code: 
  
  
  
  
  """simple docstring"""
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  __A 		=			get_logger(__name__)
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Tuple   ):
  		return TestCommand(
  		    args.dataset       ,     args.name       ,     args.cache_dir       ,     args.data_dir       ,     args.all_configs       ,     args.save_info or args.save_infos       ,     args.ignore_verifications       ,     args.force_redownload       ,     args.clear_cache       ,     )
  class 			lowerCamelCase__    (  a_			):
  			a__ :      Union[str, Any]								=     False  # to tell pytest it's not a test class
  
  
  			@staticmethod
  			def 							lowerCamelCase_				(     SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				parser.add_parser("test"				, help="Test dataset implementation."							)
  					test_parser.add_argument("--name"				, type=_snake_case				, default=_snake_case				, help="Dataset processing name"							)
  					test_parser.add_argument(
  					    "--cache_dir"				, type=_snake_case				, default=_snake_case				, help="Cache directory where the datasets are stored."				, )
  					test_parser.add_argument(
  					    "--data_dir"				, type=_snake_case				, default=_snake_case				, help="Can be used to specify a manual directory to get the files from."				, )
  					test_parser.add_argument("--all_configs"				, action="store_true"				, help="Test all dataset configurations"							)
  					test_parser.add_argument(
  					    "--save_info"				, action="store_true"				, help="Save the dataset infos in the dataset card (README.md)"							)
  					test_parser.add_argument(
  					    "--ignore_verifications"				, action="store_true"				, help="Run the test without checksums and splits checks."				, )
  					test_parser.add_argument("--force_redownload"				, action="store_true"				, help="Force dataset redownload"							)
  					test_parser.add_argument(
  					    "--clear_cache"				, action="store_true"				, help="Remove downloaded files and cached datasets after each config test"				, )
  					# aliases
  					test_parser.add_argument("--save_infos"				, action="store_true"				, help="alias to save_info"							)
  					test_parser.add_argument("dataset"				, type=_snake_case				, help="Name of the dataset to download"							)
  					test_parser.set_defaults(func=_test_command_factory							)
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				dataset
  					snake_case		:						List[Any]								=				name
  					snake_case		:						Optional[Any]								=				cache_dir
  					snake_case		:						str								=				data_dir
  					snake_case		:						int								=				all_configs
  					snake_case		:						int								=				save_infos
  					snake_case		:						str								=				ignore_verifications
  					snake_case		:						str								=				force_redownload
  					snake_case		:						Union[str, Any]								=				clear_cache
  					if clear_cache and not cache_dir:
  							print(
  							    "When --clear_cache is used, specifying a cache directory is mandatory.\n"
  							    "The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
  							    "Please provide a --cache_dir that will be used to test the dataset script."							)
  							exit(1							)
  					if save_infos:
  							snake_case		:						Optional[Any]								=				True
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					fl_logger().setLevel(_snake_case							)
  					if self._name is not None and self._all_configs:
  							print("Both parameters `config` and `all_configs` can\'t be used at once."							)
  							exit(1							)
  					snake_case		:						List[Any]								=				self._dataset, self._name
  					snake_case		:						Tuple								=				dataset_module_factory(_snake_case							)
  					snake_case		:						Optional[int]								=				import_main_class(module.module_path							)
  					snake_case		:						int								=				len(builder_cls.BUILDER_CONFIGS							) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
  					def get_builders() -> Generator[DatasetBuilder, None, None]:
  							if self._all_configs and builder_cls.BUILDER_CONFIGS:
  									for i, config in enumerate(builder_cls.BUILDER_CONFIGS							):
  											if "config_name" in module.builder_kwargs:
  													yield builder_cls(
  													    cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )
  											else:
  													yield builder_cls(
  													    config_name=config.name				, cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )
  							else:
  									if "config_name" in module.builder_kwargs:
  											yield builder_cls(cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs							)
  									else:
  											yield builder_cls(
  											    config_name=_snake_case				, cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )
  
  					for j, builder in enumerate(get_builders()							):
  							print(F'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''							)
  							snake_case		:						Any								=				os.path.exists(
  							    os.path.join(builder.get_imported_module_dir()				, datasets.config.DATASETDICT_INFOS_FILENAME							)							)  # record checksums only if we need to update a (deprecated) dataset_infos.json
  							builder.download_and_prepare(
  							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  							    if not self._force_redownload
  							    else DownloadMode.FORCE_REDOWNLOAD				, verification_mode=VerificationMode.NO_CHECKS
  							    if self._ignore_verifications
  							    else VerificationMode.ALL_CHECKS				, try_from_hf_gcs=_snake_case				, )
  							builder.as_dataset()
  							if self._save_infos:
  									builder._save_infos()
  
  							# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
  							# The dataset_infos are saved in the YAML part of the README.md
  
  							# Let's move it to the original directory of the dataset script, to allow the user to
  							# upload them on S3 at the same time afterwards.
  							if self._save_infos:
  									snake_case		:						Union[str, Any]								=				os.path.join(builder_cls.get_imported_module_dir()				, "README.md"							)
  									snake_case		:						Tuple								=				Path(_snake_case							).name + '''.py'''
  									snake_case		:						Any								=				os.path.join(_snake_case				, _snake_case							)
  									if os.path.isfile(_snake_case							):
  											snake_case		:						List[Any]								=				os.path.dirname(_snake_case							)
  									elif os.path.isfile(_snake_case							):
  											snake_case		:						Tuple								=				path
  									elif os.path.isdir(_snake_case							):  # for local directories containing only data files
  											snake_case		:						Dict								=				path
  									else:  # in case of a remote dataset
  											snake_case		:						List[str]								=				None
  											print(F'''Dataset card saved at {dataset_readme_path}'''							)
  
  									# Move dataset_info back to the user
  									if dataset_dir is not None:
  											snake_case		:						Union[str, Any]								=				os.path.join(_snake_case				, "README.md"							)
  											copyfile(_snake_case				, _snake_case							)
  											print(F'''Dataset card saved at {user_dataset_readme_path}'''							)
  
              # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
  							if self._clear_cache:
  									if os.path.isdir(builder._cache_dir							):
  											logger.warning(F'''Clearing cache at {builder._cache_dir}'''							)
  											rmtree(builder._cache_dir							)
  									snake_case		:						Optional[Any]								=				os.path.join(self._cache_dir				, datasets.config.DOWNLOADED_DATASETS_DIR							)
  									if os.path.isdir(_snake_case							):
  											logger.warning(F'''Clearing cache at {download_dir}'''							)
  											rmtree(_snake_case							)
  
  					print("Test successful."							)
  
style_context: 
  
  
  
  
  """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 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
  			a__ :      Optional[Any]								=     RePaintPipeline
  			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
  			    """latents""",
  			    """num_images_per_prompt""",
  			    """callback""",
  			    """callback_steps""",
  			}
  			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[str]								=     False
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					torch.manual_seed(0							)
  					snake_case		:						Union[str, 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")				, )
  					snake_case		:						Union[str, Any]								=				RePaintScheduler()
  					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
  					return components
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
  					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						int								=				{
  					    "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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
  					snake_case		:						Any								=				self.get_dummy_components()
  					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
  					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
  					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 32, 32, 3)
  					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)
  
  					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_local()
  
  
  			@unittest.skip("non-deterministic pipeline"							)
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_inference_batch_single_identical()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_dict_tuple_outputs_equivalent()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_optional_components()
  
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256.png"							)
  					snake_case		:						Any								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
  					snake_case		:						List[Any]								=				load_numpy(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256_result.npy"							)
  
  					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
  					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				RePaintPipeline(unet=SCREAMING_SNAKE_CASE				, scheduler=SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
  					repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  					repaint.enable_attention_slicing()
  
  					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
  					snake_case		:						int								=				repaint(
  					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
  					snake_case		:						List[Any]								=				output.images[0]
  
  					assert image.shape == (256, 256, 3)
  					assert np.abs(expected_image - image							).mean() < 1E-2
  
  