





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

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


if is_sentencepiece_available():
    from .tokenization_pegasus import PegasusTokenizer
else:
    __lowerCamelCase			:	Tuple					    =	None


__lowerCamelCase			:	int					    =	logging.get_logger(__name__)


__lowerCamelCase			:	Tuple					    =	"""▁"""

__lowerCamelCase			:	int					    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase			:	Optional[int]					    =	{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__lowerCamelCase			:	Dict					    =	{
    """google/pegasus-xsum""": 512,
}



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :str		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :Optional[Any]		       =  PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :List[Any]		       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Any		       =  PegasusTokenizer
      _UpperCAmelCase      :Any		       =  ['input_ids', 'attention_mask']





      def __init__(					self			,				A_=None			,				A_=None			,				A_="<pad>"			,				A_="</s>"			,				A_="<unk>"			,				A_="<mask_2>"			,				A_="<mask_1>"			,				A_=None			,				A_=103			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  offset

       if additional_special_tokens is not None:
        if not isinstance(A_			,				A_  ):
         raise TypeError(
             F"""additional_special_tokens should be of type {type(A_  )}, but is"""
             F""" {type(A_  )}"""  )

        UpperCamelCase      :   List[Any]			      =  (
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            F"""<unk_{i}>""" for i in range(len(A_  )			,				self.offset - 1  )
        ]

        if len(set(A_  )  ) != len(A_  ):
         raise ValueError(
             "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
             F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
        UpperCamelCase      :   str			      =  additional_special_tokens_extended
       else:
        UpperCamelCase      :   Any			      =  [mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [F"""<unk_{i}>""" for i in range(2			,				self.offset  )]

       super().__init__(
           A_			,				tokenizer_file=A_			,				pad_token=A_			,				eos_token=A_			,				unk_token=A_			,				mask_token=A_			,				mask_token_sent=A_			,				offset=A_			,				additional_special_tokens=A_			,				**A_			,				)
       UpperCamelCase      :   Optional[Any]			      =  vocab_file
       UpperCamelCase      :   Any			      =  False if not self.vocab_file else True





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  set(self.all_special_ids  )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
        raise ValueError(
            "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
            F""" {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}"""  )

       return [1 if x in all_special_ids else 0 for x in seq]





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None			,				A_ = False  ):
       '''simple docstring'''




       if already_has_special_tokens:
        return self._special_token_mask(A_  )
       elif token_ids_a is None:
        return self._special_token_mask(A_  ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]





      def 			__UpperCamelCase(					self			,				A_			,				A_=None  ):
       '''simple docstring'''




       if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       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(A_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
       UpperCamelCase      :   Optional[Any]			      =  os.path.join(
           A_			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

       return (out_vocab_file,)







from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase			:	str					    =	{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_byta import ByTaTokenizer
else:
    import sys

    __lowerCamelCase			:	List[str]					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
