



from math import pi, sqrt





def      __snake_case	(			_UpperCAmelCase				):
    if num <= 0:
        raise ValueError('''math domain error'''				)
    if num > 1_71.5:
        raise OverflowError('''math range error'''				)
    elif num - int(_UpperCAmelCase				) not in (0, 0.5):
        raise NotImplementedError('''num must be an integer or a half-integer'''				)
    elif num == 0.5:
        return sqrt(_UpperCAmelCase				)
    else:
        return 1.0 if num == 1 else (num - 1) * gamma(num - 1				)





def      __snake_case	(			):
    assert gamma(0.5				) == sqrt(_UpperCAmelCase				)
    assert gamma(1				) == 1.0
    assert gamma(2				) == 1.0


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Union[str, Any]      		=     1.0
 while num:
  __snake_case						:Union[str, Any]      		=     float(input('''Gamma of: '''))
  print(f'gamma({num}) = {gamma(num)}')
  print('''\nEnter 0 to exit...''')









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 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

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

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


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      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.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




