




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


_lowerCamelCase	:		Optional[int]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Optional[Any]							       =   {
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''swinv2'''

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


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	int=224					, UpperCAmelCase__      :	Optional[int]=4					, UpperCAmelCase__      :	int=3					, UpperCAmelCase__      :	Any=96					, UpperCAmelCase__      :	Union[str, Any]=[2, 2, 6, 2]					, UpperCAmelCase__      :	Union[str, Any]=[3, 6, 12, 24]					, UpperCAmelCase__      :	Any=7					, UpperCAmelCase__      :	Tuple=4.0					, UpperCAmelCase__      :	str=True					, UpperCAmelCase__      :	str=0.0					, UpperCAmelCase__      :	Any=0.0					, UpperCAmelCase__      :	Dict=0.1					, UpperCAmelCase__      :	Tuple="gelu"					, UpperCAmelCase__      :	Tuple=False					, UpperCAmelCase__      :	List[Any]=0.02					, UpperCAmelCase__      :	Tuple=1e-5					, UpperCAmelCase__      :	Optional[Any]=32					, **UpperCAmelCase__      :	int					, )						->Any:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    image_size
           A__       =    patch_size
           A__       =    num_channels
           A__       =    embed_dim
           A__       =    depths
           A__       =    len(UpperCAmelCase__)
           A__       =    num_heads
           A__       =    window_size
           A__       =    mlp_ratio
           A__       =    qkv_bias
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    drop_path_rate
           A__       =    hidden_act
           A__       =    use_absolute_embeddings
           A__       =    layer_norm_eps
           A__       =    initializer_range
           A__       =    encoder_stride
           # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           A__       =    int(embed_dim * 2 ** (len(UpperCAmelCase__) - 1))
           A__       =    (0, 0, 0, 0)








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       "list[int]":



       """simple docstring"""

       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(lowercase_						):
                     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	:		List[Any]							       =   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()


