



import math
from datetime import datetime, timedelta






def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					year % 19
				lowerCAmelCase__					:    List[str]												=					year % 4
				lowerCAmelCase__					:    int												=					year % 7
				lowerCAmelCase__					:    Tuple												=					math.floor(year / 100)
				lowerCAmelCase__					:    Optional[Any]												=					math.floor((13 + 8 * leap_day_inhibits) / 25)
				lowerCAmelCase__					:    List[str]												=					leap_day_inhibits / 4
				lowerCAmelCase__					:    Dict												=					(
				    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
				) % 30
				lowerCAmelCase__					:    List[str]												=					(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__					:    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(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 (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
														__snake_case    :		Optional[int]  ='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, is_vision_available


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

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

