





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 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							:


							def __init__(				self     :Optional[Any]				,					__A     :int				,					__A     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   question_encoder
									SCREAMING_SNAKE_CASE__								=   generator
									SCREAMING_SNAKE_CASE__								=   self.question_encoder


							def   _snake_case	(				self     :Tuple				,					__A     :List[str]		) -> Optional[int]:



									"""simple docstring"""



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


							@classmethod
							def   _snake_case	(				cls     :Union[str, Any]				,					__A     :List[Any]				,					**__A     :Any		) -> Union[str, Any]:



									"""simple docstring"""



									from ..auto.tokenization_auto import AutoTokenizer

									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""config"""				,					__A		)

									if config is None:
											SCREAMING_SNAKE_CASE__								=   RagConfig.from_pretrained(__A		)

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


							def __call__(				self     :Any				,					*__A     :str				,					**__A     :int		) -> str:



									"""simple docstring"""



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


							def   _snake_case	(				self     :str				,					*__A     :Any				,					**__A     :Tuple		) -> List[str]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Optional[Any]				,					**__A     :Dict		) -> Optional[Any]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.question_encoder


							def   _snake_case	(				self     :Any		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.generator


							def   _snake_case	(				self     :Dict				,					__A     :List[str]				,					__A     :Optional[List[str]] = None				,					__A     :Optional[int] = None				,					__A     :Optional[int] = None				,					__A     :str = "longest"				,					__A     :str = None				,					__A     :bool = True				,					**__A     :Union[str, Any]				,					) -> BatchEncoding:



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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""encoder"""		)
									SCREAMING_SNAKE_CASE__								=   encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""decoder"""		)
									SCREAMING_SNAKE_CASE__								=   decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   True

									return cls(encoder=encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**__A		)


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__								=   self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.__class__.model_type
									return output