




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_	     =       {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       ['ReformerTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       ['ReformerTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       [
      'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ReformerAttention',
      'ReformerForMaskedLM',
      'ReformerForQuestionAnswering',
      'ReformerForSequenceClassification',
      'ReformerLayer',
      'ReformerModel',
      'ReformerModelWithLMHead',
      'ReformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

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







"""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))
