import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_barthez import BarthezTokenizer
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			None

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

__SCREAMING_SNAKE_CASE						:   int         =			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__SCREAMING_SNAKE_CASE						:   List[str]         =			{
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__SCREAMING_SNAKE_CASE						:   List[str]         =			'▁'



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		VOCAB_FILES_NAMES
							_lowerCamelCase						=		PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase						=		['input_ids', 'attention_mask']
							_lowerCamelCase						=		BarthezTokenizer



							def __init__(						self				,    lowercase_=None				,    lowercase_=None				,    lowercase_="<s>"				,    lowercase_="</s>"				,    lowercase_="</s>"				,    lowercase_="<s>"				,    lowercase_="<unk>"				,    lowercase_="<pad>"				,    lowercase_="<mask>"				,    **lowercase_				,    ):
													# Mask token behave like a normal word, i.e. include the space before it
													_snake_case						:					Dict    =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_    ) if isinstance(lowercase_				,    lowercase_    ) else mask_token

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

													_snake_case						:					int    =   vocab_file
													_snake_case						:					List[Any]    =   False if not self.vocab_file else True



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):

													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													_snake_case						:					List[str]    =   [self.cls_token_id]
													_snake_case						:					List[Any]    =   [self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					Dict    =   [self.sep_token_id]
													_snake_case						:					List[str]    =   [self.cls_token_id]

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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = 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(lowercase_    ):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
																			return
													_snake_case						:					Optional[Any]    =   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,)
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

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