





"""simple docstring"""
import math
import random






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


# Initial Value
_snake_case     :		Optional[int]       						= 0.0_2






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							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()

		_snake_case     :		Any       						= int(input('Expected value: '))
		_snake_case     :		List[Any]       						= int(input('Number of propagations: '))
		print(forward_propagation(expected, number_propagations))









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


