



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


_lowerCamelCase    :		List[str]		      =    logging.get_logger(__name__)

_lowerCamelCase    :		int		      =    {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Dict		=		'megatron-bert'







						def __init__(  self    :				str       ,	snake_case    :				List[Any]=29056       ,	snake_case    :				Optional[int]=1024       ,	snake_case    :				Union[str, Any]=24       ,	snake_case    :				Union[str, Any]=16       ,	snake_case    :				Union[str, Any]=4096       ,	snake_case    :				List[str]="gelu"       ,	snake_case    :				Tuple=0.1       ,	snake_case    :				str=0.1       ,	snake_case    :				Tuple=512       ,	snake_case    :				Any=2       ,	snake_case    :				Optional[Any]=0.02       ,	snake_case    :				Union[str, Any]=1E-12       ,	snake_case    :				Dict=0       ,	snake_case    :				int="absolute"       ,	snake_case    :				List[Any]=True       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Any						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Any						=				hidden_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				num_hidden_layers
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				hidden_act
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				intermediate_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE							:     Any						=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     str						=				type_vocab_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				initializer_range
											SCREAMING_SNAKE_CASE							:     List[str]						=				layer_norm_eps
											SCREAMING_SNAKE_CASE							:     str						=				position_embedding_type
											SCREAMING_SNAKE_CASE							:     str						=				use_cache




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]