



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 						UpperCAmelCase_					( unittest.TestCase):







       def      snake_case__   (     self):

              '''simple docstring'''

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







       def      snake_case__   (     self,    **__a):

              '''simple docstring'''

              return AutoTokenizer.from_pretrained(self.checkpoint,    **__UpperCamelCase)







       def      snake_case__   (     self):

              '''simple docstring'''

              shutil.rmtree(self.tmpdirname)







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Union[str, Any]				     =					self.get_tokenizer()

              _lowerCAmelCase  :							int				     =					BarkProcessor(tokenizer=__UpperCamelCase)

              processor.save_pretrained(self.tmpdirname)
              _lowerCAmelCase  :							Tuple				     =					BarkProcessor.from_pretrained(self.tmpdirname)

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







       @slow
       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[int]				     =					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,    )

              _lowerCAmelCase  :							str				     =					self.get_tokenizer(bos_token="(BOS)",    eos_token="(EOS)")

              _lowerCAmelCase  :							Union[str, 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      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Dict				     =					BarkProcessor.from_pretrained(
                  pretrained_processor_name_or_path=self.checkpoint,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,    )

              _lowerCAmelCase  :							int				     =					35
              _lowerCAmelCase  :							List[str]				     =					2
              _lowerCAmelCase  :							Optional[Any]				     =					8

              _lowerCAmelCase  :							Optional[int]				     =					{
                  "semantic_prompt": np.ones(__UpperCamelCase),
                  "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
                  "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
              }

              # test providing already loaded voice_preset
              _lowerCAmelCase  :							str				     =					processor(text=self.input_string,    voice_preset=__UpperCamelCase)

              _lowerCAmelCase  :							Dict				     =					inputs["history_prompt"]
              for key in voice_preset:
                     self.assertListEqual(voice_preset[key].tolist(),    processed_voice_preset.get(__UpperCamelCase,    np.array([])).tolist())

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

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

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







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[Any]				     =					self.get_tokenizer()

              _lowerCAmelCase  :							List[Any]				     =					BarkProcessor(tokenizer=__UpperCamelCase)

              _lowerCAmelCase  :							Tuple				     =					processor(text=self.input_string)

              _lowerCAmelCase  :							Tuple				     =					tokenizer(
                  self.input_string,    padding="max_length",    max_length=256,    add_special_tokens=__UpperCamelCase,    return_attention_mask=__UpperCamelCase,    return_token_type_ids=__UpperCamelCase,    )

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





def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if length <= 0 or not isinstance(_lowerCamelCase       ,				_lowerCamelCase						):
														raise ValueError("Length must be a positive integer."						)
							return [n * (2 * n - 1) for n in range(_lowerCamelCase						)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))
