"""simple docstring"""






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCAmelCase    :       Any					     =logging.get_logger(__name__)





class  _A						:

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	question_encoder
            lowercase														=	generator
            lowercase														=	self.question_encoder

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if os.path.isfile(__lowerCAmelCase						):
                  raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'						)
            os.makedirs(__lowerCAmelCase		,       exist_ok=__lowerCAmelCase						)
            lowercase														=	os.path.join(__lowerCAmelCase		,       """question_encoder_tokenizer"""						)
            lowercase														=	os.path.join(__lowerCAmelCase		,       """generator_tokenizer"""						)
            self.question_encoder.save_pretrained(__lowerCAmelCase						)
            self.generator.save_pretrained(__lowerCAmelCase						)

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            from ..auto.tokenization_auto import AutoTokenizer

            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						)

            if config is None:
                  lowercase														=	RagConfig.from_pretrained(__lowerCAmelCase						)

            lowercase														=	AutoTokenizer.from_pretrained(
                __lowerCAmelCase		,       config=config.question_encoder		,       subfolder="""question_encoder_tokenizer"""						)
            lowercase														=	AutoTokenizer.from_pretrained(
                __lowerCAmelCase		,       config=config.generator		,       subfolder="""generator_tokenizer"""						)
            return cls(question_encoder=__lowerCAmelCase		,       generator=__lowerCAmelCase						)

      def __call__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return self.current_tokenizer(*__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return self.generator.batch_decode(*__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return self.generator.decode(*__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.question_encoder

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.generator







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None		,       __lowerCAmelCase = None		,       __lowerCAmelCase = None		,       __lowerCAmelCase = "longest"		,       __lowerCAmelCase = None		,       __lowerCAmelCase = True		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            warnings.warn(
                """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                """details"""		,       __lowerCAmelCase		,       )
            if max_length is None:
                  lowercase														=	self.current_tokenizer.model_max_length
            lowercase														=	self(
                __lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase		,       return_tensors=__lowerCAmelCase		,       max_length=__lowerCAmelCase		,       padding=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       **__lowerCAmelCase		,       )
            if tgt_texts is None:
                  return model_inputs
            # Process tgt_texts
            if max_target_length is None:
                  lowercase														=	self.current_tokenizer.model_max_length
            lowercase														=	self(
                text_target=__lowerCAmelCase		,       add_special_tokens=__lowerCAmelCase		,       return_tensors=__lowerCAmelCase		,       padding=__lowerCAmelCase		,       max_length=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       **__lowerCAmelCase		,       )
            lowercase														=	labels["""input_ids"""]
            return model_inputs




"""simple docstring"""






from decimal import Decimal, getcontext
from math import ceil, factorial


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    str:
      '''simple docstring'''


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

      lowercase														=	precision
      lowercase														=	ceil(precision / 1_4		)
      lowercase														=	4_2_6_8_8_0 * Decimal(1_0_0_0_5		).sqrt()
      lowercase														=	1
      lowercase														=	1_3_5_9_1_4_0_9
      lowercase														=	Decimal(lowerCAmelCase__		)
      for k in range(1					,		lowerCAmelCase__		):
            lowercase														=	factorial(6 * k		) // (factorial(3 * k		) * factorial(lowerCAmelCase__		) ** 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__":
       __lowerCAmelCase    :       List[Any]					     =5_0
       print(F"""The first {n} digits of pi is: {pi(n)}""")



