
import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 			__A( a			):
				snake_case_						   =    (DPMSolverSDEScheduler,)
				snake_case_						   =    1_0




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 1_100,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''noise_sampler_seed''': 0,
								}

								config.update(**_snake_case				)
								return config




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


								'''simple docstring'''




								for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case				)




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


								'''simple docstring'''




								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]		,     [0.0002, 0.002, 0.02]				):
												self.check_over_configs(beta_start=_snake_case		,     beta_end=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								scheduler.set_timesteps(self.num_inference_steps				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter * scheduler.init_noise_sigma
								__a										=    sample.to(_snake_case				)

								for i, t in enumerate(scheduler.timesteps				):
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												__a										=    model(_snake_case		,     _snake_case				)

												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				)
												__a										=    output.prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875				) < 1E-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277				) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406				) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652				) < 1E-3
								else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562				) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(prediction_type='''v_prediction'''				)
								__a										=    scheduler_class(**_snake_case				)

								scheduler.set_timesteps(self.num_inference_steps				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter * scheduler.init_noise_sigma
								__a										=    sample.to(_snake_case				)

								for i, t in enumerate(scheduler.timesteps				):
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												__a										=    model(_snake_case		,     _snake_case				)

												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				)
												__a										=    output.prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453				) < 1E-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284				) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703				) < 1E-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297				) < 1E-3
								else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125				) < 1E-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								scheduler.set_timesteps(self.num_inference_steps		,     device=_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter.to(_snake_case				) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												__a										=    model(_snake_case		,     _snake_case				)

												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				)
												__a										=    output.prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938				) < 1E-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635				) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312				) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771				) < 1E-3
								else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562				) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326				) < 1E-3




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


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case		,     use_karras_sigmas=_snake_case				)

								scheduler.set_timesteps(self.num_inference_steps		,     device=_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter.to(_snake_case				) * scheduler.init_noise_sigma
								__a										=    sample.to(_snake_case				)

								for t in scheduler.timesteps:
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												__a										=    model(_snake_case		,     _snake_case				)

												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				)
												__a										=    output.prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188				) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811				) < 1E-2
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125				) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811				) < 1E-2
								else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672				) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811				) < 1E-2

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort







class 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 			__A( unittest.TestCase			):




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


								'''simple docstring'''




								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




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


								'''simple docstring'''




								__a										=    ort.SessionOptions()
								__a										=    False
								return options




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     subfolder='''scheduler'''		,     revision='''onnx'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     scheduler=_snake_case		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    '''A red cat sitting on a park bench'''

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

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