"""
Integration tests for end-to-end pose estimation pipeline.

Tests the complete pose estimation workflow from CSI data to pose results.
"""

import pytest
import asyncio
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, Any, List, Optional
from unittest.mock import AsyncMock, MagicMock, patch
import json

from dataclasses import dataclass


@dataclass
class CSIData:
    """CSI data structure for testing."""
    timestamp: datetime
    router_id: str
    amplitude: np.ndarray
    phase: np.ndarray
    frequency: float
    bandwidth: float
    antenna_count: int
    subcarrier_count: int


@dataclass
class PoseResult:
    """Pose estimation result structure."""
    timestamp: datetime
    frame_id: str
    persons: List[Dict[str, Any]]
    zone_summary: Dict[str, int]
    processing_time_ms: float
    confidence_scores: List[float]
    metadata: Dict[str, Any]


class MockCSIProcessor:
    """Mock CSI data processor."""
    
    def __init__(self):
        self.is_initialized = False
        self.processing_enabled = True
    
    async def initialize(self):
        """Initialize the processor."""
        self.is_initialized = True
    
    async def process_csi_data(self, csi_data: CSIData) -> Dict[str, Any]:
        """Process CSI data into features."""
        if not self.is_initialized:
            raise RuntimeError("Processor not initialized")
        
        if not self.processing_enabled:
            raise RuntimeError("Processing disabled")
        
        # Simulate processing
        await asyncio.sleep(0.01)  # Simulate processing time
        
        return {
            "features": np.random.rand(64, 32).tolist(),  # Mock feature matrix
            "quality_score": 0.85,
            "signal_strength": -45.2,
            "noise_level": -78.1,
            "processed_at": datetime.utcnow().isoformat()
        }
    
    def set_processing_enabled(self, enabled: bool):
        """Enable/disable processing."""
        self.processing_enabled = enabled


class MockPoseEstimator:
    """Mock pose estimation model."""
    
    def __init__(self):
        self.is_loaded = False
        self.model_version = "1.0.0"
        self.confidence_threshold = 0.5
    
    async def load_model(self):
        """Load the pose estimation model."""
        await asyncio.sleep(0.1)  # Simulate model loading
        self.is_loaded = True
    
    async def estimate_poses(self, features: np.ndarray) -> Dict[str, Any]:
        """Estimate poses from features."""
        if not self.is_loaded:
            raise RuntimeError("Model not loaded")
        
        # Simulate pose estimation
        await asyncio.sleep(0.05)  # Simulate inference time
        
        # Generate mock pose data
        num_persons = np.random.randint(0, 4)  # 0-3 persons
        persons = []
        
        for i in range(num_persons):
            confidence = np.random.uniform(0.3, 0.95)
            if confidence >= self.confidence_threshold:
                persons.append({
                    "person_id": f"person_{i}",
                    "confidence": confidence,
                    "bounding_box": {
                        "x": np.random.uniform(0, 800),
                        "y": np.random.uniform(0, 600),
                        "width": np.random.uniform(50, 200),
                        "height": np.random.uniform(100, 400)
                    },
                    "keypoints": [
                        {
                            "name": "head",
                            "x": np.random.uniform(0, 800),
                            "y": np.random.uniform(0, 200),
                            "confidence": np.random.uniform(0.5, 0.95)
                        },
                        {
                            "name": "torso",
                            "x": np.random.uniform(0, 800),
                            "y": np.random.uniform(200, 400),
                            "confidence": np.random.uniform(0.5, 0.95)
                        }
                    ],
                    "activity": "standing" if np.random.random() > 0.2 else "sitting"
                })
        
        return {
            "persons": persons,
            "processing_time_ms": np.random.uniform(20, 80),
            "model_version": self.model_version,
            "confidence_threshold": self.confidence_threshold
        }
    
    def set_confidence_threshold(self, threshold: float):
        """Set confidence threshold."""
        self.confidence_threshold = threshold


class MockZoneManager:
    """Mock zone management system."""
    
    def __init__(self):
        self.zones = {
            "zone1": {"id": "zone1", "name": "Zone 1", "bounds": [0, 0, 400, 600]},
            "zone2": {"id": "zone2", "name": "Zone 2", "bounds": [400, 0, 800, 600]},
            "zone3": {"id": "zone3", "name": "Zone 3", "bounds": [0, 300, 800, 600]}
        }
    
    def assign_persons_to_zones(self, persons: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Assign detected persons to zones."""
        zone_summary = {zone_id: 0 for zone_id in self.zones.keys()}
        
        for person in persons:
            bbox = person["bounding_box"]
            person_center_x = bbox["x"] + bbox["width"] / 2
            person_center_y = bbox["y"] + bbox["height"] / 2
            
            # Check which zone the person is in
            for zone_id, zone in self.zones.items():
                x1, y1, x2, y2 = zone["bounds"]
                if x1 <= person_center_x <= x2 and y1 <= person_center_y <= y2:
                    zone_summary[zone_id] += 1
                    person["zone_id"] = zone_id
                    break
            else:
                person["zone_id"] = None
        
        return zone_summary


class TestPosePipelineIntegration:
    """Integration tests for the complete pose estimation pipeline."""
    
    @pytest.fixture
    def csi_processor(self):
        """Create CSI processor."""
        return MockCSIProcessor()
    
    @pytest.fixture
    def pose_estimator(self):
        """Create pose estimator."""
        return MockPoseEstimator()
    
    @pytest.fixture
    def zone_manager(self):
        """Create zone manager."""
        return MockZoneManager()
    
    @pytest.fixture
    def sample_csi_data(self):
        """Create sample CSI data."""
        return CSIData(
            timestamp=datetime.utcnow(),
            router_id="router_001",
            amplitude=np.random.rand(64, 32),
            phase=np.random.rand(64, 32),
            frequency=5.8e9,  # 5.8 GHz
            bandwidth=80e6,   # 80 MHz
            antenna_count=4,
            subcarrier_count=64
        )
    
    @pytest.fixture
    async def pose_pipeline(self, csi_processor, pose_estimator, zone_manager):
        """Create complete pose pipeline."""
        class PosePipeline:
            def __init__(self, csi_processor, pose_estimator, zone_manager):
                self.csi_processor = csi_processor
                self.pose_estimator = pose_estimator
                self.zone_manager = zone_manager
                self.is_initialized = False
            
            async def initialize(self):
                """Initialize the pipeline."""
                await self.csi_processor.initialize()
                await self.pose_estimator.load_model()
                self.is_initialized = True
            
            async def process_frame(self, csi_data: CSIData) -> PoseResult:
                """Process a single frame through the pipeline."""
                if not self.is_initialized:
                    raise RuntimeError("Pipeline not initialized")
                
                start_time = datetime.utcnow()
                
                # Step 1: Process CSI data
                processed_data = await self.csi_processor.process_csi_data(csi_data)
                
                # Step 2: Extract features
                features = np.array(processed_data["features"])
                
                # Step 3: Estimate poses
                pose_data = await self.pose_estimator.estimate_poses(features)
                
                # Step 4: Assign to zones
                zone_summary = self.zone_manager.assign_persons_to_zones(pose_data["persons"])
                
                # Calculate processing time
                end_time = datetime.utcnow()
                processing_time = (end_time - start_time).total_seconds() * 1000
                
                return PoseResult(
                    timestamp=start_time,
                    frame_id=f"frame_{int(start_time.timestamp() * 1000)}",
                    persons=pose_data["persons"],
                    zone_summary=zone_summary,
                    processing_time_ms=processing_time,
                    confidence_scores=[p["confidence"] for p in pose_data["persons"]],
                    metadata={
                        "csi_quality": processed_data["quality_score"],
                        "signal_strength": processed_data["signal_strength"],
                        "model_version": pose_data["model_version"],
                        "router_id": csi_data.router_id
                    }
                )
        
        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
        await pipeline.initialize()
        return pipeline
    
    @pytest.mark.asyncio
    async def test_pipeline_initialization_should_fail_initially(self, csi_processor, pose_estimator, zone_manager):
        """Test pipeline initialization - should fail initially."""
        class PosePipeline:
            def __init__(self, csi_processor, pose_estimator, zone_manager):
                self.csi_processor = csi_processor
                self.pose_estimator = pose_estimator
                self.zone_manager = zone_manager
                self.is_initialized = False
            
            async def initialize(self):
                await self.csi_processor.initialize()
                await self.pose_estimator.load_model()
                self.is_initialized = True
        
        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
        
        # Initially not initialized
        assert not pipeline.is_initialized
        assert not csi_processor.is_initialized
        assert not pose_estimator.is_loaded
        
        # Initialize pipeline
        await pipeline.initialize()
        
        # This will fail initially
        assert pipeline.is_initialized
        assert csi_processor.is_initialized
        assert pose_estimator.is_loaded
    
    @pytest.mark.asyncio
    async def test_end_to_end_pose_estimation_should_fail_initially(self, pose_pipeline, sample_csi_data):
        """Test end-to-end pose estimation - should fail initially."""
        result = await pose_pipeline.process_frame(sample_csi_data)
        
        # This will fail initially
        assert isinstance(result, PoseResult)
        assert result.timestamp is not None
        assert result.frame_id.startswith("frame_")
        assert isinstance(result.persons, list)
        assert isinstance(result.zone_summary, dict)
        assert result.processing_time_ms > 0
        assert isinstance(result.confidence_scores, list)
        assert isinstance(result.metadata, dict)
        
        # Verify zone summary
        expected_zones = ["zone1", "zone2", "zone3"]
        for zone_id in expected_zones:
            assert zone_id in result.zone_summary
            assert isinstance(result.zone_summary[zone_id], int)
            assert result.zone_summary[zone_id] >= 0
        
        # Verify metadata
        assert "csi_quality" in result.metadata
        assert "signal_strength" in result.metadata
        assert "model_version" in result.metadata
        assert "router_id" in result.metadata
        assert result.metadata["router_id"] == sample_csi_data.router_id
    
    @pytest.mark.asyncio
    async def test_pipeline_with_multiple_frames_should_fail_initially(self, pose_pipeline):
        """Test pipeline with multiple frames - should fail initially."""
        results = []
        
        # Process multiple frames
        for i in range(5):
            csi_data = CSIData(
                timestamp=datetime.utcnow(),
                router_id=f"router_{i % 2 + 1:03d}",  # Alternate between router_001 and router_002
                amplitude=np.random.rand(64, 32),
                phase=np.random.rand(64, 32),
                frequency=5.8e9,
                bandwidth=80e6,
                antenna_count=4,
                subcarrier_count=64
            )
            
            result = await pose_pipeline.process_frame(csi_data)
            results.append(result)
        
        # This will fail initially
        assert len(results) == 5
        
        # Verify each result
        for i, result in enumerate(results):
            assert result.frame_id != results[0].frame_id if i > 0 else True
            assert result.metadata["router_id"] in ["router_001", "router_002"]
            assert result.processing_time_ms > 0
    
    @pytest.mark.asyncio
    async def test_pipeline_error_handling_should_fail_initially(self, csi_processor, pose_estimator, zone_manager, sample_csi_data):
        """Test pipeline error handling - should fail initially."""
        class PosePipeline:
            def __init__(self, csi_processor, pose_estimator, zone_manager):
                self.csi_processor = csi_processor
                self.pose_estimator = pose_estimator
                self.zone_manager = zone_manager
                self.is_initialized = False
            
            async def initialize(self):
                await self.csi_processor.initialize()
                await self.pose_estimator.load_model()
                self.is_initialized = True
            
            async def process_frame(self, csi_data):
                if not self.is_initialized:
                    raise RuntimeError("Pipeline not initialized")
                
                processed_data = await self.csi_processor.process_csi_data(csi_data)
                features = np.array(processed_data["features"])
                pose_data = await self.pose_estimator.estimate_poses(features)
                
                return pose_data
        
        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
        
        # Test uninitialized pipeline
        with pytest.raises(RuntimeError, match="Pipeline not initialized"):
            await pipeline.process_frame(sample_csi_data)
        
        # Initialize pipeline
        await pipeline.initialize()
        
        # Test with disabled CSI processor
        csi_processor.set_processing_enabled(False)
        
        with pytest.raises(RuntimeError, match="Processing disabled"):
            await pipeline.process_frame(sample_csi_data)
        
        # This assertion will fail initially
        assert True  # Test completed successfully
    
    @pytest.mark.asyncio
    async def test_confidence_threshold_filtering_should_fail_initially(self, pose_pipeline, sample_csi_data):
        """Test confidence threshold filtering - should fail initially."""
        # Set high confidence threshold
        pose_pipeline.pose_estimator.set_confidence_threshold(0.9)
        
        result = await pose_pipeline.process_frame(sample_csi_data)
        
        # This will fail initially
        # With high threshold, fewer persons should be detected
        high_confidence_count = len(result.persons)
        
        # Set low confidence threshold
        pose_pipeline.pose_estimator.set_confidence_threshold(0.1)
        
        result = await pose_pipeline.process_frame(sample_csi_data)
        low_confidence_count = len(result.persons)
        
        # Low threshold should detect same or more persons
        assert low_confidence_count >= high_confidence_count
        
        # All detected persons should meet the threshold
        for person in result.persons:
            assert person["confidence"] >= 0.1


class TestPipelinePerformance:
    """Test pose pipeline performance characteristics."""
    
    @pytest.mark.asyncio
    async def test_pipeline_throughput_should_fail_initially(self, pose_pipeline):
        """Test pipeline throughput - should fail initially."""
        frame_count = 10
        start_time = datetime.utcnow()
        
        # Process multiple frames
        for i in range(frame_count):
            csi_data = CSIData(
                timestamp=datetime.utcnow(),
                router_id="router_001",
                amplitude=np.random.rand(64, 32),
                phase=np.random.rand(64, 32),
                frequency=5.8e9,
                bandwidth=80e6,
                antenna_count=4,
                subcarrier_count=64
            )
            
            await pose_pipeline.process_frame(csi_data)
        
        end_time = datetime.utcnow()
        total_time = (end_time - start_time).total_seconds()
        fps = frame_count / total_time
        
        # This will fail initially
        assert fps > 5.0  # Should process at least 5 FPS
        assert total_time < 5.0  # Should complete 10 frames in under 5 seconds
    
    @pytest.mark.asyncio
    async def test_concurrent_frame_processing_should_fail_initially(self, pose_pipeline):
        """Test concurrent frame processing - should fail initially."""
        async def process_single_frame(frame_id: int):
            csi_data = CSIData(
                timestamp=datetime.utcnow(),
                router_id=f"router_{frame_id % 3 + 1:03d}",
                amplitude=np.random.rand(64, 32),
                phase=np.random.rand(64, 32),
                frequency=5.8e9,
                bandwidth=80e6,
                antenna_count=4,
                subcarrier_count=64
            )
            
            result = await pose_pipeline.process_frame(csi_data)
            return result.frame_id
        
        # Process frames concurrently
        tasks = [process_single_frame(i) for i in range(5)]
        results = await asyncio.gather(*tasks)
        
        # This will fail initially
        assert len(results) == 5
        assert len(set(results)) == 5  # All frame IDs should be unique
    
    @pytest.mark.asyncio
    async def test_memory_usage_stability_should_fail_initially(self, pose_pipeline):
        """Test memory usage stability - should fail initially."""
        import psutil
        import os
        
        process = psutil.Process(os.getpid())
        initial_memory = process.memory_info().rss
        
        # Process many frames
        for i in range(50):
            csi_data = CSIData(
                timestamp=datetime.utcnow(),
                router_id="router_001",
                amplitude=np.random.rand(64, 32),
                phase=np.random.rand(64, 32),
                frequency=5.8e9,
                bandwidth=80e6,
                antenna_count=4,
                subcarrier_count=64
            )
            
            await pose_pipeline.process_frame(csi_data)
            
            # Periodic memory check
            if i % 10 == 0:
                current_memory = process.memory_info().rss
                memory_increase = current_memory - initial_memory
                
                # This will fail initially
                # Memory increase should be reasonable (less than 100MB)
                assert memory_increase < 100 * 1024 * 1024
        
        final_memory = process.memory_info().rss
        total_increase = final_memory - initial_memory
        
        # Total memory increase should be reasonable
        assert total_increase < 200 * 1024 * 1024  # Less than 200MB increase


class TestPipelineDataFlow:
    """Test data flow through the pipeline."""
    
    @pytest.mark.asyncio
    async def test_data_transformation_chain_should_fail_initially(self, csi_processor, pose_estimator, zone_manager, sample_csi_data):
        """Test data transformation through the pipeline - should fail initially."""
        # Step 1: CSI processing
        await csi_processor.initialize()
        processed_data = await csi_processor.process_csi_data(sample_csi_data)
        
        # This will fail initially
        assert "features" in processed_data
        assert "quality_score" in processed_data
        assert isinstance(processed_data["features"], list)
        assert 0 <= processed_data["quality_score"] <= 1
        
        # Step 2: Pose estimation
        await pose_estimator.load_model()
        features = np.array(processed_data["features"])
        pose_data = await pose_estimator.estimate_poses(features)
        
        assert "persons" in pose_data
        assert "processing_time_ms" in pose_data
        assert isinstance(pose_data["persons"], list)
        
        # Step 3: Zone assignment
        zone_summary = zone_manager.assign_persons_to_zones(pose_data["persons"])
        
        assert isinstance(zone_summary, dict)
        assert all(isinstance(count, int) for count in zone_summary.values())
        
        # Verify person zone assignments
        for person in pose_data["persons"]:
            if "zone_id" in person and person["zone_id"]:
                assert person["zone_id"] in zone_summary
    
    @pytest.mark.asyncio
    async def test_pipeline_state_consistency_should_fail_initially(self, pose_pipeline, sample_csi_data):
        """Test pipeline state consistency - should fail initially."""
        # Process the same frame multiple times
        results = []
        for _ in range(3):
            result = await pose_pipeline.process_frame(sample_csi_data)
            results.append(result)
        
        # This will fail initially
        # Results should be consistent (same input should produce similar output)
        assert len(results) == 3
        
        # All results should have the same router_id
        router_ids = [r.metadata["router_id"] for r in results]
        assert all(rid == router_ids[0] for rid in router_ids)
        
        # Processing times should be reasonable and similar
        processing_times = [r.processing_time_ms for r in results]
        assert all(10 <= pt <= 200 for pt in processing_times)  # Between 10ms and 200ms