import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.generation import DisjunctiveConstraint

@require_torch
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				Tuple:
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

												UpperCamelCase_		:							Any  =		[[1, 2, 4], [1, 2, 3, 4]]
												UpperCamelCase_		:							Any  =		DisjunctiveConstraint(__UpperCamelCase						)
												self.assertTrue(isinstance(dc.token_ids			,     __UpperCamelCase						)						)

												with self.assertRaises(__UpperCamelCase						):
																		DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

												with self.assertRaises(__UpperCamelCase						):
																		DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)





						def        A_							(self						)				->				Union[str, Any]:
												# We can't have constraints that are complete subsets of another. This leads to a preverse
												# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
												# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
												# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
												# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
												UpperCamelCase_		:							Optional[int]  =		[[1, 2], [1, 2, 3, 4]]

												with self.assertRaises(__UpperCamelCase						):
																		DisjunctiveConstraint(__UpperCamelCase						)  # fails here





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							List[Any]  =		[[1, 2, 3], [1, 2, 4]]

												UpperCamelCase_		:							Optional[int]  =		DisjunctiveConstraint(__UpperCamelCase						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		dc.update(1						)
												UpperCamelCase_		:							Tuple  =		stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.current_seq == [1]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Tuple  =		dc.update(2						)
												UpperCamelCase_		:							List[str]  =		stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.current_seq == [1, 2]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		dc.update(3						)
												UpperCamelCase_		:							List[Any]  =		stepped is True and completed is True and reset is False
												self.assertTrue(__UpperCamelCase						)
												self.assertTrue(dc.completed						)  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 3]						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							List[Any]  =		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												UpperCamelCase_		:							List[str]  =		DisjunctiveConstraint(__UpperCamelCase						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		dc.update(1						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.current_seq == [1]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		dc.update(2						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.current_seq == [1, 2]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		dc.update(4						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.current_seq == [1, 2, 4]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Tuple  =		dc.update(5						)
												self.assertTrue(dc.completed						)  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

												dc.reset()

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		dc.update(1						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.remaining() == 3						)
												self.assertTrue(dc.current_seq == [1]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		dc.update(2						)
												self.assertTrue(not dc.completed						)
												self.assertTrue(dc.remaining() == 2						)
												self.assertTrue(dc.current_seq == [1, 2]						)

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Tuple  =		dc.update(5						)
												self.assertTrue(dc.completed						)  # Completed!
												self.assertTrue(dc.remaining() == 0						)
												self.assertTrue(dc.current_seq == [1, 2, 5]						)







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		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     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						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

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

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






