





"""simple docstring"""




def 	a_    (							_lowercase       ):

						def merge(_lowercase	,						_lowercase       ) -> list:

												def _merge():
																		while left and right:
																								yield (left if left[0] <= right[0] else right).pop(0       )
																		yield from left
																		yield from right

												return list(_merge()       )

						if len(_lowercase       ) <= 1:
												return collection
						_UpperCamelCase      : List[str]         =   len(_lowercase       ) // 2
						return merge(merge_sort(collection[:mid]       )	,						merge_sort(collection[mid:]       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCamelCase_									=input("""Enter numbers separated by a comma:\n""").strip()
		UpperCamelCase_									=[int(item) for item in user_input.split(""",""")]
		print(*merge_sort(unsorted), sep=""",""")










"""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 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_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(
						    """--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_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



