

def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]       )					->				int:
	stooge(a__			,0			,len(a__       ) - 1       )
	return arr
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   Tuple			,a__    :   str       )					->				Any:
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		__A    ,				__A :			Optional[int]		    =			arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		__A :			List[Any]		    =			(int)((h - i + 1) / 3       )

		# Recursively sort first 2/3 elements
		stooge(a__			,a__			,(h - t)       )

		# Recursively sort last 2/3 elements
		stooge(a__			,i + t			,(a__)       )

		# Recursively sort first 2/3 elements
		stooge(a__			,a__			,(h - t)       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   input('''Enter numbers separated by a comma:\n''').strip()
			UpperCAmelCase_							:					Any		 =   [int(item) for item in user_input.split(''',''')]
			print(stooge_sort(unsorted))








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					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(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT 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.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





