



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	lowerCamelCase_    ( _A						,_A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  StableDiffusionInstructPixaPixPipeline
						a__   =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
						a__   =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						a__   =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						a__   =  IMAGE_TO_IMAGE_IMAGE_PARAMS







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				List[Any]:
									torch.manual_seed(0 )
									A							:					Union[str, Any]              =						UNetaDConditionModel(
									    block_out_channels=(32, 64)	,	layers_per_block=2	,	sample_size=32	,	in_channels=8	,	out_channels=4	,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,	cross_attention_dim=32	,	)
									A							:					str              =						PNDMScheduler(skip_prk_steps=__lowerCamelCase )
									torch.manual_seed(0 )
									A							:					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	,	)
									torch.manual_seed(0 )
									A							:					str              =						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=10_00	,	)
									A							:					Union[str, Any]              =						CLIPTextModel(__lowerCamelCase )
									A							:					Optional[int]              =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

									A							:					List[str]              =						{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]=0 )      ->				Optional[Any]:
									A							:					int              =						floats_tensor((1, 3, 32, 32)	,	rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									A							:					Optional[Any]              =						image.cpu().permute(0	,	2	,	3	,	1 )[0]
									A							:					Any              =						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" )
									if str(__lowerCamelCase ).startswith("mps" ):
												A							:					str              =						torch.manual_seed(__lowerCamelCase )
									else:
												A							:					Optional[int]              =						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									A							:					int              =						{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "image_guidance_scale": 1,
									    "output_type": "numpy",
									}
									return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				List[str]:
									A							:					Tuple              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Any              =						self.get_dummy_components()
									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					int              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					List[str]              =						sd_pipe(**__lowerCamelCase ).images
									A							:					Tuple              =						image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A							:					Optional[int]              =						np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[Any]:
									A							:					Tuple              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Tuple              =						self.get_dummy_components()
									A							:					int              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Union[str, Any]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					Tuple              =						"french fries"
									A							:					List[str]              =						sd_pipe(**__lowerCamelCase	,	negative_prompt=__lowerCamelCase )
									A							:					Dict              =						output.images
									A							:					str              =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A							:					Optional[Any]              =						np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									A							:					Optional[Any]              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					int              =						self.get_dummy_components()
									A							:					str              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					List[Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Optional[Any]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					List[Any]              =						[inputs["prompt"]] * 2

									A							:					Any              =						np.array(inputs["image"] ).astype(np.floataa ) / 255.0
									A							:					List[str]              =						torch.from_numpy(__lowerCamelCase ).unsqueeze(0 ).to(__lowerCamelCase )
									A							:					Union[str, Any]              =						image / 2 + 0.5
									A							:					Tuple              =						image.permute(0	,	3	,	1	,	2 )
									A							:					int              =						image.repeat(2	,	1	,	1	,	1 )

									A							:					Union[str, Any]              =						sd_pipe(**__lowerCamelCase ).images
									A							:					Optional[int]              =						image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A							:					List[Any]              =						np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									A							:					int              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Dict              =						self.get_dummy_components()
									A							:					Dict              =						EulerAncestralDiscreteScheduler(
									    beta_start=0.00085	,	beta_end=0.012	,	beta_schedule="scaled_linear" )
									A							:					str              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					str              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Optional[int]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					Dict              =						sd_pipe(**__lowerCamelCase ).images
									A							:					List[str]              =						image[0, -3:, -3:, -1]

									A							:					List[Any]              =						[round(__lowerCamelCase	,	4 ) for x in image_slice.flatten().tolist()]
									print(",".join([str(__lowerCamelCase ) for x in slice] ) )

									assert image.shape == (1, 32, 32, 3)
									A							:					Tuple              =						np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Tuple:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				int:
									A							:					Optional[int]              =						self.get_dummy_components()
									A							:					List[Any]              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						VaeImageProcessor(do_resize=__lowerCamelCase	,	do_normalize=__lowerCamelCase )
									A							:					Union[str, Any]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Dict              =						pipe(**self.get_dummy_inputs_by_type(__lowerCamelCase	,	input_image_type="pt" ) )[0]

									A							:					Any              =						components["vae"]
									A							:					List[Any]              =						self.get_dummy_inputs_by_type(__lowerCamelCase	,	input_image_type="pt" )

									for image_param in self.image_latents_params:
												if image_param in inputs.keys():
															A							:					Optional[Any]              =						vae.encode(inputs[image_param] ).latent_dist.mode()

									A							:					Optional[int]              =						pipe(**__lowerCamelCase )[0]

									A							:					Dict              =						np.abs(out - out_latents_inputs ).max()
									self.assertLess(__lowerCamelCase	,	1e-4	,	"passing latents as image input generate different result from passing image" )







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






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Dict:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str=0 )      ->				List[str]:
									A							:					str              =						torch.manual_seed(__lowerCamelCase )
									A							:					Tuple              =						load_image(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
									A							:					List[str]              =						{
									    "prompt": "turn him into a cyborg",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "image_guidance_scale": 1.0,
									    "output_type": "numpy",
									}
									return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									A							:					List[str]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					Union[str, Any]              =						self.get_inputs()
									A							:					Dict              =						pipe(**__lowerCamelCase ).images
									A							:					List[Any]              =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					Any              =						np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Tuple:
									A							:					Optional[int]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									A							:					Union[str, Any]              =						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					Any              =						np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									A							:					Union[str, Any]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									A							:					Union[str, Any]              =						DDIMScheduler.from_config(pipe.scheduler.config )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					int              =						self.get_inputs()
									A							:					List[str]              =						pipe(**__lowerCamelCase ).images
									A							:					Tuple              =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					int              =						np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[int]:
									A							:					List[Any]              =						0

									def callback_fn(__lowerCamelCase      :			int	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			torch.FloatTensor ) -> None:
												A							:					str              =						True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
															A							:					str              =						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A							:					Optional[Any]              =						latents[0, -3:, -3:, -1]
															A							:					int              =						np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )

															assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
												elif step == 2:
															A							:					Optional[int]              =						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A							:					Optional[int]              =						latents[0, -3:, -3:, -1]
															A							:					Union[str, Any]              =						np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )

															assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2

									A							:					Optional[int]              =						False

									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase	,	torch_dtype=torch.floataa )
									A							:					List[str]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					Optional[Any]              =						self.get_inputs()
									pipe(**__lowerCamelCase	,	callback=__lowerCamelCase	,	callback_steps=1 )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A							:					List[Any]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase	,	torch_dtype=torch.floataa )
									A							:					Optional[int]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing(1 )
									pipe.enable_sequential_cpu_offload()

									A							:					str              =						self.get_inputs()
									A							:					int              =						pipe(**__lowerCamelCase )

									A							:					Any              =						torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					Optional[Any]              =						self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A							:					int              =						inputs["image"].resize((5_04, 5_04) )

									A							:					Optional[Any]              =						"timbrooks/instruct-pix2pix"
									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    __lowerCamelCase	,	safety_checker=__lowerCamelCase	,	)
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					int              =						pipe(**__lowerCamelCase )
									A							:					Optional[Any]              =						output.images[0]

									A							:					Tuple              =						image[2_55:2_58, 3_83:3_86, -1]

									assert image.shape == (5_04, 5_04, 3)
									A							:					Dict              =						np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Any              =						self.get_scheduler_config(**__lowerCamelCase )
												A							:					int              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )
												# copy over dummy past residuals
												A							:					Tuple              =						dummy_past_residuals[:]

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

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						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"

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					str              =						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)
															A							:					Optional[Any]              =						dummy_past_residuals[:]

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						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"

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						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" ):
															A							:					List[str]              =						num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						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] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3