



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_rembert import RemBertTokenizer
else:
							__lowerCAmelCase						      =       None

__lowerCAmelCase						      =       logging.get_logger(__name__)
__lowerCAmelCase						      =       {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

__lowerCAmelCase						      =       {
    '''google/rembert''': 256,
}

__lowerCAmelCase						      =       '''▁'''

class      __magic_name__						(   _snake_case       ):
							lowerCAmelCase	:   Union[str, Any]           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   str           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Tuple           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   Optional[int]           = RemBertTokenizer







							def __init__(							self :	int     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	Any=None     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	Union[str, Any]=True     ,_UpperCAmelCase :	Optional[Any]=False     ,_UpperCAmelCase :	Optional[Any]="[CLS]"     ,_UpperCAmelCase :	List[str]="[SEP]"     ,_UpperCAmelCase :	List[Any]="<unk>"     ,_UpperCAmelCase :	Any="[SEP]"     ,_UpperCAmelCase :	int="<pad>"     ,_UpperCAmelCase :	Any="[CLS]"     ,_UpperCAmelCase :	int="[MASK]"     ,**_UpperCAmelCase :	Optional[Any]     ,):
									_a					:				str				=							AddedToken(UpperCamelCase__     ,lstrip=UpperCamelCase__     ,rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__     ,UpperCamelCase__		) else mask_token

									super().__init__(
									    UpperCamelCase__     ,tokenizer_file=UpperCamelCase__     ,do_lower_case=UpperCamelCase__     ,remove_space=UpperCamelCase__     ,keep_accents=UpperCamelCase__     ,bos_token=UpperCamelCase__     ,eos_token=UpperCamelCase__     ,unk_token=UpperCamelCase__     ,sep_token=UpperCamelCase__     ,pad_token=UpperCamelCase__     ,cls_token=UpperCamelCase__     ,mask_token=UpperCamelCase__     ,**UpperCamelCase__     ,)

									_a					:				int				=							do_lower_case
									_a					:				Any				=							remove_space
									_a					:				Dict				=							keep_accents
									_a					:				Optional[int]				=							vocab_file
									_a					:				Optional[Any]				=							False if not self.vocab_file else True







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Dict = None		):
									_a					:				Optional[int]				=							[self.sep_token_id]
									_a					:				Optional[Any]				=							[self.cls_token_id]
									if token_ids_a is None:
											return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	int = None     ,_UpperCAmelCase :	Union[str, Any] = False		):

									if already_has_special_tokens:
											if token_ids_a is not None:
													raise ValueError(
													    'You should not supply a second sequence if the provided sequence of '
													    'ids is already formatted with special tokens for the model.'		)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
											return [1] + ([0] * len(UpperCamelCase__		)) + [1] + ([0] * len(UpperCamelCase__		)) + [1]
									return [1] + ([0] * len(UpperCamelCase__		)) + [1]







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Dict = None		):
									_a					:				Optional[Any]				=							[self.sep_token_id]
									_a					:				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		) * [0] + len(token_ids_a + sep		) * [1]







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int = None		):
									if not os.path.isdir(UpperCamelCase__		):
											logger.error('Vocabulary path ({}) should be a directory'.format(UpperCamelCase__		)		)
											return
									_a					:				Tuple				=							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,)









'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''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      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = 'distilbert'
							lowerCAmelCase	:   Optional[int]           = {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]=30522     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	int=12     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	Optional[Any]=4 * 768     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Dict=0.2     ,_UpperCAmelCase :	Union[str, Any]=0     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[Any]				=							vocab_size
									_a					:				Dict				=							max_position_embeddings
									_a					:				Optional[int]				=							sinusoidal_pos_embds
									_a					:				List[Any]				=							n_layers
									_a					:				Any				=							n_heads
									_a					:				int				=							dim
									_a					:				Optional[Any]				=							hidden_dim
									_a					:				str				=							dropout
									_a					:				Optional[Any]				=							attention_dropout
									_a					:				Any				=							activation
									_a					:				Tuple				=							initializer_range
									_a					:				Union[str, Any]				=							qa_dropout
									_a					:				List[Any]				=							seq_classif_dropout
									super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase		)

class      __magic_name__						(   _UpperCamelCase       ):







							@property
							def 							__lowercase      (							self :	Union[str, Any]		):
									if self.task == "multiple-choice":
											_a					:				Union[str, Any]				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a					:				List[str]				=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)




