



'''simple docstring'''





import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   Any:
  # Initialise PyTorch model
  __lowerCamelCase : Tuple					 =				ElectraConfig.from_json_file(_lowerCAmelCase )
  print(F'Building PyTorch model from configuration: {config}' )

  if discriminator_or_generator == "discriminator":
    __lowerCamelCase : int					 =				ElectraForPreTraining(_lowerCAmelCase )
  elif discriminator_or_generator == "generator":
    __lowerCamelCase : Any					 =				ElectraForMaskedLM(_lowerCAmelCase )
  else:
    raise ValueError('The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,discriminator_or_generator=_lowerCAmelCase )

  # Save pytorch-model
  print(F'Save PyTorch model to {pytorch_dump_path}' )
  torch.save(model.state_dict() ,_lowerCAmelCase )


if __name__ == "__main__":
   _UpperCamelCase							       =		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(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--discriminator_or_generator',
       default=None,
       type=str,
       required=True,
       help=(
           'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
           '\'generator\'.'
       ),
   )
   _UpperCamelCase							       =		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
   )











'''simple docstring'''





from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase							       =		6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   dict[str, float]:
  __lowerCamelCase : Dict					 =				mass_a * mass_a

  if (force, mass_a, mass_a, distance).count(0 ) != 1:
    raise ValueError('One and only one argument must be 0' )
  if force < 0:
    raise ValueError('Gravitational force can not be negative' )
  if distance < 0:
    raise ValueError('Distance can not be negative' )
  if mass_a < 0 or mass_a < 0:
    raise ValueError('Mass can not be negative' )
  if force == 0:
    __lowerCamelCase : Optional[Any]					 =				GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
  elif mass_a == 0:
    __lowerCamelCase : Tuple					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
  elif mass_a == 0:
    __lowerCamelCase : str					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
  elif distance == 0:
    __lowerCamelCase : int					 =				(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
    return {"distance": distance}
  raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()






