#!/usr/bin/env python3
"""Standalone chat inference for kerzgrr/Senryu.

Streams a live reasoning trace (`<think>` / `<|think|>`) and runs Hermes JSON
tool-call rounds. Built-in `web-search`, `python`, and `calculator` tools
execute automatically. Other observations are sent as `<|tool_response|>`.

Examples:
  python inference.py --prompt "What is the capital of France?"
  python inference.py --tools web-search,python,calculator
  python inference.py --no-think --prompt "Reply in one sentence."
"""

from __future__ import annotations

import argparse
import importlib
import json
import os
import platform
import shutil
import subprocess
import sys
import time
import warnings
from dataclasses import fields
from pathlib import Path
from types import SimpleNamespace
from typing import Any

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from tokenizers import Tokenizer


def _silence_runtime_warnings() -> None:
    patterns = (
        r"tl\.make_block_ptr is deprecated",
        r"Memory efficient kernel not used because",
        r"Memory Efficient attention has been runtime disabled",
        r"Flash attention kernel not used because",
        r"Torch was not compiled with flash attention",
        r"cuDNN attention kernel not used because",
        r"cuDNN attention has been runtime disabled",
    )
    for pattern in patterns:
        warnings.filterwarnings("ignore", message=pattern)


REPO_ID = "kerzgrr/Senryu"
FLA_COMMIT = "cbb0a72efb55c18ca0ef4f298298317573ad2cb3"
FLA_REPO = "https://github.com/fla-org/flash-linear-attention.git"
FLA_RUNTIME_REQUIREMENT = "transformers>=4.45.0"
PATCH_FILES = (
    "fla/__init__.py",
    "fla/ops/__init__.py",
    "fla/layers/__init__.py",
    "fla/ops/simple_gla/__init__.py",
)
TINY_GDN_FILES = (
    "tiny_gdn/__init__.py",
    "tiny_gdn/config.py",
    "tiny_gdn/model.py",
    "tiny_gdn/haiku_layers.py",
    "tiny_gdn/nn_common.py",
    "tiny_gdn/fused_ops.py",
    "tiny_gdn/tools.py",
    "tiny_gdn/smoltalk_chat.py",
    "tiny_gdn/chatml.py",
    "tiny_gdn/cli_chat.py",
    "tiny_gdn/detokenize.py",
    "tiny_gdn/code_exec.py",
    "tiny_gdn/web_search.py",
)


def _download(filename: str, local_dir: Path | None) -> Path:
    return Path(
        hf_hub_download(
            repo_id=REPO_ID,
            filename=filename,
            local_dir=str(local_dir) if local_dir else None,
        )
    )


def _run(cmd: list[str], *, cwd: Path | None = None, env: dict | None = None) -> None:
    print("+", " ".join(cmd), flush=True)
    merged = os.environ.copy()
    if env:
        merged.update(env)
    merged.setdefault("PYTHONUTF8", "1")
    merged.setdefault("PYTHONIOENCODING", "utf-8")
    subprocess.check_call(cmd, cwd=str(cwd) if cwd else None, env=merged)


def _pip_install(*args: str) -> None:
    _run([sys.executable, "-m", "pip", "install", *args])


def _fla_importable() -> tuple[bool, str]:
    # fla.models.utils holds the decode cache and is the module that needs
    # transformers at runtime; the GDN-2 layer alone imports without it.
    try:
        importlib.import_module("fla.layers.gdn2")
        importlib.import_module("fla.models.utils")
    except Exception as error:  # noqa: BLE001
        return False, str(error)
    return True, ""


def _cache_root() -> Path:
    override = os.environ.get("SENRYU_CACHE") or os.environ.get("MONOSTICH_CACHE")
    if override:
        path = Path(override).expanduser().resolve()
    else:
        path = Path.home() / ".cache" / "senryu"
    path.mkdir(parents=True, exist_ok=True)
    return path


def _ensure_git() -> None:
    if shutil.which("git") is None:
        raise RuntimeError(
            "git is required to auto-install flash-linear-attention. "
            "Install Git and ensure it is on PATH."
        )


def _apply_windows_fla_patches(fla_root: Path, local_dir: Path | None) -> None:
    print("Applying Windows FLA import patches from the Hub …", flush=True)
    for relative in PATCH_FILES:
        source = _download(f"windows_fla_patches/{relative}", local_dir)
        target = fla_root / relative
        target.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(source, target)
        print(f"  patched {relative}", flush=True)


def _install_fla(local_dir: Path | None) -> None:
    print("flash-linear-attention missing/broken — installing automatically …", flush=True)
    _pip_install("einops", "numpy", FLA_RUNTIME_REQUIREMENT)
    if platform.system() != "Windows":
        _pip_install("--no-deps", f"git+{FLA_REPO}@{FLA_COMMIT}")
        return

    _ensure_git()
    fla_root = _cache_root() / "flash-linear-attention"
    if (fla_root / ".git").is_dir():
        _run(["git", "fetch", "--depth", "1", "origin", FLA_COMMIT], cwd=fla_root)
        _run(["git", "checkout", "--force", FLA_COMMIT], cwd=fla_root)
    else:
        if fla_root.exists():
            shutil.rmtree(fla_root)
        _run(["git", "clone", "--filter=blob:none", FLA_REPO, str(fla_root)])
        _run(["git", "fetch", "--depth", "1", "origin", FLA_COMMIT], cwd=fla_root)
        _run(["git", "checkout", "--force", FLA_COMMIT], cwd=fla_root)

    _apply_windows_fla_patches(fla_root, local_dir)
    _pip_install("--no-build-isolation", "--no-deps", "-e", str(fla_root))


def _ensure_fla(local_dir: Path | None) -> None:
    ok, error = _fla_importable()
    if ok:
        return
    print(f"FLA not ready ({error})", flush=True)
    try:
        _install_fla(local_dir)
    except Exception as install_error:  # noqa: BLE001
        raise RuntimeError(
            "Automatic flash-linear-attention install failed.\n"
            f"Original import error: {error}\n"
            f"Install error: {install_error}"
        ) from install_error

    for name in list(sys.modules):
        if name == "fla" or name.startswith("fla."):
            del sys.modules[name]
    importlib.invalidate_caches()

    ok, error = _fla_importable()
    if not ok:
        raise RuntimeError(
            "flash-linear-attention installed but still failed to import "
            f"GatedDeltaNet2 and its decode cache: {error}"
        )
    print("flash-linear-attention ready.", flush=True)


def _has_tiny_gdn(root: Path) -> bool:
    return all((root / name).is_file() for name in TINY_GDN_FILES)


def _ensure_tiny_gdn(local_dir: Path | None) -> Path:
    here = Path(__file__).resolve().parent
    if _has_tiny_gdn(here):
        return here
    if local_dir and _has_tiny_gdn(local_dir):
        return local_dir
    for name in TINY_GDN_FILES:
        _download(name, local_dir)
    return _download("tiny_gdn/__init__.py", local_dir).parent.parent


def _import_chat_stack() -> SimpleNamespace:
    # tiny_gdn is fetched from the Hub and FLA may be installed at runtime, so
    # these modules only become importable after _ensure_tiny_gdn/_ensure_fla.
    tiny_gdn = importlib.import_module("tiny_gdn")
    chatml = importlib.import_module("tiny_gdn.chatml")
    cli_chat = importlib.import_module("tiny_gdn.cli_chat")
    detokenize = importlib.import_module("tiny_gdn.detokenize")
    tools = importlib.import_module("tiny_gdn.tools")
    return SimpleNamespace(
        TinyGDNConfig=tiny_gdn.TinyGDNConfig,
        TinyGDNForCausalLM=tiny_gdn.TinyGDNForCausalLM,
        encode_chatml_messages=chatml.encode_chatml_messages,
        ChatLoopState=cli_chat.ChatLoopState,
        LiveReasoningStreamer=cli_chat.LiveReasoningStreamer,
        append_tool_round=cli_chat.append_tool_round,
        apply_slash_command=cli_chat.apply_slash_command,
        prompt_tool_results=cli_chat.prompt_tool_results,
        resolve_cli_tools=cli_chat.resolve_cli_tools,
        IncrementalUtf8Decoder=detokenize.IncrementalUtf8Decoder,
        parse_tool_calls=tools.parse_tool_calls,
    )


def _sample(
    logits: torch.Tensor,
    *,
    temperature: float,
    top_p: float,
    top_k: int,
    generator: torch.Generator,
) -> int:
    logits = logits.float()
    if temperature <= 1e-5:
        return int(torch.argmax(logits).item())
    logits = logits / temperature
    if 0 < top_k < logits.shape[-1]:
        threshold = torch.topk(logits, top_k).values[-1]
        logits = logits.masked_fill(logits < threshold, -torch.inf)
    if top_p < 1.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        probs = torch.softmax(sorted_logits, dim=-1)
        remove = torch.cumsum(probs, dim=-1) > top_p
        remove[1:] = remove[:-1].clone()
        remove[0] = False
        sorted_logits = sorted_logits.masked_fill(remove, -torch.inf)
        logits = torch.full_like(logits, -torch.inf)
        logits.scatter_(0, sorted_indices, sorted_logits)
    probs = torch.softmax(logits, dim=-1).cpu()
    return int(torch.multinomial(probs, 1, generator=generator).item())


def _apply_repetition_penalty(
    logits: torch.Tensor,
    token_ids: list[int],
    penalty: float,
    window: int,
) -> torch.Tensor:
    if penalty == 1.0 or not token_ids:
        return logits
    recent = token_ids[-window:] if window > 0 else token_ids
    unique = torch.tensor(list(set(recent)), dtype=torch.long, device=logits.device)
    score = logits[unique]
    logits[unique] = torch.where(score > 0, score / penalty, score * penalty)
    return logits


@torch.inference_mode()
def generate_turn(
    model,
    tokenizer: Tokenizer,
    stack: SimpleNamespace,
    streamer,
    messages: list[dict[str, Any]],
    *,
    tools: Any | None,
    enable_thinking: bool,
    max_new_tokens: int,
    context_length: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repetition_penalty: float,
    repetition_window: int,
    seed: int,
    device: torch.device,
) -> tuple[str, tuple]:
    prompt_ids = stack.encode_chatml_messages(
        tokenizer,
        messages,
        tools=tools,
        enable_thinking=enable_thinking,
    )
    if len(prompt_ids) > context_length:
        print(
            f"[warn] conversation is {len(prompt_ids)} tokens; "
            f"keeping the last {context_length} (--context-length)",
            file=sys.stderr,
            flush=True,
        )
    eos_id = int(model.config.eos_token_id)
    im_end = tokenizer.token_to_id("<|im_end|>")
    stop = {eos_id}
    if im_end is not None:
        stop.add(im_end)

    token_ids = list(prompt_ids[-context_length:])
    generated: list[int] = []
    decoder = stack.IncrementalUtf8Decoder(tokenizer)
    stop_reason = "max_new_tokens"
    generator = torch.Generator(device="cpu")
    generator.manual_seed(seed)
    started = time.perf_counter()

    try:
        input_ids = torch.tensor([token_ids], dtype=torch.long, device=device)
        output = model(
            input_ids,
            return_logits=True,
            logits_to_keep=1,
            use_cache=True,
        )

        for step in range(max_new_tokens):
            if output.logits is None:
                raise RuntimeError("Model returned no logits")
            next_logits = _apply_repetition_penalty(
                output.logits[0, -1],
                token_ids,
                repetition_penalty,
                repetition_window,
            )
            next_id = _sample(
                next_logits,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                generator=generator,
            )
            if next_id in stop:
                stop_reason = "stop"
                break
            token_ids.append(next_id)
            generated.append(next_id)
            decoder.push(next_id)
            if streamer is not None:
                streamer.update(decoder.text)
            if step + 1 < max_new_tokens:
                if output.past_key_values is None:
                    raise RuntimeError("Model returned no decode cache")
                input_ids = torch.tensor([[next_id]], dtype=torch.long, device=device)
                output = model(
                    input_ids,
                    return_logits=True,
                    logits_to_keep=1,
                    past_key_values=output.past_key_values,
                    use_cache=True,
                )
    except KeyboardInterrupt:
        stop_reason = "interrupted"

    decoded = decoder.finalize()
    if streamer is not None:
        streamer.finish()
    elapsed = time.perf_counter() - started
    tps = len(generated) / max(elapsed, 1e-9)
    print(
        f"[done] tokens={len(generated)} stop={stop_reason} {tps:.1f} tok/s",
        file=sys.stderr,
        flush=True,
    )
    if stop_reason == "interrupted":
        return decoded, ()
    return decoded, tuple(stack.parse_tool_calls(decoded))


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Senryu chat inference")
    parser.add_argument("--prompt", default=None, help="Single user prompt")
    parser.add_argument("--system", default="", help="Optional system prompt")
    parser.add_argument(
        "--think",
        dest="enable_thinking",
        action="store_true",
        default=True,
    )
    parser.add_argument(
        "--no-think",
        dest="enable_thinking",
        action="store_false",
    )
    parser.add_argument(
        "--tools",
        default="",
        help="Built-in tools: web-search, python, calculator (comma-separated)",
    )
    parser.add_argument("--tools-json", default=None)
    parser.add_argument("--max-tool-rounds", type=int, default=8)
    parser.add_argument("--max-new-tokens", type=int, default=4096)
    parser.add_argument("--temperature", type=float, default=0.6)
    parser.add_argument("--top-p", type=float, default=0.95)
    parser.add_argument("--top-k", type=int, default=50)
    parser.add_argument("--repetition-penalty", type=float, default=1.08)
    parser.add_argument("--repetition-window", type=int, default=256)
    parser.add_argument("--context-length", type=int, default=32768)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument(
        "--device",
        default="cuda",
        help="CUDA device (cuda, cuda:1, …); Senryu needs a GPU",
    )
    parser.add_argument("--no-stream", action="store_true")
    parser.add_argument("--no-color", action="store_true")
    parser.add_argument("--local-dir", default=None)
    parser.add_argument("--repo-id", default=REPO_ID)
    return parser.parse_args()


def main() -> int:
    _silence_runtime_warnings()
    args = parse_args()
    global REPO_ID
    REPO_ID = args.repo_id
    local_dir = Path(args.local_dir).resolve() if args.local_dir else None

    try:
        device = torch.device(args.device)
    except RuntimeError as error:
        print(f"Invalid --device {args.device!r}: {error}", file=sys.stderr)
        return 1
    if device.type != "cuda" or not torch.cuda.is_available():
        print(
            "Senryu needs a CUDA GPU: its Gated DeltaNet-2 layers run on "
            "flash-linear-attention Triton kernels. "
            f"torch {torch.__version__} reports CUDA available={torch.cuda.is_available()}.",
            file=sys.stderr,
        )
        return 1

    print(f"Loading Senryu from huggingface.co/{REPO_ID} …", flush=True)
    try:
        package_root = _ensure_tiny_gdn(local_dir)
    except Exception as error:  # noqa: BLE001
        print(f"Failed to resolve tiny_gdn package: {error}", file=sys.stderr)
        return 1

    if str(package_root) not in sys.path:
        sys.path.insert(0, str(package_root))

    try:
        _ensure_fla(local_dir)
    except Exception as error:  # noqa: BLE001
        print(str(error), file=sys.stderr)
        return 1

    try:
        stack = _import_chat_stack()
    except ImportError as error:
        print(f"Could not import tiny_gdn chat stack: {error}", file=sys.stderr)
        return 1

    try:
        raw_json = (
            Path(args.tools_json).read_text(encoding="utf-8")
            if args.tools_json
            else None
        )
        tools = stack.resolve_cli_tools(args.tools, raw_json)
    except (OSError, ValueError, json.JSONDecodeError) as error:
        print(error, file=sys.stderr)
        return 1

    weights_path = _download("model.safetensors", local_dir)
    tok_path = _download("tokenizer.json", local_dir)
    cfg_path = _download("config.json", local_dir)

    raw = json.loads(cfg_path.read_text(encoding="utf-8"))
    allowed = {item.name for item in fields(stack.TinyGDNConfig)}
    payload = {key: value for key, value in raw.items() if key in allowed}
    if "shared_layer_indices" in payload:
        payload["shared_layer_indices"] = tuple(payload["shared_layer_indices"])
    config = stack.TinyGDNConfig(**payload)

    print(
        f"Building Senryu ({config.num_hidden_layers}L / {config.hidden_size}d) "
        f"on {device} …",
        flush=True,
    )
    model = stack.TinyGDNForCausalLM(config)
    state = load_file(str(weights_path), device="cpu")
    model.load_state_dict(state, strict=True)
    del state
    model = model.to(device=device, dtype=torch.bfloat16)
    model.eval()
    model.requires_grad_(False)

    tokenizer = Tokenizer.from_file(str(tok_path))
    context_length = min(args.context_length, config.max_position_embeddings)
    color = not args.no_color and sys.stdout.isatty()

    def read_line(prompt: str) -> str:
        return input(prompt)

    def run_turn(state) -> None:
        for round_index in range(max(1, args.max_tool_rounds + 1)):
            streamer = (
                None
                if args.no_stream
                else stack.LiveReasoningStreamer(sys.stdout, color=color)
            )
            if streamer is not None:
                print("assistant> ", end="", flush=True)
            text, calls = generate_turn(
                model,
                tokenizer,
                stack,
                streamer,
                state.messages,
                tools=state.tools,
                enable_thinking=state.enable_thinking,
                max_new_tokens=args.max_new_tokens,
                context_length=context_length,
                temperature=args.temperature,
                top_p=args.top_p,
                top_k=args.top_k,
                repetition_penalty=args.repetition_penalty,
                repetition_window=args.repetition_window,
                seed=args.seed,
                device=device,
            )
            if args.no_stream:
                print(f"assistant> {text}")
            if not calls or round_index >= args.max_tool_rounds:
                state.messages.append({"role": "assistant", "content": text})
                return
            results = stack.prompt_tool_results(calls, read_line=read_line)
            stack.append_tool_round(state.messages, text, results)

    state = stack.ChatLoopState(
        system=args.system,
        enable_thinking=args.enable_thinking,
        tools=tools,
    )
    state.reset()

    if args.prompt is not None:
        state.add_user(args.prompt)
        try:
            run_turn(state)
        except KeyboardInterrupt:
            print("\n(interrupted)", file=sys.stderr)
            return 130
        return 0

    print(
        "Interactive Senryu chat. "
        "Commands: /think  /no_think  /system …  /reset  /exit\n"
        "web-search, python, and calculator run automatically when enabled. "
        "Other <tool_call> observations can be pasted at the prompt. "
        "Ctrl+C stops a reply.",
        flush=True,
    )
    while True:
        try:
            user_input = input("user> ")
        except (EOFError, KeyboardInterrupt):
            print()
            return 0
        text = user_input.strip()
        if not text:
            continue
        status = stack.apply_slash_command(state, text)
        if status == "exit":
            return 0
        if status is not None:
            print(f"({status})", flush=True)
            continue
        state.add_user(text)
        try:
            run_turn(state)
        except KeyboardInterrupt:
            print("\n(interrupted)", flush=True)
        print(flush=True)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())