


'''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







'''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__)



