


'''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      __snake_case       (       lowerCamelCase_ :				Tuple	,  lowerCamelCase_ :				Optional[int]	,  lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				__magic_name__	  = ElectraConfig.from_json_file(lowerCamelCase_						)
				print(F'Building PyTorch model from configuration: {config}'						)

				if discriminator_or_generator == "discriminator":
								__magic_name__	  = ElectraForPreTraining(lowerCamelCase_						)
				elif discriminator_or_generator == "generator":
								__magic_name__	  = 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__":
						__magic_name__   :   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\'.'
						    ),
						)
						__magic_name__   :   Optional[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 __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



