




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:
		__lowerCAmelCase						:							Any			     =						None


__lowerCAmelCase						:							Tuple			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							str			     =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase						:							str			     =						{
    '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',
    },
}

__lowerCAmelCase						:							Dict			     =						{
    'facebook/xglm-564M': 2048,
}




class        snake_case__				(lowercase__  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Dict									=   VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_			:				List[Any]									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_			:				List[Any]									=   ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   XGLMTokenizer





				def __init__( self							:	List[Any]   , __lowerCamelCase							:	Dict=None   , __lowerCamelCase							:	Optional[Any]=None   , __lowerCamelCase							:	Optional[Any]="<s>"   , __lowerCamelCase							:	Tuple="</s>"   , __lowerCamelCase							:	str="</s>"   , __lowerCamelCase							:	List[Any]="<s>"   , __lowerCamelCase							:	Optional[Any]="<unk>"   , __lowerCamelCase							:	str="<pad>"   , **__lowerCamelCase							:	Any   , )   ->	List[Any]:
							a       =     7
							a       =     [f"""<madeupword{i}>""" for i in range(self.num_madeup_words				)]

							a       =     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__(
							    SCREAMING_SNAKE_CASE__   , tokenizer_file=SCREAMING_SNAKE_CASE__   , bos_token=SCREAMING_SNAKE_CASE__   , eos_token=SCREAMING_SNAKE_CASE__   , sep_token=SCREAMING_SNAKE_CASE__   , cls_token=SCREAMING_SNAKE_CASE__   , unk_token=SCREAMING_SNAKE_CASE__   , pad_token=SCREAMING_SNAKE_CASE__   , **SCREAMING_SNAKE_CASE__   , )

							a       =     vocab_file
							a       =     False if not self.vocab_file else True





				def 				__UpperCAmelCase				( self							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Dict = None				)   ->	Any:

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





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict = None				)   ->	List[Any]:
							a       =     [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 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Union[str, Any] = None				)   ->	Union[str, Any]:

							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
							a       =     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_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

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





