'''simple docstring'''






import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class    a (       _a				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    super().__init__()
    self.register_modules(vqvae=_A   ,    unet=_A   ,    scheduler=_A					)







  @torch.no_grad()
  def __call__(							self   ,    snake_case_ = 1   ,    snake_case_ = None   ,    snake_case_ = 0.0   ,    snake_case_ = 50   ,    snake_case_ = "pil"   ,    snake_case_ = True   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= randn_tensor(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=_A   ,    )
    __UpperCAmelCase:					Union[str, Any]		= latents.to(self.device					)

    # scale the initial noise by the standard deviation required by the scheduler
    __UpperCAmelCase:					Tuple		= latents * self.scheduler.init_noise_sigma

    self.scheduler.set_timesteps(_A					)

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    __UpperCAmelCase:					List[Any]		= """eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)

    __UpperCAmelCase:					int		= {}
    if accepts_eta:
      __UpperCAmelCase:					Optional[int]		= eta

    for t in self.progress_bar(self.scheduler.timesteps					):
      __UpperCAmelCase:					Dict		= self.scheduler.scale_model_input(_A   ,    _A					)
      # predict the noise residual
      __UpperCAmelCase:					Dict		= self.unet(_A   ,    _A					).sample
      # compute the previous noisy sample x_t -> x_t-1
      __UpperCAmelCase:					Any		= self.scheduler.step(_A   ,    _A   ,    _A   ,    **_A					).prev_sample

    # decode the image latents with the VAE
    __UpperCAmelCase:					List[str]		= self.vqvae.decode(_A					).sample

    __UpperCAmelCase:					Dict		= (image / 2 + 0.5).clamp(0   ,    1					)
    __UpperCAmelCase:					Dict		= image.cpu().permute(0   ,    2   ,    3   ,    1					).numpy()
    if output_type == "pil":
      __UpperCAmelCase:					Optional[Any]		= self.numpy_to_pil(_A					)

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_A					)
'''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]					)					)