




'''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_      :		Tuple							      =       logging.get_logger(__name__)


class   __UpperCamelCase       :






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=question_encoder
              lowerCamelCase_							=generator
              lowerCamelCase_							=self.question_encoder






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       @classmethod
       def      lowercase__     (							cls,		lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              from ..auto.tokenization_auto import AutoTokenizer

              lowerCamelCase_							=kwargs.pop('''config''',		lowerCAmelCase  )

              if config is None:
                     lowerCamelCase_							=RagConfig.from_pretrained(lowerCAmelCase  )

              lowerCamelCase_							=AutoTokenizer.from_pretrained(
                  lowerCAmelCase,		config=config.question_encoder,		subfolder='''question_encoder_tokenizer'''  )
              lowerCamelCase_							=AutoTokenizer.from_pretrained(
                  lowerCAmelCase,		config=config.generator,		subfolder='''generator_tokenizer'''  )
              return cls(question_encoder=lowerCAmelCase,		generator=lowerCAmelCase  )






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



              """simple docstring"""







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






       def      lowercase__     (							self,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.question_encoder






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.generator






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = "longest",		lowerCAmelCase = None,		lowerCAmelCase = True,		**lowerCAmelCase,		):



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







def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


