'''simple docstring'''






from ...processing_utils import ProcessorMixin




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""image_processor""", """feature_extractor"""]
  __lowerCAmelCase				      =					"""TvltImageProcessor"""
  __lowerCAmelCase				      =					"""TvltFeatureExtractor"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    super().__init__(image_processor=snake_case_   ,    feature_extractor=snake_case_					)

    __UpperCAmelCase:					Union[str, Any]		= image_processor
    __UpperCAmelCase:					List[str]		= feature_extractor







  def __call__(							self   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=False   ,    snake_case_=False   ,    *snake_case_   ,    **snake_case_   ,    ):

    '''simple docstring'''







    if images is None and audio is None:
      raise ValueError("""You need to specify either an `images` or `audio` input to process."""					)

    __UpperCAmelCase:					Dict		= None
    if images is not None:
      __UpperCAmelCase:					Tuple		= self.image_processor(snake_case_   ,    mask_pixel=snake_case_   ,    *snake_case_   ,    **snake_case_					)
    if images_mixed is not None:
      __UpperCAmelCase:					Any		= self.image_processor(snake_case_   ,    is_mixed=snake_case_   ,    *snake_case_   ,    **snake_case_					)
    if audio is not None:
      __UpperCAmelCase:					str		= self.feature_extractor(
          snake_case_   ,    *snake_case_   ,    sampling_rate=snake_case_   ,    mask_audio=snake_case_   ,    **snake_case_					)

    __UpperCAmelCase:					str		= {}
    if audio is not None:
      output_dict.update(snake_case_					)
    if images is not None:
      output_dict.update(snake_case_					)
    if images_mixed_dict is not None:
      output_dict.update(snake_case_					)
    return output_dict







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.image_processor.model_input_names
    __UpperCAmelCase:					Union[str, Any]		= self.feature_extractor.model_input_names
    return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names					)					)
'''simple docstring'''






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class    a (       unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					JukeboxTokenizer
  __lowerCAmelCase				      =					{
      """artist""": """Zac Brown Band""",
      """genres""": """Country""",
      """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
  }







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Dict		= JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""					)
    __UpperCAmelCase:					List[str]		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
            76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
            44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
            47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
            76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
            30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
            27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
            45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
            41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
            76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
            76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
            64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
            30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
            27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
            34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
            27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
            41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
            44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
            76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
            32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
            40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
            45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
            31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
            45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
            34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
            31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
            40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
            38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
            76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
            41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
            27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
            46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
            41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
            46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
            41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
            40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
            27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
            76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
            41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
            76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
            27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
            34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
            44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
            40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
            46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
            38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
            40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
            27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
            76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
            76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
            76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
            76, 76]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
        torch.tensor([[0, 0, 0, 1069, 11]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)







  @require_torch
  def    lowercase_  (							self					):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:					Any		= JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""					)
    __UpperCAmelCase:					Tuple		= tokenizer(**self.metas					)["""input_ids"""]
    # fmt: off
    __UpperCAmelCase:					int		= [
        torch.tensor([[
            0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
            31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
            31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
            40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
            79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
            77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
            27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
            37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
            32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
            77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
            77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
            46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
            77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
            77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
            77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
            77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
            64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
            40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
            40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
            38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
            31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
            41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
            46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
            41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
            31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
            31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
            44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
            31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
            38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
            40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
            27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
            31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
            34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
            31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
            31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
            45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
            31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
            15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
            11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
            45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
            41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
            44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
            46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
            27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
            35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
            77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
            31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
            41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
            77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
            40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
            77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
            27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
            77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
            77, 77, 77, 77, 77, 77]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]					),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,    EXPECTED_OUTPUT[0]					)					)
    self.assertTrue(torch.allclose(tokens[1]   ,    EXPECTED_OUTPUT[1]					)					)
    self.assertTrue(torch.allclose(tokens[2]   ,    EXPECTED_OUTPUT[2]					)					)