code: 
  
  
  '''simple docstring'''
  
  from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
  from .base import PipelineTool
  
  
  lowerCamelCase							:     List[Any]								=			'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Optional[int]						      =							"""google/flan-t5-base"""
        lowerCAmelCase__      :  Union[str, Any]						      =							(
            """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
            """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
        )
        lowerCAmelCase__      :  Union[str, Any]						      =							"""text_qa"""
        lowerCAmelCase__      :  int						      =							AutoTokenizer
        lowerCAmelCase__      :  List[str]						      =							AutoModelForSeqaSeqLM
  
        lowerCAmelCase__      :  Union[str, Any]						      =							["""text""", """text"""]
        lowerCAmelCase__      :  str						      =							["""text"""]
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				QA_PROMPT.format(text=UpperCamelCase     ,		question=UpperCamelCase				)
               return self.pre_processor(UpperCamelCase     ,		return_tensors='''pt'''				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model.generate(**UpperCamelCase				)
  
               lowercase__    ,lowercase__       =				inputs['''input_ids'''].shape
               lowercase__       =				output_ids.shape[0]
  
               return output_ids.reshape(UpperCamelCase     ,		out_b // in_b     ,		*output_ids.shape[1:]				)[0][0]
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.pre_processor.decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase				)
  
  
  
  
  
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__)
  
  
  
  
  