



'''simple docstring'''



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(       __A      ):
						_lowerCAmelCase  : Union[str, Any]						  =					(PNDMScheduler,)
						_lowerCAmelCase  : List[str]						  =					(('num_inference_steps', 5_0),)







						def        __lowercase     (	self :   List[Any]					,     **lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      {
													    "num_train_timesteps": 10_00,
													    "beta_start": 0.0001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**lowerCAmelCase__					)
													return config







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   List[str]=0					,     **lowerCAmelCase__ :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Any         =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config(**lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Any         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)
																				# copy over dummy past residuals
																				SCREAMING_SNAKE_CASE       :   str         =      dummy_past_residuals[:]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler_class.from_pretrained(lowerCAmelCase__					)
																											new_scheduler.set_timesteps(lowerCAmelCase__					)
																											# copy over dummy past residuals
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      dummy_past_residuals[:]

																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.step_prk(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   int         =      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"

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler.step_plms(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Optional[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        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													pass







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Optional[int]=0					,     **lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Any         =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)

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

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      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)
																											SCREAMING_SNAKE_CASE       :   int         =      dummy_past_residuals[:]

																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler.step_prk(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      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"

																				SCREAMING_SNAKE_CASE       :   str         =      scheduler.step_plms(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   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        __lowercase     (	self :   Optional[int]					,     **lowerCAmelCase__ :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.get_scheduler_config(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      10
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.dummy_sample_deter
													scheduler.set_timesteps(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.prk_timesteps					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Any         =      scheduler.step_prk(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													for i, t in enumerate(scheduler.plms_timesteps					):
																				SCREAMING_SNAKE_CASE       :   Dict         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler.step_plms(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													return sample







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      dict(self.forward_default_kwargs					)

													SCREAMING_SNAKE_CASE       :   int         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   Dict         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   str         =      scheduler_class(**lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   List[str]         =      self.dummy_sample
																				SCREAMING_SNAKE_CASE       :   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'''					):
																											SCREAMING_SNAKE_CASE       :   str         =      num_inference_steps

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

																				SCREAMING_SNAKE_CASE       :   Any         =      scheduler.step_prk(lowerCAmelCase__					,     0					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   str         =      scheduler.step_prk(lowerCAmelCase__					,     1					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

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

																				SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler.step_plms(lowerCAmelCase__					,     0					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   int         =      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        __lowercase     (	self :   Dict					):

													"""simple docstring"""

													for timesteps in [1_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""

													for steps_offset in [0, 1]:
																				self.check_over_configs(steps_offset=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.get_scheduler_config(steps_offset=1					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      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        __lowercase     (	self :   Dict					):

													"""simple docstring"""

													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        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""

													for schedule in ["linear", "squaredcos_cap_v2"]:
																				self.check_over_configs(beta_schedule=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""

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







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""

													for t in [1, 5, 10]:
																				self.check_over_forward(time_step=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""

													for t, num_inference_steps in zip([1, 5, 10]					,     [10, 50, 1_00]					):
																				self.check_over_forward(num_inference_steps=lowerCAmelCase__					)







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      27

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   str         =      self.dummy_sample
																				SCREAMING_SNAKE_CASE       :   Tuple         =      0.1 * sample

																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      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]					):
																											SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.step_prk(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""

													with self.assertRaises(lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      self.scheduler_classes[0]
																				SCREAMING_SNAKE_CASE       :   List[str]         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler_class(**lowerCAmelCase__					)

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







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.full_loop()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

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







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.full_loop(prediction_type='''v_prediction'''					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   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        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__					,     beta_start=0.01					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

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







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.full_loop(set_alpha_to_one=lowerCAmelCase__					,     beta_start=0.01					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Dict         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

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







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 _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







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

				"""simple docstring"""

				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)
			@property
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							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						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, 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						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   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