



"""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     (			snake_case ,					unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   RePaintPipeline
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   False






	def 					lowerCamelCase__ (			self					):
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			_lowercase							:      Optional[Any]			=					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""")							,)
			_lowercase							:      str			=					RePaintScheduler()
			_lowercase							:      str			=					{"""unet""": unet, """scheduler""": scheduler}
			return components






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_=0					):
			if str(UpperCAmelCase_					).startswith("""mps"""					):
					_lowercase							:      Tuple			=					torch.manual_seed(UpperCAmelCase_					)
			else:
					_lowercase							:      Optional[Any]			=					torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			_lowercase							:      str			=					np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			_lowercase							:      Optional[Any]			=					torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_							,dtype=torch.floataa					)
			_lowercase							:      List[str]			=					(image > 0).to(device=UpperCAmelCase_							,dtype=torch.floataa					)
			_lowercase							:      Union[str, Any]			=					{
			    """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 					lowerCamelCase__ (			self					):
			_lowercase							:      int			=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase							:      Any			=					self.get_dummy_components()
			_lowercase							:      Dict			=					RePaintPipeline(**UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			_lowercase							:      str			=					self.get_dummy_inputs(UpperCAmelCase_					)
			_lowercase							:      int			=					sd_pipe(**UpperCAmelCase_					).images
			_lowercase							:      Tuple			=					image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			_lowercase							:      Dict			=					np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

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






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_save_load_local()






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






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_save_load_optional_components()






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			return super().test_attention_slicing_forward_pass()







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




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






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

			_lowercase							:      Dict			=					"""google/ddpm-ema-celebahq-256"""
			_lowercase							:      Tuple			=					UNetaDModel.from_pretrained(UpperCAmelCase_					)
			_lowercase							:      Optional[Any]			=					RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			_lowercase							:      Dict			=					RePaintPipeline(unet=UpperCAmelCase_							,scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			_lowercase							:      str			=					torch.manual_seed(0					)
			_lowercase							:      Any			=					repaint(
			    UpperCAmelCase_							,UpperCAmelCase_							,num_inference_steps=2_50							,eta=0.0							,jump_length=10							,jump_n_sample=10							,generator=UpperCAmelCase_							,output_type="""np"""							,)
			_lowercase							:      Any			=					output.images[0]

			assert image.shape == (2_56, 2_56, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




