import torch
from typing import Dict, Tuple, List
from dataclasses import dataclass

@dataclass
class TensorConfig:
    pad_token_id: int
    max_prompt_length: int
    max_obs_length: int
    max_start_length: int

class TensorHelper:
    def __init__(self, config: TensorConfig):
        self.config = config

    def cut_to_effective_len(self, tensor_dict: Dict[str, torch.Tensor], 
                            keys: List[str], cut_left: bool = True) -> Dict[str, torch.Tensor]:
        """Cut tensors to their effective length based on attention mask."""
        effective_len = tensor_dict['attention_mask'].sum(dim=1).max()
        result = tensor_dict.copy()
        
        for key in keys:
            if cut_left:
                result[key] = tensor_dict[key][:, -effective_len:]
            else:
                result[key] = tensor_dict[key][:, :effective_len]
        return result

    def convert_pad_structure(self, tensor: torch.Tensor, pad_to_left: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:
        """Convert padding structure and return sorted tensor with indices."""
        mask = tensor != self.config.pad_token_id if pad_to_left else tensor == self.config.pad_token_id
        sorted_indices = mask.to(torch.int64).argsort(dim=1, stable=True)
        return tensor.gather(1, sorted_indices), sorted_indices

    def create_attention_mask(self, input_ids: torch.Tensor) -> torch.Tensor:
        """Create attention mask from input ids."""
        return torch.where(input_ids != self.config.pad_token_id, 1, 0)
    
    def mask_using_kept_lengths(self, attn_mask: torch.Tensor, kept_lengths: torch.Tensor, initial_token_lengths: List[int]) -> torch.Tensor:
        for i in range(len(kept_lengths)):
            # find the last non-zero index in the i-th row of attn_mask
            non_zero_indices = (attn_mask[i] == 1).nonzero()
            last_non_zero_index = non_zero_indices[-1]
            first_non_zero_index = non_zero_indices[0]
            attn_mask[i, :last_non_zero_index - kept_lengths[i] + 1] = 0
            attn_mask[i, first_non_zero_index:first_non_zero_index + initial_token_lengths[i]] = 1

        return attn_mask


    def create_position_ids(self, attention_mask: torch.Tensor) -> torch.Tensor:
        """Create position ids from attention mask."""
        return (torch.cumsum(attention_mask, dim=1) - 1) * attention_mask

    def concatenate_with_padding(self, tensors: List[torch.Tensor], 
                               pad_to_left: bool = True) -> torch.Tensor:
        """Concatenate tensors and handle padding."""
        concatenated = torch.cat(tensors, dim=1)
        padded_tensor, _ = self.convert_pad_structure(concatenated, pad_to_left)
        return padded_tensor

    def _example_level_pad(self, responses: torch.Tensor, 
                          responses_str: List[str], 
                          active_mask: torch.Tensor) -> Tuple[torch.Tensor, List[str]]:
        """
        Pad responses for non-active examples with pad tokens.
        """
        assert active_mask.sum() == responses.shape[0]
        # Create masked responses tensor
        batch_size = active_mask.shape[0]
        seq_len = responses.shape[1]
        padded_responses = torch.full(
            (batch_size, seq_len), self.config.pad_token_id,
            dtype=responses.dtype, device=responses.device
        )
        padded_responses[active_mask] = responses
        
        # Create masked response strings
        padded_responses_str = [""] * batch_size
        
        s = 0
        for i, is_active in enumerate(active_mask):
            if is_active:
                padded_responses_str[i] = responses_str[s]
                s += 1
                
        return padded_responses, padded_responses_str