"""
BitsAndBytes quantization for GPU-constrained deployment.
4-bit NF4: reduces Whisper-large-v3-turbo from ~3GB to ~1GB VRAM.
8-bit: intermediate option with less accuracy loss.
"""
from __future__ import annotations

import logging
import time
from typing import TYPE_CHECKING

import torch
from transformers import BitsAndBytesConfig, WhisperForConditionalGeneration, WhisperProcessor

if TYPE_CHECKING:
    pass

logger = logging.getLogger(__name__)


def load_4bit(model_id: str, hf_token: str | None = None) -> WhisperForConditionalGeneration:
    """Load Whisper with 4-bit NF4 quantization. Reduces VRAM to ~1GB."""
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_use_double_quant=True,
    )
    logger.info("Loading %s with 4-bit NF4 quantization...", model_id)
    model = WhisperForConditionalGeneration.from_pretrained(
        model_id,
        quantization_config=bnb_config,
        device_map="auto",
        token=hf_token,
    )
    return model


def load_8bit(model_id: str, hf_token: str | None = None) -> WhisperForConditionalGeneration:
    """Load Whisper with 8-bit quantization. Reduces VRAM to ~1.5GB."""
    bnb_config = BitsAndBytesConfig(load_in_8bit=True)
    logger.info("Loading %s with 8-bit quantization...", model_id)
    model = WhisperForConditionalGeneration.from_pretrained(
        model_id,
        quantization_config=bnb_config,
        device_map="auto",
        token=hf_token,
    )
    return model


class ModelQuantizer:
    """Benchmarks quantized vs full-precision models."""

    def __init__(self, model_id: str, hf_token: str | None = None) -> None:
        self.model_id = model_id
        self.hf_token = hf_token

    def benchmark(
        self,
        model: WhisperForConditionalGeneration,
        processor: WhisperProcessor,
        test_audio_arrays: list,
        sample_rate: int = 16_000,
    ) -> dict:
        """Measure latency and memory for a list of audio arrays."""
        import numpy as np

        device = next(model.parameters()).device
        latencies = []

        for audio in test_audio_arrays:
            inputs = processor.feature_extractor(audio, sampling_rate=sample_rate, return_tensors="pt")
            features = inputs.input_features.to(device)

            if device.type == "cuda":
                torch.cuda.synchronize()
            t0 = time.perf_counter()

            with torch.no_grad():
                model.generate(features, max_new_tokens=50)

            if device.type == "cuda":
                torch.cuda.synchronize()
            latencies.append((time.perf_counter() - t0) * 1000)

        result = {
            "mean_latency_ms": round(sum(latencies) / len(latencies), 1),
            "max_latency_ms": round(max(latencies), 1),
        }

        if torch.cuda.is_available():
            result["vram_allocated_gb"] = round(torch.cuda.memory_allocated() / 1e9, 2)

        return result