

'''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__							)    ->   List[str]:





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



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Optional[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__							)    ->   str:





           '''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__							)    ->   Union[str, Any]:





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



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





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



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





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



    def        snake_case_      (       self							)    ->   str:





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



    def        snake_case_      (       self							)    ->   List[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'''







from math import pow
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							)      ->      tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       A_						       =   int(pow(UpperCAmelCase__,							UpperCAmelCase__					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
       return current_sum, solutions_count
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  """Invalid input\n"""
                  """needed_sum must be between 1 and 1000, power between 2 and 10."""					)

       return backtrack(UpperCAmelCase__,							UpperCAmelCase__,							1,							0,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
      import doctest

      doctest.testmod()
