





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			(UniPCMultistepScheduler,)
  a_      =			(("""num_inference_steps""", 25),)






  def     lowercase		(    self			:		Tuple ,  **lowerCAmelCase_			:		Any					)							->						int:
     __lowerCAmelCase							     =     {
         'num_train_timesteps': 1_0_0_0,
         'beta_start': 0.00_01,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'solver_order': 2,
         'solver_type': 'bh2',
     }

     config.update(**lowerCAmelCase_					)
     return config






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Optional[int]=0 ,  **lowerCAmelCase_			:		Any					)							->						List[Any]:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str=0 ,  **lowerCAmelCase_			:		Dict					)							->						Tuple:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

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

        # 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(lowerCAmelCase_					)
           __lowerCAmelCase							     =     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)
           __lowerCAmelCase							     =     dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample
        __lowerCAmelCase							     =     new_scheduler.step(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[Any] ,  lowerCAmelCase_			:		Optional[Any]=None ,  **lowerCAmelCase_			:		Tuple					)							->						List[str]:
     if scheduler is None:
        __lowerCAmelCase							     =     self.scheduler_classes[0]
        __lowerCAmelCase							     =     self.get_scheduler_config(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase_					)

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

     return sample






  def     lowercase		(    self			:		Tuple					)							->						Tuple:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)

     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config()
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

        __lowerCAmelCase							     =     self.dummy_sample
        __lowerCAmelCase							     =     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'					):
           __lowerCAmelCase							     =     num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]
        __lowerCAmelCase							     =     dummy_past_residuals[: scheduler.config.solver_order]

        __lowerCAmelCase							     =     scheduler.timesteps[5]
        __lowerCAmelCase							     =     scheduler.timesteps[6]

        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample
        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample

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






  def     lowercase		(    self			:		int					)							->						int:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     __lowerCAmelCase							     =     UniPCMultistepScheduler(**self.get_scheduler_config()					)
     __lowerCAmelCase							     =     self.full_loop(scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3

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

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

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						int:
     self.check_over_configs(thresholding=lowerCAmelCase_					)
     for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
           for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=lowerCAmelCase_ ,  prediction_type=lowerCAmelCase_ ,  sample_max_value=lowerCAmelCase_ ,  solver_order=lowerCAmelCase_ ,  solver_type=lowerCAmelCase_ ,  )






  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     for solver_type in ["bh1", "bh2"]:
        for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=lowerCAmelCase_ ,  solver_type=lowerCAmelCase_ ,  prediction_type=lowerCAmelCase_ ,  )
              __lowerCAmelCase							     =     self.full_loop(
                  solver_order=lowerCAmelCase_ ,  solver_type=lowerCAmelCase_ ,  prediction_type=lowerCAmelCase_ ,  )
              assert not torch.isnan(lowerCAmelCase_					).any(), "Samples have nan numbers"






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     self.check_over_configs(lower_order_final=lowerCAmelCase_					)
     self.check_over_configs(lower_order_final=lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_forward(num_inference_steps=lowerCAmelCase_ ,  time_step=0					)






  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     self.full_loop()
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		int					)							->						List[str]:
     __lowerCAmelCase							     =     self.full_loop(prediction_type='v_prediction'					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.10_14					) < 1e-3






  def     lowercase		(    self			:		Optional[Any]					)							->						Any:
     __lowerCAmelCase							     =     self.scheduler_classes[0]
     __lowerCAmelCase							     =     self.get_scheduler_config(thresholding=lowerCAmelCase_ ,  dynamic_thresholding_ratio=0					)
     __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCAmelCase_					)

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

     assert sample.dtype == torch.floataa






  def     lowercase		(    self			:		Union[str, Any] ,  **lowerCAmelCase_			:		Tuple					)							->						str:
     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

        scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
        assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



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

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'					)

     # using the PNDM scheduler by default
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

