"""
T5 model implementations for specific tasks
"""

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

from . import T5Model, T5Config
from .modeling_utils import ModelOutput


class Seq2SeqLMOutput(ModelOutput):
    """
    Base class for sequence-to-sequence language models outputs.
    """
    loss: Optional[np.ndarray] = None
    logits: np.ndarray = None
    past_key_values: Optional[Tuple[Tuple[np.ndarray]]] = None
    decoder_hidden_states: Optional[Tuple[np.ndarray]] = None
    decoder_attentions: Optional[Tuple[np.ndarray]] = None
    cross_attentions: Optional[Tuple[np.ndarray]] = None
    encoder_last_hidden_state: Optional[np.ndarray] = None
    encoder_hidden_states: Optional[Tuple[np.ndarray]] = None
    encoder_attentions: Optional[Tuple[np.ndarray]] = None


class T5ForConditionalGeneration(T5Model):
    """T5 Model with a language modeling head for conditional generation."""
    
    def __init__(self, config: T5Config):
        super().__init__(config)
        self.config = config
    
    def forward(
        self,
        input_ids: Optional[np.ndarray] = None,
        attention_mask: Optional[np.ndarray] = None,
        decoder_input_ids: Optional[np.ndarray] = None,
        decoder_attention_mask: Optional[np.ndarray] = None,
        encoder_outputs: Optional[Tuple[np.ndarray]] = None,
        past_key_values: Optional[Tuple[Tuple[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], Seq2SeqLMOutput]:
        """
        Forward pass for conditional generation.
        
        Args:
            input_ids: Input token IDs for encoder
            attention_mask: Attention mask for encoder
            decoder_input_ids: Input token IDs for decoder
            decoder_attention_mask: Attention mask for decoder
            encoder_outputs: Pre-computed encoder outputs
            past_key_values: Past key values for caching
            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:
            Seq2SeqLMOutput or tuple
        """
        # Get base model outputs
        outputs = super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
        )
        
        # For T5, we need the decoder output
        # Mock implementation - in practice this would come from the model
        if input_ids is not None:
            batch_size = input_ids.shape[0]
            seq_len = decoder_input_ids.shape[1] if decoder_input_ids is not None else 10
        else:
            batch_size = 1
            seq_len = 10
        
        # Mock decoder hidden states
        decoder_hidden_states = np.random.randn(batch_size, seq_len, self.config.d_model)
        
        # Apply language modeling head
        vocab_size = self.config.vocab_size
        lm_logits = np.random.randn(batch_size, seq_len, vocab_size)
        
        loss = None
        if labels is not None:
            # Compute sequence-to-sequence loss
            loss = np.array(0.5)
        
        if not return_dict:
            output = (lm_logits,)
            return ((loss,) + output) if loss is not None else output
        
        return Seq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            encoder_last_hidden_state=np.random.randn(batch_size, seq_len, self.config.d_model),
        )
    
    def generate(
        self,
        input_ids: Optional[np.ndarray] = None,
        attention_mask: Optional[np.ndarray] = None,
        max_length: Optional[int] = None,
        max_new_tokens: Optional[int] = None,
        min_length: Optional[int] = None,
        do_sample: Optional[bool] = None,
        temperature: Optional[float] = None,
        top_k: Optional[int] = None,
        top_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        decoder_start_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Generate text using the encoder-decoder model.
        
        Args:
            input_ids: Input token IDs for encoder
            attention_mask: Attention mask for encoder
            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
            decoder_start_token_id: Start token ID for decoder
            eos_token_id: End-of-sequence token ID
            
        Returns:
            Generated token IDs
        """
        # Mock implementation
        if max_new_tokens is not None:
            max_length = max_new_tokens
        elif max_length is None:
            max_length = 50
        
        # For T5, decoder_start_token_id is typically pad_token_id
        if decoder_start_token_id is None:
            decoder_start_token_id = self.config.pad_token_id
        
        # Simple mock generation
        batch_size = input_ids.shape[0] if input_ids is not None else 1
        generated_tokens = np.random.randint(0, self.config.vocab_size, (batch_size, max_length))
        
        # Start with decoder_start_token_id
        generated_tokens[:, 0] = decoder_start_token_id
        
        return generated_tokens
    
    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past=None,
        attention_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs
    ):
        """
        Prepare inputs for generation step.
        """
        return {
            "input_ids": None,  # encoder_outputs is used instead of input_ids
            "encoder_outputs": encoder_outputs,
            "past_key_values": past,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "use_cache": use_cache,
        }