


'''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				)








'''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__)




