
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase__		:     Union[str, Any]     			=      logging.get_logger(__name__)






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Dict			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  str					=       question_encoder
      _A:  Any					=       generator
      _A:  Dict					=       self.question_encoder


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Tuple     ):

      """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_     )
      _A:  int					=       os.path.join(lowerCAmelCase_			,			'''question_encoder_tokenizer'''     )
      _A:  Dict					=       os.path.join(lowerCAmelCase_			,			'''generator_tokenizer'''     )
      self.question_encoder.save_pretrained(lowerCAmelCase_     )
      self.generator.save_pretrained(lowerCAmelCase_     )


   @classmethod
   def  __magic_name__				(					cls     :							Optional[int]			,			lowerCAmelCase_     :							List[Any]			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      from ..auto.tokenization_auto import AutoTokenizer

      _A:  Tuple					=       kwargs.pop('''config'''			,			lowerCAmelCase_     )

      if config is None:
         _A:  List[str]					=       RagConfig.from_pretrained(lowerCAmelCase_     )

      _A:  int					=       AutoTokenizer.from_pretrained(
          lowerCAmelCase_			,			config=config.question_encoder			,			subfolder='''question_encoder_tokenizer'''     )
      _A:  Optional[Any]					=       AutoTokenizer.from_pretrained(
          lowerCAmelCase_			,			config=config.generator			,			subfolder='''generator_tokenizer'''     )
      return cls(question_encoder=lowerCAmelCase_			,			generator=lowerCAmelCase_     )


   def __call__(					self     :							int			,			*lowerCAmelCase_     :							Optional[Any]			,			**lowerCAmelCase_     :							str     ):

      """simple docstring"""
      return self.current_tokenizer(*lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str			,			*lowerCAmelCase_     :							Dict			,			**lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      return self.generator.batch_decode(*lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple			,			*lowerCAmelCase_     :							str			,			**lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return self.generator.decode(*lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  str					=       self.question_encoder


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       self.generator


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							int = None			,			lowerCAmelCase_     :							Any = None			,			lowerCAmelCase_     :							List[str] = None			,			lowerCAmelCase_     :							int = "longest"			,			lowerCAmelCase_     :							Union[str, Any] = None			,			lowerCAmelCase_     :							str = True			,			**lowerCAmelCase_     :							List[Any]			,			):

      """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:
         _A:  Any					=       self.current_tokenizer.model_max_length
      _A:  List[str]					=       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:
         _A:  List[str]					=       self.current_tokenizer.model_max_length
      _A:  Union[str, Any]					=       self(
          text_target=lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_			,			return_tensors=lowerCAmelCase_			,			padding=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
      _A:  List[Any]					=       labels['''input_ids''']
      return model_inputs











from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(__lowerCAmelCase	)
						if n > 1:
												factors.append(__lowerCAmelCase	)
						return factors


if __name__ == "__main__":
		import doctest

		doctest.testmod()
