


from typing import TYPE_CHECKING

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


__UpperCamelCase    :						str     =							{
    '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    :						List[Any]     =							['ConvBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Union[str, Any]     =							[
        '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    :						List[str]     =							[
        '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    :						Dict     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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




      __lowerCamelCase     :      Optional[int]       						=						[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

      __lowerCamelCase     :      str       						=						6
      __lowerCamelCase     :      Optional[int]       						=						1
      __lowerCamelCase     :      Optional[int]       						=						1_901

      __lowerCamelCase     :      Optional[Any]       						=						0

      while year < 2_001:
            day += 7

            if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
                  if day > days_per_month[month - 1] and month != 2:
                        month += 1
                        __lowerCamelCase     :      Tuple       						=						day - days_per_month[month - 2]
                  elif day > 29 and month == 2:
                        month += 1
                        __lowerCamelCase     :      str       						=						day - 29
            else:
                  if day > days_per_month[month - 1]:
                        month += 1
                        __lowerCamelCase     :      Any       						=						day - days_per_month[month - 2]

            if month > 12:
                  year += 1
                  __lowerCamelCase     :      Optional[Any]       						=						1

            if year < 2_001 and day == 1:
                  sundays += 1
      return sundays


if __name__ == "__main__":
    print(solution())



