




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:
						__SCREAMING_SNAKE_CASE      :				Any				      =None






class      A     (						unittest.TestCase						):
					"""simple docstring"""

					def __init__(      self					:				int		,					A__					:				int		,					A__					:				Union[str, Any]=7		,					A__					:				str=3		,					A__					:				Any=3_0		,					A__					:				Optional[int]=4_0_0		,					A__					:				Any=True		,					A__					:				str=None		,					A__					:				List[str]=True		,					A__					:				List[Any]=None		,					A__					:				int=None		,					A__					:				str=True		,					A__					:				str=1 / 2_5_5		,					A__					:				Optional[int]=True		,					A__					:				Optional[Any]=FLAVA_IMAGE_MEAN		,					A__					:				Tuple=FLAVA_IMAGE_STD		,					A__					:				List[Any]=1_4		,					A__					:				Any=7_5		,					A__					:				int=None		,					A__					:				List[Any]=1_6		,					A__					:				int=0.3		,					A__					:				Any=None		,					A__					:				Any=True		,					A__					:				Any=None		,					A__					:				Any=None		,					A__					:				List[str]=True		,					A__					:				Tuple=None		,					A__					:				Any=True		,					A__					:				Any=True		,					A__					:				Optional[int]=FLAVA_CODEBOOK_MEAN		,					A__					:				Union[str, Any]=FLAVA_CODEBOOK_STD		,					)			->    Tuple:
											lowerCamelCase__ 					=			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
											lowerCamelCase__ 					=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
											lowerCamelCase__ 					=			codebook_size if codebook_size is not None else {'height': 1_1_2, 'width': 1_1_2}
											lowerCamelCase__ 					=			codebook_crop_size if codebook_crop_size is not None else {'height': 1_1_2, 'width': 1_1_2}

											lowerCamelCase__ 					=			parent
											lowerCamelCase__ 					=			batch_size
											lowerCamelCase__ 					=			num_channels
											lowerCamelCase__ 					=			do_resize
											lowerCamelCase__ 					=			do_rescale
											lowerCamelCase__ 					=			rescale_factor
											lowerCamelCase__ 					=			min_resolution
											lowerCamelCase__ 					=			max_resolution
											lowerCamelCase__ 					=			size
											lowerCamelCase__ 					=			resample if resample is not None else PILImageResampling.BICUBIC
											lowerCamelCase__ 					=			do_normalize
											lowerCamelCase__ 					=			image_mean
											lowerCamelCase__ 					=			image_std
											lowerCamelCase__ 					=			do_center_crop
											lowerCamelCase__ 					=			crop_size

											lowerCamelCase__ 					=			input_size_patches
											lowerCamelCase__ 					=			total_mask_patches
											lowerCamelCase__ 					=			mask_group_max_patches
											lowerCamelCase__ 					=			mask_group_min_patches
											lowerCamelCase__ 					=			mask_group_min_aspect_ratio
											lowerCamelCase__ 					=			mask_group_max_aspect_ratio

											lowerCamelCase__ 					=			codebook_do_resize
											lowerCamelCase__ 					=			codebook_size
											lowerCamelCase__ 					=			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
											lowerCamelCase__ 					=			codebook_do_center_crop
											lowerCamelCase__ 					=			codebook_crop_size
											lowerCamelCase__ 					=			codebook_do_map_pixels
											lowerCamelCase__ 					=			codebook_do_normalize
											lowerCamelCase__ 					=			codebook_image_mean
											lowerCamelCase__ 					=			codebook_image_std




					def 	__snake_case				(      self					:				Tuple     )			->    int:
											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 	__snake_case				(      self					:				List[str]     )			->    Optional[Any]:
											return (self.size["height"], self.size["width"])




					def 	__snake_case				(      self					:				List[str]     )			->    List[str]:
											return (
											    (self.input_size_patches, self.input_size_patches)
											    if not isinstance(self.input_size_patches		,					A__     )
											    else self.input_size_patches
											)




					def 	__snake_case				(      self					:				List[str]     )			->    Union[str, Any]:
											return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class      A     (						_lowerCAmelCase     ,  unittest.TestCase						):
					"""simple docstring"""

					_snake_case:		List[str]       =FlavaImageProcessor if is_vision_available() else None
					_snake_case:		str       =None




					def 	__snake_case				(      self					:				Tuple     )			->    int:
											lowerCamelCase__ 					=			FlavaImageProcessingTester(self     )




					@property
					def 	__snake_case				(      self					:				Tuple     )			->    Union[str, Any]:
											return self.image_processor_tester.prepare_image_processor_dict()




					def 	__snake_case				(      self					:				Dict     )			->    Union[str, Any]:
											lowerCamelCase__ 					=			self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A__		,					'image_mean'     )     )
											self.assertTrue(hasattr(A__		,					'image_std'     )     )
											self.assertTrue(hasattr(A__		,					'do_normalize'     )     )
											self.assertTrue(hasattr(A__		,					'do_resize'     )     )
											self.assertTrue(hasattr(A__		,					'resample'     )     )
											self.assertTrue(hasattr(A__		,					'crop_size'     )     )
											self.assertTrue(hasattr(A__		,					'do_center_crop'     )     )
											self.assertTrue(hasattr(A__		,					'do_rescale'     )     )
											self.assertTrue(hasattr(A__		,					'rescale_factor'     )     )
											self.assertTrue(hasattr(A__		,					'masking_generator'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_do_resize'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_size'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_resample'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_do_center_crop'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_crop_size'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_do_map_pixels'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_do_normalize'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_image_mean'     )     )
											self.assertTrue(hasattr(A__		,					'codebook_image_std'     )     )




					def 	__snake_case				(      self					:				int     )			->    List[Any]:
											lowerCamelCase__ 					=			self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size		,					{'height': 2_2_4, 'width': 2_2_4}     )
											self.assertEqual(image_processor.crop_size		,					{'height': 2_2_4, 'width': 2_2_4}     )
											self.assertEqual(image_processor.codebook_size		,					{'height': 1_1_2, 'width': 1_1_2}     )
											self.assertEqual(image_processor.codebook_crop_size		,					{'height': 1_1_2, 'width': 1_1_2}     )

											lowerCamelCase__ 					=			self.image_processing_class.from_dict(
											    self.image_processor_dict		,					size=4_2		,					crop_size=8_4		,					codebook_size=3_3		,					codebook_crop_size=6_6     )
											self.assertEqual(image_processor.size		,					{'height': 4_2, 'width': 4_2}     )
											self.assertEqual(image_processor.crop_size		,					{'height': 8_4, 'width': 8_4}     )
											self.assertEqual(image_processor.codebook_size		,					{'height': 3_3, 'width': 3_3}     )
											self.assertEqual(image_processor.codebook_crop_size		,					{'height': 6_6, 'width': 6_6}     )




					def 	__snake_case				(      self					:				List[Any]     )			->    List[str]:
											pass




					def 	__snake_case				(      self					:				Optional[Any]     )			->    Union[str, Any]:
											# Initialize image_processing
											lowerCamelCase__ 					=			self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											lowerCamelCase__ 					=			prepare_image_inputs(self.image_processor_tester		,					equal_resolution=A__     )
											for image in image_inputs:
																	self.assertIsInstance(A__		,					PIL.Image.Image     )

											# Test not batched input
											lowerCamelCase__ 					=			image_processing(image_inputs[0]		,					return_tensors='pt'     )

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

											lowerCamelCase__					,						lowerCamelCase__ 					=			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
											lowerCamelCase__ 					=			image_processing(A__		,					return_tensors='pt'     )
											lowerCamelCase__					,						lowerCamelCase__ 					=			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 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Dict		,					A__					:				Tuple     )			->    Any:
											# Initialize image_processing
											lowerCamelCase__ 					=			self.image_processing_class(**self.image_processor_dict     )
											# create random tensors
											lowerCamelCase__ 					=			prepare_image_inputs(self.image_processor_tester		,					equal_resolution=A__		,					**A__     )
											for image in image_inputs:
																	self.assertIsInstance(A__		,					A__     )

											# Test not batched input
											lowerCamelCase__ 					=			image_processing(image_inputs[0]		,					return_tensors='pt'     )

											lowerCamelCase__					,						lowerCamelCase__ 					=			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)		,					)

											lowerCamelCase__ 					=			image_processing(A__		,					return_image_mask=A__		,					return_tensors='pt'     )

											lowerCamelCase__					,						lowerCamelCase__ 					=			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,
											    )		,					)

											lowerCamelCase__					,						lowerCamelCase__ 					=			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
											lowerCamelCase__ 					=			image_processing(A__		,					return_tensors='pt'     ).pixel_values

											lowerCamelCase__					,						lowerCamelCase__ 					=			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
											lowerCamelCase__ 					=			image_processing(A__		,					return_image_mask=A__		,					return_tensors='pt'     )

											lowerCamelCase__					,						lowerCamelCase__ 					=			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,
											    )		,					)

											lowerCamelCase__					,						lowerCamelCase__ 					=			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 	__snake_case				(      self					:				Any     )			->    Tuple:
											self._test_call_framework(np.ndarray		,					prepare_kwargs={'numpify': True}     )




					def 	__snake_case				(      self					:				Tuple     )			->    Tuple:
											self._test_call_framework(torch.Tensor		,					prepare_kwargs={'torchify': True}     )




					def 	__snake_case				(      self					:				Dict     )			->    Tuple:
											# Initialize image_processing
											random.seed(1_2_3_4     )
											lowerCamelCase__ 					=			self.image_processing_class(**self.image_processor_dict     )
											lowerCamelCase__ 					=			prepare_image_inputs(self.image_processor_tester		,					equal_resolution=A__		,					torchify=A__     )

											# Test not batched input
											lowerCamelCase__ 					=			image_processing(image_inputs[0]		,					return_image_mask=A__		,					return_tensors='pt'     )
											self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,					7_5     )




					def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
											# Initialize image_processing
											lowerCamelCase__ 					=			self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											lowerCamelCase__ 					=			prepare_image_inputs(self.image_processor_tester		,					equal_resolution=A__     )
											for image in image_inputs:
																	self.assertIsInstance(A__		,					PIL.Image.Image     )

											# Test not batched input
											lowerCamelCase__ 					=			image_processing(image_inputs[0]		,					return_codebook_pixels=A__		,					return_tensors='pt'     )
											lowerCamelCase__					,						lowerCamelCase__ 					=			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
											lowerCamelCase__ 					=			image_processing(A__		,					return_codebook_pixels=A__		,					return_tensors='pt'     )
											lowerCamelCase__					,						lowerCamelCase__ 					=			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,
											    )		,					)





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(DPMSolverMultistepScheduler,)
     _snake_case:		Union[str, Any]       =(("num_inference_steps", 25),)




     def 	__snake_case				(      self					:				str		,					**A__					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lower_order_final': False,
               'lambda_min_clipped': -float('inf'     ),
               'variance_type': None,
           }

           config.update(**A__     )
           return config




     def 	__snake_case				(      self					:				Dict		,					A__					:				Dict=0		,					**A__					:				Dict     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )
                 # copy over dummy past residuals
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       new_scheduler.set_timesteps(A__     )
                       # copy over dummy past residuals
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__					,						lowerCamelCase__ 					=			sample, sample
                 for t in range(A__		,					time_step + scheduler.config.solver_order + 1     ):
                       lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                       lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				List[str]     )			->    Union[str, Any]:
           pass




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]=0		,					**A__					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A__     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Any=None		,					**A__					:				Any     )			->    List[str]:
           if scheduler is None:
                 lowerCamelCase__ 					=			self.scheduler_classes[0]
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           return sample




     def 	__snake_case				(      self					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )

           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 lowerCamelCase__ 					=			self.dummy_sample
                 lowerCamelCase__ 					=			0.1 * sample

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

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.timesteps[5]
                 lowerCamelCase__ 					=			scheduler.timesteps[6]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				int     )			->    str:
           self.check_over_configs(thresholding=A__     )
           for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=A__		,					prediction_type=A__		,					sample_max_value=A__		,					algorithm_type='dpmsolver++'		,					solver_order=A__		,					solver_type=A__		,					)




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                         if order == 3:
                                               continue
                                   else:
                                         self.check_over_configs(
                                             solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   lowerCamelCase__ 					=			self.full_loop(
                                       solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   assert not torch.isnan(A__     ).any(), "Samples have nan numbers"




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           self.check_over_configs(lower_order_final=A__     )
           self.check_over_configs(lower_order_final=A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
           self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
           self.check_over_configs(lambda_min_clipped=-5.1     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    int:
           self.check_over_configs(variance_type=A__     )
           self.check_over_configs(variance_type='learned_range'     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[str]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__		,					time_step=0     )




     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           lowerCamelCase__ 					=			self.full_loop()
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    int:
           lowerCamelCase__ 					=			self.full_loop(thresholding=A__		,					dynamic_thresholding_ratio=0.87		,					sample_max_value=0.5     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 1.1364     ) < 1E-3




     def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2251     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'		,					use_karras_sigmas=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2096     ) < 1E-3




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3

           lowerCamelCase__ 					=			DPMSolverSinglestepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			UniPCMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DEISMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler.from_config(scheduler.config     )

           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				int     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(thresholding=A__		,					dynamic_thresholding_ratio=0     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.half()
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           assert sample.dtype == torch.floataa




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				int     )			->    Optional[int]:
           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
                 assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps