





from typing import TYPE_CHECKING

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


__UpperCAmelCase	:       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	:       Tuple    	=							["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	:       int    	=							[
        "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__)













import math







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:

  if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE__)

  if number < 1:
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be > 0'''
    raise ValueError(SCREAMING_SNAKE_CASE__)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    __snake_case:						List[Any]			  =						int(math.log(number // 3 , 2)) + 2

    __snake_case:						str			  =						[3, 5]
    __snake_case:						int			  =						2
    __snake_case:						List[str]			  =						3
    for block in range(1 , SCREAMING_SNAKE_CASE__):
      for _ in range(SCREAMING_SNAKE_CASE__):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for number in range(11):
            __UpperCAmelCase	:       Optional[int]    	=							0
            try:
                  __UpperCAmelCase	:       int    	=							proth(number)
            except ValueError:
                  print(f'ValueError: there is no {number}th Proth number')
                  continue

            print(f'The {number}th Proth number: {value}')






