Update FastPLMs files from Synthyra/FastPLMs 32d9951 (PR 52: ESMFold2 noise cap, DPLM2 parity)

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"""Deterministic identity for embedding inputs, models, tokenizers, and execution."""

from __future__ import annotations

import hashlib
import platform
import torch

from collections.abc import Iterable, Mapping, Sequence
from pathlib import Path
from typing import Any
from torch import Tensor

from .pooling import POOLING_SEMANTICS
from .storage import tensor_sha256
from .types import EmbeddingInput
from ..digests import json_sha256
from ..json_files import compact_json


_RUN_FINGERPRINT_SCHEMA_VERSION = 5
_MODEL_STATE_HASH_CHUNK_BYTES = 16 * 1024**2


def _model_device(model: Any) -> torch.device:
    try:
        return torch.device(next(model.parameters()).device)
    except (AttributeError, StopIteration):
        return torch.device("cpu")


def _attention_backend(model: Any) -> str | None:
    config = getattr(model, "config", None)
    for name in ("_attn_implementation", "attn_implementation", "attn_backend"):
        value = getattr(config, name, None)
        if value:
            return str(value)
    return None


def _attention_kernel_metadata(backend: str | None) -> dict[str, Any] | None:
    if backend not in {"flash_attention_2", "flash_attention_3"}:
        return None
    from fastplms.registry import get_model_registry

    spec = get_model_registry().attention_kernels[backend]
    return {
        "repository": spec.repository,
        "revision": spec.revision,
        "version": spec.version,
        "expected_variant": spec.expected_variant,
        "dtypes": list(spec.dtypes),
    }


def _fingerprint_jsonable(value: Any) -> Any:
    if isinstance(value, Mapping):
        return {str(key): _fingerprint_jsonable(item) for key, item in value.items()}
    if isinstance(value, (list, tuple)):
        return [_fingerprint_jsonable(item) for item in value]
    if isinstance(value, (set, frozenset)):
        return sorted((_fingerprint_jsonable(item) for item in value), key=repr)
    if isinstance(value, Path):
        return str(value)
    if isinstance(value, Tensor):
        return {
            "dtype": str(value.dtype).removeprefix("torch."),
            "shape": list(value.shape),
            "sha256": tensor_sha256(value),
        }
    if isinstance(value, torch.dtype):
        return str(value).removeprefix("torch.")
    if isinstance(value, torch.device):
        return str(value)
    if value is None or isinstance(value, (str, int, float, bool)):
        return value
    return {
        "class": f"{value.__class__.__module__}.{value.__class__.__qualname__}",
        "value": str(value),
    }


def _backend_tokenizer_content(backend: Any) -> str | None:
    """The backend tokenizer's serialization, without the padding and truncation of its last call.
    Transformers sets padding and truncation on the Rust tokenizer at the start of every encode
    call, from that call's own arguments, and leaves them set. What the backend holds is
    therefore the previous call's settings, which never change the next encoding. Hashing them
    gave the first run in a process a different fingerprint from every identical run after it.
    This run's own padding and truncation are fingerprinted through ``max_length``,
    ``truncate``, and the batching policy.
    The settings are cleared on a copy, so the caller's tokenizer keeps its state. A tokenizer
    that was never called already serializes without them, so this normalization itself
    does not change its identity. The enclosing run schema also versions pooling semantics.
    """
    to_str = getattr(backend, "to_str", None)
    if not callable(to_str):
        return None
    serialized = to_str()
    from_str = getattr(type(backend), "from_str", None)
    if not callable(from_str):
        return serialized
    call_free = from_str(serialized)
    call_free.no_truncation()
    call_free.no_padding()
    return call_free.to_str()


def _tokenizer_content_sha256(tokenizer: Any) -> str:
    content: dict[str, Any] = {
        "init_kwargs": getattr(tokenizer, "init_kwargs", None),
        "special_tokens_map": getattr(tokenizer, "special_tokens_map", None),
        "model_max_length": getattr(tokenizer, "model_max_length", None),
        "padding_side": getattr(tokenizer, "padding_side", None),
        "truncation_side": getattr(tokenizer, "truncation_side", None),
    }
    get_vocab = getattr(tokenizer, "get_vocab", None)
    if callable(get_vocab):
        content["vocabulary"] = get_vocab()
    get_added_vocab = getattr(tokenizer, "get_added_vocab", None)
    if callable(get_added_vocab):
        content["added_vocabulary"] = get_added_vocab()
    backend_content = _backend_tokenizer_content(getattr(tokenizer, "backend_tokenizer", None))
    if backend_content is not None:
        content["backend"] = backend_content
    return json_sha256(_fingerprint_jsonable(content), ensure_ascii=False)


def _tokenizer_metadata(model: Any, tokenizer: Any | None) -> dict[str, Any]:
    resolved = tokenizer if tokenizer is not None else getattr(model, "tokenizer", None)
    if resolved is None:
        # Raw-sequence families such as E1 retain their loader context on the
        # model/encoder rather than exposing a Transformers tokenizer. Bind the
        # non-secret source policy to resume identity without serializing a Hub
        # token or forcing lazy tokenizer initialization.
        for candidate in (model, getattr(model, "model", None)):
            settings = getattr(candidate, "__dict__", {}).get("_fastplms_tokenizer_kwargs")
            if isinstance(settings, Mapping):
                token_value = settings.get("token")
                return {
                    "mode": "native-sequence",
                    "source": (
                        str(settings.get("tokenizer_source"))
                        if settings.get("tokenizer_source") is not None
                        else None
                    ),
                    "revision": settings.get("revision"),
                    "cache_dir": (
                        str(settings.get("cache_dir"))
                        if settings.get("cache_dir") is not None
                        else None
                    ),
                    "local_files_only": bool(settings.get("local_files_only", False)),
                    "token_policy": (
                        "disabled"
                        if token_value is False
                        else "provided"
                        if token_value is not None
                        else "default"
                    ),
                }
        return {"mode": "native-sequence"}
    return {
        "mode": "tokenizer",
        "class": f"{resolved.__class__.__module__}.{resolved.__class__.__qualname__}",
        "name_or_path": getattr(resolved, "name_or_path", None),
        "vocab_size": getattr(resolved, "vocab_size", None),
        "special_token_ids": list(getattr(resolved, "all_special_ids", ())),
        "content_sha256": _tokenizer_content_sha256(resolved),
    }


def _software_versions() -> dict[str, str | None]:
    try:
        import fastplms

        fastplms_version = fastplms.__version__
    except (AttributeError, ImportError):
        fastplms_version = None
    try:
        import safetensors

        safetensors_version = safetensors.__version__
    except ImportError:
        safetensors_version = None
    try:
        import transformers

        transformers_version = transformers.__version__
    except ImportError:
        transformers_version = None
    return {
        "fastplms": fastplms_version,
        "python": platform.python_version(),
        "safetensors": safetensors_version,
        "torch": torch.__version__,
        "torch_cuda": torch.version.cuda,
        "transformers": transformers_version,
    }


def _adapter_identity_metadata(model: Any) -> dict[str, Any] | None:
    """Return deterministic PEFT/adapter identity without tensor payloads."""

    peft_config = getattr(model, "peft_config", None)
    if not isinstance(peft_config, Mapping) or not peft_config:
        return None
    configurations: dict[str, Any] = {}
    for name, config in sorted(peft_config.items(), key=lambda item: str(item[0])):
        to_dict = getattr(config, "to_dict", None)
        if callable(to_dict):
            value = to_dict()
        else:
            try:
                value = vars(config)
            except TypeError:
                value = config
        configurations[str(name)] = _fingerprint_jsonable(value)
    active_adapters = getattr(model, "active_adapters", None)
    if callable(active_adapters):
        active_adapters = active_adapters()
    return {
        "active": _fingerprint_jsonable(active_adapters),
        "configurations": configurations,
    }


def _execution_identity_metadata(model: Any) -> dict[str, Any]:
    """Capture runtime policy that can change persisted numerical results."""

    parameter_dtypes = sorted(
        {
            str(parameter.dtype).removeprefix("torch.")
            for parameter in getattr(model, "parameters", lambda: ())()
        }
    )
    return {
        "device": _model_device(model).type,
        "hf_device_map": _fingerprint_jsonable(getattr(model, "hf_device_map", None)),
        "parameter_dtypes": parameter_dtypes,
        "software": _software_versions(),
    }


def _first_metadata_value(*values: Any) -> Any:
    for value in values:
        if isinstance(value, str):
            if value.strip():
                return value
        elif value is not None:
            return value
    return None


def _model_identity_metadata(model: Any) -> dict[str, Any]:
    """Resolve model and checkpoint identity, including local artifact fallbacks."""

    config = getattr(model, "config", None)
    checkpoint_revision = _first_metadata_value(
        getattr(config, "fastplms_checkpoint_revision", None),
        getattr(config, "_commit_hash", None),
    )
    return {
        "model_id": _first_metadata_value(
            getattr(config, "fastplms_model_id", None),
            getattr(config, "_name_or_path", None),
        ),
        "model_revision": _first_metadata_value(
            getattr(config, "_commit_hash", None),
            checkpoint_revision,
        ),
        "checkpoint_repo_id": getattr(config, "fastplms_checkpoint_repo_id", None),
        "checkpoint_revision": checkpoint_revision,
        "checkpoint_hash": _first_metadata_value(
            getattr(model, "checkpoint_hash", None),
            getattr(config, "checkpoint_hash", None),
            getattr(config, "fastplms_checkpoint_hash", None),
        ),
        "weights_revision": getattr(config, "fastplms_weights_revision", None),
        "runtime_revision": getattr(config, "fastplms_runtime_revision", None),
        "source_tree_sha256": getattr(config, "fastplms_source_tree_sha256", None),
        "runtime_bundle_sha256": getattr(config, "fastplms_runtime_bundle_sha256", None),
    }


def _bounded_tensor_chunks(X: Tensor, max_elements: int) -> Iterable[Tensor]:
    """Yield X in logical row-major order without materializing a full copy."""

    # X: (...)
    if X.numel() == 0:
        return
    if X.ndim == 0:
        yield X
        return
    trailing_elements = 1
    for size in X.shape[1:]:
        trailing_elements *= int(size)
    if trailing_elements <= max_elements:
        rows_per_chunk = max(1, max_elements // trailing_elements)
        for start in range(0, X.shape[0], rows_per_chunk):
            yield X[start : start + rows_per_chunk]  # (chunk_rows, ...)
        return
    for row in X:
        yield from _bounded_tensor_chunks(row, max_elements)


def _model_state_sha256(model: Any) -> str:
    """Hash named parameters and persistent buffers using bounded CPU copies."""

    # Never cache this digest from tensor identity or ``Tensor._version``.
    # ``Parameter.data`` and independent tensor aliases can mutate shared storage
    # without changing either signal, while persisted resume identity must bind
    # the authoritative bytes visible at the start of this run.
    state = model.state_dict(keep_vars=True)
    digest = hashlib.sha256()
    for name, value in sorted(state.items()):
        if not isinstance(value, Tensor):
            raise TypeError(f"Model state entry {name!r} is not a tensor.")
        if value.is_meta:
            raise ValueError(
                f"Cannot fingerprint meta-device model state entry {name!r}; pass "
                "model_state_fingerprint with a caller-owned state identity."
            )
        header = compact_json(
            {
                "name": name,
                "dtype": str(value.dtype).removeprefix("torch."),
                "shape": list(value.shape),
            }
        ).encode()
        digest.update(len(header).to_bytes(8, "big"))
        digest.update(header)
        max_elements = max(1, _MODEL_STATE_HASH_CHUNK_BYTES // value.element_size())
        for chunk in _bounded_tensor_chunks(value.detach(), max_elements):
            cpu_chunk = chunk.to(device="cpu").contiguous()  # chunk.shape
            digest.update(cpu_chunk.reshape(-1).view(torch.uint8).numpy().tobytes())
    return digest.hexdigest()


def _input_sha256(records: Iterable[EmbeddingInput]) -> str:
    """Hash an ordered input stream without constructing a duplicate JSON payload."""

    precomputed = getattr(records, "input_fingerprint", None)
    if isinstance(precomputed, str):
        return precomputed
    digest = hashlib.sha256()
    count = 0
    for record in records:
        count += 1
        for value in (record.id, record.sequence):
            encoded = value.encode("utf-8")
            digest.update(len(encoded).to_bytes(8, "big"))
            digest.update(encoded)
    digest.update(count.to_bytes(8, "big"))
    return digest.hexdigest()


def _run_fingerprint(
    model: Any,
    records: Sequence[EmbeddingInput],
    *,
    pooling: Sequence[str],
    full_embeddings: bool,
    max_length: int | None,
    truncate: bool,
    dtype: torch.dtype | None,
    model_kwargs: dict[str, Any],
    tokenizer_metadata: dict[str, Any],
    model_state_fingerprint: str | None,
    persist_output: bool,
    embedding_context: Mapping[str, Any],
    batch_size: int,
    batch_window_size: int,
    max_tokens_per_batch: int | None,
    taps: Sequence[Mapping[str, Any]] | None = None,
) -> tuple[str, str, str | None, str]:
    input_fingerprint = _input_sha256(records)
    attention_backend = _attention_backend(model)
    model_identity = _model_identity_metadata(model)
    if model_state_fingerprint is None and persist_output:
        resolved_model_state_fingerprint = _model_state_sha256(model)
        model_state_fingerprint_source = "computed"
    elif model_state_fingerprint is not None:
        resolved_model_state_fingerprint = model_state_fingerprint.strip()
        if not resolved_model_state_fingerprint:
            raise ValueError("model_state_fingerprint must not be empty.")
        model_state_fingerprint_source = "caller"
    else:
        resolved_model_state_fingerprint = None
        model_state_fingerprint_source = "not-computed"
    payload = {
        "fingerprint_schema_version": _RUN_FINGERPRINT_SCHEMA_VERSION,
        "input_fingerprint": input_fingerprint,
        "model_state_fingerprint": resolved_model_state_fingerprint,
        "model_state_fingerprint_source": model_state_fingerprint_source,
        "model_class": f"{model.__class__.__module__}.{model.__class__.__qualname__}",
        **model_identity,
        "attention_backend": attention_backend,
        "attention_kernel": _attention_kernel_metadata(attention_backend),
        "layer": repr(
            getattr(model, "embedding_layer", model_kwargs.get("hidden_state_index", -1))
        ),
        "projection": getattr(model, "embedding_projection", None),
        "esmc_source": getattr(model, "_esmc_source", None),
        "esmc_revision": getattr(model, "_esmc_source_revision", None),
        "esmc_files": getattr(model, "_esmc_source_files", None),
        "token_policy": getattr(model, "embedding_token_policy", None),
        "tokenizer": tokenizer_metadata,
        "adapter": _adapter_identity_metadata(model),
        "execution": _execution_identity_metadata(model),
        "embedding_context": _fingerprint_jsonable(embedding_context),
        "pooling": list(pooling),
        "pooling_semantics": dict(POOLING_SEMANTICS),
        "full_embeddings": full_embeddings,
        "max_length": max_length,
        "truncate": truncate,
        "dtype": str(dtype) if dtype is not None else None,
        "batching": {
            "batch_size": batch_size,
            "batch_window_size": batch_window_size,
            "max_tokens_per_batch": max_tokens_per_batch,
        },
        "model_kwargs": {
            key: _fingerprint_jsonable(value) for key, value in sorted(model_kwargs.items())
        },
        "residue_mask_policy": "attention-mask-minus-special-tokens",
    }
    if taps is not None:
        # Each hidden tap binds its requested dtype; each reduced tap binds its own contract.
        payload["taps"] = _fingerprint_jsonable(taps)
    run_fingerprint = json_sha256(payload)
    return (
        input_fingerprint,
        run_fingerprint,
        resolved_model_state_fingerprint,
        model_state_fingerprint_source,
    )


def _ordered_string_sha256(values: Sequence[str]) -> str:
    digest = hashlib.sha256()
    for value in values:
        encoded = value.encode("utf-8")
        digest.update(len(encoded).to_bytes(8, "big"))
        digest.update(encoded)
    digest.update(len(values).to_bytes(8, "big"))
    return digest.hexdigest()


def _embedding_context(
    model: Any,
    records: Sequence[EmbeddingInput],
    *,
    hidden_state_source: str,
    decoder_inputs: Sequence[str] | None,
    decoder_input_ids: Tensor | None,
    decoder_attention_mask: Tensor | None,
    model_kwargs: Mapping[str, Any],
) -> tuple[dict[str, Any], tuple[str, ...] | None]:
    # decoder_input_ids, decoder_attention_mask: (n_records, l_decoder), aligned with records
    if hidden_state_source not in {"encoder", "decoder"}:
        raise ValueError("hidden_state_source must be 'encoder' or 'decoder'.")
    hidden_state_index = model_kwargs.get("hidden_state_index", -1)
    if not isinstance(hidden_state_index, int) or isinstance(hidden_state_index, bool):
        raise TypeError("hidden_state_index must be an integer.")
    store_all_hidden_states = model_kwargs.get("store_all_hidden_states", False)
    if not isinstance(store_all_hidden_states, bool):
        raise TypeError("store_all_hidden_states must be a boolean.")
    normalized_decoder_inputs: tuple[str, ...] | None = None
    has_decoder_inputs = decoder_inputs is not None
    has_decoder_ids = decoder_input_ids is not None
    if hidden_state_source == "encoder":
        if has_decoder_inputs or has_decoder_ids or decoder_attention_mask is not None:
            raise ValueError("Decoder inputs are only valid when hidden_state_source='decoder'.")
    else:
        if has_decoder_inputs == has_decoder_ids:
            raise ValueError(
                "Decoder embedding requires exactly one of decoder_inputs or decoder_input_ids."
            )
    decoder_input_fingerprint: str | None = None
    if decoder_inputs is not None:
        if isinstance(decoder_inputs, (str, bytes)) or not isinstance(decoder_inputs, Sequence):
            raise TypeError("decoder_inputs must be an aligned sequence of strings.")
        normalized_decoder_inputs = tuple(decoder_inputs)
        if not all(isinstance(value, str) and value for value in normalized_decoder_inputs):
            raise ValueError("decoder_inputs must contain non-empty strings.")
        if len(normalized_decoder_inputs) != len(records):
            raise ValueError("decoder_inputs must align one-to-one with embedding inputs.")
        decoder_input_fingerprint = _ordered_string_sha256(normalized_decoder_inputs)
        if decoder_attention_mask is not None:
            raise ValueError("decoder_attention_mask requires decoder_input_ids.")
    if decoder_input_ids is not None:
        if not isinstance(decoder_input_ids, Tensor) or decoder_input_ids.ndim != 2:
            raise ValueError("decoder_input_ids must have shape (batch, sequence).")
        if decoder_input_ids.shape[0] != len(records):
            raise ValueError("decoder_input_ids must align one-to-one with embedding inputs.")
        if decoder_input_ids.dtype == torch.bool or decoder_input_ids.is_floating_point():
            raise TypeError("decoder_input_ids must use an integer token dtype.")
        decoder_input_fingerprint = tensor_sha256(decoder_input_ids)
    decoder_mask_fingerprint: str | None = None
    if decoder_attention_mask is not None:
        if not isinstance(decoder_attention_mask, Tensor):
            raise TypeError("decoder_attention_mask must be a tensor.")
        if decoder_input_ids is None or decoder_attention_mask.shape != decoder_input_ids.shape:
            raise ValueError("decoder_attention_mask must match decoder_input_ids shape.")
        decoder_mask_fingerprint = tensor_sha256(decoder_attention_mask)

    context: dict[str, Any] = {
        "hidden_state_source": hidden_state_source,
        "hidden_state_index": hidden_state_index,
        "store_all_hidden_states": store_all_hidden_states,
        "decoder_input_fingerprint": decoder_input_fingerprint,
        "decoder_attention_mask_fingerprint": decoder_mask_fingerprint,
        "decoder_alignment": "input-position" if hidden_state_source == "decoder" else None,
    }
    metadata_hook = getattr(model, "_embedding_metadata", None)
    model_metadata: Mapping[str, Any] | None = None
    if callable(metadata_hook):
        model_metadata = metadata_hook(**context)
        if not isinstance(model_metadata, Mapping):
            raise TypeError("_embedding_metadata must return a mapping.")
        context["model_embedding"] = _fingerprint_jsonable(model_metadata)
    if hidden_state_source == "decoder":
        has_decoder_batch = callable(getattr(model, "_embedding_batch", None))
        declares_decoder_stack = (
            model_metadata is not None and model_metadata.get("hidden_state_stack") == "decoder"
        )
        if not has_decoder_batch or not declares_decoder_stack:
            raise ValueError(
                f"{model.__class__.__name__} does not declare decoder embedding support."
            )
    return context, normalized_decoder_inputs

	"""Deterministic identity for embedding inputs, models, tokenizers, and execution."""

	from __future__ import annotations

	import hashlib
	import platform
	import torch

	from collections.abc import Iterable, Mapping, Sequence
	from pathlib import Path
	from typing import Any
	from torch import Tensor

	from .pooling import POOLING_SEMANTICS
	from .storage import tensor_sha256
	from .types import EmbeddingInput
	from ..digests import json_sha256
	from ..json_files import compact_json


	_RUN_FINGERPRINT_SCHEMA_VERSION = 5
	_MODEL_STATE_HASH_CHUNK_BYTES = 16 * 1024**2


	def _model_device(model: Any) -> torch.device:
	try:
	return torch.device(next(model.parameters()).device)
	except (AttributeError, StopIteration):
	return torch.device("cpu")


	def _attention_backend(model: Any) -> str \| None:
	config = getattr(model, "config", None)
	for name in ("_attn_implementation", "attn_implementation", "attn_backend"):
	value = getattr(config, name, None)
	if value:
	return str(value)
	return None


	def _attention_kernel_metadata(backend: str \| None) -> dict[str, Any] \| None:
	if backend not in {"flash_attention_2", "flash_attention_3"}:
	return None
	from fastplms.registry import get_model_registry

	spec = get_model_registry().attention_kernels[backend]
	return {
	"repository": spec.repository,
	"revision": spec.revision,
	"version": spec.version,
	"expected_variant": spec.expected_variant,
	"dtypes": list(spec.dtypes),
	}


	def _fingerprint_jsonable(value: Any) -> Any:
	if isinstance(value, Mapping):
	return {str(key): _fingerprint_jsonable(item) for key, item in value.items()}
	if isinstance(value, (list, tuple)):
	return [_fingerprint_jsonable(item) for item in value]
	if isinstance(value, (set, frozenset)):
	return sorted((_fingerprint_jsonable(item) for item in value), key=repr)
	if isinstance(value, Path):
	return str(value)
	if isinstance(value, Tensor):
	return {
	"dtype": str(value.dtype).removeprefix("torch."),
	"shape": list(value.shape),
	"sha256": tensor_sha256(value),
	}
	if isinstance(value, torch.dtype):
	return str(value).removeprefix("torch.")
	if isinstance(value, torch.device):
	return str(value)
	if value is None or isinstance(value, (str, int, float, bool)):
	return value
	return {
	"class": f"{value.__class__.__module__}.{value.__class__.__qualname__}",
	"value": str(value),
	}


	def _backend_tokenizer_content(backend: Any) -> str \| None:
	"""The backend tokenizer's serialization, without the padding and truncation of its last call.

	Transformers sets padding and truncation on the Rust tokenizer at the start of every encode
	call, from that call's own arguments, and leaves them set. What the backend holds is
	therefore the previous call's settings, which never change the next encoding. Hashing them
	gave the first run in a process a different fingerprint from every identical run after it.
	This run's own padding and truncation are fingerprinted through ``max_length``,
	``truncate``, and the batching policy.

	The settings are cleared on a copy, so the caller's tokenizer keeps its state. A tokenizer
	that was never called already serializes without them, so this normalization itself
	does not change its identity. The enclosing run schema also versions pooling semantics.
	"""
	to_str = getattr(backend, "to_str", None)
	if not callable(to_str):
	return None
	serialized = to_str()
	from_str = getattr(type(backend), "from_str", None)
	if not callable(from_str):
	return serialized
	call_free = from_str(serialized)
	call_free.no_truncation()
	call_free.no_padding()
	return call_free.to_str()


	def _tokenizer_content_sha256(tokenizer: Any) -> str:
	content: dict[str, Any] = {
	"init_kwargs": getattr(tokenizer, "init_kwargs", None),
	"special_tokens_map": getattr(tokenizer, "special_tokens_map", None),
	"model_max_length": getattr(tokenizer, "model_max_length", None),
	"padding_side": getattr(tokenizer, "padding_side", None),
	"truncation_side": getattr(tokenizer, "truncation_side", None),
	}
	get_vocab = getattr(tokenizer, "get_vocab", None)
	if callable(get_vocab):
	content["vocabulary"] = get_vocab()
	get_added_vocab = getattr(tokenizer, "get_added_vocab", None)
	if callable(get_added_vocab):
	content["added_vocabulary"] = get_added_vocab()
	backend_content = _backend_tokenizer_content(getattr(tokenizer, "backend_tokenizer", None))
	if backend_content is not None:
	content["backend"] = backend_content
	return json_sha256(_fingerprint_jsonable(content), ensure_ascii=False)


	def _tokenizer_metadata(model: Any, tokenizer: Any \| None) -> dict[str, Any]:
	resolved = tokenizer if tokenizer is not None else getattr(model, "tokenizer", None)
	if resolved is None:
	# Raw-sequence families such as E1 retain their loader context on the
	# model/encoder rather than exposing a Transformers tokenizer. Bind the
	# non-secret source policy to resume identity without serializing a Hub
	# token or forcing lazy tokenizer initialization.
	for candidate in (model, getattr(model, "model", None)):
	settings = getattr(candidate, "__dict__", {}).get("_fastplms_tokenizer_kwargs")
	if isinstance(settings, Mapping):
	token_value = settings.get("token")
	return {
	"mode": "native-sequence",
	"source": (
	str(settings.get("tokenizer_source"))
	if settings.get("tokenizer_source") is not None
	else None
	),
	"revision": settings.get("revision"),
	"cache_dir": (
	str(settings.get("cache_dir"))
	if settings.get("cache_dir") is not None
	else None
	),
	"local_files_only": bool(settings.get("local_files_only", False)),
	"token_policy": (
	"disabled"
	if token_value is False
	else "provided"
	if token_value is not None
	else "default"
	),
	}
	return {"mode": "native-sequence"}
	return {
	"mode": "tokenizer",
	"class": f"{resolved.__class__.__module__}.{resolved.__class__.__qualname__}",
	"name_or_path": getattr(resolved, "name_or_path", None),
	"vocab_size": getattr(resolved, "vocab_size", None),
	"special_token_ids": list(getattr(resolved, "all_special_ids", ())),
	"content_sha256": _tokenizer_content_sha256(resolved),
	}


	def _software_versions() -> dict[str, str \| None]:
	try:
	import fastplms

	fastplms_version = fastplms.__version__
	except (AttributeError, ImportError):
	fastplms_version = None
	try:
	import safetensors

	safetensors_version = safetensors.__version__
	except ImportError:
	safetensors_version = None
	try:
	import transformers

	transformers_version = transformers.__version__
	except ImportError:
	transformers_version = None
	return {
	"fastplms": fastplms_version,
	"python": platform.python_version(),
	"safetensors": safetensors_version,
	"torch": torch.__version__,
	"torch_cuda": torch.version.cuda,
	"transformers": transformers_version,
	}


	def _adapter_identity_metadata(model: Any) -> dict[str, Any] \| None:
	"""Return deterministic PEFT/adapter identity without tensor payloads."""

	peft_config = getattr(model, "peft_config", None)
	if not isinstance(peft_config, Mapping) or not peft_config:
	return None
	configurations: dict[str, Any] = {}
	for name, config in sorted(peft_config.items(), key=lambda item: str(item[0])):
	to_dict = getattr(config, "to_dict", None)
	if callable(to_dict):
	value = to_dict()
	else:
	try:
	value = vars(config)
	except TypeError:
	value = config
	configurations[str(name)] = _fingerprint_jsonable(value)
	active_adapters = getattr(model, "active_adapters", None)
	if callable(active_adapters):
	active_adapters = active_adapters()
	return {
	"active": _fingerprint_jsonable(active_adapters),
	"configurations": configurations,
	}


	def _execution_identity_metadata(model: Any) -> dict[str, Any]:
	"""Capture runtime policy that can change persisted numerical results."""

	parameter_dtypes = sorted(
	{
	str(parameter.dtype).removeprefix("torch.")
	for parameter in getattr(model, "parameters", lambda: ())()
	}
	)
	return {
	"device": _model_device(model).type,
	"hf_device_map": _fingerprint_jsonable(getattr(model, "hf_device_map", None)),
	"parameter_dtypes": parameter_dtypes,
	"software": _software_versions(),
	}


	def _first_metadata_value(*values: Any) -> Any:
	for value in values:
	if isinstance(value, str):
	if value.strip():
	return value
	elif value is not None:
	return value
	return None


	def _model_identity_metadata(model: Any) -> dict[str, Any]:
	"""Resolve model and checkpoint identity, including local artifact fallbacks."""

	config = getattr(model, "config", None)
	checkpoint_revision = _first_metadata_value(
	getattr(config, "fastplms_checkpoint_revision", None),
	getattr(config, "_commit_hash", None),
	)
	return {
	"model_id": _first_metadata_value(
	getattr(config, "fastplms_model_id", None),
	getattr(config, "_name_or_path", None),
	),
	"model_revision": _first_metadata_value(
	getattr(config, "_commit_hash", None),
	checkpoint_revision,
	),
	"checkpoint_repo_id": getattr(config, "fastplms_checkpoint_repo_id", None),
	"checkpoint_revision": checkpoint_revision,
	"checkpoint_hash": _first_metadata_value(
	getattr(model, "checkpoint_hash", None),
	getattr(config, "checkpoint_hash", None),
	getattr(config, "fastplms_checkpoint_hash", None),
	),
	"weights_revision": getattr(config, "fastplms_weights_revision", None),
	"runtime_revision": getattr(config, "fastplms_runtime_revision", None),
	"source_tree_sha256": getattr(config, "fastplms_source_tree_sha256", None),
	"runtime_bundle_sha256": getattr(config, "fastplms_runtime_bundle_sha256", None),
	}


	def _bounded_tensor_chunks(X: Tensor, max_elements: int) -> Iterable[Tensor]:
	"""Yield X in logical row-major order without materializing a full copy."""

	# X: (...)
	if X.numel() == 0:
	return
	if X.ndim == 0:
	yield X
	return
	trailing_elements = 1
	for size in X.shape[1:]:
	trailing_elements *= int(size)
	if trailing_elements <= max_elements:
	rows_per_chunk = max(1, max_elements // trailing_elements)
	for start in range(0, X.shape[0], rows_per_chunk):
	yield X[start : start + rows_per_chunk] # (chunk_rows, ...)
	return
	for row in X:
	yield from _bounded_tensor_chunks(row, max_elements)


	def _model_state_sha256(model: Any) -> str:
	"""Hash named parameters and persistent buffers using bounded CPU copies."""

	# Never cache this digest from tensor identity or ``Tensor._version``.
	# ``Parameter.data`` and independent tensor aliases can mutate shared storage
	# without changing either signal, while persisted resume identity must bind
	# the authoritative bytes visible at the start of this run.
	state = model.state_dict(keep_vars=True)
	digest = hashlib.sha256()
	for name, value in sorted(state.items()):
	if not isinstance(value, Tensor):
	raise TypeError(f"Model state entry {name!r} is not a tensor.")
	if value.is_meta:
	raise ValueError(
	f"Cannot fingerprint meta-device model state entry {name!r}; pass "
	"model_state_fingerprint with a caller-owned state identity."
	)
	header = compact_json(
	{
	"name": name,
	"dtype": str(value.dtype).removeprefix("torch."),
	"shape": list(value.shape),
	}
	).encode()
	digest.update(len(header).to_bytes(8, "big"))
	digest.update(header)
	max_elements = max(1, _MODEL_STATE_HASH_CHUNK_BYTES // value.element_size())
	for chunk in _bounded_tensor_chunks(value.detach(), max_elements):
	cpu_chunk = chunk.to(device="cpu").contiguous() # chunk.shape
	digest.update(cpu_chunk.reshape(-1).view(torch.uint8).numpy().tobytes())
	return digest.hexdigest()


	def _input_sha256(records: Iterable[EmbeddingInput]) -> str:
	"""Hash an ordered input stream without constructing a duplicate JSON payload."""

	precomputed = getattr(records, "input_fingerprint", None)
	if isinstance(precomputed, str):
	return precomputed
	digest = hashlib.sha256()
	count = 0
	for record in records:
	count += 1
	for value in (record.id, record.sequence):
	encoded = value.encode("utf-8")
	digest.update(len(encoded).to_bytes(8, "big"))
	digest.update(encoded)
	digest.update(count.to_bytes(8, "big"))
	return digest.hexdigest()


	def _run_fingerprint(
	model: Any,
	records: Sequence[EmbeddingInput],
	*,
	pooling: Sequence[str],
	full_embeddings: bool,
	max_length: int \| None,
	truncate: bool,
	dtype: torch.dtype \| None,
	model_kwargs: dict[str, Any],
	tokenizer_metadata: dict[str, Any],
	model_state_fingerprint: str \| None,
	persist_output: bool,
	embedding_context: Mapping[str, Any],
	batch_size: int,
	batch_window_size: int,
	max_tokens_per_batch: int \| None,
	taps: Sequence[Mapping[str, Any]] \| None = None,
	) -> tuple[str, str, str \| None, str]:
	input_fingerprint = _input_sha256(records)
	attention_backend = _attention_backend(model)
	model_identity = _model_identity_metadata(model)
	if model_state_fingerprint is None and persist_output:
	resolved_model_state_fingerprint = _model_state_sha256(model)
	model_state_fingerprint_source = "computed"
	elif model_state_fingerprint is not None:
	resolved_model_state_fingerprint = model_state_fingerprint.strip()
	if not resolved_model_state_fingerprint:
	raise ValueError("model_state_fingerprint must not be empty.")
	model_state_fingerprint_source = "caller"
	else:
	resolved_model_state_fingerprint = None
	model_state_fingerprint_source = "not-computed"
	payload = {
	"fingerprint_schema_version": _RUN_FINGERPRINT_SCHEMA_VERSION,
	"input_fingerprint": input_fingerprint,
	"model_state_fingerprint": resolved_model_state_fingerprint,
	"model_state_fingerprint_source": model_state_fingerprint_source,
	"model_class": f"{model.__class__.__module__}.{model.__class__.__qualname__}",
	**model_identity,
	"attention_backend": attention_backend,
	"attention_kernel": _attention_kernel_metadata(attention_backend),
	"layer": repr(
	getattr(model, "embedding_layer", model_kwargs.get("hidden_state_index", -1))
	),
	"projection": getattr(model, "embedding_projection", None),
	"esmc_source": getattr(model, "_esmc_source", None),
	"esmc_revision": getattr(model, "_esmc_source_revision", None),
	"esmc_files": getattr(model, "_esmc_source_files", None),
	"token_policy": getattr(model, "embedding_token_policy", None),
	"tokenizer": tokenizer_metadata,
	"adapter": _adapter_identity_metadata(model),
	"execution": _execution_identity_metadata(model),
	"embedding_context": _fingerprint_jsonable(embedding_context),
	"pooling": list(pooling),
	"pooling_semantics": dict(POOLING_SEMANTICS),
	"full_embeddings": full_embeddings,
	"max_length": max_length,
	"truncate": truncate,
	"dtype": str(dtype) if dtype is not None else None,
	"batching": {
	"batch_size": batch_size,
	"batch_window_size": batch_window_size,
	"max_tokens_per_batch": max_tokens_per_batch,
	},
	"model_kwargs": {
	key: _fingerprint_jsonable(value) for key, value in sorted(model_kwargs.items())
	},
	"residue_mask_policy": "attention-mask-minus-special-tokens",
	}
	if taps is not None:
	# Each hidden tap binds its requested dtype; each reduced tap binds its own contract.
	payload["taps"] = _fingerprint_jsonable(taps)
	run_fingerprint = json_sha256(payload)
	return (
	input_fingerprint,
	run_fingerprint,
	resolved_model_state_fingerprint,
	model_state_fingerprint_source,
	)


	def _ordered_string_sha256(values: Sequence[str]) -> str:
	digest = hashlib.sha256()
	for value in values:
	encoded = value.encode("utf-8")
	digest.update(len(encoded).to_bytes(8, "big"))
	digest.update(encoded)
	digest.update(len(values).to_bytes(8, "big"))
	return digest.hexdigest()


	def _embedding_context(
	model: Any,
	records: Sequence[EmbeddingInput],
	*,
	hidden_state_source: str,
	decoder_inputs: Sequence[str] \| None,
	decoder_input_ids: Tensor \| None,
	decoder_attention_mask: Tensor \| None,
	model_kwargs: Mapping[str, Any],
	) -> tuple[dict[str, Any], tuple[str, ...] \| None]:
	# decoder_input_ids, decoder_attention_mask: (n_records, l_decoder), aligned with records
	if hidden_state_source not in {"encoder", "decoder"}:
	raise ValueError("hidden_state_source must be 'encoder' or 'decoder'.")
	hidden_state_index = model_kwargs.get("hidden_state_index", -1)
	if not isinstance(hidden_state_index, int) or isinstance(hidden_state_index, bool):
	raise TypeError("hidden_state_index must be an integer.")
	store_all_hidden_states = model_kwargs.get("store_all_hidden_states", False)
	if not isinstance(store_all_hidden_states, bool):
	raise TypeError("store_all_hidden_states must be a boolean.")
	normalized_decoder_inputs: tuple[str, ...] \| None = None
	has_decoder_inputs = decoder_inputs is not None
	has_decoder_ids = decoder_input_ids is not None
	if hidden_state_source == "encoder":
	if has_decoder_inputs or has_decoder_ids or decoder_attention_mask is not None:
	raise ValueError("Decoder inputs are only valid when hidden_state_source='decoder'.")
	else:
	if has_decoder_inputs == has_decoder_ids:
	raise ValueError(
	"Decoder embedding requires exactly one of decoder_inputs or decoder_input_ids."
	)
	decoder_input_fingerprint: str \| None = None
	if decoder_inputs is not None:
	if isinstance(decoder_inputs, (str, bytes)) or not isinstance(decoder_inputs, Sequence):
	raise TypeError("decoder_inputs must be an aligned sequence of strings.")
	normalized_decoder_inputs = tuple(decoder_inputs)
	if not all(isinstance(value, str) and value for value in normalized_decoder_inputs):
	raise ValueError("decoder_inputs must contain non-empty strings.")
	if len(normalized_decoder_inputs) != len(records):
	raise ValueError("decoder_inputs must align one-to-one with embedding inputs.")
	decoder_input_fingerprint = _ordered_string_sha256(normalized_decoder_inputs)
	if decoder_attention_mask is not None:
	raise ValueError("decoder_attention_mask requires decoder_input_ids.")
	if decoder_input_ids is not None:
	if not isinstance(decoder_input_ids, Tensor) or decoder_input_ids.ndim != 2:
	raise ValueError("decoder_input_ids must have shape (batch, sequence).")
	if decoder_input_ids.shape[0] != len(records):
	raise ValueError("decoder_input_ids must align one-to-one with embedding inputs.")
	if decoder_input_ids.dtype == torch.bool or decoder_input_ids.is_floating_point():
	raise TypeError("decoder_input_ids must use an integer token dtype.")
	decoder_input_fingerprint = tensor_sha256(decoder_input_ids)
	decoder_mask_fingerprint: str \| None = None
	if decoder_attention_mask is not None:
	if not isinstance(decoder_attention_mask, Tensor):
	raise TypeError("decoder_attention_mask must be a tensor.")
	if decoder_input_ids is None or decoder_attention_mask.shape != decoder_input_ids.shape:
	raise ValueError("decoder_attention_mask must match decoder_input_ids shape.")
	decoder_mask_fingerprint = tensor_sha256(decoder_attention_mask)

	context: dict[str, Any] = {
	"hidden_state_source": hidden_state_source,
	"hidden_state_index": hidden_state_index,
	"store_all_hidden_states": store_all_hidden_states,
	"decoder_input_fingerprint": decoder_input_fingerprint,
	"decoder_attention_mask_fingerprint": decoder_mask_fingerprint,
	"decoder_alignment": "input-position" if hidden_state_source == "decoder" else None,
	}
	metadata_hook = getattr(model, "_embedding_metadata", None)
	model_metadata: Mapping[str, Any] \| None = None
	if callable(metadata_hook):
	model_metadata = metadata_hook(**context)
	if not isinstance(model_metadata, Mapping):
	raise TypeError("_embedding_metadata must return a mapping.")
	context["model_embedding"] = _fingerprint_jsonable(model_metadata)
	if hidden_state_source == "decoder":
	has_decoder_batch = callable(getattr(model, "_embedding_batch", None))
	declares_decoder_stack = (
	model_metadata is not None and model_metadata.get("hidden_state_stack") == "decoder"
	)
	if not has_decoder_batch or not declares_decoder_stack:
	raise ValueError(
	f"{model.__class__.__name__} does not declare decoder embedding support."
	)
	return context, normalized_decoder_inputs