






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_		:    Dict   							=     {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:    Dict   							=     [
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				a_		:    List[str]   							=     _LazyModule(__name__, globals()['__file__'], _import_structure)












import math
from datetime import datetime, timedelta






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

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

   # 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 == 29 and days_from_phm_to_sunday == 6:
      return datetime(__lowercase     ,   4     ,   19)
   elif days_to_add == 28 and days_from_phm_to_sunday == 6:
      return datetime(__lowercase     ,   4     ,   18)
   else:
      return datetime(__lowercase     ,   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__   :						Dict													=   """will be""" if year > datetime.now().year else """was"""
  print(f'Easter in {year} {tense} {gauss_easter(year)}')





