





"""simple docstring"""
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		int							)      ->				Any:
      # Load checkpoint
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  chkpt['''model''']

      # We have the base model one level deeper than the original XLM repository
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {}
      for k, v in state_dict.items():
            if "pred_layer" in k:
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  v
            else:
                  __SCREAMING_SNAKE_CASE       :Any		    =  v

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  chkpt['''params''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {n: v for n, v in config.items() if not isinstance(a_			,       (torch.FloatTensor, numpy.ndarray)							)}

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  chkpt['''dico_word2id''']
      __SCREAMING_SNAKE_CASE       :Any		    =  {s + '''</w>''' if s.find('''@@'''							) == -1 and i > 13 else s.replace('''@@'''			,       ''''''							): i for s, i in vocab.items()}

      # Save pytorch-model
      __SCREAMING_SNAKE_CASE       :List[str]		    =  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      __SCREAMING_SNAKE_CASE       :int		    =  pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

      print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''							)
      torch.save(a_			,       a_							)

      print(f'''Save configuration file to {pytorch_config_dump_path}'''							)
      with open(a_			,       '''w'''			,       encoding='''utf-8'''							) as f:
            f.write(json.dumps(a_			,       indent=2							) + '''\n'''							)

      print(f'''Save vocab file to {pytorch_config_dump_path}'''							)
      with open(a_			,       '''w'''			,       encoding='''utf-8'''							) as f:
            f.write(json.dumps(a_			,       indent=2							) + '''\n'''							)


if __name__ == "__main__":
  lowerCamelCase_       =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase_       =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






"""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))