

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							(			lowerCAmelCase:					Dict					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[Any]	)				->    Union[str, Any]:
				# Initialise PyTorch model
				_UpperCAmelCase    :						Optional[Any]							=      MobileBertConfig.from_json_file(__UpperCAmelCase	)
				print(F'Building PyTorch model from configuration: {config}'	)
				_UpperCAmelCase    :						int							=      MobileBertForPreTraining(__UpperCAmelCase	)
				# Load weights from tf checkpoint
				_UpperCAmelCase    :						Union[str, Any]							=      load_tf_weights_in_mobilebert(__UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase	)
				# Save pytorch-model
				print(F'Save PyTorch model to {pytorch_dump_path}'	)
				torch.save(model.state_dict()					, __UpperCAmelCase	)


if __name__ == "__main__":
			SCREAMING_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(
			    '--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.'
			)
			SCREAMING_SNAKE_CASE_            = parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


