

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


_lowerCAmelCase					:			int  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Dict  				=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'vit_mae'





			def __init__(		self					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.0					,  __snake_case=0.0					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=224					,  __snake_case=16					,  __snake_case=3					,  __snake_case=True					,  __snake_case=16					,  __snake_case=512					,  __snake_case=8					,  __snake_case=2048					,  __snake_case=0.75					,  __snake_case=False					,  **__snake_case					,  )   -> Dict:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__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        =layer_norm_eps
						__a        =image_size
						__a        =patch_size
						__a        =num_channels
						__a        =qkv_bias
						__a        =decoder_num_attention_heads
						__a        =decoder_hidden_size
						__a        =decoder_num_hidden_layers
						__a        =decoder_intermediate_size
						__a        =mask_ratio
						__a        =norm_pix_loss









from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __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=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






