


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class  UpperCamelCase__(     _UpperCAmelCase ):
				__magic_name__       :   UNetaDModel
				__magic_name__       :   ScoreSdeVeScheduler




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				UNetaDModel   ,					lowerCAmelCase    :				ScoreSdeVeScheduler   )->  str:

										"""simple docstring"""




										super().__init__()
										self.register_modules(unet=__UpperCamelCase   ,					scheduler=__UpperCamelCase   )





				@torch.no_grad()
				def __call__(	self    :				Optional[int]   ,					lowerCAmelCase    :				int = 1   ,					lowerCAmelCase    :				int = 2000   ,					lowerCAmelCase    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,					lowerCAmelCase    :				Optional[str] = "pil"   ,					lowerCAmelCase    :				bool = True   ,					**lowerCAmelCase    :				Union[str, Any]   ,					)->  Union[ImagePipelineOutput, Tuple]:

										"""simple docstring"""




										UpperCAmelCase         =			self.unet.config.sample_size
										UpperCAmelCase         =			(batch_size, 3, img_size, img_size)

										UpperCAmelCase         =			self.unet

										UpperCAmelCase         =			randn_tensor(__UpperCamelCase   ,					generator=__UpperCamelCase   ) * self.scheduler.init_noise_sigma
										UpperCAmelCase         =			sample.to(self.device   )

										self.scheduler.set_timesteps(__UpperCamelCase   )
										self.scheduler.set_sigmas(__UpperCamelCase   )

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
																UpperCAmelCase         =			self.scheduler.sigmas[i] * torch.ones(shape[0]   ,					device=self.device   )

																# correction step
																for _ in range(self.scheduler.config.correct_steps   ):
																						UpperCAmelCase         =			self.unet(__UpperCamelCase   ,					__UpperCamelCase   ).sample
																						UpperCAmelCase         =			self.scheduler.step_correct(__UpperCamelCase   ,					__UpperCamelCase   ,					generator=__UpperCamelCase   ).prev_sample

																# prediction step
																UpperCAmelCase         =			model(__UpperCamelCase   ,					__UpperCamelCase   ).sample
																UpperCAmelCase         =			self.scheduler.step_pred(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					generator=__UpperCamelCase   )

																UpperCAmelCase       ,       UpperCAmelCase         =			output.prev_sample, output.prev_sample_mean

										UpperCAmelCase         =			sample_mean.clamp(0   ,					1   )
										UpperCAmelCase         =			sample.cpu().permute(0   ,					2   ,					3   ,					1   ).numpy()
										if output_type == "pil":
																UpperCAmelCase         =			self.numpy_to_pil(__UpperCamelCase   )

										if not return_dict:
																return (sample,)

										return ImagePipelineOutput(images=__UpperCamelCase   )









'''simple docstring'''



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    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 4], [1, 2, 3, 4]]
										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )
										self.assertTrue(isinstance(dc.token_ids   ,					lowerCAmelCase   )   )

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

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




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

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2], [1, 2, 3, 4]]

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




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(3   )
										UpperCAmelCase         =			stepped is True and completed is True and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 3]   )





				def   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(4   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2, 4]   )

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

										dc.reset()

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 3   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 2   )
										self.assertTrue(dc.current_seq == [1, 2]   )

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





