code: 
  '''simple docstring'''
  
  # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
  from .base import PipelineTool
  
  
  UpperCamelCase_     			=       """Here is a text containing a lot of information: '''{text}'''.
  
  Can you answer this question about the text: '{question}'"""
  
  
  class   a_  (_a    ):
         __lowerCAmelCase    :				Optional[Any]    =      """google/flan-t5-base"""
         __lowerCAmelCase    :				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    :				Tuple    =      """text_qa"""
         __lowerCAmelCase    :				int    =      AutoTokenizer
         __lowerCAmelCase    :				Optional[Any]    =      AutoModelForSeqaSeqLM
  
         __lowerCAmelCase    :				Optional[Any]    =      ["""text""", """text"""]
         __lowerCAmelCase    :				str    =      ["""text"""]
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
           _lowerCAmelCase       :  List[str]   				=					QA_PROMPT.format(text=snake_case_   ,   question=snake_case_  )
           return self.pre_processor(snake_case_   ,   return_tensors="""pt"""  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase       :  int   				=					self.model.generate(**snake_case_  )
  
           _lowerCAmelCase				,					_lowerCAmelCase       :  Union[str, Any]   				=					inputs["""input_ids"""].shape
           _lowerCAmelCase       :  List[str]   				=					output_ids.shape[0]
  
           return output_ids.reshape(snake_case_   ,   out_b // in_b   ,   *output_ids.shape[1:]  )[0][0]
  
  
  
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           return self.pre_processor.decode(snake_case_   ,   skip_special_tokens=snake_case_   ,   clean_up_tokenization_spaces=snake_case_  )
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  