






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				# Initialise PyTorch model
				lowercase__    =			BertConfig.from_json_file(lowerCamelCase_				)
				print(F"""Building PyTorch model from configuration: {config}"""				)
				lowercase__    =			BertForPreTraining(lowerCamelCase_				)

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
				torch.save(model.state_dict()		,   lowerCamelCase_				)


if __name__ == "__main__":
							A__	:			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(
							    '--bert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained BERT 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__	:			List[str]			   = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












from math import asin, atan, cos, radians, sin, sqrt, tan

A__	:			Optional[int]			   = 637_8137.0
A__	:			List[str]			   = 635_6752.31_4245
A__	:			Union[str, Any]			   = 6_37_81_37






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			(AXIS_A - AXIS_B) / AXIS_A
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			radians(lowerCamelCase_				)
				lowercase__    =			radians(lowerCamelCase_				)
				# Equation
				lowercase__    =			sin((phi_a - phi_a) / 2				)
				lowercase__    =			sin((lambda_a - lambda_a) / 2				)
				# Square both values
				sin_sq_phi *= sin_sq_phi
				sin_sq_lambda *= sin_sq_lambda
				lowercase__    =			sqrt(sin_sq_phi + (cos(lowerCamelCase_				) * cos(lowerCamelCase_				) * sin_sq_lambda)				)
				return 2 * RADIUS * asin(lowerCamelCase_				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




