"""simple docstring"""






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = StableDiffusionDiffEditPipeline
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
					snake_case__				    = frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					snake_case__				    = frozenset([]				)







					def 		__lowerCAmelCase						(				self   :	int			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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')    ,cross_attention_dim=32    ,attention_head_dim=(2, 4)    ,use_linear_projection=lowerCamelCase__    ,)
									UpperCAmelCase__             =							DDIMScheduler(
									    beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=lowerCamelCase__    ,set_alpha_to_one=lowerCamelCase__    ,)
									UpperCAmelCase__             =							DDIMInverseScheduler(
									    beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=lowerCamelCase__    ,set_alpha_to_zero=lowerCamelCase__    ,)
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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			)
									UpperCAmelCase__             =							CLIPTextConfig(
									    bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,hidden_act='gelu'    ,projection_dim=512    ,)
									UpperCAmelCase__             =							CLIPTextModel(lowerCamelCase__			)
									UpperCAmelCase__             =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

									UpperCAmelCase__             =							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'inverse_scheduler': inverse_scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]=0			):
									UpperCAmelCase__             =							floats_tensor((1, 16, 16)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							floats_tensor((1, 2, 4, 16, 16)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'prompt': 'a dog and a newt',
									    'mask_image': mask,
									    'image_latents': latents,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 32, 32)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							image.cpu().permute(0    ,2    ,3    ,1			)[0]
									UpperCAmelCase__             =							Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('RGB'			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': image,
									    'source_prompt': 'a cat and a frog',
									    'target_prompt': 'a dog and a newt',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'num_maps_per_mask': 2,
									    'mask_encode_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Any=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 32, 32)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							image.cpu().permute(0    ,2    ,3    ,1			)[0]
									UpperCAmelCase__             =							Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('RGB'			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': image,
									    'prompt': 'a cat and a frog',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'decode_latents': True,
									    'output_type': 'numpy',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	int			):
									if not hasattr(self.pipeline_class    ,'_optional_components'			):
													return

									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									# set all optional components to None and update pipeline config accordingly
									for optional_component in pipe._optional_components:
													setattr(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)
									pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							pipe(**lowerCamelCase__			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowerCamelCase__			)
													UpperCAmelCase__             =							self.pipeline_class.from_pretrained(lowerCamelCase__			)
													pipe_loaded.to(lowerCamelCase__			)
													pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__			)

									for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(lowerCamelCase__    ,lowerCamelCase__			) is None    ,f'''`{optional_component}` did not stay set to None after loading.'''    ,)

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							pipe_loaded(**lowerCamelCase__			)[0]

									UpperCAmelCase__             =							np.abs(output - output_loaded			).max()
									self.assertLess(lowerCamelCase__    ,1e-4			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							self.get_dummy_mask_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.generate_mask(**lowerCamelCase__			)
									UpperCAmelCase__             =							mask[0, -3:, -3:]

									self.assertEqual(mask.shape    ,(1, 16, 16)			)
									UpperCAmelCase__             =							np.array([0] * 9			)
									UpperCAmelCase__             =							np.abs(mask_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(lowerCamelCase__    ,1e-3			)
									self.assertEqual(mask[0, -3, -4]    ,0			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							self.get_dummy_inversion_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.invert(**lowerCamelCase__			).images
									UpperCAmelCase__             =							image[0, -1, -3:, -3:]

									self.assertEqual(image.shape    ,(2, 32, 32, 3)			)
									UpperCAmelCase__             =							np.array(
									    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]    ,)
									UpperCAmelCase__             =							np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(lowerCamelCase__    ,1e-3			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									super().test_inference_batch_single_identical(expected_max_diff=5e-3			)







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							{'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
									UpperCAmelCase__             =							DPMSolverMultistepScheduler(**lowerCamelCase__			)
									UpperCAmelCase__             =							DPMSolverMultistepInverseScheduler(**lowerCamelCase__			)

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							self.get_dummy_inversion_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.invert(**lowerCamelCase__			).images
									UpperCAmelCase__             =							image[0, -1, -3:, -3:]

									self.assertEqual(image.shape    ,(2, 32, 32, 3)			)
									UpperCAmelCase__             =							np.array(
									    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]    ,)
									UpperCAmelCase__             =							np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(lowerCamelCase__    ,1e-3			)






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[str]			):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	int			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

									UpperCAmelCase__             =							raw_image.convert('RGB'			).resize((768, 768)			)

									UpperCAmelCase__             =							raw_image







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							torch.manual_seed(0			)

									UpperCAmelCase__             =							StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'    ,safety_checker=lowerCamelCase__    ,torch_dtype=torch.floataa			)
									UpperCAmelCase__             =							DDIMScheduler.from_config(pipe.scheduler.config			)
									UpperCAmelCase__             =							DDIMInverseScheduler.from_config(pipe.scheduler.config			)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							'a bowl of fruit'
									UpperCAmelCase__             =							'a bowl of pears'

									UpperCAmelCase__             =							pipe.generate_mask(
									    image=self.raw_image    ,source_prompt=lowerCamelCase__    ,target_prompt=lowerCamelCase__    ,generator=lowerCamelCase__    ,)

									UpperCAmelCase__             =							pipe.invert(
									    prompt=lowerCamelCase__    ,image=self.raw_image    ,inpaint_strength=0.7    ,generator=lowerCamelCase__			).latents

									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,image_latents=lowerCamelCase__    ,generator=lowerCamelCase__    ,negative_prompt=lowerCamelCase__    ,inpaint_strength=0.7    ,output_type='numpy'    ,).images[0]

									UpperCAmelCase__             =							(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'			).resize((768, 768)			)			)
									    / 255
									)
									assert np.abs((expected_image - image).max()			) < 5e-1







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							torch.manual_seed(0			)

									UpperCAmelCase__             =							StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'    ,safety_checker=lowerCamelCase__    ,torch_dtype=torch.floataa			)
									UpperCAmelCase__             =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
									UpperCAmelCase__             =							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							'a bowl of fruit'
									UpperCAmelCase__             =							'a bowl of pears'

									UpperCAmelCase__             =							pipe.generate_mask(
									    image=self.raw_image    ,source_prompt=lowerCamelCase__    ,target_prompt=lowerCamelCase__    ,generator=lowerCamelCase__    ,)

									UpperCAmelCase__             =							pipe.invert(
									    prompt=lowerCamelCase__    ,image=self.raw_image    ,inpaint_strength=0.7    ,generator=lowerCamelCase__    ,num_inference_steps=25    ,).latents

									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,image_latents=lowerCamelCase__    ,generator=lowerCamelCase__    ,negative_prompt=lowerCamelCase__    ,inpaint_strength=0.7    ,num_inference_steps=25    ,output_type='numpy'    ,).images[0]

									UpperCAmelCase__             =							(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'			).resize((768, 768)			)			)
									    / 255
									)
									assert np.abs((expected_image - image).max()			) < 5e-1




"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = (PNDMScheduler,)
					snake_case__				    = (("num_inference_steps", 50),)







					def 		__lowerCAmelCase						(				self   :	List[str]    ,**lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**lowerCamelCase__			)
									return config







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Optional[Any]=0    ,**lowerCamelCase__   :	List[str]			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[str]=0    ,**lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

													# copy over dummy past residuals (must be after setting timesteps)
													UpperCAmelCase__             =							dummy_past_residuals[:]

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

																	# copy over dummy past residual (must be after setting timesteps)
																	UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

									UpperCAmelCase__             =							10
									UpperCAmelCase__             =							self.dummy_model()
									UpperCAmelCase__             =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__			)

									for i, t in enumerate(scheduler.prk_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									return sample







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)

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

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

													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							0.1 * sample

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

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for timesteps in [100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__			)

									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(steps_offset=1			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
									scheduler.set_timesteps(10			)
									assert torch.equal(
									    scheduler.timesteps    ,torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,[0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=lowerCamelCase__    ,beta_end=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									for t in [1, 5, 10]:
													self.check_over_forward(time_step=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for t, num_inference_steps in zip([1, 5, 10]    ,[10, 50, 100]			):
													self.check_over_forward(num_inference_steps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									UpperCAmelCase__             =							27

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

													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.set_timesteps(lowerCamelCase__			)

													# before power of 3 fix, would error on first step, so we only need to do two
													for i, t in enumerate(scheduler.prk_timesteps[:2]			):
																	UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample







					def 		__lowerCAmelCase						(				self   :	int			):
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							self.scheduler_classes[0]
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.step_plms(self.dummy_sample    ,1    ,self.dummy_sample			).prev_sample







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop()
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop(prediction_type='v_prediction'			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3



