


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class   SCREAMING_SNAKE_CASE_			(_UpperCamelCase						):




       '''simple docstring'''



       _a   							=							(DEISMultistepScheduler,)
       _a   							=							(("num_inference_steps", 25),)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				**__a				:				Optional[int]							)					->str:
          lowerCamelCase_						:   str		= {
              """num_train_timesteps""": 1_000,
              """beta_start""": 0.0_001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
          }

          config.update(**UpperCamelCase__							)
          return config



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				Union[str, Any]=0		,				**__a				:				Dict							)					->List[Any]:
          lowerCamelCase_						:   Any		= dict(self.forward_default_kwargs							)
          lowerCamelCase_						:   Union[str, Any]		= kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__							)
          lowerCamelCase_						:   List[Any]		= self.dummy_sample
          lowerCamelCase_						:   Dict		= 0.1 * sample
          lowerCamelCase_						:   int		= [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

             lowerCamelCase_,	lowerCamelCase_						:   List[str]		= sample, sample
             for t in range(UpperCamelCase__		,				time_step + scheduler.config.solver_order + 1							):
                lowerCamelCase_						:   Dict		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample
                lowerCamelCase_						:   Union[str, Any]		= new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample

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



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[Any]:
          pass



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				Optional[int]=0		,				**__a				:				int							)					->Tuple:
          lowerCamelCase_						:   int		= dict(self.forward_default_kwargs							)
          lowerCamelCase_						:   List[str]		= kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__							)
          lowerCamelCase_						:   List[str]		= self.dummy_sample
          lowerCamelCase_						:   int		= 0.1 * sample
          lowerCamelCase_						:   List[str]		= [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(UpperCamelCase__							)
                lowerCamelCase_						:   List[str]		= scheduler_class.from_pretrained(UpperCamelCase__							)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(UpperCamelCase__							)

                # copy over dummy past residual (must be after setting timesteps)
                lowerCamelCase_						:   int		= dummy_past_residuals[: new_scheduler.config.solver_order]

             lowerCamelCase_						:   Optional[Any]		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample
             lowerCamelCase_						:   Dict		= new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample

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



       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				Optional[int]=None		,				**__a				:				Union[str, Any]							)					->Tuple:
          if scheduler is None:
             lowerCamelCase_						:   Union[str, Any]		= self.scheduler_classes[0]
             lowerCamelCase_						:   Dict		= self.get_scheduler_config(**UpperCamelCase__							)
             lowerCamelCase_						:   List[str]		= scheduler_class(**UpperCamelCase__							)

          lowerCamelCase_						:   List[Any]		= self.scheduler_classes[0]
          lowerCamelCase_						:   str		= self.get_scheduler_config(**UpperCamelCase__							)
          lowerCamelCase_						:   List[Any]		= scheduler_class(**UpperCamelCase__							)

          lowerCamelCase_						:   int		= 10
          lowerCamelCase_						:   Dict		= self.dummy_model()
          lowerCamelCase_						:   List[str]		= self.dummy_sample_deter
          scheduler.set_timesteps(UpperCamelCase__							)

          for i, t in enumerate(scheduler.timesteps							):
             lowerCamelCase_						:   List[Any]		= model(UpperCamelCase__		,				UpperCamelCase__							)
             lowerCamelCase_						:   List[Any]		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__							).prev_sample

          return sample



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Dict:
          lowerCamelCase_						:   Optional[int]		= dict(self.forward_default_kwargs							)

          lowerCamelCase_						:   Optional[Any]		= kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__							)

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase_						:   int		= self.get_scheduler_config()
             lowerCamelCase_						:   Union[str, Any]		= scheduler_class(**UpperCamelCase__							)

             lowerCamelCase_						:   List[Any]		= self.dummy_sample
             lowerCamelCase_						:   Optional[int]		= 0.1 * sample

             if num_inference_steps is not None and hasattr(UpperCamelCase__		,				"""set_timesteps"""							):
                scheduler.set_timesteps(UpperCamelCase__							)
             elif num_inference_steps is not None and not hasattr(UpperCamelCase__		,				"""set_timesteps"""							):
                lowerCamelCase_						:   int		= num_inference_steps

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

             lowerCamelCase_						:   str		= scheduler.timesteps[5]
             lowerCamelCase_						:   Dict		= scheduler.timesteps[6]

             lowerCamelCase_						:   Any		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample
             lowerCamelCase_						:   str		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__							).prev_sample

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



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[str]:
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          lowerCamelCase_						:   Optional[Any]		= DEISMultistepScheduler(**self.get_scheduler_config()							)
          lowerCamelCase_						:   int		= self.full_loop(scheduler=UpperCamelCase__							)
          lowerCamelCase_						:   Any		= torch.mean(torch.abs(UpperCamelCase__							)							)

          assert abs(result_mean.item() - 0.23_916							) < 1e-3

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

          lowerCamelCase_						:   Tuple		= self.full_loop(scheduler=UpperCamelCase__							)
          lowerCamelCase_						:   Dict		= torch.mean(torch.abs(UpperCamelCase__							)							)

          assert abs(result_mean.item() - 0.23_916							) < 1e-3



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->List[Any]:
          for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Dict:
          self.check_over_configs(thresholding=UpperCamelCase__							)
          for order in [1, 2, 3]:
             for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=UpperCamelCase__		,				prediction_type=UpperCamelCase__		,				sample_max_value=UpperCamelCase__		,				algorithm_type="""deis"""		,				solver_order=UpperCamelCase__		,				solver_type=UpperCamelCase__		,				)



       def 				_lowerCAmelCase							(						self				:				Dict							)					->int:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Dict:
          for algorithm_type in ["deis"]:
             for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          solver_order=UpperCamelCase__		,				solver_type=UpperCamelCase__		,				prediction_type=UpperCamelCase__		,				algorithm_type=UpperCamelCase__		,				)
                      lowerCamelCase_						:   Any		= self.full_loop(
                          solver_order=UpperCamelCase__		,				solver_type=UpperCamelCase__		,				prediction_type=UpperCamelCase__		,				algorithm_type=UpperCamelCase__		,				)
                      assert not torch.isnan(UpperCamelCase__							).any(), "Samples have nan numbers"



       def 				_lowerCAmelCase							(						self				:				Dict							)					->Optional[Any]:
          self.check_over_configs(lower_order_final=UpperCamelCase__							)
          self.check_over_configs(lower_order_final=UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				int							)					->Dict:
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=UpperCamelCase__		,				time_step=0							)



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[Any]:
          lowerCamelCase_						:   Any		= self.full_loop()
          lowerCamelCase_						:   Optional[int]		= torch.mean(torch.abs(UpperCamelCase__							)							)

          assert abs(result_mean.item() - 0.23_916							) < 1e-3



       def 				_lowerCAmelCase							(						self				:				Any							)					->List[Any]:
          lowerCamelCase_						:   Any		= self.full_loop(prediction_type="""v_prediction"""							)
          lowerCamelCase_						:   Optional[int]		= torch.mean(torch.abs(UpperCamelCase__							)							)

          assert abs(result_mean.item() - 0.091							) < 1e-3




       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          lowerCamelCase_						:   Tuple		= self.scheduler_classes[0]
          lowerCamelCase_						:   Any		= self.get_scheduler_config(thresholding=UpperCamelCase__		,				dynamic_thresholding_ratio=0							)
          lowerCamelCase_						:   List[Any]		= scheduler_class(**UpperCamelCase__							)

          lowerCamelCase_						:   int		= 10
          lowerCamelCase_						:   List[Any]		= self.dummy_model()
          lowerCamelCase_						:   Any		= self.dummy_sample_deter.half()
          scheduler.set_timesteps(UpperCamelCase__							)

          for i, t in enumerate(scheduler.timesteps							):
             lowerCamelCase_						:   Dict		= model(UpperCamelCase__		,				UpperCamelCase__							)
             lowerCamelCase_						:   Tuple		= scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__							).prev_sample

          assert sample.dtype == torch.floataa






'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCAmelCase    :     List[Any]         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   pass







@slow
@require_torch_gpu
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
        pipe.to(UpperCamelCase__     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'     )
        _A    		=  torch.manual_seed(0     )
        _A    		=  pipe(
            image=UpperCamelCase__, generator=UpperCamelCase__, guidance_scale=7.5, num_inference_steps=50, output_type='numpy', ).images

        _A    		=  image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2



