
'''simple docstring'''
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch







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




			@require_torch
			def 					lowerCAmelCase__					(       self	):
				import torch

				a_      				=						JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""	)
				a_      				=						tokenizer(**self.metas	)["""input_ids"""]
				# fmt: off
				a_      				=						[
				    torch.tensor([[
				        0, 0, 0, 71_69, 5_07, 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, 10_69, 11]]	),
				    torch.tensor([[0, 0, 0, 10_69, 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 					lowerCAmelCase__					(       self	):
				import torch

				a_      				=						JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""	)
				a_      				=						tokenizer(**self.metas	)["""input_ids"""]
				# fmt: off
				a_      				=						[
				    torch.tensor([[
				        0, 0, 0, 10_69, 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, 10_69, 11, -1, -1, -1, -1]]	),
				    torch.tensor([[0, 0, 0, 10_69, 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]	)	)


'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
