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


lowercase__							:							Any									=			logging.get_logger(__name__)

lowercase__							:							Tuple									=			{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'cvt'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=[7, 3, 3]     ,	SCREAMING_SNAKE_CASE_=[4, 2, 2]     ,	SCREAMING_SNAKE_CASE_=[2, 1, 1]     ,	SCREAMING_SNAKE_CASE_=[64, 192, 384]     ,	SCREAMING_SNAKE_CASE_=[1, 3, 6]     ,	SCREAMING_SNAKE_CASE_=[1, 2, 10]     ,	SCREAMING_SNAKE_CASE_=[4.0, 4.0, 4.0]     ,	SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.0]     ,	SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.0]     ,	SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.1]     ,	SCREAMING_SNAKE_CASE_=[True, True, True]     ,	SCREAMING_SNAKE_CASE_=[False, False, True]     ,	SCREAMING_SNAKE_CASE_=["dw_bn", "dw_bn", "dw_bn"]     ,	SCREAMING_SNAKE_CASE_=[3, 3, 3]     ,	SCREAMING_SNAKE_CASE_=[1, 1, 1]     ,	SCREAMING_SNAKE_CASE_=[2, 2, 2]     ,	SCREAMING_SNAKE_CASE_=[1, 1, 1]     ,	SCREAMING_SNAKE_CASE_=[1, 1, 1]     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			patch_sizes
													__UpperCamelCase       =			patch_stride
													__UpperCamelCase       =			patch_padding
													__UpperCamelCase       =			embed_dim
													__UpperCamelCase       =			num_heads
													__UpperCamelCase       =			depth
													__UpperCamelCase       =			mlp_ratio
													__UpperCamelCase       =			attention_drop_rate
													__UpperCamelCase       =			drop_rate
													__UpperCamelCase       =			drop_path_rate
													__UpperCamelCase       =			qkv_bias
													__UpperCamelCase       =			cls_token
													__UpperCamelCase       =			qkv_projection_method
													__UpperCamelCase       =			kernel_qkv
													__UpperCamelCase       =			padding_kv
													__UpperCamelCase       =			stride_kv
													__UpperCamelCase       =			padding_q
													__UpperCamelCase       =			stride_q
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			layer_norm_eps






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




