


'''simple docstring'''




import math
from datetime import datetime, timedelta

def 			__UpperCamelCase    (    _A    :				int				)    ->      datetime:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= year % 19
	lowerCAmelCase					:    Optional[Any]      							= year % 4
	lowerCAmelCase					:    List[Any]      							= year % 7
	lowerCAmelCase					:    List[Any]      							= math.floor(year / 1_00				)
	lowerCAmelCase					:    List[str]      							= math.floor((13 + 8 * leap_day_inhibits) / 25				)
	lowerCAmelCase					:    Any      							= leap_day_inhibits / 4
	lowerCAmelCase					:    int      							= (
	    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 30
	lowerCAmelCase					:    Any      							= (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

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

	# PHM -> Paschal Full Moon
	lowerCAmelCase					:    List[Any]      							= (
	    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(_A					,   4					,   19				)
	elif days_to_add == 28 and days_from_phm_to_sunday == 6:
		return datetime(_A					,   4					,   18				)
	else:
		return datetime(_A					,   3					,   22				) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday				)				)


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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		str		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
