import jax
import jax.numpy as jnp
from jax import random, grad, jit, vmap
from flax import linen as nn
from flax.training import train_state
import optax
from typing import Tuple, List, Dict, Any
from .advanced_thinking import CDSTDP
from .cognitive_architecture import CognitiveArchitecture

class GenerativeAIModel(nn.Module):
    features: Tuple[int, ...]
    output_dim: int

    @nn.compact
    def __call__(self, x):
        for feat in self.features:
            x = nn.Dense(feat)(x)
            x = nn.relu(x)
        x = nn.Dense(self.output_dim)(x)
        return x

    def simulate_consciousness(self, x):
        consciousness = jax.nn.sigmoid(x)
        threshold = 0.5
        conscious_state = jnp.where(consciousness > threshold, 1.0, 0.0)
        return conscious_state

class GenerativeAIFramework:
    def __init__(self, features: Tuple[int, ...], output_dim: int, learning_rate: float = 1e-3):
        self.model = GenerativeAIModel(features=features, output_dim=output_dim)
        self.learning_rate = learning_rate
        self.cdstdp = CDSTDP()
        self.cognitive_arch = CognitiveArchitecture({"learning_rate": learning_rate})
        self.optimizer = optax.chain(
            optax.clip_by_global_norm(1.0),  # Gradient clipping
            optax.adamw(learning_rate, weight_decay=1e-4)  # AdamW optimizer
        )

    def init_model(self, rng: Any, input_shape: Tuple[int, ...]):
        params = self.model.init(rng, jnp.ones(input_shape))['params']
        return train_state.TrainState.create(
            apply_fn=self.model.apply,
            params=params,
            tx=self.optimizer
        )

    @jit
    def train_step(self, state: train_state.TrainState, batch: Dict[str, jnp.ndarray]):
        def loss_fn(params):
            logits = self.model.apply({'params': params}, batch['input'], training=True)
            loss = optax.softmax_cross_entropy_with_integer_labels(logits, batch['target']).mean()
            return loss, logits

        grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
        (loss, logits), grads = grad_fn(state.params)

        # Apply gradient accumulation
        grads = jax.tree_map(lambda g: g / 4, grads)  # Accumulate over 4 steps
        state = state.apply_gradients(grads=grads)

        # Apply CD-STDP with improved cognitive feedback
        conscious_state = self.model.simulate_consciousness(logits)
        feedback = self.cognitive_arch.apply_feedback(conscious_state, loss)
        state = state.replace(params=self.cdstdp.update_weights(
            state.params, batch['input'], conscious_state, feedback, self.learning_rate
        ))

        return state, loss, logits

    @jit
    def generate(self, state: train_state.TrainState, input_data: jnp.ndarray):
        logits = self.model.apply({'params': state.params}, input_data, training=False)
        return jax.nn.softmax(logits)

    def integrate_with_nextgentorch(self, nextgentorch_model):
        # Placeholder for integration with NextGenTorch
        # This method would be implemented based on NextGenTorch's API
        pass

    @jit
    def evaluate(self, state: train_state.TrainState, batch: Dict[str, jnp.ndarray]):
        logits = self.model.apply({'params': state.params}, batch['input'], training=False)
        loss = optax.softmax_cross_entropy_with_integer_labels(logits, batch['target']).mean()
        accuracy = jnp.mean(jnp.argmax(logits, axis=-1) == batch['target'])
        return loss, accuracy

def create_generative_ai_framework(features: Tuple[int, ...], output_dim: int) -> GenerativeAIFramework:
    return GenerativeAIFramework(features, output_dim)

# Example usage
if __name__ == "__main__":
    rng = random.PRNGKey(0)
    framework = create_generative_ai_framework((64, 32), 10)
    state = framework.init_model(rng, (1, 28, 28))  # Assuming MNIST-like input

    # Generate dummy data
    dummy_input = random.normal(rng, (32, 28, 28))
    dummy_target = random.randint(rng, (32,), 0, 10)

    # Training loop
    for _ in range(10):
        state, loss, _ = framework.train_step(state, {'input': dummy_input, 'target': dummy_target})
        print(f"Loss: {loss}")

    # Generate output
    generated = framework.generate(state, dummy_input[:1])
    print(f"Generated output shape: {generated.shape}")

    # Simulate consciousness
    conscious_state = framework.model.simulate_consciousness(generated)
    print(f"Conscious state: {conscious_state}")

    # Integrate with NextGenTorch (placeholder)
    framework.integrate_with_nextgentorch(None)
