


import argparse

import torch

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


logging.set_verbosity_info()






def 					UpperCamelCase   (      __magic_name__	:				List[Any]       ,       __magic_name__	:				int       ,       __magic_name__	:				List[str]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						MobileBertConfig.from_json_file(__magic_name__	)
		print(f'''Building PyTorch model from configuration: {config}'''	)
		lowercase__	     =						MobileBertForPreTraining(__magic_name__	)
		# Load weights from tf checkpoint
		lowercase__	     =						load_tf_weights_in_mobilebert(__magic_name__       ,       __magic_name__       ,       __magic_name__	)
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}'''	)
		torch.save(model.state_dict()       ,       __magic_name__	)


if __name__ == "__main__":
	A			:						Optional[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.'
	)
	A			:						Optional[int]        =  parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)


from __future__ import annotations

SCREAMING_SNAKE_CASE		:     Tuple         =	1.6_021E-19  # units = C






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (conductivity, electron_conc, mobility).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif conductivity < 0:
												raise ValueError("""Conductivity cannot be negative"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative"""			)
						elif mobility < 0:
												raise ValueError("""mobility cannot be negative"""			)
						elif conductivity == 0:
												return (
												    "conductivity",
												    mobility * electron_conc * ELECTRON_CHARGE,
												)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    conductivity / (mobility * ELECTRON_CHARGE),
												)
						else:
												return (
												    "mobility",
												    conductivity / (electron_conc * ELECTRON_CHARGE),
												)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






