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

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


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       A__       :   str   						=					None


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


A__       :   Any   						=					"""▁"""

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

A__       :   List[str]   						=					{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    },
    """tokenizer_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"""
        )
    },
}

A__       :   Any   						=					{
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}





class 						lowercase       (  __UpperCamelCase					):
     __a					=    VOCAB_FILES_NAMES
     __a					=    PRETRAINED_VOCAB_FILES_MAP
     __a					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a					=    ["input_ids", "attention_mask"]
     __a					=    ReformerTokenizer
     def __init__(		self ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__="</s>" ,	SCREAMING_SNAKE_CASE__="<unk>" ,	SCREAMING_SNAKE_CASE__=[] ,	**SCREAMING_SNAKE_CASE__ ,	):
        """simple docstring"""







        super().__init__(
            SCREAMING_SNAKE_CASE__ ,	tokenizer_file=SCREAMING_SNAKE_CASE__ ,	eos_token=SCREAMING_SNAKE_CASE__ ,	unk_token=SCREAMING_SNAKE_CASE__ ,	additional_special_tokens=SCREAMING_SNAKE_CASE__ ,	**SCREAMING_SNAKE_CASE__ ,	)

        lowerCAmelCase__ :    Any				      =			vocab_file
        lowerCAmelCase__ :    List[Any]				      =			False if not self.vocab_file else True
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = 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(SCREAMING_SNAKE_CASE__    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        lowerCAmelCase__ :    Any				      =			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,)





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



