code: 
  
  
  
  
  
  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
  
  
style_context: 
  
  
  
  
  
  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
  
  