




'''simple docstring'''



import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
     import numpy as np
     import torch

if is_vision_available():
     from PIL import Image







def 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Union[str, Any]=False   ,	_lowerCamelCase							:     Dict=False   ,	_lowerCamelCase							:     Optional[int]=False	)->   int:




     '''simple docstring'''





     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     __snake_case    					=  []
     for i in range(image_processor_tester.batch_size	):
          if equal_resolution:
               __snake_case    					=  __snake_case    					=  image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               __snake_case    					=  image_processor_tester.min_resolution
               if getattr(_lowerCamelCase   ,	'''size_divisor'''   ,	_lowerCamelCase	):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    __snake_case    					=  max(image_processor_tester.size_divisor   ,	_lowerCamelCase	)
               __snake_case					,    __snake_case    					=  np.random.choice(np.arange(_lowerCamelCase   ,	image_processor_tester.max_resolution	)   ,	2	)
          image_inputs.append(
              np.random.randint(2_55   ,	size=(image_processor_tester.num_channels, width, height)   ,	dtype=np.uinta	)	)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          __snake_case    					=  [Image.fromarray(np.moveaxis(_lowerCamelCase   ,	0   ,	-1	)	) for image in image_inputs]

     if torchify:
          __snake_case    					=  [torch.from_numpy(_lowerCamelCase	) for image in image_inputs]

     return image_inputs







def 			_UpperCamelCase (_lowerCamelCase							:     Optional[int]   ,	_lowerCamelCase							:     Dict=10   ,	_lowerCamelCase							:     str=10   ,	_lowerCamelCase							:     int=False   ,	_lowerCamelCase							:     Optional[Any]=False	)->   int:




     '''simple docstring'''





     __snake_case    					=  []
     for i in range(image_processor_tester.num_frames	):
          video.append(np.random.randint(2_55   ,	size=(image_processor_tester.num_channels, width, height)   ,	dtype=np.uinta	)	)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          __snake_case    					=  [Image.fromarray(np.moveaxis(_lowerCamelCase   ,	0   ,	-1	)	) for frame in video]

     if torchify:
          __snake_case    					=  [torch.from_numpy(_lowerCamelCase	) for frame in video]

     return video







def 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Union[str, Any]=False   ,	_lowerCamelCase							:     int=False   ,	_lowerCamelCase							:     Any=False	)->   Optional[Any]:




     '''simple docstring'''





     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     __snake_case    					=  []
     for i in range(image_processor_tester.batch_size	):
          if equal_resolution:
               __snake_case    					=  __snake_case    					=  image_processor_tester.max_resolution
          else:
               __snake_case					,    __snake_case    					=  np.random.choice(
                   np.arange(image_processor_tester.min_resolution   ,	image_processor_tester.max_resolution	)   ,	2	)
               __snake_case    					=  prepare_video(
                   image_processor_tester=_lowerCamelCase   ,	width=_lowerCamelCase   ,	height=_lowerCamelCase   ,	numpify=_lowerCamelCase   ,	torchify=_lowerCamelCase   ,	)
          video_inputs.append(_lowerCamelCase	)

     return video_inputs







class     lowerCAmelCase   :
       __lowercase  :      Dict        =			None


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



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )
            __snake_case    					=  json.loads(image_processor.to_json_string()  )
            for key, value in self.image_processor_dict.items():
                 self.assertEqual(obj[key]    ,  __SCREAMING_SNAKE_CASE  )


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



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 __snake_case    					=  os.path.join(__SCREAMING_SNAKE_CASE    ,  '''image_processor.json'''  )
                 image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE  )
                 __snake_case    					=  self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE  )

            self.assertEqual(image_processor_second.to_dict()    ,  image_processor_first.to_dict()  )


       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 __snake_case    					=  image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE  )[0]
                 check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE  )
                 __snake_case    					=  self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE  )

            self.assertEqual(image_processor_second.to_dict()    ,  image_processor_first.to_dict()  )


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class()
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




       @require_torch
       @require_vision
       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            if self.test_cast_dtype is not None:
                 # Initialize image_processor
                 __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )

                 # create random PyTorch tensors
                 __snake_case    					=  prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=__SCREAMING_SNAKE_CASE    ,  torchify=__SCREAMING_SNAKE_CASE  )

                 __snake_case    					=  image_processor(__SCREAMING_SNAKE_CASE    ,  return_tensors='''pt'''  )
                 # for layoutLM compatiblity
                 self.assertEqual(encoding.pixel_values.device    ,  torch.device('''cpu'''  )  )
                 self.assertEqual(encoding.pixel_values.dtype    ,  torch.floataa  )

                 __snake_case    					=  image_processor(__SCREAMING_SNAKE_CASE    ,  return_tensors='''pt'''  ).to(torch.floataa  )
                 self.assertEqual(encoding.pixel_values.device    ,  torch.device('''cpu'''  )  )
                 self.assertEqual(encoding.pixel_values.dtype    ,  torch.floataa  )

                 __snake_case    					=  image_processor(__SCREAMING_SNAKE_CASE    ,  return_tensors='''pt'''  ).to('''cpu'''    ,  torch.bfloataa  )
                 self.assertEqual(encoding.pixel_values.device    ,  torch.device('''cpu'''  )  )
                 self.assertEqual(encoding.pixel_values.dtype    ,  torch.bfloataa  )

                 with self.assertRaises(__SCREAMING_SNAKE_CASE  ):
                      __snake_case    					=  image_processor(__SCREAMING_SNAKE_CASE    ,  return_tensors='''pt'''  ).to(torch.bfloataa    ,  '''cpu'''  )

                 # Try with text + image feature
                 __snake_case    					=  image_processor(__SCREAMING_SNAKE_CASE    ,  return_tensors='''pt'''  )
                 encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]  )}  )
                 __snake_case    					=  encoding.to(torch.floataa  )

                 self.assertEqual(encoding.pixel_values.device    ,  torch.device('''cpu'''  )  )
                 self.assertEqual(encoding.pixel_values.dtype    ,  torch.floataa  )
                 self.assertEqual(encoding.input_ids.dtype    ,  torch.long  )












'''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]  )






