import re

class IonicCompiler:
    def __init__(self):
        self.code = ""
    
    def parse(self, source: str):
        # Very crude parser – just extracts model architecture and training loop
        model_match = re.search(r"model\s*\{([^}]+)\}", source, re.DOTALL)
        train_match = re.search(r"training\s*\{([^}]+)\}", source, re.DOTALL)
        if model_match:
            self.model_spec = model_match.group(1)
        if train_match:
            self.train_spec = train_match.group(1)
    
    def compile(self, source: str) -> str:
        self.parse(source)
        # Generate Python code using the IonicMLSimulator
        py_code = f"""
import torch
import torch.nn as nn
import torch.optim as optim
from ionic_ml_simulator import IonicMLSimulator

# Build model
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        {self._model_to_pytorch()}
    def forward(self, x):
        {self._forward_pass()}

model = MyModel()
sim = IonicMLSimulator()
loss_fn = nn.CrossEntropyLoss()

# Training loop
for epoch in range({self._get_epochs()}):
    for batch in dataloader:
        loss = sim.train_step(batch, model, loss_fn)
        print(f"Epoch {{epoch}}, loss {{loss:.4f}}")
"""
        return py_code
    
    def _model_to_pytorch(self):
        # Convert spec like "layer1: Dense(784,256,relu)" to PyTorch
        lines = []
        for line in self.model_spec.split('\n'):
            if 'Dense' in line:
                parts = re.findall(r'Dense\((\d+),(\d+),(\w+)\)', line)
                if parts:
                    in_dim, out_dim, act = parts[0]
                    lines.append(f'self.fc1 = nn.Linear({in_dim}, {out_dim})')
                    if act == 'relu':
                        lines.append(f'self.relu = nn.ReLU()')
        return '\n        '.join(lines)
    
    def _forward_pass(self):
        return """
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu(x)
        return x
"""
    
    def _get_epochs(self):
        match = re.search(r"repeat\s+(\d+)\s+epochs", self.train_spec)
        return match.group(1) if match else "10"

# Usage
compiler = IonicCompiler()
source = """
model {
    layer1: Dense(784,256,relu)
    layer2: Dense(256,10,softmax)
}
training {
    repeat 100 epochs
}
"""
python_code = compiler.compile(source)
exec(python_code)