"""Human to dog translation module"""

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

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


class HumanToDogTranslator(BaseModule):
    """
    Translate human speech to dog-understandable format

    This module processes human audio and returns:
    - Text representation of what the dog would understand
    - Audio in frequencies and patterns dogs can comprehend
    """

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

        Args:
            audio_path: Path to audio file or audio bytes
            **kwargs: Additional parameters
                - language: Input language (default: 'en')
                - tone: Emotional tone to convey ('friendly', 'commanding', 'playful')
                - include_transcription: Include human speech transcription (default: True)

        Returns:
            Dict with keys:
                - text: Description of translated message
                - audio: Dog-optimized audio as bytes
                - transcription: Original human speech text (optional)
                - interpretation: What the dog would understand (optional)

        Example:
            >>> translator = HumanToDogTranslator(config)
            >>> result = translator.translate("good_boy.wav", tone="friendly")
            >>> print(result['text'])
            'Translated to dog-friendly frequencies with positive reinforcement tone'
        """
        self.logger.info(f"Translating human speech to dog format: {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 human audio and translate to dog format

        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')
            tone = kwargs.get('tone', 'friendly')
            include_transcription = kwargs.get('include_transcription', True)

            # Step 1: Transcribe human speech
            transcription = self._transcribe_audio(audio_bytes, language)

            # Step 2: Interpret meaning for dogs
            interpretation = self._interpret_for_dog(transcription, tone)

            # Step 3: Generate dog-optimized audio
            dog_audio = self._generate_dog_audio(interpretation, tone)

            # Generate text description
            text = self._generate_translation_description(
                interpretation,
                tone,
                language
            )

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

            if include_transcription:
                result['transcription'] = transcription
                result['interpretation'] = interpretation

            self.logger.info(f"Translation complete: {tone} tone")
            return result

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

    def _transcribe_audio(self, audio_bytes: bytes, language: str) -> str:
        """
        Transcribe human speech to text

        Args:
            audio_bytes: Audio data
            language: Speech language

        Returns:
            Transcribed text
        """
        # TODO: Implement actual speech-to-text
        # Could use Whisper, Google Speech-to-Text, etc.
        self.logger.info("Transcribing human speech...")

        # Placeholder
        return "Good boy! You're such a good dog!"

    def _interpret_for_dog(self, text: str, tone: str) -> Dict[str, Any]:
        """
        Interpret human speech meaning for dog comprehension

        Args:
            text: Transcribed text
            tone: Emotional tone

        Returns:
            Interpretation for dog
        """
        # TODO: Implement actual NLP interpretation
        # Analyze sentiment, commands, key words

        # Dogs understand:
        # - Tone of voice
        # - Specific command words
        # - Emotional energy
        # - Repetition

        interpretation = {
            'command': self._extract_command(text),
            'emotion': self._analyze_emotion(tone),
            'key_words': self._extract_keywords(text),
            'energy_level': self._estimate_energy(tone)
        }

        return interpretation

    def _extract_command(self, text: str) -> str:
        """Extract potential command from text"""
        common_commands = ['sit', 'stay', 'come', 'down', 'heel', 'good', 'no']
        text_lower = text.lower()

        for command in common_commands:
            if command in text_lower:
                return command

        return 'none'

    def _analyze_emotion(self, tone: str) -> str:
        """Map tone to dog-understandable emotion"""
        emotion_map = {
            'friendly': 'positive',
            'commanding': 'authoritative',
            'playful': 'excited',
            'calm': 'soothing'
        }
        return emotion_map.get(tone, 'neutral')

    def _extract_keywords(self, text: str) -> list:
        """Extract important keywords dogs might recognize"""
        # TODO: More sophisticated keyword extraction
        keywords = []
        dog_words = ['good', 'walk', 'treat', 'play', 'ball', 'food', 'dinner']

        for word in dog_words:
            if word in text.lower():
                keywords.append(word)

        return keywords

    def _estimate_energy(self, tone: str) -> str:
        """Estimate energy level from tone"""
        energy_map = {
            'playful': 'high',
            'commanding': 'medium',
            'friendly': 'medium',
            'calm': 'low'
        }
        return energy_map.get(tone, 'medium')

    def _generate_dog_audio(
        self,
        interpretation: Dict[str, Any],
        tone: str
    ) -> bytes:
        """
        Generate dog-optimized audio

        Args:
            interpretation: Interpreted meaning
            tone: Emotional tone

        Returns:
            Dog-optimized audio bytes
        """
        # TODO: Implement actual audio generation
        # - Adjust frequencies to dog hearing range (67Hz - 45kHz)
        # - Emphasize 8kHz - 16kHz range (optimal for dogs)
        # - Add tone variations based on emotion
        # - Include whistle-like components for commands

        self.logger.info("Generating dog-optimized audio...")

        # Placeholder
        return b"DOG_OPTIMIZED_AUDIO_DATA"

    def _generate_translation_description(
        self,
        interpretation: Dict[str, Any],
        tone: str,
        language: str
    ) -> str:
        """
        Generate description of translation

        Args:
            interpretation: Interpretation data
            tone: Tone used
            language: Output language

        Returns:
            Description text
        """
        command = interpretation.get('command', 'none')
        emotion = interpretation.get('emotion', 'neutral')

        if language == 'zh':
            text = f"已转换为狗能理解的{tone}语调"
            if command != 'none':
                text += f"，包含'{command}'指令"
        else:
            text = f"Translated to dog-friendly frequencies with {tone} tone"
            if command != 'none':
                text += f", conveying '{command}' command"
            text += f" and {emotion} emotional energy."

        return text
