





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class    __snake_case	(							__lowerCamelCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					(DDPMScheduler,)




							def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    **A			:   Tuple		):
									__snake_case:						Union[str, Any]			  =						{
									    """num_train_timesteps""": 1_000,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									    """variance_type""": """fixed_small""",
									    """clip_sample""": True,
									}

									config.update(**A		)
									return config




							def 							UpperCAmelCase__			(  self			:   Dict		):
									for timesteps in [1, 5, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=A		)




							def 							UpperCAmelCase__			(  self			:   Any		):
									for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,    [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=A							,    beta_end=A		)




							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=A		)




							def 							UpperCAmelCase__			(  self			:   str		):
									for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=A		)




							def 							UpperCAmelCase__			(  self			:   int		):
									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=A		)




							def 							UpperCAmelCase__			(  self			:   List[str]		):
									self.check_over_configs(thresholding=A		)
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=A							,    prediction_type=A							,    sample_max_value=A							,    )




							def 							UpperCAmelCase__			(  self			:   Any		):
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=A		)




							def 							UpperCAmelCase__			(  self			:   Any		):
									for t in [0, 500, 999]:
											self.check_over_forward(time_step=A		)




							def 							UpperCAmelCase__			(  self			:   int		):
									__snake_case:						Tuple			  =						self.scheduler_classes[0]
									__snake_case:						Optional[Any]			  =						self.get_scheduler_config()
									__snake_case:						Optional[Any]			  =						scheduler_class(**A		)

									assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						Optional[Any]			  =						self.scheduler_classes[0]
									__snake_case:						str			  =						self.get_scheduler_config()
									__snake_case:						Dict			  =						scheduler_class(**A		)

									__snake_case:						str			  =						len(A		)

									__snake_case:						Dict			  =						self.dummy_model()
									__snake_case:						Tuple			  =						self.dummy_sample_deter
									__snake_case:						Optional[Any]			  =						torch.manual_seed(0		)

									for t in reversed(range(A		)		):
											# 1. predict noise residual
											__snake_case:						str			  =						model(A							,    A		)

											# 2. predict previous mean of sample x_t-1
											__snake_case:						List[Any]			  =						scheduler.step(A							,    A							,    A							,    generator=A		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__snake_case:						Optional[int]			  =						pred_prev_sample

									__snake_case:						List[Any]			  =						torch.sum(torch.abs(A		)		)
									__snake_case:						str			  =						torch.mean(torch.abs(A		)		)

									assert abs(result_sum.item() - 258.9606		) < 1E-2
									assert abs(result_mean.item() - 0.3372		) < 1E-3




							def 							UpperCAmelCase__			(  self			:   Tuple		):
									__snake_case:						Dict			  =						self.scheduler_classes[0]
									__snake_case:						Dict			  =						self.get_scheduler_config(prediction_type="""v_prediction"""		)
									__snake_case:						List[str]			  =						scheduler_class(**A		)

									__snake_case:						List[str]			  =						len(A		)

									__snake_case:						Tuple			  =						self.dummy_model()
									__snake_case:						Dict			  =						self.dummy_sample_deter
									__snake_case:						Optional[int]			  =						torch.manual_seed(0		)

									for t in reversed(range(A		)		):
											# 1. predict noise residual
											__snake_case:						Any			  =						model(A							,    A		)

											# 2. predict previous mean of sample x_t-1
											__snake_case:						int			  =						scheduler.step(A							,    A							,    A							,    generator=A		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__snake_case:						str			  =						pred_prev_sample

									__snake_case:						int			  =						torch.sum(torch.abs(A		)		)
									__snake_case:						Optional[Any]			  =						torch.mean(torch.abs(A		)		)

									assert abs(result_sum.item() - 202.0296		) < 1E-2
									assert abs(result_mean.item() - 0.2631		) < 1E-3




							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						List[Any]			  =						self.scheduler_classes[0]
									__snake_case:						Optional[Any]			  =						self.get_scheduler_config()
									__snake_case:						str			  =						scheduler_class(**A		)

									__snake_case:						List[str]			  =						[100, 87, 50, 1, 0]

									scheduler.set_timesteps(timesteps=A		)

									__snake_case:						List[str]			  =						scheduler.timesteps

									for i, timestep in enumerate(A		):
											if i == len(A		) - 1:
													__snake_case:						Dict			  =						-1
											else:
													__snake_case:						Optional[Any]			  =						timesteps[i + 1]

											__snake_case:						Union[str, Any]			  =						scheduler.previous_timestep(A		)
											__snake_case:						Tuple			  =						prev_t.item()

											self.assertEqual(A							,    A		)




							def 							UpperCAmelCase__			(  self			:   Dict		):
									__snake_case:						Optional[Any]			  =						self.scheduler_classes[0]
									__snake_case:						Optional[int]			  =						self.get_scheduler_config()
									__snake_case:						str			  =						scheduler_class(**A		)

									__snake_case:						Optional[int]			  =						[100, 87, 50, 51, 0]

									with self.assertRaises(A							,    msg="""`custom_timesteps` must be in descending order."""		):
											scheduler.set_timesteps(timesteps=A		)




							def 							UpperCAmelCase__			(  self			:   Any		):
									__snake_case:						Any			  =						self.scheduler_classes[0]
									__snake_case:						str			  =						self.get_scheduler_config()
									__snake_case:						str			  =						scheduler_class(**A		)

									__snake_case:						List[Any]			  =						[100, 87, 50, 1, 0]
									__snake_case:						List[str]			  =						len(A		)

									with self.assertRaises(A							,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""		):
											scheduler.set_timesteps(num_inference_steps=A							,    timesteps=A		)






							def 							UpperCAmelCase__			(  self			:   Tuple		):
									__snake_case:						Tuple			  =						self.scheduler_classes[0]
									__snake_case:						List[str]			  =						self.get_scheduler_config()
									__snake_case:						List[str]			  =						scheduler_class(**A		)

									__snake_case:						Dict			  =						[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    A							,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,    ):
											scheduler.set_timesteps(timesteps=A		)













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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







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





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						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






