



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= KandinskyVaaControlnetImgaImgPipeline
	_SCREAMING_SNAKE_CASE	:		Dict  						= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
	_SCREAMING_SNAKE_CASE	:		str  						= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
	_SCREAMING_SNAKE_CASE	:		int  						= [
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''strength''',
	    '''guidance_scale''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	_SCREAMING_SNAKE_CASE	:		int  						= False





	@property
	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[Any]:





					"""simple docstring"""


					return 32





	@property
	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Dict:





					"""simple docstring"""


					return 32





	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Tuple:





					"""simple docstring"""


					return self.time_input_dim





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Union[str, Any]:





					"""simple docstring"""


					return self.time_input_dim * 4





	@property
	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					return 100





	@property
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[int]:





					"""simple docstring"""


					torch.manual_seed(0    )

					SCREAMING_SNAKE_CASE						:							Dict							=     {
					    'in_channels': 8,
					    # Out channels is double in channels because predicts mean and variance
					    'out_channels': 8,
					    'addition_embed_type': 'image_hint',
					    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
					    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
					    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
					    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    'layers_per_block': 1,
					    'encoder_hid_dim': self.text_embedder_hidden_size,
					    'encoder_hid_dim_type': 'image_proj',
					    'cross_attention_dim': self.cross_attention_dim,
					    'attention_head_dim': 4,
					    'resnet_time_scale_shift': 'scale_shift',
					    'class_embed_type': None,
					}

					SCREAMING_SNAKE_CASE						:							List[Any]							=     UNetaDConditionModel(**_SCREAMING_SNAKE_CASE    )
					return model





	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					return {
					    "block_out_channels": [32, 32, 64, 64],
					    "down_block_types": [
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "AttnDownEncoderBlock2D",
					    ],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
					    "vq_embed_dim": 4,
					}





	@property
	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[Any]:





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Dict							=     VQModel(**self.dummy_movq_kwargs    )
					return model





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.dummy_unet
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_movq

					SCREAMING_SNAKE_CASE						:							int							=     {
					    'num_train_timesteps': 1_000,
					    'beta_schedule': 'linear',
					    'beta_start': 0.0_0_0_8_5,
					    'beta_end': 0.0_1_2,
					    'clip_sample': False,
					    'set_alpha_to_one': False,
					    'steps_offset': 0,
					    'prediction_type': 'epsilon',
					    'thresholding': False,
					}

					SCREAMING_SNAKE_CASE						:							Dict							=     DDIMScheduler(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     {
					    'unet': unet,
					    'scheduler': scheduler,
					    'movq': movq,
					}

					return components





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(seed + 1    )    ).to(
					    _SCREAMING_SNAKE_CASE    )
					# create init_image
					SCREAMING_SNAKE_CASE						:							List[str]							=     floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     image.cpu().permute(0     ,       2     ,       3     ,       1    )[0]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE    )    ).convert('RGB'    ).resize((256, 256)    )
					# create hint
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )

					if str(_SCREAMING_SNAKE_CASE    ).startswith('mps'    ):
									SCREAMING_SNAKE_CASE						:							Any							=     torch.manual_seed(_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     {
					    'image': init_image,
					    'image_embeds': image_embeds,
					    'negative_image_embeds': negative_image_embeds,
					    'hint': hint,
					    'generator': generator,
					    'height': 64,
					    'width': 64,
					    'num_inference_steps': 10,
					    'guidance_scale': 7.0,
					    'strength': 0.2,
					    'output_type': 'np',
					}
					return inputs







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'cpu'

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_dummy_components()

					SCREAMING_SNAKE_CASE						:							Dict							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe.to(_SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     output.images

					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )[0]

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE						:							List[str]							=     image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     np.array(
					    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]    )
					assert (
					    np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
					), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
					), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[str]:





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'    )

					SCREAMING_SNAKE_CASE						:							Any							=     load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
					SCREAMING_SNAKE_CASE						:							str							=     init_image.resize((512, 512)    )

					SCREAMING_SNAKE_CASE						:							str							=     load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinskyv22/hint_image_cat.png'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.from_numpy(np.array(_SCREAMING_SNAKE_CASE    )    ).float() / 2_5_5.0
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     hint.permute(2     ,       0     ,       1    ).unsqueeze(0    )

					SCREAMING_SNAKE_CASE						:							Any							=     'A robot, 4k photo'

					SCREAMING_SNAKE_CASE						:							List[Any]							=     KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-2-prior'     ,       torch_dtype=torch.floataa    )
					pipe_prior.to(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-2-controlnet-depth'     ,       torch_dtype=torch.floataa    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipeline.to(_SCREAMING_SNAKE_CASE    )

					pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.Generator(device='cpu'    ).manual_seed(0    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     pipe_prior(
					    _SCREAMING_SNAKE_CASE     ,       image=_SCREAMING_SNAKE_CASE     ,       strength=0.8_5     ,       generator=_SCREAMING_SNAKE_CASE     ,       negative_prompt=''     ,       ).to_tuple()

					SCREAMING_SNAKE_CASE						:							List[str]							=     pipeline(
					    image=_SCREAMING_SNAKE_CASE     ,       image_embeds=_SCREAMING_SNAKE_CASE     ,       negative_image_embeds=_SCREAMING_SNAKE_CASE     ,       hint=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       num_inference_steps=100     ,       height=512     ,       width=512     ,       strength=0.5     ,       output_type='np'     ,       )

					SCREAMING_SNAKE_CASE						:							int							=     output.images[0]

					assert image.shape == (512, 512, 3)

					assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )










"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= (DEISMultistepScheduler,)
	_SCREAMING_SNAKE_CASE	:		str  						= (('''num_inference_steps''', 25),)





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       **_SCREAMING_SNAKE_CASE				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {
					    'num_train_timesteps': 1_000,
					    'beta_start': 0.0_0_0_1,
					    'beta_end': 0.0_2,
					    'beta_schedule': 'linear',
					    'solver_order': 2,
					}

					config.update(**_SCREAMING_SNAKE_CASE    )
					return config





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							Any							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE						:							Dict							=     dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
													new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
													# copy over dummy past residuals
													SCREAMING_SNAKE_CASE						:							List[str]							=     dummy_past_residuals[: new_scheduler.config.solver_order]

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

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





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





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict=0     ,       **_SCREAMING_SNAKE_CASE				:       Dict    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							Dict							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							int							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							Any							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

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

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

									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							str							=     new_scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Any:





					"""simple docstring"""


					if scheduler is None:
									SCREAMING_SNAKE_CASE						:							int							=     self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Any							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample_deter
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

					for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE						:							int							=     model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ).prev_sample

					return sample





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     dict(self.forward_default_kwargs    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							int							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.dummy_sample
									SCREAMING_SNAKE_CASE						:							Tuple							=     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'    ):
													SCREAMING_SNAKE_CASE						:							List[str]							=     num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									SCREAMING_SNAKE_CASE						:							Tuple							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     dummy_past_residuals[: scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[5]
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[6]

									SCREAMING_SNAKE_CASE						:							str							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							List[Any]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     DEISMultistepScheduler(**self.get_scheduler_config()    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     DPMSolverSinglestepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     DPMSolverMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							Any							=     UniPCMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     DEISMultistepScheduler.from_config(scheduler.config    )

					SCREAMING_SNAKE_CASE						:							str							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





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





					"""simple docstring"""


					for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[str]:





					"""simple docstring"""


					self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
					for order in [1, 2, 3]:
									for solver_type in ["logrho"]:
													for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       sample_max_value=_SCREAMING_SNAKE_CASE     ,       algorithm_type='deis'     ,       solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       )





	def 			_lowerCAmelCase     (      self				:       str    )						->	Optional[int]:





					"""simple docstring"""


					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					for algorithm_type in ["deis"]:
									for solver_type in ["logrho"]:
													for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					SCREAMING_SNAKE_CASE						:							Dict							=     self.full_loop(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[Any]:





					"""simple docstring"""


					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE     ,       time_step=0    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.full_loop()
					SCREAMING_SNAKE_CASE						:							Tuple							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.full_loop(prediction_type='v_prediction'    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.0_9_1    ) < 1E-3







	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0    )
					SCREAMING_SNAKE_CASE						:							Any							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Dict							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_sample_deter.half()
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

					for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ).prev_sample

					assert sample.dtype == torch.floataa





