



import math
from datetime import datetime, timedelta






def      _A				(UpperCamelCase		:      int )  ->datetime:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		year % 19
			lowerCamelCase__ :      List[Any]								=		year % 4
			lowerCamelCase__ :      Optional[Any]								=		year % 7
			lowerCamelCase__ :      Optional[Any]								=		math.floor(year / 100 )
			lowerCamelCase__ :      Any								=		math.floor((13 + 8 * leap_day_inhibits) / 25 )
			lowerCamelCase__ :      Tuple								=		leap_day_inhibits / 4
			lowerCamelCase__ :      int								=		(
			    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
			) % 30
			lowerCamelCase__ :      Union[str, Any]								=		(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

			# days to be added to March 21
			lowerCamelCase__ :      Optional[Any]								=		(19 * metonic_cycle + secular_moon_shift) % 30

			# PHM -> Paschal Full Moon
			lowerCamelCase__ :      int								=		(
			    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(UpperCamelCase      ,			4      ,			19 )
			elif days_to_add == 28 and days_from_phm_to_sunday == 6:
						return datetime(UpperCamelCase      ,			4      ,			18 )
			else:
						return datetime(UpperCamelCase      ,			3      ,			22 ) + timedelta(
						    days=int(days_to_add + days_from_phm_to_sunday ) )


if __name__ == "__main__":
				for year in (19_94, 20_00, 20_10, 20_21, 20_23):
								_lowercase     							=						'''will be''' if year > datetime.now().year else '''was'''
								print(F'''Easter in {year} {tense} {gauss_easter(year)}''')








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



