






"""simple docstring"""





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': 2_56,
}

__lowerCamelCase							=  '▁'



class       __A		(   UpperCAmelCase__			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				RemBertTokenizer

					def __init__(			self  :      List[Any]     ,		__snake_case  :      int=None     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Any=True     ,		__snake_case  :      Any=False     ,		__snake_case  :      int="[CLS]"     ,		__snake_case  :      Dict="[SEP]"     ,		__snake_case  :      Optional[int]="<unk>"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="<pad>"     ,		__snake_case  :      str="[CLS]"     ,		__snake_case  :      Union[str, Any]="[MASK]"     ,		**__snake_case  :      int     ,		)			->							Dict:
											__magic_name__:	Optional[int]							=       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     ,		)

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

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Optional[int]							=       [self.sep_token_id]
											__magic_name__:	Dict							=       [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   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None     ,		__snake_case  :      bool = False					)			->							List[int]:


											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   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Optional[int]							=       [self.sep_token_id]
											__magic_name__:	Optional[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					) * [0] + len(token_ids_a + sep					) * [1]






					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:


											if not os.path.isdir(_UpperCAmelCase					):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCAmelCase					)					)
																	return
											__magic_name__:	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,)







from collections import OrderedDict
from typing import Mapping

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


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)

