





'''simple docstring'''







import math
from datetime import datetime, timedelta





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	datetime:
						UpperCAmelCase_			:      int     						=  year % 19
						UpperCAmelCase_			:      Dict     						=  year % 4
						UpperCAmelCase_			:      int     						=  year % 7
						UpperCAmelCase_			:      Union[str, Any]     						=  math.floor(year / 100   )
						UpperCAmelCase_			:      Tuple     						=  math.floor((13 + 8 * leap_day_inhibits) / 25   )
						UpperCAmelCase_			:      Tuple     						=  leap_day_inhibits / 4
						UpperCAmelCase_			:      List[Any]     						=  (
						    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
						) % 30
						UpperCAmelCase_			:      Any     						=  (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

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

						# PHM -> Paschal Full Moon
						UpperCAmelCase_			:      Dict     						=  (
						    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(SCREAMING_SNAKE_CASE__,			4,			19   )
						elif days_to_add == 28 and days_from_phm_to_sunday == 6:
												return datetime(SCREAMING_SNAKE_CASE__,			4,			18   )
						else:
												return datetime(SCREAMING_SNAKE_CASE__,			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):
										snake_case_     :       Tuple						=  "will be" if year > datetime.now().year else "was"
										print(f'''Easter in {year} {tense} {gauss_easter(year)}''')












'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





