





def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  lowercase__			    =						1  # To kept the Calculated Value
  # Since C(n, k) = C(n, n-k)
  if k > (n - k):
    lowercase__			    =						n - k
  # Calculate C(n,k)
  for i in range(_SCREAMING_SNAKE_CASE	):
    result *= n - i
    result //= i + 1
  return result




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return binomial_coefficient(2 * node_count	,		_SCREAMING_SNAKE_CASE	) // (node_count + 1)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  if n < 0:
    raise ValueError('factorial() not defined for negative values'	)
  lowercase__			    =						1
  for i in range(1	,		n + 1	):
    result *= i
  return result




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return catalan_number(_SCREAMING_SNAKE_CASE	) * factorial(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	int(input("""Enter the number of nodes: """).strip() or 0)
      if node_count <= 0:
            raise ValueError("""We need some nodes to work with.""")
      print(
          f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
          f'''binary trees and {catalan_number(node_count)} binary search trees.'''
      )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

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


