


import re






def      _lowercase(				__a							:	Optional[Any]    ):
							def docstring_decorator(__a							:	List[Any]    ):
														a__     						=fn.__doc__
														a__     						=func_doc.split('\n'    )
														a__     						=0
														while i < len(__a    ) and re.search(r'^\s*Examples?:\s*$'						,    lines[i]    ) is None:
																					i += 1
														if i < len(__a    ):
																					a__     						=example_docstring
																					a__     						="\n".join(__a    )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""    )
														a__     						=func_doc
														return fn

							return docstring_decorator





# 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


_lowerCAmelCase:	Any      							=					'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''




class 			lowercase_				(lowercase__      ):
				snake_case  						='google/flan-t5-base'
				snake_case  						=(
				    '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.'
				)
				snake_case  						='text_qa'
				snake_case  						=AutoTokenizer
				snake_case  						=AutoModelForSeqaSeqLM

				snake_case  						=['text', 'text']
				snake_case  						=['text']





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   Optional[int]:
											a__     						=QA_PROMPT.format(text=lowercase_					,     question=lowercase_)
											return self.pre_processor(lowercase_					,     return_tensors='pt')





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Dict:
											a__     						=self.model.generate(**lowercase_)

											a__						,    a__     						=inputs['input_ids'].shape
											a__     						=output_ids.shape[0]

											return output_ids.reshape(lowercase_					,     out_b // in_b					,     *output_ids.shape[1:])[0][0]





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Union[str, Any]:
											return self.pre_processor.decode(lowercase_					,     skip_special_tokens=lowercase_					,     clean_up_tokenization_spaces=lowercase_)

