






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

A__			:    int         =				{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class    _UpperCAmelCase      ( _UpperCAmelCase	,_UpperCAmelCase			):

				"""simple docstring"""




				lowercase__       							= """nat"""

				lowercase__       							= {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Optional[Any]=4,						lowerCamelCase		:					Any=3,						lowerCamelCase		:					List[str]=64,						lowerCamelCase		:					List[str]=[3, 4, 6, 5],						lowerCamelCase		:					Optional[int]=[2, 4, 8, 16],						lowerCamelCase		:					List[str]=7,						lowerCamelCase		:					int=3.0,						lowerCamelCase		:					Tuple=True,						lowerCamelCase		:					Dict=0.0,						lowerCamelCase		:					Union[str, Any]=0.0,						lowerCamelCase		:					int=0.1,						lowerCamelCase		:					str="gelu",						lowerCamelCase		:					Union[str, Any]=0.02,						lowerCamelCase		:					List[str]=1E-5,						lowerCamelCase		:					int=0.0,						lowerCamelCase		:					int=None,						lowerCamelCase		:					Union[str, Any]=None,						**lowerCamelCase		:					str,						):

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

								lowercase__     				=    patch_size
								lowercase__     				=    num_channels
								lowercase__     				=    embed_dim
								lowercase__     				=    depths
								lowercase__     				=    len(lowercase_						)
								lowercase__     				=    num_heads
								lowercase__     				=    kernel_size
								lowercase__     				=    mlp_ratio
								lowercase__     				=    qkv_bias
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    drop_path_rate
								lowercase__     				=    hidden_act
								lowercase__     				=    layer_norm_eps
								lowercase__     				=    initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								lowercase__     				=    int(embed_dim * 2 ** (len(lowercase_						) - 1)						)
								lowercase__     				=    layer_scale_init_value
								lowercase__     				=    ["""stem"""] + [F"""stage{idx}""" for idx in range(1,						len(lowercase_						) + 1						)]
								lowercase__     				=    get_aligned_output_features_output_indices(
								    out_features=lowercase_,						out_indices=lowercase_,						stage_names=self.stage_names						)







class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								# we need a list not a string, so do something to change the type
								lowercase__     				=    arr.split(''','''						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								for i in range(1,						len(self.array						)						):
												lowercase__     				=    max(
												    int(self.array[i]						) + sum_value[i - 1],						int(self.array[i]						)						)
												lowercase__     				=    max(sum_value[i],						rear[i - 1]						)
								return rear[len(self.array						) - 1]


if __name__ == "__main__":
							A__			:    Dict         =				input('please input some numbers:')
							A__			:    Union[str, Any]         =				SubArray(whole_array)
							A__			:    int         =				array.solve_sub_array()
							print(('the results is:', re))




