





def     lowerCamelCase__			(__lowerCamelCase	):
     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"	)

     _SCREAMING_SNAKE_CASE       :	Dict						    =							[0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							1
     if upper_limit > 0:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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(SCREAMING_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(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()






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab()							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)
            processor.save_pretrained(
                self.tmpdirname    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						speaker_embeddings_directory=self.speaker_embeddings_directory    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BarkProcessor.from_pretrained(
                self.tmpdirname    ,						self.speaker_embeddings_dict_path    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from npz file
            _SCREAMING_SNAKE_CASE       :	Any						    =							os.path.join(self.tmpdirname    ,						"file.npz"							)
            np.savez(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["history_prompt"]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key].squeeze().tolist()							)