

'''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		       =    logging.get_logger(__name__)


class 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      question_encoder
           A_						=      generator
           A_						=      self.question_encoder



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





           '''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__       )
           A_						=      os.path.join(UpperCamelCase__		,				"""question_encoder_tokenizer"""       )
           A_						=      os.path.join(UpperCamelCase__		,				"""generator_tokenizer"""       )
           self.question_encoder.save_pretrained(UpperCamelCase__       )
           self.generator.save_pretrained(UpperCamelCase__       )



    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           # dynamically import AutoTokenizer
           from ..auto.tokenization_auto import AutoTokenizer

           A_						=      kwargs.pop("""config"""		,				UpperCamelCase__       )

           if config is None:
                  A_						=      RagConfig.from_pretrained(UpperCamelCase__       )

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



    def __call__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return self.current_tokenizer(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return self.generator.batch_decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           return self.generator.decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      self.question_encoder



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





           '''simple docstring'''
           A_						=      self.generator







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = "longest"		,				UpperCamelCase__ = None		,				UpperCamelCase__ = True		,				**UpperCamelCase__		,				)						->      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"""		,				UpperCamelCase__		,				)
           if max_length is None:
                  A_						=      self.current_tokenizer.model_max_length
           A_						=      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:
                  A_						=      self.current_tokenizer.model_max_length
           A_						=      self(
               text_target=UpperCamelCase__		,				add_special_tokens=UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				padding=UpperCamelCase__		,				max_length=UpperCamelCase__		,				truncation=UpperCamelCase__		,				**UpperCamelCase__		,				)
           A_						=      labels["""input_ids"""]
           return model_inputs



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       return str(UpperCAmelCase__				) == str(UpperCAmelCase__				)[::-1]
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       return int(UpperCAmelCase__				) + int(str(UpperCAmelCase__				)[::-1]				)
def       UpperCAmelCase__    (      UpperCAmelCase__ = 1_00_00				)			->			int:
       A_						=      []
       for num in range(1,       UpperCAmelCase__				):
              A_						=      0
              A_						=      num
              while iterations < 50:
                     A_						=      sum_reverse(UpperCAmelCase__				)
                     iterations += 1
                     if is_palindrome(UpperCAmelCase__				):
                            break
              else:
                     lychrel_nums.append(UpperCAmelCase__				)
       return len(UpperCAmelCase__				)


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