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"""Content hashing for multimodal features.

A multimodal feature -- an image/video pixel tensor, a numpy array, or a nested
list of them -- is folded into a single unsigned 64-bit integer. The runtime
uses that integer for within-batch dedup (duplicate features encode once) and
as the seed for a per-item pad value that substitutes the placeholder token ids
so the text-only prefix cache can prefix-match across requests. The hash only
needs to be deterministic and well distributed *within a run*: values are
computed once (in the gateway producer) and travel with the item, never
persisted or compared across builds, so the concrete digest is an
implementation detail.
"""

import hashlib
from collections.abc import Iterable

import numpy as np
import torch

from tokenspeed.runtime.utils import flatten_nested_list

# blake2b emits an 8-byte digest natively, which is exactly our key width.
_KEY_BYTES = 8

ByteChunk = bytes | bytearray | memoryview


def _fold(chunks: Iterable[ByteChunk]) -> int:
    """Fold an ordered sequence of byte chunks into one unsigned 64-bit key."""
    digest = hashlib.blake2b(digest_size=_KEY_BYTES)
    for chunk in chunks:
        digest.update(chunk)
    return int.from_bytes(digest.digest(), byteorder="big")


def _raw_bytes(buffer: torch.Tensor | np.ndarray) -> memoryview:
    """Contiguous byte view of a tensor/array; CUDA tensors are pulled to host."""
    if isinstance(buffer, torch.Tensor):
        if buffer.is_cuda:
            buffer = buffer.cpu()
        return memoryview(buffer.detach().contiguous().view(torch.uint8).numpy())
    return memoryview(np.ascontiguousarray(buffer))


def hash_feature(feature) -> int:
    """Deterministic unsigned 64-bit content hash of a multimodal feature.

    Handles a single tensor or numpy array, a (possibly nested) list of those,
    and -- as a fallback -- any bytes-like or ``repr``-able object.
    """
    if isinstance(feature, (torch.Tensor, np.ndarray)):
        return _fold([_raw_bytes(feature)])

    if isinstance(feature, list):
        leaves = flatten_nested_list(feature)
        if leaves and all(isinstance(x, (torch.Tensor, np.ndarray)) for x in leaves):
            return _fold(_raw_bytes(x) for x in leaves)
        # Non-array leaves (e.g. python scalars): hash a stable serialization.
        return _fold([repr(tuple(leaves)).encode()])

    if isinstance(feature, (bytes, bytearray, memoryview)):
        return _fold([feature])

    return _fold([repr(feature).encode()])