
'''simple docstring'''
import math
import random







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if deriv:
    return value * (1 - value)
  return 1 / (1 + math.exp(-value ))


# Initial Value
A__   :		List[Any] 		=0.0_2







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int )				->       float:





  """simple docstring"""

  __A		:     Union[str, Any]												=		float(2 * (random.randint(1			,     100 )) - 1 )

  for _ in range(__SCREAMING_SNAKE_CASE ):
    # Forward propagation
    __A		:     Tuple												=		sigmoid_function(INITIAL_VALUE * weight )
    # How much did we miss?
    __A		:     Tuple												=		(expected / 100) - layer_a
    # Error delta
    __A		:     str												=		layer_1_error * sigmoid_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    # Update weight
    weight += INITIAL_VALUE * layer_1_delta

  return layer_a * 100


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__   :		int 		=int(input('Expected value: '))
     A__   :		Dict 		=int(input('Number of propagations: '))
     print(forward_propagation(expected, number_propagations))





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



