code: 
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					int     ,		UpperCamelCase					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				AutoTokenizer.from_pretrained(UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if isinstance(UpperCamelCase     ,		UpperCamelCase				) and all(isinstance(_x     ,		UpperCamelCase				) for _x in x				):
                      lowercase__       =				None
                      for l in x:
                             lowercase__       =				self.tokenizer(
                                 UpperCamelCase     ,		padding='''max_length'''     ,		max_length=384     ,		truncation=UpperCamelCase     ,		return_tensors='''pt'''     ,		)
                             lowercase__       =				torch.tensor([len(UpperCamelCase				)]				)
                             if d is not None:
                                    for k in d.keys():
                                           lowercase__       =				torch.cat((d[k], t[k])     ,		0				)
                             else:
                                    lowercase__       =				t
  
                      lowercase__       =				torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''				)				)
                      lowercase__       =				torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''				)				)
  
               elif isinstance(UpperCamelCase     ,		UpperCamelCase				) and all(isinstance(_x     ,		UpperCamelCase				) for _x in x				):
                      lowercase__       =				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					:					Tuple     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[Any]=0.70				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				[]
               for idx in range(len(W_query['''input_ids''']				)				):
                      lowercase__       =				lowercase__       =				range(p_start.shape[1]				)
  
                      lowercase__       =				[]
                      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				)
                      lowercase__       =				[]
                      for k in range(len(UpperCamelCase				)				):
                             if output[k][2] * output[k][3] >= thresh:
                                    lowercase__    ,lowercase__       =				output[k][0], output[k][1]
                                    lowercase__       =				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: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  