
'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Dict											=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__ :			Any											=		{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase__ :			Optional[Any]											=		{"mobilebert-uncased": 5_12}


UpperCAmelCase__ :			Dict											=		{}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :str   =  VOCAB_FILES_NAMES
	snake_case__   :List[Any]   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Optional[int]   =  PRETRAINED_INIT_CONFIGURATION
	snake_case__   :Any   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Union[str, Any]   =  MobileBertTokenizer





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							str=None   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							Tuple="[UNK]"   ,				__magic_name__      :							int="[SEP]"   ,				__magic_name__      :							List[str]="[PAD]"   ,				__magic_name__      :							List[str]="[CLS]"   ,				__magic_name__      :							int="[MASK]"   ,				__magic_name__      :							Dict=True   ,				__magic_name__      :							Any=None   ,				**__magic_name__      :							List[str]   ,				):


				"""simple docstring"""

				super().__init__(
				    __magic_name__   ,				tokenizer_file=__magic_name__   ,				do_lower_case=__magic_name__   ,				unk_token=__magic_name__   ,				sep_token=__magic_name__   ,				pad_token=__magic_name__   ,				cls_token=__magic_name__   ,				mask_token=__magic_name__   ,				tokenize_chinese_chars=__magic_name__   ,				strip_accents=__magic_name__   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
				if (
				    normalizer_state.get("lowercase"   ,				__magic_name__				) != do_lower_case
				    or normalizer_state.get("strip_accents"   ,				__magic_name__				) != strip_accents
				    or normalizer_state.get("handle_chinese_chars"   ,				__magic_name__				) != tokenize_chinese_chars
				):
							lowerCAmelCase__										=				getattr(__magic_name__   ,				normalizer_state.pop("type"				)				)
							lowerCAmelCase__										=				do_lower_case
							lowerCAmelCase__										=				strip_accents
							lowerCAmelCase__										=				tokenize_chinese_chars
							lowerCAmelCase__										=				normalizer_class(**__magic_name__				)

				lowerCAmelCase__										=				do_lower_case





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							Dict   ,				__magic_name__      :							List[Any]=None				):


				"""simple docstring"""

				lowerCAmelCase__										=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				[self.sep_token_id]
				lowerCAmelCase__										=				[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 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				self._tokenizer.model.save(__magic_name__   ,				name=__magic_name__				)
				return tuple(__magic_name__				)






'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

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



