





'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 							__UpperCAmelCase  (       A__		,					unittest.TestCase						):







		'''simple docstring'''







		__lowerCAmelCase 				=    RePaintPipeline
		__lowerCAmelCase 				=    IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
		__lowerCAmelCase 				=    PipelineTesterMixin.required_optional_params - {
		    '''latents''',
		    '''num_images_per_prompt''',
		    '''callback''',
		    '''callback_steps''',
		}
		__lowerCAmelCase 				=    IMAGE_INPAINTING_BATCH_PARAMS
		__lowerCAmelCase 				=    False







		def        A	(self						:      Union[str, Any]      ):
			torch.manual_seed(0      )
			torch.manual_seed(0      )
			A							  =      UNetaDModel(
			    block_out_channels=(32, 64)      ,      layers_per_block=2      ,      sample_size=32      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,      )
			A							  =      RePaintScheduler()
			A							  =      {"""unet""": unet, """scheduler""": scheduler}
			return components







		def        A	(self						:      Optional[Any]      ,      _lowerCAmelCase						:      Optional[Any]      ,      _lowerCAmelCase						:      Any=0      ):
			if str(_lowerCAmelCase      ).startswith("""mps"""      ):
				A							  =      torch.manual_seed(_lowerCAmelCase      )
			else:
				A							  =      torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
			A							  =      np.random.RandomState(_lowerCAmelCase      ).standard_normal((1, 3, 32, 32)      )
			A							  =      torch.from_numpy(_lowerCAmelCase      ).to(device=_lowerCAmelCase      ,      dtype=torch.floataa      )
			A							  =      (image > 0).to(device=_lowerCAmelCase      ,      dtype=torch.floataa      )
			A							  =      {
			    """image""": image,
			    """mask_image""": mask,
			    """generator""": generator,
			    """num_inference_steps""": 5,
			    """eta""": 0.0,
			    """jump_length""": 2,
			    """jump_n_sample""": 2,
			    """output_type""": """numpy""",
			}
			return inputs







		def        A	(self						:      List[Any]      ):
			A							  =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
			A							  =      self.get_dummy_components()
			A							  =      RePaintPipeline(**_lowerCAmelCase      )
			A							  =      sd_pipe.to(_lowerCAmelCase      )
			sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

			A							  =      self.get_dummy_inputs(_lowerCAmelCase      )
			A							  =      sd_pipe(**_lowerCAmelCase      ).images
			A							  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			A							  =      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







		@skip_mps
		def        A	(self						:      List[Any]      ):
			return super().test_save_load_local()







		@unittest.skip("""non-deterministic pipeline"""      )
		def        A	(self						:      Any      ):
			return super().test_inference_batch_single_identical()







		@skip_mps
		def        A	(self						:      Optional[Any]      ):
			return super().test_dict_tuple_outputs_equivalent()







		@skip_mps
		def        A	(self						:      Optional[int]      ):
			return super().test_save_load_optional_components()







		@skip_mps
		def        A	(self						:      Optional[Any]      ):
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 							__UpperCAmelCase  (       unittest.TestCase						):







		'''simple docstring'''







		def        A	(self						:      Optional[Any]      ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        A	(self						:      Tuple      ):
			A							  =      load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256.png"""      )
			A							  =      load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""      )
			A							  =      load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256_result.npy"""      )

			A							  =      """google/ddpm-ema-celebahq-256"""
			A							  =      UNetaDModel.from_pretrained(_lowerCAmelCase      )
			A							  =      RePaintScheduler.from_pretrained(_lowerCAmelCase      )

			A							  =      RePaintPipeline(unet=_lowerCAmelCase      ,      scheduler=_lowerCAmelCase      ).to(_lowerCAmelCase      )
			repaint.set_progress_bar_config(disable=_lowerCAmelCase      )
			repaint.enable_attention_slicing()

			A							  =      torch.manual_seed(0      )
			A							  =      repaint(
			    _lowerCAmelCase      ,      _lowerCAmelCase      ,      num_inference_steps=250      ,      eta=0.0      ,      jump_length=10      ,      jump_n_sample=10      ,      generator=_lowerCAmelCase      ,      output_type="""np"""      ,      )
			A							  =      output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image      ).mean() < 1e-2








'''simple docstring'''


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
		__lowerCAmelCase 				=    10







		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
			A							  =      {
			    """num_train_timesteps""": 1100,
			    """beta_start""": 0.0_001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			}

			config.update(**_lowerCAmelCase      )
			return config







		def        A     (self						:      Dict      ):
			for timesteps in [10, 50, 100, 1000]:
				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )







		def        A     (self						:      Optional[int]      ):
			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
				self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )







		def        A     (self						:      Tuple      ):
			for schedule in ["linear", "scaled_linear"]:
				self.check_over_configs(beta_schedule=_lowerCAmelCase      )







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
			assert abs(result_mean.item() - 18.0_331      ) < 5e-3







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







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			A							  =      torch.manual_seed(0      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 328.9_970      ) < 1e-2
			assert abs(result_mean.item() - 0.4_284      ) < 1e-3







		def        A     (self						:      Optional[int]      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
			assert abs(result_mean.item() - 18.0_331      ) < 1e-3

