

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


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


def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [redshift, radiation_density, matter_density, dark_energy]
						if any(p < 0 for p in parameters    ):
												raise ValueError("All input parameters must be positive"    )

						if any(p > 1 for p in parameters[1:4]    ):
												raise ValueError("Relative densities cannot be greater than one"    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       1 - (matter_density + radiation_density + dark_energy)

												SCREAMING_SNAKE_CASE_: Dict  		=       (
												    radiation_density * (redshift + 1) ** 4
												    + matter_density * (redshift + 1) ** 3
												    + curvature * (redshift + 1) ** 2
												    + dark_energy
												)

												SCREAMING_SNAKE_CASE_: Any  		=       hubble_constant * e_a ** (1 / 2)
												return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						lowerCAmelCase							:  List[Any]   	=						0.3

						print(
						    hubble_parameter(
						        hubble_constant=68.3,
						        radiation_density=1E-4,
						        matter_density=matter_density,
						        dark_energy=1 - matter_density,
						        redshift=0,
						    )
						)






