



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						:Tuple										=      ElectraConfig.from_json_file(lowerCamelCase__     )
		print(F'Building PyTorch model from configuration: {config}'     )

		if discriminator_or_generator == "discriminator":
				SCREAMING_SNAKE_CASE_						:int										=      ElectraForPreTraining(lowerCamelCase__     )
		elif discriminator_or_generator == "generator":
				SCREAMING_SNAKE_CASE_						:Dict										=      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__":
							SCREAMING_SNAKE_CASE__     : List[Any]				=   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'."""
							    ),
							)
							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   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 itertools import permutations




def     snake_case__     (			lowerCamelCase__					: int = 5      ) -> int:
					if gon_side < 3 or gon_side > 5:
										raise ValueError('''gon_side must be in the range [3, 5]'''      )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					A_			:  int			=				list(range(gon_side + 1      ,		0      ,		-1      )      )
					A_			:  List[Any]			=				list(range(gon_side + 2      ,		gon_side * 2 + 1      )      )

					for perm in permutations(small_numbers + big_numbers      ):
										A_			:  Optional[int]			=				generate_gon_ring(lowerCamelCase__      ,		list(lowerCamelCase__      )      )
										if is_magic_gon(lowerCamelCase__      ):
															return int(''''''.join(str(lowerCamelCase__      ) for n in numbers      )      )

					A_			:  Optional[Any]			=				f'Magic {gon_side}-gon ring is impossible'
					raise ValueError(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  List[Any]			=				[0] * (gon_side * 3)
					A_			:  Optional[Any]			=				perm[0:3]
					perm.append(perm[1]      )

					A_			:  Optional[Any]			=				1 if gon_side < 5 else 2

					for i in range(1      ,		len(lowerCamelCase__      ) // 3 + magic_number      ):
										A_			:  List[str]			=				perm[2 * i + 1]
										A_			:  str			=				result[3 * i - 1]
										A_			:  Union[str, Any]			=				perm[2 * i + 2]

					return result




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> bool:
					if len(lowerCamelCase__      ) % 3 != 0:
										raise ValueError('''a gon ring should have a length that is a multiple of 3'''      )

					if min(numbers[::3]      ) != numbers[0]:
										return False

					A_			:  Union[str, Any]			=				sum(numbers[:3]      )

					return all(sum(numbers[i : i + 3]      ) == total for i in range(3      ,		len(lowerCamelCase__      )      ,		3      )      )


if __name__ == "__main__":
							print(solution())



