






import torch

from transformers import AutoTokenizer






class 	lowercase_      (			__SCREAMING_SNAKE_CASE     ):






      '''simple docstring'''




      def __init__(   self   :		Union[str, Any]				,      __UpperCAmelCase   :		List[str]						)					->List[str]:




       """simple docstring"""


       a							=   AutoTokenizer.from_pretrained(UpperCamelCase__						)







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->Dict:




       """simple docstring"""


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

        a							=   torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''						)						)
        a							=   torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''						)						)

       elif isinstance(UpperCamelCase__				,      UpperCamelCase__						) and all(isinstance(_x				,      UpperCamelCase__						) for _x in x						):
        a							=   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    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[Any]				,      __UpperCAmelCase   :		List[Any]				,      __UpperCAmelCase   :		Any				,      __UpperCAmelCase   :		Tuple				,      __UpperCAmelCase   :		Any=0.70						)					->Dict:




       """simple docstring"""


       a							=   []
       for idx in range(len(W_query['''input_ids''']						)						):
        a							=   range(p_start.shape[1]						)

        a							=   []
        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__						)
        a							=   []
        for k in range(len(UpperCamelCase__						)						):
         if output[k][2] * output[k][3] >= thresh:
          a							=   output[k][0], output[k][1]
          a							=   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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__            =	{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "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

     UpperCAmelCase__            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


