


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



class       a							(						_a			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					Union[str, Any]				  =  "google/flan-t5-base"
     SCREAMING_SNAKE_CASE			:					Optional[int]				  =  (
         "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."
     )
     SCREAMING_SNAKE_CASE			:					List[str]				  =  "text_qa"
     SCREAMING_SNAKE_CASE			:					Optional[int]				  =  AutoTokenizer
     SCREAMING_SNAKE_CASE			:					int				  =  AutoModelForSeqaSeqLM

     SCREAMING_SNAKE_CASE			:					Tuple				  =  ["text", "text"]
     SCREAMING_SNAKE_CASE			:					int				  =  ["text"]

     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						str				,					snake_case	:						str     )		->				Dict:
          __UpperCAmelCase      :					Optional[Any]    				=     QA_PROMPT.format(text=snake_case				,					question=snake_case     )
          return self.pre_processor(snake_case				,					return_tensors='''pt'''     )

     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						int     )		->				str:
          __UpperCAmelCase      :					int    				=     self.model.generate(**snake_case     )

          __UpperCAmelCase				,  __UpperCAmelCase      :					int    				=     inputs['''input_ids'''].shape
          __UpperCAmelCase      :					Optional[int]    				=     output_ids.shape[0]

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






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						int     )		->				str:
          return self.pre_processor.decode(snake_case				,					skip_special_tokens=snake_case				,					clean_up_tokenization_spaces=snake_case     )



'''simple docstring'''



import math
from decimal import Decimal, getcontext




def       _a      (		_lowercase   :      int = 4000000    ):





     '''simple docstring'''






     try:
          __UpperCAmelCase      :					List[str]    				=     int(_lowercase    )
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''    )
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''    )
     __UpperCAmelCase      :					Tuple    				=     100
     __UpperCAmelCase      :					Optional[Any]    				=     (Decimal(5    ) ** Decimal(0.5    ) + 1) / Decimal(2    )

     __UpperCAmelCase      :					Tuple    				=     (math.floor(math.log(n * (phi + 2)     ,   _lowercase    ) - 1    ) // 3) * 3 + 2
     __UpperCAmelCase      :					List[Any]    				=     Decimal(round(phi ** Decimal(index + 1    )    )    ) / (phi + 2)
     __UpperCAmelCase      :					List[str]    				=     num // 2
     return int(_lowercase    )


if __name__ == "__main__":
      print(f"""{solution() = }""")