





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


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "nat"

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

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



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   lowerCamelCase__: Dict         =patch_size
   lowerCamelCase__: List[Any]         =num_channels
   lowerCamelCase__: Any         =embed_dim
   lowerCamelCase__: Optional[Any]         =depths
   lowerCamelCase__: Dict         =len(UpperCAmelCase_)
   lowerCamelCase__: List[Any]         =num_heads
   lowerCamelCase__: Optional[int]         =kernel_size
   lowerCamelCase__: Any         =mlp_ratio
   lowerCamelCase__: Optional[int]         =qkv_bias
   lowerCamelCase__: Optional[int]         =hidden_dropout_prob
   lowerCamelCase__: Tuple         =attention_probs_dropout_prob
   lowerCamelCase__: Tuple         =drop_path_rate
   lowerCamelCase__: Dict         =hidden_act
   lowerCamelCase__: Any         =layer_norm_eps
   lowerCamelCase__: Any         =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
   lowerCamelCase__: Union[str, Any]         =int(embed_dim * 2 ** (len(UpperCAmelCase_) - 1))
   lowerCamelCase__: Tuple         =layer_scale_init_value
   lowerCamelCase__: int         =["stem"] + [F"""stage{idx}""" for idx in range(1			,					len(UpperCAmelCase_) + 1)]
   lowerCamelCase__    ,   lowerCamelCase__: Any         =get_aligned_output_features_output_indices(
       out_features=UpperCAmelCase_			,					out_indices=UpperCAmelCase_			,					stage_names=self.stage_names)







'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  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()

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