






from typing import TYPE_CHECKING

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


snake_case     					=		{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case     					=		["""ConvBertTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case     					=		[
       """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:
   snake_case     					=		[
       """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

   snake_case     					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''




def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if length <= 0 or not isinstance(_A ,  _A					):
				raise ValueError("""Length must be a positive integer."""					)
		return [n * (2 * n - 1) for n in range(_A					)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=1_0))




