import os
import json
import torch
import triton
from pathlib import Path
from huggingface_hub import hf_hub_download
from typing import Dict, Any, List

def fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_w, bn_b, eps):
    # Fuse Conv and BN weights mathematically
    if conv_b is None:
        conv_b = torch.zeros_like(bn_rm)
    bn_var_rsqrt = torch.rsqrt(bn_rv + eps)
    fused_w = conv_w * (bn_w * bn_var_rsqrt).reshape(-1, 1, 1, 1)
    fused_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
    return fused_w, fused_b

class PretrainedModelHub:
    def __init__(self, framework="neural"):
        self.model_db = {
            "vision": {
                "resnet50": {
                    "hf_repo": "pytorch/vision",
                    "filename": "resnet50.pth",
                    "converter": self._convert_torch_weights,
                    "optimized_kernels": True
                },
                "efficientnet-b4": {
                    "hf_repo": "custom/repo",
                    "filename": "efficientnet-b4.neural",
                    "custom_kernel": "fused_conv2d_bn",
                    "quantized": True
                }
            },
            "nlp": {
                "bert-base": {
                    "hf_repo": "google-bert/bert-base-uncased",
                    "filename": "bert-base.pth",
                    "sparse_attention": True,
                    "dynamic_pruning": True
                }
            }
        }
        
    def load(self, model_name: str, pretrained: bool = True) -> Any:
        config = None
        for category in self.model_db.values():
            if model_name in category:
                config = category[model_name]
                break
        if not config:
            raise ValueError(f"Model {model_name} not found in hub")
            
        if pretrained:
            weights_path = hf_hub_download(
                repo_id=config["hf_repo"],
                filename=config["filename"]
            )
            # Assume NeuralModel is a placeholder; replace with actual model loading
            return torch.load(weights_path)
        
        return self._create_architecture(model_name)

    def _convert_torch_weights(self, model: torch.nn.Module) -> Dict:
        converted = {}
        # Iterate through modules to find Conv2D followed by BatchNorm
        for name, module in model.named_modules():
            if isinstance(module, torch.nn.Conv2d):
                # Check if next module is BatchNorm
                parts = name.split('.')
                parent = model
                for part in parts[:-1]:
                    parent = getattr(parent, part)
                idx = int(parts[-1])
                if idx + 1 < len(parent):
                    next_module = parent[idx + 1]
                    if isinstance(next_module, torch.nn.BatchNorm2d):
                        fused_w, fused_b = fuse_conv_bn_weights(
                            module.weight, module.bias,
                            next_module.running_mean, next_module.running_var,
                            next_module.weight, next_module.bias, next_module.eps
                        )
                        converted[f"{name}.weight"] = fused_w
                        converted[f"{name}.bias"] = fused_b
                        # Skip the BN layer
                        continue
                # If no BN, add original weights
                converted[f"{name}.weight"] = module.weight.detach()
                if module.bias is not None:
                    converted[f"{name}.bias"] = module.bias.detach()
        return converted

    def _create_architecture(self, model_name: str) -> torch.nn.Module:
        # Simplified architecture creation
        return torch.nn.Module()

class FusedConvBNLayer(torch.nn.Module):
    def __init__(self, layer_config: Dict):
        super().__init__()
        # Implementation for fused layer

class TritonConv2D(torch.autograd.Function):
    @staticmethod
    @triton.jit
    def forward(ctx, input, weight, bias, stride, padding, dilation):
        # Custom Triton kernel for Conv2D
        output = triton.ops.conv2d(
            input, weight, bias,
            stride, padding, dilation
        )
        ctx.save_for_backward(input, weight, bias)
        return output
    
    @staticmethod
    @triton.jit  
    def backward(ctx, grad_output):
        # Optimized backward pass
        input, weight, bias = ctx.saved_tensors
        grad_input, grad_weight, grad_bias = triton.ops.conv2d_backward(
            grad_output, input, weight,
            ctx.stride, ctx.padding, ctx.dilation
        )
        return grad_input, grad_weight, grad_bias

class OptimizedModel:
    def __init__(self, model_config: Dict):
        self.layers = self._compile_layers(model_config)
        
    def _compile_layers(self, config: Dict) -> list:
        return [self._create_optimized_layer(l) for l in config['layers']]
    
    def _create_optimized_layer(self, layer: Dict) -> torch.nn.Module:
        if layer['type'] == 'Conv2D':
            if layer.get('fused_conv_bn'):
                return FusedConvBNLayer(layer)
            return TritonConv2D(layer)
        return getattr(torch.nn, layer['type'])(**layer['params'])


class ModelOptimizer:
    def __init__(self, model):
        self.model = model
        self.optimizations = {
            'kernel_fusion': True,
            'mixed_precision': True,
            'sparse_format': 'blocked'
        }
        
    def apply(self):
        if self.optimizations['kernel_fusion']:
            self._fuse_conv_bn()
            
        if self.optimizations['mixed_precision']:
            self._convert_to_mixed_precision()
            
        return self._compile_model()

    def _fuse_conv_bn(self):
        # Automatic Conv-BN fusion
        for i, layer in enumerate(self.model.layers):
            if isinstance(layer, Conv2D) and isinstance(self.model.layers[i+1], BatchNorm):
                fused_layer = FusedConvBN(layer, self.model.layers[i+1])
                self.model.layers[i] = fused_layer
                del self.model.layers[i+1]

    def _convert_to_mixed_precision(self):
        # Automatic precision conversion
        for layer in self.model.layers:
            if hasattr(layer, 'weight'):
                layer.weight = layer.weight.half()
            if hasattr(layer, 'bias'):
                layer.bias = layer.bias.half()

    def _compile_model(self):
        # JIT compile model graph
        return torch.jit.script(self.model)