


import math
from datetime import datetime, timedelta





def   _UpperCAmelCase       (     UpperCAmelCase      :			int      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						year % 19
      __lowerCamelCase     :      Dict       						=						year % 4
      __lowerCamelCase     :      int       						=						year % 7
      __lowerCamelCase     :      List[str]       						=						math.floor(year / 100      )
      __lowerCamelCase     :      List[Any]       						=						math.floor((13 + 8 * leap_day_inhibits) / 25      )
      __lowerCamelCase     :      Optional[Any]       						=						leap_day_inhibits / 4
      __lowerCamelCase     :      Dict       						=						(
          15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
      ) % 30
      __lowerCamelCase     :      Dict       						=						(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

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

      # PHM -> Paschal Full Moon
      __lowerCamelCase     :      int       						=						(
          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):
        __UpperCamelCase    :						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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase    :						Union[str, Any]     =							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2TokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2FeatureExtractor']
    __UpperCamelCase    :						Union[str, Any]     =							['LayoutLMv2ImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMv2ForQuestionAnswering',
        'LayoutLMv2ForSequenceClassification',
        'LayoutLMv2ForTokenClassification',
        'LayoutLMv2Layer',
        'LayoutLMv2Model',
        'LayoutLMv2PreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaLayer,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



