






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		tuple[int, int]:







							"""simple docstring"""





							if len(UpperCAmelCase     ) == 1:
														return a[0], a[0]
							else:
														# split A into half.
														_a  :    Union[str, Any]			=						a[: len(UpperCAmelCase     ) // 2]
														_a  :    Optional[Any]			=						a[len(UpperCAmelCase     ) // 2 :]

														# 2 sub problems, 1/2 of original size.
														_a		,					_a  :    Union[str, Any]			=						max_difference(UpperCAmelCase     )
														_a		,					_a  :    List[Any]			=						max_difference(UpperCAmelCase     )

														# get min of first and max of second
														# linear time
														_a  :    Union[str, Any]			=						min(UpperCAmelCase     )
														_a  :    Any			=						max(UpperCAmelCase     )

														# 3 cases, either (small1, big1),
														# (min_first, max_second), (small2, big2)
														# constant comparisons
														if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																					return smalla, biga
														elif biga - smalla > max_second - min_first:
																					return smalla, biga
														else:
																					return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()







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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= 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\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)