


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    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase    		=       {
    '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    		=       {'mobilebert-uncased': 5_1_2}


__UpperCamelCase    		=       {}







class        lowerCamelCase__	(							UpperCamelCase_      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					int    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					int    =		PRETRAINED_INIT_CONFIGURATION
			_UpperCamelCase		:					Union[str, Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:					Optional[Any]    =		MobileBertTokenizer




			def __init__(							self      ,						snake_case=None      ,						snake_case=None      ,						snake_case=True      ,						snake_case="[UNK]"      ,						snake_case="[SEP]"      ,						snake_case="[PAD]"      ,						snake_case="[CLS]"      ,						snake_case="[MASK]"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


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

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

						UpperCamelCase__					=       do_lower_case




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [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 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [self.sep_token_id]
						UpperCamelCase__					=       [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 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       self._tokenizer.model.save(snake_case      ,						name=snake_case     )
						return tuple(snake_case     )







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    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


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

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







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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


