






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  '''distilbert'''
		_snake_case   :   str							=  {
		    '''hidden_size''': '''dim''',
		    '''num_attention_heads''': '''n_heads''',
		    '''num_hidden_layers''': '''n_layers''',
		}



		def __init__(							self    ,		_UpperCamelCase=3_0_5_2_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=False    ,		_UpperCamelCase=6    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=4 * 7_6_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.2    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							Dict:
							UpperCAmelCase_   :				str						=						vocab_size
							UpperCAmelCase_   :				str						=						max_position_embeddings
							UpperCAmelCase_   :				List[str]						=						sinusoidal_pos_embds
							UpperCAmelCase_   :				Union[str, Any]						=						n_layers
							UpperCAmelCase_   :				Optional[int]						=						n_heads
							UpperCAmelCase_   :				Optional[Any]						=						dim
							UpperCAmelCase_   :				List[str]						=						hidden_dim
							UpperCAmelCase_   :				List[Any]						=						dropout
							UpperCAmelCase_   :				Optional[int]						=						attention_dropout
							UpperCAmelCase_   :				List[Any]						=						activation
							UpperCAmelCase_   :				List[Any]						=						initializer_range
							UpperCAmelCase_   :				Any						=						qa_dropout
							UpperCAmelCase_   :				str						=						seq_classif_dropout
							super().__init__(**_UpperCamelCase    ,		pad_token_id=_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
												UpperCAmelCase_   :				int						=						{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
												UpperCAmelCase_   :				Tuple						=						{0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]       )














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:
		__UpperCAmelCase     							=						None

__UpperCAmelCase     							=						logging.get_logger(__name__)

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

__UpperCAmelCase     							=						{
    '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'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Dict							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Dict							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Union[str, Any]							=  BarthezTokenizer



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		**_UpperCamelCase    ,		)     ->							str:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_   :				List[Any]						=						AddedToken(_UpperCamelCase    ,		lstrip=_UpperCamelCase    ,		rstrip=_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else mask_token

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

							UpperCAmelCase_   :				Tuple						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				Union[str, Any]						=						[self.cls_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				List[Any]						=						[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    ,		_UpperCamelCase    ,		_UpperCamelCase = 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(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						os.path.join(
							    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

							return (out_vocab_file,)






