



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMForMultipleChoice''',
							    '''XLMForQuestionAnswering''',
							    '''XLMForQuestionAnsweringSimple''',
							    '''XLMForSequenceClassification''',
							    '''XLMForTokenClassification''',
							    '''XLMModel''',
							    '''XLMPreTrainedModel''',
							    '''XLMWithLMHeadModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXLMForMultipleChoice''',
							    '''TFXLMForQuestionAnsweringSimple''',
							    '''TFXLMForSequenceClassification''',
							    '''TFXLMForTokenClassification''',
							    '''TFXLMMainLayer''',
							    '''TFXLMModel''',
							    '''TFXLMPreTrainedModel''',
							    '''TFXLMWithLMHeadModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
							from .tokenization_xlm import XLMTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm import (
														    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMForMultipleChoice,
														    XLMForQuestionAnswering,
														    XLMForQuestionAnsweringSimple,
														    XLMForSequenceClassification,
														    XLMForTokenClassification,
														    XLMModel,
														    XLMPreTrainedModel,
														    XLMWithLMHeadModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm import (
														    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMForMultipleChoice,
														    TFXLMForQuestionAnsweringSimple,
														    TFXLMForSequenceClassification,
														    TFXLMForTokenClassification,
														    TFXLMMainLayer,
														    TFXLMModel,
														    TFXLMPreTrainedModel,
														    TFXLMWithLMHeadModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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))




