
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__       :   Any   						=					None


A__       :   Dict   						=					logging.get_logger(__name__)


A__       :   Any   						=					"""▁"""

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

A__       :   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"""
        )
    },
}

A__       :   List[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__ :    List[Any]				      =			vocab_file
								lowerCAmelCase__ :    int				      =			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__ :    Union[str, 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,)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """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__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



