






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			:					Optional[int]         =	logging.get_logger(__name__)



class 							_UpperCamelCase			:


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					question_encoder
           __lowerCAmelCase								=					generator
           __lowerCAmelCase								=					self.question_encoder


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           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	)
           __lowerCAmelCase								=					os.path.join(__UpperCamelCase					,						"question_encoder_tokenizer"	)
           __lowerCAmelCase								=					os.path.join(__UpperCamelCase					,						"generator_tokenizer"	)
           self.question_encoder.save_pretrained(__UpperCamelCase	)
           self.generator.save_pretrained(__UpperCamelCase	)


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						**__UpperCamelCase	)->	int:
           # dynamically import AutoTokenizer
           from ..auto.tokenization_auto import AutoTokenizer

           __lowerCAmelCase								=					kwargs.pop("config"					,						__UpperCamelCase	)

           if config is None:
                __lowerCAmelCase								=					RagConfig.from_pretrained(__UpperCamelCase	)

           __lowerCAmelCase								=					AutoTokenizer.from_pretrained(
               __UpperCamelCase					,						config=config.question_encoder					,						subfolder="question_encoder_tokenizer"	)
           __lowerCAmelCase								=					AutoTokenizer.from_pretrained(
               __UpperCamelCase					,						config=config.generator					,						subfolder="generator_tokenizer"	)
           return cls(question_encoder=__UpperCamelCase					,						generator=__UpperCamelCase	)


      def __call__(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	str:
           return self.current_tokenizer(*__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	List[Any]:
           return self.generator.batch_decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	Union[str, Any]:
           return self.generator.decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.question_encoder


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					self.generator


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = "longest"					,						__UpperCamelCase = None					,						__UpperCamelCase = True					,						**__UpperCamelCase					,						)->	BatchEncoding:
           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:
                __lowerCAmelCase								=					self.current_tokenizer.model_max_length
           __lowerCAmelCase								=					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:
                __lowerCAmelCase								=					self.current_tokenizer.model_max_length
           __lowerCAmelCase								=					self(
               text_target=__UpperCamelCase					,						add_special_tokens=__UpperCamelCase					,						return_tensors=__UpperCamelCase					,						padding=__UpperCamelCase					,						max_length=__UpperCamelCase					,						truncation=__UpperCamelCase					,						**__UpperCamelCase					,						)
           __lowerCAmelCase								=					labels["input_ids"]
           return model_inputs














lowerCamelCase			:					str         =	'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase			:					Tuple         =	[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase			:					Dict         =	{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






