





import torch

from transformers import AutoTokenizer




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      def __init__( self				,						UpperCAmelCase   )       ->   Optional[Any]:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       AutoTokenizer.from_pretrained(UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   List[Any]:






        '''simple docstring'''






        if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) and all(isinstance(_x				,						UpperCAmelCase   ) for _x in x   ):
          __snake_case   :				Dict        =       None
          for l in x:
            __snake_case   :				Dict        =       self.tokenizer(
                UpperCAmelCase				,						padding="max_length"				,						max_length=384				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
            __snake_case   :				Any        =       torch.tensor([len(UpperCAmelCase   )]   )
            if d is not None:
              for k in d.keys():
                __snake_case   :				str        =       torch.cat((d[k], t[k])				,						0   )
            else:
              __snake_case   :				str        =       t

          __snake_case   :				str        =       torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"   )   )
          __snake_case   :				Optional[Any]        =       torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"   )   )

        elif isinstance(UpperCAmelCase				,						UpperCAmelCase   ) and all(isinstance(_x				,						UpperCAmelCase   ) for _x in x   ):
          __snake_case   :				Union[str, Any]        =       self.tokenizer(
              UpperCAmelCase				,						padding="max_length"				,						max_length=384				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        else:
          raise Exception(
              "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
              " strings` for supports are supported."   )

        return d







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=0.70   )       ->   Tuple:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       []
        for idx in range(len(W_query["input_ids"]   )   ):
          __snake_case   :				Optional[int]        =       range(p_start.shape[1]   )

          __snake_case   :				List[Any]        =       []
          for start_id in start_indexes:
            for end_id in end_indexes:
              if start_id < end_id:
                output.append(
                    (
                        start_id,
                        end_id,
                        p_start[idx][start_id].item(),
                        p_end[idx][end_id].item(),
                    )   )

          output.sort(key=lambda UpperCAmelCase   : (tup[2] * tup[3])				,						reverse=UpperCAmelCase   )
          __snake_case   :				str        =       []
          for k in range(len(UpperCAmelCase   )   ):
            if output[k][2] * output[k][3] >= thresh:
              __snake_case    ,   __snake_case   :				Union[str, Any]        =       output[k][0], output[k][1]
              __snake_case   :				Dict        =       self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]   )
              temp.append((decoded, output[k][2] * output[k][3])   )

          final_outputs.append(UpperCAmelCase   )

        return final_outputs








import torch
from torch import autograd




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Dict        =       inputs.numel()
        __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
        if sigmoid:
          __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
        else:
          __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
        if mask.sum() < nb_min:
          # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
          __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
          __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        return gradOutput, None, None




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[int]        =       mask.flatten()
        __snake_case   :				Union[str, Any]        =       0
        __snake_case   :				Any        =       1
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:






        '''simple docstring'''






        return gradOutput, None




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[Any]        =       mask.flatten()
        __snake_case   :				Tuple        =       0
        __snake_case   :				Any        =       1
        return mask

