code: 
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    Dict    ):
          lowerCAmelCase				:			List[Any]    			=      AutoTokenizer.from_pretrained(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ):
  
          if isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ) and all(isinstance(_x    ,				UpperCAmelCase_    ) for _x in x    ):
              lowerCAmelCase				:			Union[str, Any]    			=      None
              for l in x:
                  lowerCAmelCase				:			Any    			=      self.tokenizer(
                      UpperCAmelCase_    ,				padding='max_length'    ,				max_length=384    ,				truncation=UpperCAmelCase_    ,				return_tensors='pt'    ,				)
                  lowerCAmelCase				:			List[Any]    			=      torch.tensor([len(UpperCAmelCase_    )]    )
                  if d is not None:
                      for k in d.keys():
                          lowerCAmelCase				:			Dict    			=      torch.cat((d[k], t[k])    ,				0    )
                  else:
                      lowerCAmelCase				:			Optional[int]    			=      t
  
              lowerCAmelCase				:			str    			=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'    )    )
              lowerCAmelCase				:			str    			=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'    )    )
  
          elif isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ) and all(isinstance(_x    ,				UpperCAmelCase_    ) for _x in x    ):
              lowerCAmelCase				:			int    			=      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 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    str=0.70    ):
          lowerCAmelCase				:			str    			=      []
          for idx in range(len(W_query['input_ids']    )    ):
              lowerCAmelCase				:			Union[str, Any]    			=      range(p_start.shape[1]    )
  
              lowerCAmelCase				:			str    			=      []
              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_    )
              lowerCAmelCase				:			Optional[Any]    			=      []
              for k in range(len(UpperCAmelCase_    )    ):
                  if output[k][2] * output[k][3] >= thresh:
                      lowerCAmelCase			,  lowerCAmelCase				:			int    			=      output[k][0], output[k][1]
                      lowerCAmelCase				:			List[Any]    			=      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: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  