"""
Performance tests for ML model inference speed.

Tests pose estimation model performance, throughput, and optimization.
"""

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


class MockPoseModel:
    """Mock pose estimation model for performance testing."""
    
    def __init__(self, model_complexity: str = "standard"):
        self.model_complexity = model_complexity
        self.is_loaded = False
        self.inference_count = 0
        self.total_inference_time = 0.0
        self.batch_size = 1
        
        # Model complexity affects inference time
        self.base_inference_time = {
            "lightweight": 0.02,  # 20ms
            "standard": 0.05,     # 50ms
            "high_accuracy": 0.15  # 150ms
        }.get(model_complexity, 0.05)
    
    async def load_model(self):
        """Load the model."""
        # Simulate model loading time
        load_time = {
            "lightweight": 0.5,
            "standard": 2.0,
            "high_accuracy": 5.0
        }.get(self.model_complexity, 2.0)
        
        await asyncio.sleep(load_time)
        self.is_loaded = True
    
    async def predict(self, features: np.ndarray) -> Dict[str, Any]:
        """Run inference on features."""
        if not self.is_loaded:
            raise RuntimeError("Model not loaded")
        
        start_time = time.time()
        
        # Simulate inference computation
        batch_size = features.shape[0] if len(features.shape) > 2 else 1
        inference_time = self.base_inference_time * batch_size
        
        # Add some variance
        inference_time *= np.random.uniform(0.8, 1.2)
        
        await asyncio.sleep(inference_time)
        
        end_time = time.time()
        actual_inference_time = end_time - start_time
        
        self.inference_count += batch_size
        self.total_inference_time += actual_inference_time
        
        # Generate mock predictions
        predictions = []
        for i in range(batch_size):
            predictions.append({
                "person_id": f"person_{i}",
                "confidence": np.random.uniform(0.5, 0.95),
                "keypoints": np.random.rand(17, 3).tolist(),  # 17 keypoints with x,y,confidence
                "bounding_box": {
                    "x": np.random.uniform(0, 640),
                    "y": np.random.uniform(0, 480),
                    "width": np.random.uniform(50, 200),
                    "height": np.random.uniform(100, 300)
                }
            })
        
        return {
            "predictions": predictions,
            "inference_time_ms": actual_inference_time * 1000,
            "model_complexity": self.model_complexity,
            "batch_size": batch_size
        }
    
    def get_performance_stats(self) -> Dict[str, Any]:
        """Get performance statistics."""
        avg_inference_time = (
            self.total_inference_time / self.inference_count 
            if self.inference_count > 0 else 0
        )
        
        return {
            "total_inferences": self.inference_count,
            "total_time_seconds": self.total_inference_time,
            "average_inference_time_ms": avg_inference_time * 1000,
            "throughput_fps": 1.0 / avg_inference_time if avg_inference_time > 0 else 0,
            "model_complexity": self.model_complexity
        }


class TestInferenceSpeed:
    """Test inference speed for different model configurations."""
    
    @pytest.fixture
    def lightweight_model(self):
        """Create lightweight model."""
        return MockPoseModel("lightweight")
    
    @pytest.fixture
    def standard_model(self):
        """Create standard model."""
        return MockPoseModel("standard")
    
    @pytest.fixture
    def high_accuracy_model(self):
        """Create high accuracy model."""
        return MockPoseModel("high_accuracy")
    
    @pytest.fixture
    def sample_features(self):
        """Create sample feature data."""
        return np.random.rand(64, 32)  # 64x32 feature matrix
    
    @pytest.mark.asyncio
    async def test_single_inference_speed_should_fail_initially(self, standard_model, sample_features):
        """Test single inference speed - should fail initially."""
        await standard_model.load_model()
        
        start_time = time.time()
        result = await standard_model.predict(sample_features)
        end_time = time.time()
        
        inference_time = (end_time - start_time) * 1000  # Convert to ms
        
        # This will fail initially
        assert inference_time < 100  # Should be less than 100ms
        assert result["inference_time_ms"] > 0
        assert len(result["predictions"]) > 0
        assert result["model_complexity"] == "standard"
    
    @pytest.mark.asyncio
    async def test_model_complexity_comparison_should_fail_initially(self, sample_features):
        """Test model complexity comparison - should fail initially."""
        models = {
            "lightweight": MockPoseModel("lightweight"),
            "standard": MockPoseModel("standard"),
            "high_accuracy": MockPoseModel("high_accuracy")
        }
        
        # Load all models
        for model in models.values():
            await model.load_model()
        
        # Run inference on each model
        results = {}
        for name, model in models.items():
            start_time = time.time()
            result = await model.predict(sample_features)
            end_time = time.time()
            
            results[name] = {
                "inference_time_ms": (end_time - start_time) * 1000,
                "result": result
            }
        
        # This will fail initially
        # Lightweight should be fastest
        assert results["lightweight"]["inference_time_ms"] < results["standard"]["inference_time_ms"]
        assert results["standard"]["inference_time_ms"] < results["high_accuracy"]["inference_time_ms"]
        
        # All should complete within reasonable time
        for name, result in results.items():
            assert result["inference_time_ms"] < 500  # Less than 500ms
    
    @pytest.mark.asyncio
    async def test_batch_inference_performance_should_fail_initially(self, standard_model):
        """Test batch inference performance - should fail initially."""
        await standard_model.load_model()
        
        # Test different batch sizes
        batch_sizes = [1, 4, 8, 16]
        results = {}
        
        for batch_size in batch_sizes:
            # Create batch of features
            batch_features = np.random.rand(batch_size, 64, 32)
            
            start_time = time.time()
            result = await standard_model.predict(batch_features)
            end_time = time.time()
            
            total_time = (end_time - start_time) * 1000
            per_sample_time = total_time / batch_size
            
            results[batch_size] = {
                "total_time_ms": total_time,
                "per_sample_time_ms": per_sample_time,
                "throughput_fps": 1000 / per_sample_time,
                "predictions": len(result["predictions"])
            }
        
        # This will fail initially
        # Batch processing should be more efficient per sample
        assert results[1]["per_sample_time_ms"] > results[4]["per_sample_time_ms"]
        assert results[4]["per_sample_time_ms"] > results[8]["per_sample_time_ms"]
        
        # Verify correct number of predictions
        for batch_size, result in results.items():
            assert result["predictions"] == batch_size
    
    @pytest.mark.asyncio
    async def test_sustained_inference_performance_should_fail_initially(self, standard_model, sample_features):
        """Test sustained inference performance - should fail initially."""
        await standard_model.load_model()
        
        # Run many inferences to test sustained performance
        num_inferences = 50
        inference_times = []
        
        for i in range(num_inferences):
            start_time = time.time()
            await standard_model.predict(sample_features)
            end_time = time.time()
            
            inference_times.append((end_time - start_time) * 1000)
        
        # This will fail initially
        # Calculate performance metrics
        avg_time = np.mean(inference_times)
        std_time = np.std(inference_times)
        min_time = np.min(inference_times)
        max_time = np.max(inference_times)
        
        assert avg_time < 100  # Average should be less than 100ms
        assert std_time < 20   # Standard deviation should be low (consistent performance)
        assert max_time < avg_time * 2  # No inference should take more than 2x average
        
        # Check model statistics
        stats = standard_model.get_performance_stats()
        assert stats["total_inferences"] == num_inferences
        assert stats["throughput_fps"] > 10  # Should achieve at least 10 FPS


class TestInferenceOptimization:
    """Test inference optimization techniques."""
    
    @pytest.mark.asyncio
    async def test_model_warmup_effect_should_fail_initially(self, standard_model, sample_features):
        """Test model warmup effect - should fail initially."""
        await standard_model.load_model()
        
        # First inference (cold start)
        start_time = time.time()
        await standard_model.predict(sample_features)
        cold_start_time = (time.time() - start_time) * 1000
        
        # Subsequent inferences (warmed up)
        warm_times = []
        for _ in range(5):
            start_time = time.time()
            await standard_model.predict(sample_features)
            warm_times.append((time.time() - start_time) * 1000)
        
        avg_warm_time = np.mean(warm_times)
        
        # This will fail initially
        # Warm inferences should be faster than cold start
        assert avg_warm_time <= cold_start_time
        assert cold_start_time > 0
        assert avg_warm_time > 0
    
    @pytest.mark.asyncio
    async def test_concurrent_inference_performance_should_fail_initially(self, sample_features):
        """Test concurrent inference performance - should fail initially."""
        # Create multiple model instances
        models = [MockPoseModel("standard") for _ in range(3)]
        
        # Load all models
        for model in models:
            await model.load_model()
        
        async def run_inference(model, features):
            start_time = time.time()
            result = await model.predict(features)
            end_time = time.time()
            return (end_time - start_time) * 1000
        
        # Run concurrent inferences
        tasks = [run_inference(model, sample_features) for model in models]
        inference_times = await asyncio.gather(*tasks)
        
        # This will fail initially
        # All inferences should complete
        assert len(inference_times) == 3
        assert all(time > 0 for time in inference_times)
        
        # Concurrent execution shouldn't be much slower than sequential
        avg_concurrent_time = np.mean(inference_times)
        assert avg_concurrent_time < 200  # Should complete within 200ms each
    
    @pytest.mark.asyncio
    async def test_memory_usage_during_inference_should_fail_initially(self, standard_model, sample_features):
        """Test memory usage during inference - should fail initially."""
        process = psutil.Process(os.getpid())
        
        await standard_model.load_model()
        initial_memory = process.memory_info().rss
        
        # Run multiple inferences
        for i in range(20):
            await standard_model.predict(sample_features)
            
            # Check memory every 5 inferences
            if i % 5 == 0:
                current_memory = process.memory_info().rss
                memory_increase = current_memory - initial_memory
                
                # This will fail initially
                # Memory increase should be reasonable (less than 50MB)
                assert memory_increase < 50 * 1024 * 1024
        
        final_memory = process.memory_info().rss
        total_increase = final_memory - initial_memory
        
        # Total memory increase should be reasonable
        assert total_increase < 100 * 1024 * 1024  # Less than 100MB


class TestInferenceAccuracy:
    """Test inference accuracy and quality metrics."""
    
    @pytest.mark.asyncio
    async def test_prediction_consistency_should_fail_initially(self, standard_model, sample_features):
        """Test prediction consistency - should fail initially."""
        await standard_model.load_model()
        
        # Run same inference multiple times
        results = []
        for _ in range(5):
            result = await standard_model.predict(sample_features)
            results.append(result)
        
        # This will fail initially
        # All results should have similar structure
        for result in results:
            assert "predictions" in result
            assert "inference_time_ms" in result
            assert len(result["predictions"]) > 0
        
        # Inference times should be consistent
        inference_times = [r["inference_time_ms"] for r in results]
        avg_time = np.mean(inference_times)
        std_time = np.std(inference_times)
        
        assert std_time < avg_time * 0.5  # Standard deviation should be less than 50% of mean
    
    @pytest.mark.asyncio
    async def test_confidence_score_distribution_should_fail_initially(self, standard_model, sample_features):
        """Test confidence score distribution - should fail initially."""
        await standard_model.load_model()
        
        # Collect confidence scores from multiple inferences
        all_confidences = []
        
        for _ in range(20):
            result = await standard_model.predict(sample_features)
            for prediction in result["predictions"]:
                all_confidences.append(prediction["confidence"])
        
        # This will fail initially
        if all_confidences:  # Only test if we have predictions
            # Confidence scores should be in valid range
            assert all(0.0 <= conf <= 1.0 for conf in all_confidences)
            
            # Should have reasonable distribution
            avg_confidence = np.mean(all_confidences)
            assert 0.3 <= avg_confidence <= 0.95  # Reasonable average confidence
    
    @pytest.mark.asyncio
    async def test_keypoint_detection_quality_should_fail_initially(self, standard_model, sample_features):
        """Test keypoint detection quality - should fail initially."""
        await standard_model.load_model()
        
        result = await standard_model.predict(sample_features)
        
        # This will fail initially
        for prediction in result["predictions"]:
            keypoints = prediction["keypoints"]
            
            # Should have correct number of keypoints
            assert len(keypoints) == 17  # Standard pose has 17 keypoints
            
            # Each keypoint should have x, y, confidence
            for keypoint in keypoints:
                assert len(keypoint) == 3
                x, y, conf = keypoint
                assert isinstance(x, (int, float))
                assert isinstance(y, (int, float))
                assert 0.0 <= conf <= 1.0


class TestInferenceScaling:
    """Test inference scaling characteristics."""
    
    @pytest.mark.asyncio
    async def test_input_size_scaling_should_fail_initially(self, standard_model):
        """Test inference scaling with input size - should fail initially."""
        await standard_model.load_model()
        
        # Test different input sizes
        input_sizes = [(32, 16), (64, 32), (128, 64), (256, 128)]
        results = {}
        
        for height, width in input_sizes:
            features = np.random.rand(height, width)
            
            start_time = time.time()
            result = await standard_model.predict(features)
            end_time = time.time()
            
            inference_time = (end_time - start_time) * 1000
            input_size = height * width
            
            results[input_size] = {
                "inference_time_ms": inference_time,
                "dimensions": (height, width),
                "predictions": len(result["predictions"])
            }
        
        # This will fail initially
        # Larger inputs should generally take longer
        sizes = sorted(results.keys())
        for i in range(len(sizes) - 1):
            current_size = sizes[i]
            next_size = sizes[i + 1]
            
            # Allow some variance, but larger inputs should generally be slower
            time_ratio = results[next_size]["inference_time_ms"] / results[current_size]["inference_time_ms"]
            assert time_ratio >= 0.8  # Next size shouldn't be much faster
    
    @pytest.mark.asyncio
    async def test_throughput_under_load_should_fail_initially(self, standard_model, sample_features):
        """Test throughput under sustained load - should fail initially."""
        await standard_model.load_model()
        
        # Simulate sustained load
        duration_seconds = 5
        start_time = time.time()
        inference_count = 0
        
        while time.time() - start_time < duration_seconds:
            await standard_model.predict(sample_features)
            inference_count += 1
        
        actual_duration = time.time() - start_time
        throughput = inference_count / actual_duration
        
        # This will fail initially
        # Should maintain reasonable throughput under load
        assert throughput > 5  # At least 5 FPS
        assert inference_count > 20  # Should complete at least 20 inferences in 5 seconds
        
        # Check model statistics
        stats = standard_model.get_performance_stats()
        assert stats["total_inferences"] >= inference_count
        assert stats["throughput_fps"] > 0


@pytest.mark.benchmark
class TestInferenceBenchmarks:
    """Benchmark tests for inference performance."""
    
    @pytest.mark.asyncio
    async def test_benchmark_lightweight_model_should_fail_initially(self, benchmark):
        """Benchmark lightweight model performance - should fail initially."""
        model = MockPoseModel("lightweight")
        await model.load_model()
        features = np.random.rand(64, 32)
        
        async def run_inference():
            return await model.predict(features)
        
        # This will fail initially
        # Benchmark the inference
        result = await run_inference()
        assert result["inference_time_ms"] < 50  # Should be less than 50ms
    
    @pytest.mark.asyncio
    async def test_benchmark_batch_processing_should_fail_initially(self, benchmark):
        """Benchmark batch processing performance - should fail initially."""
        model = MockPoseModel("standard")
        await model.load_model()
        batch_features = np.random.rand(8, 64, 32)  # Batch of 8
        
        async def run_batch_inference():
            return await model.predict(batch_features)
        
        # This will fail initially
        result = await run_batch_inference()
        assert len(result["predictions"]) == 8
        assert result["inference_time_ms"] < 200  # Batch should be efficient