


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase   :str						     =		{
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase   :Optional[int]						     =		[
        'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LiltForQuestionAnswering',
        'LiltForSequenceClassification',
        'LiltForTokenClassification',
        'LiltModel',
        'LiltPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_lilt import (
            LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            LiltForQuestionAnswering,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltModel,
            LiltPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase   :Optional[int]						     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''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))
