





import math
from datetime import datetime, timedelta

def     a_       (					lowerCAmelCase_ :     int      ):
   __lowerCAmelCase				      =      year % 19
   __lowerCAmelCase				      =      year % 4
   __lowerCAmelCase				      =      year % 7
   __lowerCAmelCase				      =      math.floor(year / 100      )
   __lowerCAmelCase				      =      math.floor((13 + 8 * leap_day_inhibits) / 25      )
   __lowerCAmelCase				      =      leap_day_inhibits / 4
   __lowerCAmelCase				      =      (
       15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
   ) % 30
   __lowerCAmelCase				      =      (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

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

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


if __name__ == "__main__":
     for year in (1994, 2000, 2010, 2021, 2023):
          _snake_case    :       Union[str, 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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase							:       List[str]					 =	{
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       str					 =	[
							    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTBigCodeForSequenceClassification""",
							    """GPTBigCodeForTokenClassification""",
							    """GPTBigCodeForCausalLM""",
							    """GPTBigCodeModel""",
							    """GPTBigCodePreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_bigcode import (
														    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTBigCodeForCausalLM,
														    GPTBigCodeForSequenceClassification,
														    GPTBigCodeForTokenClassification,
														    GPTBigCodeModel,
														    GPTBigCodePreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       List[str]					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



