response.raise_for_status()
+ return response.json()
+ except Exception:
+ logger.debug("Could not fetch agent harnesses registry from the Hub.", exc_info=True)
+ return None
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_dotenv.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_dotenv.py
new file mode 100644
index 0000000000000000000000000000000000000000..b80ec560f21f05b8c40f2d6a9ad55db19043fce5
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_dotenv.py
@@ -0,0 +1,82 @@
+# AI-generated module (ChatGPT)
+import re
+
+
+# Escape sequences expanded inside quoted values. Double-quoted values additionally
+# expand "\$" to "$"; single-quoted values keep it verbatim.
+_ESCAPES = {"n": "\n", "t": "\t", '"': '"', "\\": "\\"}
+_DOUBLE_QUOTE_ESCAPES = {**_ESCAPES, "$": "$"}
+
+
+def _unescape(value: str, escapes: dict[str, str]) -> str:
+ r"""Expand backslash escapes in a single left-to-right pass.
+
+ Processing in one pass (rather than chained `str.replace` calls) ensures an escaped
+ backslash (`\\`) is consumed as a unit and cannot merge with the following character,
+ e.g. `\\n` is a backslash followed by `n`, not a newline. Unknown escapes are kept as-is.
+ """
+ return re.sub(r"\\(.)", lambda match: escapes.get(match.group(1), match.group(0)), value)
+
+
+def load_dotenv(dotenv_str: str, environ: dict[str, str] | None = None) -> dict[str, str]:
+ """
+ Parse a DOTENV-format string and return a dictionary of key-value pairs.
+ Handles quoted values, comments, export keyword, and blank lines.
+ """
+ env: dict[str, str] = {}
+ line_pattern = re.compile(
+ r"""
+ \s*
+ (?:export[^\S\n]+)? # optional export
+ ([A-Za-z_][A-Za-z0-9_]*) # key
+ (?:
+ [^\S\n]*
+ (=) # equal sign
+ (?:
+ [^\S\n]*
+ ( # quoted value
+ '(?:\\'|[^'])*' # single-quoted
+ | \"(?:\\\"|[^\"])*\" # double-quoted
+ )
+ [^\S\n]*(?:\#[^\n\r]*)? # inline comment (needs no preceding whitespace after a quote)
+ |
+ (?:[^\S\n]+(?!\#))? # whitespace after '=' also separates an inline comment
+ ( # unquoted value (may contain '#')
+ [^\n\r]*?
+ )
+ (?:[^\S\n]+\#[^\n\r]*)? # inline comment (must be preceded by whitespace)
+ )
+ |
+ [^\S\n]*(?:\#[^\n\r]*)? # bare key (no '='), with an optional inline comment
+ )
+ """,
+ re.VERBOSE,
+ )
+
+ for line in dotenv_str.splitlines():
+ line = line.strip()
+ if not line or line.startswith("#"):
+ continue # Skip comments and empty lines
+
+ match = line_pattern.fullmatch(line)
+ if match:
+ key = match.group(1)
+ val = None
+ if match.group(2): # if there is '='
+ raw_val = match.group(3) or match.group(4) or ""
+ val = raw_val.strip()
+ # Remove surrounding quotes if quoted
+ if val.startswith('"') and val.endswith('"'):
+ # Double-quoted values expand escape sequences (\n, \t, \", \\, \$).
+ val = _unescape(val[1:-1], _DOUBLE_QUOTE_ESCAPES)
+ elif val.startswith("'") and val.endswith("'"):
+ # Single-quoted values are kept verbatim: no escape expansion.
+ val = val[1:-1]
+ elif environ is not None:
+ # Get it from the current environment
+ val = environ.get(key)
+
+ if val is not None:
+ env[key] = val
+
+ return env
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_experimental.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_experimental.py
new file mode 100644
index 0000000000000000000000000000000000000000..82753e1fe052639ee9f4cc7adcbbe2ffb595c88b
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_experimental.py
@@ -0,0 +1,67 @@
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to flag a feature as "experimental" in Huggingface Hub."""
+
+import warnings
+from collections.abc import Callable
+from functools import wraps
+
+from .. import constants
+
+
+def experimental(fn: Callable) -> Callable:
+ """Decorator to flag a feature as experimental.
+
+ An experimental feature triggers a warning when used as it might be subject to breaking changes without prior notice
+ in the future.
+
+ Warnings can be disabled by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
+
+ Args:
+ fn (`Callable`):
+ The function to flag as experimental.
+
+ Returns:
+ `Callable`: The decorated function.
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub.utils import experimental
+
+ >>> @experimental
+ ... def my_function():
+ ... print("Hello world!")
+
+ >>> my_function()
+ UserWarning: 'my_function' is experimental and might be subject to breaking changes in the future without prior
+ notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
+ Hello world!
+ ```
+ """
+ # For classes, put the "experimental" around the "__new__" method => __new__ will be removed in warning message
+ name = fn.__qualname__[: -len(".__new__")] if fn.__qualname__.endswith(".__new__") else fn.__qualname__
+
+ @wraps(fn)
+ def _inner_fn(*args, **kwargs):
+ if not constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING:
+ warnings.warn(
+ f"'{name}' is experimental and might be subject to breaking changes in the future without prior notice."
+ " You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment"
+ " variable.",
+ UserWarning,
+ )
+ return fn(*args, **kwargs)
+
+ return _inner_fn
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_fixes.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_fixes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4f755fcc0aebc229dbf75c7d0ea51a50c91afff
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_fixes.py
@@ -0,0 +1,123 @@
+import contextlib
+import os
+import shutil
+import stat
+import tempfile
+import time
+from collections.abc import Callable, Generator
+from functools import partial
+from pathlib import Path
+
+import yaml
+from filelock import BaseFileLock, FileLock, SoftFileLock, Timeout
+
+from .. import constants
+from . import logging
+
+
+logger = logging.get_logger(__name__)
+
+# Wrap `yaml.dump` to set `allow_unicode=True` by default.
+#
+# Example:
+# ```py
+# >>> yaml.dump({"emoji": "👀", "some unicode": "日本か"})
+# 'emoji: "\\U0001F440"\nsome unicode: "\\u65E5\\u672C\\u304B"\n'
+#
+# >>> yaml_dump({"emoji": "👀", "some unicode": "日本か"})
+# 'emoji: "👀"\nsome unicode: "日本か"\n'
+# ```
+yaml_dump: Callable[..., str] = partial(yaml.dump, stream=None, allow_unicode=True) # type: ignore
+
+
+@contextlib.contextmanager
+def SoftTemporaryDirectory(
+ suffix: str | None = None,
+ prefix: str | None = None,
+ dir: Path | str | None = None,
+ **kwargs,
+) -> Generator[Path, None, None]:
+ """
+ Context manager to create a temporary directory and safely delete it.
+
+ If tmp directory cannot be deleted normally, we set the WRITE permission and retry.
+ If cleanup still fails, we give up but don't raise an exception. This is equivalent
+ to `tempfile.TemporaryDirectory(..., ignore_cleanup_errors=True)` introduced in
+ Python 3.10.
+
+ See https://www.scivision.dev/python-tempfile-permission-error-windows/.
+ """
+ tmpdir = tempfile.TemporaryDirectory(prefix=prefix, suffix=suffix, dir=dir, **kwargs)
+ yield Path(tmpdir.name).resolve()
+
+ try:
+ # First once with normal cleanup
+ shutil.rmtree(tmpdir.name)
+ except Exception:
+ # If failed, try to set write permission and retry
+ try:
+ shutil.rmtree(tmpdir.name, onerror=_set_write_permission_and_retry)
+ except Exception:
+ pass
+
+ # And finally, cleanup the tmpdir.
+ # If it fails again, give up but do not throw error
+ try:
+ tmpdir.cleanup()
+ except Exception:
+ pass
+
+
+def _set_write_permission_and_retry(func, path, excinfo):
+ os.chmod(path, stat.S_IWRITE)
+ func(path)
+
+
+@contextlib.contextmanager
+def WeakFileLock(lock_file: str | Path, *, timeout: float | None = None) -> Generator[BaseFileLock, None, None]:
+ """A filelock with some custom logic.
+
+ This filelock is weaker than the default filelock in that:
+ 1. It won't raise an exception if release fails.
+ 2. It will default to a SoftFileLock if the filesystem does not support flock.
+ 3. Lock files are created with mode 0o664 (group-writable) instead of the default 0o644.
+ This allows multiple users sharing a cache directory to wait for locks.
+
+ An INFO log message is emitted every 10 seconds if the lock is not acquired immediately.
+ If a timeout is provided, a `filelock.Timeout` exception is raised if the lock is not acquired within the timeout.
+ """
+ log_interval = constants.FILELOCK_LOG_EVERY_SECONDS
+ lock = FileLock(lock_file, timeout=log_interval, mode=0o664)
+ start_time = time.time()
+
+ while True:
+ elapsed_time = time.time() - start_time
+ if timeout is not None and elapsed_time >= timeout:
+ raise Timeout(str(lock_file))
+
+ try:
+ lock.acquire(timeout=min(log_interval, timeout - elapsed_time) if timeout else log_interval)
+ except Timeout:
+ logger.info(
+ f"Still waiting to acquire lock on {lock_file} (elapsed: {time.time() - start_time:.1f} seconds)"
+ )
+ except NotImplementedError as e:
+ if "use SoftFileLock instead" in str(e):
+ logger.warning(
+ "FileSystem does not appear to support flock. Falling back to SoftFileLock for %s", lock_file
+ )
+ lock = SoftFileLock(lock_file, timeout=log_interval)
+ continue
+ else:
+ break
+
+ try:
+ yield lock
+ finally:
+ try:
+ lock.release()
+ except OSError:
+ try:
+ Path(lock_file).unlink()
+ except OSError:
+ pass
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_git_credential.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_git_credential.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ef2b2dce55c1274d89dc9e34beaf92d0989f8e3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_git_credential.py
@@ -0,0 +1,119 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to manage Git credentials."""
+
+import re
+import subprocess
+
+from ..constants import ENDPOINT
+from ._subprocess import run_interactive_subprocess, run_subprocess
+
+
+GIT_CREDENTIAL_REGEX = re.compile(
+ r"""
+ ^\s* # start of line
+ credential\.helper # credential.helper value
+ \s*=\s* # separator
+ ([\w\-\/]+) # the helper name or absolute path (group 1)
+ (\s|$) # whitespace or end of line
+ """,
+ flags=re.MULTILINE | re.IGNORECASE | re.VERBOSE,
+)
+
+
+def list_credential_helpers(folder: str | None = None) -> list[str]:
+ """Return the list of git credential helpers configured.
+
+ See https://git-scm.com/docs/gitcredentials.
+
+ Credentials are saved in all configured helpers (store, cache, macOS keychain,...).
+ Calls "`git credential approve`" internally. See https://git-scm.com/docs/git-credential.
+
+ Args:
+ folder (`str`, *optional*):
+ The folder in which to check the configured helpers.
+ """
+ try:
+ output = run_subprocess("git config --list", folder=folder).stdout
+ parsed = _parse_credential_output(output)
+ return parsed
+ except subprocess.CalledProcessError as exc:
+ raise OSError(exc.stderr)
+
+
+def set_git_credential(token: str, username: str = "hf_user", folder: str | None = None) -> None:
+ """Save a username/token pair in git credential for HF Hub registry.
+
+ Credentials are saved in all configured helpers (store, cache, macOS keychain,...).
+ Calls "`git credential approve`" internally. See https://git-scm.com/docs/git-credential.
+
+ Args:
+ username (`str`, defaults to `"hf_user"`):
+ A git username. Defaults to `"hf_user"`, the default user used in the Hub.
+ token (`str`, defaults to `"hf_user"`):
+ A git password. In practice, the User Access Token for the Hub.
+ See https://huggingface.co/settings/tokens.
+ folder (`str`, *optional*):
+ The folder in which to check the configured helpers.
+ """
+ with run_interactive_subprocess("git credential approve", folder=folder) as (
+ stdin,
+ _,
+ ):
+ stdin.write(f"url={ENDPOINT}\nusername={username.lower()}\npassword={token}\n\n")
+ stdin.flush()
+
+
+def unset_git_credential(username: str = "hf_user", folder: str | None = None) -> None:
+ """Erase credentials from git credential for HF Hub registry.
+
+ Credentials are erased from the configured helpers (store, cache, macOS
+ keychain,...), if any. If `username` is not provided, any credential configured for
+ HF Hub endpoint is erased.
+ Calls "`git credential erase`" internally. See https://git-scm.com/docs/git-credential.
+
+ Args:
+ username (`str`, defaults to `"hf_user"`):
+ A git username. Defaults to `"hf_user"`, the default user used in the Hub.
+ folder (`str`, *optional*):
+ The folder in which to check the configured helpers.
+ """
+ with run_interactive_subprocess("git credential reject", folder=folder) as (
+ stdin,
+ _,
+ ):
+ standard_input = f"url={ENDPOINT}\n"
+ if username is not None:
+ standard_input += f"username={username.lower()}\n"
+ standard_input += "\n"
+
+ stdin.write(standard_input)
+ stdin.flush()
+
+
+def _parse_credential_output(output: str) -> list[str]:
+ """Parse the output of `git credential fill` to extract the password.
+
+ Args:
+ output (`str`):
+ The output of `git credential fill`.
+ """
+ # NOTE: If user has set a helper for a custom URL, it will not be caught here.
+ # Example: `credential.https://huggingface.co.helper=store`
+ # See: https://github.com/huggingface/huggingface_hub/pull/1138#discussion_r1013324508
+ return sorted( # Sort for nice printing
+ { # Might have some duplicates
+ match[0] for match in GIT_CREDENTIAL_REGEX.findall(output)
+ }
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_headers.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa6867401da47688c66cc1183e04d3facb708262
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_headers.py
@@ -0,0 +1,208 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle headers to send in calls to Huggingface Hub."""
+
+from huggingface_hub.errors import LocalTokenNotFoundError
+
+from .. import constants
+from ._auth import get_token
+from ._detect_agent import detect_agent
+from ._runtime import (
+ get_hf_hub_version,
+ get_python_version,
+ get_torch_version,
+ is_torch_available,
+)
+from ._validators import validate_hf_hub_args
+
+
+@validate_hf_hub_args
+def build_hf_headers(
+ *,
+ token: bool | str | None = None,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+ headers: dict[str, str] | None = None,
+) -> dict[str, str]:
+ """
+ Build headers dictionary to send in a HF Hub call.
+
+ By default, authorization token is always provided either from argument (explicit
+ use) or retrieved from the cache (implicit use). To explicitly avoid sending the
+ token to the Hub, set `token=False` or set the `HF_HUB_DISABLE_IMPLICIT_TOKEN`
+ environment variable.
+
+ In case of an API call that requires write access, an error is thrown if token is
+ `None` or token is an organization token (starting with `"api_org***"`).
+
+ In addition to the auth header, a user-agent is added to provide information about
+ the installed packages (versions of python, huggingface_hub, torch).
+
+ Args:
+ token (`str`, `bool`, *optional*):
+ The token to be sent in authorization header for the Hub call:
+ - if a string, it is used as the Hugging Face token
+ - if `True`, the token is read from the machine (cache or env variable)
+ - if `False`, authorization header is not set
+ - if `None`, the token is read from the machine only except if
+ `HF_HUB_DISABLE_IMPLICIT_TOKEN` env variable is set.
+ library_name (`str`, *optional*):
+ The name of the library that is making the HTTP request. Will be added to
+ the user-agent header.
+ library_version (`str`, *optional*):
+ The version of the library that is making the HTTP request. Will be added
+ to the user-agent header.
+ user_agent (`str`, `dict`, *optional*):
+ The user agent info in the form of a dictionary or a single string. It will
+ be completed with information about the installed packages.
+ headers (`dict`, *optional*):
+ Additional headers to include in the request. Those headers take precedence
+ over the ones generated by this function.
+
+ Returns:
+ A `dict` of headers to pass in your API call.
+
+ Example:
+ ```py
+ >>> build_hf_headers(token="hf_***") # explicit token
+ {"authorization": "Bearer hf_***", "user-agent": ""}
+
+ >>> build_hf_headers(token=True) # explicitly use cached token
+ {"authorization": "Bearer hf_***",...}
+
+ >>> build_hf_headers(token=False) # explicitly don't use cached token
+ {"user-agent": ...}
+
+ >>> build_hf_headers() # implicit use of the cached token
+ {"authorization": "Bearer hf_***",...}
+
+ # HF_HUB_DISABLE_IMPLICIT_TOKEN=True # to set as env variable
+ >>> build_hf_headers() # token is not sent
+ {"user-agent": ...}
+
+ >>> build_hf_headers(library_name="transformers", library_version="1.2.3")
+ {"authorization": ..., "user-agent": "transformers/1.2.3; hf_hub/0.10.2; python/3.10.4; tensorflow/1.55"}
+ ```
+
+ Raises:
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If organization token is passed and "write" access is required.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If "write" access is required but token is not passed and not saved locally.
+ [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+ If `token=True` but token is not saved locally.
+ """
+ # Get auth token to send
+ token_to_send = get_token_to_send(token)
+
+ # Combine headers
+ hf_headers = {
+ "user-agent": _http_user_agent(
+ library_name=library_name,
+ library_version=library_version,
+ user_agent=user_agent,
+ )
+ }
+ if token_to_send is not None:
+ hf_headers["authorization"] = f"Bearer {token_to_send}"
+ if headers is not None:
+ hf_headers.update(headers)
+ return hf_headers
+
+
+def get_token_to_send(token: bool | str | None) -> str | None:
+ """Select the token to send from either `token` or the cache."""
+ # Case token is explicitly provided
+ if isinstance(token, str):
+ return token
+
+ # Case token is explicitly forbidden
+ if token is False:
+ return None
+
+ # Case token is explicitly required
+ if token is True:
+ cached_token = get_token()
+ if cached_token is None:
+ raise LocalTokenNotFoundError(
+ "Token is required (`token=True`), but no token found. You"
+ " need to provide a token or be logged in to Hugging Face with"
+ " `hf auth login` or `huggingface_hub.login`. See"
+ " https://huggingface.co/settings/tokens."
+ )
+ return cached_token
+
+ # Case implicit use of the token is forbidden by env variable. Checked before resolving the
+ # cached token: `get_token()` may refresh an OAuth token (network call + file writes), which
+ # must not happen when the resolved token wouldn't be used anyway.
+ if constants.HF_HUB_DISABLE_IMPLICIT_TOKEN:
+ return None
+
+ # Otherwise: we use the cached token as the user has not explicitly forbidden it
+ return get_token()
+
+
+def _http_user_agent(
+ *,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+) -> str:
+ """Format a user-agent string containing information about the installed packages.
+
+ Args:
+ library_name (`str`, *optional*):
+ The name of the library that is making the HTTP request.
+ library_version (`str`, *optional*):
+ The version of the library that is making the HTTP request.
+ user_agent (`str`, `dict`, *optional*):
+ The user agent info in the form of a dictionary or a single string.
+
+ Returns:
+ The formatted user-agent string.
+ """
+ if library_name is not None:
+ ua = f"{library_name}/{library_version}"
+ else:
+ ua = "unknown/None"
+ ua += f"; hf_hub/{get_hf_hub_version()}"
+ ua += f"; python/{get_python_version()}"
+
+ if not constants.HF_HUB_DISABLE_TELEMETRY:
+ if is_torch_available():
+ ua += f"; torch/{get_torch_version()}"
+
+ agent = detect_agent()
+ if agent:
+ ua += f"; agent/{agent}"
+
+ if isinstance(user_agent, dict):
+ ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
+ elif isinstance(user_agent, str):
+ ua += "; " + user_agent
+
+ # Retrieve user-agent origin headers from environment variable
+ origin = constants.HF_HUB_USER_AGENT_ORIGIN
+ if origin is not None:
+ ua += "; origin/" + origin
+
+ return _deduplicate_user_agent(ua)
+
+
+def _deduplicate_user_agent(user_agent: str) -> str:
+ """Deduplicate redundant information in the generated user-agent."""
+ # Split around ";" > Strip whitespaces > Store as dict keys (ensure unicity) > format back as string
+ # Order is implicitly preserved by dictionary structure (see https://stackoverflow.com/a/53657523).
+ return "; ".join({key.strip(): None for key in user_agent.split(";")}.keys())
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_hf_uris.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_hf_uris.py
new file mode 100644
index 0000000000000000000000000000000000000000..759ea0e76c5e51559e0d74be6907cc66623f0ee1
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_hf_uris.py
@@ -0,0 +1,642 @@
+# Copyright 2026-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Centralized parser for Hugging Face Hub URIs ('hf://...') and mount specifications.
+
+A HF URI is a URI-like string that identifies a location on the Hugging Face
+Hub: a model/dataset/space/kernel repository, a bucket, optionally a revision,
+and optionally a path inside the repo or bucket.
+
+Canonical syntax:
+
+```
+hf://[/][@][/]
+```
+
+For convenience, [`parse_hf_uri`] also accepts Hugging Face **web URLs** (the
+ones you copy-paste from your browser), e.g.
+'https://huggingface.co/datasets/my-org/my-dataset/blob/main/train.csv'. They are
+normalized to the canonical 'hf://' form before parsing. Only unambiguous URLs
+(repository / bucket pages and file/folder viewer routes) are accepted; any other
+route is rejected rather than guessed.
+
+A HF mount wraps a HF URI with a local mount path and an optional ':ro'/':rw'
+flag (used by Spaces and Jobs volumes):
+
+```
+hf://[/][@][/]:[:ro|:rw]
+```
+
+See 'docs/source/en/package_reference/hf_uris.md' for the full grammar and examples.
+"""
+
+import functools
+import re
+from dataclasses import dataclass, field
+from urllib.parse import quote, unquote, urlsplit
+
+from huggingface_hub import constants
+from huggingface_hub.errors import HfUriError, HFValidationError
+
+from ._validators import validate_repo_id
+
+
+# Inverse map (singular -> plural URI prefix). Built once from the canonical
+# 'constants.HF_URI_TYPE_PREFIXES' and used to render URIs.
+_TYPE_TO_PREFIX: dict[str, str] = {v: k for k, v in constants.HF_URI_TYPE_PREFIXES.items()}
+
+# Special revisions that contain a '/'. They take precedence when splitting
+# the part after '@' into '/'. Matches 'refs/pr/N'
+# (Pull Request refs) and 'refs/convert/' (e.g. parquet conversions).
+# The conversion name allows the typical git ref characters '[a-zA-Z0-9_.-]'
+# so names like 'parquet-v2' or 'duckdb.v1' round-trip correctly.
+_SPECIAL_REFS_REVISION_REGEX = re.compile(r"^refs/(?:convert/[\w.-]+|pr/\d+)")
+
+# Same as constants.HfUriType, but as a set of strings for easy lookup.)
+_VALID_URI_TYPES: frozenset[str] = frozenset(constants.HF_URI_TYPE_PREFIXES.values())
+
+
+# Web-viewer routes that point at a file or folder and that map cleanly onto a
+# '/' pair. Other routes (commit, commits, discussions, settings,
+# edit, ...) do not identify a Hub location and are rejected by the URL parser.
+_URL_REPO_LOCATION_ACTIONS: frozenset[str] = frozenset({"blame", "blob", "raw", "resolve", "tree"})
+# Bucket web routes that point at a file or folder. Buckets are not versioned, so
+# these are followed directly by '' (no revision segment).
+_URL_BUCKET_LOCATION_ACTIONS: frozenset[str] = frozenset({"resolve", "tree"})
+
+
+@dataclass(frozen=True)
+class HfUri:
+ """Parsed representation of a Hugging Face Hub URI ('hf://...').
+
+ Attributes:
+ type (`str`):
+ One of 'model', 'dataset', 'space', 'kernel' or 'bucket'.
+ id (`str`):
+ The repository id ('namespace/name', e.g. 'my-org/my-model') for repo URIs, or the bucket id ('namespace/name') for bucket URIs.
+ revision (`str`, *optional*):
+ The revision specified after '@' in the URI, URL-decoded. 'None' if no revision was specified, or for bucket URIs (which
+ never carry a revision). Special refs like 'refs/pr/10' and 'refs/convert/parquet' are preserved as-is.
+ path_in_repo (`str`):
+ The path inside the repo or bucket. Empty string if the URI points at the root.
+ """
+
+ type: constants.HfUriType
+ id: str
+ revision: str | None = None
+ path_in_repo: str = ""
+ _raw: str | None = field(repr=False, hash=False, compare=False, default=None)
+
+ def __post_init__(self) -> None:
+ uri = self._raw or "" # For error messages
+
+ # Check valid URI type
+ if self.type not in _VALID_URI_TYPES:
+ raise HfUriError(uri=uri, msg=f"Invalid type '{self.type}'. Must be one of {sorted(_VALID_URI_TYPES)}.")
+
+ # Check valid ID
+ if not self.id or self.id.count("/") != 1:
+ raise HfUriError(uri=uri, msg=f"Id must be 'namespace/name', got '{self.id}'.")
+ if self.type != "bucket":
+ try:
+ validate_repo_id(self.id)
+ except HFValidationError as e:
+ raise HfUriError(uri=uri, msg=str(e)) from e
+
+ # Check valid revision
+ if self.revision is not None and not self.revision:
+ raise HfUriError(uri=uri, msg="Revision must not be an empty string.")
+ if self.type == "bucket" and self.revision is not None:
+ raise HfUriError(uri=uri, msg="Bucket URIs do not support a revision.")
+
+ # Check valid path in repo
+ if self.path_in_repo:
+ if self.path_in_repo.startswith("/") or "//" in self.path_in_repo:
+ raise HfUriError(uri=uri, msg=f"Path must not contain empty segments (got '{self.path_in_repo}').")
+
+ @property
+ def is_bucket(self) -> bool:
+ """True if this URI points at a bucket."""
+ return self.type == "bucket"
+
+ @property
+ def is_repo(self) -> bool:
+ """True if this URI points at a repository (model, dataset, space or kernel)."""
+ return self.type != "bucket"
+
+ def to_uri(self) -> str:
+ """Render the URI as a canonical 'hf://' string.
+
+ The type prefix is always written explicitly (e.g. 'hf://models/my-org/my-model').
+ """
+ parts: list[str] = [constants.HF_PROTOCOL, _TYPE_TO_PREFIX[self.type], "/", self.id]
+ if self.revision is not None:
+ # Encode '/' as '%2F' for revisions that would otherwise be split as '/'
+ # at parse time. Special refs ('refs/pr/N', 'refs/convert/') are kept verbatim
+ # because the parser matches them eagerly.
+ revision = self.revision
+ if "/" in revision and _SPECIAL_REFS_REVISION_REGEX.fullmatch(revision) is None:
+ revision = revision.replace("/", "%2F")
+ parts.append(f"@{revision}")
+ if self.path_in_repo:
+ parts.append(f"/{self.path_in_repo}")
+ return "".join(parts)
+
+ def to_url(self, endpoint: str | None = None) -> str:
+ """Render the URI as a Hugging Face **web URL** (the kind you open in a browser).
+
+ This is the inverse of parsing a URL with [`parse_hf_uri`]. The returned URL points at:
+
+ - the repository / bucket landing page when no path or revision is set;
+ - the folder viewer ('/tree/') when only a revision is set;
+ - the file viewer ('/blob//') for repository files (revision defaults to 'main');
+ - the tree route ('/tree/') for bucket files (buckets are not versioned).
+
+ Args:
+ endpoint (`str`, *optional*):
+ Base endpoint to use. Defaults to 'constants.ENDPOINT' (i.e. 'https://huggingface.co').
+
+ Returns:
+ `str`: the web URL.
+
+ Example:
+ ```py
+ >>> from huggingface_hub import parse_hf_uri
+ >>> parse_hf_uri("hf://datasets/my-org/my-dataset@v1/train.csv").to_url()
+ 'https://huggingface.co/datasets/my-org/my-dataset/blob/v1/train.csv'
+ ```
+ """
+ base = (endpoint or constants.ENDPOINT).rstrip("/")
+ # Percent-encode characters that would otherwise break the URL (spaces, '#', '?', ...),
+ # keeping '/' as the path separator. This is the inverse of the decoding done when parsing.
+ path = quote(self.path_in_repo, safe="/")
+
+ if self.type == "bucket":
+ url = f"{base}/buckets/{self.id}"
+ if path:
+ url += f"/tree/{path}"
+ return url
+
+ # Models live at the root ('hf.co/'); other repos are namespaced by their plural prefix.
+ url = f"{base}/{self.id}" if self.type == "model" else f"{base}/{_TYPE_TO_PREFIX[self.type]}/{self.id}"
+ revision = self.revision
+ # Percent-encode the branch/tag name so it stays a single, URL-safe segment: a '/' would
+ # otherwise open a new path segment and '#'/'?' would be read as a fragment/query when the
+ # URL is opened or parsed back. This mirrors the decoding done when parsing. Special refs
+ # ('refs/pr/N', 'refs/convert/') are used verbatim by the Hub web routes.
+ if revision is not None and _SPECIAL_REFS_REVISION_REGEX.fullmatch(revision) is None:
+ revision = quote(revision, safe="")
+ if path:
+ url += f"/blob/{revision or constants.DEFAULT_REVISION}/{path}"
+ elif revision is not None:
+ url += f"/tree/{revision}"
+ return url
+
+
+@dataclass(frozen=True)
+class HfMount:
+ """A HF URI paired with a local mount path and optional read-only flag.
+
+ Used by Spaces and Jobs to describe volume mounts. The full syntax is:
+
+ ```
+ hf://[/][@][/]:[:ro|:rw]
+ ```
+
+ Attributes:
+ source ([`HfUri`]):
+ The parsed HF URI identifying the Hub resource to mount.
+ mount_path (`str`):
+ The local mount path (always starts with '/').
+ read_only (`bool`, *optional*):
+ True if the mount ends with ':ro', False if it ends with ':rw', 'None' if no flag was provided.
+ """
+
+ source: HfUri
+ mount_path: str
+ read_only: bool | None = None
+ _raw: str | None = field(repr=False, hash=False, compare=False, default=None)
+
+ def __post_init__(self) -> None:
+ raw = self._raw or ""
+ if not self.mount_path.startswith("/") or self.mount_path == "/":
+ raise HfUriError(
+ uri=raw,
+ msg=f"Mount path must be a non-empty absolute path starting with '/', got '{self.mount_path}'.",
+ )
+
+ def to_uri(self) -> str:
+ """Render the mount as a canonical 'hf://' string.
+
+ Example: 'hf://models/my-org/my-model:/data:ro'
+ """
+ parts = [self.source.to_uri(), ":", self.mount_path]
+ if self.read_only is not None:
+ parts.append(":ro" if self.read_only else ":rw")
+ return "".join(parts)
+
+
+def is_hf_uri(uri: str) -> bool:
+ """Check if a string is a valid HF URI ('hf://...') or a recognized Hugging Face web URL."""
+ try:
+ parse_hf_uri(uri)
+ return True
+ except HfUriError:
+ return False
+
+
+@functools.lru_cache
+def parse_hf_uri(uri: str, endpoint: str | None = None) -> HfUri:
+ """Parse a Hugging Face Hub URI ('hf://...') or a Hugging Face web URL.
+
+ A HF URI is a URI-like string identifying a location on the Hugging Face Hub. The full grammar is:
+
+ ```
+ hf://[/][@][/]
+ ```
+
+ For convenience, Hugging Face **web URLs** (the ones you copy-paste from the website) are also
+ accepted and normalized to the canonical 'hf://' form, e.g.
+ 'https://huggingface.co/datasets/my-org/my-dataset/blob/main/train.csv'. Only unambiguous URLs
+ (repository / bucket pages and file/folder viewer routes) are accepted; any other route is rejected.
+
+ See 'docs/source/en/package_reference/hf_uris.md' for the full specification.
+
+ Args:
+ uri (`str`):
+ The URI to parse. Must start with 'hf://', or be a Hugging Face URL (e.g. 'https://huggingface.co/...').
+ endpoint (`str`, *optional*):
+ A custom Hub endpoint (e.g. a self-hosted or proxied Hub like 'https://hub.my-company.com' or
+ 'http://localhost:8080/hf'). When provided, web URLs on that endpoint are recognized in addition to
+ the default Hugging Face hosts. Has no effect on 'hf://' URIs.
+
+ Returns:
+ [`HfUri`]: the parsed URI.
+
+ Raises:
+ [`HfUriError`]:
+ If the URI is malformed (missing prefix, invalid type, missing id, unsupported URL route, etc.).
+
+ Examples:
+ ```py
+ >>> from huggingface_hub.utils import parse_hf_uri
+ >>> parse_hf_uri("hf://my-org/my-model")
+ HfUri(type='model', id='my-org/my-model', revision=None, path_in_repo='')
+ >>> parse_hf_uri("hf://datasets/my-org/my-dataset@refs/pr/3/train.json")
+ HfUri(type='dataset', id='my-org/my-dataset', revision='refs/pr/3', path_in_repo='train.json')
+ >>> parse_hf_uri("https://huggingface.co/datasets/my-org/my-dataset/blob/main/train.csv")
+ HfUri(type='dataset', id='my-org/my-dataset', revision='main', path_in_repo='train.csv')
+ ```
+ """
+ raw = uri
+ if uri.startswith(constants.HF_PROTOCOL):
+ body = uri[len(constants.HF_PROTOCOL) :]
+ if not body:
+ raise HfUriError(uri, f"Empty body after '{constants.HF_PROTOCOL}'.")
+ elif _looks_like_hf_url(uri, endpoint=endpoint):
+ body = _url_to_uri_body(uri, endpoint=endpoint)
+ else:
+ raise HfUriError(
+ uri,
+ f"Must start with '{constants.HF_PROTOCOL}' or be a Hugging Face URL (e.g. 'https://huggingface.co/...'). "
+ f"Expected format: {constants.HF_PROTOCOL}[/][@][/]",
+ )
+
+ type_, location = _split_type(body, raw=raw)
+
+ if type_ == "bucket":
+ return _parse_bucket_body(location, type_, raw=raw)
+ return _parse_repo_body(location, type_, raw=raw)
+
+
+def _endpoint_host_and_path(endpoint: str | None) -> tuple[str | None, str]:
+ """Return the lowercased host and stripped path prefix of a custom Hub 'endpoint'.
+
+ E.g. 'https://hub.my-company.com' -> ('hub.my-company.com', '') and a self-hosted
+ 'http://localhost:8080/hf' -> ('localhost', 'hf'). Returns '(None, "")' when 'endpoint' is None.
+ """
+ if endpoint is None:
+ return None, ""
+ # Prefix '//' for scheme-less endpoints so 'urlsplit' populates 'netloc' instead of 'path'.
+ parsed = urlsplit(endpoint if "://" in endpoint else "//" + endpoint)
+ host = parsed.hostname.lower() if parsed.hostname else None
+ return host, parsed.path.strip("/")
+
+
+def _recognized_hosts(endpoint: str | None) -> frozenset[str]:
+ """The set of hosts whose web URLs can be parsed: the default Hugging Face hosts plus 'endpoint'."""
+ host, _ = _endpoint_host_and_path(endpoint)
+ return constants.HF_URL_HOSTS | {host} if host else constants.HF_URL_HOSTS
+
+
+def _looks_like_hf_url(uri: str, endpoint: str | None = None) -> bool:
+ """Return True if 'uri' looks like a (possibly scheme-less) Hugging Face web URL."""
+ lowered = uri.lower()
+ if lowered.startswith(("http://", "https://")):
+ return True
+ # Scheme-less host (e.g. 'huggingface.co/org/model').
+ return any(lowered == host or lowered.startswith(host + "/") for host in _recognized_hosts(endpoint))
+
+
+def _decode_url_path_segment(segment: str) -> str:
+ """Percent-decode a single URL path segment (e.g. 'file%20name.txt' -> 'file name.txt').
+
+ A decoded '/' is re-encoded as '%2F' so the segment stays atomic when the normalized body is
+ re-split by the shared parser. This decodes ordinary path characters (spaces, '#', ...) that
+ browsers encode, while keeping '%2F'-encoded revisions (e.g. 'feature%2Ffoo') intact.
+ """
+ return unquote(segment).replace("/", "%2F")
+
+
+def _url_to_uri_body(url: str, endpoint: str | None = None) -> str:
+ """Normalize a Hugging Face web URL into the body of a 'hf://' URI (everything after 'hf://').
+
+ The returned string is fed back into the regular URI parsing logic, so all validation
+ (repo id, revision, empty path segments, ...) is shared with the canonical 'hf://' path.
+ Only unambiguous URLs are accepted: any unrecognized route raises [`HfUriError`]. When 'endpoint'
+ is provided, URLs on that custom Hub host are recognized too (and its path prefix is stripped).
+ """
+ raw = url
+ # Prefix '//' for scheme-less inputs so 'urlsplit' populates 'netloc' instead of 'path'.
+ parsed = urlsplit(url if "://" in url else "//" + url)
+ host = (parsed.hostname or "").lower()
+ if host not in _recognized_hosts(endpoint):
+ raise HfUriError(
+ uri=raw,
+ msg=f"Unrecognized host '{host or url}'. Expected a Hugging Face URL (e.g. 'https://huggingface.co/...').",
+ )
+
+ # Query string and fragment are intentionally dropped (e.g. '?download=true').
+ path = parsed.path
+ # For a self-hosted endpoint with a path prefix (e.g. 'http://localhost:8080/hf'), drop it so the
+ # remaining segments are '[/]/[/...]' just like on the public Hub.
+ endpoint_host, endpoint_path = _endpoint_host_and_path(endpoint)
+ if endpoint_path and host == endpoint_host:
+ prefix = "/" + endpoint_path
+ if path == prefix or path.startswith(prefix + "/"):
+ path = path[len(prefix) :]
+ segments = [segment for segment in path.split("/") if segment]
+ if not segments:
+ raise HfUriError(uri=raw, msg=f"Missing repository or bucket identifier in URL '{url}'.")
+
+ # Optional type prefix ('datasets', 'spaces', 'kernels', 'buckets', 'models').
+ type_prefix: str | None = None
+ if segments[0] in constants.HF_URI_TYPE_PREFIXES:
+ type_prefix = segments[0]
+ segments = segments[1:]
+
+ # Everything in the web UI is namespaced ('/'); a single segment is a user or
+ # organization page (or a listing page), which we cannot map to a repository -> reject.
+ if len(segments) < 2:
+ raise HfUriError(
+ uri=raw,
+ msg=(
+ f"Cannot parse URL '{url}': expected a '/' repository or bucket. "
+ "User/organization pages and single-segment URLs are not supported."
+ ),
+ )
+ repo_id = f"{segments[0]}/{segments[1]}"
+ rest = segments[2:]
+
+ if type_prefix == "buckets":
+ if not rest:
+ return f"buckets/{repo_id}"
+ action, *tail = rest
+ if action not in _URL_BUCKET_LOCATION_ACTIONS:
+ raise HfUriError(uri=raw, msg=f"Cannot parse bucket URL '{url}': unsupported '/{action}/' route.")
+ path = "/".join(_decode_url_path_segment(segment) for segment in tail)
+ return f"buckets/{repo_id}/{path}" if path else f"buckets/{repo_id}"
+
+ prefix = f"{type_prefix}/" if type_prefix else ""
+ if not rest:
+ return f"{prefix}{repo_id}"
+ action, *tail = rest
+ if action not in _URL_REPO_LOCATION_ACTIONS:
+ raise HfUriError(
+ uri=raw,
+ msg=(
+ f"Cannot parse URL '{url}': unsupported '/{action}/' route. "
+ "Only repository pages and file/folder viewer routes (blob, resolve, raw, tree, ...) can be parsed."
+ ),
+ )
+ if not tail:
+ # e.g. '.../tree' with nothing after -> repository root.
+ return f"{prefix}{repo_id}"
+ # 'tail' is '/'; reuse the canonical '@/' splitting logic
+ # (special refs, URL-encoded slashes, ...) by handing it back to the URI parser. Each segment
+ # is percent-decoded first so file names with spaces, '#', ... resolve correctly; the revision
+ # segment's '%2F' survives (re-encoded by '_decode_url_path_segment') and is decoded downstream.
+ decoded = "/".join(_decode_url_path_segment(segment) for segment in tail)
+ return f"{prefix}{repo_id}@{decoded}"
+
+
+def parse_hf_mount(mount_str: str) -> HfMount:
+ """Parse a HF mount specification ('hf://...:[:ro|:rw]').
+
+ A mount specification is a HF URI followed by a local mount path and an optional read-only/read-write flag.
+ The full grammar is:
+
+ ```
+ hf://[/][@][/]:[:ro|:rw]
+ ```
+
+ See 'docs/source/en/package_reference/hf_uris.md' for the full specification.
+
+ Args:
+ mount_str (`str`):
+ The mount string to parse. Must start with 'hf://' and contain a ':' segment.
+
+ Returns:
+ [`HfMount`]: the parsed mount.
+
+ Raises:
+ [`HfUriError`]:
+ If the mount string is malformed (missing mount path, invalid URI, etc.).
+
+ Examples:
+ ```py
+ >>> from huggingface_hub.utils import parse_hf_mount
+ >>> parse_hf_mount("hf://my-org/my-model:/data:ro")
+ HfMount(source=HfUri(type='model', id='my-org/my-model', revision=None, path_in_repo=''), mount_path='/data', read_only=True)
+ >>> parse_hf_mount("hf://buckets/my-org/my-bucket/sub/dir:/mnt:rw")
+ HfMount(source=HfUri(type='bucket', id='my-org/my-bucket', revision=None, path_in_repo='sub/dir'), mount_path='/mnt', read_only=False)
+ ```
+ """
+ if not mount_str.startswith(constants.HF_PROTOCOL):
+ raise HfUriError(
+ uri=mount_str,
+ msg=f"Must start with '{constants.HF_PROTOCOL}'.",
+ )
+
+ raw = mount_str
+ body = mount_str[len(constants.HF_PROTOCOL) :]
+ if not body:
+ raise HfUriError(uri=raw, msg=f"Empty body after '{constants.HF_PROTOCOL}'.")
+
+ location, mount_path, read_only = _split_mount(body, raw=raw)
+
+ if mount_path is None:
+ raise HfUriError(uri=raw, msg="Missing mount path. Expected ':' (e.g. 'hf://org/model:/data').")
+
+ # Re-assemble the URI part and parse it
+ uri_str = constants.HF_PROTOCOL + location
+ try:
+ source = parse_hf_uri(uri_str)
+ except HfUriError as e:
+ raise HfUriError(uri=raw, msg=e.msg) from e
+
+ return HfMount(source=source, mount_path=mount_path, read_only=read_only, _raw=raw)
+
+
+def _split_mount(body: str, *, raw: str) -> tuple[str, str | None, bool | None]:
+ """Split the ':[:ro|:rw]' suffix from 'body'.
+
+ Returns '(location, mount_path, read_only)' where 'mount_path' is 'None' if no mount segment is present.
+ """
+ if body.endswith(":ro"):
+ read_only, body = True, body.removesuffix(":ro")
+ elif body.endswith(":rw"):
+ read_only, body = False, body.removesuffix(":rw")
+ else:
+ read_only = None
+
+ # Mount paths always start with '/', so the delimiter is ':/'.
+ # We use rfind() because the mount segment is always trailing
+ idx = body.rfind(":/")
+ if idx == -1:
+ if read_only is not None:
+ raise HfUriError(
+ uri=raw,
+ msg="':ro'/':rw' suffix is only valid when a mount path is provided (e.g. 'hf://...:/:ro').",
+ )
+ return body, None, None
+
+ location = body[:idx]
+ mount_path = body[idx + 1 :] # includes the leading '/'
+ if not location:
+ raise HfUriError(uri=raw, msg="Missing location before mount path.")
+ return location, mount_path, read_only
+
+
+def _split_type(location: str, *, raw: str) -> tuple[constants.HfUriType, str]:
+ """Detect the (optional) type prefix and return '(type, remaining_location)'.
+
+ A missing type prefix defaults to 'model'. Singular forms ('model/', 'dataset/', etc.) are explicitly rejected with a helpful error.
+ """
+ slash_idx = location.find("/")
+ if slash_idx == -1:
+ # Single segment, no prefix. Reject if it looks like a bare type name.
+ if location in constants.HF_URI_TYPE_PREFIXES:
+ raise HfUriError(
+ uri=raw,
+ msg=f"Missing identifier after '{location}'. Expected '{constants.HF_PROTOCOL}{location}/'.",
+ )
+ if (singular_plural := _TYPE_TO_PREFIX.get(location)) is not None:
+ raise HfUriError(
+ uri=raw,
+ msg=f"Type prefix must be plural. Did you mean '{constants.HF_PROTOCOL}{singular_plural}/...'?",
+ )
+ return "model", location
+
+ first = location[:slash_idx]
+ rest = location[slash_idx + 1 :]
+ if first in constants.HF_URI_TYPE_PREFIXES:
+ return constants.HF_URI_TYPE_PREFIXES[first], rest
+ if (singular_plural := _TYPE_TO_PREFIX.get(first)) is not None:
+ raise HfUriError(
+ uri=raw, msg=f"Type prefix must be plural, got '{first}/'. Did you mean '{singular_plural}/'?"
+ )
+ return "model", location
+
+
+def _parse_bucket_body(
+ location: str,
+ type_: constants.HfUriType,
+ *,
+ raw: str,
+) -> HfUri:
+ """Parse the body of a bucket URI: 'namespace/name[/path]'."""
+ location = location.strip("/")
+ parts = location.split("/", 2)
+ if len(parts) < 2 or not parts[0] or not parts[1]:
+ raise HfUriError(uri=raw, msg=f"Bucket id must be 'namespace/name', got '{location}'.")
+ bucket_id = f"{parts[0]}/{parts[1]}"
+ if "@" in bucket_id:
+ raise HfUriError(uri=raw, msg="Bucket URIs do not support a revision marker ('@').")
+ path_in_bucket = parts[2] if len(parts) >= 3 else ""
+ return HfUri(
+ type=type_,
+ id=bucket_id,
+ revision=None,
+ path_in_repo=path_in_bucket,
+ _raw=raw,
+ )
+
+
+def _parse_repo_body(
+ location: str,
+ type_: constants.HfUriType,
+ *,
+ raw: str,
+) -> HfUri:
+ """Parse the body of a repo URI: '[@][/]'."""
+ location = location.strip("/")
+ if not location:
+ raise HfUriError(uri=raw, msg="Missing repository id.")
+
+ # The '@' separates the repo_id from the revision, but only when it
+ # appears right after 'namespace/name' (at most one '/' before it).
+ # An '@' deeper in the path (e.g. in a filename like 'file@1.txt') is literal.
+ at_idx = location.find("@")
+ revision: str | None
+
+ if at_idx == -1 or location[:at_idx].count("/") > 1:
+ # No '@' at all, or the '@' is past the repo_id portion (in a filename).
+ revision = None
+ parts = location.split("/", 2)
+ if len(parts) < 2:
+ raise HfUriError(uri=raw, msg=f"Repository id must be 'namespace/name', got '{location}'. ")
+ repo_id = f"{parts[0]}/{parts[1]}"
+ path_in_repo = parts[2] if len(parts) > 2 else ""
+ else:
+ repo_id = location[:at_idx]
+ rev_and_path = location[at_idx + 1 :]
+ if not repo_id:
+ raise HfUriError(uri=raw, msg="Missing repository id before '@'.")
+ if repo_id.count("/") != 1:
+ raise HfUriError(uri=raw, msg=f"Repository id must be 'namespace/name', got '{repo_id}'.")
+ # Special refs like 'refs/pr/10' contain '/' and must be matched eagerly,
+ # otherwise we would split them at the first '/' and treat the rest as a path.
+ match = _SPECIAL_REFS_REVISION_REGEX.search(rev_and_path)
+ if match is not None:
+ revision = match.group()
+ path_in_repo = rev_and_path[len(revision) :].removeprefix("/")
+ else:
+ slash_idx = rev_and_path.find("/")
+ if slash_idx == -1:
+ revision = rev_and_path
+ path_in_repo = ""
+ else:
+ revision = rev_and_path[:slash_idx]
+ path_in_repo = rev_and_path[slash_idx + 1 :]
+ revision = unquote(revision)
+ if not revision:
+ raise HfUriError(uri=raw, msg="Empty revision after '@'.")
+
+ return HfUri(
+ type=type_,
+ id=repo_id,
+ revision=revision,
+ path_in_repo=path_in_repo,
+ _raw=raw,
+ )
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_http.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_http.py
new file mode 100644
index 0000000000000000000000000000000000000000..20c0d7f751c1641f5a1ddf6dacea851da0bada7d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_http.py
@@ -0,0 +1,1233 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle HTTP requests in huggingface_hub."""
+
+import atexit
+import io
+import json
+import os
+import re
+import threading
+import time
+import uuid
+from collections.abc import Callable, Generator, Mapping
+from contextlib import contextmanager
+from contextvars import ContextVar
+from dataclasses import dataclass
+from functools import wraps
+from shlex import quote
+from typing import Any, TypeVar
+from urllib.parse import urljoin, urlparse
+
+import httpx2
+
+from huggingface_hub.errors import OfflineModeIsEnabled
+
+from .. import constants
+from ..errors import (
+ BadRequestError,
+ BucketNotFoundError,
+ DisabledRepoError,
+ GatedRepoError,
+ HfHubHTTPError,
+ JobNotFoundError,
+ RemoteEntryNotFoundError,
+ RepositoryNotFoundError,
+ RevisionNotFoundError,
+)
+from . import logging
+from ._lfs import SliceFileObj
+from ._typing import HTTP_METHOD_T, CallableT
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass(frozen=True)
+class RateLimitInfo:
+ """
+ Parsed rate limit information from HTTP response headers.
+
+ Attributes:
+ resource_type (`str`): The type of resource being rate limited.
+ remaining (`int`): The number of requests remaining in the current window.
+ reset_in_seconds (`int`): The number of seconds until the rate limit resets.
+ limit (`int`, *optional*): The maximum number of requests allowed in the current window.
+ window_seconds (`int`, *optional*): The number of seconds in the current window.
+
+ """
+
+ resource_type: str
+ remaining: int
+ reset_in_seconds: int
+ limit: int | None = None
+ window_seconds: int | None = None
+
+
+# Regex patterns for parsing rate limit headers
+# e.g.: "api";r=0;t=55 --> resource_type="api", r=0, t=55
+_RATELIMIT_REGEX = re.compile(r"\"(?P\w+)\"\s*;\s*r\s*=\s*(?P\d+)\s*;\s*t\s*=\s*(?P\d+)")
+# e.g.: "fixed window";"api";q=500;w=300 --> q=500, w=300
+_RATELIMIT_POLICY_REGEX = re.compile(r"q\s*=\s*(?P\d+).*?w\s*=\s*(?P\d+)")
+
+
+def parse_ratelimit_headers(headers: Mapping[str, str]) -> RateLimitInfo | None:
+ """Parse rate limit information from HTTP response headers.
+
+ Follows IETF draft: https://www.ietf.org/archive/id/draft-ietf-httpapi-ratelimit-headers-09.html
+ Only a subset is implemented.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.utils import parse_ratelimit_headers
+ >>> headers = {
+ ... "ratelimit": '"api";r=0;t=55',
+ ... "ratelimit-policy": '"fixed window";"api";q=500;w=300',
+ ... }
+ >>> info = parse_ratelimit_headers(headers)
+ >>> info.remaining
+ 0
+ >>> info.reset_in_seconds
+ 55
+ ```
+ """
+
+ ratelimit: str | None = None
+ policy: str | None = None
+ for key in headers:
+ lower_key = key.lower()
+ if lower_key == "ratelimit":
+ ratelimit = headers[key]
+ elif lower_key == "ratelimit-policy":
+ policy = headers[key]
+
+ if not ratelimit:
+ return None
+
+ match = _RATELIMIT_REGEX.search(ratelimit)
+ if not match:
+ return None
+
+ resource_type = match.group("resource_type")
+ remaining = int(match.group("r"))
+ reset_in_seconds = int(match.group("t"))
+
+ limit: int | None = None
+ window_seconds: int | None = None
+
+ if policy:
+ policy_match = _RATELIMIT_POLICY_REGEX.search(policy)
+ if policy_match:
+ limit = int(policy_match.group("q"))
+ window_seconds = int(policy_match.group("w"))
+
+ return RateLimitInfo(
+ resource_type=resource_type,
+ remaining=remaining,
+ reset_in_seconds=reset_in_seconds,
+ limit=limit,
+ window_seconds=window_seconds,
+ )
+
+
+def _parse_retry_after(headers: Mapping[str, str]) -> int | None:
+ """Parse the standard `Retry-After` HTTP header into a number of seconds to wait.
+
+ The `Retry-After` header can be either a non-negative number of seconds (delay-seconds)
+ or an HTTP-date after which to retry. We handle only the delay-seconds case.
+
+ See https://developer.mozilla.org/en-US/docs/Web/HTTP/Reference/Headers/Retry-After.
+ """
+ value: str | None = None
+ for key in headers:
+ if key.lower() == "retry-after":
+ value = headers[key]
+ break
+
+ if value is None:
+ return None
+ value = value.strip()
+ if not value:
+ return None
+
+ if value.isdigit():
+ return int(value) # e.g. "Retry-After: 120"
+ return None # e.g. "Retry-After: Wed, 21 Oct 2015 07:28:00 GMT" - not supported
+
+
+# When raising an error, we include the request id in the error message for easier debugging.
+# Request ID is sourced from headers in order of precedence: "X-Request-Id", "X-Amzn-Trace-Id", "X-Amz-Cf-Id".
+X_REQUEST_ID = "x-request-id"
+X_AMZN_TRACE_ID = "X-Amzn-Trace-Id"
+X_AMZ_CF_ID = "x-amz-cf-id"
+
+REPO_API_REGEX = re.compile(
+ r"""
+ # staging or production endpoint
+ ^https://[^/]+
+ (
+ # on /api/repo_type/repo_id
+ /api/(models|datasets|spaces)/(.+)
+ |
+ # or /repo_id/resolve/revision/...
+ /(.+)/resolve/(.+)
+ )
+ """,
+ flags=re.VERBOSE,
+)
+
+BUCKET_API_REGEX = re.compile(
+ r"""
+ # staging or production endpoint
+ ^https?://[^/]+
+ # on /api/buckets/...
+ /api/buckets/
+ """,
+ flags=re.VERBOSE,
+)
+
+# Regex to extract the job_id from a (scheduled) job API URL.
+# Matches /api/jobs/{namespace}/{job_id}[/...] and /api/scheduled-jobs/{namespace}/{job_id}[/...].
+_JOB_ID_FROM_URL_REGEX = re.compile(r"^https?://[^/]+/api/(?:scheduled-jobs|jobs)/[^/]+/([^/?]+)")
+
+# Regex to extract repo_type and repo_id from API URLs.
+# Captures: group(1) = repo_type plural (models/datasets/spaces), group(2) = first path segment, group(3) = optional second segment.
+_REPO_ID_FROM_URL_REGEX = re.compile(r"^https?://[^/]+/api/(models|datasets|spaces)/([^/?]+)(?:/([^/?]+))?")
+
+# Regex to extract repo_type and repo_id from download URLs: /[{repo_type}s/]{repo_id}/resolve/...
+# Captures: group(1) = optional repo_type plural (datasets/spaces/kernels), group(2) = repo_id.
+_REPO_ID_FROM_RESOLVE_URL_REGEX = re.compile(
+ r"^https?://[^/]+/(?:(datasets|spaces|kernels)/)?([^/?]+(?:/[^/?]+)?)/resolve/"
+)
+
+# Regex to extract bucket_id (namespace/name) from bucket API URLs.
+_BUCKET_ID_FROM_URL_REGEX = re.compile(r"^https?://[^/]+/api/buckets/([^/?]+/[^/?]+)")
+
+# Sub-paths that follow a repo_id in API URLs (not part of the repo name).
+_REPO_URL_SUBPATHS = {"resolve", "tree", "blob", "raw", "refs", "commit", "discussions", "settings", "revision"}
+
+
+def _parse_repo_info_from_url(url: str) -> tuple[str | None, str | None]:
+ """Extract (repo_type, repo_id) from an API URL or a download URL.
+
+ Returns canonical repo_type values: "model", "dataset", "space" (or None).
+
+ Examples:
+ >>> _parse_repo_info_from_url("https://huggingface.co/api/models/user/repo")
+ ("model", "user/repo")
+ >>> _parse_repo_info_from_url("https://huggingface.co/api/datasets/user/repo/resolve/main/data.csv")
+ ("dataset", "user/repo")
+ >>> _parse_repo_info_from_url("https://huggingface.co/api/models/bert-base-cased/resolve/main/config.json")
+ ("model", "bert-base-cased")
+ >>> _parse_repo_info_from_url("https://huggingface.co/datasets/user/repo/resolve/main/data.csv")
+ ("dataset", "user/repo")
+ """
+ if match := _REPO_ID_FROM_RESOLVE_URL_REGEX.search(url):
+ return constants.REPO_TYPES_MAPPING[match.group(1) or "models"], match.group(2)
+ match = _REPO_ID_FROM_URL_REGEX.search(url)
+ if not match:
+ return None, None
+ repo_type = constants.REPO_TYPES_MAPPING.get(match.group(1))
+ first, second = match.group(2), match.group(3)
+ if second and second not in _REPO_URL_SUBPATHS:
+ repo_id = f"{first}/{second}"
+ else:
+ repo_id = first
+ return repo_type, repo_id
+
+
+def _parse_bucket_id_from_url(url: str) -> str | None:
+ """Extract bucket_id (namespace/name) from a bucket API URL."""
+ match = _BUCKET_ID_FROM_URL_REGEX.search(url)
+ return match.group(1) if match else None
+
+
+def _parse_job_id_from_url(url: str) -> str | None:
+ """Extract the job_id from a (scheduled) job API URL, if present."""
+ match = _JOB_ID_FROM_URL_REGEX.search(url)
+ return match.group(1) if match else None
+
+
+_IS_DOWNLOAD_CALL: ContextVar[bool] = ContextVar("_IS_DOWNLOAD_CALL", default=False)
+
+
+def flag_as_download_call(fn: CallableT) -> CallableT:
+ """Decorator to tag all HTTP calls made by `fn` with the `X-HF-Download-Counter` header.
+
+ Applied on `hf_hub_download`, `snapshot_download`, etc. so that every call they make through [`get_session`]
+ is flagged as part of a download, no matter how deep in the call stack it happens.
+ """
+
+ @wraps(fn)
+ def _inner(*args, **kwargs):
+ token = _IS_DOWNLOAD_CALL.set(True)
+ try:
+ return fn(*args, **kwargs)
+ finally:
+ _IS_DOWNLOAD_CALL.reset(token)
+
+ return _inner # type: ignore
+
+
+def hf_request_event_hook(request: httpx2.Request) -> str | None:
+ """
+ Event hook that will be used to make HTTP requests to the Hugging Face Hub.
+
+ What it does:
+ - Block requests if offline mode is enabled
+ - Add a request ID to the request headers
+ - Flag the request as part of a download, if applicable
+ - Log the request if debug mode is enabled
+ """
+ if constants.is_offline_mode():
+ raise OfflineModeIsEnabled(
+ f"Cannot reach {request.url}: offline mode is enabled. To disable it, please unset the `HF_HUB_OFFLINE` environment variable."
+ )
+
+ if _IS_DOWNLOAD_CALL.get():
+ request.headers[constants.X_HF_DOWNLOAD_COUNTER] = "1"
+
+ # Add random request ID => easier for server-side debugging
+ if X_AMZN_TRACE_ID not in request.headers:
+ request.headers[X_AMZN_TRACE_ID] = request.headers.get(X_REQUEST_ID) or str(uuid.uuid4())
+ request_id = request.headers.get(X_AMZN_TRACE_ID)
+
+ # Debug log
+ logger.debug(
+ "Request %s: %s %s (authenticated: %s)",
+ request_id,
+ request.method,
+ request.url,
+ request.headers.get("authorization") is not None,
+ )
+ if constants.HF_DEBUG:
+ logger.debug("Send: %s", _curlify(request))
+
+ return request_id
+
+
+async def async_hf_request_event_hook(request: httpx2.Request) -> str | None:
+ """
+ Async version of `hf_request_event_hook`.
+ """
+ return hf_request_event_hook(request)
+
+
+async def async_hf_response_event_hook(response: httpx2.Response) -> None:
+ if response.status_code >= 400:
+ # If response will raise, read content from stream to have it available when raising the exception
+ # If content-length is not set or is too large, skip reading the content to avoid OOM
+ if "Content-length" in response.headers:
+ try:
+ length = int(response.headers["Content-length"])
+ except ValueError:
+ return
+
+ if length < 1_000_000:
+ await response.aread()
+
+
+def default_client_factory() -> httpx2.Client:
+ """
+ Factory function to create a `httpx2.Client` with the default transport.
+ """
+ return httpx2.Client(
+ event_hooks={"request": [hf_request_event_hook]},
+ follow_redirects=True,
+ timeout=None,
+ )
+
+
+def default_async_client_factory() -> httpx2.AsyncClient:
+ """
+ Factory function to create a `httpx2.AsyncClient` with the default transport.
+ """
+ return httpx2.AsyncClient(
+ event_hooks={"request": [async_hf_request_event_hook], "response": [async_hf_response_event_hook]},
+ follow_redirects=True,
+ timeout=None,
+ )
+
+
+CLIENT_FACTORY_T = Callable[[], httpx2.Client]
+ASYNC_CLIENT_FACTORY_T = Callable[[], httpx2.AsyncClient]
+
+_CLIENT_LOCK = threading.Lock()
+_GLOBAL_CLIENT_FACTORY: CLIENT_FACTORY_T = default_client_factory
+_GLOBAL_ASYNC_CLIENT_FACTORY: ASYNC_CLIENT_FACTORY_T = default_async_client_factory
+_GLOBAL_CLIENT: httpx2.Client | None = None
+
+
+def set_client_factory(client_factory: CLIENT_FACTORY_T) -> None:
+ """
+ Set the HTTP client factory to be used by `huggingface_hub`.
+
+ The client factory is a method that returns a `httpx2.Client` object. On the first call to [`get_session`] the client factory
+ will be used to create a new `httpx2.Client` object that will be shared between all calls made by `huggingface_hub`.
+
+ This can be useful if you are running your scripts in a specific environment requiring custom configuration (e.g. custom proxy or certifications).
+
+ Use [`get_session`] to get a correctly configured `httpx2.Client`.
+ """
+ global _GLOBAL_CLIENT_FACTORY
+ with _CLIENT_LOCK:
+ close_session()
+ _GLOBAL_CLIENT_FACTORY = client_factory
+
+
+def set_async_client_factory(async_client_factory: ASYNC_CLIENT_FACTORY_T) -> None:
+ """
+ Set the HTTP async client factory to be used by `huggingface_hub`.
+
+ The async client factory is a method that returns a `httpx2.AsyncClient` object.
+ This can be useful if you are running your scripts in a specific environment requiring custom configuration (e.g. custom proxy or certifications).
+ Use [`get_async_session`] to get a correctly configured `httpx2.AsyncClient`.
+
+ > [!WARNING]
+ > Contrary to the `httpx2.Client` that is shared between all calls made by `huggingface_hub`, the `httpx2.AsyncClient` is not shared.
+ > It is recommended to use an async context manager to ensure the client is properly closed when the context is exited.
+ """
+ global _GLOBAL_ASYNC_CLIENT_FACTORY
+ _GLOBAL_ASYNC_CLIENT_FACTORY = async_client_factory
+
+
+def get_session() -> httpx2.Client:
+ """
+ Get a `httpx2.Client` object, using the transport factory from the user.
+
+ This client is shared between all calls made by `huggingface_hub`. Therefore you should not close it manually.
+
+ Use [`set_client_factory`] to customize the `httpx2.Client`.
+ """
+ global _GLOBAL_CLIENT
+ if _GLOBAL_CLIENT is None:
+ with _CLIENT_LOCK:
+ if _GLOBAL_CLIENT is None:
+ _GLOBAL_CLIENT = _GLOBAL_CLIENT_FACTORY()
+ return _GLOBAL_CLIENT
+
+
+def get_async_session() -> httpx2.AsyncClient:
+ """
+ Return a `httpx2.AsyncClient` object, using the transport factory from the user.
+
+ Use [`set_async_client_factory`] to customize the `httpx2.AsyncClient`.
+
+ > [!WARNING]
+ > Contrary to the `httpx2.Client` that is shared between all calls made by `huggingface_hub`, the `httpx2.AsyncClient` is not shared.
+ > It is recommended to use an async context manager to ensure the client is properly closed when the context is exited.
+ """
+ return _GLOBAL_ASYNC_CLIENT_FACTORY()
+
+
+def close_session() -> None:
+ """
+ Close the global `httpx2.Client` used by `huggingface_hub`.
+
+ If a Client is closed, it will be recreated on the next call to [`get_session`].
+
+ Can be useful if e.g. an SSL certificate has been updated.
+ """
+ global _GLOBAL_CLIENT
+ client = _GLOBAL_CLIENT
+
+ # First, set global client to None
+ _GLOBAL_CLIENT = None
+
+ # Then, close the clients
+ if client is not None:
+ try:
+ client.close()
+ except Exception as e:
+ logger.warning(f"Error closing client: {e}")
+
+
+atexit.register(close_session)
+if hasattr(os, "register_at_fork"):
+ os.register_at_fork(after_in_child=close_session)
+
+
+_DEFAULT_RETRY_ON_EXCEPTIONS: tuple[type[Exception], ...] = (
+ httpx2.TimeoutException,
+ httpx2.NetworkError,
+ httpx2.RemoteProtocolError,
+)
+_DEFAULT_RETRY_ON_STATUS_CODES: tuple[int, ...] = (408, 429, 500, 502, 503, 504)
+
+
+def _http_backoff_base(
+ method: HTTP_METHOD_T,
+ url: str,
+ *,
+ max_retries: int = 5,
+ base_wait_time: float = 1,
+ max_wait_time: float = 8,
+ retry_on_exceptions: type[Exception] | tuple[type[Exception], ...] = _DEFAULT_RETRY_ON_EXCEPTIONS,
+ retry_on_status_codes: int | tuple[int, ...] = _DEFAULT_RETRY_ON_STATUS_CODES,
+ stream: bool = False,
+ **kwargs,
+) -> Generator[httpx2.Response, None, None]:
+ """Internal implementation of HTTP backoff logic shared between `http_backoff` and `http_stream_backoff`."""
+ if isinstance(retry_on_exceptions, type): # Tuple from single exception type
+ retry_on_exceptions = (retry_on_exceptions,)
+
+ if isinstance(retry_on_status_codes, int): # Tuple from single status code
+ retry_on_status_codes = (retry_on_status_codes,)
+
+ nb_tries = 0
+ sleep_time = base_wait_time
+ ratelimit_reset: int | None = None # seconds to wait for rate limit reset if 429 response
+
+ # If `data` is used and is a file object (or any IO), it will be consumed on the
+ # first HTTP request. We need to save the initial position so that the full content
+ # of the file is re-sent on http backoff. See warning tip in docstring.
+ io_obj_initial_pos = None
+ if "data" in kwargs and isinstance(kwargs["data"], (io.IOBase, SliceFileObj)):
+ io_obj_initial_pos = kwargs["data"].tell()
+
+ while True:
+ nb_tries += 1
+ ratelimit_reset = None
+ # Fetched on each attempt: a previous attempt may have closed the shared client (see `close_session` below),
+ # and closed `httpx2.Client` objects cannot be reused.
+ client = get_session()
+ try:
+ # If `data` is used and is a file object (or any IO), set back cursor to
+ # initial position.
+ if io_obj_initial_pos is not None:
+ kwargs["data"].seek(io_obj_initial_pos)
+
+ # Perform request and handle response
+ def _should_retry(response: httpx2.Response) -> bool:
+ """Handle response and return True if should retry, False if should return/yield."""
+ nonlocal ratelimit_reset
+
+ if response.status_code not in retry_on_status_codes:
+ return False # Success, don't retry
+
+ # Wrong status code returned (HTTP 503 for instance)
+ logger.warning(f"HTTP Error {response.status_code} thrown while requesting {method} {url}")
+ if nb_tries > max_retries:
+ hf_raise_for_status(response) # Will raise uncaught exception
+ # Return/yield response to avoid infinite loop in the corner case where the
+ # user ask for retry on a status code that doesn't raise_for_status.
+ return False # Don't retry, return/yield response
+
+ # Check 'ratelimit' and `Retry-After` headers.
+ if (
+ response.status_code == 429
+ and (ratelimit_info := parse_ratelimit_headers(response.headers)) is not None
+ and ratelimit_info.remaining == 0
+ ):
+ ratelimit_reset = ratelimit_info.reset_in_seconds
+ elif (retry_after := _parse_retry_after(response.headers)) is not None:
+ ratelimit_reset = retry_after
+
+ return True # Should retry
+
+ if stream:
+ with client.stream(method=method, url=url, **kwargs) as response:
+ if not _should_retry(response):
+ yield response
+ return
+ else:
+ response = client.request(method=method, url=url, **kwargs)
+ if not _should_retry(response):
+ yield response
+ return
+
+ except retry_on_exceptions as err:
+ logger.warning(f"'{err}' thrown while requesting {method} {url}")
+
+ if isinstance(err, httpx2.ConnectError):
+ close_session() # In case of SSLError it's best to close the shared httpx2.Client objects
+
+ if nb_tries > max_retries:
+ raise err
+
+ if ratelimit_reset is not None:
+ actual_sleep = float(ratelimit_reset) + 1 # +1s to avoid rounding issues
+ logger.warning(f"Rate limited. Waiting {actual_sleep}s before retry [Retry {nb_tries}/{max_retries}].")
+ else:
+ actual_sleep = sleep_time
+ logger.warning(f"Retrying in {actual_sleep}s [Retry {nb_tries}/{max_retries}].")
+
+ time.sleep(actual_sleep)
+
+ # Update sleep time for next retry
+ sleep_time = min(max_wait_time, sleep_time * 2) # Exponential backoff
+
+
+def http_backoff(
+ method: HTTP_METHOD_T,
+ url: str,
+ *,
+ max_retries: int = 5,
+ base_wait_time: float = 1,
+ max_wait_time: float = 8,
+ retry_on_exceptions: type[Exception] | tuple[type[Exception], ...] = _DEFAULT_RETRY_ON_EXCEPTIONS,
+ retry_on_status_codes: int | tuple[int, ...] = _DEFAULT_RETRY_ON_STATUS_CODES,
+ **kwargs,
+) -> httpx2.Response:
+ """Wrapper around httpx2 to retry calls on an endpoint, with exponential backoff.
+
+ Endpoint call is retried on exceptions (ex: connection timeout, proxy error,...)
+ and/or on specific status codes (ex: service unavailable). If the call failed more
+ than `max_retries`, the exception is thrown or `raise_for_status` is called on the
+ response object.
+
+ Re-implement mechanisms from the `backoff` library to avoid adding an external
+ dependencies to `hugging_face_hub`. See https://github.com/litl/backoff.
+
+ Args:
+ method (`Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"]`):
+ HTTP method to perform.
+ url (`str`):
+ The URL of the resource to fetch.
+ max_retries (`int`, *optional*, defaults to `5`):
+ Maximum number of retries, defaults to 5. Set to `0` to disable retries.
+ base_wait_time (`float`, *optional*, defaults to `1`):
+ Duration (in seconds) to wait before retrying the first time.
+ Wait time between retries then grows exponentially, capped by
+ `max_wait_time`.
+ max_wait_time (`float`, *optional*, defaults to `8`):
+ Maximum duration (in seconds) to wait before retrying.
+ retry_on_exceptions (`type[Exception]` or `tuple[type[Exception]]`, *optional*):
+ Define which exceptions must be caught to retry the request. Can be a single type or a tuple of types.
+ By default, retry on `httpx2.TimeoutException`, `httpx2.NetworkError` and `httpx2.RemoteProtocolError`.
+ retry_on_status_codes (`int` or `tuple[int]`, *optional*, defaults to `(429, 500, 502, 503, 504)`):
+ Define on which status codes the request must be retried. By default, retries
+ on rate limit (429) and server errors (5xx).
+ **kwargs (`dict`, *optional*):
+ kwargs to pass to `httpx2.request`.
+
+ Example:
+ ```
+ >>> from huggingface_hub.utils import http_backoff
+
+ # Same usage as "httpx2.request".
+ >>> response = http_backoff("GET", "https://www.google.com")
+ >>> response.raise_for_status()
+
+ # If you expect a Gateway Timeout from time to time
+ >>> http_backoff("PUT", upload_url, data=data, retry_on_status_codes=504)
+ >>> response.raise_for_status()
+ ```
+
+ > [!WARNING]
+ > When using `requests` it is possible to stream data by passing an iterator to the
+ > `data` argument. On http backoff this is a problem as the iterator is not reset
+ > after a failed call. This issue is mitigated for file objects or any IO streams
+ > by saving the initial position of the cursor (with `data.tell()`) and resetting the
+ > cursor between each call (with `data.seek()`). For arbitrary iterators, http backoff
+ > will fail. If this is a hard constraint for you, please let us know by opening an
+ > issue on [Github](https://github.com/huggingface/huggingface_hub).
+ """
+ return next(
+ _http_backoff_base(
+ method=method,
+ url=url,
+ max_retries=max_retries,
+ base_wait_time=base_wait_time,
+ max_wait_time=max_wait_time,
+ retry_on_exceptions=retry_on_exceptions,
+ retry_on_status_codes=retry_on_status_codes,
+ stream=False,
+ **kwargs,
+ )
+ )
+
+
+@contextmanager
+def http_stream_backoff(
+ method: HTTP_METHOD_T,
+ url: str,
+ *,
+ max_retries: int = 5,
+ base_wait_time: float = 1,
+ max_wait_time: float = 8,
+ retry_on_exceptions: type[Exception] | tuple[type[Exception], ...] = _DEFAULT_RETRY_ON_EXCEPTIONS,
+ retry_on_status_codes: int | tuple[int, ...] = _DEFAULT_RETRY_ON_STATUS_CODES,
+ **kwargs,
+) -> Generator[httpx2.Response, None, None]:
+ """Wrapper around httpx2 to retry calls on an endpoint, with exponential backoff.
+
+ Endpoint call is retried on exceptions (ex: connection timeout, proxy error,...)
+ and/or on specific status codes (ex: service unavailable). If the call failed more
+ than `max_retries`, the exception is thrown or `raise_for_status` is called on the
+ response object.
+
+ Re-implement mechanisms from the `backoff` library to avoid adding an external
+ dependencies to `hugging_face_hub`. See https://github.com/litl/backoff.
+
+ Args:
+ method (`Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"]`):
+ HTTP method to perform.
+ url (`str`):
+ The URL of the resource to fetch.
+ max_retries (`int`, *optional*, defaults to `5`):
+ Maximum number of retries, defaults to 5. Set to `0` to disable retries.
+ base_wait_time (`float`, *optional*, defaults to `1`):
+ Duration (in seconds) to wait before retrying the first time.
+ Wait time between retries then grows exponentially, capped by
+ `max_wait_time`.
+ max_wait_time (`float`, *optional*, defaults to `8`):
+ Maximum duration (in seconds) to wait before retrying.
+ retry_on_exceptions (`type[Exception]` or `tuple[type[Exception]]`, *optional*):
+ Define which exceptions must be caught to retry the request. Can be a single type or a tuple of types.
+ By default, retry on `httpx2.TimeoutException`, `httpx2.NetworkError` and `httpx2.RemoteProtocolError`.
+ retry_on_status_codes (`int` or `tuple[int]`, *optional*, defaults to `(429, 500, 502, 503, 504)`):
+ Define on which status codes the request must be retried. By default, retries
+ on rate limit (429) and server errors (5xx).
+ **kwargs (`dict`, *optional*):
+ kwargs to pass to `httpx2.request`.
+
+ Example:
+ ```
+ >>> from huggingface_hub.utils import http_stream_backoff
+
+ # Same usage as "httpx2.stream".
+ >>> with http_stream_backoff("GET", "https://www.google.com") as response:
+ ... for chunk in response.iter_bytes():
+ ... print(chunk)
+
+ # If you expect a Gateway Timeout from time to time
+ >>> with http_stream_backoff("PUT", upload_url, data=data, retry_on_status_codes=504) as response:
+ ... response.raise_for_status()
+ ```
+
+ > [!WARNING]
+ > When using `httpx2` it is possible to stream data by passing an iterator to the
+ > `data` argument. On http backoff this is a problem as the iterator is not reset
+ > after a failed call. This issue is mitigated for file objects or any IO streams
+ > by saving the initial position of the cursor (with `data.tell()`) and resetting the
+ > cursor between each call (with `data.seek()`). For arbitrary iterators, http backoff
+ > will fail. If this is a hard constraint for you, please let us know by opening an
+ > issue on [Github](https://github.com/huggingface/huggingface_hub).
+ """
+ yield from _http_backoff_base(
+ method=method,
+ url=url,
+ max_retries=max_retries,
+ base_wait_time=base_wait_time,
+ max_wait_time=max_wait_time,
+ retry_on_exceptions=retry_on_exceptions,
+ retry_on_status_codes=retry_on_status_codes,
+ stream=True,
+ **kwargs,
+ )
+
+
+_MAX_REDIRECTS = 20 # same bound as httpx2's default max_redirects
+
+
+def _httpx2_follow_hub_redirects_with_backoff(
+ method: HTTP_METHOD_T, url: str, *, retry_on_errors: bool = False, **httpx2_kwargs
+) -> httpx2.Response:
+ """Perform an HTTP request with backoff, following redirects that stay on the Hub.
+
+ Used to fetch HEAD /resolve on repo or bucket files.
+
+ Redirects to the same host or another Hub host are followed. Redirects to any other host (CDN, storage
+ bucket) are not: the file metadata is on the redirect response itself and the auth header must not leave the Hub.
+
+ A backoff mechanism retries the HTTP call on errors (429, 5xx, timeout, network errors).
+
+ Args:
+ method (`str`):
+ HTTP method, such as 'GET' or 'HEAD'.
+ url (`str`):
+ The URL of the resource to fetch.
+ retry_on_errors (`bool`, *optional*, defaults to `False`):
+ Whether to retry on errors. If False, no retry is performed (fast fallback to local cache).
+ If True, uses default retry behavior (429, 5xx, timeout, network errors).
+ **httpx2_kwargs (`dict`, *optional*):
+ Params to pass to `httpx2.request`.
+ """
+ # if `retry_on_errors=False`, disable all retries for fast fallback to cache
+ no_retry_kwargs: dict[str, Any] = (
+ {} if retry_on_errors else {"retry_on_exceptions": (), "retry_on_status_codes": ()}
+ )
+
+ for _ in range(_MAX_REDIRECTS):
+ response = http_backoff(
+ method=method,
+ url=url,
+ **httpx2_kwargs,
+ follow_redirects=False,
+ **no_retry_kwargs,
+ )
+ hf_raise_for_status(response)
+
+ if not response.has_redirect_location:
+ return response
+
+ target = urljoin(url, response.headers["Location"])
+ if not _is_same_or_hub_host(url, target):
+ # Redirect to a CDN or storage host (signed URL): the file metadata is carried by this very response,
+ # and the authorization header must not be forwarded off the Hub => stop here.
+ return response
+
+ url = target
+
+ raise httpx2.TooManyRedirects(f"Exceeded {_MAX_REDIRECTS} redirects while resolving '{url}'.")
+
+
+_DEFAULT_PORTS = {"http": 80, "https": 443}
+
+
+def _url_origin(url: str) -> tuple[str, str, int]:
+ """Origin of a url: (scheme, hostname, port), with the scheme's default port if not explicit."""
+ parsed = urlparse(url)
+ return (parsed.scheme, (parsed.hostname or "").lower(), parsed.port or _DEFAULT_PORTS.get(parsed.scheme, 0))
+
+
+# Origins considered to be Hub endpoints. The auth header is forwarded to these: only add trusted ones.
+_HUB_ORIGINS = frozenset(
+ _url_origin(endpoint)
+ for endpoint in (
+ constants._HF_DEFAULT_ENDPOINT,
+ constants._HF_DEFAULT_STAGING_ENDPOINT,
+ constants.ENDPOINT,
+ "https://hf.co",
+ )
+)
+
+
+def _is_same_or_hub_host(url: str, target: str) -> bool:
+ """Whether `target` is served by the same origin as `url`, or by a known Hub origin."""
+ return _url_origin(target) in {_url_origin(url), *_HUB_ORIGINS}
+
+
+def fix_hf_endpoint_in_url(url: str, endpoint: str | None) -> str:
+ """Replace the default endpoint in a URL by a custom one.
+
+ This is useful when using a proxy and the Hugging Face Hub returns a URL with the default endpoint.
+ """
+ endpoint = endpoint.rstrip("/") if endpoint else constants.ENDPOINT
+ # check if a proxy has been set => if yes, update the returned URL to use the proxy
+ if endpoint not in (constants._HF_DEFAULT_ENDPOINT, constants._HF_DEFAULT_STAGING_ENDPOINT):
+ url = url.replace(constants._HF_DEFAULT_ENDPOINT, endpoint)
+ url = url.replace(constants._HF_DEFAULT_STAGING_ENDPOINT, endpoint)
+ return url
+
+
+def hf_raise_for_status(response: httpx2.Response, endpoint_name: str | None = None) -> None:
+ """
+ Internal version of `response.raise_for_status()` that will refine a potential HTTPError.
+ Raised exception will be an instance of [`~errors.HfHubHTTPError`].
+
+ This helper is meant to be the unique method to raise_for_status when making a call to the Hugging Face Hub.
+
+ Args:
+ response (`Response`):
+ Response from the server.
+ endpoint_name (`str`, *optional*):
+ Name of the endpoint that has been called. If provided, the error message will be more complete.
+
+ > [!WARNING]
+ > Raises when the request has failed:
+ >
+ > - [`~utils.RepositoryNotFoundError`]
+ > If the repository to download from cannot be found. This may be because it
+ > doesn't exist, because `repo_type` is not set correctly, or because the repo
+ > is `private` and you do not have access.
+ > - [`~utils.GatedRepoError`]
+ > If the repository exists but is gated and the user is not on the authorized
+ > list.
+ > - [`~utils.RevisionNotFoundError`]
+ > If the repository exists but the revision couldn't be found.
+ > - [`~utils.EntryNotFoundError`]
+ > If the repository exists but the entry (e.g. the requested file) couldn't be
+ > find.
+ > - [`~utils.BadRequestError`]
+ > If request failed with a HTTP 400 BadRequest error.
+ > - [`~utils.HfHubHTTPError`]
+ > If request failed for a reason not listed above.
+ """
+ try:
+ _warn_on_warning_headers(response)
+ except Exception:
+ # Never raise on warning parsing
+ logger.debug("Failed to parse warning headers", exc_info=True)
+
+ try:
+ response.raise_for_status()
+ except httpx2.HTTPStatusError as e:
+ if response.status_code // 100 == 3:
+ return # Do not raise on redirects to stay consistent with `requests`
+
+ error_code = response.headers.get("X-Error-Code")
+ error_message = response.headers.get("X-Error-Message")
+
+ # Parse repo info from request URL (used to enrich errors below)
+ request_url = (
+ str(response.request.url) if response.request is not None and response.request.url is not None else None
+ )
+ repo_type, repo_id = _parse_repo_info_from_url(request_url) if request_url else (None, None)
+
+ if error_code == "RevisionNotFound":
+ message = f"{response.status_code} Client Error." + "\n\n" + f"Revision Not Found for url: {response.url}."
+ raise _format(RevisionNotFoundError, message, response, repo_type=repo_type, repo_id=repo_id) from e
+
+ elif error_code == "EntryNotFound":
+ message = f"{response.status_code} Client Error." + "\n\n" + f"Entry Not Found for url: {response.url}."
+ raise _format(RemoteEntryNotFoundError, message, response, repo_type=repo_type, repo_id=repo_id) from e
+
+ elif error_code == "GatedRepo":
+ message = (
+ f"{response.status_code} Client Error." + "\n\n" + f"Cannot access gated repo for url {response.url}."
+ )
+ raise _format(GatedRepoError, message, response, repo_type=repo_type, repo_id=repo_id) from e
+
+ elif error_message == "Access to this resource is disabled.":
+ message = (
+ f"{response.status_code} Client Error."
+ + "\n\n"
+ + f"Cannot access repository for url {response.url}."
+ + "\n"
+ + "Access to this resource is disabled."
+ )
+ raise _format(DisabledRepoError, message, response) from e
+
+ elif (
+ error_code == "RepoNotFound"
+ and request_url is not None
+ and BUCKET_API_REGEX.search(request_url) is not None
+ ):
+ message = (
+ f"{response.status_code} Client Error."
+ + "\n\n"
+ + f"Bucket Not Found for url: {response.url}."
+ + "\nPlease make sure you specified the correct bucket id (namespace/name)."
+ + "\nIf the bucket is private, make sure you are authenticated and your token has the required permissions."
+ )
+ raise _format(
+ BucketNotFoundError, message, response, bucket_id=_parse_bucket_id_from_url(request_url)
+ ) from e
+
+ elif (
+ response.status_code == 404
+ and request_url is not None
+ and (job_id := _parse_job_id_from_url(request_url)) is not None
+ ):
+ message = (
+ f"{response.status_code} Client Error."
+ + "\n\n"
+ + f"Job Not Found for url: {response.url}."
+ + "\nPlease make sure you specified the correct job ID and namespace."
+ )
+ raise _format(JobNotFoundError, message, response, job_id=job_id) from e
+
+ elif error_code == "RepoNotFound" or (
+ response.status_code == 401
+ and error_message != "Invalid credentials in Authorization header"
+ and request_url is not None
+ and REPO_API_REGEX.search(request_url) is not None
+ ):
+ # 401 is misleading as it is returned for:
+ # - private and gated repos if user is not authenticated
+ # - missing repos
+ # => for now, we process them as `RepoNotFound` anyway.
+ # See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9
+ message = (
+ f"{response.status_code} Client Error."
+ + "\n\n"
+ + f"Repository Not Found for url: {response.url}."
+ + "\nPlease make sure you specified the correct `repo_id` and"
+ " `repo_type`.\nIf you are trying to access a private or gated repo,"
+ " make sure you are authenticated and your token has the required permissions."
+ + "\nFor more details, see https://huggingface.co/docs/huggingface_hub/authentication"
+ )
+ raise _format(RepositoryNotFoundError, message, response, repo_type=repo_type, repo_id=repo_id) from e
+
+ elif response.status_code == 400:
+ message = (
+ f"\n\nBad request for {endpoint_name} endpoint:" if endpoint_name is not None else "\n\nBad request:"
+ )
+ raise _format(BadRequestError, message, response) from e
+
+ elif response.status_code == 403:
+ message = (
+ f"\n\n{response.status_code} Forbidden: {error_message}."
+ + f"\nCannot access content at: {response.url}."
+ + "\nMake sure your token has the correct permissions."
+ )
+ raise _format(HfHubHTTPError, message, response) from e
+
+ elif response.status_code == 429:
+ ratelimit_info = parse_ratelimit_headers(response.headers)
+ # Headers are set on every response: only use them if they are the reason for the 429.
+ if ratelimit_info is not None and ratelimit_info.remaining == 0:
+ message = (
+ f"\n\n429 Too Many Requests: you have reached your '{ratelimit_info.resource_type}' rate limit."
+ )
+ message += f"\nRetry after {ratelimit_info.reset_in_seconds} seconds"
+ if ratelimit_info.limit is not None and ratelimit_info.window_seconds is not None:
+ message += (
+ f" ({ratelimit_info.remaining}/{ratelimit_info.limit} requests remaining"
+ f" in current {ratelimit_info.window_seconds}s window)."
+ )
+ else:
+ message += "."
+ message += f"\nUrl: {response.url}."
+ else:
+ message = f"\n\n429 Too Many Requests for url: {response.url}."
+ raise _format(HfHubHTTPError, message, response) from e
+
+ elif response.status_code == 416:
+ range_header = response.request.headers.get("Range")
+ message = f"{e}. Requested range: {range_header}. Content-Range: {response.headers.get('Content-Range')}."
+ raise _format(HfHubHTTPError, message, response) from e
+
+ # Convert `HTTPError` into a `HfHubHTTPError` to display request information
+ # as well (request id and/or server error message)
+ raise _format(HfHubHTTPError, str(e), response) from e
+
+
+_WARNED_TOPICS = set()
+
+
+def _warn_on_warning_headers(response: httpx2.Response) -> None:
+ """
+ Emit warnings if warning headers are present in the HTTP response.
+
+ Expected header format: 'X-HF-Warning: topic; message'
+
+ Only the first warning for each topic will be shown. Topic is optional and can be empty. Note that several warning
+ headers can be present in a single response.
+
+ Args:
+ response (`httpx2.Response`):
+ The HTTP response to check for warning headers.
+ """
+ server_warnings = response.headers.get_list("X-HF-Warning")
+ for server_warning in server_warnings:
+ topic, message = server_warning.split(";", 1) if ";" in server_warning else ("", server_warning)
+ topic = topic.strip()
+ if topic not in _WARNED_TOPICS:
+ message = message.strip()
+ if message:
+ _WARNED_TOPICS.add(topic)
+ logger.warning(message)
+
+
+_HfHubHTTPErrorT = TypeVar("_HfHubHTTPErrorT", bound=HfHubHTTPError)
+
+
+def _format(
+ error_type: type[_HfHubHTTPErrorT], custom_message: str, response: httpx2.Response, **attrs: Any
+) -> _HfHubHTTPErrorT:
+ server_errors = []
+
+ # Retrieve server error from header
+ from_headers = response.headers.get("X-Error-Message")
+ if from_headers is not None:
+ server_errors.append(from_headers)
+
+ # Retrieve server error from body
+ try:
+ # Case errors are returned in a JSON format
+ try:
+ data = response.json()
+ except httpx2.ResponseNotRead:
+ try:
+ response.read() # In case of streaming response, we need to read the response first
+ data = response.json()
+ except RuntimeError:
+ # In case of async streaming response, we can't read the stream here.
+ # In practice if user is using the default async client from `get_async_client`, the stream will have
+ # already been read in the async event hook `async_hf_response_event_hook`.
+ #
+ # Here, we are skipping reading the response to avoid RuntimeError but it happens only if async + stream + used httpx2.AsyncClient directly.
+ data = {}
+
+ error = data.get("error")
+ error_description = data.get("error_description")
+ if error is not None:
+ if isinstance(error, list):
+ # Case {'error': ['my error 1', 'my error 2']}
+ server_errors.extend(error)
+ elif error_description is not None:
+ # OAuth-style case {'error': 'invalid_grant', 'error_description': 'my description'}
+ server_errors.append(f"{error}: {error_description}")
+ else:
+ # Case {'error': 'my error'}
+ server_errors.append(error)
+ elif error_description is not None:
+ # Case {'error_description': 'my description'} (no 'error' field)
+ server_errors.append(error_description)
+
+ errors = data.get("errors")
+ if errors is not None:
+ # Case {'errors': [{'message': 'my error 1'}, {'message': 'my error 2'}]}
+ for error in errors:
+ if "message" in error:
+ server_errors.append(error["message"])
+
+ except json.JSONDecodeError:
+ # If content is not JSON and not HTML, append the text
+ content_type = response.headers.get("Content-Type", "")
+ if response.text and "html" not in content_type.lower():
+ server_errors.append(response.text)
+
+ # Strip all server messages
+ server_errors = [str(line).strip() for line in server_errors if str(line).strip()]
+
+ # Deduplicate server messages (keep order)
+ # taken from https://stackoverflow.com/a/17016257
+ server_errors = list(dict.fromkeys(server_errors))
+
+ # Format server error
+ server_message = "\n".join(server_errors)
+
+ # Add server error to custom message
+ final_error_message = custom_message
+ if server_message and server_message.lower() not in custom_message.lower():
+ if "\n\n" in custom_message:
+ final_error_message += "\n" + server_message
+ else:
+ final_error_message += "\n\n" + server_message
+
+ # Prepare Request ID message
+ request_id = ""
+ request_id_message = ""
+ for header, label in (
+ (X_REQUEST_ID, "Request ID"),
+ (X_AMZN_TRACE_ID, "Amzn Trace ID"),
+ (X_AMZ_CF_ID, "Amz CF ID"),
+ ):
+ value = response.headers.get(header)
+ if value:
+ request_id = str(value)
+ request_id_message = f" ({label}: {value})"
+ break
+
+ # Add Request ID
+ if request_id and request_id.lower() not in final_error_message.lower():
+ if "\n" in final_error_message:
+ newline_index = final_error_message.index("\n")
+ final_error_message = (
+ final_error_message[:newline_index] + request_id_message + final_error_message[newline_index:]
+ )
+ else:
+ final_error_message += request_id_message
+
+ # Return
+ err = error_type(final_error_message.strip(), response=response, server_message=server_message or None)
+ for k, v in attrs.items():
+ setattr(err, k, v)
+ return err
+
+
+# Request-body fields that carry credentials; redacted from debug-log curl commands (HF_DEBUG).
+_SENSITIVE_BODY_KEYS = ("subject_token", "access_token", "refresh_token", "client_secret", "device_code")
+_SENSITIVE_BODY_PATTERNS = [
+ pattern
+ for key in _SENSITIVE_BODY_KEYS
+ for pattern in (
+ (re.compile(rf'("{key}"\s*:\s*")[^"]*(")'), r"\1\2"), # JSON
+ (re.compile(rf"(^|&)({key}=)[^&]*"), r"\1\2"), # application/x-www-form-urlencoded
+ )
+]
+
+
+def _redact_sensitive_body(body: str) -> str:
+ """Redact OAuth credential values from a JSON or form-urlencoded request body string."""
+ for pattern, replacement in _SENSITIVE_BODY_PATTERNS:
+ body = pattern.sub(replacement, body)
+ return body
+
+
+def _curlify(request: httpx2.Request) -> str:
+ """Convert a `httpx2.Request` into a curl command (str).
+
+ Used for debug purposes only.
+
+ Implementation vendored from https://github.com/ofw/curlify/blob/master/curlify.py.
+ MIT License Copyright (c) 2016 Egor.
+ """
+ parts: list[tuple[Any, Any]] = [
+ ("curl", None),
+ ("-X", request.method),
+ ]
+
+ for k, v in sorted(request.headers.items()):
+ if k.lower() == "authorization":
+ v = "" # Hide authorization header, no matter its value (can be Bearer, Key, etc.)
+ parts += [("-H", f"{k}: {v}")]
+
+ body: str | None = None
+ try:
+ if request.content is not None:
+ body = request.content.decode("utf-8", errors="ignore")
+ if len(body) > 1000:
+ body = f"{body[:1000]} ... [truncated]"
+ body = _redact_sensitive_body(body)
+ except httpx2.RequestNotRead:
+ body = ""
+ if body is not None:
+ parts += [("-d", body.replace("\n", ""))]
+
+ parts += [(None, request.url)]
+
+ flat_parts = []
+ for k, v in parts:
+ if k:
+ flat_parts.append(quote(str(k)))
+ if v:
+ flat_parts.append(quote(str(v)))
+
+ return " ".join(flat_parts)
+
+
+# Regex to parse HTTP Range header
+RANGE_REGEX = re.compile(r"\s*bytes\s*=\s*(\d*)\s*-\s*(\d*)\s*", re.IGNORECASE)
+
+
+def _adjust_range_header(original_range: str | None, resume_size: int) -> str | None:
+ """
+ Adjust HTTP Range header to account for resume position.
+ """
+ if not original_range:
+ return f"bytes={resume_size}-"
+
+ if "," in original_range:
+ raise ValueError(f"Multiple ranges detected - {original_range!r}, not supported yet.")
+
+ match = RANGE_REGEX.fullmatch(original_range)
+ if not match:
+ raise RuntimeError(f"Invalid range format - {original_range!r}.")
+ start, end = match.groups()
+
+ if not start:
+ if not end:
+ raise RuntimeError(f"Invalid range format - {original_range!r}.")
+
+ new_suffix = int(end) - resume_size
+ new_range = f"bytes=-{new_suffix}"
+ if new_suffix <= 0:
+ raise RuntimeError(f"Empty new range - {new_range!r}.")
+ return new_range
+
+ start = int(start)
+ new_start = start + resume_size
+ if end:
+ end = int(end)
+ new_range = f"bytes={new_start}-{end}"
+ if new_start > end:
+ raise RuntimeError(f"Empty new range - {new_range!r}.")
+ return new_range
+
+ return f"bytes={new_start}-"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_lfs.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_lfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce2d002d8beab5ffcad81cb1528e94b0f33e936f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_lfs.py
@@ -0,0 +1,110 @@
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Git LFS related utilities"""
+
+import io
+import os
+from contextlib import AbstractContextManager
+from typing import BinaryIO
+
+
+class SliceFileObj(AbstractContextManager):
+ """
+ Utility context manager to read a *slice* of a seekable file-like object as a seekable, file-like object.
+
+ This is NOT thread safe
+
+ Inspired by stackoverflow.com/a/29838711/593036
+
+ Credits to @julien-c
+
+ Args:
+ fileobj (`BinaryIO`):
+ A file-like object to slice. MUST implement `tell()` and `seek()` (and `read()` of course).
+ `fileobj` will be reset to its original position when exiting the context manager.
+ seek_from (`int`):
+ The start of the slice (offset from position 0 in bytes).
+ read_limit (`int`):
+ The maximum number of bytes to read from the slice.
+
+ Attributes:
+ previous_position (`int`):
+ The previous position
+
+ Examples:
+
+ Reading 200 bytes with an offset of 128 bytes from a file (ie bytes 128 to 327):
+ ```python
+ >>> with open("path/to/file", "rb") as file:
+ ... with SliceFileObj(file, seek_from=128, read_limit=200) as fslice:
+ ... fslice.read(...)
+ ```
+
+ Reading a file in chunks of 512 bytes
+ ```python
+ >>> import os
+ >>> chunk_size = 512
+ >>> file_size = os.getsize("path/to/file")
+ >>> with open("path/to/file", "rb") as file:
+ ... for chunk_idx in range(ceil(file_size / chunk_size)):
+ ... with SliceFileObj(file, seek_from=chunk_idx * chunk_size, read_limit=chunk_size) as fslice:
+ ... chunk = fslice.read(...)
+
+ ```
+ """
+
+ def __init__(self, fileobj: BinaryIO, seek_from: int, read_limit: int):
+ self.fileobj = fileobj
+ self.seek_from = seek_from
+ self.read_limit = read_limit
+
+ def __enter__(self):
+ self._previous_position = self.fileobj.tell()
+ end_of_stream = self.fileobj.seek(0, os.SEEK_END)
+ self._len = min(self.read_limit, end_of_stream - self.seek_from)
+ # ^^ The actual number of bytes that can be read from the slice
+ self.fileobj.seek(self.seek_from, io.SEEK_SET)
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.fileobj.seek(self._previous_position, io.SEEK_SET)
+
+ def read(self, n: int = -1):
+ pos = self.tell()
+ if pos >= self._len:
+ return b""
+ remaining_amount = self._len - pos
+ data = self.fileobj.read(remaining_amount if n < 0 else min(n, remaining_amount))
+ return data
+
+ def tell(self) -> int:
+ return self.fileobj.tell() - self.seek_from
+
+ def seek(self, offset: int, whence: int = os.SEEK_SET) -> int:
+ start = self.seek_from
+ end = start + self._len
+ if whence in (os.SEEK_SET, os.SEEK_END):
+ offset = start + offset if whence == os.SEEK_SET else end + offset
+ offset = max(start, min(offset, end))
+ whence = os.SEEK_SET
+ elif whence == os.SEEK_CUR:
+ cur_pos = self.fileobj.tell()
+ offset = max(start - cur_pos, min(offset, end - cur_pos))
+ else:
+ raise ValueError(f"whence value {whence} is not supported")
+ return self.fileobj.seek(offset, whence) - self.seek_from
+
+ def __iter__(self):
+ while data := self.read(n=4 * 1024 * 1024):
+ yield data
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_oauth_device.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_oauth_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a2c6480c80b57901325f860532d8512b3b10373
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_oauth_device.py
@@ -0,0 +1,197 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Device Code OAuth (RFC 8628) for browser-based login, plus access token refresh.
+
+The flow: the client requests a device code, displays a URL and a short user code, the user
+authorizes in a browser, and the client polls ``POST {ENDPOINT}/oauth/token`` until a token is
+issued. Access tokens may come with a refresh token, used to renew them transparently (see
+``utils/_auth.py::get_token``).
+
+This module is self-contained protocol logic: no printing, no persistence. Interactive flows
+live in ``_login.py`` (human/library) and ``cli/auth.py`` (machine-readable event stream).
+"""
+
+import time
+from collections.abc import Callable
+from typing import TypedDict, cast
+
+import httpx2
+
+from .. import constants
+from ..errors import DeviceCodeError, OAuthErrorCode
+from ._http import get_session, hf_raise_for_status
+
+
+_DEVICE_CODE_GRANT_TYPE = "urn:ietf:params:oauth:grant-type:device_code"
+_REFRESH_TOKEN_GRANT_TYPE = "refresh_token"
+
+
+class DeviceCodeInfo(TypedDict):
+ device_code: str
+ user_code: str
+ verification_uri: str
+ verification_uri_complete: str # falls back to verification_uri if the server omits it
+ interval: int
+ expires_in: int
+
+
+class OAuthTokenResponse(TypedDict, total=False):
+ access_token: str
+ refresh_token: str
+ expires_in: int
+ token_type: str
+
+
+def request_device_code() -> DeviceCodeInfo:
+ """Request a device code from the Hub's OAuth device authorization endpoint.
+
+ The returned dict is normalized: `interval`, `expires_in` and `verification_uri_complete`
+ are always set (server values, or sensible defaults).
+
+ Raises:
+ [`DeviceCodeError`]: If the request fails.
+ """
+ try:
+ response = get_session().post(
+ f"{constants.ENDPOINT}/oauth/device",
+ data={"client_id": constants.DEVICE_CODE_OAUTH_CLIENT_ID},
+ timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ )
+ hf_raise_for_status(response)
+ except httpx2.HTTPError as e:
+ raise DeviceCodeError(f"Failed to request device code from {constants.ENDPOINT}/oauth/device: {e}") from e
+ info = response.json()
+ # `interval` is optional per RFC 8628 (5s is the spec-mandated fallback); `expires_in` is
+ # required but defaulted defensively so polling stays bounded if a server omits it.
+ info.setdefault("interval", 5)
+ info.setdefault("expires_in", 900)
+ if not info.get("verification_uri_complete"):
+ info["verification_uri_complete"] = info["verification_uri"]
+ return cast(DeviceCodeInfo, info)
+
+
+def poll_device_token(
+ device_info: DeviceCodeInfo, *, on_pending: Callable[[], None] | None = None
+) -> OAuthTokenResponse:
+ """Poll the token endpoint until the user authorizes the device.
+
+ Args:
+ device_info (`DeviceCodeInfo`):
+ The device authorization response from [`request_device_code`].
+ on_pending (`Callable`, *optional*):
+ Called after each "authorization pending" response (e.g. to print a progress dot).
+
+ Returns:
+ `OAuthTokenResponse`: the full token response: `access_token`, and optionally
+ `refresh_token` and `expires_in`.
+
+ Raises:
+ [`DeviceCodeError`]: If authorization is denied, the device code expires, or the server
+ returns an unexpected OAuth error.
+ """
+ interval = device_info["interval"]
+ deadline = time.monotonic() + device_info["expires_in"]
+ while time.monotonic() < deadline:
+ # Inconclusive responses (network blip, 5xx, gateway error page, rate limiting) must not
+ # abort the login: keep polling until the device code expires (RFC 8628 section 3.5).
+ # The deadline bounds the total wait even if the endpoint is genuinely broken.
+ data = None
+ try:
+ response = get_session().post(
+ f"{constants.ENDPOINT}/oauth/token",
+ data={
+ "grant_type": _DEVICE_CODE_GRANT_TYPE,
+ "device_code": device_info["device_code"],
+ "client_id": constants.DEVICE_CODE_OAUTH_CLIENT_ID,
+ },
+ timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ )
+ if response.status_code < 500:
+ data = response.json()
+ except (httpx2.HTTPError, ValueError):
+ pass
+
+ if data is not None:
+ if "access_token" in data:
+ return cast(OAuthTokenResponse, data)
+
+ match data.get("error"):
+ case None:
+ pass # JSON without an OAuth `error` field (proxy error page, ...): transient
+ case OAuthErrorCode.AUTHORIZATION_PENDING:
+ if on_pending is not None:
+ on_pending()
+ case OAuthErrorCode.SLOW_DOWN:
+ interval += 5
+ case OAuthErrorCode.EXPIRED_TOKEN:
+ raise DeviceCodeError(
+ "Device code expired. Please try again.", error_code=OAuthErrorCode.EXPIRED_TOKEN
+ )
+ case OAuthErrorCode.ACCESS_DENIED:
+ raise DeviceCodeError(
+ "Authorization was denied. Please try again.", error_code=OAuthErrorCode.ACCESS_DENIED
+ )
+ case error:
+ raise DeviceCodeError(
+ f"OAuth error: {error} - {data.get('error_description', '')}", error_code=error
+ )
+
+ time.sleep(interval)
+
+ raise DeviceCodeError("Device code expired (timeout). Please try again.", error_code=OAuthErrorCode.EXPIRED_TOKEN)
+
+
+def refresh_access_token(refresh_token: str) -> OAuthTokenResponse:
+ """Exchange a refresh token for a new access token.
+
+ Returns:
+ `OAuthTokenResponse`: the full token response: `access_token`, and optionally a rotated
+ `refresh_token` and `expires_in`.
+
+ Raises:
+ [`DeviceCodeError`]: If the server rejects the refresh (`error_code="invalid_grant"` when
+ the refresh token is expired or revoked) or returns an unexpected response.
+ """
+ try:
+ response = get_session().post(
+ f"{constants.ENDPOINT}/oauth/token",
+ data={
+ "grant_type": _REFRESH_TOKEN_GRANT_TYPE,
+ "refresh_token": refresh_token,
+ "client_id": constants.DEVICE_CODE_OAUTH_CLIENT_ID,
+ },
+ # An explicit timeout is critical here: this runs inside `get_token()`, so a hung
+ # request would otherwise block every Hub call in the process.
+ timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+ )
+ except httpx2.HTTPError as e:
+ raise DeviceCodeError(f"Failed to refresh access token: {e}") from e
+ data = _parse_token_response(response)
+ if "access_token" in data:
+ return cast(OAuthTokenResponse, data)
+ error = data.get("error")
+ raise DeviceCodeError(
+ f"Failed to refresh access token: {error or response.status_code} - {data.get('error_description', '')}",
+ error_code=error,
+ )
+
+
+def _parse_token_response(response: httpx2.Response) -> dict:
+ try:
+ return response.json()
+ except ValueError as e:
+ raise DeviceCodeError(
+ f"Failed to parse response from {constants.ENDPOINT}/oauth/token "
+ f"(status {response.status_code}): {response.text[:500]}"
+ ) from e
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py
new file mode 100644
index 0000000000000000000000000000000000000000..b060901c08d0a1fb3d7bfeb79ba7c2f90956d9b4
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py
@@ -0,0 +1,51 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle pagination on Huggingface Hub."""
+
+from collections.abc import Iterable
+from typing import Any
+
+import httpx2
+
+from . import hf_raise_for_status, http_backoff, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def paginate(path: str, params: dict | list[tuple[str, Any]], headers: dict, timeout: float | None = None) -> Iterable:
+ """Fetch a list of models/datasets/spaces and paginate through results.
+
+ This is using the same "Link" header format as GitHub.
+ See:
+ - https://requests.readthedocs.io/en/latest/api/#requests.Response.links
+ - https://docs.github.com/en/rest/guides/traversing-with-pagination#link-header
+ """
+ r = http_backoff("GET", path, params=params, headers=headers, timeout=timeout)
+ hf_raise_for_status(r)
+ yield from r.json()
+
+ # Follow pages
+ # Next link already contains query params
+ next_page = _get_next_page(r)
+ while next_page is not None:
+ logger.debug(f"Pagination detected. Requesting next page: {next_page}")
+ r = http_backoff("GET", next_page, headers=headers, timeout=timeout)
+ hf_raise_for_status(r)
+ yield from r.json()
+ next_page = _get_next_page(r)
+
+
+def _get_next_page(response: httpx2.Response) -> str | None:
+ return response.links.get("next", {}).get("url")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_parsing.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_parsing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d890f75161cc75df765d32b9338257ff2bdf8ba2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_parsing.py
@@ -0,0 +1,116 @@
+# Copyright 2025-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Parsing helpers shared across modules."""
+
+import re
+import time
+
+
+RE_NUMBER_WITH_UNIT = re.compile(r"(\d+)([a-z]+)", re.IGNORECASE)
+
+BYTE_UNITS: dict[str, int] = {
+ "k": 1_000,
+ "kb": 1_000,
+ "m": 1_000_000,
+ "mb": 1_000_000,
+ "g": 1_000_000_000,
+ "gb": 1_000_000_000,
+ "t": 1_000_000_000_000,
+ "tb": 1_000_000_000_000,
+ "p": 1_000_000_000_000_000,
+ "pb": 1_000_000_000_000_000,
+}
+
+TIME_UNITS: dict[str, int] = {
+ "s": 1,
+ "m": 60,
+ "h": 60 * 60,
+ "d": 24 * 60 * 60,
+ "w": 7 * 24 * 60 * 60,
+ "mo": 30 * 24 * 60 * 60,
+ "y": 365 * 24 * 60 * 60,
+}
+
+
+def parse_size(value: str) -> int:
+ """Parse a size expressed as a string with digits and unit (like `"10MB"`) to an integer (in bytes)."""
+ return _parse_with_unit(value, BYTE_UNITS)
+
+
+def parse_duration(value: str) -> int:
+ """Parse a duration expressed as a string with digits and unit (like `"10s"`) to an integer (in seconds)."""
+ return _parse_with_unit(value, TIME_UNITS)
+
+
+def _parse_with_unit(value: str, units: dict[str, int]) -> int:
+ """Parse a numeric value with optional unit."""
+ stripped = value.strip()
+ if not stripped:
+ raise ValueError("Value cannot be empty.")
+ try:
+ return int(value)
+ except ValueError:
+ pass
+
+ match = RE_NUMBER_WITH_UNIT.fullmatch(stripped)
+ if not match:
+ raise ValueError(f"Invalid value '{value}'. Must match pattern '\\d+[a-z]+' or be a plain number.")
+
+ number = int(match.group(1))
+ unit = match.group(2).lower()
+
+ if unit not in units:
+ raise ValueError(f"Unknown unit '{unit}'. Must be one of {list(units.keys())}.")
+
+ return number * units[unit]
+
+
+def format_duration(secs: int | None) -> str:
+ """Format a duration in seconds as a short human-readable string (e.g. `"1m 32s"`, `"2h 15m"`, `"45s"`).
+
+ Returns `"--"` when `secs` is `None` so it can be used directly as a CLI table cell.
+ """
+ if secs is None:
+ return "--"
+ secs = int(secs)
+ if secs < 60:
+ return f"{secs}s"
+ if secs < 3600:
+ return f"{secs // 60}m {secs % 60}s"
+ return f"{secs // 3600}h {(secs % 3600) // 60}m"
+
+
+def format_timesince(ts: float) -> str:
+ """Format timestamp in seconds into a human-readable string, relative to now.
+
+ Vaguely inspired by Django's `timesince` formatter.
+ """
+ _TIMESINCE_CHUNKS = (
+ # Label, divider, max value shown before rolling over to the next unit (e.g. 59 -> "1 minute ago" at 60s)
+ ("second", 1, 59),
+ ("minute", 60, 59),
+ ("hour", 60 * 60, 23),
+ ("day", 60 * 60 * 24, 6),
+ ("week", 60 * 60 * 24 * 7, 6),
+ ("month", 60 * 60 * 24 * 30, 11),
+ ("year", 60 * 60 * 24 * 365, None),
+ )
+ delta = time.time() - ts
+ if delta < 20:
+ return "a few seconds ago"
+ for label, divider, max_value in _TIMESINCE_CHUNKS: # noqa: B007
+ value = round(delta / divider)
+ if max_value is not None and value <= max_value:
+ break
+ return f"{value} {label}{'s' if value > 1 else ''} ago"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_paths.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..87e8d212de0defb14ce1a6631dec125fe2280f6a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_paths.py
@@ -0,0 +1,183 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle paths in Huggingface Hub."""
+
+import os
+from collections.abc import Callable, Generator, Iterable
+from fnmatch import fnmatchcase
+from pathlib import Path
+from typing import TypeVar
+
+
+T = TypeVar("T")
+
+# Always ignore `.git` and `.cache/huggingface` folders in commits
+DEFAULT_IGNORE_PATTERNS = [
+ ".git",
+ ".git/*",
+ "*/.git",
+ "**/.git/**",
+ ".cache/huggingface",
+ ".cache/huggingface/*",
+ "*/.cache/huggingface",
+ "**/.cache/huggingface/**",
+]
+# Forbidden to commit these folders
+FORBIDDEN_FOLDERS = [".git", ".cache"]
+
+
+def as_extended_path(path: str | Path, max_length: int = 255) -> str:
+ r"""Return `path` in its Windows extended-length form if it is too long, unchanged otherwise.
+
+ Some Windows versions do not allow for paths longer than 255 characters (247 for directories, i.e. MAX_PATH minus
+ room for an 8.3 file name). In this case, we must specify them as extended paths by using the `\\?\` prefix, which
+ only works on absolute paths. Network shares take the `\\?\UNC\server\share\...` form: prefixing them verbatim
+ would produce an invalid `\\?\\\server\...` path.
+
+ Args:
+ path (`str` or `Path`):
+ The path to convert. Returned unchanged on non-Windows platforms, if it is short enough, or if it is
+ already an extended path.
+ max_length (`int`, *optional*):
+ Length above which the path must be converted. Defaults to 255, the limit for files. Pass 247 for paths
+ whose parent directories are created by the caller.
+ """
+ path = str(path)
+ if os.name != "nt":
+ return path
+ absolute_path = os.path.abspath(path)
+ if len(absolute_path) <= max_length or absolute_path.startswith("\\\\?\\"):
+ return path
+ if absolute_path.startswith("\\\\"): # UNC share: `\\server\share\...` => `\\?\UNC\server\share\...`
+ return "\\\\?\\UNC\\" + absolute_path[2:]
+ return "\\\\?\\" + absolute_path
+
+
+def filter_repo_objects(
+ items: Iterable[T],
+ *,
+ allow_patterns: list[str] | str | None = None,
+ ignore_patterns: list[str] | str | None = None,
+ key: Callable[[T], str] | None = None,
+) -> Generator[T, None, None]:
+ """Filter repo objects based on an allowlist and a denylist.
+
+ Input must be a list of paths (`str` or `Path`) or a list of arbitrary objects.
+ In the later case, `key` must be provided and specifies a function of one argument
+ that is used to extract a path from each element in iterable.
+
+ Patterns are Standard Wildcards (globbing patterns), NOT regular expressions. The pattern matching is based on
+ Python's `fnmatch.fnmatchcase`, so it is case-sensitive on every platform. Backslashes are treated as path separators
+ and normalized to forward slashes in both patterns and paths before matching, so patterns built with `os.path.join`
+ on Windows work as expected.
+
+ Note that it matches `*` across path boundaries, unlike traditional Unix shell globbing. For example, `"data/*.json"`
+ will match both `data/file.json` and `data/subdir/file.json`.
+
+ See https://docs.python.org/3/library/fnmatch.html for more details.
+
+ Args:
+ items (`Iterable`):
+ List of items to filter.
+ allow_patterns (`str` or `list[str]`, *optional*):
+ Patterns constituting the allowlist. If provided, item paths must match at
+ least one pattern from the allowlist.
+ ignore_patterns (`str` or `list[str]`, *optional*):
+ Patterns constituting the denylist. If provided, item paths must not match
+ any patterns from the denylist.
+ key (`Callable[[T], str]`, *optional*):
+ Single-argument function to extract a path from each item. If not provided,
+ the `items` must already be `str` or `Path`.
+
+ Returns:
+ Filtered list of objects, as a generator.
+
+ Raises:
+ :class:`ValueError`:
+ If `key` is not provided and items are not `str` or `Path`.
+
+ Example usage with paths:
+ ```python
+ >>> # Filter only PDFs that are not hidden.
+ >>> list(filter_repo_objects(
+ ... ["aaa.pdf", "bbb.jpg", ".ccc.pdf", ".ddd.png"],
+ ... allow_patterns=["*.pdf"],
+ ... ignore_patterns=[".*"],
+ ... ))
+ ["aaa.pdf"]
+ ```
+
+ Example usage with objects:
+ ```python
+ >>> list(filter_repo_objects(
+ ... [
+ ... CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf")
+ ... CommitOperationAdd(path_or_fileobj="/tmp/bbb.jpg", path_in_repo="bbb.jpg")
+ ... CommitOperationAdd(path_or_fileobj="/tmp/.ccc.pdf", path_in_repo=".ccc.pdf")
+ ... CommitOperationAdd(path_or_fileobj="/tmp/.ddd.png", path_in_repo=".ddd.png")
+ ... ],
+ ... allow_patterns=["*.pdf"],
+ ... ignore_patterns=[".*"],
+ ... key=lambda x: x.path_in_repo
+ ... ))
+ [CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf")]
+ ```
+ """
+ if isinstance(allow_patterns, str):
+ allow_patterns = [allow_patterns]
+
+ if isinstance(ignore_patterns, str):
+ ignore_patterns = [ignore_patterns]
+
+ if allow_patterns is not None:
+ allow_patterns = [_add_wildcard_to_directories(_normalize_separators(p)) for p in allow_patterns]
+ if ignore_patterns is not None:
+ ignore_patterns = [_add_wildcard_to_directories(_normalize_separators(p)) for p in ignore_patterns]
+
+ if key is None:
+
+ def _identity(item: T) -> str:
+ if isinstance(item, str):
+ return item
+ if isinstance(item, Path):
+ return str(item)
+ raise ValueError(f"Please provide `key` argument in `filter_repo_objects`: `{item}` is not a string.")
+
+ key = _identity # Items must be `str` or `Path`, otherwise raise ValueError
+
+ for item in items:
+ path = _normalize_separators(key(item))
+
+ # Skip if there's an allowlist and path doesn't match any
+ if allow_patterns is not None and not any(fnmatchcase(path, r) for r in allow_patterns):
+ continue
+
+ # Skip if there's a denylist and path matches any
+ if ignore_patterns is not None and any(fnmatchcase(path, r) for r in ignore_patterns):
+ continue
+
+ yield item
+
+
+def _normalize_separators(value: str | Path) -> str:
+ # Repo paths always use `/` and `\` is not an fnmatch escape character, so treating backslashes as path separators
+ # (e.g. patterns built with `os.path.join` on Windows) is safe.
+ # `value` can be a `Path` if a custom `key` returns one; coerce to `str` first.
+ return str(value).replace("\\", "/")
+
+
+def _add_wildcard_to_directories(pattern: str) -> str:
+ if pattern.endswith("/"):
+ return pattern + "*"
+ return pattern
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2735be1ce9b7c6a77f61f60247426cf39467a7b
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py
@@ -0,0 +1,448 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Check presence of installed packages at runtime."""
+
+import importlib.metadata
+import importlib.util
+import os
+import platform
+import sys
+import warnings
+from pathlib import Path
+from typing import Any, Literal
+
+from .. import __version__, constants
+
+
+_PY_VERSION: str = sys.version.split()[0].rstrip("+")
+
+_package_versions: dict[str, str] = {}
+
+_CANDIDATES = {
+ "aiohttp": {"aiohttp"},
+ "fastai": {"fastai"},
+ "fastapi": {"fastapi"},
+ "fastcore": {"fastcore"},
+ "gradio": {"gradio"},
+ "graphviz": {"graphviz"},
+ "hf_xet": {"hf_xet"},
+ "jinja": {"Jinja2"},
+ "httpx2": {"httpx2"},
+ "keras": {"keras"},
+ "numpy": {"numpy"},
+ "pillow": {"Pillow"},
+ "pydantic": {"pydantic"},
+ "pydot": {"pydot"},
+ "safetensors": {"safetensors"},
+ "tensorboard": {"tensorboardX"},
+ "tensorflow": (
+ "tensorflow",
+ "tensorflow-cpu",
+ "tensorflow-gpu",
+ "tf-nightly",
+ "tf-nightly-cpu",
+ "tf-nightly-gpu",
+ "intel-tensorflow",
+ "intel-tensorflow-avx512",
+ "tensorflow-rocm",
+ "tensorflow-macos",
+ ),
+ "torch": {"torch"},
+}
+
+
+def _get_version(package_name: str) -> str:
+ if package_name not in _package_versions:
+ # Write once after the lookup: concurrent callers would read a placeholder as "not installed".
+ version = "N/A"
+ for distribution_name in _CANDIDATES.get(package_name, (package_name,)):
+ try:
+ version = importlib.metadata.version(distribution_name)
+ break
+ except importlib.metadata.PackageNotFoundError:
+ pass
+ _package_versions[package_name] = version
+ return _package_versions[package_name]
+
+
+def is_package_available(package_name: str) -> bool:
+ return _get_version(package_name) != "N/A"
+
+
+# Python
+def get_python_version() -> str:
+ return _PY_VERSION
+
+
+# Huggingface Hub
+def get_hf_hub_version() -> str:
+ return __version__
+
+
+# aiohttp
+def is_aiohttp_available() -> bool:
+ return is_package_available("aiohttp")
+
+
+def get_aiohttp_version() -> str:
+ return _get_version("aiohttp")
+
+
+# FastAI
+def is_fastai_available() -> bool:
+ return is_package_available("fastai")
+
+
+def get_fastai_version() -> str:
+ return _get_version("fastai")
+
+
+# FastAPI
+def is_fastapi_available() -> bool:
+ return is_package_available("fastapi")
+
+
+def get_fastapi_version() -> str:
+ return _get_version("fastapi")
+
+
+# Fastcore
+def is_fastcore_available() -> bool:
+ return is_package_available("fastcore")
+
+
+def get_fastcore_version() -> str:
+ return _get_version("fastcore")
+
+
+# FastAI
+def is_gradio_available() -> bool:
+ return is_package_available("gradio")
+
+
+def get_gradio_version() -> str:
+ return _get_version("gradio")
+
+
+# Graphviz
+def is_graphviz_available() -> bool:
+ return is_package_available("graphviz")
+
+
+def get_graphviz_version() -> str:
+ return _get_version("graphviz")
+
+
+# httpx2
+def is_httpx2_available() -> bool:
+ return is_package_available("httpx2")
+
+
+def get_httpx2_version() -> str:
+ return _get_version("httpx2")
+
+
+# xet
+def is_xet_available() -> bool:
+ # since hf_xet is automatically used if available, allow explicit disabling via environment variable
+ if constants.HF_HUB_DISABLE_XET:
+ return False
+
+ return is_package_available("hf_xet")
+
+
+def get_xet_version() -> str:
+ return _get_version("hf_xet")
+
+
+# keras
+def is_keras_available() -> bool:
+ return is_package_available("keras")
+
+
+def get_keras_version() -> str:
+ return _get_version("keras")
+
+
+# Numpy
+def is_numpy_available() -> bool:
+ return is_package_available("numpy")
+
+
+def get_numpy_version() -> str:
+ return _get_version("numpy")
+
+
+# Jinja
+def is_jinja_available() -> bool:
+ return is_package_available("jinja")
+
+
+def get_jinja_version() -> str:
+ return _get_version("jinja")
+
+
+# Pillow
+def is_pillow_available() -> bool:
+ return is_package_available("pillow")
+
+
+def get_pillow_version() -> str:
+ return _get_version("pillow")
+
+
+# Pydantic
+def is_pydantic_available() -> bool:
+ if not is_package_available("pydantic"):
+ return False
+ # For Pydantic, we add an extra check to test whether it is correctly installed or not. If both pydantic 2.x and
+ # typing_extensions<=4.5.0 are installed, then pydantic will fail at import time. This should not happen when
+ # it is installed with `pip install huggingface_hub[inference]` but it can happen when it is installed manually
+ # by the user in an environment that we don't control.
+ #
+ # Usually we won't need to do this kind of check on optional dependencies. However, pydantic is a special case
+ # as it is automatically imported when doing `from huggingface_hub import ...` even if the user doesn't use it.
+ #
+ # See https://github.com/huggingface/huggingface_hub/pull/1829 for more details.
+ try:
+ from pydantic import validator # noqa: F401
+ except ImportError:
+ # Example: "ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'"
+ warnings.warn(
+ "Pydantic is installed but cannot be imported. Please check your installation. `huggingface_hub` will "
+ "default to not using Pydantic. Error message: '{e}'"
+ )
+ return False
+ return True
+
+
+def get_pydantic_version() -> str:
+ return _get_version("pydantic")
+
+
+# Pydot
+def is_pydot_available() -> bool:
+ return is_package_available("pydot")
+
+
+def get_pydot_version() -> str:
+ return _get_version("pydot")
+
+
+# Tensorboard
+def is_tensorboard_available() -> bool:
+ return is_package_available("tensorboard")
+
+
+def get_tensorboard_version() -> str:
+ return _get_version("tensorboard")
+
+
+# Tensorflow
+def is_tf_available() -> bool:
+ return is_package_available("tensorflow")
+
+
+def get_tf_version() -> str:
+ return _get_version("tensorflow")
+
+
+# Torch
+def is_torch_available() -> bool:
+ return is_package_available("torch")
+
+
+def get_torch_version() -> str:
+ return _get_version("torch")
+
+
+# Safetensors
+def is_safetensors_available() -> bool:
+ return is_package_available("safetensors")
+
+
+# Shell-related helpers
+try:
+ # Set to `True` if script is running in a Google Colab notebook.
+ # If running in Google Colab, git credential store is set globally which makes the
+ # warning disappear. See https://github.com/huggingface/huggingface_hub/issues/1043
+ #
+ # Taken from https://stackoverflow.com/a/63519730.
+ _is_google_colab = "google.colab" in str(get_ipython()) # type: ignore # noqa: F821
+except NameError:
+ _is_google_colab = False
+
+
+def is_notebook() -> bool:
+ """Return `True` if code is executed in a notebook (Jupyter, Colab, QTconsole).
+
+ Taken from https://stackoverflow.com/a/39662359.
+ Adapted to make it work with Google colab as well.
+ """
+ try:
+ shell_class = get_ipython().__class__ # type: ignore # noqa: F821
+ for parent_class in shell_class.__mro__: # e.g. "is subclass of"
+ if parent_class.__name__ == "ZMQInteractiveShell":
+ return True # Jupyter notebook, Google colab or qtconsole
+ return False
+ except NameError:
+ return False # Probably standard Python interpreter
+
+
+def is_google_colab() -> bool:
+ """Return `True` if code is executed in a Google colab.
+
+ Taken from https://stackoverflow.com/a/63519730.
+ """
+ return _is_google_colab
+
+
+def is_colab_enterprise() -> bool:
+ """Return `True` if code is executed in a Google Colab Enterprise environment."""
+ return os.environ.get("VERTEX_PRODUCT") == "COLAB_ENTERPRISE"
+
+
+# Check how huggingface_hub has been installed
+
+
+def installation_method() -> Literal["brew", "hf_installer", "pip", "unknown"]:
+ """Return the installation method of the current environment.
+
+ - "hf_installer" if installed via the official installer script
+ - "brew" if installed via Homebrew
+ - "pip" if pip is available (default fallback for standard Python environments)
+ - "unknown" otherwise
+ """
+ # hf_installer check must come first: the installer creates a venv using the
+ # system Python, which may be Homebrew's. Checking brew first would false-positive
+ # when the resolved sys.executable points to /opt/homebrew/... inside a venv.
+ if _is_hf_installer_installation():
+ return "hf_installer"
+ if _is_brew_installation():
+ return "brew"
+ if _is_pip_available():
+ return "pip"
+ return "unknown"
+
+
+def _is_brew_installation() -> bool:
+ """Check if running from a Homebrew installation.
+
+ Homebrew installs the `hf` formula into a Cellar directory and creates a
+ libexec virtualenv at e.g. /opt/homebrew/Cellar/hf/0.30.0/libexec/.
+ We check `sys.prefix` (the venv/prefix root) for "/Cellar/hf/" rather
+ than checking `sys.executable` — the latter resolves to Homebrew's Python
+ (e.g. /opt/homebrew/Cellar/python@3.12/...) even for non-brew installs
+ when the system Python happens to come from Homebrew.
+ """
+ prefix = str(Path(sys.prefix).resolve())
+ return "/Cellar/hf/" in prefix
+
+
+def _is_hf_installer_installation() -> bool:
+ """Return `True` if the current environment was set up via the official hf installer script.
+
+ i.e. using one of
+ curl -LsSf https://hf.co/cli/install.sh | bash
+ powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"
+ """
+ venv = sys.prefix # points to venv root if active
+ marker = Path(venv) / ".hf_installer_marker"
+ return marker.exists()
+
+
+def _is_pip_available() -> bool:
+ """Return `True` if pip is importable in the current environment."""
+ return importlib.util.find_spec("pip") is not None
+
+
+def dump_environment_info() -> dict[str, Any]:
+ """Dump information about the machine to help debugging issues.
+
+ Similar helper exist in:
+ - `datasets` (https://github.com/huggingface/datasets/blob/main/src/datasets/commands/env.py)
+ - `diffusers` (https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/env.py)
+ - `transformers` (https://github.com/huggingface/transformers/blob/main/src/transformers/commands/env.py)
+ """
+ from huggingface_hub import get_token
+ from huggingface_hub.utils import is_agent, list_credential_helpers
+
+ token = get_token()
+
+ # Generic machine info
+ info: dict[str, Any] = {
+ "huggingface_hub version": get_hf_hub_version(),
+ "Platform": platform.platform(),
+ "Python version": get_python_version(),
+ }
+
+ # Interpreter info
+ try:
+ shell_class = get_ipython().__class__ # type: ignore # noqa: F821
+ info["Running in iPython ?"] = "Yes"
+ info["iPython shell"] = shell_class.__name__
+ except NameError:
+ info["Running in iPython ?"] = "No"
+ info["Running in notebook ?"] = "Yes" if is_notebook() else "No"
+ info["Running in Google Colab ?"] = "Yes" if is_google_colab() else "No"
+ info["Running in Google Colab Enterprise ?"] = "Yes" if is_colab_enterprise() else "No"
+ # Login info
+ info["Token path ?"] = constants.HF_TOKEN_PATH
+ info["Has saved token ?"] = token is not None
+ if token is not None:
+ try:
+ from huggingface_hub import whoami
+
+ info["Who am I ?"] = whoami()["name"]
+ except Exception:
+ pass
+
+ try:
+ info["Configured git credential helpers"] = ", ".join(list_credential_helpers())
+ except Exception:
+ pass
+
+ info["Run by AI agent ?"] = "Yes" if is_agent() else "No"
+
+ # How huggingface_hub has been installed?
+ info["Installation method"] = installation_method()
+
+ # Installed dependencies
+ info["httpx2"] = get_httpx2_version()
+ info["hf_xet"] = get_xet_version()
+ info["gradio"] = get_gradio_version()
+ info["tensorboard"] = get_tensorboard_version()
+
+ # Environment variables
+ info["ENDPOINT"] = constants.ENDPOINT
+ info["HF_HUB_CACHE"] = constants.HF_HUB_CACHE
+ info["HF_ASSETS_CACHE"] = constants.HF_ASSETS_CACHE
+ info["HF_TOKEN_PATH"] = constants.HF_TOKEN_PATH
+ info["HF_STORED_TOKENS_PATH"] = constants.HF_STORED_TOKENS_PATH
+ info["HF_HUB_OFFLINE"] = constants.HF_HUB_OFFLINE
+ info["HF_HUB_DISABLE_TELEMETRY"] = constants.HF_HUB_DISABLE_TELEMETRY
+ info["HF_HUB_DISABLE_PROGRESS_BARS"] = constants.HF_HUB_DISABLE_PROGRESS_BARS
+ info["HF_HUB_DISABLE_SYMLINKS"] = constants.HF_HUB_DISABLE_SYMLINKS
+ info["HF_HUB_DISABLE_SYMLINKS_WARNING"] = constants.HF_HUB_DISABLE_SYMLINKS_WARNING
+ info["HF_HUB_DISABLE_EXPERIMENTAL_WARNING"] = constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING
+ info["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = constants.HF_HUB_DISABLE_IMPLICIT_TOKEN
+ info["HF_HUB_DISABLE_XET"] = constants.HF_HUB_DISABLE_XET
+ info["HF_HUB_DISABLE_SHARED_BLOBS"] = constants.HF_HUB_DISABLE_SHARED_BLOBS
+ info["HF_HUB_ETAG_TIMEOUT"] = constants.HF_HUB_ETAG_TIMEOUT
+ info["HF_HUB_DOWNLOAD_TIMEOUT"] = constants.HF_HUB_DOWNLOAD_TIMEOUT
+ info["HF_XET_HIGH_PERFORMANCE"] = constants.HF_XET_HIGH_PERFORMANCE
+
+ print("\nCopy-and-paste the text below in your GitHub issue.\n")
+ print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n")
+ return info
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_safetensors.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_safetensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..d066f6ee636110d49bae6edf8c39be55172e292a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_safetensors.py
@@ -0,0 +1,111 @@
+import functools
+import operator
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Literal
+
+
+FILENAME_T = str
+TENSOR_NAME_T = str
+DTYPE_T = Literal["F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "U3", "BOOL"]
+
+
+@dataclass
+class TensorInfo:
+ """Information about a tensor.
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Attributes:
+ dtype (`str`):
+ The data type of the tensor ("F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "U3", "BOOL").
+ shape (`list[int]`):
+ The shape of the tensor.
+ data_offsets (`tuple[int, int]`):
+ The offsets of the data in the file as a tuple `[BEGIN, END]`.
+ parameter_count (`int`):
+ The number of parameters in the tensor.
+ """
+
+ dtype: DTYPE_T
+ shape: list[int]
+ data_offsets: tuple[int, int]
+ parameter_count: int = field(init=False)
+
+ def __post_init__(self) -> None:
+ # Taken from https://stackoverflow.com/a/13840436
+ try:
+ self.parameter_count = functools.reduce(operator.mul, self.shape)
+ except TypeError:
+ self.parameter_count = 1 # scalar value has no shape
+
+
+@dataclass
+class SafetensorsFileMetadata:
+ """Metadata for a Safetensors file hosted on the Hub.
+
+ This class is returned by [`parse_safetensors_file_metadata`].
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Attributes:
+ metadata (`dict`):
+ The metadata contained in the file.
+ tensors (`dict[str, TensorInfo]`):
+ A map of all tensors. Keys are tensor names and values are information about the corresponding tensor, as a
+ [`TensorInfo`] object.
+ parameter_count (`dict[str, int]`):
+ A map of the number of parameters per data type. Keys are data types and values are the number of parameters
+ of that data type.
+ """
+
+ metadata: dict[str, str]
+ tensors: dict[TENSOR_NAME_T, TensorInfo]
+ parameter_count: dict[DTYPE_T, int] = field(init=False)
+
+ def __post_init__(self) -> None:
+ parameter_count: dict[DTYPE_T, int] = defaultdict(int)
+ for tensor in self.tensors.values():
+ parameter_count[tensor.dtype] += tensor.parameter_count
+ self.parameter_count = dict(parameter_count)
+
+
+@dataclass
+class SafetensorsRepoMetadata:
+ """Metadata for a Safetensors repo.
+
+ A repo is considered to be a Safetensors repo if it contains either a 'model.safetensors' weight file (non-shared
+ model) or a 'model.safetensors.index.json' index file (sharded model) at its root.
+
+ This class is returned by [`get_safetensors_metadata`].
+
+ For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+ Attributes:
+ metadata (`dict`, *optional*):
+ The metadata contained in the 'model.safetensors.index.json' file, if it exists. Only populated for sharded
+ models.
+ sharded (`bool`):
+ Whether the repo contains a sharded model or not.
+ weight_map (`dict[str, str]`):
+ A map of all weights. Keys are tensor names and values are filenames of the files containing the tensors.
+ files_metadata (`dict[str, SafetensorsFileMetadata]`):
+ A map of all files metadata. Keys are filenames and values are the metadata of the corresponding file, as
+ a [`SafetensorsFileMetadata`] object.
+ parameter_count (`dict[str, int]`):
+ A map of the number of parameters per data type. Keys are data types and values are the number of parameters
+ of that data type.
+ """
+
+ metadata: dict | None
+ sharded: bool
+ weight_map: dict[TENSOR_NAME_T, FILENAME_T] # tensor name -> filename
+ files_metadata: dict[FILENAME_T, SafetensorsFileMetadata] # filename -> metadata
+ parameter_count: dict[DTYPE_T, int] = field(init=False)
+
+ def __post_init__(self) -> None:
+ parameter_count: dict[DTYPE_T, int] = defaultdict(int)
+ for file_metadata in self.files_metadata.values():
+ for dtype, nb_parameters_ in file_metadata.parameter_count.items():
+ parameter_count[dtype] += nb_parameters_
+ self.parameter_count = dict(parameter_count)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_shared_blobs.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_shared_blobs.py
new file mode 100644
index 0000000000000000000000000000000000000000..4376b02ec1433e5ffdf36a04cd844c5436c6f63b
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_shared_blobs.py
@@ -0,0 +1,622 @@
+# Copyright 2026-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Cache-wide shared blob store, deduplicating Xet files across cached repos.
+
+Store entries at `/blobs/<2-hex-prefix>/` are regular files.
+Per-repo `blobs/` entries are relative symlinks to them. A companion
+`.refs` manifest lists those symlinks so cache deletion only needs to inspect
+the blobs it touches instead of scanning the entire store.
+
+The manifest is a hint, not trusted metadata: every entry is validated against the
+filesystem before use, and any missing or unreadable manifest makes garbage collection
+leak the shared blob rather than risk deleting referenced content.
+"""
+
+import os
+import re
+import secrets
+import shutil
+import stat
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Generator
+
+from filelock import FileLock, SoftFileLock
+
+from .. import constants
+from . import logging
+
+
+logger = logging.get_logger(__name__)
+
+_XET_HASH_REGEX = re.compile(r"[0-9a-f]{64}")
+_REPO_DIR_REGEX = re.compile(rf"(?:{'|'.join(sorted(constants.REPO_TYPES_MAPPING))})--.+")
+
+SHARED_BLOBS_DIR_NAME = "blobs"
+SHARED_BLOBS_MARKER_NAME = ".huggingface-shared-blobs"
+SHARED_BLOBS_LAYOUT_VERSION = "1"
+_MANIFEST_SUFFIX = ".refs"
+_LOCK_SUFFIX = ".lock"
+_SOFT_LOCK_TIMEOUT = 10
+_MARKER_TMP_REGEX = re.compile(rf"{re.escape(SHARED_BLOBS_MARKER_NAME)}\.[0-9a-f]{{8}}\.tmp")
+
+
+def shared_blobs_dir(cache_dir: str | Path) -> Path:
+ """Return the path of the shared blob store inside a cache directory."""
+ return Path(cache_dir) / SHARED_BLOBS_DIR_NAME
+
+
+def shared_blob_path(cache_dir: str | Path, xet_hash: str) -> Path:
+ """Return the store path for a given Xet file hash.
+
+ Raises `ValueError` if `xet_hash` is not a valid Xet hash.
+ """
+ if _XET_HASH_REGEX.fullmatch(xet_hash) is None:
+ raise ValueError(f"Invalid Xet file hash: '{xet_hash}'.")
+ return shared_blobs_dir(cache_dir) / xet_hash[:2] / xet_hash
+
+
+def shared_blob_manifest_path(cache_dir: str | Path, xet_hash: str) -> Path:
+ """Return the reference manifest path for a given Xet file hash."""
+ return shared_blob_path(cache_dir, xet_hash).with_name(f"{xet_hash}{_MANIFEST_SUFFIX}")
+
+
+def _is_regular_file(path: Path) -> bool:
+ # checks if path is a regular file without following symlinks
+ try:
+ return stat.S_ISREG(path.lstat().st_mode)
+ except OSError:
+ return False
+
+
+def _is_directory(path: Path) -> bool:
+ # checks if path is a regular directory without following symlinks
+ try:
+ return stat.S_ISDIR(path.lstat().st_mode)
+ except OSError:
+ return False
+
+
+def _is_usable_store_entry(store_path: Path, expected_size: int | None) -> bool:
+ """Return whether `store_path` is a regular, readable payload of the expected size."""
+ if expected_size is None:
+ # Size unknown (HEAD without a content length): the entry cannot be validated.
+ return False
+ try:
+ store_stat = store_path.lstat()
+ except OSError:
+ return False
+ if not stat.S_ISREG(store_stat.st_mode):
+ return False
+ if store_stat.st_size != expected_size:
+ logger.warning(
+ f"Shared blob '{store_path}' has an unexpected size ({store_stat.st_size} instead of {expected_size}). "
+ "Not using it."
+ )
+ return False
+ if not os.access(store_path, os.R_OK):
+ logger.warning(f"Shared blob '{store_path}' is not readable. Not using it.")
+ return False
+ return True
+
+
+def is_shared_blobs_dir(path: str | Path) -> bool:
+ """Return whether `path` is an owned, supported shared blob store."""
+ store_dir = Path(path)
+ marker_path = store_dir / SHARED_BLOBS_MARKER_NAME
+ if not _is_directory(store_dir) or not _is_regular_file(marker_path):
+ return False
+ try:
+ return marker_path.read_text() == f"{SHARED_BLOBS_LAYOUT_VERSION}\n"
+ except OSError:
+ return False
+
+
+def _cleanup_abandoned_marker_temps(store_dir: Path) -> bool:
+ """Remove leftover marker temporaries.
+
+ Returns whether the directory is empty afterwards, i.e. safe to mark. Any foreign content returns False.
+ """
+ expected_content = f"{SHARED_BLOBS_LAYOUT_VERSION}\n"
+ entries = list(store_dir.iterdir())
+ for entry in entries:
+ if _MARKER_TMP_REGEX.fullmatch(entry.name) is None or not _is_regular_file(entry):
+ return False
+ try:
+ if not expected_content.startswith(entry.read_text()):
+ return False
+ except (OSError, UnicodeError):
+ return False
+ for entry in entries:
+ entry.unlink(missing_ok=True)
+ return not any(store_dir.iterdir())
+
+
+def _ensure_shared_blobs_dir(cache_dir: str | Path) -> bool:
+ """Create and mark the shared store, refusing to adopt an unmarked directory."""
+ store_dir = shared_blobs_dir(cache_dir)
+ marker_path = store_dir / SHARED_BLOBS_MARKER_NAME
+ try:
+ store_dir.mkdir(exist_ok=True)
+ if is_shared_blobs_dir(store_dir):
+ return True
+ if not _is_directory(store_dir) or not _cleanup_abandoned_marker_temps(store_dir):
+ logger.debug(f"Refusing to use unmarked shared blob directory '{store_dir}'.")
+ return False
+ _repair_shared_directory_mode(store_dir, cache_dir)
+
+ tmp_marker = marker_path.with_name(f"{marker_path.name}.{secrets.token_hex(4)}.tmp")
+ try:
+ tmp_marker.write_text(f"{SHARED_BLOBS_LAYOUT_VERSION}\n")
+ tmp_marker.chmod(_shared_blob_mode(cache_dir))
+ os.replace(tmp_marker, marker_path)
+ finally:
+ tmp_marker.unlink(missing_ok=True)
+ return is_shared_blobs_dir(store_dir)
+ except OSError as e:
+ logger.debug(f"Could not initialize shared blob directory '{store_dir}': {e}")
+ return False
+
+
+def _ensure_prefix_dir(cache_dir: str | Path, xet_hash: str) -> Path | None:
+ if not _ensure_shared_blobs_dir(cache_dir):
+ return None
+ prefix_dir = shared_blob_path(cache_dir, xet_hash).parent
+ try:
+ prefix_dir.mkdir(exist_ok=True)
+ except OSError as e:
+ logger.debug(f"Could not create shared blob prefix directory '{prefix_dir}': {e}")
+ return None
+ if not _is_directory(prefix_dir):
+ logger.debug(f"Refusing to use non-directory shared blob prefix '{prefix_dir}'.")
+ return None
+ try:
+ _repair_shared_directory_mode(prefix_dir, cache_dir)
+ except OSError as e:
+ logger.debug(f"Could not set shared blob prefix permissions on '{prefix_dir}': {e}")
+ return None
+ return prefix_dir
+
+
+def _shared_blob_mode(cache_dir: str | Path) -> int:
+ """Return a read-only mode accessible to users who can traverse the cache root."""
+ if os.name == "nt":
+ # Windows uses ACLs; 0444 would set the read-only attribute and block replacement and GC.
+ return 0o666
+ try:
+ cache_mode = stat.S_IMODE(Path(cache_dir).stat().st_mode)
+ except OSError:
+ return 0o400
+ return 0o400 | (0o040 if cache_mode & stat.S_IXGRP else 0) | (0o004 if cache_mode & stat.S_IXOTH else 0)
+
+
+def _shared_directory_mode(cache_dir: str | Path) -> int:
+ """Mirror cache-root access and inheritance bits on newly created store directories."""
+ try:
+ cache_mode = stat.S_IMODE(Path(cache_dir).stat().st_mode)
+ except OSError:
+ return 0o700
+ return cache_mode & (0o777 | stat.S_ISGID | stat.S_ISVTX)
+
+
+def _repair_shared_directory_mode(path: Path, cache_dir: str | Path) -> None:
+ expected_mode = _shared_directory_mode(cache_dir)
+ if stat.S_IMODE(path.lstat().st_mode) != expected_mode:
+ path.chmod(expected_mode)
+
+
+def _prepare_shared_blob_permissions(blob_path: Path, prefix_dir: Path, cache_dir: str | Path) -> None:
+ """Make a payload immutable and readable according to the shared cache policy."""
+ blob_mode = _shared_blob_mode(cache_dir)
+ os.chmod(blob_path, blob_mode)
+ if os.name == "nt" or not hasattr(os, "chown"):
+ return
+ target_gid = prefix_dir.stat().st_gid
+ if blob_path.stat().st_gid == target_gid:
+ return
+ try:
+ os.chown(blob_path, -1, target_gid)
+ except OSError:
+ # Without other-read, a wrong group makes the entry unreadable to other users: fall back to repo-local.
+ if blob_mode & stat.S_IRGRP and not blob_mode & stat.S_IROTH:
+ raise
+
+
+def _path_for_comparison(path: str | Path) -> Path:
+ """Return an absolute path without the Windows extended-length prefix."""
+ path_str = os.fspath(path)
+ if path_str[:8].lower() == "\\\\?\\unc\\":
+ path_str = f"\\\\{path_str[8:]}"
+ elif path_str.startswith("\\\\?\\"):
+ path_str = path_str[4:]
+ return Path(os.path.abspath(path_str))
+
+
+def _relative_blob_path(blob_path: str | Path, cache_dir: str | Path) -> str | None:
+ blob_path = _path_for_comparison(blob_path)
+ cache_dir = _path_for_comparison(cache_dir)
+ try:
+ relative_path = blob_path.relative_to(cache_dir)
+ except ValueError:
+ return None
+ if (
+ len(relative_path.parts) != 3
+ or _REPO_DIR_REGEX.fullmatch(relative_path.parts[0]) is None
+ or relative_path.parts[1] != "blobs"
+ or not relative_path.parts[2]
+ ):
+ return None
+ relative_str = relative_path.as_posix()
+ return None if "\n" in relative_str or "\r" in relative_str else relative_str
+
+
+def _shared_blob_target(
+ blob_path: str | Path, cache_dir: str | Path, *, raise_on_io_error: bool = False
+) -> Path | None:
+ """Return the canonical store target if `blob_path` is a managed shared symlink."""
+ blob_path = Path(blob_path)
+ if _relative_blob_path(blob_path, cache_dir) is None:
+ return None
+ try:
+ if not stat.S_ISLNK(blob_path.lstat().st_mode):
+ return None
+ link_target = Path(os.readlink(blob_path))
+ except FileNotFoundError:
+ return None
+ except OSError:
+ if raise_on_io_error:
+ raise
+ return None
+
+ comparable_blob_path = _path_for_comparison(blob_path)
+ target = link_target if link_target.is_absolute() else comparable_blob_path.parent / link_target
+ target = _path_for_comparison(target)
+ store_dir = _path_for_comparison(shared_blobs_dir(cache_dir))
+ try:
+ relative_target = target.relative_to(store_dir)
+ except ValueError:
+ return None
+ if (
+ len(relative_target.parts) != 2
+ or _XET_HASH_REGEX.fullmatch(relative_target.parts[1]) is None
+ or relative_target.parts[0] != relative_target.parts[1][:2]
+ ):
+ return None
+ return shared_blobs_dir(cache_dir) / relative_target
+
+
+def shared_blob_target(blob_path: str | Path, cache_dir: str | Path) -> Path | None:
+ """Return the canonical store target if `blob_path` is a valid shared symlink.
+
+ Callers check `is_shared_blobs_dir` once before resolving many blobs.
+ """
+ target = _shared_blob_target(blob_path, cache_dir)
+ return target if target is not None and _is_regular_file(target) else None
+
+
+def _manifest_path_for_store_path(store_path: Path) -> Path:
+ return store_path.with_name(f"{store_path.name}{_MANIFEST_SUFFIX}")
+
+
+def _lock_path_for_store_path(store_path: Path) -> Path:
+ return store_path.with_name(f"{store_path.name}{_LOCK_SUFFIX}")
+
+
+@contextmanager
+def _shared_blob_lock(store_path: Path) -> Generator[None, None, None]:
+ """Lock publication, reference creation, and GC for one content hash."""
+ lock_path = _lock_path_for_store_path(store_path)
+ # Create the lock file ourselves: `O_NOFOLLOW` refuses a planted symlink and 0o666 lets other users of a
+ # shared cache take the same lock. `FileLock` alone would follow symlinks and apply the umask.
+ flags = os.O_WRONLY | os.O_CREAT
+ if hasattr(os, "O_NOFOLLOW"):
+ flags |= os.O_NOFOLLOW
+ fd = os.open(lock_path, flags, 0o666)
+ try:
+ if not stat.S_ISREG(os.fstat(fd).st_mode):
+ raise OSError(f"Shared blob lock is not a regular file: '{lock_path}'.")
+ if hasattr(os, "fchmod"):
+ try:
+ os.fchmod(fd, 0o666)
+ except OSError:
+ pass
+ finally:
+ os.close(fd)
+ lock = FileLock(lock_path, mode=0o666)
+ try:
+ lock.acquire()
+ except NotImplementedError:
+ # SoftFileLock uses file existence as the lock, so it cannot reuse the flock file above.
+ lock = SoftFileLock(f"{lock_path}.soft", mode=0o666)
+ lock.acquire(timeout=_SOFT_LOCK_TIMEOUT)
+ try:
+ yield
+ finally:
+ try:
+ lock.release()
+ except OSError:
+ pass
+
+
+def _append_manifest_reference(manifest_path: Path, relative_blob_path: str) -> None:
+ """Append and flush one reference before its symlink is made visible."""
+ flags = os.O_WRONLY | os.O_APPEND | os.O_CREAT
+ if hasattr(os, "O_NOFOLLOW"):
+ flags |= os.O_NOFOLLOW
+ fd = os.open(manifest_path, flags, 0o666)
+ try:
+ if not stat.S_ISREG(os.fstat(fd).st_mode):
+ raise OSError(f"Shared blob manifest is not a regular file: '{manifest_path}'.")
+ if hasattr(os, "fchmod"):
+ try:
+ os.fchmod(fd, 0o666)
+ except OSError:
+ pass
+ line = f"{relative_blob_path}\n".encode()
+ if os.write(fd, line) != len(line):
+ raise OSError(f"Could not append a complete reference to '{manifest_path}'.")
+ os.fsync(fd)
+ finally:
+ os.close(fd)
+
+
+def _make_temporary_symlink(blob_path: Path, store_path: Path) -> Path:
+ tmp_link = blob_path.with_name(f".{blob_path.name}.{secrets.token_hex(4)}.shared")
+ relative_target = os.path.relpath(_path_for_comparison(store_path), start=_path_for_comparison(blob_path).parent)
+ os.symlink(relative_target, tmp_link)
+ return tmp_link
+
+
+def shared_blobs_enabled() -> bool:
+ """Return whether the shared blob store may be used."""
+ return not constants.HF_HUB_DISABLE_SHARED_BLOBS and not constants.HF_HUB_DISABLE_XET
+
+
+def try_link_from_shared_store(
+ *, blob_path: str, xet_hash: str, cache_dir: str | Path, expected_size: int | None
+) -> bool:
+ """Materialize `blobs/` as a symlink to an existing store entry, if any.
+
+ The reference manifest is flushed before the symlink becomes visible. Failures are
+ best-effort misses and leave the regular download path untouched.
+ """
+ if _XET_HASH_REGEX.fullmatch(xet_hash) is None or not is_shared_blobs_dir(shared_blobs_dir(cache_dir)):
+ return False
+ store_path = shared_blob_path(cache_dir, xet_hash)
+ relative_blob_path = _relative_blob_path(blob_path, cache_dir)
+ if relative_blob_path is None:
+ return False
+
+ tmp_link: Path | None = None
+ try:
+ with _shared_blob_lock(store_path):
+ if not _is_usable_store_entry(store_path, expected_size):
+ return False
+
+ tmp_link = _make_temporary_symlink(Path(blob_path), store_path)
+ _append_manifest_reference(_manifest_path_for_store_path(store_path), relative_blob_path)
+ os.replace(tmp_link, blob_path)
+ except OSError as e:
+ logger.debug(f"Could not symlink '{blob_path}' from shared blob store: {e}")
+ return False
+ finally:
+ if tmp_link is not None:
+ tmp_link.unlink(missing_ok=True)
+
+ logger.debug(f"Blob '{blob_path}' reused from shared blob store (no download needed).")
+ return True
+
+
+def has_shared_blob(*, xet_hash: str, cache_dir: str | Path, expected_size: int | None) -> bool:
+ """Return whether a usable store entry exists, without touching the cache."""
+ if (
+ not shared_blobs_enabled()
+ or _XET_HASH_REGEX.fullmatch(xet_hash) is None
+ or not is_shared_blobs_dir(shared_blobs_dir(cache_dir))
+ ):
+ return False
+ return _is_usable_store_entry(shared_blob_path(cache_dir, xet_hash), expected_size)
+
+
+def publish_blob_to_shared_store(
+ *,
+ blob_path: str,
+ xet_hash: str,
+ cache_dir: str | Path,
+ expected_size: int | None,
+ replace_existing: bool = False,
+) -> bool:
+ """Move a fresh Xet download into the store and replace its repo blob with a symlink.
+
+ Best-effort: on failure the repo blob remains (or is restored as) a regular local
+ file. Returns whether the repo blob was successfully shared.
+ """
+ # Publishing a payload whose size is unknown would create an entry no reader can validate.
+ if expected_size is None or _XET_HASH_REGEX.fullmatch(xet_hash) is None:
+ return False
+ prefix_dir = _ensure_prefix_dir(cache_dir, xet_hash)
+ relative_blob_path = _relative_blob_path(blob_path, cache_dir)
+ if prefix_dir is None or relative_blob_path is None:
+ return False
+
+ blob_path_obj = Path(blob_path)
+ store_path = shared_blob_path(cache_dir, xet_hash)
+ manifest_path = _manifest_path_for_store_path(store_path)
+ tmp_link: Path | None = None
+ blob_moved = False
+ try:
+ with _shared_blob_lock(store_path):
+ tmp_link = _make_temporary_symlink(blob_path_obj, store_path)
+ store_is_usable = not replace_existing and _is_usable_store_entry(store_path, expected_size)
+ _append_manifest_reference(manifest_path, relative_blob_path)
+ if not store_is_usable:
+ _prepare_shared_blob_permissions(blob_path_obj, prefix_dir, cache_dir)
+ os.replace(blob_path_obj, store_path)
+ blob_moved = True
+ try:
+ os.replace(tmp_link, blob_path_obj)
+ except OSError:
+ if blob_moved:
+ shutil.copyfile(store_path, blob_path_obj) # restore under the lock, before GC can run
+ blob_moved = False
+ raise
+ except OSError as e:
+ logger.debug(f"Could not publish '{blob_path}' to shared blob store: {e}")
+ if blob_moved:
+ raise OSError(f"Could not restore repo blob '{blob_path}' after shared-store failure") from e
+ return False
+ finally:
+ if tmp_link is not None:
+ tmp_link.unlink(missing_ok=True)
+
+ logger.debug(f"Blob '{blob_path}' published to shared blob store.")
+ return True
+
+
+def _read_valid_manifest_references(store_path: Path, cache_dir: str | Path) -> set[Path] | None:
+ manifest_path = _manifest_path_for_store_path(store_path)
+ if not _is_regular_file(manifest_path):
+ return None
+ try:
+ lines = manifest_path.read_text().splitlines()
+ except (OSError, UnicodeError):
+ return None
+
+ references: set[Path] = set()
+ cache_dir = Path(os.path.abspath(cache_dir))
+ for line in lines:
+ relative_path = Path(line)
+ if relative_path.is_absolute() or ".." in relative_path.parts:
+ continue
+ candidate = cache_dir / relative_path
+ if _relative_blob_path(candidate, cache_dir) is None:
+ continue
+ try:
+ target = _shared_blob_target(candidate, cache_dir, raise_on_io_error=True)
+ except OSError:
+ return None
+ if target == store_path:
+ references.add(candidate)
+ return references
+
+
+def expected_shared_blob_freed_size(
+ store_path: Path, *, blob_paths_to_delete: set[Path], cache_dir: str | Path
+) -> int:
+ """Predict bytes freed if all known references are part of a deletion plan."""
+ references = _read_valid_manifest_references(store_path, cache_dir)
+ if references is None or not references or not references.issubset(blob_paths_to_delete):
+ return 0
+ try:
+ return store_path.lstat().st_size if _is_regular_file(store_path) else 0
+ except OSError:
+ return 0
+
+
+def shared_store_blob_paths(cache_dir: str | Path) -> set[Path]:
+ """Return all valid canonical payload paths in a marked shared store."""
+ store_dir = shared_blobs_dir(cache_dir)
+ if not is_shared_blobs_dir(store_dir):
+ return set()
+ paths: set[Path] = set()
+ try:
+ prefix_dirs = list(store_dir.iterdir())
+ except OSError:
+ return paths
+ for prefix_dir in prefix_dirs:
+ if re.fullmatch(r"[0-9a-f]{2}", prefix_dir.name) is None or not _is_directory(prefix_dir):
+ continue
+ try:
+ entries = list(prefix_dir.iterdir())
+ except OSError:
+ continue
+ for entry in entries:
+ if (
+ _XET_HASH_REGEX.fullmatch(entry.name) is not None
+ and entry.name.startswith(prefix_dir.name)
+ and _is_regular_file(entry)
+ ):
+ paths.add(entry)
+ return paths
+
+
+def unreferenced_shared_blobs(cache_dir: str | Path) -> dict[Path, int]:
+ """Return store payloads without any valid manifest reference, with their sizes.
+
+ Payloads with a missing or unreadable manifest are not listed, consistent with `sweep_shared_blob`.
+ """
+ unreferenced: dict[Path, int] = {}
+ for store_path in shared_store_blob_paths(cache_dir):
+ references = _read_valid_manifest_references(store_path, cache_dir)
+ if references is not None and not references:
+ try:
+ unreferenced[store_path] = store_path.lstat().st_size
+ except OSError:
+ pass
+ return unreferenced
+
+
+def _rewrite_manifest(manifest_path: Path, references: set[Path], cache_dir: str | Path) -> None:
+ tmp_path = manifest_path.with_name(f"{manifest_path.name}.{secrets.token_hex(4)}.tmp")
+ cache_dir = Path(os.path.abspath(cache_dir))
+ try:
+ content = "".join(f"{path.relative_to(cache_dir).as_posix()}\n" for path in sorted(references))
+ tmp_path.write_text(content)
+ tmp_path.chmod(0o666)
+ with tmp_path.open("rb") as file:
+ os.fsync(file.fileno())
+ os.replace(tmp_path, manifest_path)
+ finally:
+ tmp_path.unlink(missing_ok=True)
+
+
+def sweep_shared_blob(store_path: Path, *, cache_dir: str | Path) -> int:
+ """Collect one shared blob if its manifest has no remaining valid references.
+
+ Missing, invalid, or unreadable metadata is never grounds for deletion. Returns the
+ number of payload bytes removed.
+ """
+ store_path = Path(os.path.abspath(store_path))
+ cache_dir = Path(os.path.abspath(cache_dir))
+ expected_store_dir = Path(os.path.abspath(shared_blobs_dir(cache_dir)))
+ try:
+ relative_store_path = store_path.relative_to(expected_store_dir)
+ except ValueError:
+ return 0
+ if (
+ not is_shared_blobs_dir(expected_store_dir)
+ or len(relative_store_path.parts) != 2
+ or _XET_HASH_REGEX.fullmatch(store_path.name) is None
+ or store_path.parent.name != store_path.name[:2]
+ ):
+ return 0
+
+ try:
+ with _shared_blob_lock(store_path):
+ references = _read_valid_manifest_references(store_path, cache_dir)
+ if references is None:
+ return 0
+ manifest_path = _manifest_path_for_store_path(store_path)
+ if references:
+ _rewrite_manifest(manifest_path, references, cache_dir)
+ return 0
+ if not _is_regular_file(store_path):
+ return 0
+ size = store_path.lstat().st_size
+ store_path.unlink()
+ manifest_path.unlink(missing_ok=True)
+ return size
+ except OSError as e:
+ logger.debug(f"Could not collect shared blob '{store_path}': {e}")
+ return 0
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_subprocess.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_subprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..e648e11146ecaa18bdb736bafedaf6cb6d8c15ff
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_subprocess.py
@@ -0,0 +1,143 @@
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Contains utilities to easily handle subprocesses in `huggingface_hub`."""
+
+import os
+import subprocess
+import sys
+from collections.abc import Generator
+from contextlib import contextmanager
+from io import StringIO
+from pathlib import Path
+from typing import IO
+
+from .logging import get_logger
+
+
+logger = get_logger(__name__)
+
+
+@contextmanager
+def capture_output() -> Generator[StringIO, None, None]:
+ """Capture output that is printed to terminal.
+
+ Taken from https://stackoverflow.com/a/34738440
+
+ Example:
+ ```py
+ >>> with capture_output() as output:
+ ... print("hello world")
+ >>> assert output.getvalue() == "hello world\n"
+ ```
+ """
+ output = StringIO()
+ previous_output = sys.stdout
+ sys.stdout = output
+ try:
+ yield output
+ finally:
+ sys.stdout = previous_output
+
+
+def run_subprocess(
+ command: str | list[str],
+ folder: str | Path | None = None,
+ check=True,
+ **kwargs,
+) -> subprocess.CompletedProcess:
+ """
+ Method to run subprocesses. Calling this will capture the `stderr` and `stdout`,
+ please call `subprocess.run` manually in case you would like for them not to
+ be captured.
+
+ Args:
+ command (`str` or `list[str]`):
+ The command to execute as a string or list of strings.
+ folder (`str`, *optional*):
+ The folder in which to run the command. Defaults to current working
+ directory (from `os.getcwd()`).
+ check (`bool`, *optional*, defaults to `True`):
+ Setting `check` to `True` will raise a `subprocess.CalledProcessError`
+ when the subprocess has a non-zero exit code.
+ kwargs (`dict[str]`):
+ Keyword arguments to be passed to the `subprocess.run` underlying command.
+
+ Returns:
+ `subprocess.CompletedProcess`: The completed process.
+ """
+ if isinstance(command, str):
+ command = command.split()
+
+ if isinstance(folder, Path):
+ folder = str(folder)
+
+ return subprocess.run(
+ command,
+ capture_output=True,
+ check=check,
+ encoding="utf-8",
+ errors="replace", # if not utf-8, replace char by �
+ cwd=folder or os.getcwd(),
+ **kwargs,
+ )
+
+
+@contextmanager
+def run_interactive_subprocess(
+ command: str | list[str],
+ folder: str | Path | None = None,
+ **kwargs,
+) -> Generator[tuple[IO[str], IO[str]], None, None]:
+ """Run a subprocess in an interactive mode in a context manager.
+
+ Args:
+ command (`str` or `list[str]`):
+ The command to execute as a string or list of strings.
+ folder (`str`, *optional*):
+ The folder in which to run the command. Defaults to current working
+ directory (from `os.getcwd()`).
+ kwargs (`dict[str]`):
+ Keyword arguments to be passed to the `subprocess.run` underlying command.
+
+ Returns:
+ `tuple[IO[str], IO[str]]`: A tuple with `stdin` and `stdout` to interact
+ with the process (input and output are utf-8 encoded).
+
+ Example:
+ ```python
+ with _interactive_subprocess("git credential-store get") as (stdin, stdout):
+ # Write to stdin
+ stdin.write("url=hf.co\nusername=obama\n".encode("utf-8"))
+ stdin.flush()
+
+ # Read from stdout
+ output = stdout.read().decode("utf-8")
+ ```
+ """
+ if isinstance(command, str):
+ command = command.split()
+
+ with subprocess.Popen(
+ command,
+ stdin=subprocess.PIPE,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.STDOUT,
+ encoding="utf-8",
+ errors="replace", # if not utf-8, replace char by �
+ cwd=folder or os.getcwd(),
+ **kwargs,
+ ) as process:
+ assert process.stdin is not None, "subprocess is opened as subprocess.PIPE"
+ assert process.stdout is not None, "subprocess is opened as subprocess.PIPE"
+ yield process.stdin, process.stdout
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_telemetry.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_telemetry.py
new file mode 100644
index 0000000000000000000000000000000000000000..a97bae52111902e0b1ffb5fdcdb91ae07396f3f2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_telemetry.py
@@ -0,0 +1,125 @@
+from queue import Queue
+from threading import Lock, Thread
+from urllib.parse import quote
+
+from .. import constants, logging
+from . import build_hf_headers, get_session, hf_raise_for_status
+
+
+logger = logging.get_logger(__name__)
+
+# Telemetry is sent by a separate thread to avoid blocking the main thread.
+# A daemon thread is started once and consume tasks from the _TELEMETRY_QUEUE.
+# If the thread stops for some reason -shouldn't happen-, we restart a new one.
+_TELEMETRY_THREAD: Thread | None = None
+_TELEMETRY_THREAD_LOCK = Lock() # Lock to avoid starting multiple threads in parallel
+_TELEMETRY_QUEUE: Queue = Queue()
+
+
+def send_telemetry(
+ topic: str,
+ *,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+) -> None:
+ """
+ Sends telemetry that helps track usage of different HF libraries.
+
+ This usage data helps us debug issues and prioritize new features. However, we understand that not everyone wants
+ to share additional information, and we respect your privacy. You can disable telemetry collection by setting the
+ `HF_HUB_DISABLE_TELEMETRY=1` as environment variable. Telemetry is also disabled in offline mode (i.e. when setting
+ `HF_HUB_OFFLINE=1`).
+
+ Telemetry collection is run in a separate thread to minimize impact for the user.
+
+ Args:
+ topic (`str`):
+ Name of the topic that is monitored. The topic is directly used to build the URL. If you want to monitor
+ subtopics, just use "/" separation. Examples: "gradio", "transformers/examples",...
+ library_name (`str`, *optional*):
+ The name of the library that is making the HTTP request. Will be added to the user-agent header.
+ library_version (`str`, *optional*):
+ The version of the library that is making the HTTP request. Will be added to the user-agent header.
+ user_agent (`str`, `dict`, *optional*):
+ The user agent info in the form of a dictionary or a single string. It will be completed with information about the installed packages.
+
+ Example:
+ ```py
+ >>> from huggingface_hub.utils import send_telemetry
+
+ # Send telemetry without library information
+ >>> send_telemetry("ping")
+
+ # Send telemetry to subtopic with library information
+ >>> send_telemetry("gradio/local_link", library_name="gradio", library_version="3.22.1")
+
+ # Send telemetry with additional data
+ >>> send_telemetry(
+ ... topic="examples",
+ ... library_name="transformers",
+ ... library_version="4.26.0",
+ ... user_agent={"pipeline": "text_classification", "framework": "flax"},
+ ... )
+ ```
+ """
+ if constants.is_offline_mode() or constants.HF_HUB_DISABLE_TELEMETRY:
+ return
+
+ _start_telemetry_thread() # starts thread only if doesn't exist yet
+ _TELEMETRY_QUEUE.put(
+ {"topic": topic, "library_name": library_name, "library_version": library_version, "user_agent": user_agent}
+ )
+
+
+def _start_telemetry_thread():
+ """Start a daemon thread to consume tasks from the telemetry queue.
+
+ If the thread is interrupted, start a new one.
+ """
+ with _TELEMETRY_THREAD_LOCK: # avoid to start multiple threads if called concurrently
+ global _TELEMETRY_THREAD
+ if _TELEMETRY_THREAD is None or not _TELEMETRY_THREAD.is_alive():
+ _TELEMETRY_THREAD = Thread(target=_telemetry_worker, daemon=True)
+ _TELEMETRY_THREAD.start()
+
+
+def _telemetry_worker():
+ """Wait for a task and consume it."""
+ while True:
+ kwargs = _TELEMETRY_QUEUE.get()
+ _send_telemetry_in_thread(**kwargs)
+ _TELEMETRY_QUEUE.task_done()
+
+
+def _send_telemetry_in_thread(
+ topic: str,
+ *,
+ library_name: str | None = None,
+ library_version: str | None = None,
+ user_agent: dict | str | None = None,
+) -> None:
+ """Contains the actual data sending data to the Hub.
+
+ This function is called directly in gradio's analytics because
+ it is not possible to send telemetry from a daemon thread.
+
+ See here: https://github.com/gradio-app/gradio/pull/8180
+
+ Please do not rename or remove this function.
+ """
+ path = "/".join(quote(part) for part in topic.split("/") if len(part) > 0)
+ try:
+ r = get_session().head(
+ f"{constants.ENDPOINT}/api/telemetry/{path}",
+ headers=build_hf_headers(
+ token=False, # no need to send a token for telemetry
+ library_name=library_name,
+ library_version=library_version,
+ user_agent=user_agent,
+ ),
+ )
+ hf_raise_for_status(r)
+ except Exception as e:
+ # We don't want to error in case of connection errors of any kind.
+ logger.debug(f"Error while sending telemetry: {e}")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_terminal.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_terminal.py
new file mode 100644
index 0000000000000000000000000000000000000000..c12285489a4ec898a641fa6f5cde8d978abd8c38
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_terminal.py
@@ -0,0 +1,268 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to print stuff to the terminal (styling, helpers)."""
+
+import ctypes
+import os
+import shutil
+import sys
+from contextlib import contextmanager
+
+from ._detect_agent import is_agent
+
+
+if sys.platform == "win32":
+ import msvcrt
+else:
+ import select
+ import termios
+ import tty
+
+
+class StatusLine:
+ """Minimal TTY status line for sync progress (stderr, single-line overwrite)."""
+
+ def __init__(self, enabled: bool = True):
+ self._active = enabled and sys.stderr.isatty()
+
+ def update(self, msg: str) -> None:
+ if not self._active:
+ return
+ width = shutil.get_terminal_size().columns
+ if len(msg) > width - 1:
+ msg = msg[: width - 4] + "..."
+ sys.stderr.write(f"\r\033[K\033[90m{msg}\033[0m")
+ sys.stderr.flush()
+
+ def done(self, msg: str) -> None:
+ if not self._active:
+ return
+ width = shutil.get_terminal_size().columns
+ if len(msg) > width - 1:
+ msg = msg[: width - 4] + "..."
+ sys.stderr.write(f"\r\033[K\033[90m{msg}\033[0m\n")
+ sys.stderr.flush()
+
+
+class ANSI:
+ """
+ Helper for en.wikipedia.org/wiki/ANSI_escape_code
+ """
+
+ _blue = "\u001b[34m"
+ _bold = "\u001b[1m"
+ _gray = "\u001b[90m"
+ _green = "\u001b[32m"
+ _red = "\u001b[31m"
+ _reset = "\u001b[0m"
+ _underline = "\u001b[4m"
+ _yellow = "\u001b[33m"
+ _enabled: bool | None = None # None: fall back to agent detection (callers outside the `hf` CLI)
+
+ @classmethod
+ def set_enabled(cls, enabled: bool) -> None:
+ cls._enabled = enabled
+
+ @classmethod
+ def blue(cls, s: str) -> str:
+ return cls._format(s, cls._blue)
+
+ @classmethod
+ def bold(cls, s: str) -> str:
+ return cls._format(s, cls._bold)
+
+ @classmethod
+ def gray(cls, s: str) -> str:
+ return cls._format(s, cls._gray)
+
+ @classmethod
+ def green(cls, s: str) -> str:
+ return cls._format(s, cls._green)
+
+ @classmethod
+ def red(cls, s: str) -> str:
+ return cls._format(s, cls._bold + cls._red)
+
+ @classmethod
+ def underline(cls, s: str) -> str:
+ return cls._format(s, cls._underline)
+
+ @classmethod
+ def yellow(cls, s: str) -> str:
+ return cls._format(s, cls._yellow)
+
+ @classmethod
+ def _format(cls, s: str, code: str) -> str:
+ if os.environ.get("NO_COLOR"):
+ # See https://no-color.org/
+ return s
+ enabled = cls._enabled if cls._enabled is not None else not is_agent()
+ return f"{code}{s}{cls._reset}" if enabled else s
+
+
+def select_choice(prompt: str, choices: list[str]) -> int:
+ """Single-choice interactive prompt. Returns the index of the selected choice.
+
+ On a TTY, renders an arrow-key menu (Up/Down to move, Enter to confirm, 1-9 to pick
+ directly, Ctrl+C to abort). Falls back to a numbered `input()` prompt when raw
+ keyboard input is not available. Callers are responsible for not prompting at all in
+ non-interactive contexts.
+ """
+ if not choices:
+ raise ValueError("select_choice() requires at least one choice.")
+ if _supports_raw_keyboard():
+ return _select_with_arrows(prompt, choices)
+ return _select_with_numbers(prompt, choices)
+
+
+def _supports_raw_keyboard() -> bool:
+ if not (sys.stdin and sys.stdin.isatty() and sys.stdout.isatty()):
+ return False
+ if sys.platform == "win32":
+ return _enable_windows_vt_processing()
+ return True
+
+
+def _enable_windows_vt_processing() -> bool:
+ """Enable VT escape-sequence processing on the Windows console (off by default in legacy
+ cmd.exe/PowerShell windows, where the menu would otherwise render as literal `←[K` garbage)."""
+ ENABLE_VIRTUAL_TERMINAL_PROCESSING = 0x0004
+ STD_OUTPUT_HANDLE = -11
+ try:
+ kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
+ handle = kernel32.GetStdHandle(STD_OUTPUT_HANDLE)
+ mode = ctypes.c_uint32()
+ if not kernel32.GetConsoleMode(handle, ctypes.byref(mode)):
+ return False
+ return bool(kernel32.SetConsoleMode(handle, mode.value | ENABLE_VIRTUAL_TERMINAL_PROCESSING))
+ except Exception:
+ return False
+
+
+@contextmanager
+def _raw_terminal():
+ """Put the terminal in cbreak mode (read keypresses without Enter). No-op on Windows."""
+ if sys.platform == "win32":
+ yield
+ return
+
+ fd = sys.stdin.fileno()
+ old_settings = termios.tcgetattr(fd)
+ try:
+ tty.setcbreak(fd)
+ yield
+ finally:
+ termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+
+
+def _read_key() -> str:
+ """Read one keypress, normalizing arrow keys to "up"/"down"."""
+ if sys.platform == "win32":
+ char = msvcrt.getwch()
+ if char in ("\x00", "\xe0"): # arrow keys come as a two-character sequence
+ return {"H": "up", "P": "down"}.get(msvcrt.getwch(), "")
+ return char
+
+ # Read the file descriptor directly: `sys.stdin.read(1)` would buffer the whole escape
+ # sequence internally, making the fd look empty to `select()` below.
+ fd = sys.stdin.fileno()
+ char = os.read(fd, 1)
+ # Disambiguate a bare Escape from an escape sequence (e.g. "\x1b[A" for Up).
+ if char == b"\x1b" and select.select([fd], [], [], 0.05)[0]:
+ if os.read(fd, 1) == b"[" and select.select([fd], [], [], 0.05)[0]:
+ return {b"A": "up", b"B": "down"}.get(os.read(fd, 1), "")
+ return ""
+ return char.decode(errors="replace")
+
+
+def _select_with_arrows(prompt: str, choices: list[str]) -> int:
+ selected = 0
+ print(ANSI.bold(f"? {prompt}") + ANSI.gray(" [Use arrows, Enter to confirm]"))
+
+ def render() -> None:
+ for i, choice in enumerate(choices):
+ line = ANSI.green("> ") + ANSI.bold(choice) if i == selected else " " + choice
+ sys.stdout.write(f"\r\x1b[K{line}\n")
+ sys.stdout.flush()
+
+ try:
+ sys.stdout.write("\x1b[?25l") # hide cursor
+ render()
+ with _raw_terminal():
+ while True:
+ key = _read_key()
+ if key == "\x03":
+ # Ctrl+C: POSIX cbreak keeps ISIG so it never reaches here, but on Windows
+ # msvcrt.getwch() returns the raw character instead of raising.
+ raise KeyboardInterrupt
+ if key == "up":
+ selected = (selected - 1) % len(choices)
+ elif key == "down":
+ selected = (selected + 1) % len(choices)
+ elif key.isdecimal() and 1 <= int(key) <= len(choices):
+ selected = int(key) - 1
+ break
+ elif key in ("\r", "\n"):
+ break
+ else:
+ continue
+ sys.stdout.write(f"\x1b[{len(choices)}A") # move back to the first option line
+ render()
+ finally:
+ sys.stdout.write("\x1b[?25h") # show cursor
+ sys.stdout.flush()
+
+ # Collapse the menu into a single "? prompt answer" summary line, like gh does.
+ sys.stdout.write(f"\x1b[{len(choices) + 1}A\r\x1b[J")
+ print(ANSI.bold(f"? {prompt} ") + ANSI.green(choices[selected]))
+ return selected
+
+
+def _select_with_numbers(prompt: str, choices: list[str]) -> int:
+ print(f"? {prompt}")
+ for i, choice in enumerate(choices, start=1):
+ print(f" {i}. {choice}")
+ while True:
+ raw = input("Choice [1]: ").strip()
+ if not raw:
+ return 0
+ if raw.isdecimal() and 1 <= int(raw) <= len(choices):
+ return int(raw) - 1
+ print(f"Invalid choice. Enter a number between 1 and {len(choices)}.")
+
+
+def tabulate(
+ rows: list[list[str | int]],
+ headers: list[str],
+ alignments: dict[str, str] | None = None,
+) -> str:
+ """
+ Inspired by:
+
+ - stackoverflow.com/a/8356620/593036
+ - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
+ """
+ _ALIGN_MAP = {"left": "<", "right": ">"}
+ for row in rows:
+ if len(row) < len(headers):
+ raise IndexError(f"Row has {len(row)} values but expected {len(headers)} (headers: {headers})")
+ col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
+ col_aligns = [_ALIGN_MAP.get((alignments or {}).get(h, "left"), "<") for h in headers]
+ row_format = " ".join(f"{{:{a}{w}}}" for a, w in zip(col_aligns, col_widths))
+ lines = []
+ lines.append(row_format.format(*headers))
+ lines.append(row_format.format(*["-" * w for w in col_widths]))
+ for row in rows:
+ lines.append(row_format.format(*row))
+ return "\n".join(lines)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_typing.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..20db0e2d3ae9ae30a2a8a641e82a7869738fe2a9
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_typing.py
@@ -0,0 +1,91 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Handle typing imports based on system compatibility."""
+
+from collections.abc import Callable
+from types import UnionType
+from typing import Any, Literal, TypeVar, Union, get_args, get_origin
+
+
+UNION_TYPES: list[Any] = [Union, UnionType]
+
+
+HTTP_METHOD_T = Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"]
+
+# type hint meaning "function signature not changed by decorator"
+CallableT = TypeVar("CallableT", bound=Callable)
+
+_JSON_SERIALIZABLE_TYPES = (int, float, str, bool, type(None))
+
+
+def is_jsonable(obj: Any, _visited: set[int] | None = None) -> bool:
+ """Check if an object is JSON serializable.
+
+ This is a weak check, as it does not check for the actual JSON serialization, but only for the types of the object.
+ It works correctly for basic use cases but do not guarantee an exhaustive check.
+
+ Object is considered to be recursively json serializable if:
+ - it is an instance of int, float, str, bool, or NoneType
+ - it is a list or tuple and all its items are json serializable
+ - it is a dict and all its keys are strings and all its values are json serializable
+
+ Uses a visited set to avoid infinite recursion on circular references. If object has already been visited, it is
+ considered not json serializable.
+ """
+ # Initialize visited set to track object ids and detect circular references
+ if _visited is None:
+ _visited = set()
+
+ # Detect circular reference
+ obj_id = id(obj)
+ if obj_id in _visited:
+ return False
+
+ # Add current object to visited before recursive checks
+ _visited.add(obj_id)
+ try:
+ if isinstance(obj, _JSON_SERIALIZABLE_TYPES):
+ return True
+ if isinstance(obj, (list, tuple)):
+ return all(is_jsonable(item, _visited) for item in obj)
+ if isinstance(obj, dict):
+ return all(
+ isinstance(key, _JSON_SERIALIZABLE_TYPES) and is_jsonable(value, _visited)
+ for key, value in obj.items()
+ )
+ if hasattr(obj, "__json__"):
+ return True
+ return False
+ except RecursionError:
+ return False
+ finally:
+ # Remove the object id from visited to avoid side‑effects for other branches
+ _visited.discard(obj_id)
+
+
+def is_simple_optional_type(type_: type) -> bool:
+ """Check if a type is optional, i.e. Optional[Type] or Union[Type, None] or Type | None, where Type is a non-composite type."""
+ if get_origin(type_) in UNION_TYPES:
+ union_args = get_args(type_)
+ if len(union_args) == 2 and type(None) in union_args:
+ return True
+ return False
+
+
+def unwrap_simple_optional_type(optional_type: type) -> type:
+ """Unwraps a simple optional type, i.e. returns Type from Optional[Type]."""
+ for arg in get_args(optional_type):
+ if arg is not type(None):
+ return arg
+ raise ValueError(f"'{optional_type}' is not an optional type")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py
new file mode 100644
index 0000000000000000000000000000000000000000..281b8df403be45f4da690c6081d3228fd4a34318
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py
@@ -0,0 +1,210 @@
+# Copyright 2022-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to validate argument values in `huggingface_hub`."""
+
+import inspect
+import re
+import warnings
+from functools import wraps
+from itertools import chain
+from typing import Any
+
+from huggingface_hub.errors import HFValidationError
+
+from ._typing import CallableT
+
+
+REPO_ID_REGEX = re.compile(
+ r"""
+ (\b[\w\-.]{1,96}\b/)? # optional namespace (username or organization)
+ \b # starts with a word boundary
+ [\w\-.]{1,96} # repo_name: alphanumeric + . _ -
+ \b # ends with a word boundary
+ """,
+ # `\w` matches Unicode word characters by default, but the allowed charset is
+ # ASCII-only (see the "Rules" docstring below and the internal Hub validation
+ # it mirrors), so `café` or `文件夹` would otherwise pass this regex unrejected.
+ flags=re.VERBOSE | re.ASCII,
+)
+
+
+def validate_hf_hub_args(fn: CallableT) -> CallableT:
+ """Validate values received as argument for any public method of `huggingface_hub`.
+
+ The goal of this decorator is to harmonize validation of arguments reused
+ everywhere. By default, all defined validators are tested.
+
+ Validators:
+ - [`~utils.validate_repo_id`]: `repo_id` must be `"repo_name"`
+ or `"namespace/repo_name"`. Namespace is a username or an organization.
+ - [`~utils.smoothly_deprecate_legacy_arguments`]: Ignore `proxies` when downloading files (should be set globally).
+
+ Example:
+ ```py
+ >>> from huggingface_hub.utils import validate_hf_hub_args
+
+ >>> @validate_hf_hub_args
+ ... def my_cool_method(repo_id: str):
+ ... print(repo_id)
+
+ >>> my_cool_method(repo_id="valid_repo_id")
+ valid_repo_id
+
+ >>> my_cool_method("other..repo..id")
+ huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'.
+
+ >>> my_cool_method(repo_id="other..repo..id")
+ huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'.
+ ```
+
+ Raises:
+ [`~utils.HFValidationError`]:
+ If an input is not valid.
+ """
+ # TODO: add an argument to opt-out validation for specific argument?
+ signature = inspect.signature(fn)
+
+ @wraps(fn)
+ def _inner_fn(*args, **kwargs):
+ for arg_name, arg_value in chain(
+ zip(signature.parameters, args), # Args values
+ kwargs.items(), # Kwargs values
+ ):
+ if arg_name in ["repo_id", "from_id", "to_id"]:
+ validate_repo_id(arg_value)
+
+ kwargs = smoothly_deprecate_legacy_arguments(fn_name=fn.__name__, kwargs=kwargs)
+
+ return fn(*args, **kwargs)
+
+ return _inner_fn # type: ignore
+
+
+def validate_repo_id(repo_id: str | None) -> None:
+ """Validate `repo_id` is valid.
+
+ This is not meant to replace the proper validation made on the Hub but rather to
+ avoid local inconsistencies whenever possible (example: passing `repo_type` in the
+ `repo_id` is forbidden).
+
+ Rules:
+ - Between 1 and 96 characters.
+ - Either "repo_name" or "namespace/repo_name"
+ - [a-zA-Z0-9] or "-", "_", "."
+ - "--" and ".." are forbidden
+
+ Valid: `"foo"`, `"foo/bar"`, `"123"`, `"Foo-BAR_foo.bar123"`
+
+ Not valid: `"datasets/foo/bar"`, `".repo_id"`, `"foo--bar"`, `"foo.git"`
+
+ Example:
+ ```py
+ >>> from huggingface_hub.utils import validate_repo_id
+ >>> validate_repo_id(repo_id="valid_repo_id")
+ >>> validate_repo_id(repo_id="other..repo..id")
+ huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'.
+ ```
+
+ Discussed in https://github.com/huggingface/huggingface_hub/issues/1008.
+ In moon-landing (internal repository):
+ - https://github.com/huggingface/moon-landing/blob/main/server/lib/Names.ts#L27
+ - https://github.com/huggingface/moon-landing/blob/main/server/views/components/NewRepoForm/NewRepoForm.svelte#L138
+ """
+ if repo_id is None:
+ # Repo id is not always required
+ return
+
+ if not isinstance(repo_id, str):
+ # Typically, a Path is not a repo_id
+ raise HFValidationError(f"Repo id must be a string, not {type(repo_id)}: '{repo_id}'.")
+
+ if repo_id.count("/") > 1:
+ raise HFValidationError(
+ "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
+ f" '{repo_id}'. Use `repo_type` argument if needed."
+ )
+
+ if not REPO_ID_REGEX.fullmatch(repo_id):
+ raise HFValidationError(
+ "Repo id must use alphanumeric chars, '-', '_' or '.'."
+ " The name cannot start or end with '-' or '.' and the maximum length is 96:"
+ f" '{repo_id}'."
+ )
+
+ if "--" in repo_id or ".." in repo_id:
+ raise HFValidationError(f"Cannot have -- or .. in repo_id: '{repo_id}'.")
+
+ if repo_id.endswith(".git"):
+ raise HFValidationError(f"Repo_id cannot end by '.git': '{repo_id}'.")
+
+
+def smoothly_deprecate_legacy_arguments(fn_name: str, kwargs: dict[str, Any]) -> dict[str, Any]:
+ """Smoothly deprecate legacy arguments in the `huggingface_hub` codebase.
+
+ This function ignores some deprecated arguments from the kwargs and warns the user they are ignored.
+ The goal is to avoid breaking existing code while guiding the user to the new way of doing things.
+
+ List of deprecated arguments:
+ - `proxies`:
+ To set up proxies, user must either use the HTTP_PROXY environment variable or configure the `httpx2.Client`
+ manually using the [`set_client_factory`] function.
+
+ In huggingface_hub 0.x, `proxies` was a dictionary directly passed to `requests.request`.
+ Since huggingface_hub 1.x, per-request `proxies` are no longer supported.
+ In particular, it is not possible to configure proxies on a per-request basis. The solution is to configure
+ it globally using the [`set_client_factory`] function or using the HTTP_PROXY environment variable.
+
+ For more details, see:
+ - https://httpx2.pydantic.dev/advanced/proxies/
+
+ - `resume_download`: deprecated without replacement. `huggingface_hub` always resumes downloads whenever possible.
+ - `force_filename`: deprecated without replacement. Filename is always the same as on the Hub.
+ - `local_dir_use_symlinks`: deprecated without replacement. Downloading to a local directory does not use symlinks anymore.
+ """
+ new_kwargs = kwargs.copy() # do not mutate input !
+
+ # proxies
+ proxies = new_kwargs.pop("proxies", None) # remove from kwargs
+ if proxies is not None:
+ warnings.warn(
+ f"The `proxies` argument is ignored in `{fn_name}`. To set up proxies, use the HTTP_PROXY / HTTPS_PROXY"
+ " environment variables or configure the `httpx2.Client` manually using `huggingface_hub.set_client_factory`."
+ " See https://httpx2.pydantic.dev/advanced/proxies/ for more details."
+ )
+
+ # resume_download
+ resume_download = new_kwargs.pop("resume_download", None) # remove from kwargs
+ if resume_download is not None:
+ warnings.warn(
+ f"The `resume_download` argument is deprecated and ignored in `{fn_name}`. Downloads always resume"
+ " whenever possible."
+ )
+
+ # force_filename
+ force_filename = new_kwargs.pop("force_filename", None) # remove from kwargs
+ if force_filename is not None:
+ warnings.warn(
+ f"The `force_filename` argument is deprecated and ignored in `{fn_name}`. Filename is always the same "
+ "as on the Hub."
+ )
+
+ # local_dir_use_symlinks
+ local_dir_use_symlinks = new_kwargs.pop("local_dir_use_symlinks", None) # remove from kwargs
+ if local_dir_use_symlinks is not None:
+ warnings.warn(
+ f"The `local_dir_use_symlinks` argument is deprecated and ignored in `{fn_name}`. Downloading to a local"
+ " directory does not use symlinks anymore."
+ )
+
+ return new_kwargs
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_verification.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_verification.py
new file mode 100644
index 0000000000000000000000000000000000000000..a954c5daba22e8be86dd0292153905fcb75a77c8
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_verification.py
@@ -0,0 +1,167 @@
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Literal, TypedDict
+
+from .. import constants
+from ..file_download import repo_folder_name
+from .sha import git_hash, sha_fileobj
+
+
+if TYPE_CHECKING:
+ from ..hf_api import RepoFile
+
+# using fullmatch for clarity and strictness
+_REGEX_COMMIT_HASH = re.compile(r"[0-9a-f]{40}")
+
+
+# Typed structure describing a checksum mismatch
+class Mismatch(TypedDict):
+ path: str
+ expected: str
+ actual: str
+ algorithm: str
+
+
+HashAlgo = Literal["sha256", "git-sha1"]
+
+
+@dataclass(frozen=True)
+class FolderVerification:
+ revision: str
+ checked_count: int
+ mismatches: list[Mismatch]
+ missing_paths: list[str]
+ extra_paths: list[str]
+ verified_path: Path
+
+
+def collect_local_files(root: Path) -> dict[str, Path]:
+ """
+ Return a mapping of repo-relative path -> absolute path for all files under `root`.
+ """
+ return {p.relative_to(root).as_posix(): p for p in root.rglob("*") if p.is_file()}
+
+
+def _resolve_commit_hash_from_cache(storage_folder: Path, revision: str | None) -> str:
+ """
+ Resolve a commit hash from a cache repo folder and an optional revision.
+ """
+ if revision and _REGEX_COMMIT_HASH.fullmatch(revision):
+ return revision
+
+ refs_dir = storage_folder / "refs"
+ snapshots_dir = storage_folder / "snapshots"
+
+ if revision:
+ ref_path = refs_dir / revision
+ if ref_path.is_file():
+ return ref_path.read_text(encoding="utf-8").strip()
+ raise ValueError(f"Revision '{revision}' could not be resolved in cache (expected file '{ref_path}').")
+
+ # No revision provided: try common defaults
+ main_ref = refs_dir / "main"
+ if main_ref.is_file():
+ return main_ref.read_text(encoding="utf-8").strip()
+
+ if not snapshots_dir.is_dir():
+ raise ValueError(f"Cache repo is missing snapshots directory: {snapshots_dir}. Provide --revision explicitly.")
+
+ candidates = [p.name for p in snapshots_dir.iterdir() if p.is_dir() and _REGEX_COMMIT_HASH.fullmatch(p.name)]
+ if len(candidates) == 1:
+ return candidates[0]
+
+ raise ValueError(
+ "Ambiguous cached revision: multiple snapshots found and no refs to disambiguate. Please pass --revision."
+ )
+
+
+def compute_file_hash(path: Path, algorithm: HashAlgo) -> str:
+ """
+ Compute the checksum of a local file using the requested algorithm.
+ """
+
+ with path.open("rb") as stream:
+ if algorithm == "sha256":
+ return sha_fileobj(stream).hex()
+ if algorithm == "git-sha1":
+ return git_hash(stream.read())
+ raise ValueError(f"Unsupported hash algorithm: {algorithm}")
+
+
+def verify_maps(
+ *,
+ remote_by_path: dict[str, "RepoFile"],
+ local_by_path: dict[str, Path],
+ revision: str,
+ verified_path: Path,
+) -> FolderVerification:
+ """Compare remote entries and local files and return a verification result."""
+ remote_paths = set(remote_by_path)
+ local_paths = set(local_by_path)
+
+ missing = sorted(remote_paths - local_paths)
+ extra = sorted(local_paths - remote_paths)
+ both = sorted(remote_paths & local_paths)
+
+ mismatches: list[Mismatch] = []
+
+ for rel_path in both:
+ remote_entry = remote_by_path[rel_path]
+ local_path = local_by_path[rel_path]
+
+ lfs = getattr(remote_entry, "lfs", None)
+ lfs_sha = getattr(lfs, "sha256", None) if lfs is not None else None
+ if lfs_sha is None and isinstance(lfs, dict):
+ lfs_sha = lfs.get("sha256")
+ if lfs_sha:
+ algorithm: HashAlgo = "sha256"
+ expected = str(lfs_sha).lower()
+ else:
+ blob_id = remote_entry.blob_id # type: ignore
+ algorithm = "git-sha1"
+ expected = str(blob_id).lower()
+
+ actual = compute_file_hash(local_path, algorithm)
+
+ if actual != expected:
+ mismatches.append(Mismatch(path=rel_path, expected=expected, actual=actual, algorithm=algorithm))
+
+ return FolderVerification(
+ revision=revision,
+ checked_count=len(both),
+ mismatches=mismatches,
+ missing_paths=missing,
+ extra_paths=extra,
+ verified_path=verified_path,
+ )
+
+
+def resolve_local_root(
+ *,
+ repo_id: str,
+ repo_type: str,
+ revision: str | None,
+ cache_dir: Path | None,
+ local_dir: Path | None,
+) -> tuple[Path, str]:
+ """
+ Resolve the root directory to scan locally and the remote revision to verify.
+ """
+ if local_dir is not None:
+ root = Path(local_dir).expanduser().resolve()
+ if not root.is_dir():
+ raise ValueError(f"Local directory does not exist or is not a directory: {root}")
+ return root, (revision or constants.DEFAULT_REVISION)
+
+ cache_root = Path(cache_dir or constants.HF_HUB_CACHE).expanduser().resolve()
+ storage_folder = cache_root / repo_folder_name(repo_id=repo_id, repo_type=repo_type)
+ if not storage_folder.exists():
+ raise ValueError(
+ f"Repo is not present in cache: {storage_folder}. Use 'hf download' first or pass --local-dir."
+ )
+ commit = _resolve_commit_hash_from_cache(storage_folder, revision)
+ snapshot_dir = storage_folder / "snapshots" / commit
+ if not snapshot_dir.is_dir():
+ raise ValueError(f"Snapshot directory does not exist for revision '{commit}': {snapshot_dir}.")
+ return snapshot_dir, commit
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet.py
new file mode 100644
index 0000000000000000000000000000000000000000..9847b9c933843450d3d93dbc4c512d4d3e8df3be
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet.py
@@ -0,0 +1,331 @@
+import os
+import re
+import threading
+import time
+from collections.abc import Mapping
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any
+
+import httpx2
+
+from .. import constants
+from . import hf_raise_for_status, http_backoff, validate_hf_hub_args
+
+
+_REGEX_XET_HASH = re.compile(r"^[0-9a-fA-F]{64}$")
+
+XET_CONNECTION_INFO_SAFETY_PERIOD = 60 # seconds
+XET_CONNECTION_INFO_CACHE_SIZE = 1_000
+XET_CONNECTION_INFO_CACHE: dict[str, "XetConnectionInfo"] = {}
+_XET_CONNECTION_INFO_LOCK = threading.Lock() # guards the cache dict and the per-key lock registry
+_XET_CONNECTION_INFO_KEY_LOCKS: dict[str, threading.Lock] = {}
+
+
+def is_valid_xet_hash(xet_hash: str) -> bool:
+ """Return whether `xet_hash` looks like a real Xet content hash (64 hex chars)."""
+ return bool(_REGEX_XET_HASH.fullmatch(xet_hash))
+
+
+class XetTokenType(str, Enum):
+ READ = "read"
+ WRITE = "write"
+
+
+@dataclass(frozen=True)
+class XetFileData:
+ file_hash: str
+ refresh_route: str
+
+
+@dataclass(frozen=True)
+class XetConnectionInfo:
+ access_token: str
+ expiration_unix_epoch: int
+ endpoint: str
+
+
+def parse_xet_file_data_from_response(response: httpx2.Response, endpoint: str | None = None) -> XetFileData | None:
+ """
+ Parse XET file metadata from an HTTP response.
+
+ This function extracts XET file metadata from the HTTP headers or HTTP links
+ of a given response object. If the required metadata is not found, it returns `None`.
+
+ Args:
+ response (`httpx2.Response`):
+ The HTTP response object containing headers dict and links dict to extract the XET metadata from.
+ Returns:
+ `Optional[XetFileData]`:
+ An instance of `XetFileData` containing the file hash and refresh route if the metadata
+ is found. Returns `None` if the required metadata is missing.
+ """
+ if response is None:
+ return None
+ try:
+ file_hash = response.headers[constants.HUGGINGFACE_HEADER_X_XET_HASH]
+
+ if constants.HUGGINGFACE_HEADER_LINK_XET_AUTH_KEY in response.links:
+ refresh_route = response.links[constants.HUGGINGFACE_HEADER_LINK_XET_AUTH_KEY]["url"]
+ else:
+ refresh_route = response.headers[constants.HUGGINGFACE_HEADER_X_XET_REFRESH_ROUTE]
+ except KeyError:
+ return None
+ endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+ if refresh_route.startswith(constants.HUGGINGFACE_CO_URL_HOME):
+ refresh_route = refresh_route.replace(constants.HUGGINGFACE_CO_URL_HOME.rstrip("/"), endpoint.rstrip("/"))
+ return XetFileData(
+ file_hash=file_hash,
+ refresh_route=refresh_route,
+ )
+
+
+def parse_xet_connection_info_from_headers(headers: Mapping[str, str]) -> XetConnectionInfo | None:
+ """
+ Parse XET connection info from the HTTP headers or return None if not found.
+ Args:
+ headers (`dict`):
+ HTTP headers to extract the XET metadata from.
+ Returns:
+ `XetConnectionInfo` or `None`:
+ The information needed to connect to the XET storage service.
+ Returns `None` if the headers do not contain the XET connection info.
+ """
+ try:
+ endpoint = headers[constants.HUGGINGFACE_HEADER_X_XET_ENDPOINT]
+ access_token = headers[constants.HUGGINGFACE_HEADER_X_XET_ACCESS_TOKEN]
+ expiration_unix_epoch = int(headers[constants.HUGGINGFACE_HEADER_X_XET_EXPIRATION])
+ except (KeyError, ValueError, TypeError):
+ return None
+
+ return XetConnectionInfo(
+ endpoint=endpoint,
+ access_token=access_token,
+ expiration_unix_epoch=expiration_unix_epoch,
+ )
+
+
+@validate_hf_hub_args
+def refresh_xet_connection_info(
+ *,
+ file_data: XetFileData,
+ headers: dict[str, str],
+) -> XetConnectionInfo:
+ """
+ Utilizes the information in the parsed metadata to request the Hub xet connection information.
+ This includes the access token, expiration, and XET service URL.
+ Args:
+ file_data: (`XetFileData`):
+ The file data needed to refresh the xet connection information.
+ headers (`dict[str, str]`):
+ Headers to use for the request, including authorization headers and user agent.
+ Returns:
+ `XetConnectionInfo`:
+ The connection information needed to make the request to the xet storage service.
+ Raises:
+ [`~utils.HfHubHTTPError`]
+ If the Hub API returned an error.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If the Hub API response is improperly formatted.
+ """
+ if file_data.refresh_route is None:
+ raise ValueError("The provided xet metadata does not contain a refresh endpoint.")
+ return _fetch_xet_connection_info_with_url(file_data.refresh_route, headers)
+
+
+@validate_hf_hub_args
+def _fetch_xet_connection_info_with_url(
+ url: str,
+ headers: dict[str, str],
+) -> XetConnectionInfo:
+ """
+ Requests the xet connection info from the supplied URL. This includes the
+ access token, expiration time, and endpoint to use for the xet storage service.
+
+ Result is cached to avoid redundant requests.
+
+ Args:
+ url: (`str`):
+ The access token endpoint URL.
+ headers (`dict[str, str]`):
+ Headers to use for the request, including authorization headers and user agent.
+ Returns:
+ `XetConnectionInfo`:
+ The connection information needed to make the request to the xet storage service.
+ Raises:
+ [`~utils.HfHubHTTPError`]
+ If the Hub API returned an error.
+ [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+ If the Hub API response is improperly formatted.
+ """
+ # Check cache first
+ cache_key = _cache_key(url, headers)
+ cached_info = XET_CONNECTION_INFO_CACHE.get(cache_key)
+ if cached_info is not None:
+ if not _is_expired(cached_info):
+ return cached_info
+
+ # A per-key lock collapses concurrent workers into a single token request per expired/missing
+ # key, without blocking fetches for other keys (e.g. other repos downloaded in parallel).
+ with _key_lock(cache_key):
+ cached_info = XET_CONNECTION_INFO_CACHE.get(cache_key)
+ if cached_info is not None and not _is_expired(cached_info):
+ return cached_info
+
+ # Fetch from server
+ resp = http_backoff("GET", url, headers=headers)
+ hf_raise_for_status(resp)
+
+ metadata = parse_xet_connection_info_from_headers(resp.headers)
+ if metadata is None:
+ raise ValueError("Xet headers have not been correctly set by the server.")
+
+ with _XET_CONNECTION_INFO_LOCK:
+ # Delete expired cache entries
+ for k, v in list(XET_CONNECTION_INFO_CACHE.items()):
+ if _is_expired(v):
+ XET_CONNECTION_INFO_CACHE.pop(k, None)
+
+ # Enforce cache size limit
+ if len(XET_CONNECTION_INFO_CACHE) >= XET_CONNECTION_INFO_CACHE_SIZE:
+ XET_CONNECTION_INFO_CACHE.pop(next(iter(XET_CONNECTION_INFO_CACHE)))
+
+ # Update cache
+ XET_CONNECTION_INFO_CACHE[cache_key] = metadata
+
+ return metadata
+
+
+def _cache_key(url: str, headers: dict[str, str]) -> str:
+ """Return a unique cache key for the given request parameters."""
+ lower_headers = {k.lower(): v for k, v in headers.items()} # casing is not guaranteed here
+ auth_header = lower_headers.get("authorization", "")
+ return f"{url}|{auth_header}"
+
+
+def _key_lock(cache_key: str) -> threading.Lock:
+ """Return the lock guarding token fetches for `cache_key`, creating it if needed."""
+ with _XET_CONNECTION_INFO_LOCK:
+ return _XET_CONNECTION_INFO_KEY_LOCKS.setdefault(cache_key, threading.Lock())
+
+
+def _is_expired(connection_info: XetConnectionInfo) -> bool:
+ """Check if the given XET connection info is expired."""
+ return connection_info.expiration_unix_epoch <= int(time.time()) + XET_CONNECTION_INFO_SAFETY_PERIOD
+
+
+@validate_hf_hub_args
+def xet_connection_info_refresh_url(
+ *,
+ token_type: XetTokenType,
+ repo_id: str,
+ repo_type: str,
+ revision: str | None = None,
+ endpoint: str | None = None,
+) -> str:
+ """
+ Build the URL used to fetch or refresh a Xet access token for a given repo.
+ Args:
+ token_type (`XetTokenType`):
+ Type of the token to request: `"read"` or `"write"`.
+ repo_id (`str`):
+ A namespace (user or an organization) and a repo name separated by a `/`.
+ repo_type (`str`):
+ Type of the repo (e.g. `"model"`, `"dataset"`, `"space"`, `"bucket"`).
+ revision (`str`, `optional`):
+ The revision of the repo to get the token for.
+ endpoint (`str`, `optional`):
+ The endpoint to use for the request. Defaults to the Hub endpoint.
+ Returns:
+ `str`:
+ The fully-qualified URL of the token refresh endpoint.
+ """
+ endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+ url = f"{endpoint}/api/{repo_type}s/{repo_id}/xet-{token_type.value}-token"
+ if repo_type != "bucket" or revision is not None:
+ # On "bucket" repo type, the revision never needed => don't use it
+ # Otherwise, use the revision.
+ # Note: when creating a PR on a git-based repo, user needs write access but they don't know the revision in advance.
+ # => pass "/None" in URL and server will return a token for PR refs.
+ url += f"/{revision}"
+ return url
+
+
+class XetSessionHolder:
+ """Holds an optional XetSession; supports safe re-creation after sigint_abort or fork.
+
+ Thread-safe: a ``threading.Lock`` guards all state mutations, which matters
+ for free-threaded Python (3.14t) where multiple threads can race on ``get()``
+ or ``sigint_abort()`` without the GIL serialising them.
+ """
+
+ def __init__(self) -> None:
+ self._lock = threading.Lock()
+ self._session: Any = None
+ self._session_pid: int | None = None
+
+ def get(self):
+ """Return the current session, creating one if needed.
+
+ Fork-safe: if the current process PID differs from the PID that created
+ the session (i.e. we are in a forked child), the old session is discarded
+ and a fresh session is created for this process.
+ """
+ with self._lock:
+ current_pid = os.getpid()
+
+ if self._session is not None and self._session_pid != current_pid:
+ # Fork detected. Discard the parent's session; the Rust Drop will
+ # call discard_runtime() (std::mem::forget) rather than the normal
+ # shutdown path, so this returns immediately without blocking.
+ self._session = None
+
+ if self._session is None:
+ from hf_xet import XetSession
+
+ self._session = XetSession()
+ self._session_pid = current_pid
+
+ return self._session
+
+ def sigint_abort(self):
+ """Abort the current session and clear it so the next get() creates a fresh one."""
+ with self._lock:
+ if self._session is not None:
+ try:
+ self._session.sigint_abort()
+ except Exception:
+ pass
+ self._session = None
+ self._session_pid = None
+
+
+_GLOBAL_XET_HOLDER = XetSessionHolder()
+
+
+def get_xet_session():
+ """Return the global :class:`hf_xet.XetSession`, creating it on first call.
+
+ The session is shared across all calls within a process, just as the HTTP
+ client returned by :func:`~huggingface_hub.utils._http.get_session` is shared.
+ It is created lazily and is fork-safe and thread-safe.
+ """
+ return _GLOBAL_XET_HOLDER.get()
+
+
+def xet_headers_without_auth(headers: dict[str, str]) -> dict[str, str]:
+ """Return a copy of headers with the authorization header removed.
+
+ Xet storage requests use a short-lived xet access token for auth, so the
+ Hub authorization header must not be forwarded to xet storage endpoints.
+ """
+ return {key: value for key, value in headers.items() if key.lower() != "authorization"}
+
+
+def abort_xet_session():
+ """Abort the global xet session after a KeyboardInterrupt.
+
+ Cancels any in-flight Rust operation and clears the session so the next
+ call to :func:`get_xet_session` starts fresh (notebook-friendly).
+ """
+ _GLOBAL_XET_HOLDER.sigint_abort()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet_progress_reporting.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet_progress_reporting.py
new file mode 100644
index 0000000000000000000000000000000000000000..e33adaff590dcfc2d33713642247bafe0882969f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/_xet_progress_reporting.py
@@ -0,0 +1,431 @@
+from collections import OrderedDict
+from typing import Any
+
+from . import is_google_colab, is_notebook
+from .tqdm import _create_progress_bar, tqdm
+
+
+def _format_speed_postfix(speed: float | None) -> str:
+ s = tqdm.format_sizeof(speed) if speed is not None else "???"
+ return f"{s}B/s ".rjust(10, " ")
+
+
+# Transfer byte count is hard to predict (dedup/compression), so we omit a total and show bytes only.
+XET_TRANSFER_BAR_FORMAT = "{desc}: {bar}| {n_fmt:>5}B{postfix:>12}"
+XET_BYTES_BAR_FORMAT = "{l_bar}{bar}| {n_fmt:>5}B / {total_fmt:>5}B{postfix:>12}"
+
+_NUM_OVERVIEW_BARS = 2 # data_processing + upload (always shown)
+_NUM_SHARD_BARS = 1 # single Validating bar (lazy; covers shard upload/validate/sync)
+
+
+def _set_monotonic_total(bar, total: int | None) -> None:
+ if total is None or not hasattr(bar, "total"):
+ return
+ bar.total = max(bar.total or 0, total)
+
+
+def _update_transfer_bar(bar, inc: int) -> None:
+ """Update the transfer bar and grow its hidden total so the bar graphic advances.
+
+ Network bytes are hard to predict (dedup/compression), so the display omits a denominator.
+ tqdm still needs an internal total for the bar width — seeded from file size when known,
+ then expanded here if bytes received exceed that estimate.
+ """
+ n_after = getattr(bar, "n", 0) + inc
+ current_total = getattr(bar, "total", 0) or 0
+ if n_after > 0 and current_total < n_after:
+ bar.total = max(current_total, int(n_after * 1.25) + 1)
+ bar.update(inc)
+
+
+def _finish_transfer_bar(bar) -> None:
+ """Snap the transfer bar to 100% when downloading stops.
+
+ Transfer totals are seeded from file size, but actual network bytes are often lower.
+ Set ``total = n`` so the bar fills completely instead of stopping partway.
+ """
+ n = getattr(bar, "n", 0)
+ if n > 0 and hasattr(bar, "total") and bar.total != n:
+ bar.total = n
+ bar.refresh()
+
+
+def _set_aggregate_rate_postfix(bar) -> None:
+ """Show a shared bar's own throughput as its rate.
+
+ When many files feed one bar (snapshot download), each file only knows its own per-item rate — a
+ fraction of the total. Deriving the rate from the shared bar's aggregated byte count reports the
+ true combined speed instead of whichever single file reported last.
+ """
+ bar.set_postfix_str(_format_speed_postfix(bar.format_dict.get("rate")), refresh=False)
+
+
+class XetDownloadProgressReporter:
+ """Dual progress bars for Xet downloads: network transfer and file reconstruction.
+
+ ``total_transfer_bytes_completed`` tracks bytes received from the network (updated continuously).
+ ``total_bytes_completed`` tracks bytes written to disk (updated after buffered chunks are flushed).
+ Showing both bars gives responsive feedback on slow connections where reconstruction lags behind transfer.
+ """
+
+ def __init__(
+ self,
+ *,
+ reconstruction_desc: str,
+ transfer_desc: str = "Downloading bytes",
+ total: int | None = None,
+ log_level: int,
+ name: str | None = None,
+ tqdm_class: type | None = None,
+ external_reconstruction_bar: Any | None = None,
+ position: int = 0,
+ ):
+ self._prev_bytes_completed = 0
+ self._prev_transfer_bytes_completed = 0
+
+ cls = tqdm_class or tqdm
+ routes_transfer_via_reconstruction = external_reconstruction_bar is not None and callable(
+ getattr(external_reconstruction_bar, "update_transfer", None)
+ )
+ uses_aggregated_tqdm_class = external_reconstruction_bar is None and callable(
+ getattr(cls, "update_transfer", None)
+ )
+
+ if external_reconstruction_bar is not None:
+ self.reconstruction_bar = external_reconstruction_bar
+ self._owns_reconstruction_bar = False
+ else:
+ self.reconstruction_bar = _create_progress_bar(
+ cls=cls, # ty: ignore[invalid-argument-type]
+ log_level=log_level,
+ name=name,
+ desc=reconstruction_desc,
+ total=total,
+ unit="B",
+ unit_scale=True,
+ position=position + 1,
+ bar_format=XET_BYTES_BAR_FORMAT,
+ leave=True,
+ )
+ self._owns_reconstruction_bar = True
+
+ if routes_transfer_via_reconstruction or uses_aggregated_tqdm_class:
+ self.transfer_bar = self.reconstruction_bar
+ self._owns_transfer_bar = False
+ elif external_reconstruction_bar is not None:
+ self.transfer_bar = None
+ self._owns_transfer_bar = False
+ else:
+ self.transfer_bar = _create_progress_bar(
+ cls=cls, # ty: ignore[invalid-argument-type]
+ log_level=log_level,
+ name=f"{name}.transfer" if name else None,
+ desc=transfer_desc,
+ total=total,
+ unit="B",
+ unit_scale=True,
+ position=position,
+ bar_format=XET_TRANSFER_BAR_FORMAT,
+ leave=True,
+ )
+ self._owns_transfer_bar = True
+
+ @property
+ def _aggregated(self) -> bool:
+ return self.transfer_bar is not None and self.transfer_bar is self.reconstruction_bar
+
+ def update_progress(self, group_report, _item_reports: dict | None = None) -> None:
+ bytes_inc = max(0, group_report.total_bytes_completed - self._prev_bytes_completed)
+ transfer_inc = max(0, group_report.total_transfer_bytes_completed - self._prev_transfer_bytes_completed)
+ self._prev_bytes_completed = group_report.total_bytes_completed
+ self._prev_transfer_bytes_completed = group_report.total_transfer_bytes_completed
+
+ if bytes_inc > 0:
+ self.reconstruction_bar.update(bytes_inc)
+ self.reconstruction_bar.set_postfix_str(
+ _format_speed_postfix(group_report.total_bytes_completion_rate), refresh=False
+ )
+
+ if transfer_inc > 0 and self.transfer_bar is not None:
+ if self._aggregated:
+ self.reconstruction_bar.update_transfer(transfer_inc)
+ self.reconstruction_bar.set_transfer_postfix_str(
+ _format_speed_postfix(group_report.total_transfer_bytes_completion_rate), refresh=False
+ )
+ else:
+ _update_transfer_bar(self.transfer_bar, transfer_inc)
+ self.transfer_bar.set_postfix_str(
+ _format_speed_postfix(group_report.total_transfer_bytes_completion_rate), refresh=False
+ )
+
+ if group_report.total_bytes:
+ _set_monotonic_total(self.reconstruction_bar, group_report.total_bytes)
+
+ def close(self) -> None:
+ """Close bars owned by this reporter.
+
+ Standalone downloads finish the transfer bar first (snap hidden total to ``n``), then close it.
+ Aggregated and external bars (e.g. snapshot ``_AggregatedTqdm`` or a reused ``_tqdm_bar``) are left
+ open — their parent caller owns their lifecycle.
+ """
+ if self.transfer_bar is not None and self._owns_transfer_bar:
+ _finish_transfer_bar(self.transfer_bar)
+ if hasattr(self.transfer_bar, "close"):
+ self.transfer_bar.close()
+ if self._owns_reconstruction_bar and hasattr(self.reconstruction_bar, "close"):
+ self.reconstruction_bar.close()
+
+ def __enter__(self) -> "XetDownloadProgressReporter":
+ return self
+
+ def __exit__(self, *args) -> None:
+ self.close()
+
+
+class XetUploadProgressReporter:
+ """
+ Reports on progress for Xet uploads.
+
+ Shows summary progress bars when running in notebooks or GUIs, and detailed per-file progress in console environments.
+ """
+
+ def __init__(self, n_lines: int = 10, description_width: int = 30, total_files: int | None = None):
+ self.n_lines = n_lines
+ self.description_width = description_width
+ self.total_files = total_files
+
+ self.per_file_progress = is_google_colab() or not is_notebook()
+
+ _nrows = (
+ _NUM_OVERVIEW_BARS + n_lines + _NUM_SHARD_BARS + 1
+ if self.per_file_progress
+ else _NUM_OVERVIEW_BARS + _NUM_SHARD_BARS + 1
+ )
+
+ self.tqdm_settings = {
+ "unit": "B",
+ "unit_scale": True,
+ "leave": True,
+ "unit_divisor": 1000,
+ "nrows": _nrows,
+ "miniters": 1,
+ "bar_format": XET_BYTES_BAR_FORMAT,
+ }
+
+ # Percentage only — validation-entry totals are opaque Xet internals.
+ self.percent_tqdm_settings = {
+ "unit": "",
+ "unit_scale": False,
+ "leave": True,
+ "nrows": _nrows,
+ "miniters": 1,
+ "bar_format": "{desc}: {bar}| {percentage:3.0f}%{postfix:>12}",
+ }
+
+ # Overview bars (always shown)
+ self.data_processing_bar = tqdm(
+ total=0, desc=self.format_desc("Processing Files (0 / 0)", False), position=0, **self.tqdm_settings
+ )
+
+ self.upload_bar = tqdm(
+ total=0, desc=self.format_desc("New Data Upload", False), position=1, **self.tqdm_settings
+ )
+
+ # Single Validating bar created lazily on the first shard event.
+ # Covers shard upload / validation / sync without exposing "shard" jargon.
+ # Positioned dynamically after the active per-file bars (see _validating_bar_position).
+ self.validating_bar: tqdm | None = None
+
+ self.known_items: set[str] = set()
+ self.completed_items: set[str] = set()
+
+ # Track previous absolute values to compute increments
+ self._prev_bytes_completed: int = 0
+ self._prev_transfer_bytes_completed: int = 0
+
+ # Item bars (scrolling view); positions start at _NUM_OVERVIEW_BARS
+ self.item_state: OrderedDict[str, Any] = OrderedDict()
+ self.current_bars: list = [None] * self.n_lines
+
+ def format_desc(self, name: str, indent: bool) -> str:
+ """
+ if name is longer than width characters, prints ... at the start and then the last width-3 characters of the name, otherwise
+ the whole name right justified into description_width characters. Also adds some padding.
+ """
+
+ if not self.per_file_progress:
+ # Here we just use the defaults.
+ return name
+
+ padding = " " if indent else ""
+ width = self.description_width - len(padding)
+
+ if len(name) > width:
+ name = f"...{name[-(width - 3) :]}"
+
+ return f"{padding}{name.ljust(width)}"
+
+ def _validating_bar_position(self) -> int:
+ """Place Validating right after overview bars and any active per-file bars."""
+ if not self.per_file_progress:
+ return _NUM_OVERVIEW_BARS
+ n_active = sum(1 for bar in self.current_bars if bar is not None)
+ return _NUM_OVERVIEW_BARS + n_active
+
+ def _init_validating_bar(self) -> None:
+ """Create the Validating progress bar on first shard event."""
+ self.validating_bar = tqdm(
+ total=0,
+ desc=self.format_desc("Validating", False),
+ position=self._validating_bar_position(),
+ **self.percent_tqdm_settings,
+ )
+
+ def reset_for_next_commit(self):
+ """Reset per-commit state so the reporter can be reused across multiple upload commits."""
+ self._prev_bytes_completed = 0
+ self._prev_transfer_bytes_completed = 0
+ self.known_items.clear()
+ self.completed_items.clear()
+ self.item_state.clear()
+ # Shard counters are absolute per commit — restart Validating at 0% for the next chunk.
+ if self.validating_bar is not None:
+ self.validating_bar.n = 0
+ self.validating_bar.total = 0
+ self.validating_bar.refresh()
+
+ def update_progress(self, group_report, item_reports: dict):
+ # Update all the per-item values.
+ for item in item_reports.values():
+ item_name = item.item_name
+
+ self.known_items.add(item_name)
+
+ # Only care about items where the processing has already started.
+ if item.bytes_completed == 0:
+ continue
+
+ # Overwrite the existing value in there.
+ self.item_state[item_name] = item
+
+ bar_idx = 0
+ new_completed = []
+
+ # Now, go through and update all the bars
+ for name, item in self.item_state.items():
+ # Is this ready to be removed on the next update?
+ if item.bytes_completed == item.total_bytes:
+ self.completed_items.add(name)
+ new_completed.append(name)
+
+ # If we're only showing summary information, then don't update the individual bars
+ if not self.per_file_progress:
+ continue
+
+ # If we've run out of bars to use, then collapse the last ones together.
+ if bar_idx >= len(self.current_bars):
+ bar = self.current_bars[-1]
+ in_final_bar_mode = True
+ final_bar_aggregation_count = bar_idx + 1 - len(self.current_bars)
+ else:
+ bar = self.current_bars[bar_idx]
+ in_final_bar_mode = False
+
+ if bar is None:
+ # Move Validating before creating a file bar in its current row.
+ if self.validating_bar is not None and self.validating_bar.pos == -(_NUM_OVERVIEW_BARS + bar_idx):
+ self.validating_bar.clear()
+ self.validating_bar.pos -= 1
+ self.validating_bar.refresh()
+ self.current_bars[bar_idx] = tqdm(
+ desc=self.format_desc(name, True),
+ position=_NUM_OVERVIEW_BARS + bar_idx,
+ total=item.total_bytes,
+ initial=item.bytes_completed,
+ **self.tqdm_settings,
+ )
+
+ elif in_final_bar_mode:
+ bar.n += item.bytes_completed
+ bar.total += item.total_bytes
+ bar.set_description(self.format_desc(f"[+ {final_bar_aggregation_count} files]", True), refresh=False)
+ else:
+ bar.set_description(self.format_desc(name, True), refresh=False)
+ bar.n = item.bytes_completed
+ bar.total = item.total_bytes
+
+ bar_idx += 1
+
+ # Remove all the completed ones from the ordered dictionary
+ for name in new_completed:
+ # Only remove ones from consideration to make room for more items coming in.
+ if len(self.item_state) <= self.n_lines:
+ break
+
+ del self.item_state[name]
+
+ if self.per_file_progress:
+ # Now manually refresh each of the bars
+ for bar in self.current_bars:
+ if bar:
+ bar.refresh()
+
+ # Update overall bars
+ bytes_inc = max(0, group_report.total_bytes_completed - self._prev_bytes_completed)
+ transfer_inc = max(0, group_report.total_transfer_bytes_completed - self._prev_transfer_bytes_completed)
+ self._prev_bytes_completed = group_report.total_bytes_completed
+ self._prev_transfer_bytes_completed = group_report.total_transfer_bytes_completed
+
+ self.data_processing_bar.total = group_report.total_bytes
+ total_files_count = self.total_files if self.total_files is not None else len(self.known_items)
+ self.data_processing_bar.set_description(
+ self.format_desc(f"Processing Files ({len(self.completed_items)} / {total_files_count})", False),
+ refresh=False,
+ )
+ self.data_processing_bar.set_postfix_str(
+ _format_speed_postfix(group_report.total_bytes_completion_rate), refresh=False
+ )
+ self.data_processing_bar.update(bytes_inc)
+
+ self.upload_bar.total = group_report.total_transfer_bytes
+ self.upload_bar.set_postfix_str(
+ _format_speed_postfix(group_report.total_transfer_bytes_completion_rate), refresh=False
+ )
+ self.upload_bar.update(transfer_inc)
+
+ # Single Validating bar for shard finalization (v2 shard API).
+ # Prefer validation-entry counts (the long phase). Fall back to shard-completion
+ # counts when entries are absent (e.g. V1 synthetic Result with no Validating frames).
+ # Set absolute n/total for the current commit; reset_for_next_commit restarts at 0%.
+ shard = getattr(group_report, "shard", None)
+ if shard is not None and shard.total_shards > 0:
+ if self.validating_bar is None:
+ self._init_validating_bar()
+
+ assert self.validating_bar is not None
+
+ if shard.total_shard_validation_entries > 0:
+ done_n, total_n = (
+ shard.total_shard_validation_entries_completed,
+ shard.total_shard_validation_entries,
+ )
+ else:
+ done_n, total_n = shard.total_shards_completed, shard.total_shards
+
+ self.validating_bar.total = total_n or None
+ self.validating_bar.n = done_n
+ # Keep Validating snug under the active file bars as more slots fill in.
+ self.validating_bar.pos = -self._validating_bar_position()
+ self.validating_bar.refresh()
+
+ def close(self):
+ self.data_processing_bar.close()
+ self.upload_bar.close()
+
+ if self.validating_bar is not None:
+ self.validating_bar.close()
+
+ if self.per_file_progress:
+ for bar in self.current_bars:
+ if bar:
+ bar.close()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/endpoint_helpers.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/endpoint_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..85cd86011b78bcdc57034aeebc3c01e9e721ab50
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/endpoint_helpers.py
@@ -0,0 +1,66 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Helpful utility functions and classes in relation to exploring API endpoints
+with the aim for a user-friendly interface.
+"""
+
+import math
+import re
+from typing import TYPE_CHECKING
+
+from ..repocard_data import ModelCardData
+
+
+if TYPE_CHECKING:
+ from ..hf_api import ModelInfo
+
+
+def _is_emission_within_threshold(model_info: "ModelInfo", minimum_threshold: float, maximum_threshold: float) -> bool:
+ """Checks if a model's emission is within a given threshold.
+
+ Args:
+ model_info (`ModelInfo`):
+ A model info object containing the model's emission information.
+ minimum_threshold (`float`):
+ A minimum carbon threshold to filter by, such as 1.
+ maximum_threshold (`float`):
+ A maximum carbon threshold to filter by, such as 10.
+
+ Returns:
+ `bool`: Whether the model's emission is within the given threshold.
+ """
+ if minimum_threshold is None and maximum_threshold is None:
+ raise ValueError("Both `minimum_threshold` and `maximum_threshold` cannot both be `None`")
+ if minimum_threshold is None:
+ minimum_threshold = -1
+ if maximum_threshold is None:
+ maximum_threshold = math.inf
+
+ card_data = getattr(model_info, "card_data", None)
+ if card_data is None or not isinstance(card_data, (dict, ModelCardData)):
+ return False
+
+ # Get CO2 emission metadata
+ emission = card_data.get("co2_eq_emissions", None)
+ if isinstance(emission, dict):
+ emission = emission["emissions"]
+ if not emission:
+ return False
+
+ # Filter out if value is missing or out of range
+ matched = re.search(r"\d+\.\d+|\d+", str(emission))
+ if matched is None:
+ return False
+
+ emission_value = float(matched.group(0))
+ return minimum_threshold <= emission_value <= maximum_threshold
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/insecure_hashlib.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/insecure_hashlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..639e04460b0338d4642ea522490bedf038bfb557
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/insecure_hashlib.py
@@ -0,0 +1,32 @@
+# Taken from https://github.com/mlflow/mlflow/pull/10119
+#
+# DO NOT use this function for security purposes (e.g., password hashing).
+#
+# In Python >= 3.9, insecure hashing algorithms such as MD5 fail in FIPS-compliant
+# environments unless `usedforsecurity=False` is explicitly passed.
+#
+# References:
+# - https://github.com/mlflow/mlflow/issues/9905
+# - https://github.com/mlflow/mlflow/pull/10119
+# - https://docs.python.org/3/library/hashlib.html
+# - https://github.com/huggingface/transformers/pull/27038
+#
+# Usage:
+# ```python
+# # Use
+# from huggingface_hub.utils.insecure_hashlib import sha256
+# # instead of
+# from hashlib import sha256
+#
+# # Use
+# from huggingface_hub.utils import insecure_hashlib
+# # instead of
+# import hashlib
+# ```
+import functools
+import hashlib
+
+
+md5 = functools.partial(hashlib.md5, usedforsecurity=False)
+sha1 = functools.partial(hashlib.sha1, usedforsecurity=False)
+sha256 = functools.partial(hashlib.sha256, usedforsecurity=False)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/logging.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..48d6317fa7e6ad6083c6a4d486f0cab456d0c1da
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/logging.py
@@ -0,0 +1,183 @@
+# Copyright 2020 Optuna, Hugging Face
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Logging utilities."""
+
+import logging
+import os
+from logging import (
+ CRITICAL, # NOQA
+ DEBUG, # NOQA
+ ERROR, # NOQA
+ FATAL, # NOQA
+ INFO, # NOQA
+ NOTSET, # NOQA
+ WARN, # NOQA
+ WARNING, # NOQA
+)
+
+from .. import constants
+
+
+log_levels = {
+ "debug": logging.DEBUG,
+ "info": logging.INFO,
+ "warning": logging.WARNING,
+ "error": logging.ERROR,
+ "critical": logging.CRITICAL,
+}
+
+_default_log_level = logging.WARNING
+
+
+def _get_library_name() -> str:
+ return __name__.split(".")[0]
+
+
+def _get_library_root_logger() -> logging.Logger:
+ return logging.getLogger(_get_library_name())
+
+
+def _get_default_logging_level():
+ """
+ If `HF_HUB_VERBOSITY` env var is set to one of the valid choices return that as the new default level. If it is not
+ - fall back to `_default_log_level`
+ """
+ env_level_str = os.getenv("HF_HUB_VERBOSITY", None)
+ if env_level_str:
+ if env_level_str in log_levels:
+ return log_levels[env_level_str]
+ else:
+ logging.getLogger().warning(
+ f"Unknown option HF_HUB_VERBOSITY={env_level_str}, has to be one of: {', '.join(log_levels.keys())}"
+ )
+ return _default_log_level
+
+
+def _configure_library_root_logger() -> None:
+ library_root_logger = _get_library_root_logger()
+ library_root_logger.addHandler(logging.StreamHandler())
+ library_root_logger.setLevel(_get_default_logging_level())
+
+
+def _reset_library_root_logger() -> None:
+ library_root_logger = _get_library_root_logger()
+ library_root_logger.setLevel(logging.NOTSET)
+
+
+def get_logger(name: str | None = None) -> logging.Logger:
+ """
+ Returns a logger with the specified name. This function is not supposed
+ to be directly accessed by library users.
+
+ Args:
+ name (`str`, *optional*):
+ The name of the logger to get, usually the filename
+
+ Example:
+
+ ```python
+ >>> from huggingface_hub import get_logger
+
+ >>> logger = get_logger(__file__)
+ >>> logger.set_verbosity_info()
+ ```
+ """
+
+ if name is None:
+ name = _get_library_name()
+
+ return logging.getLogger(name)
+
+
+def get_verbosity() -> int:
+ """Return the current level for the HuggingFace Hub's root logger.
+
+ Returns:
+ Logging level, e.g., `huggingface_hub.logging.DEBUG` and
+ `huggingface_hub.logging.INFO`.
+
+ > [!TIP]
+ > HuggingFace Hub has following logging levels:
+ >
+ > - `huggingface_hub.logging.CRITICAL`, `huggingface_hub.logging.FATAL`
+ > - `huggingface_hub.logging.ERROR`
+ > - `huggingface_hub.logging.WARNING`, `huggingface_hub.logging.WARN`
+ > - `huggingface_hub.logging.INFO`
+ > - `huggingface_hub.logging.DEBUG`
+ """
+ return _get_library_root_logger().getEffectiveLevel()
+
+
+def set_verbosity(verbosity: int) -> None:
+ """
+ Sets the level for the HuggingFace Hub's root logger.
+
+ Args:
+ verbosity (`int`):
+ Logging level, e.g., `huggingface_hub.logging.DEBUG` and
+ `huggingface_hub.logging.INFO`.
+ """
+ _get_library_root_logger().setLevel(verbosity)
+
+
+def set_verbosity_info():
+ """
+ Sets the verbosity to `logging.INFO`.
+ """
+ return set_verbosity(INFO)
+
+
+def set_verbosity_warning():
+ """
+ Sets the verbosity to `logging.WARNING`.
+ """
+ return set_verbosity(WARNING)
+
+
+def set_verbosity_debug():
+ """
+ Sets the verbosity to `logging.DEBUG`.
+ """
+ return set_verbosity(DEBUG)
+
+
+def set_verbosity_error():
+ """
+ Sets the verbosity to `logging.ERROR`.
+ """
+ return set_verbosity(ERROR)
+
+
+def disable_propagation() -> None:
+ """
+ Disable propagation of the library log outputs. Note that log propagation is
+ disabled by default.
+ """
+ _get_library_root_logger().propagate = False
+
+
+def enable_propagation() -> None:
+ """
+ Enable propagation of the library log outputs. Please disable the
+ HuggingFace Hub's default handler to prevent double logging if the root
+ logger has been configured.
+ """
+ _get_library_root_logger().propagate = True
+
+
+_configure_library_root_logger()
+
+if constants.HF_DEBUG:
+ # If `HF_DEBUG` environment variable is set, set the verbosity of `huggingface_hub` logger to `DEBUG`.
+ set_verbosity_debug()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/sha.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/sha.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc2f8f614c9ec02579153eb3308603f7ff24adef
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/sha.py
@@ -0,0 +1,64 @@
+"""Utilities to efficiently compute the SHA 256 hash of a bunch of bytes."""
+
+from typing import BinaryIO
+
+from .insecure_hashlib import sha1, sha256
+
+
+def sha_fileobj(fileobj: BinaryIO, chunk_size: int | None = None) -> bytes:
+ """
+ Computes the sha256 hash of the given file object, by chunks of size `chunk_size`.
+
+ Args:
+ fileobj (file-like object):
+ The File object to compute sha256 for, typically obtained with `open(path, "rb")`
+ chunk_size (`int`, *optional*):
+ The number of bytes to read from `fileobj` at once, defaults to 1MB.
+
+ Returns:
+ `bytes`: `fileobj`'s sha256 hash as bytes
+ """
+ chunk_size = chunk_size if chunk_size is not None else 1024 * 1024
+
+ sha = sha256()
+ while True:
+ chunk = fileobj.read(chunk_size)
+ sha.update(chunk)
+ if not chunk:
+ break
+ return sha.digest()
+
+
+def git_hash(data: bytes) -> str:
+ """
+ Computes the git-sha1 hash of the given bytes, using the same algorithm as git.
+
+ This is equivalent to running `git hash-object`. See https://git-scm.com/docs/git-hash-object
+ for more details.
+
+ Note: this method is valid for regular files. For LFS files, the proper git hash is supposed to be computed on the
+ pointer file content, not the actual file content. However, for simplicity, we directly compare the sha256 of
+ the LFS file content when we want to compare LFS files.
+
+ Args:
+ data (`bytes`):
+ The data to compute the git-hash for.
+
+ Returns:
+ `str`: the git-hash of `data` as an hexadecimal string.
+
+ Example:
+ ```python
+ >>> from huggingface_hub.utils.sha import git_hash
+ >>> git_hash(b"Hello, World!")
+ 'b45ef6fec89518d314f546fd6c3025367b721684'
+ ```
+ """
+ # Taken from https://gist.github.com/msabramo/763200
+ # Note: no need to optimize by reading the file in chunks as we're not supposed to hash huge files (5MB maximum).
+ sha = sha1()
+ sha.update(b"blob ")
+ sha.update(str(len(data)).encode())
+ sha.update(b"\0")
+ sha.update(data)
+ return sha.hexdigest()
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/tqdm.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/tqdm.py
new file mode 100644
index 0000000000000000000000000000000000000000..667b43c214fcc565ed2a38354a86c63710d3f8e7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/huggingface_hub/utils/tqdm.py
@@ -0,0 +1,449 @@
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Utility helpers to handle progress bars in `huggingface_hub`.
+
+Example:
+ 1. Use `huggingface_hub.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`.
+ 2. To disable progress bars, either use `disable_progress_bars()` helper or set the
+ environment variable `HF_HUB_DISABLE_PROGRESS_BARS` to 1.
+ 3. To re-enable progress bars, use `enable_progress_bars()`.
+ 4. To check whether progress bars are disabled, use `are_progress_bars_disabled()`.
+
+NOTE: Environment variable `HF_HUB_DISABLE_PROGRESS_BARS` has the priority.
+
+Example:
+ ```py
+ >>> from huggingface_hub.utils import are_progress_bars_disabled, disable_progress_bars, enable_progress_bars, tqdm
+
+ # Disable progress bars globally
+ >>> disable_progress_bars()
+
+ # Use as normal `tqdm`
+ >>> for _ in tqdm(range(5)):
+ ... pass
+
+ # Still not showing progress bars, as `disable=False` is overwritten to `True`.
+ >>> for _ in tqdm(range(5), disable=False):
+ ... pass
+
+ >>> are_progress_bars_disabled()
+ True
+
+ # Re-enable progress bars globally
+ >>> enable_progress_bars()
+
+ # Progress bar will be shown !
+ >>> for _ in tqdm(range(5)):
+ ... pass
+ 100%|███████████████████████████████████████| 5/5 [00:00<00:00, 117817.53it/s]
+ ```
+
+Group-based control:
+ ```python
+ # Disable progress bars for a specific group
+ >>> disable_progress_bars("peft.foo")
+
+ # Check state of different groups
+ >>> assert not are_progress_bars_disabled("peft"))
+ >>> assert not are_progress_bars_disabled("peft.something")
+ >>> assert are_progress_bars_disabled("peft.foo"))
+ >>> assert are_progress_bars_disabled("peft.foo.bar"))
+
+ # Enable progress bars for a subgroup
+ >>> enable_progress_bars("peft.foo.bar")
+
+ # Check if enabling a subgroup affects the parent group
+ >>> assert are_progress_bars_disabled("peft.foo"))
+ >>> assert not are_progress_bars_disabled("peft.foo.bar"))
+
+ # No progress bar for `name="peft.foo"`
+ >>> for _ in tqdm(range(5), name="peft.foo"):
+ ... pass
+
+ # Progress bar will be shown for `name="peft.foo.bar"`
+ >>> for _ in tqdm(range(5), name="peft.foo.bar"):
+ ... pass
+ 100%|███████████████████████████████████████| 5/5 [00:00<00:00, 117817.53it/s]
+
+ ```
+"""
+
+import io
+import logging
+import os
+import threading
+import warnings
+from collections.abc import Iterable, Iterator
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from contextlib import contextmanager, nullcontext
+from pathlib import Path
+from typing import Callable, ContextManager, TypeVar, cast
+
+from tqdm.auto import tqdm as old_tqdm
+
+from ..constants import HF_HUB_DISABLE_PROGRESS_BARS
+
+
+# The `HF_HUB_DISABLE_PROGRESS_BARS` environment variable can be True, False, or not set (None),
+# allowing for control over progress bar visibility. When set, this variable takes precedence
+# over programmatic settings, dictating whether progress bars should be shown or hidden globally.
+# Essentially, the environment variable's setting overrides any code-based configurations.
+#
+# If `HF_HUB_DISABLE_PROGRESS_BARS` is not defined (None), it implies that users can manage
+# progress bar visibility through code. By default, progress bars are turned on.
+
+progress_bar_states: dict[str, bool] = {}
+
+
+class disable_progress_bars:
+ """
+ Disable progress bars either globally or for a specified group.
+
+ This function updates the state of progress bars based on a group name.
+ If no group name is provided, all progress bars are disabled. The operation
+ respects the `HF_HUB_DISABLE_PROGRESS_BARS` environment variable's setting.
+
+ Works as both a regular call and a context manager:
+ disable_progress_bars() # disables until enable_progress_bars()
+ with disable_progress_bars(): # disables for the block, re-enables on exit
+ ...
+
+ Args:
+ name (`str`, *optional*):
+ The name of the group for which to disable the progress bars. If None,
+ progress bars are disabled globally.
+
+ Raises:
+ Warning: If the environment variable precludes changes.
+ """
+
+ def __init__(self, name: str | None = None) -> None:
+ self.name = name
+
+ if HF_HUB_DISABLE_PROGRESS_BARS is False:
+ warnings.warn(
+ "Cannot disable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=0` is set and has priority."
+ )
+ self._should_reenable = False
+ return
+
+ self._should_reenable = not are_progress_bars_disabled(name)
+ if name is None:
+ progress_bar_states.clear()
+ progress_bar_states["_global"] = False
+ else:
+ keys_to_remove = [key for key in progress_bar_states if key.startswith(f"{name}.")]
+ for key in keys_to_remove:
+ del progress_bar_states[key]
+ progress_bar_states[name] = False
+
+ def __enter__(self) -> "disable_progress_bars":
+ return self
+
+ def __exit__(self, *exc) -> None:
+ if self._should_reenable:
+ enable_progress_bars(self.name)
+
+
+def enable_progress_bars(name: str | None = None) -> None:
+ """
+ Enable progress bars either globally or for a specified group.
+
+ This function sets the progress bars to enabled for the specified group or globally
+ if no group is specified. The operation is subject to the `HF_HUB_DISABLE_PROGRESS_BARS`
+ environment setting.
+
+ Args:
+ name (`str`, *optional*):
+ The name of the group for which to enable the progress bars. If None,
+ progress bars are enabled globally.
+
+ Raises:
+ Warning: If the environment variable precludes changes.
+ """
+ if HF_HUB_DISABLE_PROGRESS_BARS is True:
+ warnings.warn(
+ "Cannot enable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=1` is set and has priority."
+ )
+ return
+
+ if name is None:
+ progress_bar_states.clear()
+ progress_bar_states["_global"] = True
+ else:
+ keys_to_remove = [key for key in progress_bar_states if key.startswith(f"{name}.")]
+ for key in keys_to_remove:
+ del progress_bar_states[key]
+ progress_bar_states[name] = True
+
+
+def are_progress_bars_disabled(name: str | None = None) -> bool:
+ """
+ Check if progress bars are disabled globally or for a specific group.
+
+ This function returns whether progress bars are disabled for a given group or globally.
+ It checks the `HF_HUB_DISABLE_PROGRESS_BARS` environment variable first, then the programmatic
+ settings.
+
+ Args:
+ name (`str`, *optional*):
+ The group name to check; if None, checks the global setting.
+
+ Returns:
+ `bool`: True if progress bars are disabled, False otherwise.
+ """
+ if HF_HUB_DISABLE_PROGRESS_BARS is True:
+ return True
+
+ if name is None:
+ return not progress_bar_states.get("_global", True)
+
+ while name:
+ if name in progress_bar_states:
+ return not progress_bar_states[name]
+ name = ".".join(name.split(".")[:-1])
+
+ return not progress_bar_states.get("_global", True)
+
+
+def is_tqdm_disabled(log_level: int) -> bool | None:
+ """
+ Determine if tqdm progress bars should be disabled based on logging level and environment settings.
+
+ see https://github.com/huggingface/huggingface_hub/pull/2000 and https://github.com/huggingface/huggingface_hub/pull/2698.
+ """
+ if log_level == logging.NOTSET:
+ return True
+ if os.getenv("TQDM_POSITION") == "-1":
+ return False
+ return None
+
+
+class tqdm(old_tqdm):
+ """
+ Class to override `disable` argument in case progress bars are globally disabled.
+
+ Taken from https://github.com/tqdm/tqdm/issues/619#issuecomment-619639324.
+ """
+
+ def __init__(self, *args, **kwargs):
+ name = kwargs.pop("name", None) # do not pass `name` to `tqdm`
+ if are_progress_bars_disabled(name):
+ kwargs["disable"] = True
+ super().__init__(*args, **kwargs)
+
+ def __delattr__(self, attr: str) -> None:
+ """Fix for https://github.com/huggingface/huggingface_hub/issues/1603"""
+ try:
+ super().__delattr__(attr)
+ except AttributeError:
+ if attr != "_lock":
+ raise
+
+
+# Prevent tqdm's default multiprocessing write-lock from spawning a resource
+# tracker subprocess via fork_exec(). That path fails when stderr has an invalid
+# fd (e.g. Textual TUIs that return -1 from sys.stderr.fileno()). Inter-process
+# bar coordination on the HF subclass is not a supported use case. See #4065.
+tqdm.set_lock(threading.RLock())
+
+
+class silent_tqdm:
+ """Fake tqdm object that does nothing."""
+
+ def __init__(self, *args, **kwargs):
+ pass
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ pass
+
+ def update(self, n: int | float | None = 1) -> None:
+ pass
+
+
+@contextmanager
+def tqdm_stream_file(path: Path | str) -> Iterator[io.BufferedReader]:
+ """
+ Open a file as binary and wrap the `read` method to display a progress bar when it's streamed.
+
+ First implemented in `transformers` in 2019 but removed when switched to git-lfs. Used in `huggingface_hub` to show
+ progress bar when uploading an LFS file to the Hub. See github.com/huggingface/transformers/pull/2078#discussion_r354739608
+ for implementation details.
+
+ Note: currently implementation handles only files stored on disk as it is the most common use case. Could be
+ extended to stream any `BinaryIO` object but we might have to debug some corner cases.
+
+ Example:
+ ```py
+ >>> with tqdm_stream_file("config.json") as f:
+ >>> httpx2.put(url, data=f)
+ config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
+ ```
+ """
+ if isinstance(path, str):
+ path = Path(path)
+
+ with path.open("rb") as f:
+ total_size = path.stat().st_size
+ pbar = tqdm(
+ unit="B",
+ unit_scale=True,
+ total=total_size,
+ initial=0,
+ desc=path.name,
+ )
+
+ f_read = f.read
+
+ def _inner_read(size: int | None = -1) -> bytes:
+ data = f_read(size)
+ pbar.update(len(data))
+ return data
+
+ f.read = _inner_read # type: ignore
+
+ yield f
+
+ pbar.close()
+
+
+def _create_progress_bar(*, cls: type[old_tqdm], log_level: int, name: str | None = None, **kwargs) -> old_tqdm:
+ """Create a progress bar.
+
+ For our `tqdm` subclass (or subclasses of it): respects all disable signals
+ (`HF_HUB_DISABLE_PROGRESS_BARS`, `disable_progress_bars()`, log level) and uses
+ `disable=None` for TTY auto-detection (see https://github.com/huggingface/huggingface_hub/pull/2000),
+ unless `TQDM_POSITION=-1` forces bars on (https://github.com/huggingface/huggingface_hub/pull/2698).
+
+ For other classes: does not inject `disable` or `name`. the custom class is fully
+ responsible for its own behavior. Vanilla tqdm defaults to `disable=False` (bar shows).
+ Omits `name` which vanilla tqdm rejects with `TqdmKeyError`. See https://github.com/huggingface/huggingface_hub/issues/4050.
+ """
+ # issubclass() crashes on non-class callables (e.g. functools.partial), guard with isinstance.
+ if not (isinstance(cls, type) and issubclass(cls, tqdm)):
+ return cls(**kwargs) # type: ignore[return-value]
+
+ # HF subclass: keep the historical log-level / TTY behavior. Group-based
+ # disabling is already handled in `tqdm.__init__`.
+ disable = is_tqdm_disabled(log_level)
+ return cls(disable=disable, name=name, **kwargs) # type: ignore[return-value]
+
+
+def _get_progress_bar_context(
+ *,
+ desc: str,
+ log_level: int,
+ total: int | None = None,
+ initial: int = 0,
+ unit: str = "B",
+ unit_scale: bool = True,
+ name: str | None = None,
+ tqdm_class: type[old_tqdm] | None = None,
+ _tqdm_bar: tqdm | None = None,
+) -> ContextManager[tqdm]:
+ if _tqdm_bar is not None:
+ return nullcontext(_tqdm_bar)
+ # ^ `contextlib.nullcontext` mimics a context manager that does nothing
+ # Makes it easier to use the same code path for both cases but in the later
+ # case, the progress bar is not closed when exiting the context manager.
+
+ return _create_progress_bar( # type: ignore
+ cls=tqdm_class or tqdm,
+ log_level=log_level,
+ name=name,
+ unit=unit,
+ unit_scale=unit_scale,
+ total=total,
+ initial=initial,
+ desc=desc,
+ )
+
+
+T = TypeVar("T")
+R = TypeVar("R")
+
+
+def _make_thread_pool_executor(*, max_workers: int | None, bar_class: type[old_tqdm]) -> ThreadPoolExecutor:
+ """Create a `ThreadPoolExecutor` that shares the tqdm lock with its worker threads.
+
+ Like `tqdm.contrib.concurrent.thread_map`, we make sure the bar class has a lock and pass it to the
+ worker threads, so that bars created inside the mapped function (e.g. the per-file download bars in
+ `snapshot_download`) don't race on concurrent `update()` calls. `get_lock()` creates the shared
+ class-level lock if needed. Classes that don't implement tqdm's locking API (e.g. `silent_tqdm`)
+ render nothing anyway, so no lock is needed.
+ """
+ if hasattr(bar_class, "get_lock") and hasattr(bar_class, "set_lock"):
+ return ThreadPoolExecutor(
+ max_workers=max_workers, initializer=bar_class.set_lock, initargs=(bar_class.get_lock(),)
+ )
+ return ThreadPoolExecutor(max_workers=max_workers)
+
+
+def hf_thread_map(
+ fn: Callable[[T], R],
+ iterable: Iterable[T],
+ *,
+ max_workers: int | None = None,
+ tqdm_class: type[old_tqdm] | None = None,
+ **tqdm_kwargs,
+) -> list[R]:
+ """Drop-in replacement for `tqdm.contrib.concurrent.thread_map`.
+
+ The version shipped by `tqdm` consumes results in submission order and the progress bar only advances when the "next
+ item in input order" finishes. A single slow early item pins the bar near zero while later items
+ complete, with everything flushed at once at the end. This helper avoids that problem by consuming items in completion
+ order.
+
+ See https://github.com/huggingface/huggingface_hub/issues/4518 for more details.
+
+ Args:
+ fn (`Callable`):
+ Function to apply to each item.
+ iterable (`Iterable`):
+ Items to process.
+ max_workers (`int`, *optional*):
+ Maximum number of worker threads. Defaults to tqdm's own default.
+ tqdm_class (`type`, *optional*):
+ Progress bar class to use. Defaults to `huggingface_hub`'s `tqdm`.
+ **tqdm_kwargs:
+ Additional keyword arguments forwarded to the progress bar (e.g. `desc`).
+
+ Returns:
+ `list`: Results in the same order as `iterable`.
+ """
+ items = list(iterable)
+ if max_workers is None:
+ max_workers = min(32, (os.cpu_count() or 1) + 4)
+ tqdm_kwargs.setdefault("total", len(items))
+ bar_class = tqdm_class or tqdm
+ results: list[R | None] = [None] * len(items)
+ with (
+ bar_class(**tqdm_kwargs) as pbar,
+ _make_thread_pool_executor(max_workers=max_workers, bar_class=bar_class) as executor,
+ ):
+ future_to_index = {executor.submit(fn, item): index for index, item in enumerate(items)}
+ try:
+ for future in as_completed(future_to_index):
+ results[future_to_index[future]] = future.result()
+ pbar.update(1)
+ except BaseException:
+ # Cancel not-yet-started tasks so a single failure doesn't keep the whole queue running
+ # (mirrors `tqdm.contrib.concurrent.thread_map`, which relies on `Executor.map` for this).
+ for future in future_to_index:
+ future.cancel()
+ raise
+ return cast("list[R]", results)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/INSTALLER b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/METADATA b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..edbc446bb4ec693451e3aaaa64b0185f2f7ce511
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/METADATA
@@ -0,0 +1,176 @@
+Metadata-Version: 2.5
+Name: idna
+Version: 3.20
+Summary: Internationalized Domain Names in Applications (IDNA)
+Author-email: Kim Davies
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+License-Expression: BSD-3-Clause
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: System Administrators
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3.15
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Internet :: Name Service (DNS)
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Utilities
+License-File: LICENSE.md
+Requires-Dist: ruff >= 0.16.0 ; extra == "all"
+Requires-Dist: mypy >= 1.11.2 ; extra == "all"
+Requires-Dist: ty >= 0.0.37 ; extra == "all"
+Requires-Dist: pytest >= 8.3.2 ; extra == "all"
+Requires-Dist: hypothesis >= 6.141.1 ; extra == "all"
+Requires-Dist: coverage >= 7.10.0 ; extra == "all"
+Project-URL: Changelog, https://github.com/kjd/idna/blob/master/HISTORY.md
+Project-URL: Issue tracker, https://github.com/kjd/idna/issues
+Project-URL: Source, https://github.com/kjd/idna
+Provides-Extra: all
+Import-Name: idna
+
+# Internationalized Domain Names in Applications (IDNA)
+
+Support for [Internationalized Domain Names in Applications
+(IDNA)](https://tools.ietf.org/html/rfc5891) and [Unicode IDNA
+Compatibility Processing](https://unicode.org/reports/tr46/). It
+supersedes the standard library's `encodings.idna`, which only
+implements the 2003 specification, offering broader script coverage and
+limiting domains with known security vulnerabilities.
+
+## Usage
+
+Package may be installed from [PyPI](https://pypi.org/project/idna/) via
+the typical methods (e.g. `python3 -m pip install idna`)
+
+For typical usage, the `encode` and `decode` functions will take a
+domain name argument and perform a conversion to ASCII-compatible encoding
+(known as A-labels), or to Unicode strings (known as U-labels)
+respectively.
+
+```pycon
+>>> import idna
+>>> idna.encode('ドメイン.テスト')
+b'xn--eckwd4c7c.xn--zckzah'
+>>> print(idna.decode('xn--eckwd4c7c.xn--zckzah'))
+ドメイン.テスト
+```
+
+Conversions can be applied at a per-label basis using the `ulabel` or
+`alabel` functions for specialized use cases.
+
+
+### Compatibility Mapping (UTS #46)
+
+This library provides support for [Unicode IDNA Compatibility
+Processing](https://unicode.org/reports/tr46/) which normalizes input from
+different potential ways a user may input a domain prior to performing the IDNA
+conversion operations. This functionality, known as a
+[mapping](https://tools.ietf.org/html/rfc5895), is considered by the
+specification to be a local user-interface issue distinct from IDNA
+conversion functionality.
+
+For example, "Königsgäßchen" is not a permissible label as capital letters
+are not allowed. UTS #46 will convert this into lower case prior to applying
+the IDNA conversion.
+
+```pycon
+>>> import idna
+>>> idna.encode('Königsgäßchen')
+...
+idna.core.InvalidCodepoint: Codepoint U+004B at position 1 of 'Königsgäßchen' not allowed
+>>> idna.encode('Königsgäßchen', uts46=True)
+b'xn--knigsgchen-b4a3dun'
+>>> idna.decode('xn--knigsgchen-b4a3dun')
+'königsgäßchen'
+```
+
+When performing a decode operation for display purposes, `decode()`
+accepts a `display=True` argument that leaves any `xn--` label that
+fails to decode unchanged. This is useful for user interface display
+where a domain is in use, the A-label form can be presented when it
+is not a valid IDN.
+
+
+## Exceptions
+
+All errors raised during conversion derive from the `idna.IDNAError`
+base class. The more specific exceptions are:
+
+* `idna.IDNABidiError` — raised when a label contains an illegal
+ combination of left-to-right and right-to-left characters.
+* `idna.InvalidCodepoint` — raised when a label contains a codepoint
+ that is INVALID for IDNA.
+* `idna.InvalidCodepointContext` — raised when a CONTEXTO or CONTEXTJ
+ codepoint appears in a position whose contextual requirements are
+ not satisfied.
+
+Exceptions carry machine-readable attributes so that applications do
+not need to parse the message: `code` is a short, stable identifier
+for the rule that failed (such as `disallowed_codepoint` or
+`label_too_long`); and, when the failure can be attributed to a
+particular character, `text` (the label or domain being validated),
+`codepoint` (the offending codepoint as an integer) and `position`
+are set.
+
+
+## Command-line tool
+
+The package supports command-line usage to convert domain names
+between their Unicode and ASCII-compatible forms. It can be run either
+as a module (`python3 -m idna`) or, once installed (such as with `uv
+tool` or `pipx`), via the `idna` script:
+
+```bash
+$ uv tool install idna
+$ idna xn--e1afmkfd.xn--p1ai
+пример.рф
+$ idna пример.рф
+xn--e1afmkfd.xn--p1ai
+```
+
+Mode can be specified with `-e`/`--encode` or `-d`/`--decode`, otherwise
+it will be chosen automatically based on the first input. Multiple
+domains can be supplied either as arguments or through standard input.
+UTS #46 mapping is applied by default, which lets the tool accept
+inputs that aren't strictly valid IDNA 2008 by normalising them first,
+pass `--strict` to disable UTS #46.
+
+Conversion failures are reported on stderr together with the
+offending input; processing continues with the remaining domains and
+the tool exits with a non-zero status if any conversion failed.
+
+
+## Additional Notes
+
+* **Python version support**. This library supports Python 3.9 and higher.
+ As this library serves as a low-level toolkit for a variety of
+ applications, we strive to support all versions of Python that are
+ not beyond end-of-life. Free-threaded Python is also supported,
+ as the library holds no mutable global state the functions can be
+ called concurrently from multiple threads.
+
+* **Unicode version**. The IDNA and UTS #46 lookup tables are generated
+ from a specific Unicode release. Some Unicode data depends on the
+ running Python's `unicodedata` module, so on an older Python a
+ character new to Unicode may be rejected as unknown even if this
+ library knows about it.
+
+* **Emoji**. It is an occasional request to support emoji domains in
+ this library. Encoding of symbols like emoji is expressly prohibited by
+ the IDNA technical standard, and emoji domains are broadly phased
+ out across the domain industry due to associated security risks.
+
+* **Regenerating lookup tables**. The IDNA and UTS #46 functionality
+ relies upon pre-calculated lookup tables, generated using the
+ `idna-data` script in [`tools/`](tools/README.md).
+
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/RECORD b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..76bddfb99a40fd8d12df156537882efb4382d95f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/RECORD
@@ -0,0 +1,28 @@
+../../../bin/idna,sha256=8q_ry9TmCsHSKYuDkLxLD9t8KQhAUpwL8gHzefb8LMg,243
+idna-3.20.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+idna-3.20.dist-info/METADATA,sha256=29jBTB5MoeDJgkptvHy794iE8461LZEUjNMCX2ceS4U,7207
+idna-3.20.dist-info/RECORD,,
+idna-3.20.dist-info/WHEEL,sha256=lqN8jXt_QloFOGcx9kStvWbryhfEXoI2F2z-YURm350,81
+idna-3.20.dist-info/entry_points.txt,sha256=7H3nGOHap3jnLE5e7q7Ywr9Vq8axB7WIj5-C_4N2vhw,38
+idna-3.20.dist-info/licenses/LICENSE.md,sha256=GppPDj1HmickDd1ZqRN6ZqtKD539yMphiMwL_YUYfwQ,1541
+idna/__init__.py,sha256=hRTD7VMTajWW699RL6SHu919pama3K7YLgNj0sMG068,944
+idna/__main__.py,sha256=4JMK66Wj4uLZTKbF-sT3LAxOsr6buig77PmOkJCRRxw,83
+idna/__pycache__/__init__.cpython-311.pyc,,
+idna/__pycache__/__main__.cpython-311.pyc,,
+idna/__pycache__/cli.cpython-311.pyc,,
+idna/__pycache__/codec.cpython-311.pyc,,
+idna/__pycache__/compat.cpython-311.pyc,,
+idna/__pycache__/core.cpython-311.pyc,,
+idna/__pycache__/idnadata.cpython-311.pyc,,
+idna/__pycache__/intranges.cpython-311.pyc,,
+idna/__pycache__/package_data.cpython-311.pyc,,
+idna/__pycache__/uts46data.cpython-311.pyc,,
+idna/cli.py,sha256=LgGXKiXmev37pdgkATwB6AiJORZTnA2zP3LzfEK4iWo,4245
+idna/codec.py,sha256=ff9CR9debiZcvqEmHxDRIrqSmaNxQpz6AY4JHsC3YC0,8002
+idna/compat.py,sha256=T_eZXOUt0b5Tga56cPybQu9CCYK0FTK1mdiPb96na8A,1376
+idna/core.py,sha256=PdTD_-5P8iMgf6xl84EqHmiVrorR56btYBFXaPNVFSA,32580
+idna/idnadata.py,sha256=I6mowdFd6cY00L8hPXeYEPSFCD_hsPfgkEStBKacx-s,45763
+idna/intranges.py,sha256=g49scLSkqJtAhLmOODa7hVHriSjmb60tiTsEoocJdBI,1851
+idna/package_data.py,sha256=rnWVNKPFgqAKyFbPykZKOe6CUdOTII2K8GtgW8IOAqk,21
+idna/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+idna/uts46data.py,sha256=dw6Em_oVbHGCiolEClf-mupwc18QOuRhbuT2guE9bAA,237174
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/WHEEL b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..1ab9a08b3a505f2f5a1a39db576a05c639c3baf8
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 4.1.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/entry_points.txt b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..59ca7ac02ea0a40938ca867305e020981f52a077
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/entry_points.txt
@@ -0,0 +1,3 @@
+[console_scripts]
+idna=idna.cli:main
+
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/licenses/LICENSE.md b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/licenses/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..f706835ab3a5dd709a6e1f57aee0a94ae1415df6
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna-3.20.dist-info/licenses/LICENSE.md
@@ -0,0 +1,31 @@
+BSD 3-Clause License
+
+Copyright (c) 2013-2026, Kim Davies and contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6c890a9a93ee51b0e519213d82ce5eb0340c4c2
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__init__.py
@@ -0,0 +1,47 @@
+from .core import (
+ IDNABidiError,
+ IDNAError,
+ InvalidCodepoint,
+ InvalidCodepointContext,
+ alabel,
+ check_bidi,
+ check_hyphen_ok,
+ check_initial_combiner,
+ check_label,
+ check_nfc,
+ decode,
+ encode,
+ ulabel,
+ uts46_remap,
+ valid_contextj,
+ valid_contexto,
+ valid_label_length,
+ valid_string_length,
+)
+from .idnadata import __version__ as unicode_version
+from .intranges import intranges_contain
+from .package_data import __version__
+
+__all__ = [
+ "__version__",
+ "unicode_version",
+ "IDNABidiError",
+ "IDNAError",
+ "InvalidCodepoint",
+ "InvalidCodepointContext",
+ "alabel",
+ "check_bidi",
+ "check_hyphen_ok",
+ "check_initial_combiner",
+ "check_label",
+ "check_nfc",
+ "decode",
+ "encode",
+ "intranges_contain",
+ "ulabel",
+ "uts46_remap",
+ "valid_contextj",
+ "valid_contexto",
+ "valid_label_length",
+ "valid_string_length",
+]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__main__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbdd066172160ef2d47b29f0caa00e27c62ebc20
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/__main__.py
@@ -0,0 +1,6 @@
+import sys
+
+from .cli import main
+
+if __name__ == "__main__":
+ sys.exit(main())
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/cli.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..894d8a0637bc6542ab4d7bfb61bd91d40f438358
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/cli.py
@@ -0,0 +1,132 @@
+"""Command-line interface for the :mod:`idna` package.
+
+Invoked via ``python -m idna``. See :func:`main` for the entry point.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from itertools import chain
+from typing import IO, TYPE_CHECKING
+
+from . import IDNAError, decode, encode, unicode_version
+from .core import _alabel_prefix, _unicode_dots_re
+from .package_data import __version__
+
+if TYPE_CHECKING:
+ from collections.abc import Iterable
+
+
+def _looks_like_alabel(s: str) -> bool:
+ """Return True if any label in ``s`` carries the ``xn--`` ACE prefix."""
+ prefix = _alabel_prefix.decode("ascii")
+ return any(label.lower().startswith(prefix) for label in _unicode_dots_re.split(s))
+
+
+def _build_parser() -> argparse.ArgumentParser:
+ parser = argparse.ArgumentParser(
+ prog="python -m idna",
+ description=(
+ "Convert a domain name between its Unicode (U-label) and "
+ "ASCII-compatible (A-label) forms. With no mode flag, the "
+ "direction is chosen from the first input — if it contains "
+ "an xn-- label the stream is decoded, otherwise it is "
+ "encoded — and the same mode is applied to every remaining "
+ "input. UTS #46 mapping is applied by default; pass "
+ "--strict to disable it. When no domains are given on the "
+ "command line and stdin is piped, one domain per line is "
+ "read from stdin."
+ ),
+ )
+ mode = parser.add_mutually_exclusive_group()
+ mode.add_argument(
+ "-e",
+ "--encode",
+ dest="mode",
+ action="store_const",
+ const="encode",
+ help="Encode the input to its ASCII A-label form.",
+ )
+ mode.add_argument(
+ "-d",
+ "--decode",
+ dest="mode",
+ action="store_const",
+ const="decode",
+ help="Decode the input from its ASCII A-label form.",
+ )
+ parser.add_argument(
+ "--strict",
+ action="store_true",
+ help="Disable the default UTS #46 mapping and apply IDNA 2008 rules verbatim.",
+ )
+ parser.add_argument(
+ "--version",
+ action="version",
+ version=f"idna {__version__} (Unicode {unicode_version})",
+ )
+ parser.add_argument(
+ "domain",
+ nargs="*",
+ help="One or more domain names to convert. Omit to read from stdin.",
+ )
+ return parser
+
+
+def _iter_stdin(stream: IO[str]) -> Iterable[str]:
+ """Yield non-empty stripped lines from ``stream``, ignoring blanks."""
+ for line in stream:
+ stripped = line.strip()
+ if stripped:
+ yield stripped
+
+
+def _convert_one(domain: str, mode: str, uts46: bool) -> bool:
+ """Convert ``domain`` and write the result; return ``False`` on failure."""
+ try:
+ if mode == "decode":
+ print(decode(domain, uts46=uts46))
+ else:
+ print(encode(domain, uts46=uts46).decode("ascii"))
+ except IDNAError as err:
+ print(f"idna: {mode} failed for {domain!r}: {err}", file=sys.stderr)
+ return False
+ return True
+
+
+def main(argv: list[str] | None = None) -> int:
+ """Entry point for ``python -m idna``.
+
+ When more than one domain is supplied (via positional arguments or
+ piped stdin) and no mode flag is given, the first input determines
+ the direction and that mode is applied uniformly to the rest.
+
+ :param argv: Argument list excluding the program name. Defaults to
+ :data:`sys.argv` when ``None``.
+ :returns: ``0`` on success, ``1`` if any conversion fails.
+ """
+ parser = _build_parser()
+ args = parser.parse_args(argv)
+ uts46 = not args.strict
+
+ if args.domain:
+ domains: Iterable[str] = args.domain
+ elif not sys.stdin.isatty():
+ domains = _iter_stdin(sys.stdin)
+ else:
+ parser.error("a domain argument is required when stdin is a terminal")
+
+ iterator = iter(domains)
+ first = next(iterator, None)
+ if first is None:
+ return 0
+
+ mode = args.mode or ("decode" if _looks_like_alabel(first) else "encode")
+
+ results = [_convert_one(domain, mode, uts46) for domain in chain([first], iterator)]
+ return 0 if all(results) else 1
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/codec.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/codec.py
new file mode 100644
index 0000000000000000000000000000000000000000..8efc1eed7364a1a9b08b00405752ddf4fb2068dc
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/codec.py
@@ -0,0 +1,225 @@
+from __future__ import annotations
+
+import codecs
+from typing import Any
+
+from .core import IDNAError, _max_domain_length, _unicode_dots_re, alabel, decode, encode, ulabel
+
+
+class Codec(codecs.Codec):
+ """Stateless IDNA 2008 codec.
+
+ Implements the :class:`codecs.Codec` protocol so that the whole-domain
+ encoder (:func:`idna.encode`) and decoder (:func:`idna.decode`) are
+ accessible through the standard codec machinery as ``"idna2008"``.
+
+ Only the ``"strict"`` error handler is supported; any other handler
+ raises :exc:`~idna.IDNAError`.
+ """
+
+ def encode(self, data: str, errors: str = "strict") -> tuple[bytes, int]: # ty: ignore[invalid-method-override]
+ if errors != "strict":
+ raise IDNAError(f'Unsupported error handling "{errors}"', code="unsupported_errors")
+
+ if not data:
+ return b"", 0
+
+ return encode(data), len(data)
+
+ def decode(self, data: bytes, errors: str = "strict") -> tuple[str, int]: # ty: ignore[invalid-method-override]
+ if errors != "strict":
+ raise IDNAError(f'Unsupported error handling "{errors}"', code="unsupported_errors")
+
+ if not data:
+ return "", 0
+
+ return decode(data), len(data)
+
+
+class IncrementalEncoder(codecs.BufferedIncrementalEncoder):
+ """Incremental IDNA 2008 encoder.
+
+ Buffers a partial trailing label across calls until either the next
+ label separator is seen or ``final=True``, so that streamed input is
+ encoded one whole label at a time. Any of the four Unicode label
+ separators (``U+002E``, ``U+3002``, ``U+FF0E``, ``U+FF61``) ends a
+ label; the result always uses ``U+002E`` as the separator.
+
+ The 253-octet domain length limit (254 with a trailing dot) that
+ :func:`idna.encode` enforces is applied to the accumulated output, so
+ that streaming a name and encoding it in one shot either both succeed
+ with the same result or both raise :exc:`~idna.IDNAError`.
+
+ Only the ``"strict"`` error handler is supported.
+ """
+
+ def __init__(self, errors: str = "strict") -> None:
+ super().__init__(errors)
+ self._emitted = 0 # octets returned so far
+ self._trailing_dot = False # whether the output so far ends with "."
+
+ def reset(self) -> None:
+ super().reset()
+ self._emitted = 0
+ self._trailing_dot = False
+
+ def getstate(self) -> Any:
+ if not self.buffer and not self._emitted:
+ return 0
+ return (self.buffer, self._emitted, self._trailing_dot)
+
+ def setstate(self, state: Any) -> None:
+ if state:
+ self.buffer, self._emitted, self._trailing_dot = state
+ else:
+ self.reset()
+
+ def _buffer_encode(self, data: str, errors: str, final: bool) -> tuple[bytes, int]: # ty: ignore[invalid-method-override]
+ if errors != "strict":
+ raise IDNAError(f'Unsupported error handling "{errors}"', code="unsupported_errors")
+
+ result_bytes = b""
+ size = 0
+ if data:
+ labels = _unicode_dots_re.split(data)
+ trailing_dot = b""
+ if labels:
+ if not labels[-1]:
+ trailing_dot = b"."
+ del labels[-1]
+ elif not final:
+ # Keep potentially unfinished label until the next call
+ del labels[-1]
+ if labels:
+ trailing_dot = b"."
+
+ result = []
+ for label in labels:
+ result.append(alabel(label))
+ if size:
+ size += 1
+ size += len(label)
+
+ result_bytes = b".".join(result) + trailing_dot
+ size += len(trailing_dot)
+
+ # Mirror encode(): the whole name may not exceed 253 octets, or 254
+ # when it ends with a dot. Until the input is final a trailing dot
+ # may still arrive, so only the 254-octet ceiling applies before then.
+ self._emitted += len(result_bytes)
+ if result_bytes:
+ self._trailing_dot = result_bytes.endswith(b".")
+ may_end_with_dot = self._trailing_dot or not final
+ if self._emitted > _max_domain_length + may_end_with_dot:
+ raise IDNAError("Domain too long", code="domain_too_long")
+ return result_bytes, size
+
+
+class IncrementalDecoder(codecs.BufferedIncrementalDecoder):
+ """Incremental IDNA 2008 decoder.
+
+ Buffers a partial trailing label across calls until either the next
+ label separator is seen or ``final=True``, so that streamed input is
+ decoded one whole label at a time.
+
+ The 254-octet input length limit that :func:`idna.decode` enforces is
+ applied to the accumulated input, so that streaming a name and decoding
+ it in one shot either both succeed with the same result or both raise
+ :exc:`~idna.IDNAError`.
+
+ Only the ``"strict"`` error handler is supported.
+ """
+
+ def __init__(self, errors: str = "strict") -> None:
+ super().__init__(errors)
+ self._consumed = 0 # input octets consumed so far
+
+ def reset(self) -> None:
+ super().reset()
+ self._consumed = 0
+
+ def getstate(self) -> tuple[bytes, int]:
+ return (self.buffer, self._consumed)
+
+ def setstate(self, state: tuple[bytes, int]) -> None:
+ self.buffer, self._consumed = state
+
+ def _buffer_decode(self, data: Any, errors: str, final: bool) -> tuple[str, int]: # ty: ignore[invalid-method-override]
+ if errors != "strict":
+ raise IDNAError(f'Unsupported error handling "{errors}"', code="unsupported_errors")
+
+ if not data:
+ return ("", 0)
+
+ if not isinstance(data, str):
+ try:
+ data = str(data, "ascii")
+ except UnicodeDecodeError as err:
+ raise IDNAError("Invalid ASCII in A-label", code="invalid_ascii") from err
+
+ # Mirror decode(), which rejects input longer than 254 characters
+ # (253 plus a possible trailing dot) before looking at any label.
+ # ``data`` is the unconsumed buffer plus the new input, so this is
+ # the total seen so far.
+ if self._consumed + len(data) > _max_domain_length + 1:
+ raise IDNAError("Domain too long", code="domain_too_long")
+
+ labels = _unicode_dots_re.split(data)
+ trailing_dot = ""
+ if labels:
+ if not labels[-1]:
+ trailing_dot = "."
+ del labels[-1]
+ elif not final:
+ # Keep potentially unfinished label until the next call
+ del labels[-1]
+ if labels:
+ trailing_dot = "."
+
+ result = []
+ size = 0
+ for label in labels:
+ result.append(ulabel(label))
+ if size:
+ size += 1
+ size += len(label)
+
+ result_str = ".".join(result) + trailing_dot
+ size += len(trailing_dot)
+ self._consumed += size
+ return (result_str, size)
+
+
+class StreamWriter(Codec, codecs.StreamWriter):
+ pass
+
+
+class StreamReader(Codec, codecs.StreamReader):
+ pass
+
+
+def search_function(name: str) -> codecs.CodecInfo | None:
+ """Codec search function registered with :mod:`codecs`.
+
+ Returns a :class:`codecs.CodecInfo` for the ``"idna2008"`` codec name
+ so that ``str.encode("idna2008")`` and ``bytes.decode("idna2008")``
+ invoke the IDNA 2008 codec defined in this module.
+
+ :param name: The codec name being looked up.
+ :returns: A :class:`codecs.CodecInfo` instance if ``name`` is
+ ``"idna2008"``, otherwise ``None``.
+ """
+ if name != "idna2008":
+ return None
+ return codecs.CodecInfo(
+ name=name,
+ encode=Codec().encode,
+ decode=Codec().decode, # type: ignore
+ incrementalencoder=IncrementalEncoder,
+ incrementaldecoder=IncrementalDecoder,
+ streamwriter=StreamWriter,
+ streamreader=StreamReader,
+ )
+
+
+codecs.register(search_function)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/compat.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bedee9bad9936202c60f9696d6f39b7e2423094
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/compat.py
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from typing import Any
+
+from .core import decode, encode
+
+
+def ToASCII(label: str) -> bytes:
+ """Compatibility shim for :rfc:`3490` ``ToASCII``.
+
+ Delegates to :func:`idna.encode` (IDNA 2008). Provided to ease porting
+ of code written against the legacy :mod:`encodings.idna` API; new code
+ should call :func:`idna.encode` directly.
+
+ :param label: The label or domain to encode.
+ :returns: The encoded form as ASCII :class:`bytes`.
+ """
+ return encode(label)
+
+
+def ToUnicode(label: bytes | bytearray) -> str:
+ """Compatibility shim for :rfc:`3490` ``ToUnicode``.
+
+ Delegates to :func:`idna.decode` (IDNA 2008). Provided to ease porting
+ of code written against the legacy :mod:`encodings.idna` API; new code
+ should call :func:`idna.decode` directly.
+
+ :param label: The label or domain to decode.
+ :returns: The decoded Unicode form.
+ """
+ return decode(label)
+
+
+def nameprep(s: Any) -> None:
+ """Stub for :rfc:`3491` Nameprep, which is not used by IDNA 2008.
+
+ IDNA 2008 (:rfc:`5891`) replaces Nameprep with the per-codepoint
+ validity classes from :rfc:`5892`; this function exists only to
+ return a clear error if legacy code attempts to call it.
+
+ :raises NotImplementedError: Always.
+ """
+ raise NotImplementedError("IDNA 2008 does not utilise nameprep protocol")
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/core.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4a6f0e3a7decaa48b44c901e7ed74393e93b728
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/core.py
@@ -0,0 +1,863 @@
+from __future__ import annotations
+
+import bisect
+import re
+import unicodedata
+import warnings
+from typing import Literal
+
+from . import idnadata
+from .intranges import intranges_contain
+
+_virama_combining_class = 9
+_alabel_prefix = b"xn--"
+_max_input_length = 1024
+_max_domain_length = 253 # RFC 1035 octets, excluding any trailing dot
+_STATUS_VALID, _STATUS_MAPPED, _STATUS_DEVIATION, _STATUS_IGNORED = b"VMDI"
+_unicode_dots_re = re.compile("[\u002e\u3002\uff0e\uff61]")
+_std3_disallowed_re = re.compile("[\x00-\x2c\x2f\x3a-\x40A-Z\x5b-\x60\x7b-\x7f]")
+_bidi_rtl_first = frozenset({"R", "AL"})
+_bidi_rtl_categories = frozenset({"R", "AL", "AN"})
+_bidi_rtl_allowed = frozenset({"R", "AL", "AN", "EN", "ES", "CS", "ET", "ON", "BN", "NSM"})
+_bidi_rtl_valid_ending = frozenset({"R", "AL", "EN", "AN"})
+_bidi_rtl_numeric = frozenset({"AN", "EN"})
+_bidi_ltr_allowed = frozenset({"L", "EN", "ES", "CS", "ET", "ON", "BN", "NSM"})
+_bidi_ltr_valid_ending = frozenset({"L", "EN"})
+_bidi_joiner_l_or_d = frozenset({"L", "D"})
+_bidi_joiner_r_or_d = frozenset({"R", "D"})
+
+
+def _joining_type(cp: int) -> str | None:
+ for jt, ranges in idnadata.joining_types.items():
+ if intranges_contain(cp, ranges):
+ return jt
+ return None
+
+
+# Machine-readable identifiers for the rule an :class:`IDNAError` reports.
+# These strings are stable and documented; exception message wording is not.
+_ErrorCode = Literal[
+ "input_too_long",
+ "label_too_long",
+ "domain_too_long",
+ "empty_label",
+ "empty_domain",
+ "not_nfc",
+ "hyphen_3_4",
+ "hyphen_start_end",
+ "leading_combiner",
+ "disallowed_codepoint",
+ "contextj",
+ "contexto",
+ "unknown_codepoint",
+ "bidi_rule_1",
+ "bidi_rule_2",
+ "bidi_rule_3",
+ "bidi_rule_4",
+ "bidi_rule_5",
+ "bidi_rule_6",
+ "bidi_unknown_direction",
+ "invalid_alabel",
+ "non_canonical_alabel",
+ "invalid_ascii",
+ "invalid_utf8",
+ "uts46_disallowed",
+ "uts46_std3",
+ "unsupported_errors",
+]
+
+
+class IDNAError(UnicodeError):
+ """Base exception for all IDNA-encoding related problems.
+
+ ``str(err)`` is a human-readable description of the failure. The
+ exception also carries machine-readable attributes so callers do not
+ need to parse the message:
+
+ * ``code`` -- a short, stable identifier for the rule that failed, such
+ as ``"disallowed_codepoint"`` or ``"bidi_rule_2"``; the full list is
+ documented in the README. Message wording, by contrast, may change
+ between releases.
+ * ``text`` -- the label (or, for UTS #46 processing, the domain) that
+ was being validated;
+ * ``codepoint`` -- the offending codepoint, as an ``int``;
+ * ``position`` -- the 1-based index of the offending character within
+ ``text``, matching the position quoted in the message.
+
+ Each is ``None`` when it does not apply.
+ """
+
+ code: str | None
+ text: str | None
+ codepoint: int | None
+ position: int | None
+
+ def __init__(
+ self,
+ *args: object,
+ code: _ErrorCode | None = None,
+ text: str | None = None,
+ codepoint: int | None = None,
+ position: int | None = None,
+ ) -> None:
+ super().__init__(*args)
+ self.code = code
+ self.text = text
+ self.codepoint = codepoint
+ self.position = position
+
+
+class IDNABidiError(IDNAError):
+ """Exception when bidirectional requirements are not satisfied"""
+
+
+class InvalidCodepoint(IDNAError):
+ """Exception when a disallowed or unallocated codepoint is used"""
+
+
+class InvalidCodepointContext(IDNAError):
+ """Exception when the codepoint is not valid in the context it is used"""
+
+
+def _combining_class(cp: int) -> int:
+ v = unicodedata.combining(chr(cp))
+ if v == 0 and not unicodedata.name(chr(cp)):
+ raise ValueError("Unknown character in unicodedata")
+ return v
+
+
+def _is_script(cp: str, script: str) -> bool:
+ return intranges_contain(ord(cp), idnadata.scripts[script])
+
+
+def _punycode(s: str) -> bytes:
+ return s.encode("punycode")
+
+
+def _unot(s: int) -> str:
+ return f"U+{s:04X}"
+
+
+def valid_label_length(label: bytes | str) -> bool:
+ """Check that a label does not exceed the maximum permitted length.
+
+ Per :rfc:`1035` (and :rfc:`5891` §4.2.4) a DNS label must not exceed
+ 63 octets. The argument may be either a :class:`str` (a U-label, where
+ length is measured in characters) or :class:`bytes` (an A-label, where
+ length is measured in octets).
+
+ :param label: The label to check.
+ :returns: ``True`` if the label is within the length limit, otherwise
+ ``False``.
+ """
+ return len(label) <= 63
+
+
+def valid_string_length(domain: bytes | str, trailing_dot: bool) -> bool:
+ """Check that a full domain name does not exceed the maximum length.
+
+ Per :rfc:`1035`, a domain name is limited to 253 octets when no trailing
+ dot is present, or 254 octets when one is included.
+
+ :param domain: The full (possibly multi-label) domain name.
+ :param trailing_dot: ``True`` if ``domain`` includes a trailing ``.``.
+ :returns: ``True`` if the domain is within the length limit, otherwise
+ ``False``.
+ """
+ return len(domain) <= _max_domain_length + trailing_dot
+
+
+def check_bidi(label: str, check_ltr: bool = False) -> bool:
+ """Validate the Bidi Rule from :rfc:`5893` for a single label.
+
+ The Bidi Rule constrains how bidirectional characters (Hebrew, Arabic,
+ etc.) may appear within a label. By default the check is only applied
+ when the label contains at least one right-to-left character (Unicode
+ bidirectional categories ``R``, ``AL``, or ``AN``); set ``check_ltr``
+ to ``True`` to apply it to LTR-only labels as well.
+
+ :param label: The label to validate, as a Unicode string.
+ :param check_ltr: If ``True``, apply the rules even when the label
+ contains no RTL characters.
+ :returns: ``True`` if the label satisfies the Bidi Rule.
+ :raises IDNABidiError: If any of Bidi Rule conditions 1-6 are violated,
+ or if the directional category of a codepoint cannot be determined.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ # Bidi rules should only be applied if string contains RTL characters
+ bidi_label = False
+ for idx, cp in enumerate(label, 1):
+ direction = unicodedata.bidirectional(cp)
+ if direction == "":
+ # String likely comes from a newer version of Unicode
+ raise IDNABidiError(
+ f"Unknown directionality in label {label!r} at position {idx}",
+ code="bidi_unknown_direction",
+ text=label,
+ codepoint=ord(cp),
+ position=idx,
+ )
+ if direction in _bidi_rtl_categories:
+ bidi_label = True
+ if not bidi_label and not check_ltr:
+ return True
+
+ # Bidi rule 1
+ direction = unicodedata.bidirectional(label[0])
+ if direction in _bidi_rtl_first:
+ rtl = True
+ elif direction == "L":
+ rtl = False
+ else:
+ raise IDNABidiError(
+ f"First codepoint in label {label!r} must be directionality L, R or AL",
+ code="bidi_rule_1",
+ text=label,
+ codepoint=ord(label[0]),
+ position=1,
+ )
+
+ valid_ending = False
+ ending_idx = 1
+ number_type: str | None = None
+ for idx, cp in enumerate(label, 1):
+ direction = unicodedata.bidirectional(cp)
+
+ if rtl:
+ # Bidi rule 2
+ if direction not in _bidi_rtl_allowed:
+ raise IDNABidiError(
+ f"Invalid direction for codepoint at position {idx} in a right-to-left label",
+ code="bidi_rule_2",
+ text=label,
+ codepoint=ord(cp),
+ position=idx,
+ )
+ # Bidi rule 3
+ if direction in _bidi_rtl_valid_ending:
+ valid_ending = True
+ ending_idx = idx
+ elif direction != "NSM":
+ valid_ending = False
+ ending_idx = idx
+ # Bidi rule 4
+ if direction in _bidi_rtl_numeric:
+ if not number_type:
+ number_type = direction
+ elif number_type != direction:
+ raise IDNABidiError(
+ "Can not mix numeral types in a right-to-left label",
+ code="bidi_rule_4",
+ text=label,
+ codepoint=ord(cp),
+ position=idx,
+ )
+ else:
+ # Bidi rule 5
+ if direction not in _bidi_ltr_allowed:
+ raise IDNABidiError(
+ f"Invalid direction for codepoint at position {idx} in a left-to-right label",
+ code="bidi_rule_5",
+ text=label,
+ codepoint=ord(cp),
+ position=idx,
+ )
+ # Bidi rule 6
+ if direction in _bidi_ltr_valid_ending:
+ valid_ending = True
+ ending_idx = idx
+ elif direction != "NSM":
+ valid_ending = False
+ ending_idx = idx
+
+ if not valid_ending:
+ # Rules 3 and 6 concern the last character that is not a
+ # non-spacing mark, which is what ``ending_idx`` tracks.
+ raise IDNABidiError(
+ "Label ends with illegal codepoint directionality",
+ code="bidi_rule_3" if rtl else "bidi_rule_6",
+ text=label,
+ codepoint=ord(label[ending_idx - 1]),
+ position=ending_idx,
+ )
+
+ return True
+
+
+def check_initial_combiner(label: str) -> bool:
+ """Reject labels that begin with a combining mark.
+
+ Per :rfc:`5891` §4.2.3.2 a label must not start with a character of
+ Unicode general category ``M`` (Mark).
+
+ :param label: The label to check.
+ :returns: ``True`` if the first character is not a combining mark.
+ :raises IDNAError: If the label begins with a combining character.
+ """
+ if label and unicodedata.category(label[0])[0] == "M":
+ raise IDNAError(
+ "Label begins with an illegal combining character",
+ code="leading_combiner",
+ text=label,
+ codepoint=ord(label[0]),
+ position=1,
+ )
+ return True
+
+
+def check_hyphen_ok(label: str) -> bool:
+ """Validate the hyphen restrictions for a label.
+
+ Per :rfc:`5891` §4.2.3.1 a label must not start or end with a hyphen
+ (``U+002D``), and must not have hyphens in both the third and fourth
+ positions (the prefix reserved for A-labels).
+
+ :param label: The label to check.
+ :returns: ``True`` if the hyphen restrictions are satisfied.
+ :raises IDNAError: If any of the hyphen restrictions are violated.
+ """
+ if label[2:4] == "--":
+ raise IDNAError("Label has disallowed hyphens in 3rd and 4th position", code="hyphen_3_4")
+ if label.startswith("-") or label.endswith("-"):
+ raise IDNAError("Label must not start or end with a hyphen", code="hyphen_start_end")
+ return True
+
+
+def check_nfc(label: str) -> None:
+ """Require that a label is in Unicode Normalization Form C.
+
+ :param label: The label to check.
+ :raises IDNAError: If ``label`` differs from its NFC normalisation.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ if unicodedata.normalize("NFC", label) != label:
+ raise IDNAError("Label must be in Normalization Form C", code="not_nfc")
+
+
+def valid_contextj(label: str, pos: int) -> bool:
+ """Validate the CONTEXTJ rules from :rfc:`5892` Appendix A.
+
+ These rules govern the contextual use of the joiner codepoints
+ ``U+200C`` (ZERO WIDTH NON-JOINER, Appendix A.1) and ``U+200D``
+ (ZERO WIDTH JOINER, Appendix A.2) within a label.
+
+ :param label: The label containing the codepoint.
+ :param pos: Index of the joiner codepoint within ``label``.
+ :returns: ``True`` if the codepoint at ``pos`` satisfies its CONTEXTJ
+ rule, ``False`` otherwise (including when the codepoint at
+ ``pos`` is not a recognised joiner).
+ :raises ValueError: If an adjacent codepoint has no Unicode name when
+ determining its combining class.
+ :raises IDNAError: If ``label`` exceeds the defensive input length limit.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ cp_value = ord(label[pos])
+
+ if cp_value == 0x200C:
+ if pos > 0 and _combining_class(ord(label[pos - 1])) == _virama_combining_class:
+ return True
+
+ ok = False
+ for i in range(pos - 1, -1, -1):
+ joining_type = _joining_type(ord(label[i]))
+ if joining_type == "T":
+ continue
+ if joining_type in _bidi_joiner_l_or_d:
+ ok = True
+ break
+ break
+
+ if not ok:
+ return False
+
+ ok = False
+ for i in range(pos + 1, len(label)):
+ joining_type = _joining_type(ord(label[i]))
+ if joining_type == "T":
+ continue
+ if joining_type in _bidi_joiner_r_or_d:
+ ok = True
+ break
+ break
+ return ok
+
+ if cp_value == 0x200D:
+ return pos > 0 and _combining_class(ord(label[pos - 1])) == _virama_combining_class
+
+ return False
+
+
+def valid_contexto(label: str, pos: int, exception: bool = False) -> bool:
+ """Validate the CONTEXTO rules from :rfc:`5892` Appendix A.
+
+ Covers the contextual rules for codepoints such as MIDDLE DOT
+ (``U+00B7``), Greek lower numeral sign, Hebrew punctuation, Katakana
+ middle dot, and the Arabic-Indic / Extended Arabic-Indic digit ranges.
+
+ :param label: The label containing the codepoint.
+ :param pos: Index of the codepoint within ``label``.
+ :param exception: Reserved for forward compatibility; currently unused.
+ :returns: ``True`` if the codepoint at ``pos`` satisfies its CONTEXTO
+ rule, ``False`` otherwise (including when the codepoint is not a
+ recognised CONTEXTO codepoint).
+ :raises IDNAError: If ``label`` exceeds the defensive input length limit.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ cp_value = ord(label[pos])
+
+ if cp_value == 0x00B7:
+ return 0 < pos < len(label) - 1 and ord(label[pos - 1]) == 0x006C and ord(label[pos + 1]) == 0x006C
+
+ if cp_value == 0x0375:
+ if pos < len(label) - 1 and len(label) > 1:
+ return _is_script(label[pos + 1], "Greek")
+ return False
+
+ if cp_value in {0x05F3, 0x05F4}:
+ if pos > 0:
+ return _is_script(label[pos - 1], "Hebrew")
+ return False
+
+ if cp_value == 0x30FB:
+ for cp in label:
+ if cp == "\u30fb":
+ continue
+ if _is_script(cp, "Hiragana") or _is_script(cp, "Katakana") or _is_script(cp, "Han"):
+ return True
+ return False
+
+ if 0x660 <= cp_value <= 0x669:
+ return not any(0x6F0 <= ord(cp) <= 0x06F9 for cp in label)
+
+ if 0x6F0 <= cp_value <= 0x6F9:
+ return not any(0x660 <= ord(cp) <= 0x0669 for cp in label)
+
+ return False
+
+
+def check_label(label: str | bytes | bytearray) -> None:
+ """Run the full set of IDNA 2008 validity checks on a single label.
+
+ Applies, in order: NFC normalisation (:func:`check_nfc`), hyphen
+ restrictions (:func:`check_hyphen_ok`), the no-leading-combiner rule
+ (:func:`check_initial_combiner`), per-codepoint validity (PVALID,
+ CONTEXTJ, CONTEXTO classes from :rfc:`5892`), and the Bidi Rule
+ (:func:`check_bidi`).
+
+ :param label: The label to validate. ``bytes`` or ``bytearray`` input
+ is decoded as UTF-8 first.
+ :raises IDNAError: If the label is empty or fails a structural rule.
+ :raises InvalidCodepoint: If the label contains a DISALLOWED or
+ UNASSIGNED codepoint.
+ :raises InvalidCodepointContext: If a CONTEXTJ or CONTEXTO codepoint
+ is not valid in its context.
+ :raises IDNABidiError: If the Bidi Rule is violated.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ if isinstance(label, (bytes, bytearray)):
+ try:
+ label = label.decode("utf-8")
+ except UnicodeDecodeError as err:
+ raise IDNAError("Invalid UTF-8 in label", code="invalid_utf8") from err
+ if len(label) == 0:
+ raise IDNAError("Empty Label", code="empty_label")
+
+ # Check against the domain length rather than the label length to
+ # support some UTS #46 use cases, while still bounding the work done
+ # by the label contextual rules below.
+ if not valid_string_length(label, trailing_dot=True):
+ raise IDNAError("Label too long", code="label_too_long")
+
+ check_nfc(label)
+ check_hyphen_ok(label)
+ check_initial_combiner(label)
+
+ for pos, cp in enumerate(label):
+ cp_value = ord(cp)
+ if intranges_contain(cp_value, idnadata.codepoint_classes["PVALID"]):
+ continue
+ if intranges_contain(cp_value, idnadata.codepoint_classes["CONTEXTJ"]):
+ try:
+ contextj_ok = valid_contextj(label, pos)
+ except ValueError as err:
+ raise IDNAError(
+ f"Unknown codepoint adjacent to joiner {_unot(cp_value)} at position {pos + 1} in {label!r}",
+ code="unknown_codepoint",
+ text=label,
+ codepoint=cp_value,
+ position=pos + 1,
+ ) from err
+ if not contextj_ok:
+ raise InvalidCodepointContext(
+ f"Joiner {_unot(cp_value)} not allowed at position {pos + 1} in {label!r}",
+ code="contextj",
+ text=label,
+ codepoint=cp_value,
+ position=pos + 1,
+ )
+ elif intranges_contain(cp_value, idnadata.codepoint_classes["CONTEXTO"]):
+ if not valid_contexto(label, pos):
+ raise InvalidCodepointContext(
+ f"Codepoint {_unot(cp_value)} not allowed at position {pos + 1} in {label!r}",
+ code="contexto",
+ text=label,
+ codepoint=cp_value,
+ position=pos + 1,
+ )
+ else:
+ raise InvalidCodepoint(
+ f"Codepoint {_unot(cp_value)} at position {pos + 1} of {label!r} not allowed",
+ code="disallowed_codepoint",
+ text=label,
+ codepoint=cp_value,
+ position=pos + 1,
+ )
+
+ check_bidi(label)
+
+
+def alabel(label: str) -> bytes:
+ """Convert a single U-label into its A-label form.
+
+ The result is the ASCII-Compatible Encoding (ACE) form per :rfc:`5891`
+ §4: the label is validated, Punycode-encoded, and prefixed with
+ ``xn--``. Pure ASCII labels that are already valid IDNA labels are
+ returned unchanged (as :class:`bytes`).
+
+ :param label: The label to convert, as a Unicode string.
+ :returns: The A-label as ASCII-encoded :class:`bytes`.
+ :raises IDNAError: If the label is invalid or the resulting A-label
+ exceeds 63 octets.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ try:
+ label_bytes = label.encode("ascii")
+ except UnicodeEncodeError:
+ pass
+ else:
+ ulabel(label_bytes)
+ if not valid_label_length(label_bytes):
+ raise IDNAError("Label too long", code="label_too_long")
+ return label_bytes
+
+ check_label(label)
+ label_bytes = _alabel_prefix + _punycode(label)
+
+ if not valid_label_length(label_bytes):
+ raise IDNAError("Label too long", code="label_too_long")
+
+ return label_bytes
+
+
+def ulabel(label: str | bytes | bytearray) -> str:
+ """Convert a single A-label into its U-label form.
+
+ Performs the inverse of :func:`alabel`: an ``xn--``-prefixed label is
+ Punycode-decoded and validated, and is rejected unless it is the
+ canonical A-label for the decoded U-label (:rfc:`5891` §5.3). Labels
+ that are already Unicode (or plain ASCII without the ACE prefix) are
+ validated and returned as a Unicode string.
+
+ :param label: The label to convert. ``bytes`` or ``bytearray`` input
+ is treated as ASCII.
+ :returns: The U-label as a Unicode string.
+ :raises IDNAError: If the label is malformed or fails validation.
+ """
+ if len(label) > _max_input_length:
+ raise IDNAError("Label too long", code="input_too_long")
+ if not isinstance(label, (bytes, bytearray)):
+ try:
+ label_bytes = label.encode("ascii")
+ except UnicodeEncodeError:
+ check_label(label)
+ return label
+ else:
+ label_bytes = bytes(label)
+ if not label_bytes.isascii():
+ raise IDNAError("Invalid ASCII in A-label", code="invalid_ascii")
+
+ label_bytes = label_bytes.lower()
+ if label_bytes.startswith(_alabel_prefix):
+ label_bytes = label_bytes[len(_alabel_prefix) :]
+ if not label_bytes:
+ raise IDNAError("Malformed A-label, no Punycode eligible content found", code="invalid_alabel")
+ if label_bytes.endswith(b"-"):
+ raise IDNAError("A-label must not end with a hyphen", code="invalid_alabel")
+ else:
+ check_label(label_bytes)
+ return label_bytes.decode("ascii")
+
+ try:
+ label = label_bytes.decode("punycode")
+ except UnicodeError as err:
+ raise IDNAError("Invalid A-label", code="invalid_alabel") from err
+ # RFC 5891 §5.3: the label is rejected unless re-encoding the decoded
+ # form reproduces the (lowercased) input. This catches "fake A-labels"
+ # (RFC 5890 §2.3.2.1) such as ``xn---bbk``, a non-canonical Punycode
+ # spelling of ``xn--bbk`` that would otherwise decode to the same
+ # U-label and so display identically to a different wire-format name.
+ if _punycode(label) != label_bytes:
+ raise IDNAError("A-label is not the canonical Punycode encoding of its U-label", code="non_canonical_alabel")
+ check_label(label)
+ return label
+
+
+def _check_std3(text: str, domain: str, offset: int) -> None:
+ """Raise if ``text``, a slice of ``domain`` starting at ``offset`` that
+ UTS #46 mapping left unchanged, contains an ASCII character disallowed
+ under ``UseSTD3ASCIIRules``."""
+ match = _std3_disallowed_re.search(text)
+ if match:
+ codepoint = ord(match.group())
+ position = offset + match.start() + 1
+ raise InvalidCodepoint(
+ f"Codepoint {_unot(codepoint)} not allowed at position {position} in {domain!r}",
+ code="uts46_std3",
+ text=domain,
+ codepoint=codepoint,
+ position=position,
+ )
+
+
+def _warn_transitional() -> None:
+ warnings.warn(
+ "Transitional processing is deprecated in UTS #46 and has no effect. "
+ "The transitional argument will be removed in a future version.",
+ DeprecationWarning,
+ stacklevel=3,
+ )
+
+
+def uts46_remap(domain: str, std3_rules: bool = True, transitional: bool = False) -> str:
+ """Apply the UTS #46 character mapping to a domain string.
+
+ Implements the mapping table from `UTS #46 §4
+ `_: each character is kept,
+ replaced, or rejected based on its status (``V``, ``M``, ``D``,
+ ``I``, ``X``). The result is returned in Normalisation Form C.
+
+ :param domain: The full domain name to remap.
+ :param std3_rules: If ``True``, apply UTS #46's ``UseSTD3ASCIIRules``:
+ after mapping, any ASCII character other than a lowercase letter,
+ digit, hyphen or the label separator ``.`` is rejected, whether it
+ appeared in the input or was produced by a mapping (e.g. U+FF01
+ FULLWIDTH EXCLAMATION MARK maps to ``!``). If ``False``, such
+ characters are passed through.
+ :param transitional: Deprecated and ignored. UTS #46 deprecated
+ transitional processing in Unicode 15.1 and deviation (status
+ ``D``) codepoints are now always kept, so this has no effect
+ beyond emitting a :class:`DeprecationWarning`. It will be removed
+ in a future version.
+ :returns: The remapped domain, in Normalisation Form C.
+ :raises InvalidCodepoint: If the domain contains a disallowed
+ codepoint under the chosen rules.
+ :raises IDNAError: If ``domain`` exceeds the defensive input length limit.
+ """
+ if transitional:
+ _warn_transitional()
+ if len(domain) > _max_input_length:
+ raise IDNAError("Domain too long", code="input_too_long")
+ if domain.isascii():
+ # The only ASCII mapping in UTS #46 is upper- to lowercase, and
+ # ASCII is invariant under NFC, so lowercasing is the whole job.
+ result = domain.lower()
+ if std3_rules:
+ _check_std3(result, domain, 0)
+ return result
+
+ from .uts46data import uts46_replacements, uts46_starts, uts46_statuses
+
+ # ``start`` marks the run of unchanged input not yet copied; a run is
+ # only sliced out when a character must be replaced or dropped, so the
+ # common no-change case makes no copy. STD3 is checked per output piece
+ # to report a violation at its input position.
+ output: list[str] = []
+ start = 0
+ for pos, char in enumerate(domain):
+ code_point = ord(char)
+ i = code_point if code_point < 256 else bisect.bisect_right(uts46_starts, code_point) - 1
+ status = uts46_statuses[i]
+ # UTS #46 §4: V valid, D deviation (kept), M mapped, I ignored,
+ # anything else disallowed.
+ if status == _STATUS_VALID:
+ continue
+ if status == _STATUS_MAPPED:
+ replacement = uts46_replacements[i]
+ elif status == _STATUS_DEVIATION:
+ continue
+ elif status == _STATUS_IGNORED:
+ replacement = None
+ else:
+ raise InvalidCodepoint(
+ f"Codepoint {_unot(code_point)} not allowed at position {pos + 1} in {domain!r}",
+ code="uts46_disallowed",
+ text=domain,
+ codepoint=code_point,
+ position=pos + 1,
+ )
+ if start < pos:
+ run = domain[start:pos]
+ if std3_rules:
+ _check_std3(run, domain, start)
+ output.append(run)
+ if replacement:
+ if std3_rules and _std3_disallowed_re.search(replacement):
+ raise InvalidCodepoint(
+ f"Codepoint {_unot(code_point)} not allowed at position {pos + 1} in {domain!r}",
+ code="uts46_std3",
+ text=domain,
+ codepoint=code_point,
+ position=pos + 1,
+ )
+ output.append(replacement)
+ start = pos + 1
+
+ if start == 0:
+ if std3_rules:
+ _check_std3(domain, domain, 0)
+ return unicodedata.normalize("NFC", domain)
+ tail = domain[start:]
+ if std3_rules:
+ _check_std3(tail, domain, start)
+ output.append(tail)
+ return unicodedata.normalize("NFC", "".join(output))
+
+
+def encode(
+ s: str | bytes | bytearray,
+ strict: bool = False,
+ uts46: bool = False,
+ std3_rules: bool = False,
+ transitional: bool = False,
+) -> bytes:
+ """Encode a Unicode domain name into its ASCII (A-label) form.
+
+ Splits the input on label separators (only ``U+002E`` if ``strict`` is
+ set; otherwise also IDEOGRAPHIC FULL STOP ``U+3002``, FULLWIDTH FULL
+ STOP ``U+FF0E``, and HALFWIDTH IDEOGRAPHIC FULL STOP ``U+FF61``),
+ encodes each label with :func:`alabel`, and rejoins them with ``.``.
+ Optionally pre-processes the input through :func:`uts46_remap`.
+
+ :param s: The domain name to encode.
+ :param strict: If ``True``, only ``U+002E`` is recognised as a label
+ separator.
+ :param uts46: If ``True``, apply UTS #46 mapping before encoding.
+ :param std3_rules: Forwarded to :func:`uts46_remap` when ``uts46`` is
+ ``True``.
+ :param transitional: Deprecated and ignored (see :func:`uts46_remap`):
+ emits a :class:`DeprecationWarning` and will be removed in a
+ future version.
+ :returns: The encoded domain as ASCII :class:`bytes`.
+ :raises IDNAError: If the domain is empty, contains an invalid label,
+ or exceeds the maximum domain length.
+ """
+ if transitional:
+ _warn_transitional()
+ if not isinstance(s, str):
+ try:
+ s = str(s, "ascii")
+ except (UnicodeDecodeError, TypeError) as err:
+ raise IDNAError(
+ "should pass a unicode string to the function rather than a byte string.", code="invalid_ascii"
+ ) from err
+ if len(s) > _max_input_length:
+ raise IDNAError("Domain too long", code="input_too_long")
+ if uts46:
+ s = uts46_remap(s, std3_rules)
+
+ if not valid_string_length(s, trailing_dot=True):
+ raise IDNAError("Domain too long", code="domain_too_long")
+
+ trailing_dot = False
+ result = []
+ labels = s.split(".") if strict else _unicode_dots_re.split(s)
+ if not labels or labels == [""]:
+ raise IDNAError("Empty domain", code="empty_domain")
+ if labels[-1] == "":
+ del labels[-1]
+ trailing_dot = True
+ for label in labels:
+ s = alabel(label)
+ if s:
+ result.append(s)
+ else:
+ raise IDNAError("Empty label", code="empty_label")
+ if trailing_dot:
+ result.append(b"")
+ s = b".".join(result)
+ if not valid_string_length(s, trailing_dot):
+ raise IDNAError("Domain too long", code="domain_too_long")
+ return s
+
+
+def decode(
+ s: str | bytes | bytearray,
+ strict: bool = False,
+ uts46: bool = False,
+ std3_rules: bool = False,
+ display: bool = False,
+) -> str:
+ """Decode an A-label-encoded domain name back to Unicode.
+
+ Splits the input on label separators (see :func:`encode` for the
+ rules), decodes each label with :func:`ulabel`, and rejoins them
+ with ``.``. Optionally pre-processes the input through
+ :func:`uts46_remap`.
+
+ :param s: The domain name to decode.
+ :param strict: If ``True``, only ``U+002E`` is recognised as a label
+ separator.
+ :param uts46: If ``True``, apply UTS #46 mapping before decoding.
+ :param std3_rules: Forwarded to :func:`uts46_remap` when ``uts46`` is
+ ``True``.
+ :param display: If ``True``, any ``xn--`` label that fails IDNA
+ validation is passed through unchanged (lowercased) rather than
+ aborting the whole call. Intended for "decode for display"
+ consumers (e.g. URL libraries, HTTP clients) that want to show
+ the user the label as it appears on the wire when it cannot be
+ rendered as Unicode. Matches the per-label recovery prescribed
+ by UTS #46 §4 and the WHATWG URL "domain to Unicode" algorithm.
+ :returns: The decoded domain as a Unicode string.
+ :raises IDNAError: If the input is not valid ASCII, contains an
+ invalid label, or is empty.
+ """
+ if not isinstance(s, str):
+ try:
+ s = str(s, "ascii")
+ except (UnicodeDecodeError, TypeError) as err:
+ raise IDNAError("Invalid ASCII in A-label", code="invalid_ascii") from err
+ if len(s) > _max_input_length:
+ raise IDNAError("Domain too long", code="input_too_long")
+ if uts46:
+ s = uts46_remap(s, std3_rules, False)
+ if not valid_string_length(s, trailing_dot=True):
+ raise IDNAError("Domain too long", code="domain_too_long")
+ trailing_dot = False
+ result = []
+ labels = s.split(".") if strict else _unicode_dots_re.split(s)
+ if not labels or labels == [""]:
+ raise IDNAError("Empty domain", code="empty_domain")
+ if not labels[-1]:
+ del labels[-1]
+ trailing_dot = True
+ for label in labels:
+ try:
+ u = ulabel(label)
+ except IDNAError:
+ if display and label[:4].lower() == "xn--":
+ u = label.lower()
+ else:
+ raise
+ if u:
+ result.append(u)
+ else:
+ raise IDNAError("Empty label", code="empty_label")
+ if trailing_dot:
+ result.append("")
+ return ".".join(result)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/idnadata.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/idnadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..471c047d1f213bad330de01760212af9f9da6d13
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/idnadata.py
@@ -0,0 +1,1933 @@
+# This file is automatically generated by tools/idna-data
+
+__version__ = "18.0.0"
+
+scripts = {
+ "Greek": (
+ 0x37000000374,
+ 0x37500000378,
+ 0x37A0000037E,
+ 0x37F00000380,
+ 0x38400000385,
+ 0x38600000387,
+ 0x3880000038B,
+ 0x38C0000038D,
+ 0x38E000003A2,
+ 0x3A3000003E2,
+ 0x3F000000400,
+ 0x1D2600001D2B,
+ 0x1D5D00001D62,
+ 0x1D6600001D6B,
+ 0x1DBF00001DC0,
+ 0x1F0000001F16,
+ 0x1F1800001F1E,
+ 0x1F2000001F46,
+ 0x1F4800001F4E,
+ 0x1F5000001F58,
+ 0x1F5900001F5A,
+ 0x1F5B00001F5C,
+ 0x1F5D00001F5E,
+ 0x1F5F00001F7E,
+ 0x1F8000001FB5,
+ 0x1FB600001FC5,
+ 0x1FC600001FD4,
+ 0x1FD600001FDC,
+ 0x1FDD00001FF0,
+ 0x1FF200001FF5,
+ 0x1FF600001FFF,
+ 0x212600002127,
+ 0xAB650000AB66,
+ 0x101400001018F,
+ 0x101A0000101A1,
+ 0x1D2000001D246,
+ 0x1DFF30001DFF5,
+ ),
+ "Han": (
+ 0x2E8000002E9A,
+ 0x2E9B00002EF4,
+ 0x2F0000002FD6,
+ 0x300500003006,
+ 0x300700003008,
+ 0x30210000302A,
+ 0x30380000303C,
+ 0x340000004DC0,
+ 0x4E000000A000,
+ 0xF9000000FA6E,
+ 0xFA700000FADA,
+ 0x16FE200016FE4,
+ 0x16FF000016FF7,
+ 0x200000002A6E0,
+ 0x2A7000002B81F,
+ 0x2B8200002CEAE,
+ 0x2CEB00002EBE1,
+ 0x2EBF00002EE5E,
+ 0x2F8000002FA1E,
+ 0x300000003134B,
+ 0x313500003347A,
+ ),
+ "Hebrew": (
+ 0x591000005CA,
+ 0x5D0000005EB,
+ 0x5EF000005F5,
+ 0xFB1D0000FB37,
+ 0xFB380000FB3D,
+ 0xFB3E0000FB3F,
+ 0xFB400000FB42,
+ 0xFB430000FB45,
+ 0xFB460000FB50,
+ ),
+ "Hiragana": (
+ 0x304100003097,
+ 0x309D000030A0,
+ 0x1B0010001B120,
+ 0x1B1230001B124,
+ 0x1B1320001B133,
+ 0x1B1500001B153,
+ 0x1F2000001F201,
+ ),
+ "Katakana": (
+ 0x30A1000030FB,
+ 0x30FD00003100,
+ 0x31F000003200,
+ 0x32D0000032FF,
+ 0x330000003358,
+ 0xFF660000FF70,
+ 0xFF710000FF9E,
+ 0x1AFF00001AFF4,
+ 0x1AFF50001AFFC,
+ 0x1AFFD0001AFFF,
+ 0x1B0000001B001,
+ 0x1B1200001B123,
+ 0x1B1240001B129,
+ 0x1B1550001B156,
+ 0x1B1640001B169,
+ ),
+}
+
+
+joining_types = {
+ "C": (
+ 0x64000000641,
+ 0x7FA000007FB,
+ 0x88300000886,
+ 0x180A0000180B,
+ 0x200D0000200E,
+ ),
+ "D": (
+ 0x62000000621,
+ 0x62600000627,
+ 0x62800000629,
+ 0x62A0000062F,
+ 0x63300000640,
+ 0x64100000648,
+ 0x6490000064B,
+ 0x66E00000670,
+ 0x67800000688,
+ 0x69A000006C0,
+ 0x6C1000006C3,
+ 0x6CC000006CD,
+ 0x6CE000006CF,
+ 0x6D0000006D2,
+ 0x6FA000006FD,
+ 0x6FF00000700,
+ 0x71200000715,
+ 0x71A0000071E,
+ 0x71F00000728,
+ 0x7290000072A,
+ 0x72B0000072C,
+ 0x72D0000072F,
+ 0x74E00000759,
+ 0x75C0000076B,
+ 0x76D00000771,
+ 0x77200000773,
+ 0x77500000778,
+ 0x77A00000780,
+ 0x7CA000007EB,
+ 0x84100000846,
+ 0x84800000849,
+ 0x84A00000854,
+ 0x85500000856,
+ 0x86000000861,
+ 0x86200000866,
+ 0x86800000869,
+ 0x88600000887,
+ 0x8890000088E,
+ 0x88F00000890,
+ 0x8A0000008AA,
+ 0x8AF000008B1,
+ 0x8B3000008B9,
+ 0x8BA000008C9,
+ 0x180700001808,
+ 0x182000001879,
+ 0x1887000018A9,
+ 0x18AA000018AB,
+ 0xA8400000A872,
+ 0x10AC000010AC5,
+ 0x10AD300010AD7,
+ 0x10AD800010ADD,
+ 0x10ADE00010AE1,
+ 0x10AEB00010AEF,
+ 0x10B8000010B81,
+ 0x10B8200010B83,
+ 0x10B8600010B89,
+ 0x10B8A00010B8C,
+ 0x10B8D00010B8E,
+ 0x10B9000010B91,
+ 0x10BAD00010BAF,
+ 0x10D0100010D22,
+ 0x10D2300010D24,
+ 0x10EC300010EC5,
+ 0x10EC600010EC8,
+ 0x10F3000010F33,
+ 0x10F3400010F45,
+ 0x10F5100010F54,
+ 0x10F7000010F74,
+ 0x10F7600010F82,
+ 0x10FB000010FB1,
+ 0x10FB200010FB4,
+ 0x10FB800010FB9,
+ 0x10FBB00010FBD,
+ 0x10FBE00010FC0,
+ 0x10FC100010FC2,
+ 0x10FC400010FC5,
+ 0x10FCA00010FCB,
+ 0x1E9000001E944,
+ ),
+ "L": (
+ 0xA8720000A873,
+ 0x10ACD00010ACE,
+ 0x10AD700010AD8,
+ 0x10D0000010D01,
+ 0x10ED900010EEF,
+ 0x10FCB00010FCC,
+ ),
+ "R": (
+ 0x62200000626,
+ 0x62700000628,
+ 0x6290000062A,
+ 0x62F00000633,
+ 0x64800000649,
+ 0x67100000674,
+ 0x67500000678,
+ 0x6880000069A,
+ 0x6C0000006C1,
+ 0x6C3000006CC,
+ 0x6CD000006CE,
+ 0x6CF000006D0,
+ 0x6D2000006D4,
+ 0x6D5000006D6,
+ 0x6EE000006F0,
+ 0x71000000711,
+ 0x7150000071A,
+ 0x71E0000071F,
+ 0x72800000729,
+ 0x72A0000072B,
+ 0x72C0000072D,
+ 0x72F00000730,
+ 0x74D0000074E,
+ 0x7590000075C,
+ 0x76B0000076D,
+ 0x77100000772,
+ 0x77300000775,
+ 0x7780000077A,
+ 0x84000000841,
+ 0x84600000848,
+ 0x8490000084A,
+ 0x85400000855,
+ 0x85600000859,
+ 0x86700000868,
+ 0x8690000086B,
+ 0x87000000883,
+ 0x88E0000088F,
+ 0x8AA000008AD,
+ 0x8AE000008AF,
+ 0x8B1000008B3,
+ 0x8B9000008BA,
+ 0x10AC500010AC6,
+ 0x10AC700010AC8,
+ 0x10AC900010ACB,
+ 0x10ACE00010AD3,
+ 0x10ADD00010ADE,
+ 0x10AE100010AE2,
+ 0x10AE400010AE5,
+ 0x10AEF00010AF0,
+ 0x10B8100010B82,
+ 0x10B8300010B86,
+ 0x10B8900010B8A,
+ 0x10B8C00010B8D,
+ 0x10B8E00010B90,
+ 0x10B9100010B92,
+ 0x10BA900010BAD,
+ 0x10D2200010D23,
+ 0x10EC200010EC3,
+ 0x10F3300010F34,
+ 0x10F5400010F55,
+ 0x10F7400010F76,
+ 0x10FB400010FB7,
+ 0x10FB900010FBB,
+ 0x10FBD00010FBE,
+ 0x10FC200010FC4,
+ 0x10FC900010FCA,
+ ),
+ "T": (
+ 0xAD000000AE,
+ 0x30000000370,
+ 0x4830000048A,
+ 0x591000005BE,
+ 0x5BF000005C0,
+ 0x5C1000005C3,
+ 0x5C4000005C6,
+ 0x5C7000005CA,
+ 0x6100000061B,
+ 0x61C0000061D,
+ 0x64B00000660,
+ 0x67000000671,
+ 0x6D6000006DD,
+ 0x6DF000006E5,
+ 0x6E7000006E9,
+ 0x6EA000006EE,
+ 0x70F00000710,
+ 0x71100000712,
+ 0x7300000074B,
+ 0x7A6000007B1,
+ 0x7EB000007F4,
+ 0x7FD000007FE,
+ 0x8160000081A,
+ 0x81B00000824,
+ 0x82500000828,
+ 0x8290000082E,
+ 0x8590000085C,
+ 0x897000008A0,
+ 0x8CA000008E2,
+ 0x8E300000903,
+ 0x93A0000093B,
+ 0x93C0000093D,
+ 0x94100000949,
+ 0x94D0000094E,
+ 0x95100000958,
+ 0x96200000964,
+ 0x98100000982,
+ 0x9BC000009BD,
+ 0x9C1000009C5,
+ 0x9CD000009CE,
+ 0x9E2000009E4,
+ 0x9FE000009FF,
+ 0xA0100000A03,
+ 0xA3C00000A3D,
+ 0xA4100000A43,
+ 0xA4700000A49,
+ 0xA4B00000A4E,
+ 0xA5100000A52,
+ 0xA7000000A72,
+ 0xA7500000A76,
+ 0xA8100000A83,
+ 0xABC00000ABD,
+ 0xAC100000AC6,
+ 0xAC700000AC9,
+ 0xACD00000ACE,
+ 0xAE200000AE4,
+ 0xAFA00000B00,
+ 0xB0100000B02,
+ 0xB3C00000B3D,
+ 0xB3F00000B40,
+ 0xB4100000B45,
+ 0xB4D00000B4E,
+ 0xB5300000B57,
+ 0xB6200000B64,
+ 0xB8200000B83,
+ 0xBC000000BC1,
+ 0xBCD00000BCE,
+ 0xC0000000C01,
+ 0xC0400000C05,
+ 0xC3C00000C3D,
+ 0xC3E00000C41,
+ 0xC4600000C49,
+ 0xC4A00000C4E,
+ 0xC5500000C57,
+ 0xC6200000C64,
+ 0xC8100000C82,
+ 0xCBC00000CBD,
+ 0xCBF00000CC0,
+ 0xCC600000CC7,
+ 0xCCC00000CCE,
+ 0xCE200000CE4,
+ 0xD0000000D02,
+ 0xD3B00000D3D,
+ 0xD4100000D45,
+ 0xD4D00000D4E,
+ 0xD6200000D64,
+ 0xD8100000D82,
+ 0xDCA00000DCB,
+ 0xDD200000DD5,
+ 0xDD600000DD7,
+ 0xE3100000E32,
+ 0xE3400000E3B,
+ 0xE4700000E4F,
+ 0xEB100000EB2,
+ 0xEB400000EBD,
+ 0xEC800000ECF,
+ 0xF1800000F1A,
+ 0xF3500000F36,
+ 0xF3700000F38,
+ 0xF3900000F3A,
+ 0xF7100000F7F,
+ 0xF8000000F85,
+ 0xF8600000F88,
+ 0xF8D00000F98,
+ 0xF9900000FBD,
+ 0xFC600000FC7,
+ 0x102D00001031,
+ 0x103200001038,
+ 0x10390000103B,
+ 0x103D0000103F,
+ 0x10580000105A,
+ 0x105E00001061,
+ 0x107100001075,
+ 0x108200001083,
+ 0x108500001087,
+ 0x108D0000108E,
+ 0x109D0000109E,
+ 0x135D00001360,
+ 0x171200001715,
+ 0x173200001734,
+ 0x175200001754,
+ 0x177200001774,
+ 0x17B4000017B6,
+ 0x17B7000017BE,
+ 0x17C6000017C7,
+ 0x17C9000017D4,
+ 0x17DD000017DE,
+ 0x180B0000180E,
+ 0x180F00001810,
+ 0x188500001887,
+ 0x18A9000018AA,
+ 0x192000001923,
+ 0x192700001929,
+ 0x193200001933,
+ 0x19390000193C,
+ 0x1A1700001A19,
+ 0x1A1B00001A1C,
+ 0x1A5600001A57,
+ 0x1A5800001A5F,
+ 0x1A6000001A61,
+ 0x1A6200001A63,
+ 0x1A6500001A6D,
+ 0x1A7300001A7D,
+ 0x1A7F00001A80,
+ 0x1AB000001AF1,
+ 0x1B0000001B04,
+ 0x1B3400001B35,
+ 0x1B3600001B3B,
+ 0x1B3C00001B3D,
+ 0x1B4200001B43,
+ 0x1B6B00001B74,
+ 0x1B8000001B82,
+ 0x1BA200001BA6,
+ 0x1BA800001BAA,
+ 0x1BAB00001BAE,
+ 0x1BE600001BE7,
+ 0x1BE800001BEA,
+ 0x1BED00001BEE,
+ 0x1BEF00001BF2,
+ 0x1C2C00001C34,
+ 0x1C3600001C38,
+ 0x1CD000001CD3,
+ 0x1CD400001CE1,
+ 0x1CE200001CE9,
+ 0x1CED00001CEE,
+ 0x1CF400001CF5,
+ 0x1CF800001CFA,
+ 0x1DC000001E00,
+ 0x200B0000200C,
+ 0x200E00002010,
+ 0x202A0000202F,
+ 0x206000002065,
+ 0x206A00002070,
+ 0x20D0000020F1,
+ 0x2CEF00002CF2,
+ 0x2D7F00002D80,
+ 0x2DE000002E00,
+ 0x302A0000302E,
+ 0x30990000309B,
+ 0xA66F0000A673,
+ 0xA6740000A67E,
+ 0xA69E0000A6A0,
+ 0xA6F00000A6F2,
+ 0xA8020000A803,
+ 0xA8060000A807,
+ 0xA80B0000A80C,
+ 0xA8250000A827,
+ 0xA82C0000A82D,
+ 0xA8C40000A8C6,
+ 0xA8E00000A8F2,
+ 0xA8FF0000A900,
+ 0xA9260000A92E,
+ 0xA9470000A952,
+ 0xA9800000A983,
+ 0xA9B30000A9B4,
+ 0xA9B60000A9BA,
+ 0xA9BC0000A9BE,
+ 0xA9E50000A9E6,
+ 0xAA290000AA2F,
+ 0xAA310000AA33,
+ 0xAA350000AA37,
+ 0xAA430000AA44,
+ 0xAA4C0000AA4D,
+ 0xAA7C0000AA7D,
+ 0xAAB00000AAB1,
+ 0xAAB20000AAB5,
+ 0xAAB70000AAB9,
+ 0xAABE0000AAC0,
+ 0xAAC10000AAC2,
+ 0xAAEC0000AAEE,
+ 0xAAF60000AAF7,
+ 0xABE50000ABE6,
+ 0xABE80000ABE9,
+ 0xABED0000ABEE,
+ 0xFB1E0000FB1F,
+ 0xFE000000FE10,
+ 0xFE200000FE30,
+ 0xFEFF0000FF00,
+ 0xFFF90000FFFC,
+ 0x101FD000101FE,
+ 0x102E0000102E1,
+ 0x103760001037B,
+ 0x10A0100010A04,
+ 0x10A0500010A07,
+ 0x10A0C00010A10,
+ 0x10A3800010A3B,
+ 0x10A3F00010A40,
+ 0x10AE500010AE7,
+ 0x10D2400010D28,
+ 0x10D6900010D6E,
+ 0x10EAB00010EAD,
+ 0x10ECB00010ED0,
+ 0x10EF000010F00,
+ 0x10F4600010F51,
+ 0x10F8200010F86,
+ 0x1100100011002,
+ 0x1103800011047,
+ 0x1107000011071,
+ 0x1107300011075,
+ 0x1107F00011082,
+ 0x110B3000110B7,
+ 0x110B9000110BB,
+ 0x110C2000110C3,
+ 0x1110000011103,
+ 0x111270001112C,
+ 0x1112D00011135,
+ 0x1117300011174,
+ 0x1118000011182,
+ 0x111B6000111BF,
+ 0x111C9000111CD,
+ 0x111CF000111D0,
+ 0x1122F00011232,
+ 0x1123400011235,
+ 0x1123600011238,
+ 0x1123E0001123F,
+ 0x1124100011242,
+ 0x112DF000112E0,
+ 0x112E3000112EB,
+ 0x1130000011302,
+ 0x1133B0001133D,
+ 0x1134000011341,
+ 0x113660001136D,
+ 0x1137000011375,
+ 0x113BB000113C1,
+ 0x113CE000113CF,
+ 0x113D0000113D1,
+ 0x113D2000113D3,
+ 0x113E1000113E3,
+ 0x1143800011440,
+ 0x1144200011445,
+ 0x1144600011447,
+ 0x1145E0001145F,
+ 0x114B3000114B9,
+ 0x114BA000114BB,
+ 0x114BF000114C1,
+ 0x114C2000114C4,
+ 0x115B2000115B6,
+ 0x115BC000115BE,
+ 0x115BF000115C1,
+ 0x115DC000115DE,
+ 0x116330001163B,
+ 0x1163D0001163E,
+ 0x1163F00011641,
+ 0x116AB000116AC,
+ 0x116AD000116AE,
+ 0x116B0000116B6,
+ 0x116B7000116B8,
+ 0x1171D0001171E,
+ 0x1171F00011720,
+ 0x1172200011726,
+ 0x117270001172C,
+ 0x1182F00011838,
+ 0x118390001183B,
+ 0x1193B0001193D,
+ 0x1193E0001193F,
+ 0x1194300011944,
+ 0x119D4000119D8,
+ 0x119DA000119DC,
+ 0x119E0000119E1,
+ 0x11A0100011A0B,
+ 0x11A3300011A39,
+ 0x11A3B00011A3F,
+ 0x11A4700011A48,
+ 0x11A5100011A57,
+ 0x11A5900011A5C,
+ 0x11A8A00011A97,
+ 0x11A9800011A9A,
+ 0x11B6000011B61,
+ 0x11B6200011B65,
+ 0x11B6600011B67,
+ 0x11C3000011C37,
+ 0x11C3800011C3E,
+ 0x11C3F00011C40,
+ 0x11C9200011CA8,
+ 0x11CAA00011CB1,
+ 0x11CB200011CB4,
+ 0x11CB500011CB7,
+ 0x11D3100011D37,
+ 0x11D3A00011D3B,
+ 0x11D3C00011D3E,
+ 0x11D3F00011D46,
+ 0x11D4700011D48,
+ 0x11D9000011D92,
+ 0x11D9500011D96,
+ 0x11D9700011D98,
+ 0x11DF000011DF1,
+ 0x11EF300011EF5,
+ 0x11F0000011F02,
+ 0x11F3600011F3B,
+ 0x11F4000011F41,
+ 0x11F4200011F43,
+ 0x11F5A00011F5B,
+ 0x1343000013441,
+ 0x1344700013456,
+ 0x1611E0001612A,
+ 0x1612D00016130,
+ 0x16AF000016AF5,
+ 0x16B3000016B37,
+ 0x16F4F00016F50,
+ 0x16F8F00016F93,
+ 0x16FE400016FE5,
+ 0x1BC9D0001BC9F,
+ 0x1BCA00001BCA4,
+ 0x1CF000001CF2E,
+ 0x1CF300001CF47,
+ 0x1D1270001D129,
+ 0x1D1670001D16A,
+ 0x1D1730001D183,
+ 0x1D1850001D18C,
+ 0x1D1AA0001D1AE,
+ 0x1D2420001D245,
+ 0x1D25B0001D25D,
+ 0x1DA000001DA37,
+ 0x1DA3B0001DA6D,
+ 0x1DA750001DA76,
+ 0x1DA840001DA85,
+ 0x1DA9B0001DAA0,
+ 0x1DAA10001DAB0,
+ 0x1E0000001E007,
+ 0x1E0080001E019,
+ 0x1E01B0001E022,
+ 0x1E0230001E025,
+ 0x1E0260001E02B,
+ 0x1E08F0001E090,
+ 0x1E1300001E137,
+ 0x1E2AE0001E2AF,
+ 0x1E2EC0001E2F0,
+ 0x1E4EC0001E4F0,
+ 0x1E5EE0001E5F0,
+ 0x1E6E30001E6E4,
+ 0x1E6E60001E6E7,
+ 0x1E6EE0001E6F0,
+ 0x1E6F50001E6F6,
+ 0x1E8D00001E8D7,
+ 0x1E9440001E94C,
+ 0xE0001000E0002,
+ 0xE0020000E0080,
+ 0xE0100000E01F0,
+ ),
+}
+
+
+codepoint_classes = {
+ "PVALID": (
+ 0x2D0000002E,
+ 0x300000003A,
+ 0x610000007B,
+ 0xDF000000F7,
+ 0xF800000100,
+ 0x10100000102,
+ 0x10300000104,
+ 0x10500000106,
+ 0x10700000108,
+ 0x1090000010A,
+ 0x10B0000010C,
+ 0x10D0000010E,
+ 0x10F00000110,
+ 0x11100000112,
+ 0x11300000114,
+ 0x11500000116,
+ 0x11700000118,
+ 0x1190000011A,
+ 0x11B0000011C,
+ 0x11D0000011E,
+ 0x11F00000120,
+ 0x12100000122,
+ 0x12300000124,
+ 0x12500000126,
+ 0x12700000128,
+ 0x1290000012A,
+ 0x12B0000012C,
+ 0x12D0000012E,
+ 0x12F00000130,
+ 0x13100000132,
+ 0x13500000136,
+ 0x13700000139,
+ 0x13A0000013B,
+ 0x13C0000013D,
+ 0x13E0000013F,
+ 0x14200000143,
+ 0x14400000145,
+ 0x14600000147,
+ 0x14800000149,
+ 0x14B0000014C,
+ 0x14D0000014E,
+ 0x14F00000150,
+ 0x15100000152,
+ 0x15300000154,
+ 0x15500000156,
+ 0x15700000158,
+ 0x1590000015A,
+ 0x15B0000015C,
+ 0x15D0000015E,
+ 0x15F00000160,
+ 0x16100000162,
+ 0x16300000164,
+ 0x16500000166,
+ 0x16700000168,
+ 0x1690000016A,
+ 0x16B0000016C,
+ 0x16D0000016E,
+ 0x16F00000170,
+ 0x17100000172,
+ 0x17300000174,
+ 0x17500000176,
+ 0x17700000178,
+ 0x17A0000017B,
+ 0x17C0000017D,
+ 0x17E0000017F,
+ 0x18000000181,
+ 0x18300000184,
+ 0x18500000186,
+ 0x18800000189,
+ 0x18C0000018E,
+ 0x19200000193,
+ 0x19500000196,
+ 0x1990000019C,
+ 0x19E0000019F,
+ 0x1A1000001A2,
+ 0x1A3000001A4,
+ 0x1A5000001A6,
+ 0x1A8000001A9,
+ 0x1AA000001AC,
+ 0x1AD000001AE,
+ 0x1B0000001B1,
+ 0x1B4000001B5,
+ 0x1B6000001B7,
+ 0x1B9000001BC,
+ 0x1BD000001C4,
+ 0x1CE000001CF,
+ 0x1D0000001D1,
+ 0x1D2000001D3,
+ 0x1D4000001D5,
+ 0x1D6000001D7,
+ 0x1D8000001D9,
+ 0x1DA000001DB,
+ 0x1DC000001DE,
+ 0x1DF000001E0,
+ 0x1E1000001E2,
+ 0x1E3000001E4,
+ 0x1E5000001E6,
+ 0x1E7000001E8,
+ 0x1E9000001EA,
+ 0x1EB000001EC,
+ 0x1ED000001EE,
+ 0x1EF000001F1,
+ 0x1F5000001F6,
+ 0x1F9000001FA,
+ 0x1FB000001FC,
+ 0x1FD000001FE,
+ 0x1FF00000200,
+ 0x20100000202,
+ 0x20300000204,
+ 0x20500000206,
+ 0x20700000208,
+ 0x2090000020A,
+ 0x20B0000020C,
+ 0x20D0000020E,
+ 0x20F00000210,
+ 0x21100000212,
+ 0x21300000214,
+ 0x21500000216,
+ 0x21700000218,
+ 0x2190000021A,
+ 0x21B0000021C,
+ 0x21D0000021E,
+ 0x21F00000220,
+ 0x22100000222,
+ 0x22300000224,
+ 0x22500000226,
+ 0x22700000228,
+ 0x2290000022A,
+ 0x22B0000022C,
+ 0x22D0000022E,
+ 0x22F00000230,
+ 0x23100000232,
+ 0x2330000023A,
+ 0x23C0000023D,
+ 0x23F00000241,
+ 0x24200000243,
+ 0x24700000248,
+ 0x2490000024A,
+ 0x24B0000024C,
+ 0x24D0000024E,
+ 0x24F000002B0,
+ 0x2B9000002C2,
+ 0x2C6000002D2,
+ 0x2EC000002ED,
+ 0x2EE000002EF,
+ 0x30000000340,
+ 0x34200000343,
+ 0x3460000034F,
+ 0x35000000370,
+ 0x37100000372,
+ 0x37300000374,
+ 0x37700000378,
+ 0x37B0000037E,
+ 0x39000000391,
+ 0x3AC000003CF,
+ 0x3D7000003D8,
+ 0x3D9000003DA,
+ 0x3DB000003DC,
+ 0x3DD000003DE,
+ 0x3DF000003E0,
+ 0x3E1000003E2,
+ 0x3E3000003E4,
+ 0x3E5000003E6,
+ 0x3E7000003E8,
+ 0x3E9000003EA,
+ 0x3EB000003EC,
+ 0x3ED000003EE,
+ 0x3EF000003F0,
+ 0x3F3000003F4,
+ 0x3F8000003F9,
+ 0x3FB000003FD,
+ 0x43000000460,
+ 0x46100000462,
+ 0x46300000464,
+ 0x46500000466,
+ 0x46700000468,
+ 0x4690000046A,
+ 0x46B0000046C,
+ 0x46D0000046E,
+ 0x46F00000470,
+ 0x47100000472,
+ 0x47300000474,
+ 0x47500000476,
+ 0x47700000478,
+ 0x4790000047A,
+ 0x47B0000047C,
+ 0x47D0000047E,
+ 0x47F00000480,
+ 0x48100000482,
+ 0x48300000488,
+ 0x48B0000048C,
+ 0x48D0000048E,
+ 0x48F00000490,
+ 0x49100000492,
+ 0x49300000494,
+ 0x49500000496,
+ 0x49700000498,
+ 0x4990000049A,
+ 0x49B0000049C,
+ 0x49D0000049E,
+ 0x49F000004A0,
+ 0x4A1000004A2,
+ 0x4A3000004A4,
+ 0x4A5000004A6,
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+ 0x4A9000004AA,
+ 0x4AB000004AC,
+ 0x4AD000004AE,
+ 0x4AF000004B0,
+ 0x4B1000004B2,
+ 0x4B3000004B4,
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+ 0x4B9000004BA,
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+ 0x4BF000004C0,
+ 0x4C2000004C3,
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+ 0x4ED000004EE,
+ 0x4EF000004F0,
+ 0x4F1000004F2,
+ 0x4F3000004F4,
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+ 0x4FD000004FE,
+ 0x4FF00000500,
+ 0x50100000502,
+ 0x50300000504,
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+ 0x5190000051A,
+ 0x51B0000051C,
+ 0x51D0000051E,
+ 0x51F00000520,
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+ 0x52300000524,
+ 0x52500000526,
+ 0x52700000528,
+ 0x5290000052A,
+ 0x52B0000052C,
+ 0x52D0000052E,
+ 0x52F00000530,
+ 0x5590000055A,
+ 0x56000000587,
+ 0x58800000589,
+ 0x591000005BE,
+ 0x5BF000005C0,
+ 0x5C1000005C3,
+ 0x5C4000005C6,
+ 0x5C7000005CA,
+ 0x5D0000005EB,
+ 0x5EF000005F3,
+ 0x6100000061B,
+ 0x62000000640,
+ 0x64100000660,
+ 0x66E00000675,
+ 0x679000006D4,
+ 0x6D5000006DD,
+ 0x6DF000006E9,
+ 0x6EA000006F0,
+ 0x6FA00000700,
+ 0x7100000074B,
+ 0x74D000007B2,
+ 0x7C0000007F6,
+ 0x7FD000007FE,
+ 0x8000000082E,
+ 0x8400000085C,
+ 0x8600000086B,
+ 0x87000000888,
+ 0x88900000890,
+ 0x897000008E2,
+ 0x8E300000958,
+ 0x96000000964,
+ 0x96600000970,
+ 0x97100000984,
+ 0x9850000098D,
+ 0x98F00000991,
+ 0x993000009A9,
+ 0x9AA000009B1,
+ 0x9B2000009B3,
+ 0x9B6000009BA,
+ 0x9BC000009C5,
+ 0x9C7000009C9,
+ 0x9CB000009CF,
+ 0x9D7000009D8,
+ 0x9E0000009E4,
+ 0x9E6000009F2,
+ 0x9FC000009FD,
+ 0x9FE000009FF,
+ 0xA0100000A04,
+ 0xA0500000A0B,
+ 0xA0F00000A11,
+ 0xA1300000A29,
+ 0xA2A00000A31,
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+ 0xA3500000A36,
+ 0xA3800000A3A,
+ 0xA3C00000A3D,
+ 0xA3E00000A43,
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+ 0xA4B00000A4E,
+ 0xA5100000A52,
+ 0xA5C00000A5D,
+ 0xA6600000A76,
+ 0xA8100000A84,
+ 0xA8500000A8E,
+ 0xA8F00000A92,
+ 0xA9300000AA9,
+ 0xAAA00000AB1,
+ 0xAB200000AB4,
+ 0xAB500000ABA,
+ 0xABC00000AC6,
+ 0xAC700000ACA,
+ 0xACB00000ACE,
+ 0xAD000000AD1,
+ 0xAE000000AE4,
+ 0xAE600000AF0,
+ 0xAF900000B00,
+ 0xB0100000B04,
+ 0xB0500000B0D,
+ 0xB0F00000B11,
+ 0xB1300000B29,
+ 0xB2A00000B31,
+ 0xB3200000B34,
+ 0xB3500000B3A,
+ 0xB3C00000B45,
+ 0xB4700000B49,
+ 0xB4B00000B4E,
+ 0xB5300000B58,
+ 0xB5F00000B64,
+ 0xB6600000B70,
+ 0xB7100000B72,
+ 0xB8200000B84,
+ 0xB8500000B8B,
+ 0xB8E00000B91,
+ 0xB9200000B96,
+ 0xB9900000B9B,
+ 0xB9C00000B9D,
+ 0xB9E00000BA0,
+ 0xBA300000BA5,
+ 0xBA800000BAB,
+ 0xBAE00000BBA,
+ 0xBBE00000BC3,
+ 0xBC600000BC9,
+ 0xBCA00000BCE,
+ 0xBD000000BD1,
+ 0xBD700000BD8,
+ 0xBE600000BF0,
+ 0xC0000000C0D,
+ 0xC0E00000C11,
+ 0xC1200000C29,
+ 0xC2A00000C3A,
+ 0xC3C00000C45,
+ 0xC4600000C49,
+ 0xC4A00000C4E,
+ 0xC5500000C57,
+ 0xC5800000C5B,
+ 0xC5C00000C5E,
+ 0xC6000000C64,
+ 0xC6600000C70,
+ 0xC8000000C84,
+ 0xC8500000C8D,
+ 0xC8E00000C91,
+ 0xC9200000CA9,
+ 0xCAA00000CB4,
+ 0xCB500000CBA,
+ 0xCBC00000CC5,
+ 0xCC600000CC9,
+ 0xCCA00000CCE,
+ 0xCD500000CD7,
+ 0xCDC00000CDF,
+ 0xCE000000CE4,
+ 0xCE600000CF0,
+ 0xCF100000CF4,
+ 0xD0000000D0D,
+ 0xD0E00000D11,
+ 0xD1200000D45,
+ 0xD4600000D49,
+ 0xD4A00000D4F,
+ 0xD5400000D58,
+ 0xD5F00000D64,
+ 0xD6600000D70,
+ 0xD7A00000D80,
+ 0xD8100000D84,
+ 0xD8500000D97,
+ 0xD9A00000DB2,
+ 0xDB300000DBC,
+ 0xDBD00000DBE,
+ 0xDC000000DC7,
+ 0xDCA00000DCB,
+ 0xDCF00000DD5,
+ 0xDD600000DD7,
+ 0xDD800000DE0,
+ 0xDE600000DF0,
+ 0xDF200000DF4,
+ 0xE0100000E33,
+ 0xE3400000E3B,
+ 0xE4000000E4F,
+ 0xE5000000E5A,
+ 0xE8100000E83,
+ 0xE8400000E85,
+ 0xE8600000E8B,
+ 0xE8C00000EA4,
+ 0xEA500000EA6,
+ 0xEA700000EB3,
+ 0xEB400000EBE,
+ 0xEC000000EC5,
+ 0xEC600000EC7,
+ 0xEC800000ECF,
+ 0xED000000EDA,
+ 0xEDE00000EE0,
+ 0xF0000000F01,
+ 0xF0B00000F0C,
+ 0xF1800000F1A,
+ 0xF2000000F2A,
+ 0xF3500000F36,
+ 0xF3700000F38,
+ 0xF3900000F3A,
+ 0xF3E00000F43,
+ 0xF4400000F48,
+ 0xF4900000F4D,
+ 0xF4E00000F52,
+ 0xF5300000F57,
+ 0xF5800000F5C,
+ 0xF5D00000F69,
+ 0xF6A00000F6D,
+ 0xF7100000F73,
+ 0xF7400000F75,
+ 0xF7A00000F81,
+ 0xF8200000F85,
+ 0xF8600000F93,
+ 0xF9400000F98,
+ 0xF9900000F9D,
+ 0xF9E00000FA2,
+ 0xFA300000FA7,
+ 0xFA800000FAC,
+ 0xFAD00000FB9,
+ 0xFBA00000FBD,
+ 0xFC600000FC7,
+ 0x10000000104A,
+ 0x10500000109E,
+ 0x10D0000010FB,
+ 0x10FD00001100,
+ 0x120000001249,
+ 0x124A0000124E,
+ 0x125000001257,
+ 0x125800001259,
+ 0x125A0000125E,
+ 0x126000001289,
+ 0x128A0000128E,
+ 0x1290000012B1,
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+ 0x12C0000012C1,
+ 0x12C2000012C6,
+ 0x12C8000012D7,
+ 0x12D800001311,
+ 0x131200001316,
+ 0x13180000135B,
+ 0x135D00001360,
+ 0x138000001390,
+ 0x13A0000013F6,
+ 0x14010000166D,
+ 0x166F00001680,
+ 0x16810000169B,
+ 0x16A0000016EB,
+ 0x16F1000016F9,
+ 0x170000001716,
+ 0x171F00001735,
+ 0x174000001754,
+ 0x17600000176D,
+ 0x176E00001771,
+ 0x177200001774,
+ 0x1780000017B4,
+ 0x17B6000017D4,
+ 0x17D7000017D8,
+ 0x17DC000017DE,
+ 0x17E0000017EA,
+ 0x18100000181A,
+ 0x182000001879,
+ 0x1880000018AB,
+ 0x18B0000018F6,
+ 0x19000000191F,
+ 0x19200000192C,
+ 0x19300000193C,
+ 0x19460000196E,
+ 0x197000001975,
+ 0x1980000019AC,
+ 0x19B0000019CA,
+ 0x19D0000019DA,
+ 0x1A0000001A1C,
+ 0x1A2000001A5F,
+ 0x1A6000001A7D,
+ 0x1A7F00001A8A,
+ 0x1A9000001A9A,
+ 0x1AA700001AA8,
+ 0x1AB000001ABE,
+ 0x1ABF00001AF1,
+ 0x1B0000001B4D,
+ 0x1B5000001B5A,
+ 0x1B6B00001B74,
+ 0x1B8000001BF4,
+ 0x1C0000001C38,
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+ 0x1C4D00001C7E,
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+ 0x1CD000001CD3,
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+ 0x1D0000001D2C,
+ 0x1D2F00001D30,
+ 0x1D3B00001D3C,
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+ 0x1E1900001E1A,
+ 0x1E1B00001E1C,
+ 0x1E1D00001E1E,
+ 0x1E1F00001E20,
+ 0x1E2100001E22,
+ 0x1E2300001E24,
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+ 0x1E2700001E28,
+ 0x1E2900001E2A,
+ 0x1E2B00001E2C,
+ 0x1E2D00001E2E,
+ 0x1E2F00001E30,
+ 0x1E3100001E32,
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+ 0x1E3500001E36,
+ 0x1E3700001E38,
+ 0x1E3900001E3A,
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+ 0x1E3D00001E3E,
+ 0x1E3F00001E40,
+ 0x1E4100001E42,
+ 0x1E4300001E44,
+ 0x1E4500001E46,
+ 0x1E4700001E48,
+ 0x1E4900001E4A,
+ 0x1E4B00001E4C,
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+ 0x1E4F00001E50,
+ 0x1E5100001E52,
+ 0x1E5300001E54,
+ 0x1E5500001E56,
+ 0x1E5700001E58,
+ 0x1E5900001E5A,
+ 0x1E5B00001E5C,
+ 0x1E5D00001E5E,
+ 0x1E5F00001E60,
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+ 0x1E6500001E66,
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+ 0x1E6900001E6A,
+ 0x1E6B00001E6C,
+ 0x1E6D00001E6E,
+ 0x1E6F00001E70,
+ 0x1E7100001E72,
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+ 0x1E8900001E8A,
+ 0x1E8B00001E8C,
+ 0x1E8D00001E8E,
+ 0x1E8F00001E90,
+ 0x1E9100001E92,
+ 0x1E9300001E94,
+ 0x1E9500001E9A,
+ 0x1E9C00001E9E,
+ 0x1E9F00001EA0,
+ 0x1EA100001EA2,
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+ 0x1EAD00001EAE,
+ 0x1EAF00001EB0,
+ 0x1EB100001EB2,
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+ 0x1EC100001EC2,
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+ 0x1EC500001EC6,
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+ 0x1EC900001ECA,
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+ 0x1ECD00001ECE,
+ 0x1ECF00001ED0,
+ 0x1ED100001ED2,
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+ 0x1EDD00001EDE,
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+ 0x1EE100001EE2,
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+ 0x1EEF00001EF0,
+ 0x1EF100001EF2,
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+ 0x214E0000214F,
+ 0x218400002185,
+ 0x2C3000002C60,
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+ 0x2C8F00002C90,
+ 0x2C9100002C92,
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+ 0x2C9500002C96,
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+ 0x2C9900002C9A,
+ 0x2C9B00002C9C,
+ 0x2C9D00002C9E,
+ 0x2C9F00002CA0,
+ 0x2CA100002CA2,
+ 0x2CA300002CA4,
+ 0x2CA500002CA6,
+ 0x2CA700002CA8,
+ 0x2CA900002CAA,
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+ ),
+}
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/intranges.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/intranges.py
new file mode 100644
index 0000000000000000000000000000000000000000..19d77810caf332a1f310bd3b734dab7bbd82f64c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/intranges.py
@@ -0,0 +1,55 @@
+"""
+Given a list of integers, made up of (hopefully) a small number of long runs
+of consecutive integers, compute a representation of the form
+((start1, end1), (start2, end2) ...). Then answer the question "was x present
+in the original list?" in time O(log(# runs)).
+"""
+
+import bisect
+
+
+def intranges_from_list(list_: list[int]) -> tuple[int, ...]:
+ """Represent a list of integers as a sequence of ranges:
+ ((start_0, end_0), (start_1, end_1), ...), such that the original
+ integers are exactly those x such that start_i <= x < end_i for some i.
+
+ Ranges are encoded as single integers (start << 32 | end), not as tuples.
+ """
+
+ sorted_list = sorted(list_)
+ ranges = []
+ last_write = -1
+ for i in range(len(sorted_list)):
+ if i + 1 < len(sorted_list) and sorted_list[i] == sorted_list[i + 1] - 1:
+ continue
+ current_range = sorted_list[last_write + 1 : i + 1]
+ ranges.append(_encode_range(current_range[0], current_range[-1] + 1))
+ last_write = i
+
+ return tuple(ranges)
+
+
+def _encode_range(start: int, end: int) -> int:
+ return (start << 32) | end
+
+
+def _decode_range(r: int) -> tuple[int, int]:
+ return (r >> 32), (r & ((1 << 32) - 1))
+
+
+def intranges_contain(int_: int, ranges: tuple[int, ...]) -> bool:
+ """Determine if `int_` falls into one of the ranges in `ranges`."""
+ tuple_ = _encode_range(int_, 0)
+ pos = bisect.bisect_left(ranges, tuple_)
+ # we could be immediately ahead of a tuple (start, end)
+ # with start < int_ <= end
+ if pos > 0:
+ left, right = _decode_range(ranges[pos - 1])
+ if left <= int_ < right:
+ return True
+ # or we could be immediately behind a tuple (int_, end)
+ if pos < len(ranges):
+ left, _ = _decode_range(ranges[pos])
+ if left == int_:
+ return True
+ return False
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/package_data.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/package_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f9ff7e825c609b14a30a845044034c64c46101a
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/package_data.py
@@ -0,0 +1 @@
+__version__ = "3.20"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/py.typed b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/uts46data.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/uts46data.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbfb60ff4b34a950babe9409cffd08d1b86913d3
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/idna/uts46data.py
@@ -0,0 +1,17117 @@
+# This file is automatically generated by tools/idna-data
+
+from __future__ import annotations
+
+from array import array
+
+"""IDNA Mapping Table from UTS46."""
+
+
+__version__ = "18.0.0"
+
+uts46_starts: array[int] = array(
+ "I",
+ (
+ 0x0,
+ 0x1,
+ 0x2,
+ 0x3,
+ 0x4,
+ 0x5,
+ 0x6,
+ 0x7,
+ 0x8,
+ 0x9,
+ 0xA,
+ 0xB,
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+ "(오전)",
+ "(오후)",
+ None,
+ "(一)",
+ "(二)",
+ "(三)",
+ "(四)",
+ "(五)",
+ "(六)",
+ "(七)",
+ "(八)",
+ "(九)",
+ "(十)",
+ "(月)",
+ "(火)",
+ "(水)",
+ "(木)",
+ "(金)",
+ "(土)",
+ "(日)",
+ "(株)",
+ "(有)",
+ "(社)",
+ "(名)",
+ "(特)",
+ "(財)",
+ "(祝)",
+ "(労)",
+ "(代)",
+ "(呼)",
+ "(学)",
+ "(監)",
+ "(企)",
+ "(資)",
+ "(協)",
+ "(祭)",
+ "(休)",
+ "(自)",
+ "(至)",
+ "問",
+ "幼",
+ "文",
+ "箏",
+ None,
+ "pte",
+ "21",
+ "22",
+ "23",
+ "24",
+ "25",
+ "26",
+ "27",
+ "28",
+ "29",
+ "30",
+ "31",
+ "32",
+ "33",
+ "34",
+ "35",
+ "ᄀ",
+ "ᄂ",
+ "ᄃ",
+ "ᄅ",
+ "ᄆ",
+ "ᄇ",
+ "ᄉ",
+ "ᄋ",
+ "ᄌ",
+ "ᄎ",
+ "ᄏ",
+ "ᄐ",
+ "ᄑ",
+ "ᄒ",
+ "가",
+ "나",
+ "다",
+ "라",
+ "마",
+ "바",
+ "사",
+ "아",
+ "자",
+ "차",
+ "카",
+ "타",
+ "파",
+ "하",
+ "참고",
+ "주의",
+ "우",
+ None,
+ "一",
+ "二",
+ "三",
+ "四",
+ "五",
+ "六",
+ "七",
+ "八",
+ "九",
+ "十",
+ "月",
+ "火",
+ "水",
+ "木",
+ "金",
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+ "日",
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+ "特",
+ "財",
+ "祝",
+ "労",
+ "秘",
+ "男",
+ "女",
+ "適",
+ "優",
+ "印",
+ "注",
+ "項",
+ "休",
+ "写",
+ "正",
+ "上",
+ "中",
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+ "右",
+ "医",
+ "宗",
+ "学",
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+ "夜",
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+ "37",
+ "38",
+ "39",
+ "40",
+ "41",
+ "42",
+ "43",
+ "44",
+ "45",
+ "46",
+ "47",
+ "48",
+ "49",
+ "50",
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+ "2月",
+ "3月",
+ "4月",
+ "5月",
+ "6月",
+ "7月",
+ "8月",
+ "9月",
+ "10月",
+ "11月",
+ "12月",
+ "hg",
+ "erg",
+ "ev",
+ "ltd",
+ "ア",
+ "イ",
+ "ウ",
+ "エ",
+ "オ",
+ "カ",
+ "キ",
+ "ク",
+ "ケ",
+ "コ",
+ "サ",
+ "シ",
+ "ス",
+ "セ",
+ "ソ",
+ "タ",
+ "チ",
+ "ツ",
+ "テ",
+ "ト",
+ "ナ",
+ "ニ",
+ "ヌ",
+ "ネ",
+ "ノ",
+ "ハ",
+ "ヒ",
+ "フ",
+ "ヘ",
+ "ホ",
+ "マ",
+ "ミ",
+ "ム",
+ "メ",
+ "モ",
+ "ヤ",
+ "ユ",
+ "ヨ",
+ "ラ",
+ "リ",
+ "ル",
+ "レ",
+ "ロ",
+ "ワ",
+ "ヰ",
+ "ヱ",
+ "ヲ",
+ "令和",
+ "アパート",
+ "アルファ",
+ "アンペア",
+ "アール",
+ "イニング",
+ "インチ",
+ "ウォン",
+ "エスクード",
+ "エーカー",
+ "オンス",
+ "オーム",
+ "カイリ",
+ "カラット",
+ "カロリー",
+ "ガロン",
+ "ガンマ",
+ "ギガ",
+ "ギニー",
+ "キュリー",
+ "ギルダー",
+ "キロ",
+ "キログラム",
+ "キロメートル",
+ "キロワット",
+ "グラム",
+ "グラムトン",
+ "クルゼイロ",
+ "クローネ",
+ "ケース",
+ "コルナ",
+ "コーポ",
+ "サイクル",
+ "サンチーム",
+ "シリング",
+ "センチ",
+ "セント",
+ "ダース",
+ "デシ",
+ "ドル",
+ "トン",
+ "ナノ",
+ "ノット",
+ "ハイツ",
+ "パーセント",
+ "パーツ",
+ "バーレル",
+ "ピアストル",
+ "ピクル",
+ "ピコ",
+ "ビル",
+ "ファラッド",
+ "フィート",
+ "ブッシェル",
+ "フラン",
+ "ヘクタール",
+ "ペソ",
+ "ペニヒ",
+ "ヘルツ",
+ "ペンス",
+ "ページ",
+ "ベータ",
+ "ポイント",
+ "ボルト",
+ "ホン",
+ "ポンド",
+ "ホール",
+ "ホーン",
+ "マイクロ",
+ "マイル",
+ "マッハ",
+ "マルク",
+ "マンション",
+ "ミクロン",
+ "ミリ",
+ "ミリバール",
+ "メガ",
+ "メガトン",
+ "メートル",
+ "ヤード",
+ "ヤール",
+ "ユアン",
+ "リットル",
+ "リラ",
+ "ルピー",
+ "ルーブル",
+ "レム",
+ "レントゲン",
+ "ワット",
+ "0点",
+ "1点",
+ "2点",
+ "3点",
+ "4点",
+ "5点",
+ "6点",
+ "7点",
+ "8点",
+ "9点",
+ "10点",
+ "11点",
+ "12点",
+ "13点",
+ "14点",
+ "15点",
+ "16点",
+ "17点",
+ "18点",
+ "19点",
+ "20点",
+ "21点",
+ "22点",
+ "23点",
+ "24点",
+ "hpa",
+ "da",
+ "au",
+ "bar",
+ "ov",
+ "pc",
+ "dm",
+ "dm2",
+ "dm3",
+ "iu",
+ "平成",
+ "昭和",
+ "大正",
+ "明治",
+ "株式会社",
+ "pa",
+ "na",
+ "μa",
+ "ma",
+ "ka",
+ "kb",
+ "mb",
+ "gb",
+ "cal",
+ "kcal",
+ "pf",
+ "nf",
+ "μf",
+ "μg",
+ "mg",
+ "kg",
+ "hz",
+ "khz",
+ "mhz",
+ "ghz",
+ "thz",
+ "μl",
+ "ml",
+ "dl",
+ "kl",
+ "fm",
+ "nm",
+ "μm",
+ "mm",
+ "cm",
+ "km",
+ "mm2",
+ "cm2",
+ "m2",
+ "km2",
+ "mm3",
+ "cm3",
+ "m3",
+ "km3",
+ "m∕s",
+ "m∕s2",
+ "pa",
+ "kpa",
+ "mpa",
+ "gpa",
+ "rad",
+ "rad∕s",
+ "rad∕s2",
+ "ps",
+ "ns",
+ "μs",
+ "ms",
+ "pv",
+ "nv",
+ "μv",
+ "mv",
+ "kv",
+ "mv",
+ "pw",
+ "nw",
+ "μw",
+ "mw",
+ "kw",
+ "mw",
+ "kω",
+ "mω",
+ None,
+ "bq",
+ "cc",
+ "cd",
+ "c∕kg",
+ None,
+ "db",
+ "gy",
+ "ha",
+ "hp",
+ "in",
+ "kk",
+ "km",
+ "kt",
+ "lm",
+ "ln",
+ "log",
+ "lx",
+ "mb",
+ "mil",
+ "mol",
+ "ph",
+ None,
+ "ppm",
+ "pr",
+ "sr",
+ "sv",
+ "wb",
+ "v∕m",
+ "a∕m",
+ "1日",
+ "2日",
+ "3日",
+ "4日",
+ "5日",
+ "6日",
+ "7日",
+ "8日",
+ "9日",
+ "10日",
+ "11日",
+ "12日",
+ "13日",
+ "14日",
+ "15日",
+ "16日",
+ "17日",
+ "18日",
+ "19日",
+ "20日",
+ "21日",
+ "22日",
+ "23日",
+ "24日",
+ "25日",
+ "26日",
+ "27日",
+ "28日",
+ "29日",
+ "30日",
+ "31日",
+ "gal",
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ "ꙁ",
+ None,
+ "ꙃ",
+ None,
+ "ꙅ",
+ None,
+ "ꙇ",
+ None,
+ "ꙉ",
+ None,
+ "ꙋ",
+ None,
+ "ꙍ",
+ None,
+ "ꙏ",
+ None,
+ "ꙑ",
+ None,
+ "ꙓ",
+ None,
+ "ꙕ",
+ None,
+ "ꙗ",
+ None,
+ "ꙙ",
+ None,
+ "ꙛ",
+ None,
+ "ꙝ",
+ None,
+ "ꙟ",
+ None,
+ "ꙡ",
+ None,
+ "ꙣ",
+ None,
+ "ꙥ",
+ None,
+ "ꙧ",
+ None,
+ "ꙩ",
+ None,
+ "ꙫ",
+ None,
+ "ꙭ",
+ None,
+ "ꚁ",
+ None,
+ "ꚃ",
+ None,
+ "ꚅ",
+ None,
+ "ꚇ",
+ None,
+ "ꚉ",
+ None,
+ "ꚋ",
+ None,
+ "ꚍ",
+ None,
+ "ꚏ",
+ None,
+ "ꚑ",
+ None,
+ "ꚓ",
+ None,
+ "ꚕ",
+ None,
+ "ꚗ",
+ None,
+ "ꚙ",
+ None,
+ "ꚛ",
+ None,
+ "ъ",
+ "ь",
+ None,
+ None,
+ None,
+ "ꜣ",
+ None,
+ "ꜥ",
+ None,
+ "ꜧ",
+ None,
+ "ꜩ",
+ None,
+ "ꜫ",
+ None,
+ "ꜭ",
+ None,
+ "ꜯ",
+ None,
+ "ꜳ",
+ None,
+ "ꜵ",
+ None,
+ "ꜷ",
+ None,
+ "ꜹ",
+ None,
+ "ꜻ",
+ None,
+ "ꜽ",
+ None,
+ "ꜿ",
+ None,
+ "ꝁ",
+ None,
+ "ꝃ",
+ None,
+ "ꝅ",
+ None,
+ "ꝇ",
+ None,
+ "ꝉ",
+ None,
+ "ꝋ",
+ None,
+ "ꝍ",
+ None,
+ "ꝏ",
+ None,
+ "ꝑ",
+ None,
+ "ꝓ",
+ None,
+ "ꝕ",
+ None,
+ "ꝗ",
+ None,
+ "ꝙ",
+ None,
+ "ꝛ",
+ None,
+ "ꝝ",
+ None,
+ "ꝟ",
+ None,
+ "ꝡ",
+ None,
+ "ꝣ",
+ None,
+ "ꝥ",
+ None,
+ "ꝧ",
+ None,
+ "ꝩ",
+ None,
+ "ꝫ",
+ None,
+ "ꝭ",
+ None,
+ "ꝯ",
+ None,
+ "ꝯ",
+ None,
+ "ꝺ",
+ None,
+ "ꝼ",
+ None,
+ "ᵹ",
+ "ꝿ",
+ None,
+ "ꞁ",
+ None,
+ "ꞃ",
+ None,
+ "ꞅ",
+ None,
+ "ꞇ",
+ None,
+ "ꞌ",
+ None,
+ "ɥ",
+ None,
+ "ꞑ",
+ None,
+ "ꞓ",
+ None,
+ "ꞗ",
+ None,
+ "ꞙ",
+ None,
+ "ꞛ",
+ None,
+ "ꞝ",
+ None,
+ "ꞟ",
+ None,
+ "ꞡ",
+ None,
+ "ꞣ",
+ None,
+ "ꞥ",
+ None,
+ "ꞧ",
+ None,
+ "ꞩ",
+ None,
+ "ɦ",
+ "ɜ",
+ "ɡ",
+ "ɬ",
+ "ɪ",
+ None,
+ "ʞ",
+ "ʇ",
+ "ʝ",
+ "ꭓ",
+ "ꞵ",
+ None,
+ "ꞷ",
+ None,
+ "ꞹ",
+ None,
+ "ꞻ",
+ None,
+ "ꞽ",
+ None,
+ "ꞿ",
+ None,
+ "ꟁ",
+ None,
+ "ꟃ",
+ None,
+ "ꞔ",
+ "ʂ",
+ "ᶎ",
+ "ꟈ",
+ None,
+ "ꟊ",
+ None,
+ "ɤ",
+ "",
+ None,
+ "",
+ None,
+ "ꟑ",
+ None,
+ "ꟓ",
+ None,
+ "ꟕ",
+ None,
+ "ꟗ",
+ None,
+ "ꟙ",
+ None,
+ "",
+ None,
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+ "ɷ",
+ None,
+ "ɼ",
+ None,
+ "s",
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+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ "ꜧ",
+ "ꬷ",
+ "ɫ",
+ "ꭒ",
+ None,
+ "ʍ",
+ None,
+ "ꭋ",
+ "ꭌ",
+ None,
+ "Ꭰ",
+ "Ꭱ",
+ "Ꭲ",
+ "Ꭳ",
+ "Ꭴ",
+ "Ꭵ",
+ "Ꭶ",
+ "Ꭷ",
+ "Ꭸ",
+ "Ꭹ",
+ "Ꭺ",
+ "Ꭻ",
+ "Ꭼ",
+ "Ꭽ",
+ "Ꭾ",
+ "Ꭿ",
+ "Ꮀ",
+ "Ꮁ",
+ "Ꮂ",
+ "Ꮃ",
+ "Ꮄ",
+ "Ꮅ",
+ "Ꮆ",
+ "Ꮇ",
+ "Ꮈ",
+ "Ꮉ",
+ "Ꮊ",
+ "Ꮋ",
+ "Ꮌ",
+ "Ꮍ",
+ "Ꮎ",
+ "Ꮏ",
+ "Ꮐ",
+ "Ꮑ",
+ "Ꮒ",
+ "Ꮓ",
+ "Ꮔ",
+ "Ꮕ",
+ "Ꮖ",
+ "Ꮗ",
+ "Ꮘ",
+ "Ꮙ",
+ "Ꮚ",
+ "Ꮛ",
+ "Ꮜ",
+ "Ꮝ",
+ "Ꮞ",
+ "Ꮟ",
+ "Ꮠ",
+ "Ꮡ",
+ "Ꮢ",
+ "Ꮣ",
+ "Ꮤ",
+ "Ꮥ",
+ "Ꮦ",
+ "Ꮧ",
+ "Ꮨ",
+ "Ꮩ",
+ "Ꮪ",
+ "Ꮫ",
+ "Ꮬ",
+ "Ꮭ",
+ "Ꮮ",
+ "Ꮯ",
+ "Ꮰ",
+ "Ꮱ",
+ "Ꮲ",
+ "Ꮳ",
+ "Ꮴ",
+ "Ꮵ",
+ "Ꮶ",
+ "Ꮷ",
+ "Ꮸ",
+ "Ꮹ",
+ "Ꮺ",
+ "Ꮻ",
+ "Ꮼ",
+ "Ꮽ",
+ "Ꮾ",
+ "Ꮿ",
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ "豈",
+ "更",
+ "車",
+ "賈",
+ "滑",
+ "串",
+ "句",
+ "龜",
+ "契",
+ "金",
+ "喇",
+ "奈",
+ "懶",
+ "癩",
+ "羅",
+ "蘿",
+ "螺",
+ "裸",
+ "邏",
+ "樂",
+ "洛",
+ "烙",
+ "珞",
+ "落",
+ "酪",
+ "駱",
+ "亂",
+ "卵",
+ "欄",
+ "爛",
+ "蘭",
+ "鸞",
+ "嵐",
+ "濫",
+ "藍",
+ "襤",
+ "拉",
+ "臘",
+ "蠟",
+ "廊",
+ "朗",
+ "浪",
+ "狼",
+ "郎",
+ "來",
+ "冷",
+ "勞",
+ "擄",
+ "櫓",
+ "爐",
+ "盧",
+ "老",
+ "蘆",
+ "虜",
+ "路",
+ "露",
+ "魯",
+ "鷺",
+ "碌",
+ "祿",
+ "綠",
+ "菉",
+ "錄",
+ "鹿",
+ "論",
+ "壟",
+ "弄",
+ "籠",
+ "聾",
+ "牢",
+ "磊",
+ "賂",
+ "雷",
+ "壘",
+ "屢",
+ "樓",
+ "淚",
+ "漏",
+ "累",
+ "縷",
+ "陋",
+ "勒",
+ "肋",
+ "凜",
+ "凌",
+ "稜",
+ "綾",
+ "菱",
+ "陵",
+ "讀",
+ "拏",
+ "樂",
+ "諾",
+ "丹",
+ "寧",
+ "怒",
+ "率",
+ "異",
+ "北",
+ "磻",
+ "便",
+ "復",
+ "不",
+ "泌",
+ "數",
+ "索",
+ "參",
+ "塞",
+ "省",
+ "葉",
+ "說",
+ "殺",
+ "辰",
+ "沈",
+ "拾",
+ "若",
+ "掠",
+ "略",
+ "亮",
+ "兩",
+ "凉",
+ "梁",
+ "糧",
+ "良",
+ "諒",
+ "量",
+ "勵",
+ "呂",
+ "女",
+ "廬",
+ "旅",
+ "濾",
+ "礪",
+ "閭",
+ "驪",
+ "麗",
+ "黎",
+ "力",
+ "曆",
+ "歷",
+ "轢",
+ "年",
+ "憐",
+ "戀",
+ "撚",
+ "漣",
+ "煉",
+ "璉",
+ "秊",
+ "練",
+ "聯",
+ "輦",
+ "蓮",
+ "連",
+ "鍊",
+ "列",
+ "劣",
+ "咽",
+ "烈",
+ "裂",
+ "說",
+ "廉",
+ "念",
+ "捻",
+ "殮",
+ "簾",
+ "獵",
+ "令",
+ "囹",
+ "寧",
+ "嶺",
+ "怜",
+ "玲",
+ "瑩",
+ "羚",
+ "聆",
+ "鈴",
+ "零",
+ "靈",
+ "領",
+ "例",
+ "禮",
+ "醴",
+ "隸",
+ "惡",
+ "了",
+ "僚",
+ "寮",
+ "尿",
+ "料",
+ "樂",
+ "燎",
+ "療",
+ "蓼",
+ "遼",
+ "龍",
+ "暈",
+ "阮",
+ "劉",
+ "杻",
+ "柳",
+ "流",
+ "溜",
+ "琉",
+ "留",
+ "硫",
+ "紐",
+ "類",
+ "六",
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+ "陸",
+ "倫",
+ "崙",
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+ "律",
+ "慄",
+ "栗",
+ "率",
+ "隆",
+ "利",
+ "吏",
+ "履",
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+ "李",
+ "梨",
+ "泥",
+ "理",
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+ "裏",
+ "裡",
+ "里",
+ "離",
+ "匿",
+ "溺",
+ "吝",
+ "燐",
+ "璘",
+ "藺",
+ "隣",
+ "鱗",
+ "麟",
+ "林",
+ "淋",
+ "臨",
+ "立",
+ "笠",
+ "粒",
+ "狀",
+ "炙",
+ "識",
+ "什",
+ "茶",
+ "刺",
+ "切",
+ "度",
+ "拓",
+ "糖",
+ "宅",
+ "洞",
+ "暴",
+ "輻",
+ "行",
+ "降",
+ "見",
+ "廓",
+ "兀",
+ "嗀",
+ None,
+ "塚",
+ None,
+ "晴",
+ None,
+ "凞",
+ "猪",
+ "益",
+ "礼",
+ "神",
+ "祥",
+ "福",
+ "靖",
+ "精",
+ "羽",
+ None,
+ "蘒",
+ None,
+ "諸",
+ None,
+ "逸",
+ "都",
+ None,
+ "飯",
+ "飼",
+ "館",
+ "鶴",
+ "郞",
+ "隷",
+ "侮",
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+ "𐓧",
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+ "𐓺",
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+ "𐖞",
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+ "𐖡",
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+ "𐳩",
+ "𐳪",
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+ "𐳭",
+ "𐳮",
+ "𐳯",
+ "𐳰",
+ "𐳱",
+ "𐳲",
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+ None,
+ None,
+ None,
+ None,
+ None,
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+ "𑣋",
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+ "𑣍",
+ "𑣎",
+ "𑣏",
+ "𑣐",
+ "𑣑",
+ "𑣒",
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+ "𑣔",
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+ "𑣗",
+ "𑣘",
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+ "𑣜",
+ "𑣝",
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+ "𖹩",
+ "𖹪",
+ "𖹫",
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+ "𖹭",
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+ "𖹯",
+ "𖹰",
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+ "𖹹",
+ "𖹺",
+ "𖹻",
+ "𖹼",
+ "𖹽",
+ "𖹾",
+ "𖹿",
+ None,
+ None,
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+ "𝅘𝅥𝅱",
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+ "憎",
+ "憲",
+ "憤",
+ "憯",
+ "懞",
+ "懲",
+ "懶",
+ "成",
+ "戛",
+ "扝",
+ "抱",
+ "拔",
+ "捐",
+ "𢬌",
+ "挽",
+ "拼",
+ "捨",
+ "掃",
+ "揤",
+ "𢯱",
+ "搢",
+ "揅",
+ "掩",
+ "㨮",
+ "摩",
+ "摾",
+ "撝",
+ "摷",
+ "㩬",
+ "敏",
+ "敬",
+ "𣀊",
+ "旣",
+ "書",
+ "晉",
+ "㬙",
+ "暑",
+ "㬈",
+ "㫤",
+ "冒",
+ "冕",
+ "最",
+ "暜",
+ "肭",
+ "䏙",
+ "朗",
+ "望",
+ "朡",
+ "杞",
+ "杓",
+ "𣏃",
+ "㭉",
+ "柺",
+ "枅",
+ "桒",
+ "梅",
+ "𣑭",
+ "梎",
+ "栟",
+ "椔",
+ "㮝",
+ "楂",
+ "榣",
+ "槪",
+ "檨",
+ "𣚣",
+ "櫛",
+ "㰘",
+ "次",
+ "𣢧",
+ "歔",
+ "㱎",
+ "歲",
+ "殟",
+ "殺",
+ "殻",
+ "𣪍",
+ "𡴋",
+ "𣫺",
+ "汎",
+ "𣲼",
+ "沿",
+ "泍",
+ "汧",
+ "洖",
+ "派",
+ "海",
+ "流",
+ "浩",
+ "浸",
+ "涅",
+ "𣴞",
+ "洴",
+ "港",
+ "湮",
+ "㴳",
+ "滋",
+ "滇",
+ "𣻑",
+ "淹",
+ "潮",
+ "𣽞",
+ "𣾎",
+ "濆",
+ "瀹",
+ "瀞",
+ "瀛",
+ "㶖",
+ "灊",
+ "災",
+ "灷",
+ "炭",
+ "𠔥",
+ "煅",
+ "𤉣",
+ "熜",
+ "𤎫",
+ "爨",
+ "爵",
+ "牐",
+ "𤘈",
+ "犀",
+ "犕",
+ "𤜵",
+ "𤠔",
+ "獺",
+ "王",
+ "㺬",
+ "玥",
+ "㺸",
+ "瑇",
+ "瑜",
+ "瑱",
+ "璅",
+ "瓊",
+ "㼛",
+ "甤",
+ "𤰶",
+ "甾",
+ "𤲒",
+ "異",
+ "𢆟",
+ "瘐",
+ "𤾡",
+ "𤾸",
+ "𥁄",
+ "㿼",
+ "䀈",
+ "直",
+ "𥃳",
+ "𥃲",
+ "𥄙",
+ "𥄳",
+ "眞",
+ "真",
+ "睊",
+ "䀹",
+ "瞋",
+ "䁆",
+ "䂖",
+ "𥐝",
+ "硎",
+ "碌",
+ "磌",
+ "䃣",
+ "𥘦",
+ "祖",
+ "𥚚",
+ "𥛅",
+ "福",
+ "秫",
+ "䄯",
+ "穀",
+ "穊",
+ "穏",
+ "𥥼",
+ "𥪧",
+ "竮",
+ "䈂",
+ "𥮫",
+ "篆",
+ "築",
+ "䈧",
+ "𥲀",
+ "糒",
+ "䊠",
+ "糨",
+ "糣",
+ "紀",
+ "𥾆",
+ "絣",
+ "䌁",
+ "緇",
+ "縂",
+ "繅",
+ "䌴",
+ "𦈨",
+ "𦉇",
+ "䍙",
+ "𦋙",
+ "罺",
+ "𦌾",
+ "羕",
+ "翺",
+ "者",
+ "𦓚",
+ "𦔣",
+ "聠",
+ "𦖨",
+ "聰",
+ "𣍟",
+ "䏕",
+ "育",
+ "脃",
+ "䐋",
+ "脾",
+ "媵",
+ "𦞧",
+ "𦞵",
+ "𣎓",
+ "𣎜",
+ "舁",
+ "舄",
+ "辞",
+ "䑫",
+ "芑",
+ "芋",
+ "芝",
+ "劳",
+ "花",
+ "芳",
+ "芽",
+ "苦",
+ "𦬼",
+ "若",
+ "茝",
+ "荣",
+ "莭",
+ "茣",
+ "莽",
+ "菧",
+ "著",
+ "荓",
+ "菊",
+ "菌",
+ "菜",
+ "𦰶",
+ "𦵫",
+ "𦳕",
+ "䔫",
+ "蓱",
+ "蓳",
+ "蔖",
+ "𧏊",
+ "蕤",
+ "𦼬",
+ "䕝",
+ "䕡",
+ "𦾱",
+ "𧃒",
+ "䕫",
+ "虐",
+ "虜",
+ "虧",
+ "虩",
+ "蚩",
+ "蚈",
+ "蜎",
+ "蛢",
+ "蝹",
+ "蜨",
+ "蝫",
+ "螆",
+ "䗗",
+ "蟡",
+ "蠁",
+ "䗹",
+ "衠",
+ "衣",
+ "𧙧",
+ "裗",
+ "裞",
+ "䘵",
+ "裺",
+ "㒻",
+ "𧢮",
+ "𧥦",
+ "䚾",
+ "䛇",
+ "誠",
+ "諭",
+ "變",
+ "豕",
+ "𧲨",
+ "貫",
+ "賁",
+ "贛",
+ "起",
+ "𧼯",
+ "𠠄",
+ "跋",
+ "趼",
+ "跰",
+ "𠣞",
+ "軔",
+ "輸",
+ "𨗒",
+ "𨗭",
+ "邔",
+ "郱",
+ "鄑",
+ "𨜮",
+ "鄛",
+ "鈸",
+ "鋗",
+ "鋘",
+ "鉼",
+ "鏹",
+ "鐕",
+ "𨯺",
+ "開",
+ "䦕",
+ "閷",
+ "𨵷",
+ "䧦",
+ "雃",
+ "嶲",
+ "霣",
+ "𩅅",
+ "𩈚",
+ "䩮",
+ "䩶",
+ "韠",
+ "𩐊",
+ "䪲",
+ "𩒖",
+ "頋",
+ "頩",
+ "𩖶",
+ "飢",
+ "䬳",
+ "餩",
+ "馧",
+ "駂",
+ "駾",
+ "䯎",
+ "𩬰",
+ "鬒",
+ "鱀",
+ "鳽",
+ "䳎",
+ "䳭",
+ "鵧",
+ "𪃎",
+ "䳸",
+ "𪄅",
+ "𪈎",
+ "𪊑",
+ "麻",
+ "䵖",
+ "黹",
+ "黾",
+ "鼅",
+ "鼏",
+ "鼖",
+ "鼻",
+ "𪘀",
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+ None,
+)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/INSTALLER b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/METADATA b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..d6ac7172ea84fd3acee121323b1bc6b446a1a290
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/METADATA
@@ -0,0 +1,239 @@
+Metadata-Version: 2.4
+Name: ImageIO
+Version: 2.38.1
+Summary: Read and write images and video across all major formats. Supports scientific and volumetric data.
+Author: ImageIO contributors
+Maintainer-email: Sebastian Wallkotter , Almar Klein
+License-Expression: BSD-2-Clause
+Project-URL: homepage, https://github.com/imageio/imageio
+Project-URL: download, http://pypi.python.org/pypi/imageio
+Project-URL: source, https://github.com/imageio/imageio
+Project-URL: documentation, https://imageio.readthedocs.io
+Keywords: image,video,imread,imwrite,io,animation,ffmpeg,image processing,numpy,format conversion,scientific imaging,medical imaging,volumetric,video encoding,multimedia,gif,tiff,png,jpeg
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Developers
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Requires-Python: >=3.11
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy
+Requires-Dist: pillow>=8.3.2
+Provides-Extra: bsdf
+Provides-Extra: dicom
+Provides-Extra: feisem
+Provides-Extra: ffmpeg
+Requires-Dist: imageio-ffmpeg; extra == "ffmpeg"
+Requires-Dist: psutil; extra == "ffmpeg"
+Provides-Extra: freeimage
+Requires-Dist: fsspec[http]; extra == "freeimage"
+Provides-Extra: lytro
+Provides-Extra: numpy
+Provides-Extra: pillow-heif
+Requires-Dist: pillow-heif; extra == "pillow-heif"
+Provides-Extra: pillow
+Provides-Extra: pydicom
+Requires-Dist: pydicom; extra == "pydicom"
+Provides-Extra: simpleitk
+Provides-Extra: spe
+Provides-Extra: swf
+Provides-Extra: tifffile
+Requires-Dist: tifffile; extra == "tifffile"
+Provides-Extra: pyav
+Requires-Dist: av; extra == "pyav"
+Provides-Extra: fits
+Requires-Dist: astropy; extra == "fits"
+Provides-Extra: rawpy
+Requires-Dist: rawpy; extra == "rawpy"
+Requires-Dist: numpy>2; extra == "rawpy"
+Provides-Extra: gdal
+Requires-Dist: gdal; extra == "gdal"
+Provides-Extra: itk
+Requires-Dist: itk; extra == "itk"
+Provides-Extra: linting
+Requires-Dist: black; extra == "linting"
+Requires-Dist: flake8; extra == "linting"
+Provides-Extra: test
+Requires-Dist: pytest; extra == "test"
+Requires-Dist: pytest-cov; extra == "test"
+Requires-Dist: fsspec[github]; extra == "test"
+Provides-Extra: docs
+Requires-Dist: sphinx<6; extra == "docs"
+Requires-Dist: numpydoc; extra == "docs"
+Requires-Dist: pydata-sphinx-theme; extra == "docs"
+Provides-Extra: dev
+Requires-Dist: pytest; extra == "dev"
+Requires-Dist: pytest-cov; extra == "dev"
+Requires-Dist: fsspec[github]; extra == "dev"
+Requires-Dist: black; extra == "dev"
+Requires-Dist: flake8; extra == "dev"
+Provides-Extra: all-plugins
+Requires-Dist: av; extra == "all-plugins"
+Requires-Dist: astropy; extra == "all-plugins"
+Requires-Dist: fsspec[http]; extra == "all-plugins"
+Requires-Dist: imageio-ffmpeg; extra == "all-plugins"
+Requires-Dist: numpy>2; extra == "all-plugins"
+Requires-Dist: pillow-heif; extra == "all-plugins"
+Requires-Dist: psutil; extra == "all-plugins"
+Requires-Dist: pydicom; extra == "all-plugins"
+Requires-Dist: rawpy; extra == "all-plugins"
+Requires-Dist: tifffile; extra == "all-plugins"
+Provides-Extra: all-plugins-pypy
+Requires-Dist: fsspec[http]; extra == "all-plugins-pypy"
+Requires-Dist: imageio-ffmpeg; extra == "all-plugins-pypy"
+Requires-Dist: psutil; extra == "all-plugins-pypy"
+Provides-Extra: full
+Requires-Dist: astropy; extra == "full"
+Requires-Dist: av; extra == "full"
+Requires-Dist: black; extra == "full"
+Requires-Dist: flake8; extra == "full"
+Requires-Dist: fsspec[github,http]; extra == "full"
+Requires-Dist: imageio-ffmpeg; extra == "full"
+Requires-Dist: numpydoc; extra == "full"
+Requires-Dist: numpy>2; extra == "full"
+Requires-Dist: pillow-heif; extra == "full"
+Requires-Dist: psutil; extra == "full"
+Requires-Dist: pydicom; extra == "full"
+Requires-Dist: pydata-sphinx-theme; extra == "full"
+Requires-Dist: pytest; extra == "full"
+Requires-Dist: pytest-cov; extra == "full"
+Requires-Dist: rawpy; extra == "full"
+Requires-Dist: sphinx<6; extra == "full"
+Requires-Dist: tifffile; extra == "full"
+Dynamic: license-file
+
+# IMAGEIO
+
+[](https://github.com/imageio/imageio/actions/workflows/ci.yml)
+[](https://github.com/imageio/imageio/actions/workflows/cd.yml)
+[](https://codecov.io/gh/imageio/imageio)
+[](https://imageio.readthedocs.io)
+
+[](https://pypi.python.org/pypi/imageio/)
+[](https://pypi.python.org/pypi/imageio/)
+
+[](https://doi.org/10.5281/zenodo.1488561)
+
+Website:
+
+Imageio is a mature Python library that makes it easy to read and write image
+and video data. This includes animated images, video, volumetric data, and
+scientific formats. It is cross-platform, runs on Python 3.11 through 3.14, and is easy to
+install.
+
+Professional support is available via
+[Tidelift](https://tidelift.com/funding/github/pypi/imageio).
+
+## Example
+
+Here's a minimal example of how to use imageio. See the docs for [more
+examples](https://imageio.readthedocs.io/en/stable/examples.html).
+
+```python
+import imageio.v3 as iio
+im = iio.imread('imageio:chelsea.png') # read a standard image
+im.shape # im is a NumPy array of shape (300, 451, 3)
+iio.imwrite('chelsea.jpg', im) # convert to jpg
+```
+
+## API in a nutshell
+
+You just have to remember a handful of functions:
+
+```python
+imread() # for reading
+imwrite() # for writing
+imiter() # for iterating image series (animations/videos/OME-TIFF/...)
+improps() # for standardized metadata
+immeta() # for format-specific metadata
+imopen() # for advanced usage
+```
+
+See the [API docs](https://imageio.readthedocs.io/en/stable/reference/index.html) for more information.
+
+## Features
+
+- Simple interface via a concise set of functions
+- Easy to
+ [install](https://imageio.readthedocs.io/en/stable/getting_started/installation.html)
+ using Conda or pip
+- Few core dependencies (only NumPy and Pillow)
+- Pure Python, runs on Python 3.11 through 3.14, and PyPy
+- Cross platform, runs on Windows, Linux, macOS
+- More than 295 supported
+ [formats](https://imageio.readthedocs.io/en/stable/formats/index.html)
+- Read/Write support for various
+ [resources](https://imageio.readthedocs.io/en/stable/getting_started/requests.html)
+ (files, URLs, bytes, FileLike objects, ...)
+- High code quality and large test suite including functional, regression, and
+ integration tests
+
+## Dependencies
+
+Minimal requirements:
+
+- Python 3.11 through 3.14
+- NumPy
+- Pillow >= 8.3.2
+
+Optional Python packages:
+
+- imageio-ffmpeg (for working with video files)
+- pyav (for working with video files)
+- tifffile (for working with TIFF files)
+- itk or SimpleITK (for ITK plugin)
+- astropy (for FITS plugin)
+- [imageio-flif](https://codeberg.org/monilophyta/imageio-flif) (for working
+ with [FLIF](https://github.com/FLIF-hub/FLIF) image files)
+
+## Security contact information
+
+To report a security vulnerability, please use the [Tidelift security
+contact](https://tidelift.com/security). Tidelift will coordinate the fix and
+disclosure.
+
+## ImageIO for enterprise
+
+Available as part of the Tidelift Subscription.
+
+The maintainers of imageio and thousands of other packages are working with
+Tidelift to deliver commercial support and maintenance for the open source
+dependencies you use to build your applications. Save time, reduce risk, and
+improve code health, while paying the maintainers of the exact dependencies you
+use. ([Learn
+more](https://tidelift.com/subscription/pkg/pypi-imageio?utm_source=pypi-imageio&utm_medium=referral&utm_campaign=readme))
+
+## Details
+
+The core of ImageIO is a set of user-facing APIs combined with a plugin manager.
+API calls choose sensible defaults and then call the plugin manager, which
+deduces the correct plugin/backend to use for the given resource and file
+format. The plugin manager adds sensible backend-specific defaults and then
+calls one of ImageIOs many backends to perform the actual loading. This allows
+ImageIO to take care of most of the gory details of loading images for you,
+while still allowing you to customize the behavior when and where you need to.
+You can find a more detailed explanation of this process in [our
+documentation](https://imageio.readthedocs.io/en/stable/user_guide/overview.html).
+
+## Contributing
+
+We welcome contributions of any kind. Here are some suggestions on how you are
+able to contribute
+
+- add missing formats to the format list
+- suggest/implement support for new backends
+- report/fix any bugs you encounter while using ImageIO
+
+To assist you in getting started with contributing code, take a look at the
+[development
+section](https://imageio.readthedocs.io/en/stable/development/index.html) of the
+docs. You will find instructions on setting up the dev environment as well as
+examples on how to contribute code.
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/RECORD b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..de8ad52310db22461a04f55075a2f581da720907
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/RECORD
@@ -0,0 +1,119 @@
+../../../bin/imageio_download_bin,sha256=jobL9gSQd2VqZTt2fuoeS_swYt6pohy6nrrdYlUfhzk,277
+../../../bin/imageio_remove_bin,sha256=kowoWNdA9SpN9XuijG0iNkVRboAM17jWpBy6fEEryds,273
+imageio-2.38.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+imageio-2.38.1.dist-info/METADATA,sha256=6d7tgyVvInR4nHI6wSXXKjePTttjO5H8qSqYly5okOk,9697
+imageio-2.38.1.dist-info/RECORD,,
+imageio-2.38.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+imageio-2.38.1.dist-info/WHEEL,sha256=YVMoNqKzERt-wjUZwJ33xBGAwnFl-4cqbYkTtWa4itE,91
+imageio-2.38.1.dist-info/entry_points.txt,sha256=0-yB6XGfrx1OMPw_xigPramTcwi5M4jX6L5Edrz0OoU,130
+imageio-2.38.1.dist-info/licenses/LICENSE,sha256=xFPPE2i7ngCeTX5TNPx4UR5mUxamEu9U7k_hJFZ4_iw,1307
+imageio-2.38.1.dist-info/top_level.txt,sha256=iSUjc-wEw-xbMTvMOSKg85n0-E7Ms--Mo4FLMC-J2YM,8
+imageio/__init__.py,sha256=sNTBhCbAmhCt2d4Lsax29o2wUUhYfS_F1tolZUvnT2k,3355
+imageio/__main__.py,sha256=ML406biyXilwpd81xaoG7-eC32DkJi3jW3oSkWf_SC0,5266
+imageio/__pycache__/__init__.cpython-311.pyc,,
+imageio/__pycache__/__main__.cpython-311.pyc,,
+imageio/__pycache__/freeze.cpython-311.pyc,,
+imageio/__pycache__/testing.cpython-311.pyc,,
+imageio/__pycache__/typing.cpython-311.pyc,,
+imageio/__pycache__/v2.cpython-311.pyc,,
+imageio/__pycache__/v3.cpython-311.pyc,,
+imageio/config/__init__.py,sha256=8NOpL5ePrkiioJb9hRBw3rydc4iNZkMwp7VdQlP4jDc,307
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+imageio/config/extensions.pyi,sha256=X9d4plQJc8zFsHRzIl6kcdgmN-x9ldEtQ9iVxepkRHI,606
+imageio/config/plugins.py,sha256=soMiQHl_nyHIc14hoCBGtzgmsxviNPdRd-Nqd1Vh9e8,20406
+imageio/config/plugins.pyi,sha256=pzH8pacqU5uldsvYOee_nhd2Hkk3mR8VQBtjeVnkkHY,706
+imageio/core/__init__.py,sha256=ka3YhBNXYqsnu9n1Db60DhHTn5tylYzfED_RYXgMz8U,638
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+imageio/core/__pycache__/findlib.cpython-311.pyc,,
+imageio/core/__pycache__/format.cpython-311.pyc,,
+imageio/core/__pycache__/imopen.cpython-311.pyc,,
+imageio/core/__pycache__/legacy_plugin_wrapper.cpython-311.pyc,,
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/REQUESTED b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/WHEEL b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..13013d7b49be5a84c3ee5931b06285b9553cf5d0
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (84.0.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/entry_points.txt b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa30161a1450bf11c7bfa6c548a23df8ff3636f5
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/entry_points.txt
@@ -0,0 +1,3 @@
+[console_scripts]
+imageio_download_bin = imageio.__main__:download_bin_main
+imageio_remove_bin = imageio.__main__:remove_bin_main
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/licenses/LICENSE b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..dc92c2fa6996fcf76968b38f50639700673314f9
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/licenses/LICENSE
@@ -0,0 +1,24 @@
+Copyright (c) 2014-2026, imageio developers
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/top_level.txt b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a464e4cd37851148ffe3b7ae88921620c50cfe03
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio-2.38.1.dist-info/top_level.txt
@@ -0,0 +1 @@
+imageio
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0df85521a2df5a8c8d9f0171876466049f7ba4aa
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__init__.py
@@ -0,0 +1,134 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) 2014-2026, imageio contributors
+# imageio is distributed under the terms of the (new) BSD License.
+
+# This docstring is used at the index of the documentation pages, and
+# gets inserted into a slightly larger description (in setup.py) for
+# the page on Pypi:
+"""
+Imageio is a Python library that provides an easy interface to read and
+write a wide range of image data, including animated images, volumetric
+data, and scientific formats. It is cross-platform, runs on Python 3.11 through 3.14,
+and is easy to install.
+
+Main website: https://imageio.readthedocs.io/
+"""
+
+# flake8: noqa
+
+from importlib.metadata import version as get_version
+
+__version__ = get_version("imageio")
+
+
+import warnings
+
+# Load some bits from core
+from .core import FormatManager, RETURN_BYTES
+
+# Instantiate the old format manager
+formats = FormatManager()
+show_formats = formats.show
+
+from . import v2
+from . import v3
+from . import plugins
+
+# import config after core to avoid circular import
+from . import config
+
+# import all APIs into the top level (meta API)
+from .v2 import (
+ imread as imread_v2,
+ mimread,
+ volread,
+ mvolread,
+ imwrite,
+ mimwrite,
+ volwrite,
+ mvolwrite,
+ # aliases
+ get_reader as read,
+ get_writer as save,
+ imwrite as imsave,
+ mimwrite as mimsave,
+ volwrite as volsave,
+ mvolwrite as mvolsave,
+ # misc
+ help,
+ get_reader,
+ get_writer,
+)
+from .v3 import (
+ imopen,
+ # imread, # Will take over once v3 is released
+ # imwrite, # Will take over once v3 is released
+ imiter,
+)
+
+
+def imread(uri, format=None, **kwargs):
+ """imread(uri, format=None, **kwargs)
+
+ Reads an image from the specified file. Returns a numpy array, which
+ comes with a dict of meta data at its 'meta' attribute.
+
+ Note that the image data is returned as-is, and may not always have
+ a dtype of uint8 (and thus may differ from what e.g. PIL returns).
+
+ Parameters
+ ----------
+ uri : {str, pathlib.Path, bytes, file}
+ The resource to load the image from, e.g. a filename, pathlib.Path,
+ http address or file object, see the docs for more info.
+ format : str
+ The format to use to read the file. By default imageio selects
+ the appropriate for you based on the filename and its contents.
+ kwargs : ...
+ Further keyword arguments are passed to the reader. See :func:`.help`
+ to see what arguments are available for a particular format.
+ """
+
+ warnings.warn(
+ "Starting with ImageIO v3 the behavior of this function will switch to that of"
+ " iio.v3.imread. To keep the current behavior (and make this warning disappear)"
+ " use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+
+ return imread_v2(uri, format=format, **kwargs)
+
+
+__all__ = [
+ "v2",
+ "v3",
+ "config",
+ "plugins",
+ # v3 API
+ "imopen",
+ "imread",
+ "imwrite",
+ "imiter",
+ # v2 API
+ "mimread",
+ "volread",
+ "mvolread",
+ "imwrite",
+ "mimwrite",
+ "volwrite",
+ "mvolwrite",
+ # v2 aliases
+ "read",
+ "save",
+ "imsave",
+ "mimsave",
+ "volsave",
+ "mvolsave",
+ # functions to deprecate
+ "help",
+ "get_reader",
+ "get_writer",
+ "formats",
+ "show_formats",
+]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__main__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a12662656c58464e4d5a0a3c961a991c6d8ea02
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/__main__.py
@@ -0,0 +1,167 @@
+"""
+Console scripts and associated helper methods for imageio.
+"""
+
+import argparse
+import os
+from os import path as op
+import shutil
+import sys
+
+
+from . import plugins
+from .core import util
+
+# A list of plugins that require binaries from the imageio-binaries
+# repository. These plugins must implement the `download` method.
+PLUGINS_WITH_BINARIES = ["freeimage"]
+
+
+def download_bin(plugin_names=["all"], package_dir=False):
+ """Download binary dependencies of plugins
+
+ This is a convenience method for downloading the binaries
+ (e.g. for freeimage) from the imageio-binaries
+ repository.
+
+ Parameters
+ ----------
+ plugin_names: list
+ A list of imageio plugin names. If it contains "all", all
+ binary dependencies are downloaded.
+ package_dir: bool
+ If set to `True`, the binaries will be downloaded to the
+ `resources` directory of the imageio package instead of
+ to the users application data directory. Note that this
+ might require administrative rights if imageio is installed
+ in a system directory.
+ """
+ if plugin_names.count("all"):
+ # Use all plugins
+ plugin_names = PLUGINS_WITH_BINARIES
+
+ plugin_names.sort()
+ print("Ascertaining binaries for: {}.".format(", ".join(plugin_names)))
+
+ if package_dir:
+ # Download the binaries to the `resources` directory of imageio.
+ # see `imageio.core.util.resource_dirs`
+ # and `imageio.core.util.resource_package_dir`
+ directory = util.resource_package_dir()
+ else:
+ directory = None
+
+ for plg in plugin_names:
+ if plg not in PLUGINS_WITH_BINARIES:
+ msg = "Plugin {} not registered for binary download!".format(plg)
+ raise Exception(msg)
+ mod = getattr(plugins, plg)
+ mod.download(directory=directory)
+
+
+def download_bin_main():
+ """Argument-parsing wrapper for `download_bin`"""
+ description = "Download plugin binary dependencies"
+ phelp = (
+ "Plugin name for which to download the binary. "
+ + "If no argument is given, all binaries are downloaded."
+ )
+ dhelp = (
+ "Download the binaries to the package directory "
+ + "(default is the users application data directory). "
+ + "This might require administrative rights."
+ )
+ example_text = (
+ "examples:\n"
+ + " imageio_download_bin all\n"
+ + " imageio_download_bin freeimage\n"
+ )
+ parser = argparse.ArgumentParser(
+ description=description,
+ epilog=example_text,
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument("plugin", type=str, nargs="*", default="all", help=phelp)
+ parser.add_argument(
+ "--package-dir",
+ dest="package_dir",
+ action="store_true",
+ default=False,
+ help=dhelp,
+ )
+ args = parser.parse_args()
+ download_bin(plugin_names=args.plugin, package_dir=args.package_dir)
+
+
+def remove_bin(plugin_names=["all"]):
+ """Remove binary dependencies of plugins
+
+ This is a convenience method that removes all binaries
+ dependencies for plugins downloaded by imageio.
+
+ Notes
+ -----
+ It only makes sense to use this method if the binaries
+ are corrupt.
+ """
+ if plugin_names.count("all"):
+ # Use all plugins
+ plugin_names = PLUGINS_WITH_BINARIES
+
+ print("Removing binaries for: {}.".format(", ".join(plugin_names)))
+
+ rdirs = util.resource_dirs()
+
+ for plg in plugin_names:
+ if plg not in PLUGINS_WITH_BINARIES:
+ msg = "Plugin {} not registered for binary download!".format(plg)
+ raise Exception(msg)
+
+ not_removed = []
+ for rd in rdirs:
+ # plugin name is in subdirectories
+ for rsub in os.listdir(rd):
+ if rsub in plugin_names:
+ plgdir = op.join(rd, rsub)
+ try:
+ shutil.rmtree(plgdir)
+ except Exception:
+ not_removed.append(plgdir)
+ if not_removed:
+ nrs = ",".join(not_removed)
+ msg2 = (
+ "These plugins files could not be removed: {}\n".format(nrs)
+ + "Make sure they are not used by any program and try again."
+ )
+ raise Exception(msg2)
+
+
+def remove_bin_main():
+ """Argument-parsing wrapper for `remove_bin`"""
+ description = "Remove plugin binary dependencies"
+ phelp = (
+ "Plugin name for which to remove the binary. "
+ + "If no argument is given, all binaries are removed."
+ )
+ example_text = (
+ "examples:\n"
+ + " imageio_remove_bin all\n"
+ + " imageio_remove_bin freeimage\n"
+ )
+ parser = argparse.ArgumentParser(
+ description=description,
+ epilog=example_text,
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument("plugin", type=str, nargs="*", default="all", help=phelp)
+ args = parser.parse_args()
+ remove_bin(plugin_names=args.plugin)
+
+
+if __name__ == "__main__":
+ if len(sys.argv) > 1 and sys.argv[1] == "download_bin":
+ download_bin_main()
+ elif len(sys.argv) > 1 and sys.argv[1] == "remove_bin":
+ remove_bin_main()
+ else:
+ raise RuntimeError("Invalid use of the imageio CLI")
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca78dd22f2e690bc5115565e9e0c11b67929031c
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/__init__.py
@@ -0,0 +1,16 @@
+from .extensions import (
+ extension_list,
+ known_extensions,
+ FileExtension,
+ video_extensions,
+)
+from .plugins import known_plugins, PluginConfig
+
+__all__ = [
+ "known_plugins",
+ "PluginConfig",
+ "extension_list",
+ "known_extensions",
+ "FileExtension",
+ "video_extensions",
+]
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b4735bca9e24a31b531fbf642552d95a18113e7
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.py
@@ -0,0 +1,2002 @@
+"""
+A set of objects representing each file extension recognized by ImageIO. If an
+extension is not listed here it is still supported, as long as there exists a
+supporting backend.
+
+"""
+
+
+class FileExtension:
+ """File Extension Metadata
+
+ This class holds information about a image file format associated with a
+ given extension. This information is used to track plugins that are known to
+ be able to handle a particular format. It also contains additional
+ information about a format, which is used when creating the supported format
+ docs.
+
+ Plugins known to be able to handle this format are ordered by a ``priority``
+ list. This list is used to determine the ideal plugin to use when choosing a
+ plugin based on file extension.
+
+ Parameters
+ ----------
+ extension : str
+ The name of the extension including the initial dot, e.g. ".png".
+ priority : List
+ A list of plugin names (entries in config.known_plugins) that can handle
+ this format. The position of a plugin expresses a preference, e.g.
+ ["plugin1", "plugin2"] indicates that, if available, plugin1 should be
+ preferred over plugin2 when handling a request related to this format.
+ name : str
+ The full name of the format.
+ description : str
+ A description of the format.
+ external_link : str
+ A link to further information about the format. Typically, the format's
+ specification.
+ volume_support : str
+ If True, the format/extension supports volumetric image data.
+
+ Examples
+ --------
+ >>> FileExtension(
+ name="Bitmap",
+ extension=".bmp",
+ priority=["pillow", "BMP-PIL", "BMP-FI", "ITK"],
+ external_link="https://en.wikipedia.org/wiki/BMP_file_format",
+ )
+
+ """
+
+ def __init__(
+ self,
+ *,
+ extension,
+ priority,
+ name=None,
+ description=None,
+ external_link=None,
+ volume_support=False,
+ ):
+ self.extension = extension
+ self.priority = priority
+ self.name = name
+ self.description = description
+ self.external_link = external_link
+ self.default_priority = priority.copy()
+ self.volume_support = volume_support
+
+ def reset(self):
+ self.priority = self.default_priority.copy()
+
+
+extension_list = [
+ FileExtension(
+ name="Hasselblad raw",
+ extension=".3fr",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Sony alpha",
+ extension=".arw",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Animated Portable Network Graphics",
+ external_link="https://en.wikipedia.org/wiki/APNG",
+ extension=".apng",
+ priority=["pillow", "pyav"],
+ ),
+ FileExtension(
+ name="Audio Video Interleave",
+ extension=".avi",
+ priority=["FFMPEG"],
+ ),
+ FileExtension(
+ name="Casio raw format",
+ extension=".bay",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".blp",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ name="Bitmap",
+ extension=".bmp",
+ priority=["pillow", "BMP-PIL", "BMP-FI", "ITK", "pyav", "opencv"],
+ external_link="https://en.wikipedia.org/wiki/BMP_file_format",
+ ),
+ FileExtension(
+ name="Device-Independent Bitmap",
+ extension=".dip",
+ priority=["opencv"],
+ external_link="https://en.wikipedia.org/wiki/BMP_file_format",
+ ),
+ FileExtension(
+ name="Re-Volt mipmap",
+ extension=".bmq",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Binary Structured Data Format",
+ extension=".bsdf",
+ priority=["BSDF"],
+ external_link="http://bsdf.io/",
+ ),
+ FileExtension(
+ name="Binary Universal Form for the Representation of meteorological data",
+ extension=".bufr",
+ priority=["pillow", "BUFR-PIL"],
+ ),
+ FileExtension(
+ name="Silicon Graphics Image",
+ extension=".bw",
+ priority=["pillow", "SGI-PIL", "SGI-FI"],
+ ),
+ FileExtension(
+ name="Scirra Construct",
+ extension=".cap",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="AMETEK High Speed Camera Format",
+ extension=".cine",
+ priority=["RAW-FI"],
+ external_link="https://phantomhighspeed-knowledge.secure.force.com/servlet/fileField?id=0BE1N000000kD2i#:~:text=Cine%20is%20a%20video%20file,camera%20model%20and%20image%20resolution",
+ ),
+ FileExtension(extension=".cr2", priority=["RAW-FI"]),
+ FileExtension(
+ extension=".crw",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".cs1",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Computerized Tomography",
+ extension=".ct",
+ priority=["DICOM", "pydicom"],
+ ),
+ FileExtension(
+ name="Windows Cursor Icons",
+ extension=".cur",
+ priority=["pillow", "CUR-PIL"],
+ ),
+ FileExtension(
+ name="Dr. Halo",
+ extension=".cut",
+ priority=["CUT-FI"],
+ ),
+ FileExtension(
+ extension=".dc2",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="DICOM file format",
+ extension=".dcm",
+ priority=["DICOM", "pydicom", "ITK"],
+ ),
+ FileExtension(
+ extension=".dcr",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Intel DCX",
+ extension=".dcx",
+ priority=["pillow", "DCX-PIL"],
+ ),
+ FileExtension(
+ name="DirectX Texture Container",
+ extension=".dds",
+ priority=["pillow", "DDS-FI", "DDS-PIL"],
+ ),
+ FileExtension(
+ name="Windows Bitmap",
+ extension=".dib",
+ priority=["pillow", "DIB-PIL"],
+ ),
+ FileExtension(
+ name="DICOM file format",
+ extension=".dicom",
+ priority=["ITK", "pydicom"],
+ ),
+ FileExtension(
+ extension=".dng",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".drf",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".dsc",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Enhanced Compression Wavelet",
+ extension=".ecw",
+ priority=["GDAL"],
+ ),
+ FileExtension(
+ name="Windows Metafile",
+ extension=".emf",
+ priority=["pillow", "WMF-PIL"],
+ ),
+ FileExtension(
+ name="Encapsulated Postscript",
+ extension=".eps",
+ priority=["pillow", "EPS-PIL"],
+ ),
+ FileExtension(
+ extension=".erf",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="OpenEXR",
+ extension=".exr",
+ external_link="https://openexr.readthedocs.io/en/latest/",
+ priority=["EXR-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
+ extension=".fff",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Flexible Image Transport System File",
+ extension=".fit",
+ priority=["pillow", "FITS-PIL", "FITS"],
+ ),
+ FileExtension(
+ name="Flexible Image Transport System File",
+ extension=".fits",
+ priority=["pillow", "FITS-PIL", "FITS", "pyav"],
+ ),
+ FileExtension(
+ name="Autodesk FLC Animation",
+ extension=".flc",
+ priority=["pillow", "FLI-PIL"],
+ ),
+ FileExtension(
+ name="Autodesk FLI Animation",
+ extension=".fli",
+ priority=["pillow", "FLI-PIL"],
+ ),
+ FileExtension(
+ name="Kodak FlashPix",
+ extension=".fpx",
+ priority=["pillow", "FPX-PIL"],
+ ),
+ FileExtension(
+ name="Independence War 2: Edge Of Chaos Texture Format",
+ extension=".ftc",
+ priority=["pillow", "FTEX-PIL"],
+ ),
+ FileExtension(
+ name="Flexible Image Transport System File",
+ extension=".fts",
+ priority=["FITS"],
+ ),
+ FileExtension(
+ name="Independence War 2: Edge Of Chaos Texture Format",
+ extension=".ftu",
+ priority=["pillow", "FTEX-PIL"],
+ ),
+ FileExtension(
+ name="Flexible Image Transport System File",
+ extension=".fz",
+ priority=["FITS"],
+ ),
+ FileExtension(
+ name="Raw fax format CCITT G.3",
+ extension=".g3",
+ priority=["G3-FI"],
+ ),
+ FileExtension(
+ name="GIMP brush file",
+ extension=".gbr",
+ priority=["pillow", "GBR-PIL"],
+ ),
+ FileExtension(
+ name="Grassroots DICOM",
+ extension=".gdcm",
+ priority=["ITK", "pydicom"],
+ ),
+ FileExtension(
+ name="Graphics Interchange Format",
+ extension=".gif",
+ priority=["pillow", "GIF-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="UMDS GIPL",
+ extension=".gipl",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="gridded meteorological data",
+ extension=".grib",
+ priority=["pillow", "GRIB-PIL"],
+ ),
+ FileExtension(
+ name="Hierarchical Data Format 5",
+ extension=".h5",
+ priority=["pillow", "HDF5-PIL"],
+ ),
+ FileExtension(
+ name="Hierarchical Data Format 5",
+ extension=".hdf",
+ priority=["pillow", "HDF5-PIL"],
+ ),
+ FileExtension(
+ name="Hierarchical Data Format 5",
+ extension=".hdf5",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="JPEG Extended Range",
+ extension=".hdp",
+ priority=["JPEG-XR-FI"],
+ ),
+ FileExtension(
+ name="High Dynamic Range Image",
+ extension=".hdr",
+ priority=["HDR-FI", "ITK", "opencv"],
+ ),
+ FileExtension(
+ extension=".ia",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".icb",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ name="Mac OS Icon File",
+ extension=".icns",
+ priority=["pillow", "ICNS-PIL"],
+ ),
+ FileExtension(
+ name="Windows Icon File",
+ extension=".ico",
+ priority=["pillow", "ICO-FI", "ICO-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="ILBM Interleaved Bitmap",
+ extension=".iff",
+ priority=["IFF-FI"],
+ ),
+ FileExtension(
+ name="IPTC/NAA",
+ extension=".iim",
+ priority=["pillow", "IPTC-PIL"],
+ ),
+ FileExtension(
+ extension=".iiq",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="IFUNC Image Memory",
+ extension=".im",
+ priority=["pillow", "IM-PIL"],
+ ),
+ FileExtension(
+ extension=".img",
+ priority=["ITK", "GDAL"],
+ ),
+ FileExtension(
+ extension=".img.gz",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="IM Tools",
+ extension=".IMT",
+ priority=["pillow", "IMT-PIL"],
+ ),
+ FileExtension(
+ name="Image Processing Lab",
+ extension=".ipl",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".j2c",
+ priority=["pillow", "J2K-FI", "JPEG2000-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".j2k",
+ priority=["pillow", "J2K-FI", "JPEG2000-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="JPEG",
+ extension=".jfif",
+ priority=["pillow", "JPEG-PIL"],
+ ),
+ FileExtension(
+ name="JPEG",
+ extension=".jif",
+ priority=["JPEG-FI"],
+ ),
+ FileExtension(
+ name="JPEG Network Graphics",
+ extension=".jng",
+ priority=["JNG-FI"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".jp2",
+ priority=["pillow", "JP2-FI", "JPEG2000-PIL", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".jpc",
+ priority=["pillow", "JPEG2000-PIL"],
+ ),
+ FileExtension(
+ name="JPEG",
+ extension=".jpe",
+ priority=["pillow", "JPEG-FI", "JPEG-PIL", "opencv"],
+ ),
+ FileExtension(
+ name="Joint Photographic Experts Group",
+ extension=".jpeg",
+ priority=["pillow", "JPEG-PIL", "JPEG-FI", "ITK", "GDAL", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".jpf",
+ priority=["pillow", "JPEG2000-PIL"],
+ ),
+ FileExtension(
+ name="Joint Photographic Experts Group",
+ extension=".jpg",
+ priority=["pillow", "JPEG-PIL", "JPEG-FI", "ITK", "GDAL", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="JPEG 2000",
+ extension=".jpx",
+ priority=["pillow", "JPEG2000-PIL"],
+ ),
+ FileExtension(
+ name="JPEG Extended Range",
+ extension=".jxr",
+ priority=["JPEG-XR-FI"],
+ ),
+ FileExtension(
+ extension=".k25",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".kc2",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".kdc",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="C64 Koala Graphics",
+ extension=".koa",
+ priority=["KOALA-FI"],
+ ),
+ FileExtension(
+ name="ILBM Interleaved Bitmap",
+ extension=".lbm",
+ priority=["IFF-FI"],
+ ),
+ FileExtension(
+ name="Lytro F01",
+ extension=".lfp",
+ priority=["LYTRO-LFP"],
+ ),
+ FileExtension(
+ name="Lytro Illum",
+ extension=".lfr",
+ priority=["LYTRO-LFR"],
+ ),
+ FileExtension(
+ name="ZEISS LSM",
+ extension=".lsm",
+ priority=["tifffile", "ITK", "TIFF"],
+ ),
+ FileExtension(
+ name="McIdas area file",
+ extension=".MCIDAS",
+ priority=["pillow", "MCIDAS-PIL"],
+ external_link="https://www.ssec.wisc.edu/mcidas/doc/prog_man/2003print/progman2003-formats.html",
+ ),
+ FileExtension(
+ extension=".mdc",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".mef",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="FreeSurfer File Format",
+ extension=".mgh",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="ITK MetaImage",
+ extension=".mha",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="ITK MetaImage Header",
+ extension=".mhd",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="Microsoft Image Composer",
+ extension=".mic",
+ priority=["pillow", "MIC-PIL"],
+ ),
+ FileExtension(
+ name="Matroska Multimedia Container",
+ extension=".mkv",
+ priority=["FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="Medical Imaging NetCDF",
+ extension=".mnc",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="Medical Imaging NetCDF 2",
+ extension=".mnc2",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="Leaf Raw Image Format",
+ extension=".mos",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="QuickTime File Format",
+ extension=".mov",
+ priority=["FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="MPEG-4 Part 14",
+ extension=".mp4",
+ priority=["FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="MPEG-1 Moving Picture Experts Group",
+ extension=".mpeg",
+ priority=["FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="Moving Picture Experts Group",
+ extension=".mpg",
+ priority=["pillow", "FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="JPEG Multi-Picture Format",
+ extension=".mpo",
+ priority=["pillow", "MPO-PIL"],
+ ),
+ FileExtension(
+ name="Magnetic resonance imaging",
+ extension=".mri",
+ priority=["DICOM", "pydicom"],
+ ),
+ FileExtension(
+ extension=".mrw",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Windows Paint",
+ extension=".msp",
+ priority=["pillow", "MSP-PIL"],
+ ),
+ FileExtension(
+ extension=".nef",
+ priority=["RAW-FI", "rawpy"],
+ ),
+ FileExtension(
+ extension=".nhdr",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ extension=".nia",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ extension=".nii",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="nii.gz",
+ extension=".nii.gz",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="Numpy Array",
+ extension=".npz",
+ priority=["NPZ"],
+ volume_support=True,
+ ),
+ FileExtension(
+ extension=".nrrd",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ extension=".nrw",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".orf",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".palm",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ name="Portable Bitmap",
+ extension=".pbm",
+ priority=["PGM-FI", "PGMRAW-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
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+ extension=".pcd",
+ priority=["pillow", "PCD-FI", "PCD-PIL"],
+ ),
+ FileExtension(
+ name="Macintosh PICT",
+ extension=".pct",
+ priority=["PICT-FI"],
+ ),
+ FileExtension(
+ name="Zsoft Paintbrush",
+ extension=".PCX",
+ priority=["pillow", "PCX-FI", "PCX-PIL"],
+ ),
+ FileExtension(
+ extension=".pdf",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ extension=".pef",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".pfm",
+ priority=["PFM-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="Portable Greymap",
+ extension=".pgm",
+ priority=["pillow", "PGM-FI", "PGMRAW-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="Macintosh PICT",
+ extension=".pic",
+ priority=["PICT-FI", "ITK", "opencv"],
+ ),
+ FileExtension(
+ name="Macintosh PICT",
+ extension=".pict",
+ priority=["PICT-FI"],
+ ),
+ FileExtension(
+ name="Portable Network Graphics",
+ extension=".png",
+ priority=["pillow", "PNG-PIL", "PNG-FI", "ITK", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="Portable Image Format",
+ extension=".pnm",
+ priority=["pillow", "opencv"],
+ ),
+ FileExtension(
+ name="Pbmplus image",
+ extension=".ppm",
+ priority=["pillow", "PPM-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="Pbmplus image",
+ extension=".pbm",
+ priority=["pillow", "PPM-PIL", "PPM-FI"],
+ ),
+ FileExtension(
+ name="Portable image format",
+ extension=".pxm",
+ priority=["opencv"],
+ ),
+ FileExtension(
+ name="Portable Pixelmap (ASCII)",
+ extension=".ppm",
+ priority=["PPM-FI", "opencv"],
+ ),
+ FileExtension(
+ name="Portable Pixelmap (Raw)",
+ extension=".ppm",
+ priority=["PPMRAW-FI"],
+ ),
+ FileExtension(
+ name="Ghostscript",
+ extension=".ps",
+ priority=["pillow", "EPS-PIL"],
+ ),
+ FileExtension(
+ name="Adope Photoshop 2.5 and 3.0",
+ extension=".psd",
+ priority=["pillow", "PSD-PIL", "PSD-FI"],
+ ),
+ FileExtension(
+ extension=".ptx",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".pxn",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="PIXAR raster image",
+ extension=".pxr",
+ priority=["pillow", "PIXAR-PIL"],
+ ),
+ FileExtension(
+ extension=".qtk",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".raf",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Sun Raster File",
+ extension=".ras",
+ priority=["pillow", "SUN-PIL", "RAS-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="Sun Raster File",
+ extension=".sr",
+ priority=["opencv"],
+ ),
+ FileExtension(
+ extension=".raw",
+ priority=["RAW-FI", "LYTRO-ILLUM-RAW", "LYTRO-F01-RAW", "rawpy"],
+ ),
+ FileExtension(
+ extension=".rdc",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Silicon Graphics Image",
+ extension=".rgb",
+ priority=["pillow", "SGI-PIL"],
+ ),
+ FileExtension(
+ name="Silicon Graphics Image",
+ extension=".rgba",
+ priority=["pillow", "SGI-PIL"],
+ ),
+ FileExtension(
+ extension=".rw2",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".rwl",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".rwz",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ name="Silicon Graphics Image",
+ extension=".sgi",
+ priority=["pillow", "SGI-PIL", "pyav"],
+ ),
+ FileExtension(
+ name="SPE File Format",
+ extension=".spe",
+ priority=["SPE"],
+ ),
+ FileExtension(
+ extension=".SPIDER",
+ priority=["pillow", "SPIDER-PIL"],
+ ),
+ FileExtension(
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+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".srf",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
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+ priority=["RAW-FI"],
+ ),
+ FileExtension(
+ extension=".sti",
+ priority=["RAW-FI"],
+ ),
+ FileExtension(
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+ priority=["tifffile", "TIFF"],
+ ),
+ FileExtension(
+ name="ShockWave Flash",
+ extension=".swf",
+ priority=["SWF", "pyav"],
+ ),
+ FileExtension(
+ name="Truevision TGA",
+ extension=".targa",
+ priority=["pillow", "TARGA-FI"],
+ ),
+ FileExtension(
+ name="Truevision TGA",
+ extension=".tga",
+ priority=["pillow", "TGA-PIL", "TARGA-FI", "pyav"],
+ ),
+ FileExtension(
+ name="Tagged Image File",
+ extension=".tif",
+ priority=[
+ "tifffile",
+ "TIFF",
+ "pillow",
+ "TIFF-PIL",
+ "TIFF-FI",
+ "FEI",
+ "ITK",
+ "GDAL",
+ "pyav",
+ "opencv",
+ ],
+ volume_support=True,
+ ),
+ FileExtension(
+ name="Tagged Image File Format",
+ extension=".tiff",
+ priority=[
+ "tifffile",
+ "TIFF",
+ "pillow",
+ "TIFF-PIL",
+ "TIFF-FI",
+ "FEI",
+ "ITK",
+ "GDAL",
+ "pyav",
+ "opencv",
+ ],
+ volume_support=True,
+ ),
+ FileExtension(
+ extension=".vda",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ extension=".vst",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ extension=".vtk",
+ priority=["ITK"],
+ ),
+ FileExtension(
+ name="Wireless Bitmap",
+ extension=".wap",
+ priority=["WBMP-FI"],
+ ),
+ FileExtension(
+ name="Wireless Bitmap",
+ extension=".wbm",
+ priority=["WBMP-FI"],
+ ),
+ FileExtension(
+ name="Wireless Bitmap",
+ extension=".wbmp",
+ priority=["WBMP-FI"],
+ ),
+ FileExtension(
+ name="JPEG Extended Range",
+ extension=".wdp",
+ priority=["JPEG-XR-FI"],
+ ),
+ FileExtension(
+ name="Matroska",
+ extension=".webm",
+ priority=["FFMPEG", "pyav"],
+ ),
+ FileExtension(
+ name="Google WebP",
+ extension=".webp",
+ priority=["pillow", "WEBP-FI", "pyav", "opencv"],
+ ),
+ FileExtension(
+ name="Windows Meta File",
+ extension=".wmf",
+ priority=["pillow", "WMF-PIL"],
+ ),
+ FileExtension(
+ name="Windows Media Video",
+ extension=".wmv",
+ priority=["FFMPEG"],
+ ),
+ FileExtension(
+ name="X11 Bitmap",
+ extension=".xbm",
+ priority=["pillow", "XBM-PIL", "XBM-FI", "pyav"],
+ ),
+ FileExtension(
+ name="X11 Pixel Map",
+ extension=".xpm",
+ priority=["pillow", "XPM-PIL", "XPM-FI"],
+ ),
+ FileExtension(
+ name="Thumbnail Image",
+ extension=".XVTHUMB",
+ priority=["pillow", "XVTHUMB-PIL"],
+ ),
+ FileExtension(
+ extension=".dpx",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ extension=".im1",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ extension=".im24",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
+ extension=".pgmyuv",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".3g2",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".3gp",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".f4v",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".ism",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".isma",
+ priority=["pyav"],
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+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".ismv",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".m4a",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".m4b",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".mj2",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="3GP (3GPP file format)",
+ extension=".psp",
+ priority=["pyav"],
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+ FileExtension(
+ name="3GP2 (3GPP2 file format)",
+ extension=".3g2",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".3gp",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".f4v",
+ priority=["pyav"],
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+ FileExtension(
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+ extension=".ism",
+ priority=["pyav"],
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+ FileExtension(
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+ extension=".isma",
+ priority=["pyav"],
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+ FileExtension(
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+ extension=".ismv",
+ priority=["pyav"],
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+ FileExtension(
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+ extension=".m4a",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".m4b",
+ priority=["pyav"],
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+ FileExtension(
+ name="3GP2 (3GPP2 file format)",
+ extension=".mj2",
+ priority=["pyav"],
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+ FileExtension(
+ name="3GP2 (3GPP2 file format)",
+ extension=".psp",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".A64",
+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
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+ extension=".amv",
+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ ),
+ FileExtension(
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+ priority=["pyav"],
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+ extension=".avi",
+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Beam Software SIFF",
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+ FileExtension(
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+ extension=".cdg",
+ priority=["pyav"],
+ ),
+ FileExtension(
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+ priority=["pyav"],
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+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ ),
+ FileExtension(
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+ extension=".gxf",
+ priority=["pyav"],
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+ FileExtension(
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+ ),
+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ FileExtension(
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+ FileExtension(
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+ FileExtension(
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+ FileExtension(
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
+ name="raw IPU Video",
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+ FileExtension(
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+ priority=["pyav"],
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+ FileExtension(
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+ extension=".mjpeg",
+ priority=["pyav"],
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+ FileExtension(
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+ extension=".mjpg",
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+ FileExtension(
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+ extension=".mpo",
+ priority=["pyav"],
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+ extension=".m1v",
+ priority=["pyav"],
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+ FileExtension(
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+ ),
+ FileExtension(
+ name="raw MPEG-1 video",
+ extension=".mpg",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw MPEG-2 video",
+ extension=".m2v",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw MPEG-4 video",
+ extension=".m4v",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw VC-1 video",
+ extension=".vc1",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw video",
+ extension=".cif",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw video",
+ extension=".qcif",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw video",
+ extension=".rgb",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="raw video",
+ extension=".yuv",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="RealMedia",
+ extension=".rm",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="SDR2",
+ extension=".sdr2",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Sega FILM / CPK",
+ extension=".cpk",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="SER (Simple uncompressed video format for astronomical capturing)",
+ extension=".ser",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Simbiosis Interactive IMX",
+ extension=".imx",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Square SVS",
+ extension=".svs",
+ priority=["tifffile", "pyav"],
+ ),
+ FileExtension(
+ name="TiVo TY Stream",
+ extension=".ty",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="TiVo TY Stream",
+ extension=".ty+",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Uncompressed 4:2:2 10-bit",
+ extension=".v210",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Uncompressed 4:2:2 10-bit",
+ extension=".yuv10",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="VC-1 test bitstream",
+ extension=".rcv",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Video CCTV DAT",
+ extension=".dat",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Video DAV",
+ extension=".dav",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Vivo",
+ extension=".viv",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="WebM Chunk Muxer",
+ extension=".chk",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="WebM",
+ extension=".mk3d",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="WebM",
+ extension=".mka",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="WebM",
+ extension=".mks",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Windows Television (WTV)",
+ extension=".wtv",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="Xilam DERF",
+ extension=".adp",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ name="YUV4MPEG pipe",
+ extension=".y4m",
+ priority=["pyav"],
+ ),
+ FileExtension(
+ extension=".qpi",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="PCO Camera",
+ extension=".pcoraw",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="PCO Camera",
+ extension=".rec",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Perkin Elmer Vectra",
+ extension=".qptiff",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Pyramid Encoded TIFF",
+ extension=".ptiff",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Pyramid Encoded TIFF",
+ extension=".ptif",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Opticks Gel",
+ extension=".gel",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Zoomify Image Format",
+ extension=".zif",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Hamamatsu Slide Scanner",
+ extension=".ndpi",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="Roche Digital Pathology",
+ extension=".bif",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ extension=".tf8",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ extension=".btf",
+ priority=["tifffile"],
+ ),
+ FileExtension(
+ name="High Efficiency Image File Format",
+ extension=".heic",
+ priority=["pillow"],
+ ),
+ FileExtension(
+ name="AV1 Image File Format",
+ extension=".avif",
+ priority=["pillow"],
+ ),
+]
+extension_list.sort(key=lambda x: x.extension)
+
+
+known_extensions = dict()
+for ext in extension_list:
+ if ext.extension not in known_extensions:
+ known_extensions[ext.extension] = list()
+ known_extensions[ext.extension].append(ext)
+
+extension_list = [ext for ext_list in known_extensions.values() for ext in ext_list]
+
+_video_extension_strings = [
+ ".264",
+ ".265",
+ ".3g2",
+ ".3gp",
+ ".a64",
+ ".A64",
+ ".adp",
+ ".amr",
+ ".amv",
+ ".asf",
+ ".avc",
+ ".avi",
+ ".avr",
+ ".avs",
+ ".avs2",
+ ".avs3",
+ ".bmv",
+ ".cavs",
+ ".cdg",
+ ".cdxl",
+ ".cgi",
+ ".chk",
+ ".cif",
+ ".cpk",
+ ".dat",
+ ".dav",
+ ".dif",
+ ".dnxhd",
+ ".dnxhr",
+ ".drc",
+ ".dv",
+ ".dvd",
+ ".f4v",
+ ".flm",
+ ".flv",
+ ".gsm",
+ ".gxf",
+ ".h261",
+ ".h263",
+ ".h264",
+ ".h265",
+ ".h26l",
+ ".hevc",
+ ".idf",
+ ".ifv",
+ ".imx",
+ ".ipu",
+ ".ism",
+ ".isma",
+ ".ismv",
+ ".ivf",
+ ".ivr",
+ ".j2k",
+ ".kux",
+ ".lvf",
+ ".m1v",
+ ".m2t",
+ ".m2ts",
+ ".m2v",
+ ".m4a",
+ ".m4b",
+ ".m4v",
+ ".mj2",
+ ".mjpeg",
+ ".mjpg",
+ ".mk3d",
+ ".mka",
+ ".mks",
+ ".mkv",
+ ".mods",
+ ".moflex",
+ ".mov",
+ ".mp4",
+ ".mpc",
+ ".mpd",
+ ".mpeg",
+ ".mpg",
+ ".mpo",
+ ".mts",
+ ".mvi",
+ ".mxf",
+ ".mxg",
+ ".nut",
+ ".obu",
+ ".ogg",
+ ".ogv",
+ ".psp",
+ ".qcif",
+ ".rcv",
+ ".rgb",
+ ".rm",
+ ".roq",
+ ".sdr2",
+ ".ser",
+ ".sga",
+ ".svag",
+ ".svs",
+ ".ts",
+ ".ty",
+ ".ty+",
+ ".v",
+ ".v210",
+ ".vb",
+ ".vc1",
+ ".vc2",
+ ".viv",
+ ".vob",
+ ".webm",
+ ".wmv",
+ ".wtv",
+ ".xl",
+ ".xmv",
+ ".y4m",
+ ".yop",
+ ".yuv",
+ ".yuv10",
+]
+video_extensions = list()
+for ext_string in _video_extension_strings:
+ formats = known_extensions[ext_string]
+ video_extensions.append(formats[0])
+video_extensions.sort(key=lambda x: x.extension)
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.pyi b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4e0f4fcbbc12531b7988ab22e0fee1264a458437
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/extensions.pyi
@@ -0,0 +1,24 @@
+from typing import List, Dict, Optional
+
+class FileExtension:
+ extension: str
+ priority: List[str]
+ name: Optional[str] = None
+ description: Optional[str] = None
+ external_link: Optional[str] = None
+ volume_support: bool
+
+ def __init__(
+ self,
+ *,
+ extension: str,
+ priority: List[str],
+ name: str = None,
+ description: str = None,
+ external_link: str = None,
+ ) -> None: ...
+ def reset(self) -> None: ...
+
+extension_list: List[FileExtension]
+known_extensions: Dict[str, List[FileExtension]]
+video_extensions: List[FileExtension]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.py
new file mode 100644
index 0000000000000000000000000000000000000000..d18a4a1d7057d11cf5635fe463e74b5c24fe1b59
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.py
@@ -0,0 +1,785 @@
+import importlib
+
+from ..core.legacy_plugin_wrapper import LegacyPlugin
+
+
+class PluginConfig:
+ """Plugin Configuration Metadata
+
+ This class holds the information needed to lazy-import plugins.
+
+ Parameters
+ ----------
+ name : str
+ The name of the plugin.
+ class_name : str
+ The name of the plugin class inside the plugin module.
+ module_name : str
+ The name of the module/package from which to import the plugin.
+ is_legacy : bool
+ If True, this plugin is a v2 plugin and will be wrapped in a
+ LegacyPlugin. Default: False.
+ package_name : str
+ If the given module name points to a relative module, then the package
+ name determines the package it is relative to.
+ install_name : str
+ The name of the optional dependency that can be used to install this
+ plugin if it is missing.
+ legacy_args : Dict
+ A dictionary of kwargs to pass to the v2 plugin (Format) upon construction.
+
+ Examples
+ --------
+ >>> PluginConfig(
+ name="TIFF",
+ class_name="TiffFormat",
+ module_name="imageio.plugins.tifffile",
+ is_legacy=True,
+ install_name="tifffile",
+ legacy_args={
+ "description": "TIFF format",
+ "extensions": ".tif .tiff .stk .lsm",
+ "modes": "iIvV",
+ },
+ )
+ >>> PluginConfig(
+ name="pillow",
+ class_name="PillowPlugin",
+ module_name="imageio.plugins.pillow"
+ )
+
+ """
+
+ def __init__(
+ self,
+ name,
+ class_name,
+ module_name,
+ *,
+ is_legacy=False,
+ package_name=None,
+ install_name=None,
+ legacy_args=None,
+ ):
+ legacy_args = legacy_args or dict()
+
+ self.name = name
+ self.class_name = class_name
+ self.module_name = module_name
+ self.package_name = package_name
+
+ self.is_legacy = is_legacy
+ self.install_name = install_name or self.name
+ self.legacy_args = {"name": name, "description": "A legacy plugin"}
+ self.legacy_args.update(legacy_args)
+
+ @property
+ def format(self):
+ """For backwards compatibility with FormatManager
+
+ Delete when migrating to v3
+ """
+ if not self.is_legacy:
+ raise RuntimeError("Can only get format for legacy plugins.")
+
+ module = importlib.import_module(self.module_name, self.package_name)
+ clazz = getattr(module, self.class_name)
+ return clazz(**self.legacy_args)
+
+ @property
+ def plugin_class(self):
+ """Get the plugin class (import if needed)
+
+ Returns
+ -------
+ plugin_class : Any
+ The class that can be used to instantiate plugins.
+
+ """
+
+ module = importlib.import_module(self.module_name, self.package_name)
+ clazz = getattr(module, self.class_name)
+
+ if self.is_legacy:
+ legacy_plugin = clazz(**self.legacy_args)
+
+ def partial_legacy_plugin(request):
+ return LegacyPlugin(request, legacy_plugin)
+
+ clazz = partial_legacy_plugin
+
+ return clazz
+
+
+known_plugins = dict()
+known_plugins["pillow"] = PluginConfig(
+ name="pillow", class_name="PillowPlugin", module_name="imageio.plugins.pillow"
+)
+known_plugins["pyav"] = PluginConfig(
+ name="pyav", class_name="PyAVPlugin", module_name="imageio.plugins.pyav"
+)
+known_plugins["opencv"] = PluginConfig(
+ name="opencv", class_name="OpenCVPlugin", module_name="imageio.plugins.opencv"
+)
+known_plugins["tifffile"] = PluginConfig(
+ name="tifffile",
+ class_name="TifffilePlugin",
+ module_name="imageio.plugins.tifffile_v3",
+)
+known_plugins["SPE"] = PluginConfig(
+ name="spe", class_name="SpePlugin", module_name="imageio.plugins.spe"
+)
+known_plugins["rawpy"] = PluginConfig(
+ name="rawpy", class_name="RawPyPlugin", module_name="imageio.plugins.rawpy"
+)
+known_plugins["pydicom"] = PluginConfig(
+ name="pydicom", class_name="PydicomPlugin", module_name="imageio.plugins.pydicom"
+)
+
+# Legacy plugins
+# ==============
+#
+# Which are partly registered by format, partly by plugin, and partly by a mix
+# of both. We keep the naming here for backwards compatibility.
+# In v3 this should become a single entry per plugin named after the plugin
+# We can choose extension-specific priority in ``config.extensions``.
+#
+# Note: Since python 3.7 order of insertion determines the order of dict().keys()
+# This means that the order here determines the order by which plugins are
+# checked during the full fallback search. We don't advertise this downstream,
+# but it could be a useful thing to keep in mind to choose a sensible default
+# search order.
+
+known_plugins["TIFF"] = PluginConfig(
+ name="TIFF",
+ class_name="TiffFormat",
+ module_name="imageio.plugins.tifffile",
+ is_legacy=True,
+ install_name="tifffile",
+ legacy_args={
+ "description": "TIFF format",
+ "extensions": ".tif .tiff .stk .lsm",
+ "modes": "iIvV",
+ },
+)
+
+# PILLOW plugin formats (legacy)
+PILLOW_FORMATS = [
+ ("BMP", "Windows Bitmap", ".bmp", "PillowFormat"),
+ ("BUFR", "BUFR", ".bufr", "PillowFormat"),
+ ("CUR", "Windows Cursor", ".cur", "PillowFormat"),
+ ("DCX", "Intel DCX", ".dcx", "PillowFormat"),
+ ("DDS", "DirectDraw Surface", ".dds", "PillowFormat"),
+ ("DIB", "Windows Bitmap", "", "PillowFormat"),
+ ("EPS", "Encapsulated Postscript", ".ps .eps", "PillowFormat"),
+ ("FITS", "FITS", ".fit .fits", "PillowFormat"),
+ ("FLI", "Autodesk FLI/FLC Animation", ".fli .flc", "PillowFormat"),
+ ("FPX", "FlashPix", ".fpx", "PillowFormat"),
+ ("FTEX", "Texture File Format (IW2:EOC)", ".ftc .ftu", "PillowFormat"),
+ ("GBR", "GIMP brush file", ".gbr", "PillowFormat"),
+ ("GIF", "Compuserve GIF", ".gif", "GIFFormat"),
+ ("GRIB", "GRIB", ".grib", "PillowFormat"),
+ ("HDF5", "HDF5", ".h5 .hdf", "PillowFormat"),
+ ("ICNS", "Mac OS icns resource", ".icns", "PillowFormat"),
+ ("ICO", "Windows Icon", ".ico", "PillowFormat"),
+ ("IM", "IFUNC Image Memory", ".im", "PillowFormat"),
+ ("IMT", "IM Tools", "", "PillowFormat"),
+ ("IPTC", "IPTC/NAA", ".iim", "PillowFormat"),
+ ("JPEG", "JPEG (ISO 10918)", ".jfif .jpe .jpg .jpeg", "JPEGFormat"),
+ (
+ "JPEG2000",
+ "JPEG 2000 (ISO 15444)",
+ ".jp2 .j2k .jpc .jpf .jpx .j2c",
+ "JPEG2000Format",
+ ),
+ ("MCIDAS", "McIdas area file", "", "PillowFormat"),
+ ("MIC", "Microsoft Image Composer", ".mic", "PillowFormat"),
+ # skipped in legacy pillow
+ # ("MPEG", "MPEG", ".mpg .mpeg", "PillowFormat"),
+ ("MPO", "MPO (CIPA DC-007)", ".mpo", "PillowFormat"),
+ ("MSP", "Windows Paint", ".msp", "PillowFormat"),
+ ("PCD", "Kodak PhotoCD", ".pcd", "PillowFormat"),
+ ("PCX", "Paintbrush", ".pcx", "PillowFormat"),
+ ("PIXAR", "PIXAR raster image", ".pxr", "PillowFormat"),
+ ("PNG", "Portable network graphics", ".png", "PNGFormat"),
+ ("PPM", "Pbmplus image", ".pbm .pgm .ppm", "PillowFormat"),
+ ("PSD", "Adobe Photoshop", ".psd", "PillowFormat"),
+ ("SGI", "SGI Image File Format", ".bw .rgb .rgba .sgi", "PillowFormat"),
+ ("SPIDER", "Spider 2D image", "", "PillowFormat"),
+ ("SUN", "Sun Raster File", ".ras", "PillowFormat"),
+ ("TGA", "Targa", ".tga", "PillowFormat"),
+ ("TIFF", "Adobe TIFF", ".tif .tiff", "TIFFFormat"),
+ ("WMF", "Windows Metafile", ".wmf .emf", "PillowFormat"),
+ ("XBM", "X11 Bitmap", ".xbm", "PillowFormat"),
+ ("XPM", "X11 Pixel Map", ".xpm", "PillowFormat"),
+ ("XVTHUMB", "XV thumbnail image", "", "PillowFormat"),
+]
+for id, summary, ext, class_name in PILLOW_FORMATS:
+ config = PluginConfig(
+ name=id.upper() + "-PIL",
+ class_name=class_name,
+ module_name="imageio.plugins.pillow_legacy",
+ is_legacy=True,
+ install_name="pillow",
+ legacy_args={
+ "description": summary + " via Pillow",
+ "extensions": ext,
+ "modes": "iI" if class_name == "GIFFormat" else "i",
+ "plugin_id": id,
+ },
+ )
+ known_plugins[config.name] = config
+
+known_plugins["FFMPEG"] = PluginConfig(
+ name="FFMPEG",
+ class_name="FfmpegFormat",
+ module_name="imageio.plugins.ffmpeg",
+ is_legacy=True,
+ install_name="ffmpeg",
+ legacy_args={
+ "description": "Many video formats and cameras (via ffmpeg)",
+ "extensions": ".mov .avi .mpg .mpeg .mp4 .mkv .webm .wmv .h264",
+ "modes": "I",
+ },
+)
+
+known_plugins["BSDF"] = PluginConfig(
+ name="BSDF",
+ class_name="BsdfFormat",
+ module_name="imageio.plugins.bsdf",
+ is_legacy=True,
+ install_name="bsdf",
+ legacy_args={
+ "description": "Format based on the Binary Structured Data Format",
+ "extensions": ".bsdf",
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["DICOM"] = PluginConfig(
+ name="DICOM",
+ class_name="DicomFormat",
+ module_name="imageio.plugins.dicom",
+ is_legacy=True,
+ install_name="dicom",
+ legacy_args={
+ "description": "Digital Imaging and Communications in Medicine",
+ "extensions": ".dcm .ct .mri",
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["FEI"] = PluginConfig(
+ name="FEI",
+ class_name="FEISEMFormat",
+ module_name="imageio.plugins.feisem",
+ is_legacy=True,
+ install_name="feisem",
+ legacy_args={
+ "description": "FEI-SEM TIFF format",
+ "extensions": [".tif", ".tiff"],
+ "modes": "iv",
+ },
+)
+
+known_plugins["FITS"] = PluginConfig(
+ name="FITS",
+ class_name="FitsFormat",
+ module_name="imageio.plugins.fits",
+ is_legacy=True,
+ install_name="fits",
+ legacy_args={
+ "description": "Flexible Image Transport System (FITS) format",
+ "extensions": ".fits .fit .fts .fz",
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["GDAL"] = PluginConfig(
+ name="GDAL",
+ class_name="GdalFormat",
+ module_name="imageio.plugins.gdal",
+ is_legacy=True,
+ install_name="gdal",
+ legacy_args={
+ "description": "Geospatial Data Abstraction Library",
+ "extensions": ".tiff .tif .img .ecw .jpg .jpeg",
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["ITK"] = PluginConfig(
+ name="ITK",
+ class_name="ItkFormat",
+ module_name="imageio.plugins.simpleitk",
+ is_legacy=True,
+ install_name="simpleitk",
+ legacy_args={
+ "description": "Insight Segmentation and Registration Toolkit (ITK) format",
+ "extensions": " ".join(
+ (
+ ".gipl",
+ ".ipl",
+ ".mha",
+ ".mhd",
+ ".nhdr",
+ ".nia",
+ ".hdr",
+ ".nrrd",
+ ".nii",
+ ".nii.gz",
+ ".img",
+ ".img.gz",
+ ".vtk",
+ ".hdf5",
+ ".lsm",
+ ".mnc",
+ ".mnc2",
+ ".mgh",
+ ".mnc",
+ ".pic",
+ ".bmp",
+ ".jpeg",
+ ".jpg",
+ ".png",
+ ".tiff",
+ ".tif",
+ ".dicom",
+ ".dcm",
+ ".gdcm",
+ )
+ ),
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["NPZ"] = PluginConfig(
+ name="NPZ",
+ class_name="NpzFormat",
+ module_name="imageio.plugins.npz",
+ is_legacy=True,
+ install_name="numpy",
+ legacy_args={
+ "description": "Numpy's compressed array format",
+ "extensions": ".npz",
+ "modes": "iIvV",
+ },
+)
+
+known_plugins["SWF"] = PluginConfig(
+ name="SWF",
+ class_name="SWFFormat",
+ module_name="imageio.plugins.swf",
+ is_legacy=True,
+ install_name="swf",
+ legacy_args={
+ "description": "Shockwave flash",
+ "extensions": ".swf",
+ "modes": "I",
+ },
+)
+
+known_plugins["SCREENGRAB"] = PluginConfig(
+ name="SCREENGRAB",
+ class_name="ScreenGrabFormat",
+ module_name="imageio.plugins.grab",
+ is_legacy=True,
+ install_name="pillow",
+ legacy_args={
+ "description": "Grab screenshots (Windows and OS X only)",
+ "extensions": [],
+ "modes": "i",
+ },
+)
+
+known_plugins["CLIPBOARDGRAB"] = PluginConfig(
+ name="CLIPBOARDGRAB",
+ class_name="ClipboardGrabFormat",
+ module_name="imageio.plugins.grab",
+ is_legacy=True,
+ install_name="pillow",
+ legacy_args={
+ "description": "Grab from clipboard (Windows only)",
+ "extensions": [],
+ "modes": "i",
+ },
+)
+
+# LYTRO plugin (legacy)
+lytro_formats = [
+ ("lytro-lfr", "Lytro Illum lfr image file", ".lfr", "i", "LytroLfrFormat"),
+ (
+ "lytro-illum-raw",
+ "Lytro Illum raw image file",
+ ".raw",
+ "i",
+ "LytroIllumRawFormat",
+ ),
+ ("lytro-lfp", "Lytro F01 lfp image file", ".lfp", "i", "LytroLfpFormat"),
+ ("lytro-f01-raw", "Lytro F01 raw image file", ".raw", "i", "LytroF01RawFormat"),
+]
+for name, des, ext, mode, class_name in lytro_formats:
+ config = PluginConfig(
+ name=name.upper(),
+ class_name=class_name,
+ module_name="imageio.plugins.lytro",
+ is_legacy=True,
+ install_name="lytro",
+ legacy_args={
+ "description": des,
+ "extensions": ext,
+ "modes": mode,
+ },
+ )
+ known_plugins[config.name] = config
+
+# FreeImage plugin (legacy)
+FREEIMAGE_FORMATS = [
+ (
+ "BMP",
+ 0,
+ "Windows or OS/2 Bitmap",
+ ".bmp",
+ "i",
+ "FreeimageBmpFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "CUT",
+ 21,
+ "Dr. Halo",
+ ".cut",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "DDS",
+ 24,
+ "DirectX Surface",
+ ".dds",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "EXR",
+ 29,
+ "ILM OpenEXR",
+ ".exr",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "G3",
+ 27,
+ "Raw fax format CCITT G.3",
+ ".g3",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "GIF",
+ 25,
+ "Static and animated gif (FreeImage)",
+ ".gif",
+ "iI",
+ "GifFormat",
+ "imageio.plugins.freeimagemulti",
+ ),
+ (
+ "HDR",
+ 26,
+ "High Dynamic Range Image",
+ ".hdr",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "ICO",
+ 1,
+ "Windows Icon",
+ ".ico",
+ "iI",
+ "IcoFormat",
+ "imageio.plugins.freeimagemulti",
+ ),
+ (
+ "IFF",
+ 5,
+ "IFF Interleaved Bitmap",
+ ".iff .lbm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "J2K",
+ 30,
+ "JPEG-2000 codestream",
+ ".j2k .j2c",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "JNG",
+ 3,
+ "JPEG Network Graphics",
+ ".jng",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "JP2",
+ 31,
+ "JPEG-2000 File Format",
+ ".jp2",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "JPEG",
+ 2,
+ "JPEG - JFIF Compliant",
+ ".jpg .jif .jpeg .jpe",
+ "i",
+ "FreeimageJpegFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "JPEG-XR",
+ 36,
+ "JPEG XR image format",
+ ".jxr .wdp .hdp",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "KOALA",
+ 4,
+ "C64 Koala Graphics",
+ ".koa",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ # not registered in legacy pillow
+ # ("MNG", 6, "Multiple-image Network Graphics", ".mng", "i", "FreeimageFormat", "imageio.plugins.freeimage"),
+ (
+ "PBM",
+ 7,
+ "Portable Bitmap (ASCII)",
+ ".pbm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PBMRAW",
+ 8,
+ "Portable Bitmap (RAW)",
+ ".pbm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PCD",
+ 9,
+ "Kodak PhotoCD",
+ ".pcd",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PCX",
+ 10,
+ "Zsoft Paintbrush",
+ ".pcx",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PFM",
+ 32,
+ "Portable floatmap",
+ ".pfm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PGM",
+ 11,
+ "Portable Greymap (ASCII)",
+ ".pgm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PGMRAW",
+ 12,
+ "Portable Greymap (RAW)",
+ ".pgm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PICT",
+ 33,
+ "Macintosh PICT",
+ ".pct .pict .pic",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PNG",
+ 13,
+ "Portable Network Graphics",
+ ".png",
+ "i",
+ "FreeimagePngFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PPM",
+ 14,
+ "Portable Pixelmap (ASCII)",
+ ".ppm",
+ "i",
+ "FreeimagePnmFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PPMRAW",
+ 15,
+ "Portable Pixelmap (RAW)",
+ ".ppm",
+ "i",
+ "FreeimagePnmFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "PSD",
+ 20,
+ "Adobe Photoshop",
+ ".psd",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "RAS",
+ 16,
+ "Sun Raster Image",
+ ".ras",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "RAW",
+ 34,
+ "RAW camera image",
+ ".3fr .arw .bay .bmq .cap .cine .cr2 .crw .cs1 .dc2 "
+ ".dcr .drf .dsc .dng .erf .fff .ia .iiq .k25 .kc2 .kdc .mdc .mef .mos .mrw .nef .nrw .orf "
+ ".pef .ptx .pxn .qtk .raf .raw .rdc .rw2 .rwl .rwz .sr2 .srf .srw .sti",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "SGI",
+ 28,
+ "SGI Image Format",
+ ".sgi .rgb .rgba .bw",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "TARGA",
+ 17,
+ "Truevision Targa",
+ ".tga .targa",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "TIFF",
+ 18,
+ "Tagged Image File Format",
+ ".tif .tiff",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "WBMP",
+ 19,
+ "Wireless Bitmap",
+ ".wap .wbmp .wbm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "WebP",
+ 35,
+ "Google WebP image format",
+ ".webp",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "XBM",
+ 22,
+ "X11 Bitmap Format",
+ ".xbm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+ (
+ "XPM",
+ 23,
+ "X11 Pixmap Format",
+ ".xpm",
+ "i",
+ "FreeimageFormat",
+ "imageio.plugins.freeimage",
+ ),
+]
+for name, i, des, ext, mode, class_name, module_name in FREEIMAGE_FORMATS:
+ config = PluginConfig(
+ name=name.upper() + "-FI",
+ class_name=class_name,
+ module_name=module_name,
+ is_legacy=True,
+ install_name="freeimage",
+ legacy_args={
+ "description": des,
+ "extensions": ext,
+ "modes": mode,
+ "fif": i,
+ },
+ )
+ known_plugins[config.name] = config
+
+# exists for backwards compatibility with FormatManager
+# delete in V3
+_original_order = [x for x, config in known_plugins.items() if config.is_legacy]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.pyi b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ab5d4a816257e0f501fce1c087e74a3d1f66dc13
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/config/plugins.pyi
@@ -0,0 +1,28 @@
+from typing import Any, Dict, Optional
+from ..core.v3_plugin_api import PluginV3
+
+class PluginConfig:
+ name: str
+ class_name: str
+ module_name: str
+ is_legacy: bool
+ package_name: Optional[str] = None
+ install_name: Optional[str] = None
+ legacy_args: Optional[dict] = None
+ @property
+ def format(self) -> Any: ...
+ @property
+ def plugin_class(self) -> PluginV3: ...
+ def __init__(
+ self,
+ name: str,
+ class_name: str,
+ module_name: str,
+ *,
+ is_legacy: bool = False,
+ package_name: str = None,
+ install_name: str = None,
+ legacy_args: dict = None,
+ ) -> None: ...
+
+known_plugins: Dict[str, PluginConfig]
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/__init__.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7ef5515cf120156bc3f87e488fb81ae31543138
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/__init__.py
@@ -0,0 +1,16 @@
+# -*- coding: utf-8 -*-
+# Distributed under the (new) BSD License. See LICENSE.txt for more info.
+
+"""This subpackage provides the core functionality of imageio
+(everything but the plugins).
+"""
+
+# flake8: noqa
+
+from .util import Image, Array, Dict, asarray, image_as_uint, urlopen
+from .util import BaseProgressIndicator, StdoutProgressIndicator, IS_PYPY
+from .util import get_platform, appdata_dir, resource_dirs, has_module
+from .findlib import load_lib
+from .fetching import get_remote_file, InternetNotAllowedError, NeedDownloadError
+from .request import Request, read_n_bytes, RETURN_BYTES
+from .format import Format, FormatManager
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/__pycache__/__init__.cpython-311.pyc b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/__pycache__/__init__.cpython-311.pyc
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diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/fetching.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/fetching.py
new file mode 100644
index 0000000000000000000000000000000000000000..b289340b61b5d932bbf245becd7585563aa8a05d
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/fetching.py
@@ -0,0 +1,246 @@
+# -*- coding: utf-8 -*-
+# Based on code from the vispy project
+# Distributed under the (new) BSD License. See LICENSE.txt for more info.
+
+"""Data downloading and reading functions"""
+
+from math import log
+import os
+from os import path as op
+import sys
+import shutil
+import time
+
+from . import appdata_dir, resource_dirs
+from . import StdoutProgressIndicator, urlopen
+
+
+class InternetNotAllowedError(IOError):
+ """Plugins that need resources can just use get_remote_file(), but
+ should catch this error and silently ignore it.
+ """
+
+ pass
+
+
+class NeedDownloadError(IOError):
+ """Is raised when a remote file is requested that is not locally
+ available, but which needs to be explicitly downloaded by the user.
+ """
+
+
+def get_remote_file(fname, directory=None, force_download=False, auto=True):
+ """Get a the filename for the local version of a file from the web
+
+ Parameters
+ ----------
+ fname : str
+ The relative filename on the remote data repository to download.
+ These correspond to paths on
+ ``https://github.com/imageio/imageio-binaries/``.
+ directory : str | None
+ The directory where the file will be cached if a download was
+ required to obtain the file. By default, the appdata directory
+ is used. This is also the first directory that is checked for
+ a local version of the file. If the directory does not exist,
+ it will be created.
+ force_download : bool | str
+ If True, the file will be downloaded even if a local copy exists
+ (and this copy will be overwritten). Can also be a YYYY-MM-DD date
+ to ensure a file is up-to-date (modified date of a file on disk,
+ if present, is checked).
+ auto : bool
+ Whether to auto-download the file if its not present locally. Default
+ True. If False and a download is needed, raises NeedDownloadError.
+
+ Returns
+ -------
+ fname : str
+ The path to the file on the local system.
+ """
+ _url_root = "https://github.com/imageio/imageio-binaries/raw/master/"
+ url = _url_root + fname
+ nfname = op.normcase(fname) # convert to native
+ # Get dirs to look for the resource
+ given_directory = directory
+ directory = given_directory or appdata_dir("imageio")
+ dirs = resource_dirs()
+ dirs.insert(0, directory) # Given dir has preference
+ # Try to find the resource locally
+ for dir in dirs:
+ filename = op.join(dir, nfname)
+ if op.isfile(filename):
+ if not force_download: # we're done
+ if given_directory and given_directory != dir:
+ filename2 = os.path.join(given_directory, nfname)
+ # Make sure the output directory exists
+ if not op.isdir(op.dirname(filename2)):
+ os.makedirs(op.abspath(op.dirname(filename2)))
+ shutil.copy(filename, filename2)
+ return filename2
+ return filename
+ if isinstance(force_download, str):
+ ntime = time.strptime(force_download, "%Y-%m-%d")
+ ftime = time.gmtime(op.getctime(filename))
+ if ftime >= ntime:
+ if given_directory and given_directory != dir:
+ filename2 = os.path.join(given_directory, nfname)
+ # Make sure the output directory exists
+ if not op.isdir(op.dirname(filename2)):
+ os.makedirs(op.abspath(op.dirname(filename2)))
+ shutil.copy(filename, filename2)
+ return filename2
+ return filename
+ else:
+ print("File older than %s, updating..." % force_download)
+ break
+
+ # If we get here, we're going to try to download the file
+ if os.getenv("IMAGEIO_NO_INTERNET", "").lower() in ("1", "true", "yes"):
+ raise InternetNotAllowedError(
+ "Will not download resource from the "
+ "internet because environment variable "
+ "IMAGEIO_NO_INTERNET is set."
+ )
+
+ # Can we proceed with auto-download?
+ if not auto:
+ raise NeedDownloadError()
+
+ # Get filename to store to and make sure the dir exists
+ filename = op.join(directory, nfname)
+ if not op.isdir(op.dirname(filename)):
+ os.makedirs(op.abspath(op.dirname(filename)))
+ # let's go get the file
+ if os.getenv("CI"): # pragma: no cover
+ # On CI, we retry a few times ...
+ for i in range(2):
+ try:
+ _fetch_file(url, filename)
+ return filename
+ except IOError:
+ time.sleep(0.5)
+ else:
+ _fetch_file(url, filename)
+ return filename
+ else: # pragma: no cover
+ _fetch_file(url, filename)
+ return filename
+
+
+def _fetch_file(url, file_name, print_destination=True):
+ """Load requested file, downloading it if needed or requested
+
+ Parameters
+ ----------
+ url: string
+ The url of file to be downloaded.
+ file_name: string
+ Name, along with the path, of where downloaded file will be saved.
+ print_destination: bool, optional
+ If true, destination of where file was saved will be printed after
+ download finishes.
+ resume: bool, optional
+ If true, try to resume partially downloaded files.
+ """
+ # Adapted from NISL:
+ # https://github.com/nisl/tutorial/blob/master/nisl/datasets.py
+
+ print(
+ "Imageio: %r was not found on your computer; "
+ "downloading it now." % os.path.basename(file_name)
+ )
+
+ temp_file_name = file_name + ".part"
+ local_file = None
+ initial_size = 0
+ errors = []
+ for tries in range(4):
+ try:
+ # Checking file size and displaying it alongside the download url
+ remote_file = urlopen(url, timeout=5.0)
+ file_size = int(remote_file.headers["Content-Length"].strip())
+ size_str = _sizeof_fmt(file_size)
+ print("Try %i. Download from %s (%s)" % (tries + 1, url, size_str))
+ # Downloading data (can be extended to resume if need be)
+ local_file = open(temp_file_name, "wb")
+ _chunk_read(remote_file, local_file, initial_size=initial_size)
+ # temp file must be closed prior to the move
+ if not local_file.closed:
+ local_file.close()
+ shutil.move(temp_file_name, file_name)
+ if print_destination is True:
+ sys.stdout.write("File saved as %s.\n" % file_name)
+ break
+ except Exception as e:
+ errors.append(e)
+ print("Error while fetching file: %s." % str(e))
+ finally:
+ if local_file is not None:
+ if not local_file.closed:
+ local_file.close()
+ else:
+ raise IOError(
+ "Unable to download %r. Perhaps there is no internet "
+ "connection? If there is, please report this problem."
+ % os.path.basename(file_name)
+ )
+
+
+def _chunk_read(response, local_file, chunk_size=8192, initial_size=0):
+ """Download a file chunk by chunk and show advancement
+
+ Can also be used when resuming downloads over http.
+
+ Parameters
+ ----------
+ response: urllib.response.addinfourl
+ Response to the download request in order to get file size.
+ local_file: file
+ Hard disk file where data should be written.
+ chunk_size: integer, optional
+ Size of downloaded chunks. Default: 8192
+ initial_size: int, optional
+ If resuming, indicate the initial size of the file.
+ """
+ # Adapted from NISL:
+ # https://github.com/nisl/tutorial/blob/master/nisl/datasets.py
+
+ bytes_so_far = initial_size
+ # Returns only amount left to download when resuming, not the size of the
+ # entire file
+ total_size = int(response.headers["Content-Length"].strip())
+ total_size += initial_size
+
+ progress = StdoutProgressIndicator("Downloading")
+ progress.start("", "bytes", total_size)
+
+ while True:
+ chunk = response.read(chunk_size)
+ bytes_so_far += len(chunk)
+ if not chunk:
+ break
+ _chunk_write(chunk, local_file, progress)
+ progress.finish("Done")
+
+
+def _chunk_write(chunk, local_file, progress):
+ """Write a chunk to file and update the progress bar"""
+ local_file.write(chunk)
+ progress.increase_progress(len(chunk))
+ time.sleep(0) # Give other threads a chance, e.g. those that handle stdout pipes
+
+
+def _sizeof_fmt(num):
+ """Turn number of bytes into human-readable str"""
+ units = ["bytes", "kB", "MB", "GB", "TB", "PB"]
+ decimals = [0, 0, 1, 2, 2, 2]
+ """Human friendly file size"""
+ if num > 1:
+ exponent = min(int(log(num, 1024)), len(units) - 1)
+ quotient = float(num) / 1024**exponent
+ unit = units[exponent]
+ num_decimals = decimals[exponent]
+ format_string = "{0:.%sf} {1}" % num_decimals
+ return format_string.format(quotient, unit)
+ return "0 bytes" if num == 0 else "1 byte"
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/findlib.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/findlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..27bdf4b129d108c320a81d7f271135ac3d22ed59
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/findlib.py
@@ -0,0 +1,159 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) 2015-2026, imageio contributors
+# Copyright (C) 2013, Zach Pincus, Almar Klein and others
+
+"""This module contains generic code to find and load a dynamic library."""
+
+import os
+import sys
+import ctypes
+
+LOCALDIR = os.path.abspath(os.path.dirname(__file__))
+
+# Flag that can be patched / set to True to disable loading non-system libs
+SYSTEM_LIBS_ONLY = False
+
+
+def looks_lib(fname):
+ """Returns True if the given filename looks like a dynamic library.
+ Based on extension, but cross-platform and more flexible.
+ """
+ fname = fname.lower()
+ if sys.platform.startswith("win"):
+ return fname.endswith(".dll")
+ elif sys.platform.startswith("darwin"):
+ return fname.endswith(".dylib")
+ else:
+ return fname.endswith(".so") or ".so." in fname
+
+
+def generate_candidate_libs(lib_names, lib_dirs=None):
+ """Generate a list of candidate filenames of what might be the dynamic
+ library corresponding with the given list of names.
+ Returns (lib_dirs, lib_paths)
+ """
+ lib_dirs = lib_dirs or []
+
+ # Get system dirs to search
+ sys_lib_dirs = [
+ "/lib",
+ "/usr/lib",
+ "/usr/lib/x86_64-linux-gnu",
+ "/usr/lib/aarch64-linux-gnu",
+ "/usr/local/lib",
+ "/opt/local/lib",
+ ]
+
+ # Get Python dirs to search (shared if for Pyzo)
+ py_sub_dirs = ["bin", "lib", "DLLs", "Library/bin", "shared"]
+ py_lib_dirs = [os.path.join(sys.prefix, d) for d in py_sub_dirs]
+ if hasattr(sys, "base_prefix"):
+ py_lib_dirs += [os.path.join(sys.base_prefix, d) for d in py_sub_dirs]
+
+ # Get user dirs to search (i.e. HOME)
+ home_dir = os.path.expanduser("~")
+ user_lib_dirs = [os.path.join(home_dir, d) for d in ["lib"]]
+
+ # Select only the dirs for which a directory exists, and remove duplicates
+ potential_lib_dirs = lib_dirs + sys_lib_dirs + py_lib_dirs + user_lib_dirs
+ lib_dirs = []
+ for ld in potential_lib_dirs:
+ if os.path.isdir(ld) and ld not in lib_dirs:
+ lib_dirs.append(ld)
+
+ # Now attempt to find libraries of that name in the given directory
+ # (case-insensitive)
+ lib_paths = []
+ for lib_dir in lib_dirs:
+ # Get files, prefer short names, last version
+ files = os.listdir(lib_dir)
+ files = reversed(sorted(files))
+ files = sorted(files, key=len)
+ for lib_name in lib_names:
+ # Test all filenames for name and ext
+ for fname in files:
+ if fname.lower().startswith(lib_name) and looks_lib(fname):
+ lib_paths.append(os.path.join(lib_dir, fname))
+
+ # Return (only the items which are files)
+ lib_paths = [lp for lp in lib_paths if os.path.isfile(lp)]
+ return lib_dirs, lib_paths
+
+
+def load_lib(exact_lib_names, lib_names, lib_dirs=None):
+ """load_lib(exact_lib_names, lib_names, lib_dirs=None)
+
+ Load a dynamic library.
+
+ This function first tries to load the library from the given exact
+ names. When that fails, it tries to find the library in common
+ locations. It searches for files that start with one of the names
+ given in lib_names (case insensitive). The search is performed in
+ the given lib_dirs and a set of common library dirs.
+
+ Returns ``(ctypes_library, library_path)``
+ """
+
+ # Checks
+ assert isinstance(exact_lib_names, list)
+ assert isinstance(lib_names, list)
+ if lib_dirs is not None:
+ assert isinstance(lib_dirs, list)
+ exact_lib_names = [n for n in exact_lib_names if n]
+ lib_names = [n for n in lib_names if n]
+
+ # Get reference name (for better messages)
+ if lib_names:
+ the_lib_name = lib_names[0]
+ elif exact_lib_names:
+ the_lib_name = exact_lib_names[0]
+ else:
+ raise ValueError("No library name given.")
+
+ # Collect filenames of potential libraries
+ # First try a few bare library names that ctypes might be able to find
+ # in the default locations for each platform.
+ if SYSTEM_LIBS_ONLY:
+ lib_dirs, lib_paths = [], []
+ else:
+ lib_dirs, lib_paths = generate_candidate_libs(lib_names, lib_dirs)
+ lib_paths = exact_lib_names + lib_paths
+
+ # Select loader
+ if sys.platform.startswith("win"):
+ loader = ctypes.windll
+ else:
+ loader = ctypes.cdll
+
+ # Try to load until success
+ the_lib = None
+ errors = []
+ for fname in lib_paths:
+ try:
+ the_lib = loader.LoadLibrary(fname)
+ break
+ except Exception as err:
+ # Don't record errors when it couldn't load the library from an
+ # exact name -- this fails often, and doesn't provide any useful
+ # debugging information anyway, beyond "couldn't find library..."
+ if fname not in exact_lib_names:
+ errors.append((fname, err))
+
+ # No success ...
+ if the_lib is None:
+ if errors:
+ # No library loaded, and load-errors reported for some
+ # candidate libs
+ err_txt = ["%s:\n%s" % (lib, str(e)) for lib, e in errors]
+ msg = (
+ "One or more %s libraries were found, but "
+ + "could not be loaded due to the following errors:\n%s"
+ )
+ raise OSError(msg % (the_lib_name, "\n\n".join(err_txt)))
+ else:
+ # No errors, because no potential libraries found at all!
+ msg = "Could not find a %s library in any of:\n%s"
+ raise OSError(msg % (the_lib_name, "\n".join(lib_dirs)))
+
+ # Done
+ return the_lib, fname
diff --git a/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/format.py b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/format.py
new file mode 100644
index 0000000000000000000000000000000000000000..00eac70959270cd685ae9252228e6ed259786d8f
--- /dev/null
+++ b/physworld_p1_20261018/envs/p1/lib/python3.11/site-packages/imageio/core/format.py
@@ -0,0 +1,880 @@
+# -*- coding: utf-8 -*-
+# imageio is distributed under the terms of the (new) BSD License.
+
+"""
+
+.. note::
+ imageio is under construction, some details with regard to the
+ Reader and Writer classes may change.
+
+These are the main classes of imageio. They expose an interface for
+advanced users and plugin developers. A brief overview:
+
+ * imageio.FormatManager - for keeping track of registered formats.
+ * imageio.Format - representation of a file format reader/writer
+ * imageio.Format.Reader - object used during the reading of a file.
+ * imageio.Format.Writer - object used during saving a file.
+ * imageio.Request - used to store the filename and other info.
+
+Plugins need to implement a Format class and register
+a format object using ``imageio.formats.add_format()``.
+
+"""
+
+# todo: do we even use the known extensions?
+
+# Some notes:
+#
+# The classes in this module use the Request object to pass filename and
+# related info around. This request object is instantiated in
+# imageio.get_reader and imageio.get_writer.
+
+import sys
+import warnings
+import contextlib
+
+import numpy as np
+from pathlib import Path
+
+from . import Array, asarray
+from .request import ImageMode
+from ..config import known_plugins, known_extensions, PluginConfig, FileExtension
+from ..config.plugins import _original_order
+from .imopen import imopen
+
+# survived for backwards compatibility
+# I don't know if external plugin code depends on it existing
+# We no longer do
+MODENAMES = ImageMode
+
+
+def _get_config(plugin):
+ """Old Plugin resolution logic.
+
+ Remove once we remove the old format manager.
+ """
+
+ extension_name = None
+
+ if Path(plugin).suffix.lower() in known_extensions:
+ extension_name = Path(plugin).suffix.lower()
+ elif plugin in known_plugins:
+ pass
+ elif plugin.lower() in known_extensions:
+ extension_name = plugin.lower()
+ elif "." + plugin.lower() in known_extensions:
+ extension_name = "." + plugin.lower()
+ else:
+ raise IndexError(f"No format known by name `{plugin}`.")
+
+ if extension_name is not None:
+ for plugin_name in [
+ x
+ for file_extension in known_extensions[extension_name]
+ for x in file_extension.priority
+ ]:
+ if known_plugins[plugin_name].is_legacy:
+ plugin = plugin_name
+ break
+
+ return known_plugins[plugin]
+
+
+class Format(object):
+ """Represents an implementation to read/write a particular file format
+
+ A format instance is responsible for 1) providing information about
+ a format; 2) determining whether a certain file can be read/written
+ with this format; 3) providing a reader/writer class.
+
+ Generally, imageio will select the right format and use that to
+ read/write an image. A format can also be explicitly chosen in all
+ read/write functions. Use ``print(format)``, or ``help(format_name)``
+ to see its documentation.
+
+ To implement a specific format, one should create a subclass of
+ Format and the Format.Reader and Format.Writer classes. See
+ :class:`imageio.plugins` for details.
+
+ Parameters
+ ----------
+ name : str
+ A short name of this format. Users can select a format using its name.
+ description : str
+ A one-line description of the format.
+ extensions : str | list | None
+ List of filename extensions that this format supports. If a
+ string is passed it should be space or comma separated. The
+ extensions are used in the documentation and to allow users to
+ select a format by file extension. It is not used to determine
+ what format to use for reading/saving a file.
+ modes : str
+ A string containing the modes that this format can handle ('iIvV'),
+ “i” for an image, “I” for multiple images, “v” for a volume,
+ “V” for multiple volumes.
+ This attribute is used in the documentation and to select the
+ formats when reading/saving a file.
+ """
+
+ def __init__(self, name, description, extensions=None, modes=None):
+ """Initialize the Plugin.
+
+ Parameters
+ ----------
+ name : str
+ A short name of this format. Users can select a format using its name.
+ description : str
+ A one-line description of the format.
+ extensions : str | list | None
+ List of filename extensions that this format supports. If a
+ string is passed it should be space or comma separated. The
+ extensions are used in the documentation and to allow users to
+ select a format by file extension. It is not used to determine
+ what format to use for reading/saving a file.
+ modes : str
+ A string containing the modes that this format can handle ('iIvV'),
+ “i” for an image, “I” for multiple images, “v” for a volume,
+ “V” for multiple volumes.
+ This attribute is used in the documentation and to select the
+ formats when reading/saving a file.
+ """
+
+ # Store name and description
+ self._name = name.upper()
+ self._description = description
+
+ # Store extensions, do some effort to normalize them.
+ # They are stored as a list of lowercase strings without leading dots.
+ if extensions is None:
+ extensions = []
+ elif isinstance(extensions, str):
+ extensions = extensions.replace(",", " ").split(" ")
+ #
+ if isinstance(extensions, (tuple, list)):
+ self._extensions = tuple(
+ ["." + e.strip(".").lower() for e in extensions if e]
+ )
+ else:
+ raise ValueError("Invalid value for extensions given.")
+
+ # Store mode
+ self._modes = modes or ""
+ if not isinstance(self._modes, str):
+ raise ValueError("Invalid value for modes given.")
+ for m in self._modes:
+ if m not in "iIvV?":
+ raise ValueError("Invalid value for mode given.")
+
+ def __repr__(self):
+ # Short description
+ return "" % (self.name, self.description)
+
+ def __str__(self):
+ return self.doc
+
+ @property
+ def doc(self):
+ """The documentation for this format (name + description + docstring)."""
+ # Our docsring is assumed to be indented by four spaces. The
+ # first line needs special attention.
+ return "%s - %s\n\n %s\n" % (
+ self.name,
+ self.description,
+ self.__doc__.strip(),
+ )
+
+ @property
+ def name(self):
+ """The name of this format."""
+ return self._name
+
+ @property
+ def description(self):
+ """A short description of this format."""
+ return self._description
+
+ @property
+ def extensions(self):
+ """A list of file extensions supported by this plugin.
+ These are all lowercase with a leading dot.
+ """
+ return self._extensions
+
+ @property
+ def modes(self):
+ """A string specifying the modes that this format can handle."""
+ return self._modes
+
+ def get_reader(self, request):
+ """get_reader(request)
+
+ Return a reader object that can be used to read data and info
+ from the given file. Users are encouraged to use
+ imageio.get_reader() instead.
+ """
+ select_mode = request.mode[1] if request.mode[1] in "iIvV" else ""
+ if select_mode not in self.modes:
+ raise RuntimeError(
+ f"Format {self.name} cannot read in {request.mode.image_mode} mode"
+ )
+ return self.Reader(self, request)
+
+ def get_writer(self, request):
+ """get_writer(request)
+
+ Return a writer object that can be used to write data and info
+ to the given file. Users are encouraged to use
+ imageio.get_writer() instead.
+ """
+ select_mode = request.mode[1] if request.mode[1] in "iIvV" else ""
+ if select_mode not in self.modes:
+ raise RuntimeError(
+ f"Format {self.name} cannot write in {request.mode.image_mode} mode"
+ )
+ return self.Writer(self, request)
+
+ def can_read(self, request):
+ """can_read(request)
+
+ Get whether this format can read data from the specified uri.
+ """
+ return self._can_read(request)
+
+ def can_write(self, request):
+ """can_write(request)
+
+ Get whether this format can write data to the speciefed uri.
+ """
+ return self._can_write(request)
+
+ def _can_read(self, request): # pragma: no cover
+ """Check if Plugin can read from ImageResource.
+
+ This method is called when the format manager is searching for a format
+ to read a certain image. Return True if this format can do it.
+
+ The format manager is aware of the extensions and the modes that each
+ format can handle. It will first ask all formats that *seem* to be able
+ to read it whether they can. If none can, it will ask the remaining
+ formats if they can: the extension might be missing, and this allows
+ formats to provide functionality for certain extensions, while giving
+ preference to other plugins.
+
+ If a format says it can, it should live up to it. The format would
+ ideally check the request.firstbytes and look for a header of some kind.
+
+ Parameters
+ ----------
+ request : Request
+ A request that can be used to access the ImageResource and obtain
+ metadata about it.
+
+ Returns
+ -------
+ can_read : bool
+ True if the plugin can read from the ImageResource, False otherwise.
+
+ """
+ return None # Plugins must implement this
+
+ def _can_write(self, request): # pragma: no cover
+ """Check if Plugin can write to ImageResource.
+
+ Parameters
+ ----------
+ request : Request
+ A request that can be used to access the ImageResource and obtain
+ metadata about it.
+
+ Returns
+ -------
+ can_read : bool
+ True if the plugin can write to the ImageResource, False otherwise.
+
+ """
+ return None # Plugins must implement this
+
+ # -----
+
+ class _BaseReaderWriter(object):
+ """Base class for the Reader and Writer class to implement common
+ functionality. It implements a similar approach for opening/closing
+ and context management as Python's file objects.
+ """
+
+ def __init__(self, format, request):
+ self.__closed = False
+ self._BaseReaderWriter_last_index = -1
+ self._format = format
+ self._request = request
+ # Open the reader/writer
+ self._open(**self.request.kwargs.copy())
+
+ @property
+ def format(self):
+ """The :class:`.Format` object corresponding to the current
+ read/write operation.
+ """
+ return self._format
+
+ @property
+ def request(self):
+ """The :class:`.Request` object corresponding to the
+ current read/write operation.
+ """
+ return self._request
+
+ def __enter__(self):
+ self._checkClosed()
+ return self
+
+ def __exit__(self, type, value, traceback):
+ if value is None:
+ # Otherwise error in close hide the real error.
+ self.close()
+
+ def __del__(self):
+ try:
+ self.close()
+ except Exception: # pragma: no cover
+ pass # Suppress noise when called during interpreter shutdown
+
+ def close(self):
+ """Flush and close the reader/writer.
+ This method has no effect if it is already closed.
+ """
+ if self.__closed:
+ return
+ self.__closed = True
+ self._close()
+ # Process results and clean request object
+ self.request.finish()
+
+ @property
+ def closed(self):
+ """Whether the reader/writer is closed."""
+ return self.__closed
+
+ def _checkClosed(self, msg=None):
+ """Internal: raise an ValueError if reader/writer is closed"""
+ if self.closed:
+ what = self.__class__.__name__
+ msg = msg or ("I/O operation on closed %s." % what)
+ raise RuntimeError(msg)
+
+ # To implement
+
+ def _open(self, **kwargs):
+ """_open(**kwargs)
+
+ Plugins should probably implement this.
+
+ It is called when reader/writer is created. Here the
+ plugin can do its initialization. The given keyword arguments
+ are those that were given by the user at imageio.read() or
+ imageio.write().
+ """
+ raise NotImplementedError()
+
+ def _close(self):
+ """_close()
+
+ Plugins should probably implement this.
+
+ It is called when the reader/writer is closed. Here the plugin
+ can do a cleanup, flush, etc.
+
+ """
+ raise NotImplementedError()
+
+ # -----
+
+ class Reader(_BaseReaderWriter):
+ """
+ The purpose of a reader object is to read data from an image
+ resource, and should be obtained by calling :func:`.get_reader`.
+
+ A reader can be used as an iterator to read multiple images,
+ and (if the format permits) only reads data from the file when
+ new data is requested (i.e. streaming). A reader can also be
+ used as a context manager so that it is automatically closed.
+
+ Plugins implement Reader's for different formats. Though rare,
+ plugins may provide additional functionality (beyond what is
+ provided by the base reader class).
+ """
+
+ def get_length(self):
+ """get_length()
+
+ Get the number of images in the file. (Note: you can also
+ use ``len(reader_object)``.)
+
+ The result can be:
+ * 0 for files that only have meta data
+ * 1 for singleton images (e.g. in PNG, JPEG, etc.)
+ * N for image series
+ * inf for streams (series of unknown length)
+ """
+ return self._get_length()
+
+ def get_data(self, index, **kwargs):
+ """get_data(index, **kwargs)
+
+ Read image data from the file, using the image index. The
+ returned image has a 'meta' attribute with the meta data.
+ Raises IndexError if the index is out of range.
+
+ Some formats may support additional keyword arguments. These are
+ listed in the documentation of those formats.
+ """
+ self._checkClosed()
+ self._BaseReaderWriter_last_index = index
+ try:
+ im, meta = self._get_data(index, **kwargs)
+ except StopIteration:
+ raise IndexError(index)
+ return Array(im, meta) # Array tests im and meta
+
+ def get_next_data(self, **kwargs):
+ """get_next_data(**kwargs)
+
+ Read the next image from the series.
+
+ Some formats may support additional keyword arguments. These are
+ listed in the documentation of those formats.
+ """
+ return self.get_data(self._BaseReaderWriter_last_index + 1, **kwargs)
+
+ def set_image_index(self, index, **kwargs):
+ """set_image_index(index)
+
+ Set the internal pointer such that the next call to
+ get_next_data() returns the image specified by the index
+ """
+ self._checkClosed()
+ n = self.get_length()
+ self._BaseReaderWriter_last_index = min(max(index - 1, -1), n)
+
+ def get_meta_data(self, index=None):
+ """get_meta_data(index=None)
+
+ Read meta data from the file. using the image index. If the
+ index is omitted or None, return the file's (global) meta data.
+
+ Note that ``get_data`` also provides the meta data for the returned
+ image as an attribute of that image.
+
+ The meta data is a dict, which shape depends on the format.
+ E.g. for JPEG, the dict maps group names to subdicts and each
+ group is a dict with name-value pairs. The groups represent
+ the different metadata formats (EXIF, XMP, etc.).
+ """
+ self._checkClosed()
+ meta = self._get_meta_data(index)
+ if not isinstance(meta, dict):
+ raise ValueError(
+ "Meta data must be a dict, not %r" % meta.__class__.__name__
+ )
+ return meta
+
+ def iter_data(self):
+ """iter_data()
+
+ Iterate over all images in the series. (Note: you can also
+ iterate over the reader object.)
+
+ """
+ self._checkClosed()
+ n = self.get_length()
+ i = 0
+ while i < n:
+ try:
+ im, meta = self._get_data(i)
+ except StopIteration:
+ return
+ except IndexError:
+ if n == float("inf"):
+ return
+ raise
+ yield Array(im, meta)
+ i += 1
+
+ # Compatibility
+
+ def __iter__(self):
+ return self.iter_data()
+
+ def __len__(self):
+ n = self.get_length()
+ if n == float("inf"):
+ n = sys.maxsize
+ return n
+
+ # To implement
+
+ def _get_length(self):
+ """_get_length()
+
+ Plugins must implement this.
+
+ The returned scalar specifies the number of images in the series.
+ See Reader.get_length for more information.
+ """
+ raise NotImplementedError()
+
+ def _get_data(self, index):
+ """_get_data()
+
+ Plugins must implement this, but may raise an IndexError in
+ case the plugin does not support random access.
+
+ It should return the image and meta data: (ndarray, dict).
+ """
+ raise NotImplementedError()
+
+ def _get_meta_data(self, index):
+ """_get_meta_data(index)
+
+ Plugins must implement this.
+
+ It should return the meta data as a dict, corresponding to the
+ given index, or to the file's (global) meta data if index is
+ None.
+ """
+ raise NotImplementedError()
+
+ # -----
+
+ class Writer(_BaseReaderWriter):
+ """
+ The purpose of a writer object is to write data to an image
+ resource, and should be obtained by calling :func:`.get_writer`.
+
+ A writer will (if the format permits) write data to the file
+ as soon as new data is provided (i.e. streaming). A writer can
+ also be used as a context manager so that it is automatically
+ closed.
+
+ Plugins implement Writer's for different formats. Though rare,
+ plugins may provide additional functionality (beyond what is
+ provided by the base writer class).
+ """
+
+ def append_data(self, im, meta=None):
+ """append_data(im, meta={})
+
+ Append an image (and meta data) to the file. The final meta
+ data that is used consists of the meta data on the given
+ image (if applicable), updated with the given meta data.
+ """
+ self._checkClosed()
+ # Check image data
+ if not isinstance(im, np.ndarray):
+ raise ValueError("append_data requires ndarray as first arg")
+ # Get total meta dict
+ total_meta = {}
+ if hasattr(im, "meta") and isinstance(im.meta, dict):
+ total_meta.update(im.meta)
+ if meta is None:
+ pass
+ elif not isinstance(meta, dict):
+ raise ValueError("Meta must be a dict.")
+ else:
+ total_meta.update(meta)
+
+ # Decouple meta info
+ im = asarray(im)
+ # Call
+ return self._append_data(im, total_meta)
+
+ def set_meta_data(self, meta):
+ """set_meta_data(meta)
+
+ Sets the file's (global) meta data. The meta data is a dict which
+ shape depends on the format. E.g. for JPEG the dict maps
+ group names to subdicts, and each group is a dict with
+ name-value pairs. The groups represents the different
+ metadata formats (EXIF, XMP, etc.).
+
+ Note that some meta formats may not be supported for
+ writing, and individual fields may be ignored without
+ warning if they are invalid.
+ """
+ self._checkClosed()
+ if not isinstance(meta, dict):
+ raise ValueError("Meta must be a dict.")
+ else:
+ return self._set_meta_data(meta)
+
+ # To implement
+
+ def _append_data(self, im, meta):
+ # Plugins must implement this
+ raise NotImplementedError()
+
+ def _set_meta_data(self, meta):
+ # Plugins must implement this
+ raise NotImplementedError()
+
+
+class FormatManager(object):
+ """
+ The FormatManager is a singleton plugin factory.
+
+ The format manager supports getting a format object using indexing (by
+ format name or extension). When used as an iterator, this object
+ yields all registered format objects.
+
+ See also :func:`.help`.
+ """
+
+ @property
+ def _formats(self):
+ available_formats = list()
+
+ for config in known_plugins.values():
+ with contextlib.suppress(ImportError):
+ # if an exception is raised, then format not installed
+ if config.is_legacy and config.format is not None:
+ available_formats.append(config)
+
+ return available_formats
+
+ def __repr__(self):
+ return f""
+
+ def __iter__(self):
+ return iter(x.format for x in self._formats)
+
+ def __len__(self):
+ return len(self._formats)
+
+ def __str__(self):
+ ss = []
+ for config in self._formats:
+ ext = config.legacy_args["extensions"]
+ desc = config.legacy_args["description"]
+ s = f"{config.name} - {desc} [{ext}]"
+ ss.append(s)
+ return "\n".join(ss)
+
+ def __getitem__(self, name):
+ warnings.warn(
+ "The usage of `FormatManager` is deprecated and it will be "
+ "removed in Imageio v3. Use `iio.imopen` instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+
+ if not isinstance(name, str):
+ raise ValueError(
+ "Looking up a format should be done by name or by extension."
+ )
+
+ if name == "":
+ raise ValueError("No format matches the empty string.")
+
+ # Test if name is existing file
+ if Path(name).is_file():
+ # legacy compatibility - why test reading here??
+ try:
+ return imopen(name, "r", legacy_mode=True)._format
+ except ValueError:
+ # no plugin can read the file
+ pass
+
+ config = _get_config(name.upper())
+
+ try:
+ return config.format
+ except ImportError:
+ raise ImportError(
+ f"The `{config.name}` format is not installed. "
+ f"Use `pip install imageio[{config.install_name}]` to install it."
+ )
+
+ def sort(self, *names):
+ """sort(name1, name2, name3, ...)
+
+ Sort the formats based on zero or more given names; a format with
+ a name that matches one of the given names will take precedence
+ over other formats. A match means an equal name, or ending with
+ that name (though the former counts higher). Case insensitive.
+
+ Format preference will match the order of the given names: using
+ ``sort('TIFF', '-FI', '-PIL')`` would prefer the FreeImage formats
+ over the Pillow formats, but prefer TIFF even more. Each time
+ this is called, the starting point is the default format order,
+ and calling ``sort()`` with no arguments will reset the order.
+
+ Be aware that using the function can affect the behavior of
+ other code that makes use of imageio.
+
+ Also see the ``IMAGEIO_FORMAT_ORDER`` environment variable.
+ """
+
+ warnings.warn(
+ "`FormatManager` is deprecated and it will be removed in ImageIO v3."
+ " Migrating `FormatManager.sort` depends on your use-case:\n"
+ "\t- modify `iio.config.known_plugins` to specify the search order for "
+ "unrecognized formats.\n"
+ "\t- modify `iio.config.known_extensions[
+ 
+ 
+ 
+ 
+