
"""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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					if gpta_config_file == "":
										_UpperCamelCase									=    GPTaConfig()
					else:
										_UpperCamelCase									=    GPTaConfig.from_json_file(__snake_case      )
					_UpperCamelCase									=    GPTaModel(__snake_case      )

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

					# 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(),	__snake_case      )
					print(F'''Save configuration file to {pytorch_config_dump_path}'''      )
					with open(__snake_case,	'''w''',	encoding='''utf-8'''      ) as f:
										f.write(config.to_json_string()      )


if __name__ == "__main__":
				_a								=						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."""
				    ),
				)
				_a								=						parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









from math import isqrt, loga







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       __snake_case       	=							[True] * max_number
       for i in range(2	,isqrt(max_number - 1		) + 1		):
              if is_prime[i]:
                     for j in range(i**2	,__A	,__A		):
                            __snake_case       	=							False

       return [i for i in range(2	,__A		) if is_prime[i]]







def 						lowerCamelCase__      ( __A    :int = 8_0_0_8_0_0	,__A    :int = 8_0_0_8_0_0		):


       """simple docstring"""




       __snake_case       	=							degree * loga(__A		)
       __snake_case       	=							int(__A		)
       __snake_case       	=							calculate_prime_numbers(__A		)

       __snake_case       	=							0
       __snake_case       	=							0
       __snake_case       	=							len(__A		) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]		)
                  + prime_numbers[left] * loga(prime_numbers[right]		)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
    print(F'{solution() = }')

