from typing import List, Dict, Any, Callable
import jax
import jax.numpy as jnp
import flax.linen as nn
import tensorflow as tf
from abc import ABC, abstractmethod
from NeuroFlex.utils.utils import tokenize_text, get_activation_function

class AgenticBehavior(ABC):
    @abstractmethod
    def zero_shot(self, prompt: str) -> str:
        pass

    @abstractmethod
    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        pass

    @abstractmethod
    def chain_of_thought(self, prompt: str) -> str:
        pass

    @abstractmethod
    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        pass

    @abstractmethod
    def self_correct(self, output: str) -> str:
        pass

    @abstractmethod
    def self_update(self, feedback: str) -> None:
        pass

class BaseAgent(AgenticBehavior):
    def __init__(self, model: nn.Module):
        self.model = model

    def _encode_input(self, tokens: List[str]) -> jnp.ndarray:
        # Placeholder for input encoding
        # In a real implementation, this would use a proper tokenizer and embedding
        return jnp.array([hash(token) % 10000 for token in tokens])

    def _decode_output(self, output: jnp.ndarray) -> str:
        # Placeholder for output decoding
        # In a real implementation, this would convert model output to text
        return " ".join([str(int(x)) for x in output])

    def self_correct(self, output: str) -> str:
        # Implement self-correction mechanism
        correction_prompt = f"The previous output was:\n{output}\n\nPlease review and correct any errors in the above output:"
        tokens = tokenize_text(correction_prompt)
        encoded_input = self._encode_input(tokens)
        corrected_output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(corrected_output)

    def self_update(self, feedback: str) -> None:
        # Implement self-updating mechanism
        # This method would typically involve fine-tuning the model based on feedback
        print(f"Received feedback for self-update: {feedback}")
        # TODO: Implement actual model update logic using JAX/Flax optimization

class NeuroFlexAgenticBehavior(BaseAgent):
    pass

    def zero_shot(self, prompt: str) -> str:
        # Implement zero-shot learning using the NeuroFlex model
        tokens = tokenize_text(prompt)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        # Implement few-shot learning using the NeuroFlex model and provided examples
        context = self._format_examples(examples) + "\n" + prompt
        tokens = tokenize_text(context)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def chain_of_thought(self, prompt: str) -> str:
        # Implement chain-of-thought reasoning
        cot_prompt = f"Let's approach this step-by-step:\n1) {prompt}\n2) "
        tokens = tokenize_text(cot_prompt)
        encoded_input = self._encode_input(tokens)

        thoughts = []
        for _ in range(5):  # Generate up to 5 steps
            output = self.model.apply({'params': self.model.params}, encoded_input)
            step = self._decode_output(output)
            thoughts.append(step)
            encoded_input = jnp.concatenate([encoded_input, self._encode_input(tokenize_text(step))])

        return "\n".join(thoughts)

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        # Implement meta-prompting
        full_prompt = f"{meta_prompt}\n\nTask: {prompt}"
        tokens = tokenize_text(full_prompt)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def self_correct(self, output: str) -> str:
        # Implement self-correction mechanism
        correction_prompt = f"The previous output was:\n{output}\n\nPlease review and correct any errors in the above output:"
        tokens = tokenize_text(correction_prompt)
        encoded_input = self._encode_input(tokens)
        corrected_output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(corrected_output)

    def self_update(self, feedback: str) -> None:
        # Implement self-updating mechanism
        # This method would typically involve fine-tuning the model based on feedback
        print(f"Received feedback for self-update: {feedback}")
        # TODO: Implement actual model update logic using JAX/Flax optimization
        # For example:
        # optimizer = optax.adam(learning_rate=1e-4)
        # opt_state = optimizer.init(self.model.params)
        # loss = self._compute_loss(feedback)
        # grads = jax.grad(loss)(self.model.params)
        # updates, opt_state = optimizer.update(grads, opt_state)
        # self.model.params = optax.apply_updates(self.model.params, updates)

    def _encode_input(self, tokens: List[str]) -> jnp.ndarray:
        # Placeholder for input encoding
        # In a real implementation, this would use a proper tokenizer and embedding
        return jnp.array([hash(token) % 10000 for token in tokens])

    def _decode_output(self, output: jnp.ndarray) -> str:
        # Placeholder for output decoding
        # In a real implementation, this would convert model output to text
        return " ".join([str(int(x)) for x in output])

    def _format_examples(self, examples: List[Dict[str, str]]) -> str:
        formatted = ""
        for example in examples:
            formatted += f"Input: {example['input']}\nOutput: {example['output']}\n\n"
        return formatted.strip()

# Helper function to create a NeuroFlexAgenticBehavior instance
def create_neuroflex_agentic_behavior(model: nn.Module) -> NeuroFlexAgenticBehavior:
    return NeuroFlexAgenticBehavior(model)

class BaseAgent(AgenticBehavior):
    def __init__(self, model: nn.Module):
        self.model = model

    def _encode_input(self, tokens: List[str]) -> jnp.ndarray:
        return jnp.array([hash(token) % 10000 for token in tokens])

    def _decode_output(self, output: jnp.ndarray) -> str:
        return " ".join([str(int(x)) for x in output])

    def self_correct(self, output: str) -> str:
        correction_prompt = f"The previous output was:\n{output}\n\nPlease review and correct any errors in the above output:"
        tokens = tokenize_text(correction_prompt)
        encoded_input = self._encode_input(tokens)
        corrected_output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(corrected_output)

    def self_update(self, feedback: str) -> None:
        print(f"Received feedback for self-update: {feedback}")
        # TODO: Implement actual model update logic using JAX/Flax optimization

class ZeroShotAgent(BaseAgent):
    def zero_shot(self, prompt: str) -> str:
        tokens = tokenize_text(prompt)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        raise NotImplementedError("ZeroShotAgent does not support few-shot learning")

    def chain_of_thought(self, prompt: str) -> str:
        raise NotImplementedError("ZeroShotAgent does not support chain-of-thought reasoning")

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        raise NotImplementedError("ZeroShotAgent does not support meta-prompting")

class FewShotAgent(BaseAgent):
    def zero_shot(self, prompt: str) -> str:
        raise NotImplementedError("FewShotAgent does not support zero-shot learning")

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        context = self._format_examples(examples) + "\n" + prompt
        tokens = tokenize_text(context)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def chain_of_thought(self, prompt: str) -> str:
        raise NotImplementedError("FewShotAgent does not support chain-of-thought reasoning")

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        raise NotImplementedError("FewShotAgent does not support meta-prompting")

    def _format_examples(self, examples: List[Dict[str, str]]) -> str:
        formatted = ""
        for example in examples:
            formatted += f"Input: {example['input']}\nOutput: {example['output']}\n\n"
        return formatted.strip()

class ChainOfThoughtAgent(BaseAgent):
    def zero_shot(self, prompt: str) -> str:
        raise NotImplementedError("ChainOfThoughtAgent does not support zero-shot learning")

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        raise NotImplementedError("ChainOfThoughtAgent does not support few-shot learning")

    def chain_of_thought(self, prompt: str) -> str:
        cot_prompt = f"Let's approach this step-by-step:\n1) {prompt}\n2) "
        tokens = tokenize_text(cot_prompt)
        encoded_input = self._encode_input(tokens)

        thoughts = []
        for _ in range(5):  # Generate up to 5 steps
            output = self.model.apply({'params': self.model.params}, encoded_input)
            step = self._decode_output(output)
            thoughts.append(step)
            encoded_input = jnp.concatenate([encoded_input, self._encode_input(tokenize_text(step))])

        return "\n".join(thoughts)

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        raise NotImplementedError("ChainOfThoughtAgent does not support meta-prompting")

class MetaPromptingAgent(BaseAgent):
    def zero_shot(self, prompt: str) -> str:
        raise NotImplementedError("MetaPromptingAgent does not support zero-shot learning")

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        raise NotImplementedError("MetaPromptingAgent does not support few-shot learning")

    def chain_of_thought(self, prompt: str) -> str:
        raise NotImplementedError("MetaPromptingAgent does not support chain-of-thought reasoning")

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        full_prompt = f"{meta_prompt}\n\nTask: {prompt}"
        tokens = tokenize_text(full_prompt)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

class SelfConsistencyAgent(BaseAgent):
    def __init__(self, model: nn.Module, num_samples: int = 5):
        super().__init__(model)
        self.num_samples = num_samples

    def generate_samples(self, prompt: str) -> List[str]:
        samples = []
        for _ in range(self.num_samples):
            tokens = tokenize_text(prompt)
            encoded_input = self._encode_input(tokens)
            output = self.model.apply({'params': self.model.params}, encoded_input)
            samples.append(self._decode_output(output))
        return samples

    def select_most_consistent(self, samples: List[str]) -> str:
        # Simple implementation: return the most common sample
        from collections import Counter
        return Counter(samples).most_common(1)[0][0]

    def zero_shot(self, prompt: str) -> str:
        samples = self.generate_samples(prompt)
        return self.select_most_consistent(samples)

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        raise NotImplementedError("SelfConsistencyAgent does not support few-shot learning")

    def chain_of_thought(self, prompt: str) -> str:
        raise NotImplementedError("SelfConsistencyAgent does not support chain-of-thought reasoning")

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        raise NotImplementedError("SelfConsistencyAgent does not support meta-prompting")

class GenerateKnowledgePromptingAgent(BaseAgent):
    def __init__(self, model: nn.Module, knowledge_base: Dict[str, str]):
        super().__init__(model)
        self.knowledge_base = knowledge_base

    def generate_knowledge(self, prompt: str) -> str:
        # Simple implementation: retrieve relevant knowledge from the knowledge base
        relevant_knowledge = []
        for key, value in self.knowledge_base.items():
            if key.lower() in prompt.lower():
                relevant_knowledge.append(value)
        return " ".join(relevant_knowledge)

    def integrate_knowledge(self, prompt: str, knowledge: str) -> str:
        return f"Given the following knowledge: {knowledge}\n\nAnswer the question: {prompt}"

    def zero_shot(self, prompt: str) -> str:
        knowledge = self.generate_knowledge(prompt)
        integrated_prompt = self.integrate_knowledge(prompt, knowledge)
        tokens = tokenize_text(integrated_prompt)
        encoded_input = self._encode_input(tokens)
        output = self.model.apply({'params': self.model.params}, encoded_input)
        return self._decode_output(output)

    def few_shot(self, prompt: str, examples: List[Dict[str, str]]) -> str:
        raise NotImplementedError("GenerateKnowledgePromptingAgent does not support few-shot learning")

    def chain_of_thought(self, prompt: str) -> str:
        raise NotImplementedError("GenerateKnowledgePromptingAgent does not support chain-of-thought reasoning")

    def meta_prompting(self, prompt: str, meta_prompt: str) -> str:
        raise NotImplementedError("GenerateKnowledgePromptingAgent does not support meta-prompting")
