





"""simple docstring"""






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def     __A      (			a_						:List[str]   ,       a_						:Optional[Any]   ,       a_						:Optional[int])		->		Optional[Any]:
				# Initialise PyTorch model
				__a :						Optional[int]			       =  RemBertConfig.from_json_file(a_)
				print('''Building PyTorch model from configuration: {}'''.format(str(a_)))
				__a :						Optional[Any]			       =  RemBertModel(a_)

				# Load weights from tf checkpoint
				load_tf_weights_in_rembert(a_   ,       a_   ,       a_)

				# Save pytorch-model
				print('''Save PyTorch model to {}'''.format(a_))
				torch.save(model.state_dict()   ,       a_)


if __name__ == "__main__":
		A 			=   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(
		    '''--rembert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained RemBERT 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 			=   parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float   ,       )		->		float:

				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()