



'''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 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	Union[str, Any]					, UpperCAmelCase_      :	int						) ->							List[str]:
							lowerCAmelCase__  =   ElectraConfig.from_json_file(lowercase_						)
							print(F'''Building PyTorch model from configuration: {config}'''						)

							if discriminator_or_generator == "discriminator":
														lowerCAmelCase__  =   ElectraForPreTraining(lowercase_						)
							elif discriminator_or_generator == "generator":
														lowerCAmelCase__  =   ElectraForMaskedLM(lowercase_						)
							else:
														raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'"""						)

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

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


if __name__ == "__main__":
		_UpperCamelCase  						=			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\'."""
		    ),
		)
		_UpperCamelCase  						=			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'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





