


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 math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

