"""Dog to human translation module"""

from typing import Dict, Any, Union, List
from pathlib import Path

from .base import BaseModule
from ..utils.validators import validate_audio
from ..utils.exceptions import ProcessingError


class DogToHumanTranslator(BaseModule):
    """
    Translate dog sounds to human language

    This module processes dog vocalizations and returns:
    - Text interpretation of what the dog is communicating
    - Audio narration in human language
    """

    def translate(
        self,
        audio_path: Union[str, Path, bytes],
        **kwargs
    ) -> Dict[str, Any]:
        """
        Translate dog sounds to human language

        Args:
            audio_path: Path to audio file or audio bytes
            **kwargs: Additional parameters
                - language: Output language (default: 'en')
                - include_sound_type: Include detected sound type (default: True)
                - detail_level: 'simple' or 'detailed' (default: 'simple')

        Returns:
            Dict with keys:
                - text: Human interpretation of dog sounds
                - audio: Human narration as bytes
                - sound_type: Type of vocalization (optional)
                - emotion: Detected emotion (optional)
                - context: Behavioral context (optional)

        Example:
            >>> translator = DogToHumanTranslator(config)
            >>> result = translator.translate("dog_bark.wav")
            >>> print(result['text'])
            'Alert bark - the dog is warning about something in their territory'
        """
        self.logger.info(f"Translating dog sounds to human language: {audio_path}")

        # Validate input
        validate_audio(audio_path)

        # Process audio
        return self.process(audio_path, **kwargs)

    def process(
        self,
        input_data: Union[str, Path, bytes],
        **kwargs
    ) -> Dict[str, Any]:
        """
        Process dog audio and translate to human language

        Args:
            input_data: Audio data
            **kwargs: Additional parameters

        Returns:
            Translation results
        """
        try:
            # Load audio
            if isinstance(input_data, bytes):
                audio_bytes = input_data
            else:
                audio_bytes = self._load_file(str(input_data))

            # Extract parameters
            language = kwargs.get('language', 'en')
            include_sound_type = kwargs.get('include_sound_type', True)
            detail_level = kwargs.get('detail_level', 'simple')

            # Step 1: Classify dog sound type
            sound_classification = self._classify_dog_sound(audio_bytes)

            # Step 2: Analyze acoustic features
            acoustic_features = self._analyze_acoustic_features(audio_bytes)

            # Step 3: Interpret meaning
            interpretation = self._interpret_dog_communication(
                sound_classification,
                acoustic_features
            )

            # Generate text description
            text = self._generate_interpretation_text(
                interpretation,
                language=language,
                detail_level=detail_level
            )

            # Generate audio narration
            audio = self._generate_audio(text)

            # Build result
            result = {
                'text': text,
                'audio': audio,
            }

            if include_sound_type:
                result['sound_type'] = sound_classification['type']
                result['emotion'] = interpretation['emotion']
                result['context'] = interpretation['context']

            self.logger.info(f"Translation complete: {sound_classification['type']}")
            return result

        except Exception as e:
            self.logger.error(f"Error translating dog audio: {str(e)}")
            raise ProcessingError(f"Failed to translate dog audio: {str(e)}")

    def _classify_dog_sound(self, audio_bytes: bytes) -> Dict[str, Any]:
        """
        Classify type of dog vocalization

        Args:
            audio_bytes: Dog audio data

        Returns:
            Classification results
        """
        # TODO: Implement actual sound classification model
        # Dog vocalizations include:
        # - Bark (various types)
        # - Growl
        # - Whine
        # - Howl
        # - Yelp
        # - Panting
        # - Groan

        self.logger.info("Classifying dog sound type...")

        # Placeholder classification
        sound_types = [
            'bark_alert',
            'bark_playful',
            'bark_demand',
            'growl_warning',
            'whine_anxious',
            'howl',
            'yelp_pain'
        ]

        # In real implementation, would use ML model
        return {
            'type': 'bark_alert',
            'confidence': 0.89,
            'alternatives': [
                {'type': 'bark_demand', 'confidence': 0.08}
            ]
        }

    def _analyze_acoustic_features(self, audio_bytes: bytes) -> Dict[str, Any]:
        """
        Analyze acoustic features of dog sound

        Args:
            audio_bytes: Audio data

        Returns:
            Acoustic features
        """
        # TODO: Implement actual acoustic analysis
        # Features to analyze:
        # - Pitch (frequency)
        # - Duration
        # - Intensity (volume)
        # - Repetition rate
        # - Harmonic structure

        self.logger.info("Analyzing acoustic features...")

        # Placeholder features
        return {
            'pitch_hz': 450,  # Higher pitch = more urgent/excited
            'duration_ms': 300,  # Shorter = more alert
            'intensity_db': 75,  # Louder = more emphatic
            'repetition_rate': 2.5,  # Barks per second
            'pitch_variation': 'moderate'  # Variation indicates emotion
        }

    def _interpret_dog_communication(
        self,
        sound_classification: Dict[str, Any],
        acoustic_features: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Interpret what the dog is trying to communicate

        Args:
            sound_classification: Sound type classification
            acoustic_features: Acoustic analysis

        Returns:
            Interpretation of communication
        """
        sound_type = sound_classification['type']

        # Interpretation based on sound type and features
        interpretations = {
            'bark_alert': {
                'emotion': 'alert',
                'context': 'territorial_warning',
                'meaning': 'The dog is warning about something in their territory',
                'likely_cause': 'Stranger approaching, unusual sound, or unfamiliar presence'
            },
            'bark_playful': {
                'emotion': 'excited',
                'context': 'play_invitation',
                'meaning': 'The dog wants to play',
                'likely_cause': 'Sees toy, another dog, or playful energy'
            },
            'bark_demand': {
                'emotion': 'demanding',
                'context': 'request',
                'meaning': 'The dog wants something (food, walk, attention)',
                'likely_cause': 'Near food time, needs to go outside, or wants attention'
            },
            'growl_warning': {
                'emotion': 'defensive',
                'context': 'warning',
                'meaning': 'The dog is warning - stay back',
                'likely_cause': 'Feels threatened, protecting resource, or in pain'
            },
            'whine_anxious': {
                'emotion': 'anxious',
                'context': 'distress',
                'meaning': 'The dog is anxious or uncomfortable',
                'likely_cause': 'Separation anxiety, fear, or discomfort'
            },
            'howl': {
                'emotion': 'loneliness',
                'context': 'social',
                'meaning': 'The dog is calling out (loneliness or response to sounds)',
                'likely_cause': 'Alone, hears sirens, or communicating with other dogs'
            },
            'yelp_pain': {
                'emotion': 'pain',
                'context': 'distress',
                'meaning': 'The dog is in pain or startled',
                'likely_cause': 'Physical pain, sudden fright, or injury'
            }
        }

        base_interpretation = interpretations.get(
            sound_type,
            {
                'emotion': 'unknown',
                'context': 'general',
                'meaning': 'Dog vocalization detected',
                'likely_cause': 'Various possible causes'
            }
        )

        # Adjust interpretation based on acoustic features
        intensity = acoustic_features.get('intensity_db', 0)
        pitch = acoustic_features.get('pitch_hz', 0)

        if intensity > 80:
            base_interpretation['urgency'] = 'high'
        elif intensity > 65:
            base_interpretation['urgency'] = 'medium'
        else:
            base_interpretation['urgency'] = 'low'

        if pitch > 500:
            base_interpretation['arousal'] = 'high'
        elif pitch > 300:
            base_interpretation['arousal'] = 'medium'
        else:
            base_interpretation['arousal'] = 'low'

        return base_interpretation

    def _generate_interpretation_text(
        self,
        interpretation: Dict[str, Any],
        language: str = 'en',
        detail_level: str = 'simple'
    ) -> str:
        """
        Generate human-readable interpretation text

        Args:
            interpretation: Interpretation data
            language: Output language
            detail_level: Level of detail

        Returns:
            Interpretation text
        """
        meaning = interpretation.get('meaning', 'Unknown vocalization')
        emotion = interpretation.get('emotion', 'unknown')
        context = interpretation.get('context', 'general')
        likely_cause = interpretation.get('likely_cause', '')

        if language == 'zh':
            if detail_level == 'simple':
                text = f"{meaning}。"
            else:
                text = f"{meaning}。情绪状态：{emotion}。可能原因：{likely_cause}。"
        else:
            if detail_level == 'simple':
                text = f"{meaning}."
            else:
                text = f"{meaning}. "
                text += f"Emotional state: {emotion}. "
                text += f"Context: {context}. "
                text += f"Likely cause: {likely_cause}."

        return text
