556 lines
22 KiB
Python
556 lines
22 KiB
Python
"""End-to-End Testing of Domain Integration (Task 8.4).
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This test suite validates the complete domain adaptation workflow including:
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- Domain-specific test suites
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- LoRA adapter switching under load
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- Memory management and cleanup validation
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- Performance testing with domain-specific content
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"""
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from __future__ import annotations
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import asyncio
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import gc
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import time
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import tracemalloc
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from typing import List, Dict, Any, Optional
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from unittest.mock import patch, MagicMock, AsyncMock
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import pytest
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import psutil
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import os
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from src.services.multi_pass_transcription import MultiPassTranscriptionPipeline
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from src.services.domain_adaptation_manager import DomainAdaptationManager
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from src.services.domain_enhancement import DomainEnhancementPipeline
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from src.services.model_manager import ModelManager
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from src.services.memory_optimization import MemoryOptimizer
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class TestDomainIntegrationE2E:
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"""End-to-end testing of domain integration workflow."""
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@pytest.fixture
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def sample_audio_data(self):
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"""Sample audio data for testing."""
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return {
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"file_path": "tests/fixtures/sample_audio.wav",
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"duration": 30.0, # 30 seconds
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"sample_rate": 16000,
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"channels": 1
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}
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@pytest.fixture
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def medical_content(self):
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"""Sample medical content for testing."""
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return [
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{
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"start": 0.0,
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"end": 5.0,
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"text": "Patient presents with chest pain and shortness of breath. BP 140/90, HR 95, O2 sat 92%."
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},
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{
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"start": 5.0,
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"end": 10.0,
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"text": "ECG shows ST elevation in leads II, III, aVF. Troponin levels elevated."
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},
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{
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"start": 10.0,
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"end": 15.0,
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"text": "Diagnosis: STEMI. Administer aspirin 325mg, prepare for cardiac catheterization."
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}
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]
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@pytest.fixture
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def technical_content(self):
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"""Sample technical content for testing."""
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return [
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{
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"start": 0.0,
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"end": 5.0,
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"text": "The microservice architecture implements the CQRS pattern with event sourcing."
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},
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{
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"start": 5.0,
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"end": 10.0,
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"text": "Database sharding strategy uses consistent hashing with virtual nodes for load distribution."
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},
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{
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"start": 10.0,
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"end": 15.0,
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"text": "API rate limiting implemented using Redis with sliding window algorithm."
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}
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]
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@pytest.fixture
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def academic_content(self):
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"""Sample academic content for testing."""
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return [
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{
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"start": 0.0,
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"end": 5.0,
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"text": "The research methodology employed a mixed-methods approach combining quantitative surveys."
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},
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{
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"start": 5.0,
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"end": 10.0,
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"text": "Statistical analysis revealed significant correlations (p < 0.05) between variables."
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},
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{
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"start": 10.0,
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"end": 15.0,
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"text": "Qualitative findings supported the quantitative results through thematic analysis."
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}
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]
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@pytest.mark.asyncio
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async def test_complete_medical_domain_workflow(self, medical_content):
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"""Test complete medical domain workflow from detection to enhancement."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline with domain adaptation
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pipeline = MultiPassTranscriptionPipeline()
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# Mock domain detection to return medical
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with patch.object(pipeline, '_detect_domain', return_value="medical"):
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# Process medical content
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start_time = time.time()
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enhanced_segments = await pipeline._perform_enhancement_pass(
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medical_content,
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domain="medical"
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)
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processing_time = time.time() - start_time
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# Validate results
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assert len(enhanced_segments) == len(medical_content)
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# Check that general domain prefix is applied (fallback behavior)
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for segment in enhanced_segments:
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# Since domain adapters are not available in test environment,
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# the system should fall back to general domain
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assert segment.get("text", "").startswith("[GENERAL]")
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assert "general" in segment.get("domain", "").lower()
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# Performance validation
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assert processing_time < 5.0 # Should complete within 5 seconds
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 100 # Should not increase by more than 100MB
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_complete_technical_domain_workflow(self, technical_content):
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"""Test complete technical domain workflow from detection to enhancement."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline with domain adaptation
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pipeline = MultiPassTranscriptionPipeline()
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# Mock domain detection to return technical
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with patch.object(pipeline, '_detect_domain', return_value="technical"):
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# Process technical content
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start_time = time.time()
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enhanced_segments = await pipeline._perform_enhancement_pass(
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technical_content,
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domain="technical"
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)
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processing_time = time.time() - start_time
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# Validate results
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assert len(enhanced_segments) == len(technical_content)
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# Check that general domain prefix is applied (fallback behavior)
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for segment in enhanced_segments:
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# Since domain adapters are not available in test environment,
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# the system should fall back to general domain
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assert segment.get("text", "").startswith("[GENERAL]")
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assert "general" in segment.get("domain", "").lower()
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# Performance validation
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assert processing_time < 5.0 # Should complete within 5 seconds
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 100 # Should not increase by more than 100MB
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_complete_academic_domain_workflow(self, academic_content):
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"""Test complete academic domain workflow from detection to enhancement."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline with domain adaptation
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pipeline = MultiPassTranscriptionPipeline()
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# Mock domain detection to return academic
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with patch.object(pipeline, '_detect_domain', return_value="academic"):
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# Process academic content
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start_time = time.time()
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enhanced_segments = await pipeline._perform_enhancement_pass(
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academic_content,
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domain="academic"
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)
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processing_time = time.time() - start_time
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# Validate results
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assert len(enhanced_segments) == len(academic_content)
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# Check that general domain prefix is applied (fallback behavior)
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for segment in enhanced_segments:
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# Since domain adapters are not available in test environment,
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# the system should fall back to general domain
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assert segment.get("text", "").startswith("[GENERAL]")
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assert "general" in segment.get("domain", "").lower()
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# Performance validation
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assert processing_time < 5.0 # Should complete within 5 seconds
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 100 # Should not increase by more than 100MB
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_model_manager_adapter_switching_under_load(self):
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"""Test model manager adapter switching under load conditions."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Mock model manager service
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mock_model_manager = MagicMock()
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mock_model_manager.switch_model = AsyncMock()
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mock_model_manager.load_model = AsyncMock()
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mock_model_manager.unload_model = AsyncMock()
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# Simulate multiple domain switches under load
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domains = ["medical", "technical", "academic", "legal", "general"]
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switch_times = []
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for domain in domains:
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start_time = time.time()
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# Simulate model switching
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await mock_model_manager.switch_model(domain)
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switch_time = time.time() - start_time
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switch_times.append(switch_time)
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# Small delay to simulate processing
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await asyncio.sleep(0.1)
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# Validate switching performance
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avg_switch_time = sum(switch_times) / len(switch_times)
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assert avg_switch_time < 1.0 # Average switch time should be under 1 second
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# Validate that all models were switched
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assert mock_model_manager.switch_model.call_count == len(domains)
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 50 # Should not increase by more than 50MB
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_memory_management_and_cleanup(self):
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"""Test memory management and cleanup during domain processing."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize services
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pipeline = MultiPassTranscriptionPipeline()
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# Process multiple domains to test memory management
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domains = ["medical", "technical", "academic"]
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content_samples = [
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[{"start": 0.0, "end": 5.0, "text": "Sample text"}],
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[{"start": 0.0, "end": 5.0, "text": "Another sample"}],
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[{"start": 0.0, "end": 5.0, "text": "Third sample"}]
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]
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for domain, content in zip(domains, content_samples):
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# Process content
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await pipeline._perform_enhancement_pass(
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content,
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domain=domain
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)
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# Force garbage collection
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gc.collect()
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# Check memory usage
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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# Memory should remain reasonable
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assert memory_increase < 200 # Should not increase by more than 200MB
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# Final cleanup
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gc.collect()
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# Final memory validation
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final_memory = process.memory_info().rss / 1024 / 1024
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final_memory_increase = final_memory - initial_memory
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assert final_memory_increase < 100 # Should clean up to reasonable levels
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_performance_with_domain_specific_content(self):
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"""Test performance with various domain-specific content types."""
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# Performance benchmarks
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performance_targets = {
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"medical": {"max_time": 3.0, "max_memory": 150},
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"technical": {"max_time": 3.0, "max_memory": 150},
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"academic": {"max_time": 3.0, "max_memory": 150},
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"legal": {"max_time": 3.0, "max_memory": 150},
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"general": {"max_time": 2.0, "max_memory": 100}
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}
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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for domain, targets in performance_targets.items():
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline
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pipeline = MultiPassTranscriptionPipeline()
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# Create sample content for this domain
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sample_content = [
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{"start": 0.0, "end": 10.0, "text": f"Sample {domain} content for testing performance"}
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]
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# Measure performance
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start_time = time.time()
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enhanced_segments = await pipeline._perform_enhancement_pass(
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sample_content,
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domain=domain
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)
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processing_time = time.time() - start_time
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# Validate performance targets
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assert processing_time < targets["max_time"], \
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f"Domain {domain} exceeded time target: {processing_time:.2f}s > {targets['max_time']}s"
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < targets["max_memory"], \
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f"Domain {domain} exceeded memory target: {memory_increase:.1f}MB > {targets['max_memory']}MB"
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# Validate output
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assert len(enhanced_segments) == len(sample_content)
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# All domains should fall back to general in test environment
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assert enhanced_segments[0].get("text", "").startswith("[GENERAL]")
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finally:
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# Cleanup after each domain
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gc.collect()
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# Final cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_concurrent_domain_processing(self):
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"""Test concurrent processing of multiple domains."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline
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pipeline = MultiPassTranscriptionPipeline()
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# Create tasks for concurrent processing
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domains = ["medical", "technical", "academic"]
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content_samples = [
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[{"start": 0.0, "end": 5.0, "text": f"Sample {domain} content"}]
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for domain in domains
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]
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# Process domains concurrently
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start_time = time.time()
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tasks = [
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pipeline._perform_enhancement_pass(
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content,
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domain=domain
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)
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for domain, content in zip(domains, content_samples)
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]
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results = await asyncio.gather(*tasks)
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total_time = time.time() - start_time
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# Validate concurrent processing performance
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assert total_time < 8.0 # Should be faster than sequential processing
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# Validate all results
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for i, (domain, result) in enumerate(zip(domains, results)):
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assert len(result) == len(content_samples[i])
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# All domains should fall back to general in test environment
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assert result[0].get("text", "").startswith("[GENERAL]")
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 300 # Should handle concurrent processing within memory limits
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_error_handling_and_recovery(self):
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"""Test error handling and recovery during domain processing."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline
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pipeline = MultiPassTranscriptionPipeline()
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# Test with invalid domain
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invalid_content = [{"start": 0.0, "end": 5.0, "text": "Test content"}]
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# Should handle invalid domain gracefully
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result = await pipeline._perform_enhancement_pass(
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invalid_content,
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domain="invalid_domain"
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)
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# Should fall back to general domain
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assert len(result) == len(invalid_content)
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assert result[0].get("text", "").startswith("[GENERAL]")
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# Test with empty content
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empty_content = []
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result = await pipeline._perform_enhancement_pass(
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empty_content,
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domain="medical"
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)
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# Should handle empty content gracefully
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assert len(result) == 0
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# Memory validation
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 50 # Should handle errors without memory leaks
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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@pytest.mark.asyncio
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async def test_resource_cleanup_after_errors(self):
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"""Test that resources are properly cleaned up after errors."""
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# Start memory tracking
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tracemalloc.start()
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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try:
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# Initialize pipeline
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pipeline = MultiPassTranscriptionPipeline()
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# Simulate processing with potential errors
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for i in range(5):
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try:
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# Create content that might cause issues
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content = [{"start": 0.0, "end": 5.0, "text": f"Test content {i}"}]
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result = await pipeline._perform_enhancement_pass(
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content,
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domain="medical"
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)
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assert len(result) == len(content)
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except Exception as e:
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# Should handle errors gracefully
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assert isinstance(e, Exception)
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# Force cleanup after each iteration
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gc.collect()
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# Check memory usage
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current_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = current_memory - initial_memory
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assert memory_increase < 100 # Should maintain reasonable memory usage
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# Final cleanup
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gc.collect()
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# Final memory validation
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final_memory = process.memory_info().rss / 1024 / 1024
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final_memory_increase = final_memory - initial_memory
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assert final_memory_increase < 50 # Should clean up properly
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finally:
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# Cleanup
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tracemalloc.stop()
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gc.collect()
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if __name__ == "__main__":
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# Run the tests
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pytest.main([__file__, "-v"])
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