"""Custom inference handler for HuggingFace Inference Endpoints."""

import base64
import binascii
import os
from pathlib import Path
from typing import TYPE_CHECKING, Any

if TYPE_CHECKING:
    from .alignment import QwenForcedAligner
    from .asr_modeling import ASRModel
    from .asr_pipeline import ASRPipeline
    from .diarization import NemotronDiarizer
    from .diarization import get_device as _best_device
else:
    try:
        # For remote execution, imports are relative
        from .alignment import QwenForcedAligner
        from .asr_modeling import ASRModel
        from .asr_pipeline import ASRPipeline
        from .diarization import NemotronDiarizer
        from .diarization import get_device as _best_device
    except ImportError:
        # For local execution, imports are not relative
        from alignment import QwenForcedAligner
        from asr_modeling import ASRModel
        from asr_pipeline import ASRPipeline
        from diarization import NemotronDiarizer
        from diarization import get_device as _best_device


def decode_inputs(inputs: Any) -> Any:
    """A JSON request's base64 audio as bytes; paths, raw bytes and arrays unchanged.

    The toolkit hands `inputs` over as raw bytes for an `audio/*` body, but a
    JSON body can only carry audio as a base64 string, which the pipeline
    would otherwise read as a file path.
    """
    if not isinstance(inputs, str) or Path(inputs).exists():
        return inputs
    try:
        return base64.b64decode(inputs, validate=True)
    except (binascii.Error, ValueError):
        return inputs


class EndpointHandler:
    """HuggingFace Inference Endpoints handler for ASR model.

    Handles model loading, warmup, and inference requests for deployment
    on HuggingFace Inference Endpoints or similar services.
    """

    def __init__(self, path: str = ""):
        """Initialize the endpoint handler.

        Args:
            path: Path to model directory or HuggingFace model ID
        """
        os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")

        # `ASRModel.from_pretrained` constructs its own submodules and forwards
        # **kwargs to `ASRModel.__init__`, which discards them -- no loader
        # reads `device_map`, `torch_dtype` or `low_cpu_mem_usage`, and it sets
        # `_is_loading_from_pretrained` precisely to keep `device_map="auto"`
        # out of the sub-model loaders. Passing them here did nothing, so the
        # model stayed on CPU and a GPU Inference Endpoint silently decoded on
        # CPU. Place it explicitly instead. dtype comes from
        # `config.model_dtype` and the attention backend from
        # `config.attn_implementation`, which already downgrades FA2 when
        # flash_attn is missing.
        self.model = ASRModel.from_pretrained(path)
        self.device = _best_device()
        # PreTrainedModel.to is functools.wraps'd, which pyright cannot bind as a method.
        self.model.to(self.device)  # pyright: ignore[reportArgumentType]
        self.model.eval()

        self.pipe = ASRPipeline(
            model=self.model,
            feature_extractor=self.model.feature_extractor,
            tokenizer=self.model.tokenizer,
            device=self.device,
        )
        # Load the forced aligner (return_timestamps) and Nemotron diarizer
        # (return_speakers) at boot, as the Spaces demo does, so the first
        # request that asks for them doesn't pay the download and load.
        QwenForcedAligner.get_instance()
        NemotronDiarizer.get_instance()

    def __call__(self, data: dict[str, Any]) -> dict[str, Any] | list[dict[str, Any]]:
        """Process an inference request.

        Args:
            data: Request data containing 'inputs' (audio path, bytes or base64
                string) and optional 'parameters' for the pipeline, e.g.
                `return_timestamps`, `return_speakers`, `num_speakers`,
                `max_speakers` -- the same options as the Spaces demo

        Returns:
            Transcription result with 'text' key; 'words' with timestamps, plus
            speaker labels and 'speaker_segments' when diarizing
        """
        inputs = data.get("inputs")
        if inputs is None:
            msg = "Missing 'inputs' in request data"
            raise ValueError(msg)

        # Pass through any parameters from request, let model config provide defaults
        params = data.get("parameters", {})

        result: dict[str, Any] | list[dict[str, Any]] = self.pipe(decode_inputs(inputs), **params)
        return result