
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       a		(   __UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						Tuple   							=  KandinskyInpaintPipeline
							lowercase_		:						int   							=  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							lowercase_		:						Tuple   							=  [
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							lowercase_		:						Any   							=  [
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_		:						Union[str, Any]   							=  False







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 32







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return 32







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								return self.time_input_dim







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return self.time_input_dim * 4







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return 100







							@property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
								return tokenizer







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								torch.manual_seed(0 )
								__lowerCAmelCase         =							MCLIPConfig(
								    numDims=self.cross_attention_dim      ,				transformerDimensions=self.text_embedder_hidden_size      ,				hidden_size=self.text_embedder_hidden_size      ,				intermediate_size=37      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				vocab_size=1_005      ,				)

								__lowerCAmelCase         =							MultilingualCLIP(snake_case__ )
								__lowerCAmelCase         =							text_encoder.eval()

								return text_encoder







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								torch.manual_seed(0 )

								__lowerCAmelCase         =							{
								    "in_channels": 9,
								    # Out channels is double in channels because predicts mean and variance
								    "out_channels": 8,
								    "addition_embed_type": "text_image",
								    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
								    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
								    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
								    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    "layers_per_block": 1,
								    "encoder_hid_dim": self.text_embedder_hidden_size,
								    "encoder_hid_dim_type": "text_image_proj",
								    "cross_attention_dim": self.cross_attention_dim,
								    "attention_head_dim": 4,
								    "resnet_time_scale_shift": "scale_shift",
								    "class_embed_type": None,
								}

								__lowerCAmelCase         =							UNetaDConditionModel(**snake_case__ )
								return model







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							@property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								torch.manual_seed(0 )
								__lowerCAmelCase         =							VQModel(**self.dummy_movq_kwargs )
								return model







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.dummy_text_encoder
								__lowerCAmelCase         =							self.dummy_tokenizer
								__lowerCAmelCase         =							self.dummy_unet
								__lowerCAmelCase         =							self.dummy_movq

								__lowerCAmelCase         =							DDIMScheduler(
								    num_train_timesteps=1_000      ,				beta_schedule="linear"      ,				beta_start=0.0_0_0_8_5      ,				beta_end=0.0_1_2      ,				clip_sample=snake_case__      ,				set_alpha_to_one=snake_case__      ,				steps_offset=1      ,				prediction_type="epsilon"      ,				thresholding=snake_case__      ,				)

								__lowerCAmelCase         =							{
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  List[Any]=0 ):
								"""simple docstring"""







								__lowerCAmelCase         =							floats_tensor((1, self.cross_attention_dim)      ,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
								__lowerCAmelCase         =							floats_tensor((1, self.cross_attention_dim)      ,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
								# create init_image
								__lowerCAmelCase         =							floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
								__lowerCAmelCase         =							image.cpu().permute(0      ,				2      ,				3      ,				1 )[0]
								__lowerCAmelCase         =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((256, 256) )
								# create mask
								__lowerCAmelCase         =							np.ones((64, 64)      ,				dtype=np.floataa )
								__lowerCAmelCase         =							0

								if str(snake_case__ ).startswith("mps" ):
									__lowerCAmelCase         =							torch.manual_seed(snake_case__ )
								else:
									__lowerCAmelCase         =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
								__lowerCAmelCase         =							{
								    "prompt": "horse",
								    "image": init_image,
								    "mask_image": mask,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 2,
								    "guidance_scale": 4.0,
								    "output_type": "np",
								}
								return inputs







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							"cpu"

								__lowerCAmelCase         =							self.get_dummy_components()

								__lowerCAmelCase         =							self.pipeline_class(**snake_case__ )
								__lowerCAmelCase         =							pipe.to(snake_case__ )

								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							pipe(**self.get_dummy_inputs(snake_case__ ) )
								__lowerCAmelCase         =							output.images

								__lowerCAmelCase         =							pipe(
								    **self.get_dummy_inputs(snake_case__ )      ,				return_dict=snake_case__      ,				)[0]

								__lowerCAmelCase         =							image[0, -3:, -3:, -1]
								__lowerCAmelCase         =							image_from_tuple[0, -3:, -3:, -1]

								print(F"image.shape {image.shape}" )

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

								__lowerCAmelCase         =							np.array(
								    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6] )

								assert (
								    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
								), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
								), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







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

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







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
								__lowerCAmelCase         =							np.ones((768, 768)      ,				dtype=np.floataa )
								__lowerCAmelCase         =							0

								__lowerCAmelCase         =							"a hat"

								__lowerCAmelCase         =							KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior"      ,				torch_dtype=torch.floataa )
								pipe_prior.to(snake_case__ )

								__lowerCAmelCase         =							KandinskyInpaintPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-inpaint"      ,				torch_dtype=torch.floataa )
								__lowerCAmelCase         =							pipeline.to(snake_case__ )
								pipeline.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							torch.Generator(device="cpu" ).manual_seed(0 )
								__lowerCAmelCase   ,						__lowerCAmelCase         =							pipe_prior(
								    snake_case__      ,				generator=snake_case__      ,				num_inference_steps=5      ,				negative_prompt=""      ,				).to_tuple()

								__lowerCAmelCase         =							pipeline(
								    snake_case__      ,				image=snake_case__      ,				mask_image=snake_case__      ,				image_embeds=snake_case__      ,				negative_image_embeds=snake_case__      ,				generator=snake_case__      ,				num_inference_steps=100      ,				height=768      ,				width=768      ,				output_type="np"      ,				)

								__lowerCAmelCase         =							output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(snake_case__      ,				snake_case__ )








import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  (DPMSolverSinglestepScheduler,)
							lowercase_		:						Optional[Any]   							=  (('num_inference_steps', 25),)







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								    "sample_max_value": 1.0,
								    "algorithm_type": "dpmsolver++",
								    "solver_type": "midpoint",
								    "lambda_min_clipped": -float("inf" ),
								    "variance_type": None,
								}

								config.update(**snake_case__ )
								return config







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config()
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )

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

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

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

									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
									__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







								if scheduler is None:
									__lowerCAmelCase         =							self.scheduler_classes[0]
									__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

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

								return sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							50
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:] ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3

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

								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								self.check_over_configs(thresholding=snake_case__ )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=snake_case__      ,				prediction_type=snake_case__      ,				sample_max_value=snake_case__      ,				algorithm_type="dpmsolver++"      ,				solver_order=snake_case__      ,				solver_type=snake_case__      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												__lowerCAmelCase         =							self.full_loop(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.check_over_configs(lower_order_final=snake_case__ )
								self.check_over_configs(lower_order_final=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								self.check_over_configs(lambda_min_clipped=-float("inf" ) )
								self.check_over_configs(lambda_min_clipped=-5.1 )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								self.check_over_configs(variance_type=snake_case__ )
								self.check_over_configs(variance_type="learned_range" )







							def       UpperCAmelCase__					(						self  :  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=snake_case__      ,				time_step=0 )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop()
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction"      ,				use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case__ )

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

								assert sample.dtype == torch.floataa






