"""simple docstring"""






import math
from datetime import datetime, timedelta



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							year % 1_9
				UpperCAmelCase__             =							year % 4
				UpperCAmelCase__             =							year % 7
				UpperCAmelCase__             =							math.floor(year / 1_0_0       )
				UpperCAmelCase__             =							math.floor((1_3 + 8 * leap_day_inhibits) / 2_5       )
				UpperCAmelCase__             =							leap_day_inhibits / 4
				UpperCAmelCase__             =							(
				    1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
				) % 3_0
				UpperCAmelCase__             =							(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

				# days to be added to March 21
				UpperCAmelCase__             =							(1_9 * metonic_cycle + secular_moon_shift) % 3_0

				# 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 == 2_9 and days_from_phm_to_sunday == 6:
								return datetime(lowerCamelCase					,			4					,			1_9       )
				elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
								return datetime(lowerCamelCase					,			4					,			1_8       )
				else:
								return datetime(lowerCamelCase					,			3					,			2_2       ) + timedelta(
								    days=int(days_to_add + days_from_phm_to_sunday       )       )


if __name__ == "__main__":
						for year in (1_994, 2_000, 2_010, 2_021, 2_023):
												lowerCAmelCase__  :  Any						     =	'will be' if year > datetime.now().year else 'was'
												print(F"""Easter in {year} {tense} {gauss_easter(year)}""")






from typing import TYPE_CHECKING

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


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



