



"""simple docstring"""





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						List[str]     ,				A	:						int       ):

  '''simple docstring'''


  if gpta_config_file == "":
    _UpperCAmelCase							 =		GPTaConfig()
  else:
    _UpperCAmelCase							 =		GPTaConfig.from_json_file(A       )
  _UpperCAmelCase							 =		GPTaModel(A       )

  # Load weights from numpy
  load_tf_weights_in_gpta(A     ,				A     ,				A       )

  # Save pytorch-model
  _UpperCAmelCase							 =		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _UpperCAmelCase							 =		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
  torch.save(model.state_dict()     ,				A       )
  print(f'Save configuration file to {pytorch_config_dump_path}'       )
  with open(A     ,				'w'     ,				encoding='utf-8'       ) as f:
    f.write(config.to_json_string()       )


if __name__ == "__main__":
     lowercase  							=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--gpt2_config_file''',
         default='''''',
         type=str,
         help=(
             '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
             '''This specifies the model architecture.'''
         ),
     )
     lowercase  							=					parser.parse_args()
     convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







"""simple docstring"""





from __future__ import annotations

from cmath import sqrt




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		if a == 0:
				raise ValueError('Coefficient \'a\' must not be zero.'       )
		_UpperCAmelCase							 =		b * b - 4 * a * c

		_UpperCAmelCase							 =		(-b + sqrt(A       )) / (2 * a)
		_UpperCAmelCase							 =		(-b - sqrt(A       )) / (2 * a)

		return (
		    root_a.real if not root_a.imag else root_a,
		    root_a.real if not root_a.imag else root_a,
		)




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase ,					_UpperCAmelCase							 =		quadratic_roots(a=5     ,				b=6     ,				c=1       )
		print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
					main()


