'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     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:
         _UpperCAmelCase    :     int         =       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()




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



