




import numpy as np
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       a.shape
			SCREAMING_SNAKE_CASE     =       min(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       np.eye(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       a.copy()

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

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

						SCREAMING_SNAKE_CASE     =       q @ q_k.T
						SCREAMING_SNAKE_CASE     =       q_k @ r

			return q, r


if __name__ == "__main__":
		import doctest

		doctest.testmod()












import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				str    						=	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(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






