

"""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


_UpperCamelCase		     =    logging.get_logger(__name__)






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				question_encoder
        lowerCAmelCase__   :				Dict 			=				generator
        lowerCAmelCase__   :				Dict 			=				self.question_encoder






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        if os.path.isfile(lowercase__   ):
         raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""   )
        os.makedirs(lowercase__			,					exist_ok=lowercase__   )
        lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(lowercase__			,					"question_encoder_tokenizer"   )
        lowerCAmelCase__   :				Optional[int] 			=				os.path.join(lowercase__			,					"generator_tokenizer"   )
        self.question_encoder.save_pretrained(lowercase__   )
        self.generator.save_pretrained(lowercase__   )






       @classmethod
       def 			SCREAMING_SNAKE_CASE_	(   cls			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        from ..auto.tokenization_auto import AutoTokenizer

        lowerCAmelCase__   :				Tuple 			=				kwargs.pop("config"			,					lowercase__   )

        if config is None:
         lowerCAmelCase__   :				Union[str, Any] 			=				RagConfig.from_pretrained(lowercase__   )

        lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(
            lowercase__			,					config=config.question_encoder			,					subfolder="question_encoder_tokenizer"   )
        lowerCAmelCase__   :				Optional[Any] 			=				AutoTokenizer.from_pretrained(
            lowercase__			,					config=config.generator			,					subfolder="generator_tokenizer"   )
        return cls(question_encoder=lowercase__			,					generator=lowercase__   )






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





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.question_encoder






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.generator






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None			,					snake_case = None			,					snake_case = None			,					snake_case = "longest"			,					snake_case = None			,					snake_case = True			,					**snake_case			,					):





        """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"			,					lowercase__			,					)
        if max_length is None:
         lowerCAmelCase__   :				Dict 			=				self.current_tokenizer.model_max_length
        lowerCAmelCase__   :				Union[str, Any] 			=				self(
            lowercase__			,					add_special_tokens=lowercase__			,					return_tensors=lowercase__			,					max_length=lowercase__			,					padding=lowercase__			,					truncation=lowercase__			,					**lowercase__			,					)
        if tgt_texts is None:
         return model_inputs
        # Process tgt_texts
        if max_target_length is None:
         lowerCAmelCase__   :				Optional[int] 			=				self.current_tokenizer.model_max_length
        lowerCAmelCase__   :				Optional[int] 			=				self(
            text_target=lowercase__			,					add_special_tokens=lowercase__			,					return_tensors=lowercase__			,					padding=lowercase__			,					max_length=lowercase__			,					truncation=lowercase__			,					**lowercase__			,					)
        lowerCAmelCase__   :				Optional[Any] 			=				labels["""input_ids"""]
        return model_inputs












'''simple docstring'''


from ..utils import DummyObject, requires_backends







class      SCREAMING_SNAKE_CASE    (						metaclass=snake_case_				):
      __magic_name__  :       Dict   							=					['''transformers''', '''torch''', '''note_seq''']






      def __init__( self   :  List[str]						,						*lowercase__   :  Dict						,						**lowercase__   :  int       ):


           '''simple docstring'''

           requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						*lowercase__   :  Optional[int]						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )
