


import os
import warnings
from typing import List, Optional

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


__a     =			logging.get_logger(__name__)






class   __lowercase				:





  def __init__(		self						:					int					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]       )    ->   str:






     """simple docstring"""







     UpperCAmelCase							=     question_encoder
     UpperCAmelCase							=     generator
     UpperCAmelCase							=     self.question_encoder





  def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Union[str, Any]       )    ->   Dict:






     """simple docstring"""







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





  @classmethod
  def        _lowercase				(		cls						:					Optional[Any]					,       __lowerCamelCase						:					List[str]					,       **__lowerCamelCase						:					int       )    ->   Any:






     """simple docstring"""







     from ..auto.tokenization_auto import AutoTokenizer

     UpperCAmelCase							=     kwargs.pop("""config"""					,       __A       )

     if config is None:
        UpperCAmelCase							=     RagConfig.from_pretrained(__A       )

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





  def __call__(		self						:					int					,       *__lowerCamelCase						:					Optional[int]					,       **__lowerCamelCase						:					List[str]       )    ->   int:






     """simple docstring"""







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





  def        _lowercase				(		self						:					Dict					,       *__lowerCamelCase						:					List[str]					,       **__lowerCamelCase						:					List[str]       )    ->   Dict:






     """simple docstring"""







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





  def        _lowercase				(		self						:					Union[str, Any]					,       *__lowerCamelCase						:					Tuple					,       **__lowerCamelCase						:					List[str]       )    ->   Tuple:






     """simple docstring"""







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





  def        _lowercase				(		self						:					Dict       )    ->   List[str]:






     """simple docstring"""







     UpperCAmelCase							=     self.question_encoder





  def        _lowercase				(		self						:					Union[str, Any]       )    ->   int:






     """simple docstring"""







     UpperCAmelCase							=     self.generator





  def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Optional[List[str]] = None					,       __lowerCamelCase						:					Optional[int] = None					,       __lowerCamelCase						:					Optional[int] = None					,       __lowerCamelCase						:					str = "longest"					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					bool = True					,       **__lowerCamelCase						:					Tuple					,       )    ->   BatchEncoding:






     """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"""					,       __A					,       )
     if max_length is None:
        UpperCAmelCase							=     self.current_tokenizer.model_max_length
     UpperCAmelCase							=     self(
         __A					,       add_special_tokens=__A					,       return_tensors=__A					,       max_length=__A					,       padding=__A					,       truncation=__A					,       **__A					,       )
     if tgt_texts is None:
        return model_inputs
     # Process tgt_texts
     if max_target_length is None:
        UpperCAmelCase							=     self.current_tokenizer.model_max_length
     UpperCAmelCase							=     self(
         text_target=__A					,       add_special_tokens=__A					,       return_tensors=__A					,       padding=__A					,       max_length=__A					,       truncation=__A					,       **__A					,       )
     UpperCAmelCase							=     labels["""input_ids"""]
     return model_inputs







from math import isqrt
def   _UpperCamelCase				(						lowerCAmelCase_   )						->bool:
   return all(number % divisor != 0 for divisor in range(2  ,						isqrt(lowerCAmelCase_   ) + 1   )   )
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0**6   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     1
   UpperCAmelCase							=     7
   while prime_candidate < max_prime:
      primes_count += is_prime(lowerCAmelCase_   )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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



