



"""simple docstring"""







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:
     lowercase__						:							List[Any]        =      None


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


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

lowercase__						:							Union[str, Any]        =      {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase__						:							Any        =      {
    """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"""
        )
    },
}

lowercase__						:							List[Any]        =      {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class  UpperCamelCase__  (      __lowerCAmelCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE    							=	PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE    							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE    							=	["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE    							=	ReformerTokenizer



      def __init__( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Tuple=None       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]=None       ,     SCREAMING_SNAKE_CASE_							: str="</s>"       ,     SCREAMING_SNAKE_CASE_							: Optional[int]="<unk>"       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=[]       ,     **SCREAMING_SNAKE_CASE_							: int       ,     ):
        super().__init__(
            __UpperCamelCase       ,     tokenizer_file=__UpperCamelCase       ,     eos_token=__UpperCamelCase       ,     unk_token=__UpperCamelCase       ,     additional_special_tokens=__UpperCamelCase       ,     **__UpperCamelCase       ,     )

        lowerCAmelCase_  :							Optional[Any]								=					vocab_file
        lowerCAmelCase_  :							int								=					False if not self.vocab_file else True





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: Optional[str] = None			):






        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_  :							Optional[int]								=					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_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

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