import jax
import jax.numpy as jnp
from flax import linen as nn
import optax
from typing import List, Dict, Any, Tuple
from functools import partial
import torch

class NeuroscienceModel:
    def __init__(self, input_size: int, hidden_size: int, output_size: int, model_type: str = 'SNN'):
        self.model_parameters = {}
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.model_type = model_type
        self.model = self._create_model()

    def _create_model(self):
        if self.model_type == 'SNN':
            return SpikingNeuralNetwork(self.input_size, self.hidden_size, self.output_size)
        elif self.model_type == 'LSTM':
            return LSTMNetwork(self.input_size, self.hidden_size, self.output_size)
        elif self.model_type == 'TCN':
            return TemporalConvNet(self.input_size, self.hidden_size, self.output_size)
        else:
            raise ValueError(f"Unsupported model type: {self.model_type}")

    def set_parameters(self, parameters: Dict[str, Any]):
        """Set model parameters."""
        self.model_parameters.update(parameters)

    def predict(self, data: torch.Tensor) -> torch.Tensor:
        """Make predictions using the neuroscience model."""
        self.model.eval()
        with torch.no_grad():
            return self.model(data)

    def train(self, data: torch.Tensor, labels: torch.Tensor):
        """Train the neuroscience model."""
        self.model.train()
        optimizer = torch.optim.Adam(self.model.parameters(), lr=self.model_parameters.get('learning_rate', 0.001))
        criterion = nn.CrossEntropyLoss()

        for epoch in range(self.model_parameters.get('epochs', 100)):
            optimizer.zero_grad()
            outputs = self.model(data)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

    def evaluate(self, data: torch.Tensor, labels: torch.Tensor) -> Dict[str, float]:
        """Evaluate the neuroscience model."""
        self.model.eval()
        with torch.no_grad():
            outputs = self.model(data)
            _, predicted = torch.max(outputs.data, 1)
            accuracy = (predicted == labels).sum().item() / labels.size(0)
        return {"accuracy": accuracy}

    def interpret_results(self, results: torch.Tensor) -> Dict[str, Any]:
        """Interpret the results of the neuroscience model."""
        # Placeholder for result interpretation
        return {"interpretation": "Advanced interpretation not implemented"}

class SpikingNeuralNetwork(nn.Module):
    input_size: int
    hidden_size: int
    output_size: int

    @nn.compact
    def __call__(self, x):
        fc1 = nn.Dense(features=self.hidden_size)
        fc2 = nn.Dense(features=self.output_size)

        mem = jnp.zeros((x.shape[0], self.hidden_size))
        spk = jnp.zeros((x.shape[0], self.hidden_size))

        for t in range(x.shape[1]):
            cur = fc1(x[:, t, :])
            mem = mem * 0.9 + cur * (1 - 0.9)  # Leaky integration
            spk = jnp.where(mem > 1.0, 1.0, 0.0)  # Spike if membrane potential > threshold
            mem = jnp.where(mem > 1.0, 0.0, mem)  # Reset membrane potential after spike

        output = fc2(spk)
        return output

class LSTMNetwork(nn.Module):
    hidden_size: int
    output_size: int

    @nn.compact
    def __call__(self, x):
        ScanLSTM = nn.scan(
            nn.OptimizedLSTMCell,
            variable_broadcast="params",
            split_rngs={"params": False},
            in_axes=1,
            out_axes=1
        )
        lstm = ScanLSTM(self.hidden_size)
        batch_size, seq_len, input_size = x.shape
        carry = lstm.initialize_carry(jax.random.PRNGKey(0), (batch_size,), self.hidden_size)
        carry, outputs = lstm(carry, x)
        return nn.Dense(features=self.output_size)(outputs[:, -1, :])

class TemporalConvNet(nn.Module):
    input_size: int
    hidden_size: int
    output_size: int

    @nn.compact
    def __call__(self, x):
        x = jnp.transpose(x, (0, 2, 1))  # (batch, time, features) -> (batch, features, time)
        x = nn.Conv(features=self.hidden_size, kernel_size=(3,), padding='SAME')(x)
        x = nn.relu(x)
        x = nn.Conv(features=self.hidden_size, kernel_size=(3,), padding='SAME')(x)
        x = nn.relu(x)
        x = jnp.mean(x, axis=-1)  # Global average pooling
        x = nn.Dense(features=self.output_size)(x)
        return x

# Example usage
if __name__ == "__main__":
    input_size = 32
    hidden_size = 64
    output_size = 2
    model = NeuroscienceModel(input_size, hidden_size, output_size, model_type='SNN')
    model.set_parameters({"learning_rate": 0.01, "epochs": 100})

    # Simulated data
    data = torch.rand(100, 10, input_size)  # 100 samples, 10 time steps, 32 features
    labels = torch.randint(0, 2, (100,))  # Binary labels

    model.train(data, labels)
    predictions = model.predict(data)
    evaluation = model.evaluate(data, labels)
    interpretation = model.interpret_results(predictions)

    print(f"Evaluation: {evaluation}")
    print(f"Interpretation: {interpretation}")
