





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:
    _UpperCAmelCase				:       Dict	   =				None


_UpperCAmelCase				:       Union[str, Any]	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       Optional[int]	   =				"""▁"""

_UpperCAmelCase				:       Optional[Any]	   =				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       Union[str, Any]	   =				{
    """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"""
    },
}

_UpperCAmelCase				:       List[Any]	   =				{
    """google/pegasus-xsum""": 5_12,
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		PegasusTokenizer
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 def __init__(      self  :      int			,     UpperCAmelCase  :      Optional[int]=None			,     UpperCAmelCase  :      List[Any]=None			,     UpperCAmelCase  :      Any="<pad>"			,     UpperCAmelCase  :      Union[str, Any]="</s>"			,     UpperCAmelCase  :      Dict="<unk>"			,     UpperCAmelCase  :      Dict="<mask_2>"			,     UpperCAmelCase  :      List[str]="<mask_1>"			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      str=103			,     **UpperCAmelCase  :      str			,     )			->	Any:
     lowerCamelCase__    :      Optional[int]					 =		offset

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

         lowerCamelCase__    :      Optional[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(UpperCAmelCase	)			,     self.offset - 1	)
         ]

         if len(set(UpperCAmelCase	)	) != len(UpperCAmelCase	):
             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}."""	)
         lowerCamelCase__    :      Optional[Any]					 =		additional_special_tokens_extended
     else:
         lowerCamelCase__    :      Tuple					 =		[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__(
         UpperCAmelCase			,     tokenizer_file=UpperCAmelCase			,     pad_token=UpperCAmelCase			,     eos_token=UpperCAmelCase			,     unk_token=UpperCAmelCase			,     mask_token=UpperCAmelCase			,     mask_token_sent=UpperCAmelCase			,     offset=UpperCAmelCase			,     additional_special_tokens=UpperCAmelCase			,     **UpperCAmelCase			,     )
     lowerCamelCase__    :      int					 =		vocab_file
     lowerCamelCase__    :      Any					 =		False if not self.vocab_file else True
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      str	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[Any]					 =		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      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      List			,     UpperCAmelCase  :      Optional[List] = None			,     UpperCAmelCase  :      bool = False	)			->	List[int]:
     if already_has_special_tokens:
         return self._special_token_mask(UpperCAmelCase	)
     elif token_ids_a is None:
         return self._special_token_mask(UpperCAmelCase	) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a	) + [1]
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      int=None	)			->	List[int]:
     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      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = 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(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
         return
     lowerCamelCase__    :      List[Any]					 =		os.path.join(
         UpperCAmelCase			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

     return (out_vocab_file,)












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
