




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

class 				snake_case_			(					__snake_case			):







   """simple docstring"""




   def __init__(		self       ,					*lowerCamelCase_       ,					**lowerCamelCase_)				->   None:
       warnings.warn(
           '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use PerceiverImageProcessor instead.'''       ,					lowerCamelCase_       ,					)
       super().__init__(*lowerCamelCase_       ,					**lowerCamelCase_)







"""simple docstring"""







import math
import random


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


# Initial Value
_lowerCAmelCase            =	0.02


def      UpperCamelCase       ( _A  ,				_A )     ->       float:
				lowercase    :      Any       = float(2 * (random.randint(1  ,				100 )) - 1 )

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

				return layer_a * 100


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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