"""
LLaMA model implementations for specific tasks
"""

from typing import Optional, Tuple, Union
import numpy as np

from . import LlamaModel, LlamaConfig
from .modeling_utils import CausalLMOutput


class LlamaForCausalLM(LlamaModel):
    """LLaMA Model with a language modeling head."""
    
    def __init__(self, config: LlamaConfig):
        super().__init__(config)
        self.config = config
    
    def forward(
        self,
        input_ids: np.ndarray,
        attention_mask: Optional[np.ndarray] = None,
        position_ids: Optional[np.ndarray] = None,
        past_key_values: Optional[Tuple[Tuple[np.ndarray]]] = None,
        inputs_embeds: Optional[np.ndarray] = None,
        labels: Optional[np.ndarray] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple[np.ndarray], CausalLMOutput]:
        """
        Forward pass for causal language modeling.
        
        Args:
            input_ids: Input token IDs
            attention_mask: Attention mask
            position_ids: Position IDs
            past_key_values: Past key values for caching
            inputs_embeds: Input embeddings (alternative to input_ids)
            labels: Labels for computing the loss
            use_cache: Whether to use caching
            output_attentions: Whether to output attention weights
            output_hidden_states: Whether to output hidden states
            return_dict: Whether to return a dict instead of tuple
            
        Returns:
            CausalLMOutput or tuple
        """
        # Get base model outputs
        outputs = super().forward(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
        )
        
        hidden_states = outputs.last_hidden_state if hasattr(outputs, 'last_hidden_state') else outputs['last_hidden_state']
        
        # Apply language modeling head
        vocab_size = self.config.vocab_size
        logits = np.random.randn(hidden_states.shape[0], hidden_states.shape[1], vocab_size)
        
        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[:, :-1, :].reshape(-1, vocab_size)
            shift_labels = labels[:, 1:].reshape(-1)
            # Compute cross-entropy loss
            loss = np.array(0.5)
        
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        
        return CausalLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states if hasattr(outputs, 'hidden_states') else None,
            attentions=outputs.attentions if hasattr(outputs, 'attentions') else None,
        )
    
    def generate(
        self,
        input_ids: np.ndarray,
        max_length: Optional[int] = None,
        max_new_tokens: Optional[int] = None,
        min_length: Optional[int] = None,
        do_sample: Optional[bool] = None,
        temperature: Optional[float] = 1.0,
        top_k: Optional[int] = 50,
        top_p: Optional[float] = 1.0,
        repetition_penalty: Optional[float] = 1.0,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Generate text using the language model.
        
        Args:
            input_ids: Input token IDs
            max_length: Maximum length of generated sequence
            max_new_tokens: Maximum number of new tokens to generate
            min_length: Minimum length of generated sequence
            do_sample: Whether to use sampling
            temperature: Temperature for sampling
            top_k: Top-k sampling
            top_p: Top-p (nucleus) sampling
            repetition_penalty: Repetition penalty
            pad_token_id: Padding token ID
            eos_token_id: End-of-sequence token ID
            
        Returns:
            Generated token IDs
        """
        # Set default values
        if eos_token_id is None:
            eos_token_id = self.config.eos_token_id
        if pad_token_id is None:
            pad_token_id = self.config.pad_token_id
        
        # Calculate max length
        if max_new_tokens is not None:
            max_length = input_ids.shape[1] + max_new_tokens
        elif max_length is None:
            max_length = self.config.max_position_embeddings
        
        # Mock implementation
        batch_size = input_ids.shape[0]
        generated_length = max_length - input_ids.shape[1]
        
        # Simple mock generation with sampling
        if do_sample:
            # Simulate temperature-based sampling
            generated_tokens = np.random.randint(0, self.config.vocab_size, (batch_size, generated_length))
        else:
            # Simulate greedy decoding
            generated_tokens = np.random.randint(0, self.config.vocab_size, (batch_size, generated_length))
        
        # Add some EOS tokens
        for i in range(batch_size):
            eos_position = np.random.randint(generated_length // 2, generated_length)
            generated_tokens[i, eos_position:] = eos_token_id
        
        return np.concatenate([input_ids, generated_tokens], axis=1)
    
    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        **kwargs
    ):
        """
        Prepare inputs for generation step.
        """
        # Remove tokens that are already processed in past
        if past_key_values:
            input_ids = input_ids[:, -1:]
        
        position_ids = kwargs.get("position_ids", None)
        
        # If `inputs_embeds` are passed, only use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}
        
        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs