code: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						AutoTokenizer.from_pretrained(__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if isinstance(__a		,       __a							) and all(isinstance(_x		,       __a							) for _x in x							):
  											__a    : Dict      					=						None
  											for l in x:
  																__a    : Optional[Any]      					=						self.tokenizer(
  																    __a		,       padding='max_length'		,       max_length=384		,       truncation=__a		,       return_tensors='pt'		,       )
  																__a    : Union[str, Any]      					=						torch.tensor([len(__a							)]							)
  																if d is not None:
  																					for k in d.keys():
  																										__a    : str      					=						torch.cat((d[k], t[k])		,       0							)
  																else:
  																					__a    : List[str]      					=						t
  
  											__a    : List[Any]      					=						torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'							)							)
  											__a    : str      					=						torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'							)							)
  
  						elif isinstance(__a		,       __a							) and all(isinstance(_x		,       __a							) for _x in x							):
  											__a    : Optional[Any]      					=						self.tokenizer(
  											    __a		,       padding='max_length'		,       max_length=384		,       truncation=__a		,       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		,       __a		,       __a		,       __a		,       __a		,       __a=0.70							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						[]
  						for idx in range(len(W_query['input_ids']							)							):
  											__a    : Optional[int]      					=						range(p_start.shape[1]							)
  
  											__a    : int      					=						[]
  											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 __a							: (tup[2] * tup[3])		,       reverse=__a							)
  											__a    : Optional[Any]      					=						[]
  											for k in range(len(__a							)							):
  																if output[k][2] * output[k][3] >= thresh:
  																					__a					,       __a    : Any      					=						output[k][0], output[k][1]
  																					__a    : Optional[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(__a							)
  
  						return final_outputs
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		Optional[int] =			[
  	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'VisualBertForMultipleChoice',
  	    'VisualBertForPreTraining',
  	    'VisualBertForQuestionAnswering',
  	    'VisualBertForRegionToPhraseAlignment',
  	    'VisualBertForVisualReasoning',
  	    'VisualBertLayer',
  	    'VisualBertModel',
  	    'VisualBertPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_visual_bert import (
  		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    VisualBertForMultipleChoice,
  		    VisualBertForPreTraining,
  		    VisualBertForQuestionAnswering,
  		    VisualBertForRegionToPhraseAlignment,
  		    VisualBertForVisualReasoning,
  		    VisualBertLayer,
  		    VisualBertModel,
  		    VisualBertPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  