"""simple docstring"""
def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Union[str, Any]:
    _lowerCamelCase	: Optional[int]       =       1  # To kept the Calculated Value
    # Since C(n, k) = C(n, n-k)
    if k > (n - k):
        _lowerCamelCase	: Any       =       n - k
    # Calculate C(n,k)
    for i in range(_A ):
        result *= n - i
        result //= i + 1
    return result



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Tuple:
    return binomial_coefficient(2 * node_count						,	_A ) // (node_count + 1)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Tuple:







    if n < 0:
        raise ValueError('''factorial() not defined for negative values''' )
    _lowerCamelCase	: Dict       =       1
    for i in range(1						,	n + 1 ):
        result *= i
    return result



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->str:
    return catalan_number(_A ) * factorial(_A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      List[Any]							=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."""
  )






'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_A ,    _A ,    _A       )

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'       )
       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

