





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

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


__A       =		{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       =		["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       =		[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A       =		[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __A       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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



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


# Initial Value
lowerCamelCase_       =		0.02



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :Any		    =  float(2 * (random.randint(1			,       1_00							)) - 1							)

      for _ in range(a_							):
            # Forward propagation
            __SCREAMING_SNAKE_CASE       :Any		    =  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (expected / 1_00) - layer_a
            # Error delta
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  layer_1_error * sigmoid_function(a_			,       a_							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 1_00


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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