




'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





@skip_mps
class     lowerCAmelCase   ( lowerCAmelCase__				,   lowerCAmelCase__				,   lowerCAmelCase__				,   unittest.TestCase):
       __lowercase  :      Tuple        =			StableDiffusionModelEditingPipeline
       __lowercase  :      Optional[Any]        =			TEXT_TO_IMAGE_PARAMS
       __lowercase  :      str        =			TEXT_TO_IMAGE_BATCH_PARAMS
       __lowercase  :      Union[str, Any]        =			TEXT_TO_IMAGE_IMAGE_PARAMS
       __lowercase  :      Any        =			TEXT_TO_IMAGE_IMAGE_PARAMS


       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            torch.manual_seed(0  )
            __snake_case    					=  UNetaDConditionModel(
                block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=4    ,  out_channels=4    ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,  cross_attention_dim=32    ,  )
            __snake_case    					=  DDIMScheduler()
            torch.manual_seed(0  )
            __snake_case    					=  AutoencoderKL(
                block_out_channels=[32, 64]    ,  in_channels=3    ,  out_channels=3    ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,  latent_channels=4    ,  )
            torch.manual_seed(0  )
            __snake_case    					=  CLIPTextConfig(
                bos_token_id=0    ,  eos_token_id=2    ,  hidden_size=32    ,  intermediate_size=37    ,  layer_norm_eps=1E-05    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  pad_token_id=1    ,  vocab_size=1000    ,  )
            __snake_case    					=  CLIPTextModel(lowerCAmelCase_  )
            __snake_case    					=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

            __snake_case    					=  {
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=0  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  torch.manual_seed(lowerCAmelCase_  )
            __snake_case    					=  {
                '''prompt''': '''A field of roses''',
                '''generator''': generator,
                # Setting height and width to None to prevent OOMs on CPU.
                '''height''': None,
                '''width''': None,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''output_type''': '''numpy''',
            }
            return inputs


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case    					=  self.get_dummy_components()
            __snake_case    					=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
            __snake_case    					=  sd_pipe.to(lowerCAmelCase_  )
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

            __snake_case    					=  self.get_dummy_inputs(lowerCAmelCase_  )
            __snake_case    					=  sd_pipe(**lowerCAmelCase_  ).images
            __snake_case    					=  image[0, -3:, -3:, -1]
            assert image.shape == (1, 64, 64, 3)

            __snake_case    					=  np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]  )

            assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2


       def       lowerCAmelCase      (   self  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case    					=  self.get_dummy_components()
            __snake_case    					=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
            __snake_case    					=  sd_pipe.to(lowerCAmelCase_  )
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

            __snake_case    					=  self.get_dummy_inputs(lowerCAmelCase_  )
            __snake_case    					=  '''french fries'''
            __snake_case    					=  sd_pipe(**lowerCAmelCase_    ,  negative_prompt=lowerCAmelCase_  )
            __snake_case    					=  output.images
            __snake_case    					=  image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)

            __snake_case    					=  np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]  )

            assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case    					=  self.get_dummy_components()
            __snake_case    					=  EulerAncestralDiscreteScheduler(
                beta_start=0.00_085    ,  beta_end=0.012    ,  beta_schedule='''scaled_linear'''  )
            __snake_case    					=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
            __snake_case    					=  sd_pipe.to(lowerCAmelCase_  )
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

            __snake_case    					=  self.get_dummy_inputs(lowerCAmelCase_  )
            __snake_case    					=  sd_pipe(**lowerCAmelCase_  ).images
            __snake_case    					=  image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)

            __snake_case    					=  np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]  )

            assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2


       def       lowerCAmelCase      (   self  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            __snake_case    					=  self.get_dummy_components()
            __snake_case    					=  PNDMScheduler()
            __snake_case    					=  StableDiffusionModelEditingPipeline(**lowerCAmelCase_  )
            __snake_case    					=  sd_pipe.to(lowerCAmelCase_  )
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

            __snake_case    					=  self.get_dummy_inputs(lowerCAmelCase_  )
            # the pipeline does not expect pndm so test if it raises error.
            with self.assertRaises(lowerCAmelCase_  ):
                 __snake_case    					=  sd_pipe(**lowerCAmelCase_  ).images


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=5E-3  )




       def       lowerCAmelCase      (   self  )      ->  Tuple:



            '''simple docstring'''




            super().test_attention_slicing_forward_pass(expected_max_diff=5E-3  )





@slow
@require_torch_gpu
class     lowerCAmelCase   ( unittest.TestCase):


       def       lowerCAmelCase      (   self  )      ->  Optional[Any]:



            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE=0  )      ->  Dict:



            '''simple docstring'''




            __snake_case    					=  torch.manual_seed(lowerCAmelCase_  )
            __snake_case    					=  {
                '''prompt''': '''A field of roses''',
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs


       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  '''CompVis/stable-diffusion-v1-4'''
            __snake_case    					=  StableDiffusionModelEditingPipeline.from_pretrained(lowerCAmelCase_    ,  safety_checker=lowerCAmelCase_  )
            pipe.to(lowerCAmelCase_  )
            pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
            pipe.enable_attention_slicing()

            __snake_case    					=  self.get_inputs()
            __snake_case    					=  pipe(**lowerCAmelCase_  ).images
            __snake_case    					=  image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)

            __snake_case    					=  np.array(
                [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]  )

            assert np.abs(expected_slice - image_slice  ).max() < 1E-2

            # make sure image changes after editing
            pipe.edit_model('''A pack of roses'''    ,  '''A pack of blue roses'''  )

            __snake_case    					=  pipe(**lowerCAmelCase_  ).images
            __snake_case    					=  image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)

            assert np.abs(expected_slice - image_slice  ).max() > 1E-1




       def       lowerCAmelCase      (   self  )      ->  Optional[int]:



            '''simple docstring'''




            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            __snake_case    					=  '''CompVis/stable-diffusion-v1-4'''
            __snake_case    					=  DDIMScheduler.from_pretrained(lowerCAmelCase_    ,  subfolder='''scheduler'''  )
            __snake_case    					=  StableDiffusionModelEditingPipeline.from_pretrained(
                lowerCAmelCase_    ,  scheduler=lowerCAmelCase_    ,  safety_checker=lowerCAmelCase_  )
            __snake_case    					=  pipe.to(lowerCAmelCase_  )
            pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
            pipe.enable_attention_slicing(1  )
            pipe.enable_sequential_cpu_offload()

            __snake_case    					=  self.get_inputs()
            __snake_case    					=  pipe(**lowerCAmelCase_  )

            __snake_case    					=  torch.cuda.max_memory_allocated()
            # make sure that less than 4.4 GB is allocated
            assert mem_bytes < 4.4 * 10**9












'''simple docstring'''



import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
     import torch

     from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer





@require_torch
class     lowerCAmelCase   ( unittest.TestCase):
       __lowercase  :      int        =			(JukeboxModel,) if is_torch_available() else ()
       __lowercase  :      Tuple        =			'''openai/jukebox-1b-lyrics'''
       __lowercase  :      Any        =			{
           '''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
    ''',
       }
       # fmt: off
       __lowercase  :      int        =			[
           1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
           1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
           333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
           1405, 1276, 1455, 1228
       ]

       __lowercase  :      Optional[int]        =			[
           1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653
       ]

       __lowercase  :      Union[str, Any]        =			[
           1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
           844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
           191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
           1262, 869, 1728, 747
       ]
       __lowercase  :      Tuple        =			[
           416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416
       ]

       __lowercase  :      str        =			[
           1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
           616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
           293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
           1186, 1015, 1777, 268
       ]
       __lowercase  :      Optional[int]        =			[
           854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
           185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
           591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
           185, 842, 591, 89
       ]

       __lowercase  :      Dict        =			[105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

       __lowercase  :      Union[str, Any]        =			[
           390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
           1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
           1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
           1907, 1788, 596, 1626
       ]
       __lowercase  :      Tuple        =			[
           1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
           1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
           1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
           1063, 1433, 1433, 1599
       ]
       __lowercase  :      Union[str, Any]        =			[
           1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
           96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
           1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
           197, 391, 302, 1930
       ]
       __lowercase  :      Union[str, Any]        =			[
           390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
           1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
           1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
           1907, 1788, 596, 1626
       ]
       __lowercase  :      List[Any]        =			[
           -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
           -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
           0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
           0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
           0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
       ]
       __lowercase  :      Optional[int]        =			[
           0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
           0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
           -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
           -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
       ]
       __lowercase  :      List[Any]        =			[
           0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
           0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
           -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
           -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
       ]
       __lowercase  :      Optional[int]        =			[
           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
       ]


       def       lowerCAmelCase      (   self  )      ->  Union[str, Any]:



            '''simple docstring'''




            __snake_case    					=  JukeboxTokenizer.from_pretrained(self.model_id  )
            __snake_case    					=  tokenizer(**self.metas  )['''input_ids''']
            return tokens


       @slow
       def       lowerCAmelCase      (   self  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  self.prepare_inputs()

            set_seed(0  )
            __snake_case    					=  [torch.zeros(1    ,  0    ,  dtype=torch.long  ).cpu() for _ in range(3  )]
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [0]    ,  sample_length=40 * model.priors[0].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[0][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [1]    ,  sample_length=40 * model.priors[1].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[1][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [2]    ,  sample_length=40 * model.priors[2].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[2][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )


       @slow
       def       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




            __snake_case    					=  False
            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()

            __snake_case    					=  self.prepare_inputs()
            set_seed(0  )
            __snake_case    					=  [torch.zeros(1    ,  0    ,  dtype=torch.long  ) for _ in range(3  )]

            __snake_case    					=  model.priors[0]
            __snake_case    					=  0
            __snake_case    					=  top_prior.get_music_tokens_conds(__SCREAMING_SNAKE_CASE    ,  start=__SCREAMING_SNAKE_CASE    ,  end=start + top_prior.n_ctx  )
            __snake_case    					=  top_prior.get_metadata(labels[0].clone()    ,  __SCREAMING_SNAKE_CASE    ,  105_8304    ,  0  )

            self.assertIsNone(__SCREAMING_SNAKE_CASE  )
            self.assertListEqual(metadata.numpy()[0][:10].tolist()    ,  self.EXPECTED_Y_COND  )

            __snake_case					,    __snake_case					,    __snake_case    					=  top_prior.get_cond(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            torch.testing.assert_allclose(
                audio_conditioning[0][0][:30].detach()    ,  torch.tensor(self.EXPECTED_AUDIO_COND  )    ,  atol=1E-4    ,  rtol=1E-4  )
            torch.testing.assert_allclose(
                metadata_conditioning[0][0][:30].detach()    ,  torch.tensor(self.EXPECTED_META_COND  )    ,  atol=1E-4    ,  rtol=1E-4  )
            torch.testing.assert_allclose(
                lyric_tokens[0, :30].detach()    ,  torch.tensor(self.EXPECTED_LYRIC_COND  )    ,  atol=1E-4    ,  rtol=1E-4  )


       @slow
       def       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




            __snake_case    					=  False

            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            set_seed(0  )
            __snake_case    					=  torch.rand((1, 5120, 1)  )
            __snake_case    					=  list(self.prepare_inputs()  )

            __snake_case    					=  [model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=2    ,  bs_chunks=waveform.shape[0]  )[0], None, None]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[0]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  sample_length=40 * model.priors[0].raw_to_tokens  )
            torch.testing.assert_allclose(zs[0][0][:40]    ,  torch.tensor(self.EXPECTED_PRIMED_0  )  )

            __snake_case    					=  torch.cat((zs[0], torch.zeros(1    ,  2048 - zs[0].shape[-1]  ))    ,  dim=-1  ).long()
            __snake_case    					=  [upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=1    ,  bs_chunks=waveform.shape[0]  )[0], None]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[1]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  sample_length=40 * model.priors[1].raw_to_tokens  )
            torch.testing.assert_allclose(zs[1][0][:40]    ,  torch.tensor(self.EXPECTED_PRIMED_1  )  )

            __snake_case    					=  torch.cat((zs[1], torch.zeros(1    ,  2048 - zs[1].shape[-1]  ))    ,  dim=-1  ).long()
            __snake_case    					=  [upper_a, upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=0    ,  bs_chunks=waveform.shape[0]  )[0]]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[2]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  sample_length=40 * model.priors[2].raw_to_tokens  )
            torch.testing.assert_allclose(zs[2][0][:40].cpu()    ,  torch.tensor(self.EXPECTED_PRIMED_2  )  )




       @slow
       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            set_seed(0  )
            __snake_case    					=  torch.rand((1, 5120, 1)  )
            with torch.no_grad():
                 __snake_case    					=  model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=2    ,  bs_chunks=x.shape[0]  )
            torch.testing.assert_allclose(zs[0][0]    ,  torch.tensor(self.EXPECTED_VQVAE_ENCODE  )  )

            with torch.no_grad():
                 __snake_case    					=  model.vqvae.decode(__SCREAMING_SNAKE_CASE    ,  start_level=2    ,  bs_chunks=x.shape[0]  )
            torch.testing.assert_allclose(x[0, :40, 0]    ,  torch.tensor(self.EXPECTED_VQVAE_DECODE  )    ,  atol=1E-4    ,  rtol=1E-4  )





@require_torch
class     lowerCAmelCase   ( unittest.TestCase):
       __lowercase  :      Tuple        =			(JukeboxModel,) if is_torch_available() else ()
       __lowercase  :      List[str]        =			'''openai/jukebox-5b-lyrics'''
       __lowercase  :      Any        =			{
           '''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
    ''',
       }

       # fmt: off
       __lowercase  :      List[str]        =			[
           1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
       ]
       __lowercase  :      Optional[int]        =			[
           1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
       ]

       __lowercase  :      Dict        =			[
           1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
       ]
       __lowercase  :      Union[str, Any]        =			[
           416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416
       ]

       __lowercase  :      List[str]        =			[
           1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
           616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
           290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
           234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
           1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
       ]
       __lowercase  :      Any        =			[
           854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
           417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
           1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
           89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
           185, 89, 591, 842, 185, 842, 591, 632
       ]

       __lowercase  :      str        =			[
           1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
       ]
       __lowercase  :      List[str]        =			[
           1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
           653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
           710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
           1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
           252, 1229, 1434, 2001
       ]

       __lowercase  :      List[str]        =			[
           1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
           416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
       ]
       __lowercase  :      int        =			[
           491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
           844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
           185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
           307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
           307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
       ]


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  JukeboxTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  tokenizer(**self.metas  )['''input_ids''']
            return tokens


       @slow
       def       lowerCAmelCase      (   self  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  self.prepare_inputs(self.model_id  )

            set_seed(0  )
            __snake_case    					=  [torch.zeros(1    ,  0    ,  dtype=torch.long  ).cpu() for _ in range(3  )]
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [0]    ,  sample_length=60 * model.priors[0].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[0][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [1]    ,  sample_length=60 * model.priors[1].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[1][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [2]    ,  sample_length=60 * model.priors[2].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[2][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )


       @slow
       @skip('''Not enough GPU memory on CI runners'''  )
       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  [i.cuda() for i in self.prepare_inputs(self.model_id  )]

            set_seed(0  )
            model.priors[0].cuda()
            __snake_case    					=  [torch.zeros(1    ,  0    ,  dtype=torch.long  ).cuda() for _ in range(3  )]
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [0]    ,  sample_length=60 * model.priors[0].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            torch.testing.assert_allclose(zs[0][0].cpu()    ,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_2  )  )
            model.priors[0].cpu()

            set_seed(0  )
            model.priors[1].cuda()
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [1]    ,  sample_length=60 * model.priors[1].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            torch.testing.assert_allclose(zs[1][0].cpu()    ,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_1  )  )
            model.priors[1].cpu()

            set_seed(0  )
            model.priors[2].cuda()
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [2]    ,  sample_length=60 * model.priors[2].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            torch.testing.assert_allclose(zs[2][0].cpu()    ,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_0  )  )




       @slow
       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  '''ArthurZ/jukebox_prior_0'''
            __snake_case    					=  JukeboxPrior.from_pretrained(__SCREAMING_SNAKE_CASE    ,  min_duration=0  ).eval().half().to('''cuda'''  )

            __snake_case    					=  self.prepare_inputs(__SCREAMING_SNAKE_CASE  )[0].cuda()
            __snake_case    					=  model.get_metadata(__SCREAMING_SNAKE_CASE    ,  0    ,  7680    ,  0  )
            set_seed(0  )
            __snake_case    					=  model.sample(1    ,  metadata=__SCREAMING_SNAKE_CASE    ,  sample_tokens=60  )
            self.assertIn(outputs[0].cpu().tolist()    ,  [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )






