




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


SCREAMING_SNAKE_CASE__     :     Tuple		    =       logging.get_logger(__name__)

# TODO: upload to AWS
SCREAMING_SNAKE_CASE__     :     Any		    =       {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''retribert'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3_0522      ,     SCREAMING_SNAKE_CASE=768      ,     SCREAMING_SNAKE_CASE=8      ,     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=128      ,     SCREAMING_SNAKE_CASE=0      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Optional[Any]:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						vocab_size
				SCREAMING_SNAKE_CASE_								=						hidden_size
				SCREAMING_SNAKE_CASE_								=						num_hidden_layers
				SCREAMING_SNAKE_CASE_								=						num_attention_heads
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						intermediate_size
				SCREAMING_SNAKE_CASE_								=						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_								=						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_								=						max_position_embeddings
				SCREAMING_SNAKE_CASE_								=						type_vocab_size
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						share_encoders
				SCREAMING_SNAKE_CASE_								=						projection_dim






def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

			if not isinstance(SCREAMING_SNAKE_CASE       ,    (list, tuple) ) or not all(
			    isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) for number in numbers ):
						raise ValueError('numbers must be an iterable of integers' )

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
