



from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :List[Any]			      =  c_float

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




									return sizeof(self.value						)




def 			lowerCAmelCase_  (						_lowercase       :     int		,     _lowercase       :     int		,     _lowercase       :     int = 0)							-> int:







			"""simple docstring"""
			# < 0 is possible if using a dynamic axis
			if dimension <= 0:
						a__     :		Union[str, Any]		 =						fixed_dimension

			dimension -= num_token_to_add
			return dimension

def 			lowerCAmelCase_  (						_lowercase       :     int		,     _lowercase       :     ParameterFormat)							-> int:







			"""simple docstring"""
			return num_parameters * dtype.size





def 			lowerCAmelCase_  (						_lowercase       :     str)							-> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:







			"""simple docstring"""
			from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

			try:
						return AutoProcessor.from_pretrained(_lowercase)
			except (ValueError, OSError, KeyError):
						a__ ,		a__     :		Tuple		 =						None, None
						try:
									a__     :		Optional[Any]		 =						AutoTokenizer.from_pretrained(_lowercase)
						except (OSError, KeyError):
									pass
						try:
									a__     :		Optional[int]		 =						AutoFeatureExtractor.from_pretrained(_lowercase)
						except (OSError, KeyError):
									pass

						if tokenizer is not None and feature_extractor is not None:
									raise ValueError(
									    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.''')
						elif tokenizer is None and feature_extractor is None:
									return None
						elif tokenizer is not None:
									return tokenizer
						else:
									return feature_extractor









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	:   Optional[int]    	=None


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


_lowercase	:   Any    	="▁"

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

_lowercase	:   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"
        )
    },
}

_lowercase	:   List[str]    	={
    "google/reformer-crime-and-punishment": 52_4288,
}

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Union[str, Any]			      =  VOCAB_FILES_NAMES
						__lowerCAmelCase  :int			      =  PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase  :List[Any]			      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase  :Any			      =  ["input_ids", "attention_mask"]
						__lowerCAmelCase  :Tuple			      =  ReformerTokenizer

						def __init__(     self      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase="</s>"      ,     __lowercase="<unk>"      ,     __lowercase=[]      ,     **__lowercase      ,     )      ->   int:
									"""simple docstring"""




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

									a__     :		Union[str, Any]		 =						vocab_file
									a__     :		str		 =						False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   Tuple[str]:
									"""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(__lowercase						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__     :		Tuple		 =						os.path.join(
									    __lowercase      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

									return (out_vocab_file,)




