
'''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:
					__lowercase							:				Tuple					=       None


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

__lowercase							:				List[Any]					=       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase							:				Optional[int]					=       {
    '''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''',
    },
}

__lowercase							:				Union[str, Any]					=       {
    '''facebook/xglm-564M''': 2048,
}

class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						Optional[Any]		 =     VOCAB_FILES_NAMES
			lowerCamelCase     :						Union[str, Any]		 =     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase     :						Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase     :						Optional[int]		 =     ["input_ids", "attention_mask"]
			lowerCamelCase     :						Optional[int]		 =     XGLMTokenizer

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

								lowerCamelCase_   : Union[str, 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__(
								    A     ,				tokenizer_file=A     ,				bos_token=A     ,				eos_token=A     ,				sep_token=A     ,				cls_token=A     ,				unk_token=A     ,				pad_token=A     ,				**A     ,				)

								lowerCamelCase_   : Tuple       =						vocab_file
								lowerCamelCase_   : Any       =						False if not self.vocab_file else True

			def     UpperCAmelCase__	(self     ,				A     ,				A = None ):

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

			def     UpperCAmelCase__	(self     ,				A     ,				A = None ):
								lowerCamelCase_   : Optional[int]       =						[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     ,				A     ,				A = 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(A ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
													return
								lowerCamelCase_   : List[Any]       =						os.path.join(
								    A     ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

								return (out_vocab_file,)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


