





'''simple docstring'''







def    __UpperCamelCase	(	a    : int     ) ->"list[int]":
 if upper_limit < 0:
  raise ValueError('''Limit for the Catalan sequence must be ≥ 0'''     )

 snake_case				=			[0] * (upper_limit + 1)

 # Base case: C(0) = C(1) = 1
 snake_case				=			1
 if upper_limit > 0:
  snake_case				=			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(a     ):
   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:
         _lowercase       						=			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'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''char'''
     _UpperCAmelCase										=					'''bpe'''
     _UpperCAmelCase										=					'''wp'''




_lowercase       						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''char_tokenizer''']
     _UpperCAmelCase										=					'''ViTImageProcessor'''
     _UpperCAmelCase										=					'''MgpstrTokenizer'''







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[Any]:
      snake_case				=			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''    ,   A__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''    )
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''    )

      snake_case				=			tokenizer
      snake_case				=			AutoTokenizer.from_pretrained('''gpt2'''    )
      snake_case				=			AutoTokenizer.from_pretrained('''bert-base-uncased'''    )

      super().__init__(A__    ,   A__    )







     def __call__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[str]:
      if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''    )

      if images is not None:
       snake_case				=			self.image_processor(A__    ,   return_tensors=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.char_tokenizer(A__    ,   return_tensors=A__    ,   **A__    )

      if text is None:
       return inputs
      elif images is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      snake_case				,		snake_case				,		snake_case				=			sequences
      snake_case				=			char_preds.size(0    )

      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''char'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''bpe'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''wp'''    )

      snake_case				=			[]
      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
       snake_case				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
       snake_case				=			scores.index(max(A__    )    )
       final_strs.append(strs[max_score_index]    )
       final_scores.append(scores[max_score_index]    )

      snake_case				=			{}
      snake_case				=			final_strs
      snake_case				=			final_scores
      snake_case				=			char_strs
      snake_case				=			bpe_strs
      snake_case				=			wp_strs
      return out







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      if format == DecodeType.CHARACTER:
       snake_case				=			self.char_decode
       snake_case				=			1
       snake_case				=			'''[s]'''
      elif format == DecodeType.BPE:
       snake_case				=			self.bpe_decode
       snake_case				=			2
       snake_case				=			'''#'''
      elif format == DecodeType.WORDPIECE:
       snake_case				=			self.wp_decode
       snake_case				=			1_02
       snake_case				=			'''[SEP]'''
      else:
       raise ValueError(F"""Format {format} is not supported."""    )

      snake_case				,		snake_case				=			[], []
      snake_case				=			pred_logits.size(0    )
      snake_case				=			pred_logits.size(1    )
      snake_case				,		snake_case				=			pred_logits.topk(1    ,   dim=-1    ,   largest=A__    ,   sorted=A__    )
      snake_case				=			preds_index.view(-1    ,   A__    )[:, 1:]
      snake_case				=			decoder(A__    )
      snake_case				,		snake_case				=			torch.nn.functional.softmax(A__    ,   dim=2    ).max(dim=2    )
      snake_case				=			preds_max_prob[:, 1:]

      for index in range(A__    ):
       snake_case				=			preds_str[index].find(A__    )
       snake_case				=			preds_str[index][:pred_eos]
       snake_case				=			preds_index[index].cpu().tolist()
       snake_case				=			pred_index.index(A__    ) if eos_token in pred_index else -1
       snake_case				=			preds_max_prob[index][: pred_eos_index + 1]
       snake_case				=			pred_max_prob.cumprod(dim=0    )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(A__    )
       conf_scores.append(A__    )

      return dec_strs, conf_scores







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.char_tokenizer.batch_decode(A__    )]
      return decode_strs







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      return self.bpe_tokenizer.batch_decode(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.wp_tokenizer.batch_decode(A__    )]
      return decode_strs

