



def      UpperCAmelCase__			(				__snake_case				) ->			Dict:

  if upper_limit < 0:
    raise ValueError('''Limit for the Catalan sequence must be ≥ 0'''				)

  _A							     =     [0] * (upper_limit + 1)

  # Base case: C(0) = C(1) = 1
  _A							     =     1
  if upper_limit > 0:
    _A							     =     1

  # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
  for i in range(2    ,					upper_limit + 1				):
    for j in range(__snake_case				):
      catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

  return catalan_list


if __name__ == "__main__":
    print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
    print("""\n*** Enter -1 at any time to quit ***""")
    print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
    try:
        while True:
            __lowerCamelCase						=       int(input().strip())
            if N < 0:
                print("""\n********* Goodbye!! ************""")
                break
            else:
                print(f'''The Catalan numbers from 0 through {N} are:''')
                print(catalan_numbers(N))
                print("""Try another upper limit for the sequence: """, end="""""")
    except (NameError, ValueError):
        print("""\n********* Invalid input, goodbye! ************\n""")

    import doctest

    doctest.testmod()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



