import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):


   if openai_config_file == "":
      lowerCAmelCase_:							Any       =	OpenAIGPTConfig()
   else:
      lowerCAmelCase_:							Optional[Any]       =	OpenAIGPTConfig.from_json_file(lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	OpenAIGPTModel(lowercase					)

   # Load weights from numpy
   load_tf_weights_in_openai_gpt(lowercase							,				lowercase							,				lowercase					)

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


if __name__ == "__main__":
 a  :      Optional[int]            =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--openai_checkpoint_folder_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(
     """--openai_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  :      Union[str, Any]            =    parser.parse_args()
 convert_openai_checkpoint_to_pytorch(
     args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
 )

def  __snake_case							(	__magic_name__		,    __magic_name__ = False						):







				'''simple docstring'''


				if n == 2:
								return True
				if not n % 2 or n < 2:
								return False
				if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
								return False
				if n > 3317044064679887385961981 and not allow_probable:
								raise ValueError(
								    "Warning: upper bound of deterministic test is exceeded. "
								    "Pass allow_probable=True to allow probabilistic test. "
								    "A return value of True indicates a probable prime."						)
				# array bounds provided by analysis
				lowercase         =      [
				    2047,
				    1373653,
				    25326001,
				    3215031751,
				    2152302898747,
				    3474749660383,
				    341550071728321,
				    1,
				    3825123056546413051,
				    1,
				    1,
				    318665857834031151167461,
				    3317044064679887385961981,
				]

				lowercase         =      [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
				for idx, _p in enumerate(__magic_name__		,    1						):
								if n < _p:
												# then we have our last prime to check
												lowercase         =      primes[:idx]
												break
				lowercase     ,	lowercase         =      n - 1, 0
				# break up n -1 into a power of 2 (s) and
				# remaining odd component
				# essentially, solve for d * 2 ** s == n - 1
				while d % 2 == 0:
								d //= 2
								s += 1
				for prime in plist:
								lowercase         =      False
								for r in range(__magic_name__						):
												lowercase         =      pow(__magic_name__		,    d * 2**r		,    __magic_name__						)
												# see article for analysis explanation for m
												if (r == 0 and m == 1) or ((m + 1) % n == 0):
																lowercase         =      True
																# this loop will not determine compositeness
																break
								if pr:
												continue
								# if pr is False, then the above loop never evaluated to true,
								# and the n MUST be composite
								return False
				return True

def  __snake_case							(	):







				'''simple docstring'''


				assert not miller_rabin(561						)
				assert miller_rabin(563						)
				# 2047

				assert not miller_rabin(838201						)
				assert miller_rabin(838207						)
				# 1_373_653

				assert not miller_rabin(17316001						)
				assert miller_rabin(17316017						)
				# 25_326_001

				assert not miller_rabin(3078386641						)
				assert miller_rabin(3078386653						)
				# 3_215_031_751

				assert not miller_rabin(1713045574801						)
				assert miller_rabin(1713045574819						)
				# 2_152_302_898_747

				assert not miller_rabin(2779799728307						)
				assert miller_rabin(2779799728327						)
				# 3_474_749_660_383

				assert not miller_rabin(113850023909441						)
				assert miller_rabin(113850023909527						)
				# 341_550_071_728_321

				assert not miller_rabin(1275041018848804351						)
				assert miller_rabin(1275041018848804391						)
				# 3_825_123_056_546_413_051

				assert not miller_rabin(79666464458507787791867						)
				assert miller_rabin(79666464458507787791951						)
				# 318_665_857_834_031_151_167_461

				assert not miller_rabin(552840677446647897660333						)
				assert miller_rabin(552840677446647897660359						)
				# 3_317_044_064_679_887_385_961_981
				# upper limit for probabilistic test


if __name__ == "__main__":
						test_miller_rabin()
