



"""simple docstring"""
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_xglm import XGLMTokenizer
else:
						__a           =							None


__a           =							logging.get_logger(__name__)

__a           =							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__a           =							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__a           =							{
    "facebook/xglm-564M": 20_48,
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     int		=	VOCAB_FILES_NAMES
					_A						:     Optional[int]		=	PRETRAINED_VOCAB_FILES_MAP
					_A						:     Optional[Any]		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A						:     Union[str, Any]		=	["""input_ids""", """attention_mask"""]
					_A						:     Optional[Any]		=	XGLMTokenizer






					def __init__(			self:				str		,	snake_case:				Optional[Any]=None		,	snake_case:				List[str]=None		,	snake_case:				Dict="<s>"		,	snake_case:				Dict="</s>"		,	snake_case:				Optional[Any]="</s>"		,	snake_case:				Optional[int]="<s>"		,	snake_case:				Optional[Any]="<unk>"		,	snake_case:				List[str]="<pad>"		,	**snake_case:				Optional[Any]		,	)  ->      Optional[Any]:
											# Compatibility with the original tokenizer
											snake_case_					:List[str]		=     7
											snake_case_					:int		=     [f"""<madeupword{i}>""" for i in range(self.num_madeup_words		)]

											snake_case_					:List[Any]		=     kwargs.get("""additional_special_tokens"""		,	[]		)
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    snake_case		,	tokenizer_file=snake_case		,	bos_token=snake_case		,	eos_token=snake_case		,	sep_token=snake_case		,	cls_token=snake_case		,	unk_token=snake_case		,	pad_token=snake_case		,	**snake_case		,	)

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






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				List[int]		,	snake_case:				Optional[List[int]] = None		)  ->      List[int]:

											if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
											snake_case_					:Any		=     [self.sep_token_id]
											return sep + token_ids_a + sep + sep + token_ids_a






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				List[int]		,	snake_case:				Optional[List[int]] = None		)  ->      List[int]:
											snake_case_					:Tuple		=     [self.sep_token_id]

											if token_ids_a is None:
																	return len(sep + token_ids_a		) * [0]
											return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				str		,	snake_case:				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(snake_case		):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""		)
																	return
											snake_case_					:Any		=     os.path.join(
											    snake_case		,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

											return (out_vocab_file,)


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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								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





