





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ConditionalDetrImageProcessor






class 						UpperCamelCase_  (  unittest.TestCase							):



					def __init__(		self     :     Any       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Dict=7       ,      lowerCamelCase     :     List[Any]=3       ,      lowerCamelCase     :     str=30       ,      lowerCamelCase     :     Union[str, Any]=4_00       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     Tuple=None       ,      lowerCamelCase     :     Optional[int]=True       ,      lowerCamelCase     :     Union[str, Any]=[0.5, 0.5, 0.5]       ,      lowerCamelCase     :     List[str]=[0.5, 0.5, 0.5]       ,      lowerCamelCase     :     List[Any]=True       ,      lowerCamelCase     :     int=1 / 2_55       ,      lowerCamelCase     :     Dict=True       ,      ):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowerCamelCase_	:     Optional[int] 						=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
							lowerCamelCase_	:     Optional[int] 						=      parent
							lowerCamelCase_	:     Optional[Any] 						=      batch_size
							lowerCamelCase_	:     str 						=      num_channels
							lowerCamelCase_	:     Dict 						=      min_resolution
							lowerCamelCase_	:     str 						=      max_resolution
							lowerCamelCase_	:     Optional[Any] 						=      do_resize
							lowerCamelCase_	:     Any 						=      size
							lowerCamelCase_	:     Optional[Any] 						=      do_normalize
							lowerCamelCase_	:     Dict 						=      image_mean
							lowerCamelCase_	:     List[Any] 						=      image_std
							lowerCamelCase_	:     Optional[int] 						=      do_rescale
							lowerCamelCase_	:     List[str] 						=      rescale_factor
							lowerCamelCase_	:     str 						=      do_pad



					def   __a     (		self     :     Optional[int]						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}





					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     Optional[Any]=False						):
							if not batched:
									lowerCamelCase_	:     int 						=      image_inputs[0]
									if isinstance(lowerCamelCase       ,      Image.Image						):
											lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      image.size
									else:
											lowerCamelCase_						,      lowerCamelCase_	:     List[str] 						=      image.shape[1], image.shape[2]
									if w < h:
											lowerCamelCase_	:     List[Any] 						=      int(self.size['shortest_edge'] * h / w						)
											lowerCamelCase_	:     str 						=      self.size['shortest_edge']
									elif w > h:
											lowerCamelCase_	:     int 						=      self.size['shortest_edge']
											lowerCamelCase_	:     Any 						=      int(self.size['shortest_edge'] * w / h						)
									else:
											lowerCamelCase_	:     List[Any] 						=      self.size['shortest_edge']
											lowerCamelCase_	:     Tuple 						=      self.size['shortest_edge']

							else:
									lowerCamelCase_	:     List[str] 						=      []
									for image in image_inputs:
											lowerCamelCase_						,      lowerCamelCase_	:     List[Any] 						=      self.get_expected_values([image]						)
											expected_values.append((expected_height, expected_width)						)
									lowerCamelCase_	:     Any 						=      max(lowerCamelCase       ,      key=lambda lowerCamelCase						: item[0]						)[0]
									lowerCamelCase_	:     Optional[int] 						=      max(lowerCamelCase       ,      key=lambda lowerCamelCase						: item[1]						)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class 						UpperCamelCase_  (  snake_case__  ,     unittest.TestCase							):
					_a      :						Tuple						    =					ConditionalDetrImageProcessor if is_vision_available() else None



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     Optional[Any] 						=      ConditionalDetrImageProcessingTester(self						)



					@property
					def   __a     (		self     :     Tuple						):
							return self.image_processor_tester.prepare_image_processor_dict()



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Tuple 						=      self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowerCamelCase       ,      'image_mean'						)						)
							self.assertTrue(hasattr(lowerCamelCase       ,      'image_std'						)						)
							self.assertTrue(hasattr(lowerCamelCase       ,      'do_normalize'						)						)
							self.assertTrue(hasattr(lowerCamelCase       ,      'do_resize'						)						)
							self.assertTrue(hasattr(lowerCamelCase       ,      'size'						)						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size       ,      {'shortest_edge': 18, 'longest_edge': 13_33}						)
							self.assertEqual(image_processor.do_pad       ,      lowerCamelCase						)

							lowerCamelCase_	:     List[str] 						=      self.image_processing_class.from_dict(
							    self.image_processor_dict       ,      size=42       ,      max_size=84       ,      pad_and_return_pixel_mask=lowerCamelCase						)
							self.assertEqual(image_processor.size       ,      {'shortest_edge': 42, 'longest_edge': 84}						)
							self.assertEqual(image_processor.do_pad       ,      lowerCamelCase						)



					def   __a     (		self     :     List[str]						):
							pass



					def   __a     (		self     :     Any						):
							# Initialize image_processing
							lowerCamelCase_	:     Dict 						=      self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowerCamelCase_	:     Optional[Any] 						=      prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase						)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase       ,      Image.Image						)

							# Test not batched input
							lowerCamelCase_	:     Any 						=      image_processing(image_inputs[0]       ,      return_tensors='pt'						).pixel_values

							lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      self.image_processor_tester.get_expected_values(lowerCamelCase						)

							self.assertEqual(
							    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

							# Test batched
							lowerCamelCase_						,      lowerCamelCase_	:     List[str] 						=      self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      image_processing(lowerCamelCase       ,      return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )       ,      )



					def   __a     (		self     :     Union[str, Any]						):
							# Initialize image_processing
							lowerCamelCase_	:     Optional[Any] 						=      self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowerCamelCase_	:     Optional[int] 						=      prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase       ,      numpify=lowerCamelCase						)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase       ,      np.ndarray						)

							# Test not batched input
							lowerCamelCase_	:     List[Any] 						=      image_processing(image_inputs[0]       ,      return_tensors='pt'						).pixel_values

							lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      self.image_processor_tester.get_expected_values(lowerCamelCase						)

							self.assertEqual(
							    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

							# Test batched
							lowerCamelCase_	:     int 						=      image_processing(lowerCamelCase       ,      return_tensors='pt'						).pixel_values

							lowerCamelCase_						,      lowerCamelCase_	:     Optional[Any] 						=      self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase						)

							self.assertEqual(
							    encoded_images.shape       ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )       ,      )



					def   __a     (		self     :     Optional[Any]						):
							# Initialize image_processing
							lowerCamelCase_	:     str 						=      self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowerCamelCase_	:     List[str] 						=      prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=lowerCamelCase       ,      torchify=lowerCamelCase						)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase       ,      torch.Tensor						)

							# Test not batched input
							lowerCamelCase_	:     Tuple 						=      image_processing(image_inputs[0]       ,      return_tensors='pt'						).pixel_values

							lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      self.image_processor_tester.get_expected_values(lowerCamelCase						)

							self.assertEqual(
							    encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

							# Test batched
							lowerCamelCase_	:     Optional[int] 						=      image_processing(lowerCamelCase       ,      return_tensors='pt'						).pixel_values

							lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      self.image_processor_tester.get_expected_values(lowerCamelCase       ,      batched=lowerCamelCase						)

							self.assertEqual(
							    encoded_images.shape       ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )       ,      )



					@slow
					def   __a     (		self     :     Optional[Any]						):
							# prepare image and target
							lowerCamelCase_	:     Optional[int] 						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,      'r'						) as f:
									lowerCamelCase_	:     Optional[Any] 						=      json.loads(f.read()						)

							lowerCamelCase_	:     str 						=      {'image_id': 3_97_69, 'annotations': target}

							# encode them
							lowerCamelCase_	:     List[str] 						=      ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'						)
							lowerCamelCase_	:     Union[str, Any] 						=      image_processing(images=lowerCamelCase       ,      annotations=lowerCamelCase       ,      return_tensors='pt'						)

							# verify pixel values
							lowerCamelCase_	:     Any 						=      torch.Size([1, 3, 8_00, 10_66]						)
							self.assertEqual(encoding['pixel_values'].shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     Tuple 						=      torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,      lowerCamelCase       ,      atol=1E-4						)						)

							# verify area
							lowerCamelCase_	:     Optional[Any] 						=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,      lowerCamelCase						)						)
							# verify boxes
							lowerCamelCase_	:     Tuple 						=      torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape       ,      lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,      lowerCamelCase       ,      atol=1E-3						)						)
							# verify image_id
							lowerCamelCase_	:     Optional[int] 						=      torch.tensor([3_97_69]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,      lowerCamelCase						)						)
							# verify is_crowd
							lowerCamelCase_	:     Any 						=      torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,      lowerCamelCase						)						)
							# verify class_labels
							lowerCamelCase_	:     Dict 						=      torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,      lowerCamelCase						)						)
							# verify orig_size
							lowerCamelCase_	:     int 						=      torch.tensor([4_80, 6_40]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,      lowerCamelCase						)						)
							# verify size
							lowerCamelCase_	:     Union[str, Any] 						=      torch.tensor([8_00, 10_66]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,      lowerCamelCase						)						)





					@slow
					def   __a     (		self     :     str						):
							# prepare image, target and masks_path
							lowerCamelCase_	:     List[str] 						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,      'r'						) as f:
									lowerCamelCase_	:     Optional[Any] 						=      json.loads(f.read()						)

							lowerCamelCase_	:     Dict 						=      {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

							lowerCamelCase_	:     str 						=      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

							# encode them
							lowerCamelCase_	:     Union[str, Any] 						=      ConditionalDetrImageProcessor(format='coco_panoptic'						)
							lowerCamelCase_	:     Tuple 						=      image_processing(images=lowerCamelCase       ,      annotations=lowerCamelCase       ,      masks_path=lowerCamelCase       ,      return_tensors='pt'						)

							# verify pixel values
							lowerCamelCase_	:     Tuple 						=      torch.Size([1, 3, 8_00, 10_66]						)
							self.assertEqual(encoding['pixel_values'].shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     Optional[int] 						=      torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,      lowerCamelCase       ,      atol=1E-4						)						)

							# verify area
							lowerCamelCase_	:     List[Any] 						=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,      lowerCamelCase						)						)
							# verify boxes
							lowerCamelCase_	:     Union[str, Any] 						=      torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape       ,      lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,      lowerCamelCase       ,      atol=1E-3						)						)
							# verify image_id
							lowerCamelCase_	:     List[Any] 						=      torch.tensor([3_97_69]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,      lowerCamelCase						)						)
							# verify is_crowd
							lowerCamelCase_	:     Dict 						=      torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,      lowerCamelCase						)						)
							# verify class_labels
							lowerCamelCase_	:     Optional[int] 						=      torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,      lowerCamelCase						)						)
							# verify masks
							lowerCamelCase_	:     int 						=      82_28_73
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,      lowerCamelCase						)
							# verify orig_size
							lowerCamelCase_	:     List[Any] 						=      torch.tensor([4_80, 6_40]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,      lowerCamelCase						)						)
							# verify size
							lowerCamelCase_	:     List[str] 						=      torch.tensor([8_00, 10_66]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,      lowerCamelCase						)						)







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Union[str, Any]						    =					(DPMSolverSDEScheduler,)
					_a      :						List[Any]						    =					1_0



					def   __a     (		self     :     Any       ,      **lowerCamelCase     :     str						):
							lowerCamelCase_	:     Any 						=      {
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'noise_sampler_seed': 0,
							}

							config.update(**lowerCamelCase						)
							return config



					def   __a     (		self     :     Tuple						):
							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase						)



					def   __a     (		self     :     int						):
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,      [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase						)



					def   __a     (		self     :     Any						):
							for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     str 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Optional[Any] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_model()
							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     Union[str, Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     str 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      output.prev_sample

							lowerCamelCase_	:     List[str] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Optional[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47_821_044_921_875						) < 1E-2
									assert abs(result_mean.item() - 0.2_178_705_964_565_277						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_352_111_816_406						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_906_892_299_652						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     Any 						=      self.get_scheduler_config(prediction_type='v_prediction'						)
							lowerCamelCase_	:     Tuple 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Any 						=      self.dummy_model()
							lowerCamelCase_	:     Tuple 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     List[Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     Dict 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Union[str, Any] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Dict 						=      output.prev_sample

							lowerCamelCase_	:     Optional[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     List[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77_149_200_439_453						) < 1E-2
									assert abs(result_mean.item() - 0.16_226_289_014_816_284						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1_663_360_595_703						) < 1E-2
									assert abs(result_mean.item() - 0.16_688_326_001_167_297						) < 1E-3
							else:
									assert abs(result_sum.item() - 119.8_487_548_828_125						) < 1E-2
									assert abs(result_mean.item() - 0.1_560_530_662_536_621						) < 1E-3



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     List[str] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      self.dummy_model()
							lowerCamelCase_	:     Dict 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Tuple 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     int 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      output.prev_sample

							lowerCamelCase_	:     List[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     str 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46_957_397_460_938						) < 1E-2
									assert abs(result_mean.item() - 0.21_805_934_607_982_635						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_353_637_695_312						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_908_382_415_771						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Union[str, Any] 						=      scheduler_class(**lowerCamelCase       ,      use_karras_sigmas=lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      self.dummy_model()
							lowerCamelCase_	:     str 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma
							lowerCamelCase_	:     Dict 						=      sample.to(lowerCamelCase						)

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Optional[Any] 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Any 						=      output.prev_sample

							lowerCamelCase_	:     Optional[int] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66_974_135_742_188						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63_653_564_453_125						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							else:
									assert abs(result_sum.item() - 170.3_135_223_388_672						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
