



from decimal import Decimal, getcontext
from math import ceil, factorial
def        lowercase_							(A : int      ):

       if not isinstance(A		,					A      ):
              raise TypeError('Undefined for non-integers'      )
       elif precision < 1:
              raise ValueError('Undefined for non-natural numbers'      )

       snake_case__							:	str   	=	precision
       snake_case__							:	Optional[int]   	=	ceil(precision / 1_4      )
       snake_case__							:	int   	=	4_2_6_8_8_0 * Decimal(1_0_0_0_5      ).sqrt()
       snake_case__							:	Dict   	=	1
       snake_case__							:	Optional[int]   	=	1_3_5_9_1_4_0_9
       snake_case__							:	Tuple   	=	Decimal(A      )
       for k in range(1		,					A      ):
              snake_case__							:	Any   	=	factorial(6 * k      ) // (factorial(3 * k      ) * factorial(A      ) ** 3)
              linear_term += 5_4_5_1_4_0_1_3_4
              exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
              partial_sum += Decimal(multinomial_term * linear_term      ) / exponential_term
       return str(constant_term / partial_sum      )[:-1]


if __name__ == "__main__":
 a_   :Optional[Any]        =      50
 print(F"""The first {n} digits of pi is: {pi(n)}""")











# 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




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""philschmid/bart-large-cnn-samsum"""
     _SCREAMING_SNAKE_CASE						=	(
         """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
         """and returns a summary of the text."""
     )
     _SCREAMING_SNAKE_CASE						=	"""summarizer"""
     _SCREAMING_SNAKE_CASE						=	AutoTokenizer
     _SCREAMING_SNAKE_CASE						=	AutoModelForSeqaSeqLM

     _SCREAMING_SNAKE_CASE						=	["""text"""]
     _SCREAMING_SNAKE_CASE						=	["""text"""]






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Any:
            return self.pre_processor(_snake_case, return_tensors='pt', truncation=_snake_case					)






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any]					)   ->Any:
            return self.model.generate(**_snake_case					)[0]






     def    lowercase_   (       self  :  int, _snake_case  :  int					)   ->str:
            return self.pre_processor.decode(_snake_case, skip_special_tokens=_snake_case, clean_up_tokenization_spaces=_snake_case					)






