



import math
from datetime import datetime, timedelta







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							datetime:
		UpperCAmelCase_						=				year % 19
		UpperCAmelCase_						=				year % 4
		UpperCAmelCase_						=				year % 7
		UpperCAmelCase_						=				math.floor(year / 100 )
		UpperCAmelCase_						=				math.floor((13 + 8 * leap_day_inhibits) / 25 )
		UpperCAmelCase_						=				leap_day_inhibits / 4
		UpperCAmelCase_						=				(
		    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
		) % 30
		UpperCAmelCase_						=				(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

		# days to be added to March 21
		UpperCAmelCase_						=				(19 * metonic_cycle + secular_moon_shift) % 30

		# PHM -> Paschal Full Moon
		UpperCAmelCase_						=				(
		    2 * julian_leap_year
		    + 4 * non_leap_year
		    + 6 * days_to_add
		    + century_starting_point
		) % 7

		if days_to_add == 29 and days_from_phm_to_sunday == 6:
				return datetime(__SCREAMING_SNAKE_CASE			,     4			,     19 )
		elif days_to_add == 28 and days_from_phm_to_sunday == 6:
				return datetime(__SCREAMING_SNAKE_CASE			,     4			,     18 )
		else:
				return datetime(__SCREAMING_SNAKE_CASE			,     3			,     22 ) + timedelta(
				    days=int(days_to_add + days_from_phm_to_sunday ) )


if __name__ == "__main__":
			for year in (1994, 2000, 2010, 2021, 2023):
						SCREAMING_SNAKE_CASE     				=						"will be" if year > datetime.now().year else "was"
						print(f'''Easter in {year} {tense} {gauss_easter(year)}''')






import argparse

import torch

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


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				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__":
			SCREAMING_SNAKE_CASE     				=						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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

