
'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


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

lowercase			:				Union[str, Any]    		=			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''convbert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=9						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






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

         A       :   List[str]					=						vocab_size
         A       :   List[Any]					=						hidden_size
         A       :   Optional[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Tuple					=						intermediate_size
         A       :   str					=						hidden_act
         A       :   Any					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   str					=						max_position_embeddings
         A       :   int					=						type_vocab_size
         A       :   str					=						initializer_range
         A       :   List[Any]					=						layer_norm_eps
         A       :   int					=						embedding_size
         A       :   List[str]					=						head_ratio
         A       :   Optional[Any]					=						conv_kernel_size
         A       :   Optional[Any]					=						num_groups
         A       :   int					=						classifier_dropout





class  A				(							__snake_case						):





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






         """simple docstring"""






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






'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




