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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Tuple    		=	'swinv2'

	UpperCamelCase_  :					str    		=	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(     self      :List[str]   ,     lowercase      :Any=2_2_4   ,     lowercase      :List[Any]=4   ,     lowercase      :List[str]=3   ,     lowercase      :List[Any]=9_6   ,     lowercase      :int=[2, 2, 6, 2]   ,     lowercase      :int=[3, 6, 1_2, 2_4]   ,     lowercase      :List[str]=7   ,     lowercase      :int=4.0   ,     lowercase      :Tuple=True   ,     lowercase      :Union[str, Any]=0.0   ,     lowercase      :List[Any]=0.0   ,     lowercase      :List[Any]=0.1   ,     lowercase      :Union[str, Any]="gelu"   ,     lowercase      :int=False   ,     lowercase      :Optional[Any]=0.02   ,     lowercase      :Union[str, Any]=1e-5   ,     lowercase      :Tuple=3_2   ,     **lowercase      :str   ,     )		->      Dict:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= embed_dim
				SCREAMING_SNAKE_CASE     						= depths
				SCREAMING_SNAKE_CASE     						= len(lowercase			)
				SCREAMING_SNAKE_CASE     						= num_heads
				SCREAMING_SNAKE_CASE     						= window_size
				SCREAMING_SNAKE_CASE     						= mlp_ratio
				SCREAMING_SNAKE_CASE     						= qkv_bias
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= drop_path_rate
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= use_absolute_embeddings
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= encoder_stride
				# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				SCREAMING_SNAKE_CASE     						= int(embed_dim * 2 ** (len(lowercase			) - 1)			)
				SCREAMING_SNAKE_CASE     						= (0, 0, 0, 0)

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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'mgp-str'







	def __init__(     self      :Union[str, Any]   ,     lowercase      :Union[str, Any]=[3_2, 1_2_8]   ,     lowercase      :Tuple=4   ,     lowercase      :Optional[int]=3   ,     lowercase      :Union[str, Any]=2_7   ,     lowercase      :List[str]=3_8   ,     lowercase      :Optional[Any]=5_0_2_5_7   ,     lowercase      :str=3_0_5_2_2   ,     lowercase      :Dict=7_6_8   ,     lowercase      :List[Any]=1_2   ,     lowercase      :List[str]=1_2   ,     lowercase      :Optional[Any]=4.0   ,     lowercase      :Optional[Any]=True   ,     lowercase      :str=False   ,     lowercase      :Optional[Any]=1e-5   ,     lowercase      :List[str]=0.0   ,     lowercase      :List[Any]=0.0   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Dict=False   ,     lowercase      :Optional[Any]=0.02   ,     **lowercase      :Optional[Any]   ,     )		->      Dict:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= max_token_length
				SCREAMING_SNAKE_CASE     						= num_character_labels
				SCREAMING_SNAKE_CASE     						= num_bpe_labels
				SCREAMING_SNAKE_CASE     						= num_wordpiece_labels
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= mlp_ratio
				SCREAMING_SNAKE_CASE     						= distilled
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= drop_rate
				SCREAMING_SNAKE_CASE     						= qkv_bias
				SCREAMING_SNAKE_CASE     						= attn_drop_rate
				SCREAMING_SNAKE_CASE     						= drop_path_rate
				SCREAMING_SNAKE_CASE     						= output_aa_attentions
				SCREAMING_SNAKE_CASE     						= initializer_range