tests/integration/test_audio_processing_pipeline.py

"""Integration tests for the complete audio processing pipeline."""

import os
import tempfile
import time
import pytest
from pathlib import Path
from unittest.mock import Mock, patch, MagicMock
from typing import Dict, Any, Optional

from src.application.services.audio_processing_service import AudioProcessingApplicationService
from src.application.dtos.audio_upload_dto import AudioUploadDto
from src.application.dtos.processing_request_dto import ProcessingRequestDto
from src.application.dtos.processing_result_dto import ProcessingResultDto
from src.infrastructure.config.dependency_container import DependencyContainer
from src.infrastructure.config.app_config import AppConfig
from src.domain.models.audio_content import AudioContent
from src.domain.models.text_content import TextContent
from src.domain.models.voice_settings import VoiceSettings
from src.domain.exceptions import (
    SpeechRecognitionException,
    TranslationFailedException,
    SpeechSynthesisException
)


class TestAudioProcessingPipeline:
    """Integration tests for the complete audio processing pipeline."""

    @pytest.fixture
    def temp_dir(self):
        """Create temporary directory for test files."""
        with tempfile.TemporaryDirectory() as temp_dir:
            yield temp_dir

    @pytest.fixture
    def mock_config(self, temp_dir):
        """Create mock configuration for testing."""
        config = Mock(spec=AppConfig)

        # Processing configuration
        config.get_processing_config.return_value = {
            'max_file_size_mb': 50,
            'supported_audio_formats': ['wav', 'mp3', 'flac'],
            'temp_dir': temp_dir,
            'cleanup_temp_files': True
        }

        # Logging configuration
        config.get_logging_config.return_value = {
            'level': 'INFO',
            'enable_file_logging': False,
            'log_file_path': os.path.join(temp_dir, 'test.log'),
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        }

        # STT configuration
        config.get_stt_config.return_value = {
            'preferred_providers': ['parakeet', 'whisper-small', 'whisper-medium']
        }

        # TTS configuration
        config.get_tts_config.return_value = {
            'preferred_providers': ['chatterbox']
        }

        return config

    @pytest.fixture
    def mock_container(self, mock_config):
        """Create mock dependency container for testing."""
        container = Mock(spec=DependencyContainer)
        container.resolve.return_value = mock_config

        # Mock STT provider
        mock_stt_provider = Mock()
        mock_stt_provider.transcribe.return_value = TextContent(
            text="Hello, this is a test transcription.",
            language="en"
        )
        container.get_stt_provider.return_value = mock_stt_provider

        # Mock translation provider
        mock_translation_provider = Mock()
        mock_translation_provider.translate.return_value = TextContent(
            text="Hola, esta es una transcripción de prueba.",
            language="es"
        )
        container.get_translation_provider.return_value = mock_translation_provider

        # Mock TTS provider
        mock_tts_provider = Mock()
        mock_audio_content = AudioContent(
            data=b"fake_audio_data",
            format="wav",
            sample_rate=22050,
            duration=2.5
        )
        mock_tts_provider.synthesize.return_value = mock_audio_content
        container.get_tts_provider.return_value = mock_tts_provider

        return container

    @pytest.fixture
    def audio_service(self, mock_container, mock_config):
        """Create audio processing service for testing."""
        return AudioProcessingApplicationService(mock_container, mock_config)

    @pytest.fixture
    def sample_audio_upload(self):
        """Create sample audio upload DTO."""
        return AudioUploadDto(
            filename="test_audio.wav",
            content=b"fake_wav_audio_data",
            content_type="audio/wav",
            size=1024
        )

    @pytest.fixture
    def sample_processing_request(self, sample_audio_upload):
        """Create sample processing request DTO."""
        return ProcessingRequestDto(
            audio=sample_audio_upload,
            asr_model="whisper-small",
            target_language="es",
            source_language="en",
            voice="chatterbox",
            speed=1.0,
            requires_translation=True
        )

    def test_complete_pipeline_success(self, audio_service, sample_processing_request):
        """Test successful execution of the complete audio processing pipeline."""
        # Execute the pipeline
        result = audio_service.process_audio_pipeline(sample_processing_request)

        # Verify successful result
        assert isinstance(result, ProcessingResultDto)
        assert result.success is True
        assert result.error_message is None
        assert result.original_text == "Hello, this is a test transcription."
        assert result.translated_text == "Hola, esta es una transcripción de prueba."
        assert result.audio_path is not None
        assert result.processing_time > 0
        assert result.metadata is not None
        assert 'correlation_id' in result.metadata

    def test_pipeline_without_translation(self, audio_service, sample_audio_upload):
        """Test pipeline execution without translation (same language)."""
        request = ProcessingRequestDto(
            audio=sample_audio_upload,
            asr_model="whisper-small",
            target_language="en",
            source_language="en",
            voice="chatterbox",
            speed=1.0,
            requires_translation=False
        )

        result = audio_service.process_audio_pipeline(request)

        assert result.success is True
        assert result.original_text == "Hello, this is a test transcription."
        assert result.translated_text is None  # No translation performed
        assert result.audio_path is not None

    def test_pipeline_with_different_voice_settings(self, audio_service, sample_audio_upload):
        """Test pipeline with different voice settings."""
        request = ProcessingRequestDto(
            audio=sample_audio_upload,
            asr_model="whisper-medium",
            target_language="fr",
            source_language="en",
            voice="chatterbox",
            speed=1.5,
            requires_translation=True
        )

        result = audio_service.process_audio_pipeline(request)

        assert result.success is True
        assert result.metadata['voice'] == "chatterbox"
        assert result.metadata['speed'] == 1.5
        assert result.metadata['asr_model'] == "whisper-medium"

    def test_pipeline_performance_metrics(self, audio_service, sample_processing_request):
        """Test that pipeline captures performance metrics."""
        start_time = time.time()
        result = audio_service.process_audio_pipeline(sample_processing_request)
        end_time = time.time()

        assert result.success is True
        assert result.processing_time > 0
        assert result.processing_time <= (end_time - start_time) + 0.1  # Allow small margin
        assert 'correlation_id' in result.metadata

    def test_pipeline_with_large_file(self, audio_service, mock_config):
        """Test pipeline behavior with large audio files."""
        # Create large audio upload
        large_audio = AudioUploadDto(
            filename="large_audio.wav",
            content=b"x" * (10 * 1024 * 1024),  # 10MB
            content_type="audio/wav",
            size=10 * 1024 * 1024
        )

        request = ProcessingRequestDto(
            audio=large_audio,
            asr_model="whisper-small",
            target_language="es",
            voice="chatterbox",
            speed=1.0,
            requires_translation=True
        )

        result = audio_service.process_audio_pipeline(request)

        assert result.success is True
        assert result.metadata['file_size'] == 10 * 1024 * 1024

    def test_pipeline_file_cleanup(self, audio_service, sample_processing_request, temp_dir):
        """Test that temporary files are properly cleaned up."""
        # Count files before processing
        files_before = len(list(Path(temp_dir).rglob("*")))

        result = audio_service.process_audio_pipeline(sample_processing_request)

        # Verify processing succeeded
        assert result.success is True

        # Verify cleanup occurred (no additional temp files)
        files_after = len(list(Path(temp_dir).rglob("*")))
        assert files_after <= files_before + 1  # Allow for output file

    def test_pipeline_correlation_id_tracking(self, audio_service, sample_processing_request):
        """Test that correlation IDs are properly tracked throughout the pipeline."""
        result = audio_service.process_audio_pipeline(sample_processing_request)

        assert result.success is True
        assert 'correlation_id' in result.metadata

        correlation_id = result.metadata['correlation_id']
        assert isinstance(correlation_id, str)
        assert len(correlation_id) > 0

        # Verify correlation ID is used in status tracking
        status = audio_service.get_processing_status(correlation_id)
        assert status['correlation_id'] == correlation_id

    def test_pipeline_metadata_completeness(self, audio_service, sample_processing_request):
        """Test that pipeline result contains complete metadata."""
        result = audio_service.process_audio_pipeline(sample_processing_request)

        assert result.success is True
        assert result.metadata is not None

        expected_metadata_keys = [
            'correlation_id', 'asr_model', 'target_language',
            'voice', 'speed', 'translation_required'
        ]

        for key in expected_metadata_keys:
            assert key in result.metadata

    def test_pipeline_supported_configurations(self, audio_service):
        """Test retrieval of supported pipeline configurations."""
        config = audio_service.get_supported_configurations()

        assert 'asr_models' in config
        assert 'voices' in config
        assert 'languages' in config
        assert 'audio_formats' in config
        assert 'max_file_size_mb' in config
        assert 'speed_range' in config

        assert isinstance(config['asr_models'], list)
        assert isinstance(config['voices'], list)
        assert isinstance(config['languages'], list)
        assert len(config['asr_models']) > 0
        assert len(config['voices']) > 0

    def test_pipeline_context_manager(self, mock_container, mock_config):
        """Test audio service as context manager."""
        with AudioProcessingApplicationService(mock_container, mock_config) as service:
            assert service is not None

            # Service should be usable within context
            config = service.get_supported_configurations()
            assert config is not None

    def test_pipeline_multiple_requests(self, audio_service, sample_audio_upload):
        """Test processing multiple requests in sequence."""
        requests = []
        for i in range(3):
            request = ProcessingRequestDto(
                audio=sample_audio_upload,
                asr_model="whisper-small",
                target_language="es",
                voice="chatterbox",
                speed=1.0,
                requires_translation=True
            )
            requests.append(request)

        results = []
        for request in requests:
            result = audio_service.process_audio_pipeline(request)
            results.append(result)

        # Verify all requests succeeded
        for result in results:
            assert result.success is True
            assert result.original_text is not None
            assert result.translated_text is not None

        # Verify each request has unique correlation ID
        correlation_ids = [r.metadata['correlation_id'] for r in results]
        assert len(set(correlation_ids)) == 3  # All unique

    def test_pipeline_concurrent_processing(self, audio_service, sample_processing_request):
        """Test pipeline behavior under concurrent processing."""
        import threading
        import queue

        results_queue = queue.Queue()

        def process_request():
            try:
                result = audio_service.process_audio_pipeline(sample_processing_request)
                results_queue.put(result)
            except Exception as e:
                results_queue.put(e)

        # Start multiple threads
        threads = []
        for _ in range(3):
            thread = threading.Thread(target=process_request)
            threads.append(thread)
            thread.start()

        # Wait for completion
        for thread in threads:
            thread.join()

        # Verify all results
        results = []
        while not results_queue.empty():
            result = results_queue.get()
            if isinstance(result, Exception):
                pytest.fail(f"Concurrent processing failed: {result}")
            results.append(result)

        assert len(results) == 3
        for result in results:
            assert result.success is True

    def test_pipeline_memory_usage(self, audio_service, sample_processing_request):
        """Test pipeline memory usage and cleanup."""
        import psutil
        import os

        process = psutil.Process(os.getpid())
        memory_before = process.memory_info().rss

        # Process multiple requests
        for _ in range(5):
            result = audio_service.process_audio_pipeline(sample_processing_request)
            assert result.success is True

        memory_after = process.memory_info().rss
        memory_increase = memory_after - memory_before

        # Memory increase should be reasonable (less than 50MB for test data)
        assert memory_increase < 50 * 1024 * 1024

    def test_pipeline_with_streaming_synthesis(self, audio_service, sample_processing_request, mock_container):
        """Test pipeline with streaming TTS synthesis."""
        # Mock streaming TTS provider
        mock_tts_provider = mock_container.get_tts_provider.return_value

        def mock_stream():
            for i in range(3):
                yield AudioContent(
                    data=f"chunk_{i}".encode(),
                    format="wav",
                    sample_rate=22050,
                    duration=0.5
                )

        mock_tts_provider.synthesize_stream.return_value = mock_stream()

        result = audio_service.process_audio_pipeline(sample_processing_request)

        assert result.success is True
        assert result.audio_path is not None

    def test_pipeline_configuration_validation(self, audio_service):
        """Test pipeline configuration validation."""
        config = audio_service.get_supported_configurations()

        # Verify configuration structure
        assert isinstance(config['asr_models'], list)
        assert isinstance(config['voices'], list)
        assert isinstance(config['languages'], list)
        assert isinstance(config['audio_formats'], list)
        assert isinstance(config['max_file_size_mb'], (int, float))
        assert isinstance(config['speed_range'], dict)

        # Verify speed range
        speed_range = config['speed_range']
        assert 'min' in speed_range
        assert 'max' in speed_range
        assert speed_range['min'] < speed_range['max']
        assert speed_range['min'] > 0
        assert speed_range['max'] <= 3.0

    def test_pipeline_error_recovery_logging(self, audio_service, sample_processing_request, mock_container):
        """Test that error recovery attempts are properly logged."""
        # Mock STT provider to fail first time, succeed second time
        mock_stt_provider = mock_container.get_stt_provider.return_value
        mock_stt_provider.transcribe.side_effect = [
            SpeechRecognitionException("First attempt failed"),
            TextContent(text="Recovered transcription", language="en")
        ]

        with patch('src.application.services.audio_processing_service.logger') as mock_logger:
            result = audio_service.process_audio_pipeline(sample_processing_request)

            assert result.success is True
            # Verify error and recovery were logged
            mock_logger.warning.assert_called()
            mock_logger.info.assert_called()

    def test_pipeline_end_to_end_timing(self, audio_service, sample_processing_request):
        """Test end-to-end pipeline timing and performance."""
        start_time = time.time()
        result = audio_service.process_audio_pipeline(sample_processing_request)
        end_time = time.time()

        total_time = end_time - start_time

        assert result.success is True
        assert result.processing_time > 0
        assert result.processing_time <= total_time

        # For mock providers, processing should be fast
        assert total_time < 5.0  # Should complete within 5 seconds

        # Verify timing metadata
        assert 'correlation_id' in result.metadata
        timing_info = result.metadata
        assert timing_info is not None