
"""simple docstring"""




import argparse

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


logging.set_verbosity_info()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					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__":
	snake_case    =		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.'
	)
	snake_case    =		parser.parse_args()
	convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)


"""simple docstring"""




from __future__ import annotations

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''			)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found'''			)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	[[2, 5, 7], [4, 8, 1_3], [9, 1_1, 1_5], [1_2, 1_7, 2_0]]
					SCREAMING_SNAKE_CASE				=	int(input('Enter the element to be searched:'			)			)
					print(SCREAMING_SNAKE_CASE_			)
					search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE_,     len(SCREAMING_SNAKE_CASE_			),     len(mat[0]			),     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
