


import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class 						_UpperCAmelCase      (     unittest.TestCase							):
		lowerCamelCase_	:			Union[str, Any]       			=  JukeboxTokenizer
		lowerCamelCase_	:			List[str]       			=  {
		    """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 _snake_case		(						self				:     int):
				import torch

				SCREAMING_SNAKE_CASE_				:List[str]				     =						JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						tokenizer(**self.metas)["input_ids"]
				# fmt: off
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[
				    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 _snake_case		(						self				:     List[Any]):
				import torch

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						tokenizer(**self.metas)["input_ids"]
				# fmt: off
				SCREAMING_SNAKE_CASE_				:Dict				     =						[
				    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]))










import unittest

import torch

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


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  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)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

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

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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))






