



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     "roc_bert"


			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__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="absolute"						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=910						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=24858						,	SCREAMING_SNAKE_CASE__=True						,	**SCREAMING_SNAKE_CASE__						,	)      ->	str:
				A__											=					vocab_size
				A__											=					max_position_embeddings
				A__											=					hidden_size
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					type_vocab_size
				A__											=					layer_norm_eps
				A__											=					use_cache
				A__											=					enable_pronunciation
				A__											=					enable_shape
				A__											=					pronunciation_embed_dim
				A__											=					pronunciation_vocab_size
				A__											=					shape_embed_dim
				A__											=					shape_vocab_size
				A__											=					concat_input
				A__											=					position_embedding_type
				A__											=					classifier_dropout
				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)









"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> int:






	"""simple docstring"""





	assert (
	    isinstance(UpperCAmelCase_,	UpperCAmelCase_     ) and number_of_steps > 0
	), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
	if number_of_steps == 1:
		return 1
	A__							,							A__											=					1, 1
	for _ in range(number_of_steps - 1     ):
		A__							,							A__											=					current + previous, current
	return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()




