



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


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


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= StableDiffusionXLImgaImgPipeline
	_SCREAMING_SNAKE_CASE	:		int  						= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
	_SCREAMING_SNAKE_CASE	:		Any  						= PipelineTesterMixin.required_optional_params - {'''latents'''}
	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_SCREAMING_SNAKE_CASE	:		Tuple  						= IMAGE_TO_IMAGE_IMAGE_PARAMS
	_SCREAMING_SNAKE_CASE	:		int  						= IMAGE_TO_IMAGE_IMAGE_PARAMS





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





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Dict							=     UNetaDConditionModel(
					    block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=4     ,       out_channels=4     ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,       attention_head_dim=(2, 4)     ,       use_linear_projection=_SCREAMING_SNAKE_CASE     ,       addition_embed_type='text_time'     ,       addition_time_embed_dim=8     ,       transformer_layers_per_block=(1, 2)     ,       projection_class_embeddings_input_dim=80     ,       cross_attention_dim=64     ,       )
					SCREAMING_SNAKE_CASE						:							Dict							=     EulerDiscreteScheduler(
					    beta_start=0.0_0_0_8_5     ,       beta_end=0.0_1_2     ,       steps_offset=1     ,       beta_schedule='scaled_linear'     ,       timestep_spacing='leading'     ,       )
					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     AutoencoderKL(
					    block_out_channels=[32, 64]     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,       latent_channels=4     ,       sample_size=128     ,       )
					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     CLIPTextConfig(
					    bos_token_id=0     ,       eos_token_id=2     ,       hidden_size=32     ,       intermediate_size=37     ,       layer_norm_eps=1E-0_5     ,       num_attention_heads=4     ,       num_hidden_layers=5     ,       pad_token_id=1     ,       vocab_size=1_000     ,       hidden_act='gelu'     ,       projection_dim=32     ,       )
					SCREAMING_SNAKE_CASE						:							str							=     CLIPTextModel(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     ,       local_files_only=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     ,       local_files_only=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     {
					    'unet': unet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'text_encoder_2': text_encoder_a,
					    'tokenizer_2': tokenizer_a,
					    # "safety_checker": None,
					    # "feature_extractor": None,
					}
					return components





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     image / 2 + 0.5
					if str(_SCREAMING_SNAKE_CASE    ).startswith('mps'    ):
									SCREAMING_SNAKE_CASE						:							Any							=     torch.manual_seed(_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							List[str]							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     {
					    'prompt': 'A painting of a squirrel eating a burger',
					    'image': image,
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 5.0,
					    'output_type': 'numpy',
					    'strength': 0.7_5,
					}
					return inputs





	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_dummy_components()
					SCREAMING_SNAKE_CASE						:							List[Any]							=     StableDiffusionXLImgaImgPipeline(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     sd_pipe(**_SCREAMING_SNAKE_CASE    ).images
					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





	def 			_lowerCAmelCase     (      self				:       Any    )						->	int:





					"""simple docstring"""


					super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					super().test_inference_batch_single_identical(expected_max_diff=3E-3    )





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





					"""simple docstring"""


					pass







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_dummy_components()
					SCREAMING_SNAKE_CASE						:							Tuple							=     StableDiffusionXLImgaImgPipeline(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					# forward without prompt embeds
					SCREAMING_SNAKE_CASE						:							int							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     3 * ['this is a negative prompt']
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     negative_prompt
					SCREAMING_SNAKE_CASE						:							List[str]							=     3 * [inputs['prompt']]

					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     3 * ['this is a negative prompt']
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     3 * [inputs.pop('prompt'    )]

					(
					    (
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						
					)						:							List[Any]							=     sd_pipe.encode_prompt(_SCREAMING_SNAKE_CASE     ,       negative_prompt=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     sd_pipe(
					    **_SCREAMING_SNAKE_CASE     ,       prompt_embeds=_SCREAMING_SNAKE_CASE     ,       negative_prompt_embeds=_SCREAMING_SNAKE_CASE     ,       pooled_prompt_embeds=_SCREAMING_SNAKE_CASE     ,       negative_pooled_prompt_embeds=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							int							=     output.images[0, -3:, -3:, -1]

					# make sure that it's equal
					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4




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


	'''simple docstring'''







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





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="cpu"     ,       _SCREAMING_SNAKE_CASE				:       str=torch.floataa     ,       _SCREAMING_SNAKE_CASE				:       Any=0    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     np.random.RandomState(_SCREAMING_SNAKE_CASE    ).standard_normal((1, 4, 64, 64)    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.from_numpy(_SCREAMING_SNAKE_CASE    ).to(device=_SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {
					    'prompt': 'a photograph of an astronaut riding a horse',
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipe(**_SCREAMING_SNAKE_CASE    ).images
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]    )
					assert np.abs(image_slice - expected_slice    ).max() < 7E-3










"""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





