





'''simple docstring'''

import math
from datetime import datetime, timedelta

def 						__magic_name__       (							__UpperCAmelCase      )						->					datetime:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       year % 19
   __SCREAMING_SNAKE_CASE      							=       year % 4
   __SCREAMING_SNAKE_CASE      							=       year % 7
   __SCREAMING_SNAKE_CASE      							=       math.floor(year / 100      )
   __SCREAMING_SNAKE_CASE      							=       math.floor((13 + 8 * leap_day_inhibits) / 25      )
   __SCREAMING_SNAKE_CASE      							=       leap_day_inhibits / 4
   __SCREAMING_SNAKE_CASE      							=       (
       15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
   ) % 30
   __SCREAMING_SNAKE_CASE      							=       (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

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

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


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












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





