




'''simple docstring'''




import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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():
							import PIL

							from transformers import FlavaImageProcessor
							from transformers.image_utils import PILImageResampling
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
else:
							snake_case__ 							=    None






class      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def __init__(  self   :							Union[str, Any]				,			_lowerCamelCase   :							str				,			_lowerCamelCase   :							int=7				,			_lowerCamelCase   :							Dict=3				,			_lowerCamelCase   :							List[str]=30				,			_lowerCamelCase   :							List[Any]=400				,			_lowerCamelCase   :							Any=True				,			_lowerCamelCase   :							Union[str, Any]=None				,			_lowerCamelCase   :							Optional[Any]=True				,			_lowerCamelCase   :							Optional[Any]=None				,			_lowerCamelCase   :							Optional[Any]=None				,			_lowerCamelCase   :							List[str]=True				,			_lowerCamelCase   :							Union[str, Any]=1 / 255				,			_lowerCamelCase   :							Any=True				,			_lowerCamelCase   :							Optional[Any]=FLAVA_IMAGE_MEAN				,			_lowerCamelCase   :							str=FLAVA_IMAGE_STD				,			_lowerCamelCase   :							str=14				,			_lowerCamelCase   :							List[Any]=75				,			_lowerCamelCase   :							Dict=None				,			_lowerCamelCase   :							str=16				,			_lowerCamelCase   :							Union[str, Any]=0.3				,			_lowerCamelCase   :							Tuple=None				,			_lowerCamelCase   :							Any=True				,			_lowerCamelCase   :							int=None				,			_lowerCamelCase   :							Optional[int]=None				,			_lowerCamelCase   :							str=True				,			_lowerCamelCase   :							Optional[Any]=None				,			_lowerCamelCase   :							List[Any]=True				,			_lowerCamelCase   :							Optional[Any]=True				,			_lowerCamelCase   :							List[str]=FLAVA_CODEBOOK_MEAN				,			_lowerCamelCase   :							List[Any]=FLAVA_CODEBOOK_STD				,			):





											"""simple docstring"""







											A_			:  int			=				size if size is not None else {'''height''': 224, '''width''': 224}
											A_			:  Optional[Any]			=				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											A_			:  Tuple			=				codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
											A_			:  List[str]			=				codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

											A_			:  Dict			=				parent
											A_			:  Dict			=				batch_size
											A_			:  Tuple			=				num_channels
											A_			:  Union[str, Any]			=				do_resize
											A_			:  str			=				do_rescale
											A_			:  str			=				rescale_factor
											A_			:  str			=				min_resolution
											A_			:  List[Any]			=				max_resolution
											A_			:  Dict			=				size
											A_			:  Any			=				resample if resample is not None else PILImageResampling.BICUBIC
											A_			:  Any			=				do_normalize
											A_			:  int			=				image_mean
											A_			:  Tuple			=				image_std
											A_			:  int			=				do_center_crop
											A_			:  int			=				crop_size

											A_			:  List[str]			=				input_size_patches
											A_			:  List[str]			=				total_mask_patches
											A_			:  Dict			=				mask_group_max_patches
											A_			:  List[str]			=				mask_group_min_patches
											A_			:  Union[str, Any]			=				mask_group_min_aspect_ratio
											A_			:  Optional[Any]			=				mask_group_max_aspect_ratio

											A_			:  List[str]			=				codebook_do_resize
											A_			:  List[str]			=				codebook_size
											A_			:  Dict			=				codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
											A_			:  str			=				codebook_do_center_crop
											A_			:  str			=				codebook_crop_size
											A_			:  List[Any]			=				codebook_do_map_pixels
											A_			:  Union[str, Any]			=				codebook_do_normalize
											A_			:  Dict			=				codebook_image_mean
											A_			:  int			=				codebook_image_std







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "resample": self.resample,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "input_size_patches": self.input_size_patches,
											    "total_mask_patches": self.total_mask_patches,
											    "mask_group_max_patches": self.mask_group_max_patches,
											    "mask_group_min_patches": self.mask_group_min_patches,
											    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
											    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
											    "codebook_do_resize": self.codebook_do_resize,
											    "codebook_size": self.codebook_size,
											    "codebook_resample": self.codebook_resample,
											    "codebook_do_center_crop": self.codebook_do_center_crop,
											    "codebook_crop_size": self.codebook_crop_size,
											    "codebook_do_map_pixels": self.codebook_do_map_pixels,
											    "codebook_do_normalize": self.codebook_do_normalize,
											    "codebook_image_mean": self.codebook_image_mean,
											    "codebook_image_std": self.codebook_image_std,
											}







						def    _a     (  self   :							str     ):





											"""simple docstring"""







											return (self.size["height"], self.size["width"])







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											return (
											    (self.input_size_patches, self.input_size_patches)
											    if not isinstance(self.input_size_patches				,			_SCREAMING_SNAKE_CASE     )
											    else self.input_size_patches
											)







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class      UpperCamelCase_  (_UpperCamelCase,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       FlavaImageProcessor if is_vision_available() else None
						_lowerCAmelCase         =       None







						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







											A_			:  Any			=				FlavaImageProcessingTester(self     )







						@property
						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''image_mean'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''image_std'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''do_normalize'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''do_resize'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''resample'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''crop_size'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''do_center_crop'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''do_rescale'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''rescale_factor'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''masking_generator'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_do_resize'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_size'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_resample'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_do_center_crop'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_crop_size'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_do_map_pixels'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_do_normalize'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_image_mean'''     )     )
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			'''codebook_image_std'''     )     )







						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size				,			{'''height''': 224, '''width''': 224}     )
											self.assertEqual(image_processor.crop_size				,			{'''height''': 224, '''width''': 224}     )
											self.assertEqual(image_processor.codebook_size				,			{'''height''': 112, '''width''': 112}     )
											self.assertEqual(image_processor.codebook_crop_size				,			{'''height''': 112, '''width''': 112}     )

											A_			:  str			=				self.image_processing_class.from_dict(
											    self.image_processor_dict				,			size=42				,			crop_size=84				,			codebook_size=33				,			codebook_crop_size=66     )
											self.assertEqual(image_processor.size				,			{'''height''': 42, '''width''': 42}     )
											self.assertEqual(image_processor.crop_size				,			{'''height''': 84, '''width''': 84}     )
											self.assertEqual(image_processor.codebook_size				,			{'''height''': 33, '''width''': 33}     )
											self.assertEqual(image_processor.codebook_crop_size				,			{'''height''': 66, '''width''': 66}     )







						def    _a     (  self   :							str     ):





											"""simple docstring"""







											pass







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											A_			:  Dict			=				prepare_image_inputs(self.image_processor_tester				,			equal_resolution=_SCREAMING_SNAKE_CASE     )
											for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE				,			PIL.Image.Image     )

											# Test not batched input
											A_			:  Optional[Any]			=				image_processing(image_inputs[0]				,			return_tensors='''pt'''     )

											# Test no bool masked pos
											self.assertFalse('''bool_masked_pos''' in encoded_images     )

											A_			,A_			:  Any			=				self.image_processor_tester.get_expected_image_size()

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

											# Test batched
											A_			:  List[Any]			=				image_processing(_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )
											A_			,A_			:  List[Any]			=				self.image_processor_tester.get_expected_image_size()

											# Test no bool masked pos
											self.assertFalse('''bool_masked_pos''' in encoded_images     )

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







						def    _a     (  self   :							int				,			_lowerCamelCase   :							Optional[Any]				,			_lowerCamelCase   :							int     ):





											"""simple docstring"""







											A_			:  str			=				self.image_processing_class(**self.image_processor_dict     )
											# create random tensors
											A_			:  int			=				prepare_image_inputs(self.image_processor_tester				,			equal_resolution=_SCREAMING_SNAKE_CASE				,			**_SCREAMING_SNAKE_CASE     )
											for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE     )

											# Test not batched input
											A_			:  List[str]			=				image_processing(image_inputs[0]				,			return_tensors='''pt'''     )

											A_			,A_			:  int			=				self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

											A_			:  Optional[Any]			=				image_processing(_SCREAMING_SNAKE_CASE				,			return_image_mask=_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )

											A_			,A_			:  int			=				self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape				,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)

											A_			,A_			:  Tuple			=				self.image_processor_tester.get_expected_mask_size()
											self.assertEqual(
											    encoded_images.bool_masked_pos.shape				,			(
											        self.image_processor_tester.batch_size,
											        expected_height,
											        expected_width,
											    )				,			)

											# Test batched
											A_			:  Any			=				image_processing(_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     ).pixel_values

											A_			,A_			:  List[str]			=				self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.shape				,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)

											# Test masking
											A_			:  Tuple			=				image_processing(_SCREAMING_SNAKE_CASE				,			return_image_mask=_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )

											A_			,A_			:  List[str]			=				self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape				,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)

											A_			,A_			:  Tuple			=				self.image_processor_tester.get_expected_mask_size()
											self.assertEqual(
											    encoded_images.bool_masked_pos.shape				,			(
											        self.image_processor_tester.batch_size,
											        expected_height,
											        expected_width,
											    )				,			)







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											self._test_call_framework(np.ndarray				,			prepare_kwargs={'''numpify''': True}     )







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											self._test_call_framework(torch.Tensor				,			prepare_kwargs={'''torchify''': True}     )







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											random.seed(1234     )
											A_			:  str			=				self.image_processing_class(**self.image_processor_dict     )
											A_			:  str			=				prepare_image_inputs(self.image_processor_tester				,			equal_resolution=_SCREAMING_SNAKE_CASE				,			torchify=_SCREAMING_SNAKE_CASE     )

											# Test not batched input
											A_			:  Any			=				image_processing(image_inputs[0]				,			return_image_mask=_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )
											self.assertEqual(encoded_images.bool_masked_pos.sum().item()				,			75     )







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											A_			:  Tuple			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											A_			:  Dict			=				prepare_image_inputs(self.image_processor_tester				,			equal_resolution=_SCREAMING_SNAKE_CASE     )
											for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE				,			PIL.Image.Image     )

											# Test not batched input
											A_			:  Any			=				image_processing(image_inputs[0]				,			return_codebook_pixels=_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )
											A_			,A_			:  str			=				self.image_processor_tester.get_expected_codebook_image_size()
											self.assertEqual(
											    encoded_images.codebook_pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

											# Test batched
											A_			:  Optional[int]			=				image_processing(_SCREAMING_SNAKE_CASE				,			return_codebook_pixels=_SCREAMING_SNAKE_CASE				,			return_tensors='''pt'''     )
											A_			,A_			:  int			=				self.image_processor_tester.get_expected_codebook_image_size()
											self.assertEqual(
											    encoded_images.codebook_pixel_values.shape				,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)




'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   str  					=							(ScoreSdeVeScheduler,)
       __lowercase   :   List[str]  					=							()

       @property
       def    __A			( self					)						->	List[str]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  torch.rand((batch_size, num_channels, height, width)					)

           return sample

       @property
       def    __A			( self					)						->	Optional[Any]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  batch_size * num_channels * height * width
           A_      =  torch.arange(_SCREAMING_SNAKE_CASE					)
           A_      =  sample.reshape(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  sample / num_elems
           A_      =  sample.permute(3       ,	0       ,	1       ,	2					)

           return sample

       def    __A			( self					)						->	Union[str, Any]:
           def model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					):
               return sample * t / (t + 1)

           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)
           kwargs.update(_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self					)						->	Any:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]					):
               self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       ,	sigma_max=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           A_      =  3

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter

           scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                       A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               with torch.no_grad():
                   A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_  ,A_      =  output.prev_sample, output.prev_sample_mean

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert np.isclose(result_sum.item()       ,	14_372_758_528.0					)
           assert np.isclose(result_mean.item()       ,	18_714_530.0					)

       def    __A			( self					)						->	List[Any]:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
               elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   A_      =  num_inference_steps

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	0       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	1       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               self.assertEqual(output_a.shape       ,	sample.shape					)
               self.assertEqual(output_a.shape       ,	output_a.shape					)






