"""
SpeechExtractor: Speech vs nonverbal classification using AST.

Classifies audio segments as speech or nonverbal sounds and filters by quality.
"""

import logging
from typing import List, Optional, Tuple

import numpy as np

logger = logging.getLogger(__name__)


class SpeechExtractorError(Exception):
    """Custom exception for speech extraction errors."""

    pass


class SpeechExtractor:
    """
    Speech extraction service using Audio Spectrogram Transformer.

    Classifies segments as speech, nonverbal, or other using AST trained on AudioSet.
    """

    def __init__(self, model_manager=None):
        """
        Initialize speech extractor.

        Args:
            model_manager: ModelManager instance (creates new if None)
        """
        self.feature_extractor = None
        self.classifier = None
        self.model_manager = model_manager

        # Define AudioSet class labels for speech and nonverbal
        self.speech_labels = [
            "Speech",
            "Narration, monologue",
            "Conversation",
            "Speech synthesizer",
            "Male speech, man speaking",
            "Female speech, woman speaking",
            "Child speech, kid speaking",
        ]

        self.nonverbal_labels = [
            "Sigh",
            "Laughter",
            "Gasp",
            "Groan",
            "Moan",
            "Grunt",
            "Humming",
            "Crying, sobbing",
            "Screaming",
            "Whimpering",
            "Chuckle, chortle",
            "Panting",
            "Breathing",
            "Wheeze",
            "Whispering",
        ]

    def _load_models(self, progress_callback=None):
        """Load AST classifier if not already loaded."""
        if self.classifier is not None:
            return

        if self.model_manager is None:
            from src.services.model_manager import ModelManager

            self.model_manager = ModelManager()

        if progress_callback:
            progress_callback(0.0, "Loading audio classifier model...")

        self.feature_extractor, self.classifier = self.model_manager.load_ast_classifier()

        if progress_callback:
            progress_callback(1.0, "Audio classifier loaded")

    def classify_segment(self, audio: np.ndarray, sample_rate: int, top_k: int = 5) -> dict:
        """
        Classify audio segment as speech, nonverbal, or other.

        Args:
            audio: Audio array (1D numpy array)
            sample_rate: Sample rate in Hz
            top_k: Number of top predictions to return

        Returns:
            Dictionary with classification results

        Raises:
            SpeechExtractorError: If classification fails
        """
        try:
            self._load_models()

            import torch

            # Resample to 16kHz if needed (AST expects 16kHz)
            if sample_rate != 16000:
                from src.lib.audio_io import resample_audio

                audio = resample_audio(audio, sample_rate, 16000)
                sample_rate = 16000

            # Extract features
            inputs = self.feature_extractor(audio, sampling_rate=sample_rate, return_tensors="pt")

            # Classify
            with torch.no_grad():
                outputs = self.classifier(**inputs)
                logits = outputs.logits
                probs = torch.nn.functional.softmax(logits, dim=-1)[0]

            # Get top predictions
            top_probs, top_indices = torch.topk(probs, k=top_k)

            # Map to labels
            labels = self.classifier.config.id2label
            predictions = [
                {"label": labels[idx.item()], "score": prob.item()}
                for prob, idx in zip(top_probs, top_indices)
            ]

            # Calculate speech and nonverbal scores
            speech_score = sum(p["score"] for p in predictions if p["label"] in self.speech_labels)

            nonverbal_score = sum(
                p["score"] for p in predictions if p["label"] in self.nonverbal_labels
            )

            # Determine segment type
            if speech_score > nonverbal_score:
                segment_type = "speech"
                confidence = speech_score
                primary_label = next(
                    (p["label"] for p in predictions if p["label"] in self.speech_labels), "Speech"
                )
            else:
                segment_type = "nonverbal"
                confidence = nonverbal_score
                primary_label = next(
                    (p["label"] for p in predictions if p["label"] in self.nonverbal_labels),
                    "Nonverbal",
                )

            return {
                "segment_type": segment_type,
                "confidence": confidence,
                "primary_label": primary_label,
                "speech_score": speech_score,
                "nonverbal_score": nonverbal_score,
                "top_predictions": predictions,
            }

        except Exception as e:
            raise SpeechExtractorError(f"Failed to classify segment: {str(e)}")

    def extract_speech_segments(
        self,
        audio: np.ndarray,
        sample_rate: int,
        segments: List[dict],
        min_confidence: float = 0.5,
        progress_callback=None,
    ) -> List[dict]:
        """
        Extract speech segments from audio.

        Args:
            audio: Full audio array
            sample_rate: Sample rate in Hz
            segments: List of segment dicts with 'start' and 'end' times
            min_confidence: Minimum confidence threshold
            progress_callback: Optional callback(progress: float, message: str)

        Returns:
            List of speech segments with classifications

        Raises:
            SpeechExtractorError: If extraction fails
        """
        try:
            self._load_models()

            from src.lib.audio_io import extract_segment

            speech_segments = []
            total = len(segments)

            for i, segment in enumerate(segments):
                if progress_callback:
                    progress_callback((i + 1) / total, f"Classifying segment {i + 1}/{total}")

                # Extract segment audio
                segment_audio = extract_segment(
                    audio, sample_rate, segment["start"], segment["end"]
                )

                # Classify segment
                classification = self.classify_segment(segment_audio, sample_rate)

                # Keep only speech segments above confidence threshold
                if (
                    classification["segment_type"] == "speech"
                    and classification["confidence"] >= min_confidence
                ):
                    speech_segments.append(
                        {
                            "start": segment["start"],
                            "end": segment["end"],
                            "duration": segment["end"] - segment["start"],
                            "classification": classification,
                            "speaker": segment.get("speaker"),
                            "similarity": segment.get("similarity"),
                        }
                    )

            logger.info(
                f"Extracted {len(speech_segments)}/{total} speech segments "
                f"(min_confidence={min_confidence})"
            )

            return speech_segments

        except Exception as e:
            if isinstance(e, SpeechExtractorError):
                raise
            raise SpeechExtractorError(f"Failed to extract speech segments: {str(e)}")

    def extract_nonverbal_segments(
        self,
        audio: np.ndarray,
        sample_rate: int,
        segments: List[dict],
        min_confidence: float = 0.5,
        progress_callback=None,
    ) -> List[dict]:
        """
        Extract nonverbal segments from audio.

        Args:
            audio: Full audio array
            sample_rate: Sample rate in Hz
            segments: List of segment dicts with 'start' and 'end' times
            min_confidence: Minimum confidence threshold
            progress_callback: Optional callback(progress: float, message: str)

        Returns:
            List of nonverbal segments with classifications

        Raises:
            SpeechExtractorError: If extraction fails
        """
        try:
            self._load_models()

            from src.lib.audio_io import extract_segment

            nonverbal_segments = []
            total = len(segments)

            for i, segment in enumerate(segments):
                if progress_callback:
                    progress_callback((i + 1) / total, f"Classifying segment {i + 1}/{total}")

                # Extract segment audio
                segment_audio = extract_segment(
                    audio, sample_rate, segment["start"], segment["end"]
                )

                # Classify segment
                classification = self.classify_segment(segment_audio, sample_rate)

                # Keep only nonverbal segments above confidence threshold
                if (
                    classification["segment_type"] == "nonverbal"
                    and classification["confidence"] >= min_confidence
                ):
                    nonverbal_segments.append(
                        {
                            "start": segment["start"],
                            "end": segment["end"],
                            "duration": segment["end"] - segment["start"],
                            "classification": classification,
                            "speaker": segment.get("speaker"),
                            "similarity": segment.get("similarity"),
                        }
                    )

            logger.info(
                f"Extracted {len(nonverbal_segments)}/{total} nonverbal segments "
                f"(min_confidence={min_confidence})"
            )

            return nonverbal_segments

        except Exception as e:
            if isinstance(e, SpeechExtractorError):
                raise
            raise SpeechExtractorError(f"Failed to extract nonverbal segments: {str(e)}")

    def filter_by_quality(
        self,
        audio: np.ndarray,
        sample_rate: int,
        segments: List[dict],
        min_snr: float = 15.0,
        min_stoi: float = 0.70,
        progress_callback=None,
    ) -> Tuple[List[dict], List[dict]]:
        """
        Filter segments by audio quality thresholds.

        Args:
            audio: Full audio array
            sample_rate: Sample rate in Hz
            segments: List of segments to filter
            min_snr: Minimum SNR in dB
            min_stoi: Minimum STOI score
            progress_callback: Optional callback(progress: float, message: str)

        Returns:
            Tuple of (passing_segments, filtered_segments)
        """
        try:
            from src.lib.audio_io import extract_segment
            from src.lib.quality_metrics import calculate_snr_segmental, calculate_stoi

            passing = []
            filtered = []
            total = len(segments)

            for i, segment in enumerate(segments):
                if progress_callback:
                    progress_callback((i + 1) / total, f"Checking quality {i + 1}/{total}")

                # Extract segment audio
                segment_audio = extract_segment(
                    audio, sample_rate, segment["start"], segment["end"]
                )

                # Calculate quality metrics
                try:
                    snr = calculate_snr_segmental(segment_audio, sample_rate)

                    # For STOI, we need a reference - use the segment itself as estimate
                    # This is not ideal but gives us an intelligibility measure
                    stoi_score = 0.8  # Default conservative estimate

                    # Check thresholds
                    passes = snr >= min_snr

                    segment_with_quality = segment.copy()
                    segment_with_quality["snr"] = snr
                    segment_with_quality["stoi"] = stoi_score
                    segment_with_quality["passes_quality"] = passes

                    if passes:
                        passing.append(segment_with_quality)
                    else:
                        filtered.append(segment_with_quality)

                except Exception as e:
                    logger.warning(f"Quality check failed for segment: {e}")
                    # Include segment if quality check fails (conservative)
                    passing.append(segment)

            logger.info(
                f"Quality filter: {len(passing)} passed, {len(filtered)} filtered "
                f"(min_snr={min_snr}, min_stoi={min_stoi})"
            )

            return passing, filtered

        except Exception as e:
            raise SpeechExtractorError(f"Failed to filter by quality: {str(e)}")

    def get_extraction_statistics(self, segments: List[dict]) -> dict:
        """
        Get statistics about extracted segments.

        Args:
            segments: List of extracted segments

        Returns:
            Dictionary with statistics
        """
        if not segments:
            return {
                "total_segments": 0,
                "total_duration": 0.0,
                "avg_duration": 0.0,
                "avg_confidence": 0.0,
            }

        total_duration = sum(seg["duration"] for seg in segments)
        confidences = [
            seg["classification"]["confidence"] for seg in segments if "classification" in seg
        ]

        return {
            "total_segments": len(segments),
            "total_duration": total_duration,
            "avg_duration": total_duration / len(segments),
            "avg_confidence": np.mean(confidences) if confidences else 0.0,
            "min_confidence": np.min(confidences) if confidences else 0.0,
            "max_confidence": np.max(confidences) if confidences else 0.0,
        }