




import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					["""a""", """b""", """c"""]

										# Defaults to last layer if both are None
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_aligned_output_features_output_indices(a       ,     a       ,     a			)
										self.assertEqual(a       ,     ["""c"""]			)
										self.assertEqual(a       ,     [2]			)

										# Out indices set to match out features
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_aligned_output_features_output_indices(["""a""", """c"""]       ,     a       ,     a			)
										self.assertEqual(a       ,     ["""a""", """c"""]			)
										self.assertEqual(a       ,     [0, 2]			)

										# Out features set to match out indices
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_aligned_output_features_output_indices(a       ,     [0, 2]       ,     a			)
										self.assertEqual(a       ,     ["""a""", """c"""]			)
										self.assertEqual(a       ,     [0, 2]			)

										# Out features selected from negative indices
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_aligned_output_features_output_indices(a       ,     [-3, -1]       ,     a			)
										self.assertEqual(a       ,     ["""a""", """c"""]			)
										self.assertEqual(a       ,     [-3, -1]			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[Any]:
										# Stage names must be set
										with self.assertRaises(a			):
													verify_out_features_out_indices(["""a""", """b"""]       ,     (0, 1)       ,     a			)

										# Out features must be a list
										with self.assertRaises(a			):
													verify_out_features_out_indices(("""a""", """b""")       ,     (0, 1)       ,     ["""a""", """b"""]			)

										# Out features must be a subset of stage names
										with self.assertRaises(a			):
													verify_out_features_out_indices(["""a""", """b"""]       ,     (0, 1)       ,     ["""a"""]			)

										# Out indices must be a list or tuple
										with self.assertRaises(a			):
													verify_out_features_out_indices(a       ,     0       ,     ["""a""", """b"""]			)

										# Out indices must be a subset of stage names
										with self.assertRaises(a			):
													verify_out_features_out_indices(a       ,     (0, 1)       ,     ["""a"""]			)

										# Out features and out indices must be the same length
										with self.assertRaises(a			):
													verify_out_features_out_indices(["""a""", """b"""]       ,     (0,)       ,     ["""a""", """b""", """c"""]			)

										# Out features should match out indices
										with self.assertRaises(a			):
													verify_out_features_out_indices(["""a""", """b"""]       ,     (0, 2)       ,     ["""a""", """b""", """c"""]			)

										# Out features and out indices should be in order
										with self.assertRaises(a			):
													verify_out_features_out_indices(["""b""", """a"""]       ,     (0, 1)       ,     ["""a""", """b"""]			)

										# Check passes with valid inputs
										verify_out_features_out_indices(["""a""", """b""", """d"""]       ,     (0, 1, -1)       ,     ["""a""", """b""", """c""", """d"""]			)







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					BackboneMixin()

										SCREAMING_SNAKE_CASE           =					["""a""", """b""", """c"""]
										SCREAMING_SNAKE_CASE           =					["""a""", """c"""]
										SCREAMING_SNAKE_CASE           =					[0, 2]

										# Check that the output features and indices are set correctly
										self.assertEqual(backbone.out_features       ,     ["""a""", """c"""]			)
										self.assertEqual(backbone.out_indices       ,     [0, 2]			)

										# Check out features and indices are updated correctly
										SCREAMING_SNAKE_CASE           =					["""a""", """b"""]
										self.assertEqual(backbone.out_features       ,     ["""a""", """b"""]			)
										self.assertEqual(backbone.out_indices       ,     [0, 1]			)

										SCREAMING_SNAKE_CASE           =					[-3, -1]
										self.assertEqual(backbone.out_features       ,     ["""a""", """c"""]			)
										self.assertEqual(backbone.out_indices       ,     [-3, -1]			)












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_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

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

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

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

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

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






