


"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 							A_      (       _a ):
  lowerCAmelCase__      			=	(DDIMParallelScheduler,)
  lowerCAmelCase__      			=	(('eta', 0.0), ('num_inference_steps', 5_0))


  def 							_lowercase					(			self:						List[str]							,**__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     {
         "num_train_timesteps": 1_000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**__lowerCAmelCase    )
     return config


  def 							_lowercase					(			self:						int							,**__lowerCAmelCase:						Optional[Any]    ):







     '''simple docstring'''







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

     _lowerCamelCase,					_lowerCamelCase   :				Optional[Any]							=     10, 0.0

     _lowerCamelCase   :				List[Any]							=     self.dummy_model()
     _lowerCamelCase   :				Optional[Any]							=     self.dummy_sample_deter

     scheduler.set_timesteps(__lowerCAmelCase    )

     for t in scheduler.timesteps:
        _lowerCamelCase   :				Optional[Any]							=     model(__lowerCAmelCase							,__lowerCAmelCase    )
        _lowerCamelCase   :				int							=     scheduler.step(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    ).prev_sample

     return sample


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     for timesteps in [100, 500, 1_000]:
        self.check_over_configs(num_train_timesteps=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







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

     _lowerCamelCase   :				Union[str, Any]							=     self.scheduler_classes[0]
     _lowerCamelCase   :				Dict							=     self.get_scheduler_config(steps_offset=1    )
     _lowerCamelCase   :				Union[str, Any]							=     scheduler_class(**__lowerCAmelCase    )
     scheduler.set_timesteps(5    )
     assert torch.equal(scheduler.timesteps							,torch.LongTensor([801, 601, 401, 201, 1]    )    )


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]							,[0.0_02, 0.02, 0.2, 2]    ):
        self.check_over_configs(beta_start=__lowerCAmelCase							,beta_end=__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=__lowerCAmelCase    )


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







     '''simple docstring'''







     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=__lowerCAmelCase    )


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







     '''simple docstring'''







     self.check_over_configs(thresholding=__lowerCAmelCase    )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=__lowerCAmelCase							,prediction_type=__lowerCAmelCase							,sample_max_value=__lowerCAmelCase							,)


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







     '''simple docstring'''







     for t in [1, 10, 49]:
        self.check_over_forward(time_step=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     for t, num_inference_steps in zip([1, 10, 50]							,[10, 50, 500]    ):
        self.check_over_forward(time_step=__lowerCAmelCase							,num_inference_steps=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     for t, eta in zip([1, 10, 49]							,[0.0, 0.5, 1.0]    ):
        self.check_over_forward(time_step=__lowerCAmelCase							,eta=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     self.scheduler_classes[0]
     _lowerCamelCase   :				str							=     self.get_scheduler_config()
     _lowerCamelCase   :				List[str]							=     scheduler_class(**__lowerCAmelCase    )

     assert torch.sum(torch.abs(scheduler._get_variance(0							,0    ) - 0.0    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(420							,400    ) - 0.1_47_71    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(980							,960    ) - 0.3_24_60    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0							,0    ) - 0.0    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487							,486    ) - 0.0_09_79    )    ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999							,998    ) - 0.02    )    ) < 1e-5


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.scheduler_classes[0]
     _lowerCamelCase   :				Union[str, Any]							=     self.get_scheduler_config()
     _lowerCamelCase   :				str							=     scheduler_class(**__lowerCAmelCase    )

     _lowerCamelCase,					_lowerCamelCase   :				Optional[int]							=     10, 0.0
     scheduler.set_timesteps(__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     self.dummy_model()
     _lowerCamelCase   :				Optional[int]							=     self.dummy_sample_deter
     _lowerCamelCase   :				List[str]							=     self.dummy_sample_deter + 0.1
     _lowerCamelCase   :				Dict							=     self.dummy_sample_deter - 0.1

     _lowerCamelCase   :				Union[str, Any]							=     samplea.shape[0]
     _lowerCamelCase   :				List[Any]							=     torch.stack([samplea, samplea, samplea]							,dim=0    )
     _lowerCamelCase   :				Dict							=     torch.arange(__lowerCAmelCase    )[0:3, None].repeat(1							,__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     model(samples.flatten(0							,1    )							,timesteps.flatten(0							,1    )    )
     _lowerCamelCase   :				List[str]							=     scheduler.batch_step_no_noise(__lowerCAmelCase							,timesteps.flatten(0							,1    )							,samples.flatten(0							,1    )							,__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     torch.sum(torch.abs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				List[Any]							=     torch.mean(torch.abs(__lowerCAmelCase    )    )

     assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
     assert abs(result_mean.item() - 0.49_82    ) < 1e-3


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     self.full_loop()

     _lowerCamelCase   :				Optional[Any]							=     torch.sum(torch.abs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				int							=     torch.mean(torch.abs(__lowerCAmelCase    )    )

     assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
     assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     self.full_loop(prediction_type="v_prediction"    )

     _lowerCamelCase   :				Optional[int]							=     torch.sum(torch.abs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				List[str]							=     torch.mean(torch.abs(__lowerCAmelCase    )    )

     assert abs(result_sum.item() - 52.53_02    ) < 1e-2
     assert abs(result_mean.item() - 0.06_84    ) < 1e-3


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     self.full_loop(set_alpha_to_one=__lowerCAmelCase							,beta_start=0.01    )
     _lowerCamelCase   :				List[str]							=     torch.sum(torch.abs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				Dict							=     torch.mean(torch.abs(__lowerCAmelCase    )    )

     assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
     assert abs(result_mean.item() - 0.19_51    ) < 1e-3






  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     self.full_loop(set_alpha_to_one=__lowerCAmelCase							,beta_start=0.01    )
     _lowerCamelCase   :				int							=     torch.sum(torch.abs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				Union[str, Any]							=     torch.mean(torch.abs(__lowerCAmelCase    )    )

     assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
     assert abs(result_mean.item() - 0.19_41    ) < 1e-3







"""simple docstring"""



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard




@slow
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

        UpperCAmelCase__						:    Tuple							=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							jnp.array(
            [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]							)
        print(f'output_slice: {output_slice}'							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


