




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     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[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 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # 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: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             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





'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      A__		:					Dict												=  None


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

A__		:					str												=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A__		:					Union[str, Any]												=  {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

A__		:					Any												=  {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

A__		:					Dict												=  """▁"""

# Segments (not really needed)
A__		:					List[str]												=  0
A__		:					List[Any]												=  1
A__		:					Union[str, Any]												=  2
A__		:					List[Any]												=  3
A__		:					str												=  4





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					str							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Dict							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Tuple							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					List[Any]							     =			'left'
    lowerCamelCase  :					Optional[Any]							     =			XLNetTokenizer







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<sep>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_="<cls>"			,     SCREAMING_SNAKE_CASE_="<mask>"			,     SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"]			,     **SCREAMING_SNAKE_CASE_			,     )				->     Union[str, Any]:
         # Mask token behave like a normal word, i.e. include the space before it
         __lowerCamelCase   :	str								=		AddedToken(SCREAMING_SNAKE_CASE_			,     lstrip=SCREAMING_SNAKE_CASE_			,     rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) else mask_token

         super().__init__(
             vocab_file=SCREAMING_SNAKE_CASE_			,     tokenizer_file=SCREAMING_SNAKE_CASE_			,     do_lower_case=SCREAMING_SNAKE_CASE_			,     remove_space=SCREAMING_SNAKE_CASE_			,     keep_accents=SCREAMING_SNAKE_CASE_			,     bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     sep_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     cls_token=SCREAMING_SNAKE_CASE_			,     mask_token=SCREAMING_SNAKE_CASE_			,     additional_special_tokens=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	Union[str, Any]								=		3
         __lowerCamelCase   :	int								=		do_lower_case
         __lowerCamelCase   :	Optional[Any]								=		remove_space
         __lowerCamelCase   :	int								=		keep_accents
         __lowerCamelCase   :	Any								=		vocab_file
         __lowerCamelCase   :	Any								=		False if not self.vocab_file else True







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	Optional[Any]								=		[self.sep_token_id]
         __lowerCamelCase   :	Any								=		[self.cls_token_id]
         if token_ids_a is None:
              return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	int								=		[self.sep_token_id]
         __lowerCamelCase   :	Dict								=		[2]

         if token_ids_a is None:
              return len(token_ids_a + sep	) * [0] + cls_segment_id
         return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                  'tokenizer.'	)

         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	):
              copyfile(self.vocab_file			,     SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)






