




UpperCamelCase_						:      List[Any]       =						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCamelCase_						:      List[Any]       =						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCamelCase_						:      int       =						{
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   int ,						_UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     assert len(str(_UpperCamelCase   )   ) > 2, "year should be in YYYY format"
     assert 1 <= month <= 12, "month should be between 1 to 12"
     assert 1 <= day <= 31, "day should be between 1 to 31"

     # Doomsday algorithm:
     _snake_case							=				year // 100
     _snake_case							=				(5 * (century % 4) + 2) % 7
     _snake_case							=				year % 100
     _snake_case							=				centurian % 12
     _snake_case							=				(
         (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
     ) % 7
     _snake_case							=				(
         DOOMSDAY_NOT_LEAP[month - 1]
         if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
         else DOOMSDAY_LEAP[month - 1]
     )
     _snake_case							=				(dooms_day + day - day_anchor) % 7
     return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      List[Any]       =						{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[Any]       =						['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						[
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Tuple       =						[
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      Tuple       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


