



"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   int:
			# Initialise PyTorch model
			__lowerCAmelCase      :  List[Any]												=       MobileBertConfig.from_json_file(__snake_case	)
			print(F"""Building PyTorch model from configuration: {config}"""	)
			__lowerCAmelCase      :  str												=       MobileBertForPreTraining(__snake_case	)
			# Load weights from tf checkpoint
			__lowerCAmelCase      :  Any												=       load_tf_weights_in_mobilebert(__snake_case					,__snake_case					,__snake_case	)
			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
			torch.save(model.state_dict()					,__snake_case	)


if __name__ == "__main__":
	__snake_case							:   List[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(
	    '--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.'
	)
	__snake_case							:   Tuple      =    parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




"""simple docstring"""






from __future__ import annotations


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   list:
			__lowerCAmelCase      :  Dict												=       []
			__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0	)	)
			__lowerCAmelCase      :  int												=       result + left + right
			return input_list


def 			_lowercase				(  __snake_case	)						->   list:
			if len(__snake_case	) <= 1:
						return input_list
			__lowerCAmelCase      :  int												=       list(__snake_case	)

			# iteration for two-way merging
			__lowerCAmelCase      :  Optional[int]												=       2
			while p <= len(__snake_case	):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0					,len(__snake_case	)					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       i
									__lowerCAmelCase      :  Tuple												=       i + p - 1
									__lowerCAmelCase      :  Optional[Any]												=       (low + high + 1) // 2
									__lowerCAmelCase      :  Any												=       merge(__snake_case					,__snake_case					,__snake_case					,__snake_case	)
						# final merge of last two parts
						if p * 2 >= len(__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       i
									__lowerCAmelCase      :  Union[str, Any]												=       merge(__snake_case					,0					,__snake_case					,len(__snake_case	) - 1	)
									break
						p *= 2

			return input_list


if __name__ == "__main__":
	__snake_case							:   Union[str, Any]      =    input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		__snake_case							:   Optional[int]      =    []
	else:
		__snake_case							:   int      =    [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))