
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			List[Any]        =    (UniPCMultistepScheduler,)
      A__	:			Any        =    (('''num_inference_steps''', 2_5),)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       Union[str, Any]				):
          __lowercase		:			List[str]	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
              '''solver_type''': '''bh2''',
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       str				,       _snake_case		:       List[str]=0				,       **_snake_case		:       str				):
          __lowercase		:			Tuple	    =       dict(self.forward_default_kwargs				)
          __lowercase		:			Dict	    =       kwargs.pop('''num_inference_steps'''				,       _snake_case				)
          __lowercase		:			int	    =       self.dummy_sample
          __lowercase		:			int	    =       0.1 * sample
          __lowercase		:			List[Any]	    =       [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              __lowercase		:			List[Any]	    =       self.get_scheduler_config(**_snake_case				)
              __lowercase		:			Tuple	    =       scheduler_class(**_snake_case				)
              scheduler.set_timesteps(_snake_case				)
              # copy over dummy past residuals
              __lowercase		:			str	    =       dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_snake_case				)
                  __lowercase		:			List[Any]	    =       scheduler_class.from_pretrained(_snake_case				)
                  new_scheduler.set_timesteps(_snake_case				)
                  # copy over dummy past residuals
                  __lowercase		:			Union[str, Any]	    =       dummy_past_residuals[: new_scheduler.config.solver_order]

              __lowercase		:			Dict	    =       sample, sample
              for t in range(_snake_case				,       time_step + scheduler.config.solver_order + 1				):
                  __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       **_snake_case				).prev_sample
                  __lowercase		:			int	    =       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 		snake_case_			(      self		:       Dict				,       _snake_case		:       Optional[int]=0				,       **_snake_case		:       Dict				):
          __lowercase		:			Any	    =       dict(self.forward_default_kwargs				)
          __lowercase		:			List[Any]	    =       kwargs.pop('''num_inference_steps'''				,       _snake_case				)
          __lowercase		:			int	    =       self.dummy_sample
          __lowercase		:			Optional[Any]	    =       0.1 * sample
          __lowercase		:			Optional[Any]	    =       [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              __lowercase		:			Any	    =       self.get_scheduler_config()
              __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)
              scheduler.set_timesteps(_snake_case				)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_snake_case				)
                  __lowercase		:			Tuple	    =       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)
                  __lowercase		:			Tuple	    =       dummy_past_residuals[: new_scheduler.config.solver_order]

              __lowercase		:			str	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       **_snake_case				).prev_sample
              __lowercase		:			Union[str, Any]	    =       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 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Dict=None				,       **_snake_case		:       List[str]				):
          if scheduler is None:
              __lowercase		:			List[str]	    =       self.scheduler_classes[0]
              __lowercase		:			List[Any]	    =       self.get_scheduler_config(**_snake_case				)
              __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			int	    =       self.get_scheduler_config(**_snake_case				)
          __lowercase		:			Any	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       10
          __lowercase		:			Union[str, Any]	    =       self.dummy_model()
          __lowercase		:			Any	    =       self.dummy_sample_deter
          scheduler.set_timesteps(_snake_case				)

          for i, t in enumerate(scheduler.timesteps				):
              __lowercase		:			List[str]	    =       model(_snake_case				,       _snake_case				)
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				).prev_sample

          return sample




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			List[Any]	    =       dict(self.forward_default_kwargs				)

          __lowercase		:			str	    =       kwargs.pop('''num_inference_steps'''				,       _snake_case				)

          for scheduler_class in self.scheduler_classes:
              __lowercase		:			List[Any]	    =       self.get_scheduler_config()
              __lowercase		:			List[Any]	    =       scheduler_class(**_snake_case				)

              __lowercase		:			str	    =       self.dummy_sample
              __lowercase		:			Any	    =       0.1 * sample

              if num_inference_steps is not None and hasattr(_snake_case				,       '''set_timesteps'''				):
                  scheduler.set_timesteps(_snake_case				)
              elif num_inference_steps is not None and not hasattr(_snake_case				,       '''set_timesteps'''				):
                  __lowercase		:			List[str]	    =       num_inference_steps

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

              __lowercase		:			Optional[int]	    =       scheduler.timesteps[5]
              __lowercase		:			List[str]	    =       scheduler.timesteps[6]

              __lowercase		:			Any	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       **_snake_case				).prev_sample
              __lowercase		:			List[Any]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       **_snake_case				).prev_sample

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




      def 		snake_case_			(      self		:       List[Any]				):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          __lowercase		:			Any	    =       UniPCMultistepScheduler(**self.get_scheduler_config()				)
          __lowercase		:			Union[str, Any]	    =       self.full_loop(scheduler=_snake_case				)
          __lowercase		:			Union[str, Any]	    =       torch.mean(torch.abs(_snake_case				)				)

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

          __lowercase		:			Dict	    =       DPMSolverSinglestepScheduler.from_config(scheduler.config				)
          __lowercase		:			List[Any]	    =       DEISMultistepScheduler.from_config(scheduler.config				)
          __lowercase		:			List[str]	    =       DPMSolverMultistepScheduler.from_config(scheduler.config				)
          __lowercase		:			Tuple	    =       UniPCMultistepScheduler.from_config(scheduler.config				)

          __lowercase		:			Dict	    =       self.full_loop(scheduler=_snake_case				)
          __lowercase		:			Optional[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

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




      def 		snake_case_			(      self		:       int				):
          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          self.check_over_configs(thresholding=_snake_case				)
          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=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       solver_order=_snake_case				,       solver_type=_snake_case				,       )




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          for solver_type in ["bh1", "bh2"]:
              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				,       )
                      __lowercase		:			str	    =       self.full_loop(
                          solver_order=_snake_case				,       solver_type=_snake_case				,       prediction_type=_snake_case				,       )
                      assert not torch.isnan(_snake_case				).any(), "Samples have nan numbers"




      def 		snake_case_			(      self		:       Any				):
          self.check_over_configs(lower_order_final=_snake_case				)
          self.check_over_configs(lower_order_final=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=_snake_case				,       time_step=0				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			int	    =       self.full_loop()
          __lowercase		:			Union[str, Any]	    =       torch.mean(torch.abs(_snake_case				)				)

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




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Dict	    =       self.full_loop(prediction_type='''v_prediction'''				)
          __lowercase		:			Dict	    =       torch.mean(torch.abs(_snake_case				)				)

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




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Dict	    =       self.get_scheduler_config(thresholding=_snake_case				,       dynamic_thresholding_ratio=0				)
          __lowercase		:			str	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       10
          __lowercase		:			Optional[int]	    =       self.dummy_model()
          __lowercase		:			Optional[Any]	    =       self.dummy_sample_deter.half()
          scheduler.set_timesteps(_snake_case				)

          for i, t in enumerate(scheduler.timesteps				):
              __lowercase		:			Union[str, Any]	    =       model(_snake_case				,       _snake_case				)
              __lowercase		:			Union[str, Any]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				).prev_sample

          assert sample.dtype == torch.floataa




      def 		snake_case_			(      self		:       Optional[Any]				,       **_snake_case		:       List[str]				):
          for scheduler_class in self.scheduler_classes:
              __lowercase		:			int	    =       self.get_scheduler_config(**_snake_case				)
              __lowercase		:			Optional[Any]	    =       scheduler_class(**_snake_case				)

              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 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







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




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       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
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       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				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


