

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     :   Tuple						     =  logging.get_logger(__name__)
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     question_encoder
        SCREAMING_SNAKE_CASE_				:  Any           =     generator
        SCREAMING_SNAKE_CASE_				:  Any           =     self.question_encoder

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        from ..auto.tokenization_auto import AutoTokenizer

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     kwargs.pop('config' ,					_SCREAMING_SNAKE_CASE					)

        if config is None:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     AutoTokenizer.from_pretrained(
            _SCREAMING_SNAKE_CASE ,					config=config.question_encoder ,					subfolder='question_encoder_tokenizer'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     AutoTokenizer.from_pretrained(
            _SCREAMING_SNAKE_CASE ,					config=config.generator ,					subfolder='generator_tokenizer'					)
        return cls(question_encoder=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE					)

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





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.question_encoder

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.generator



  def  UpperCAmelCase    (				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 ,					):





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









def    A_  (							a  ):

      """simple docstring"""



      if upper_limit < 0:
            raise ValueError('Limit for the Catalan sequence must be ≥ 0'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     [0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      SCREAMING_SNAKE_CASE_				:  Any           =     1
      if upper_limit > 0:
            SCREAMING_SNAKE_CASE_				:  int           =     1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2					,   upper_limit + 1  ):
            for j in range(a  ):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
    print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
    print('\n*** Enter -1 at any time to quit ***')
    print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
    try:
        while True:
            lowerCAmelCase     :   Union[str, Any]						     =  int(input().strip())
            if N < 0:
                print('\n********* Goodbye!! ************')
                break
            else:
                print(F'The Catalan numbers from 0 through {N} are:')
                print(catalan_numbers(N))
                print('Try another upper limit for the sequence: ', end='')
    except (NameError, ValueError):
        print('\n********* Invalid input, goodbye! ************\n')

    import doctest

    doctest.testmod()






