
import os
import warnings
from typing import List, Optional

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


_SCREAMING_SNAKE_CASE    :      List[Any]          =			logging.get_logger(__name__)







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	int         =							question_encoder
					A__  :	int         =							generator
					A__  :	Optional[int]         =							self.question_encoder





	def   __snake_case				(			self				,       UpperCamelCase__		):
					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__  :	Optional[Any]         =							os.path.join(UpperCamelCase__				,       '''question_encoder_tokenizer'''		)
					A__  :	List[Any]         =							os.path.join(UpperCamelCase__				,       '''generator_tokenizer'''		)
					self.question_encoder.save_pretrained(UpperCamelCase__		)
					self.generator.save_pretrained(UpperCamelCase__		)





	@classmethod
	def   __snake_case				(			cls				,       UpperCamelCase__				,       **UpperCamelCase__		):
					# dynamically import AutoTokenizer
					from ..auto.tokenization_auto import AutoTokenizer

					A__  :	int         =							kwargs.pop('''config'''				,       UpperCamelCase__		)

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

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





	def __call__(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.current_tokenizer(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.generator.batch_decode(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					return self.generator.decode(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.question_encoder





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.generator





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "longest"				,       UpperCamelCase__ = None				,       UpperCamelCase__ = True				,       **UpperCamelCase__				,       ):
					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__  :	str         =							self.current_tokenizer.model_max_length
					A__  :	Any         =							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__  :	List[Any]         =							self.current_tokenizer.model_max_length
					A__  :	Dict         =							self(
					    text_target=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__				,       return_tensors=UpperCamelCase__				,       padding=UpperCamelCase__				,       max_length=UpperCamelCase__				,       truncation=UpperCamelCase__				,       **UpperCamelCase__				,       )
					A__  :	Any         =							labels['''input_ids''']
					return model_inputs

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def   __snake_case				(			UpperCamelCase__		):
					raise NotImplementedError()





	@abstractmethod
	def   __snake_case				(			self		):
					raise NotImplementedError()