


import argparse

import torch

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


logging.set_verbosity_info()







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			int							 =   MobileBertConfig.from_json_file(lowerCamelCase__	)
							print(F'Building PyTorch model from configuration: {config}'	)
							__snake_case		:			str							 =   MobileBertForPreTraining(lowerCamelCase__	)
							# Load weights from tf checkpoint
							__snake_case		:			Dict							 =   load_tf_weights_in_mobilebert(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	)
							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'	)
							torch.save(model.state_dict()							,	lowerCamelCase__	)


if __name__ == "__main__":
			_snake_case     :		Tuple						=				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     :		Dict						=				parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)






'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not sentence:
						return ""
			A_	:						Optional[int]	=     dict(zip(lowerCamelCase__						,      lowerCamelCase__    )    )
			return lower_to_upper.get(sentence[0]						,      sentence[0]    ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()