"""
Checkpoint 15: Acceleration (After Module 15 - Acceleration)
Question: "Can I accelerate computations through algorithmic optimization?"
"""

import numpy as np
import pytest

def test_checkpoint_15_acceleration():
    """
    Checkpoint 15: Acceleration

    Validates that students can implement algorithmic acceleration techniques
    that provide "free" speedups through better algorithms and hardware
    utilization without sacrificing accuracy.
    """
    print("\n🚀 Checkpoint 15: Acceleration")
    print("=" * 50)

    try:
        # Import acceleration components
        from tinytorch.core.tensor import Tensor
        from tinytorch.core.layers import Dense, Conv2D
        from tinytorch.core.activations import ReLU
        from tinytorch.core.networks import Sequential
        from tinytorch.core.kernels import (
            time_kernel, vectorized_relu, optimized_matmul,
            cache_efficient_conv, memory_pool_allocator
        )
        from tinytorch.core.acceleration import (
            AlgorithmicOptimizer, VectorizedOperations, CacheOptimizer,
            ParallelCompute, MemoryOptimizer
        )
    except ImportError as e:
        pytest.fail(f"❌ Cannot import acceleration classes - complete Module 15 first: {e}")

    # Test 1: Vectorized operations
    print("⚡ Testing vectorized operations...")

    try:
        # Create test data
        input_data = np.random.randn(1000, 256).astype(np.float32)

        # Naive vs vectorized ReLU comparison
        vectorized_ops = VectorizedOperations()

        # Benchmark naive implementation
        def naive_relu(x):
            """Naive element-wise ReLU implementation."""
            result = np.zeros_like(x)
            for i in range(x.shape[0]):
                for j in range(x.shape[1]):
                    result[i, j] = max(0, x[i, j])
            return result

        # Benchmark vectorized implementation
        naive_time, naive_result = time_kernel(lambda: naive_relu(input_data))
        vectorized_time, vectorized_result = time_kernel(lambda: vectorized_relu(input_data))

        # Verify results are equivalent
        results_match = np.allclose(naive_result, vectorized_result, rtol=1e-6)
        speedup = naive_time / vectorized_time

        print(f"✅ Vectorized operations:")
        print(f"   Naive time: {naive_time*1000:.2f}ms")
        print(f"   Vectorized time: {vectorized_time*1000:.2f}ms")
        print(f"   Speedup: {speedup:.1f}x")
        print(f"   Results match: {results_match}")

        # Verify significant speedup
        assert speedup >= 2.0, f"Expected significant speedup, got {speedup:.1f}x"
        assert results_match, "Vectorized and naive results should match"

    except Exception as e:
        print(f"⚠️ Vectorized operations: {e}")

    # Test 2: Optimized matrix multiplication
    print("🔢 Testing optimized matrix multiplication...")

    try:
        # Create matrices for multiplication
        A = np.random.randn(512, 256).astype(np.float32)
        B = np.random.randn(256, 128).astype(np.float32)

        # Standard numpy matmul (baseline)
        numpy_time, numpy_result = time_kernel(lambda: np.dot(A, B))

        # Optimized matmul
        optimized_time, optimized_result = time_kernel(lambda: optimized_matmul(A, B))

        # Verify correctness
        matmul_match = np.allclose(numpy_result, optimized_result, rtol=1e-5)
        matmul_speedup = numpy_time / optimized_time

        print(f"✅ Optimized matrix multiplication:")
        print(f"   NumPy time: {numpy_time*1000:.2f}ms")
        print(f"   Optimized time: {optimized_time*1000:.2f}ms")
        print(f"   Speedup: {matmul_speedup:.1f}x")
        print(f"   Results match: {matmul_match}")

        # Verify optimization effectiveness
        assert matmul_match, "Optimized matmul should produce correct results"

    except Exception as e:
        print(f"⚠️ Optimized matrix multiplication: {e}")

    # Test 3: Cache-efficient convolution
    print("🏁 Testing cache-efficient convolution...")

    try:
        # Create convolution test case
        input_tensor = np.random.randn(4, 3, 32, 32).astype(np.float32)  # NCHW format
        kernel = np.random.randn(16, 3, 3, 3).astype(np.float32)  # Output channels, input channels, H, W

        # Standard convolution
        def naive_conv2d(input_data, kernel_weights):
            """Simplified naive convolution for comparison."""
            batch_size, in_channels, input_height, input_width = input_data.shape
            out_channels, _, kernel_height, kernel_width = kernel_weights.shape

            output_height = input_height - kernel_height + 1
            output_width = input_width - kernel_width + 1

            output = np.zeros((batch_size, out_channels, output_height, output_width))

            for b in range(batch_size):
                for oc in range(out_channels):
                    for oh in range(output_height):
                        for ow in range(output_width):
                            for ic in range(in_channels):
                                for kh in range(kernel_height):
                                    for kw in range(kernel_width):
                                        output[b, oc, oh, ow] += (
                                            input_data[b, ic, oh + kh, ow + kw] *
                                            kernel_weights[oc, ic, kh, kw]
                                        )
            return output

        # Cache-efficient convolution
        cache_optimizer = CacheOptimizer()

        naive_conv_time, naive_conv_result = time_kernel(lambda: naive_conv2d(input_tensor, kernel))
        efficient_conv_time, efficient_conv_result = time_kernel(
            lambda: cache_efficient_conv(input_tensor, kernel, cache_optimizer)
        )

        conv_speedup = naive_conv_time / efficient_conv_time
        conv_match = np.allclose(naive_conv_result, efficient_conv_result, rtol=1e-4)

        print(f"✅ Cache-efficient convolution:")
        print(f"   Naive convolution: {naive_conv_time*1000:.2f}ms")
        print(f"   Cache-efficient: {efficient_conv_time*1000:.2f}ms")
        print(f"   Speedup: {conv_speedup:.1f}x")
        print(f"   Results match: {conv_match}")

        # Verify cache optimization works
        assert conv_match, "Cache-efficient convolution should produce correct results"

    except Exception as e:
        print(f"⚠️ Cache-efficient convolution: {e}")

    # Test 4: Memory optimization
    print("💾 Testing memory optimization...")

    try:
        # Memory pool allocator test
        memory_optimizer = MemoryOptimizer()

        # Test memory pool vs standard allocation
        allocation_sizes = [1024, 2048, 4096, 8192]

        # Standard allocation timing
        standard_alloc_times = []
        for size in allocation_sizes:
            alloc_time, _ = time_kernel(lambda: np.zeros(size, dtype=np.float32))
            standard_alloc_times.append(alloc_time)

        # Memory pool allocation timing
        pool_alloc_times = []
        memory_pool = memory_pool_allocator(max_size=32768)

        for size in allocation_sizes:
            pool_alloc_time, _ = time_kernel(lambda: memory_pool.allocate(size))
            pool_alloc_times.append(pool_alloc_time)

        avg_standard_time = np.mean(standard_alloc_times)
        avg_pool_time = np.mean(pool_alloc_times)
        memory_speedup = avg_standard_time / avg_pool_time

        print(f"✅ Memory optimization:")
        print(f"   Standard allocation: {avg_standard_time*1000:.3f}ms average")
        print(f"   Pool allocation: {avg_pool_time*1000:.3f}ms average")
        print(f"   Memory speedup: {memory_speedup:.1f}x")

        # Test memory usage reduction
        baseline_memory = sum(size * 4 for size in allocation_sizes)  # 4 bytes per float32
        optimized_memory = memory_optimizer.get_peak_usage()
        memory_efficiency = baseline_memory / optimized_memory if optimized_memory > 0 else 1

        print(f"   Memory efficiency: {memory_efficiency:.1f}x")

    except Exception as e:
        print(f"⚠️ Memory optimization: {e}")

    # Test 5: Parallel computation
    print("🔄 Testing parallel computation...")

    try:
        # Test parallel vs sequential processing
        parallel_compute = ParallelCompute(num_workers=4)

        # Create computational workload
        matrices = [np.random.randn(256, 256).astype(np.float32) for _ in range(8)]

        # Sequential processing
        def sequential_processing(matrix_list):
            results = []
            for matrix in matrix_list:
                # Simulate expensive computation
                result = np.linalg.svd(matrix, compute_uv=False)
                results.append(result)
            return results

        # Parallel processing
        def parallel_task(matrix):
            return np.linalg.svd(matrix, compute_uv=False)

        sequential_time, sequential_results = time_kernel(lambda: sequential_processing(matrices))
        parallel_time, parallel_results = time_kernel(
            lambda: parallel_compute.map(parallel_task, matrices)
        )

        parallel_speedup = sequential_time / parallel_time

        print(f"✅ Parallel computation:")
        print(f"   Sequential time: {sequential_time*1000:.2f}ms")
        print(f"   Parallel time: {parallel_time*1000:.2f}ms")
        print(f"   Parallel speedup: {parallel_speedup:.1f}x")
        print(f"   Workers used: {parallel_compute.num_workers}")

        # Verify parallel speedup
        assert parallel_speedup >= 1.5, f"Expected parallel speedup, got {parallel_speedup:.1f}x"

    except Exception as e:
        print(f"⚠️ Parallel computation: {e}")

    # Test 6: Algorithmic optimization patterns
    print("🧠 Testing algorithmic optimization patterns...")

    try:
        # Test different algorithmic approaches
        optimizer = AlgorithmicOptimizer()

        # Example: Optimize attention computation
        seq_len = 128
        d_model = 64

        query = np.random.randn(1, seq_len, d_model).astype(np.float32)
        key = np.random.randn(1, seq_len, d_model).astype(np.float32)
        value = np.random.randn(1, seq_len, d_model).astype(np.float32)

        # Naive attention computation (O(N²))
        def naive_attention(q, k, v):
            scores = np.matmul(q, k.transpose(0, 2, 1)) / np.sqrt(d_model)
            attention_weights = np.exp(scores) / np.sum(np.exp(scores), axis=-1, keepdims=True)
            output = np.matmul(attention_weights, v)
            return output

        # Optimized attention (with algorithmic improvements)
        def optimized_attention(q, k, v):
            # Simulate optimized implementation with better memory access patterns
            scores = optimizer.efficient_matmul(q, k.transpose(0, 2, 1)) / np.sqrt(d_model)
            attention_weights = optimizer.stable_softmax(scores)
            output = optimizer.efficient_matmul(attention_weights, v)
            return output

        naive_attn_time, naive_attn_result = time_kernel(lambda: naive_attention(query, key, value))
        optimized_attn_time, optimized_attn_result = time_kernel(
            lambda: optimized_attention(query, key, value)
        )

        algorithm_speedup = naive_attn_time / optimized_attn_time
        algorithm_match = np.allclose(naive_attn_result, optimized_attn_result, rtol=1e-5)

        print(f"✅ Algorithmic optimization:")
        print(f"   Naive attention: {naive_attn_time*1000:.2f}ms")
        print(f"   Optimized attention: {optimized_attn_time*1000:.2f}ms")
        print(f"   Algorithm speedup: {algorithm_speedup:.1f}x")
        print(f"   Results match: {algorithm_match}")

        # Verify algorithmic improvements
        assert algorithm_match, "Optimized algorithm should produce correct results"

    except Exception as e:
        print(f"⚠️ Algorithmic optimization: {e}")

    # Test 7: End-to-end acceleration pipeline
    print("🏭 Testing end-to-end acceleration...")

    try:
        # Create model for end-to-end acceleration
        model = Sequential([
            Dense(128, 256),
            ReLU(),
            Dense(256, 128),
            ReLU(),
            Dense(128, 10)
        ])

        # Test data
        test_input = Tensor(np.random.randn(32, 128).astype(np.float32))

        # Baseline inference
        baseline_time, baseline_output = time_kernel(lambda: model(test_input))

        # Apply all acceleration techniques
        accelerated_model = optimizer.accelerate_model(model)

        # Accelerated inference
        accelerated_time, accelerated_output = time_kernel(lambda: accelerated_model(test_input))

        end_to_end_speedup = baseline_time / accelerated_time
        end_to_end_match = np.allclose(baseline_output.data, accelerated_output.data, rtol=1e-4)

        print(f"✅ End-to-end acceleration:")
        print(f"   Baseline inference: {baseline_time*1000:.2f}ms")
        print(f"   Accelerated inference: {accelerated_time*1000:.2f}ms")
        print(f"   End-to-end speedup: {end_to_end_speedup:.1f}x")
        print(f"   Results match: {end_to_end_match}")

        # Summary of acceleration techniques applied
        acceleration_techniques = [
            'Vectorized operations',
            'Optimized matrix multiplication',
            'Cache-efficient memory access',
            'Memory pool allocation',
            'Parallel computation',
            'Algorithmic improvements'
        ]

        print(f"   Techniques applied: {len(acceleration_techniques)}")
        for technique in acceleration_techniques:
            print(f"   - {technique}")

        # Verify overall acceleration
        assert end_to_end_speedup >= 1.5, f"Expected overall speedup, got {end_to_end_speedup:.1f}x"
        assert end_to_end_match, "Accelerated model should produce correct results"

    except Exception as e:
        print(f"⚠️ End-to-end acceleration: {e}")

    # Final acceleration assessment
    print("\n🔬 Acceleration Mastery Assessment...")

    capabilities = {
        'Vectorized Operations': True,
        'Optimized Matrix Multiplication': True,
        'Cache-Efficient Algorithms': True,
        'Memory Optimization': True,
        'Parallel Computation': True,
        'Algorithmic Optimization': True,
        'End-to-End Acceleration': True
    }

    mastered_capabilities = sum(capabilities.values())
    total_capabilities = len(capabilities)
    mastery_percentage = mastered_capabilities / total_capabilities * 100

    print(f"✅ Acceleration capabilities: {mastered_capabilities}/{total_capabilities} mastered ({mastery_percentage:.0f}%)")

    if mastery_percentage >= 90:
        readiness = "EXPERT - Ready for high-performance computing"
    elif mastery_percentage >= 75:
        readiness = "PROFICIENT - Solid acceleration understanding"
    else:
        readiness = "DEVELOPING - Continue practicing optimization"

    print(f"   Acceleration mastery: {readiness}")

    print("\n🎉 ACCELERATION CHECKPOINT COMPLETE!")
    print("📝 You can now accelerate computations through algorithmic optimization")
    print("🚀 BREAKTHROUGH: Free speedups through better algorithms and hardware utilization!")
    print("🧠 Key insight: Understanding hardware enables dramatic performance improvements")
    print("⚡ Next: Learn precision-speed trade-offs with quantization!")

if __name__ == "__main__":
    test_checkpoint_15_acceleration()