



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    "dinat"

					A__				:		Optional[Any]			       =    {
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}



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






								"""simple docstring"""


								super().__init__(**_snake_case					)

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









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




