code: 
  
  
  # 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
  
  
  __a   				=      '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.
  
  Can you answer this question about the text: \'{question}\''''
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							Union[str, Any]    							=     'google/flan-t5-base'
        A				:							List[str]    							=     (
            '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.'
        )
        A				:							List[str]    							=     'text_qa'
        A				:							List[Any]    							=     AutoTokenizer
        A				:							Optional[Any]    							=     AutoModelForSeqaSeqLM
  
        A				:							Optional[int]    							=     ['text', 'text']
        A				:							Any    							=     ['text']
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Any					   =		QA_PROMPT.format(text=SCREAMING_SNAKE_CASE__							,						question=SCREAMING_SNAKE_CASE__		)
              return self.pre_processor(SCREAMING_SNAKE_CASE__							,						return_tensors='''pt'''		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Any					   =		self.model.generate(**SCREAMING_SNAKE_CASE__		)
  
              lowercase   ,			lowercase			:   int					   =		inputs['''input_ids'''].shape
              lowercase			:   Optional[Any]					   =		output_ids.shape[0]
  
              return output_ids.reshape(SCREAMING_SNAKE_CASE__							,						out_b // in_b							,						*output_ids.shape[1:]		)[0][0]
  
  
  
  
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              return self.pre_processor.decode(SCREAMING_SNAKE_CASE__							,						skip_special_tokens=SCREAMING_SNAKE_CASE__							,						clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__		)
  
  
style_context: 
  
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      is_torch_available,
      is_transformers_available,
      is_transformers_version,
  )
  
  
  try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        AudioLDMPipeline,
    )
  else:
    from .pipeline_audioldm import AudioLDMPipeline
  
  