






"""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


A:     List[str]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			question_encoder
    UpperCAmelCase			:      Optional[int]			=			generator
    UpperCAmelCase			:      Optional[int]			=			self.question_encoder

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




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

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    from ..auto.tokenization_auto import AutoTokenizer

    UpperCAmelCase			:      Tuple			=			kwargs.pop("""config"""   ,  UpperCAmelCase_		)

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

    UpperCAmelCase			:      int			=			AutoTokenizer.from_pretrained(
        UpperCAmelCase_   ,  config=config.question_encoder   ,  subfolder="""question_encoder_tokenizer"""		)
    UpperCAmelCase			:      Dict			=			AutoTokenizer.from_pretrained(
        UpperCAmelCase_   ,  config=config.generator   ,  subfolder="""generator_tokenizer"""		)
    return cls(question_encoder=UpperCAmelCase_   ,  generator=UpperCAmelCase_		)

   def __call__(     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.question_encoder

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.generator




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "longest"   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''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"""   ,  UpperCAmelCase_   ,  )
    if max_length is None:
     UpperCAmelCase			:      int			=			self.current_tokenizer.model_max_length
    UpperCAmelCase			:      List[Any]			=			self(
        UpperCAmelCase_   ,  add_special_tokens=UpperCAmelCase_   ,  return_tensors=UpperCAmelCase_   ,  max_length=UpperCAmelCase_   ,  padding=UpperCAmelCase_   ,  truncation=UpperCAmelCase_   ,  **UpperCAmelCase_   ,  )
    if tgt_texts is None:
     return model_inputs
    # Process tgt_texts
    if max_target_length is None:
     UpperCAmelCase			:      List[Any]			=			self.current_tokenizer.model_max_length
    UpperCAmelCase			:      Tuple			=			self(
        text_target=UpperCAmelCase_   ,  add_special_tokens=UpperCAmelCase_   ,  return_tensors=UpperCAmelCase_   ,  padding=UpperCAmelCase_   ,  max_length=UpperCAmelCase_   ,  truncation=UpperCAmelCase_   ,  **UpperCAmelCase_   ,  )
    UpperCAmelCase			:      List[str]			=			labels['input_ids']
    return model_inputs





"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator

def    _snake_case		(		):
 UpperCAmelCase			:      dict[int, int]			=			{}
 UpperCAmelCase			:      str			=			2
 while True:
  UpperCAmelCase			:      List[str]			=			factor_map.pop(UpperCamelCase    ,	UpperCamelCase		)
  if factor:
   UpperCAmelCase			:      Dict			=			factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase			:      Tuple			=			factor
  else:
   UpperCAmelCase			:      Optional[int]			=			prime
   yield prime
  prime += 1

def    _snake_case		(		UpperCamelCase		:	float = 1e1_0		):
 UpperCAmelCase			:      Tuple			=			sieve()

 UpperCAmelCase			:      Optional[Any]			=			1
 while True:
  UpperCAmelCase			:      List[str]			=			next(UpperCamelCase		)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(UpperCamelCase		)
  n += 2


if __name__ == "__main__":
      print(solution())
