


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class   __a			(    lowerCAmelCase_      ):
 __lowercase :							Tuple        = (UniPCMultistepScheduler,)
 __lowercase :							List[Any]        = (('num_inference_steps', 25),)
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   {
            'num_train_timesteps': 1_000,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'solver_type': 'bh2',
        }

        config.update(**__snake_case )
        return config
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0				, **lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   str  			=   dict(self.forward_default_kwargs )
        lowercase__:   str  			=   kwargs.pop('num_inference_steps'				, __snake_case )
        lowercase__:   str  			=   self.dummy_sample
        lowercase__:   List[Any]  			=   0.1 * sample
        lowercase__:   int  			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
               lowercase__:   Optional[Any]  			=   self.get_scheduler_config(**__snake_case )
               lowercase__:   List[str]  			=   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__:   str  			=   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__			,						lowercase__:   List[str]  			=   sample, sample
               for t in range(__snake_case				, time_step + scheduler.config.solver_order + 1 ):
                      lowercase__:   Dict  			=   scheduler.step(__snake_case				, __snake_case				, __snake_case				, **__snake_case ).prev_sample
                      lowercase__:   List[str]  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0				, **lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   List[str]  			=   dict(self.forward_default_kwargs )
        lowercase__:   Tuple  			=   kwargs.pop('num_inference_steps'				, __snake_case )
        lowercase__:   int  			=   self.dummy_sample
        lowercase__:   List[Any]  			=   0.1 * sample
        lowercase__:   List[str]  			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
               lowercase__:   List[Any]  			=   self.get_scheduler_config()
               lowercase__:   List[str]  			=   scheduler_class(**__snake_case )
               scheduler.set_timesteps(__snake_case )

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__snake_case )
                      lowercase__:   Union[str, Any]  			=   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__:   List[Any]  			=   dummy_past_residuals[: new_scheduler.config.solver_order]

               lowercase__:   Tuple  			=   scheduler.step(__snake_case				, __snake_case				, __snake_case				, **__snake_case ).prev_sample
               lowercase__:   Dict  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=None				, **lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        if scheduler is None:
               lowercase__:   Union[str, Any]  			=   self.scheduler_classes[0]
               lowercase__:   Dict  			=   self.get_scheduler_config(**__snake_case )
               lowercase__:   Dict  			=   scheduler_class(**__snake_case )

        lowercase__:   List[Any]  			=   self.scheduler_classes[0]
        lowercase__:   Optional[Any]  			=   self.get_scheduler_config(**__snake_case )
        lowercase__:   Dict  			=   scheduler_class(**__snake_case )

        lowercase__:   List[Any]  			=   10
        lowercase__:   Tuple  			=   self.dummy_model()
        lowercase__:   List[Any]  			=   self.dummy_sample_deter
        scheduler.set_timesteps(__snake_case )

        for i, t in enumerate(scheduler.timesteps ):
               lowercase__:   int  			=   model(__snake_case				, __snake_case )
               lowercase__:   Optional[int]  			=   scheduler.step(__snake_case				, __snake_case				, __snake_case ).prev_sample

        return sample
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   dict(self.forward_default_kwargs )

        lowercase__:   Union[str, Any]  			=   kwargs.pop('num_inference_steps'				, __snake_case )

        for scheduler_class in self.scheduler_classes:
               lowercase__:   Optional[Any]  			=   self.get_scheduler_config()
               lowercase__:   Any  			=   scheduler_class(**__snake_case )

               lowercase__:   Optional[Any]  			=   self.dummy_sample
               lowercase__:   int  			=   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[Any]  			=   num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               lowercase__:   Optional[Any]  			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               lowercase__:   Optional[int]  			=   dummy_past_residuals[: scheduler.config.solver_order]

               lowercase__:   Any  			=   scheduler.timesteps[5]
               lowercase__:   Optional[Any]  			=   scheduler.timesteps[6]

               lowercase__:   int  			=   scheduler.step(__snake_case				, __snake_case				, __snake_case				, **__snake_case ).prev_sample
               lowercase__:   List[str]  			=   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 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   UniPCMultistepScheduler(**self.get_scheduler_config() )
        lowercase__:   Optional[int]  			=   self.full_loop(scheduler=__snake_case )
        lowercase__:   Optional[Any]  			=   torch.mean(torch.abs(__snake_case ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3

        lowercase__:   List[str]  			=   DPMSolverSinglestepScheduler.from_config(scheduler.config )
        lowercase__:   Union[str, Any]  			=   DEISMultistepScheduler.from_config(scheduler.config )
        lowercase__:   Optional[int]  			=   DPMSolverMultistepScheduler.from_config(scheduler.config )
        lowercase__:   int  			=   UniPCMultistepScheduler.from_config(scheduler.config )

        lowercase__:   Tuple  			=   self.full_loop(scheduler=__snake_case )
        lowercase__:   Dict  			=   torch.mean(torch.abs(__snake_case ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=__snake_case )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__snake_case )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        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__:   Tuple  			=   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 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        self.check_over_configs(lower_order_final=__snake_case )
        self.check_over_configs(lower_order_final=__snake_case )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=__snake_case				, time_step=0 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.full_loop()
        lowercase__:   str  			=   torch.mean(torch.abs(__snake_case ) )

        assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Tuple  			=   self.full_loop(prediction_type='v_prediction' )
        lowercase__:   Dict  			=   torch.mean(torch.abs(__snake_case ) )

        assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   self.scheduler_classes[0]
        lowercase__:   List[Any]  			=   self.get_scheduler_config(thresholding=__snake_case				, dynamic_thresholding_ratio=0 )
        lowercase__:   Union[str, Any]  			=   scheduler_class(**__snake_case )

        lowercase__:   Dict  			=   10
        lowercase__:   Any  			=   self.dummy_model()
        lowercase__:   Tuple  			=   self.dummy_sample_deter.half()
        scheduler.set_timesteps(__snake_case )

        for i, t in enumerate(scheduler.timesteps ):
               lowercase__:   str  			=   model(__snake_case				, __snake_case )
               lowercase__:   Any  			=   scheduler.step(__snake_case				, __snake_case				, __snake_case ).prev_sample

        assert sample.dtype == torch.floataa
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        for scheduler_class in self.scheduler_classes:
               lowercase__:   List[str]  			=   self.get_scheduler_config(**__snake_case )
               lowercase__:   Union[str, 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
import torch

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


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__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=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

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






