"""
Model Analysis Engine

Advanced analysis engine for PyTorch models to identify optimization opportunities:
- Architecture pattern detection and analysis
- Parameter and memory footprint analysis
- Operation-level optimization detection
- Compilation readiness assessment
"""

import inspect
from collections import defaultdict
from dataclasses import dataclass
from typing import Any

import torch.nn as nn


@dataclass
class ModelAnalysisResult:
    """Result of comprehensive model analysis."""
    model_info: dict[str, Any]
    performance_bottlenecks: list[str]
    optimization_opportunities: list[Any]  # Will be OptimizationRecommendation
    current_optimizations: list[str]
    overall_optimization_score: float
    improvement_potential: str


class CodeAnalyzer:
    """
    Analyzes PyTorch code to identify optimization opportunities.

    Performs static analysis of model architecture, forward pass implementation,
    and training loops to detect optimization patterns.
    """

    def __init__(self):
        self.optimization_patterns = self._load_optimization_patterns()

    def analyze_model(self, model: nn.Module) -> dict[str, Any]:
        """
        Comprehensive model analysis for optimization opportunities.

        Args:
            model: PyTorch model to analyze

        Returns:
            Dictionary with analysis results
        """
        analysis = {
            'architecture': self._analyze_architecture(model),
            'parameters': self._analyze_parameters(model),
            'operations': self._analyze_operations(model),
            'memory_patterns': self._analyze_memory_patterns(model),
            'compilation_readiness': self._analyze_compilation_readiness(model)
        }

        return analysis

    def _analyze_architecture(self, model: nn.Module) -> dict[str, Any]:
        """Analyze model architecture for optimization patterns."""
        module_types = defaultdict(int)
        fusion_opportunities = []
        sequential_patterns = []

        # Count module types
        for name, module in model.named_modules():
            module_type = type(module).__name__
            module_types[module_type] += 1

            # Detect fusion opportunities
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                # Look for activation functions after linear/conv layers
                fusion_opportunities.append({
                    'location': name,
                    'type': 'linear_activation_fusion',
                    'description': 'Linear layer followed by activation function'
                })

        # Detect sequential patterns
        for name, module in model.named_modules():
            if isinstance(module, nn.Sequential):
                seq_pattern = self._analyze_sequential_pattern(module)
                if seq_pattern:
                    sequential_patterns.append({
                        'location': name,
                        'pattern': seq_pattern
                    })

        return {
            'module_counts': dict(module_types),
            'total_modules': len(list(model.modules())),
            'fusion_opportunities': fusion_opportunities,
            'sequential_patterns': sequential_patterns
        }

    def _analyze_parameters(self, model: nn.Module) -> dict[str, Any]:
        """Analyze model parameters for optimization insights."""
        total_params = sum(p.numel() for p in model.parameters())
        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

        # Analyze parameter shapes for tensor core compatibility
        tensor_core_compatible = 0
        tensor_core_total = 0

        for module in model.modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                tensor_core_total += 1
                if self._is_tensor_core_compatible(module):
                    tensor_core_compatible += 1

        return {
            'total_parameters': total_params,
            'trainable_parameters': trainable_params,
            'model_size_mb': total_params * 4 / 1024**2,  # Assuming float32
            'tensor_core_compatibility': tensor_core_compatible / tensor_core_total if tensor_core_total > 0 else 0,
            'memory_footprint': self._estimate_memory_footprint(model)
        }

    def _analyze_operations(self, model: nn.Module) -> dict[str, Any]:
        """Analyze operations for optimization patterns."""
        operations = []
        activation_functions = []
        normalization_layers = []

        for name, module in model.named_modules():
            if isinstance(module, (nn.ReLU, nn.GELU, nn.SiLU, nn.Tanh)):
                activation_functions.append({
                    'name': name,
                    'type': type(module).__name__,
                    'inplace': getattr(module, 'inplace', False)
                })

            if isinstance(module, (nn.LayerNorm, nn.BatchNorm1d, nn.BatchNorm2d)):
                normalization_layers.append({
                    'name': name,
                    'type': type(module).__name__,
                    'normalized_shape': getattr(module, 'normalized_shape', None)
                })

        return {
            'activation_functions': activation_functions,
            'normalization_layers': normalization_layers,
            'operations_count': len(operations)
        }

    def _analyze_memory_patterns(self, model: nn.Module) -> dict[str, Any]:
        """Analyze memory usage patterns."""
        # This is a simplified analysis - real implementation would be more sophisticated
        estimated_forward_memory = 0
        estimated_backward_memory = 0

        for module in model.modules():
            if isinstance(module, nn.Linear):
                # Estimate memory for linear layer
                input_size = module.in_features
                output_size = module.out_features
                estimated_forward_memory += (input_size + output_size) * 4  # bytes
                estimated_backward_memory += estimated_forward_memory * 2  # Rough estimate

        return {
            'estimated_forward_memory_mb': estimated_forward_memory / 1024**2,
            'estimated_backward_memory_mb': estimated_backward_memory / 1024**2,
            'memory_efficiency_score': min(1.0, 100 / (estimated_forward_memory / 1024**2 + 1))
        }

    def _analyze_compilation_readiness(self, model: nn.Module) -> dict[str, Any]:
        """Analyze model readiness for torch.compile."""
        compilation_issues = []
        compatibility_score = 1.0

        # Check for common compilation issues
        for name, module in model.named_modules():
            if hasattr(module, 'forward'):
                # Check for dynamic control flow
                try:
                    source = inspect.getsource(module.forward) if hasattr(module, 'forward') else ""
                    if 'if ' in source and 'training' not in source:
                        compilation_issues.append({
                            'module': name,
                            'issue': 'dynamic_control_flow',
                            'description': 'Dynamic control flow may prevent optimization'
                        })
                        compatibility_score *= 0.8
                except (OSError, TypeError):
                    # Handle cases where source is not available
                    pass

        return {
            'compilation_compatibility_score': compatibility_score,
            'compilation_issues': compilation_issues,
            'recommended_compile_mode': 'default' if compatibility_score > 0.8 else 'reduce-overhead'
        }

    def _analyze_sequential_pattern(self, sequential: nn.Sequential) -> str | None:
        """Analyze sequential module for common patterns."""
        modules = list(sequential.children())
        if len(modules) < 2:
            return None

        # Detect common patterns
        pattern_names = [type(m).__name__ for m in modules]

        if pattern_names == ['Linear', 'GELU']:
            return 'linear_gelu'
        elif pattern_names == ['Linear', 'ReLU']:
            return 'linear_relu'
        elif pattern_names == ['LayerNorm', 'Linear']:
            return 'norm_linear'
        elif len(pattern_names) >= 3 and pattern_names[:3] == ['Linear', 'GELU', 'Linear']:
            return 'ffn_block'

        return None

    def _is_tensor_core_compatible(self, module: nn.Module) -> bool:
        """Check if module is compatible with Tensor Cores."""
        if isinstance(module, nn.Linear):
            # Tensor Cores require dimensions to be multiples of 8 (simplified)
            return (module.in_features % 8 == 0 and
                   module.out_features % 8 == 0)
        elif isinstance(module, nn.Conv2d):
            return (module.in_channels % 8 == 0 and
                   module.out_channels % 8 == 0)
        return False

    def _estimate_memory_footprint(self, model: nn.Module) -> dict[str, float]:
        """Estimate memory footprint of the model."""
        param_memory = sum(p.numel() * 4 for p in model.parameters()) / 1024**2  # MB
        buffer_memory = sum(b.numel() * 4 for b in model.buffers()) / 1024**2  # MB

        return {
            'parameters_mb': param_memory,
            'buffers_mb': buffer_memory,
            'total_mb': param_memory + buffer_memory
        }

    def _load_optimization_patterns(self) -> dict[str, Any]:
        """Load common optimization patterns."""
        return {
            'linear_activation_fusion': {
                'description': 'Fuse linear layer with activation function',
                'benefit': 'Reduces memory access and kernel launches',
                'difficulty': 'easy'
            },
            'attention_optimization': {
                'description': 'Use optimized attention implementations',
                'benefit': 'Significant memory and speed improvements',
                'difficulty': 'medium'
            },
            'normalization_optimization': {
                'description': 'Use optimized normalization layers',
                'benefit': 'Improved numerical stability and speed',
                'difficulty': 'easy'
            }
        }


class PerformanceBottleneckDetector:
    """
    Detects performance bottlenecks in PyTorch models.
    """

    def __init__(self):
        self.bottleneck_patterns = self._load_bottleneck_patterns()

    def detect_bottlenecks(self, model: nn.Module, analysis: dict[str, Any]) -> list[str]:
        """
        Detect performance bottlenecks based on model analysis.

        Args:
            model: PyTorch model
            analysis: Model analysis results

        Returns:
            List of detected bottlenecks
        """
        bottlenecks = []

        # Check for large parameter count
        if analysis['parameters']['total_parameters'] > 1_000_000_000:  # 1B params
            bottlenecks.append("Large model size may cause memory bottlenecks")

        # Check tensor core compatibility
        if analysis['parameters']['tensor_core_compatibility'] < 0.5:
            bottlenecks.append("Low Tensor Core compatibility reduces throughput")

        # Check for inefficient activation functions
        activations = analysis['operations']['activation_functions']
        if any(act['type'] in ['Tanh', 'Sigmoid'] for act in activations):
            bottlenecks.append("Inefficient activation functions detected")

        # Check compilation readiness
        if analysis['compilation_readiness']['compilation_compatibility_score'] < 0.7:
            bottlenecks.append("Model not optimized for torch.compile")

        return bottlenecks

    def _load_bottleneck_patterns(self) -> dict[str, Any]:
        """Load known bottleneck patterns."""
        return {
            'large_model': {
                'threshold': 1_000_000_000,
                'description': 'Model has over 1B parameters'
            },
            'low_tensor_core': {
                'threshold': 0.5,
                'description': 'Less than 50% Tensor Core compatibility'
            }
        }
