


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A	:		Union[str, Any]    =       logging.get_logger(__name__)  # pylint: disable=invalid-name






def 			UpperCamelCase (     __magic_name__					:							Union[List, PIL.Image.Image, torch.Tensor]					)       ->  List[str]:
		"""simple docstring"""




		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""   ,   __magic_name__   ,   )
		if isinstance(__magic_name__   ,   torch.Tensor					):
				return image
		elif isinstance(__magic_name__   ,   PIL.Image.Image					):
				lowercase__					      =		[image]

		if isinstance(image[0]   ,   PIL.Image.Image					):
				lowercase__      ,     lowercase__					      =		image[0].size
				lowercase__      ,     lowercase__					      =		(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				lowercase__					      =		[np.array(i.resize((w, h)   ,   resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
				lowercase__					      =		np.concatenate(__magic_name__   ,   axis=0					)
				lowercase__					      =		np.array(__magic_name__					).astype(np.floataa					) / 255.0
				lowercase__					      =		image.transpose(0   ,   3   ,   1   ,   2					)
				lowercase__					      =		2.0 * image - 1.0
				lowercase__					      =		torch.from_numpy(__magic_name__					)
		elif isinstance(image[0]   ,   torch.Tensor					):
				lowercase__					      =		torch.cat(__magic_name__   ,   dim=0					)
		return image






def 			UpperCamelCase (     __magic_name__					:							Union[List, PIL.Image.Image, torch.Tensor]					)       ->  Union[str, Any]:
		"""simple docstring"""




		if isinstance(__magic_name__   ,   torch.Tensor					):
				return mask
		elif isinstance(__magic_name__   ,   PIL.Image.Image					):
				lowercase__					      =		[mask]

		if isinstance(mask[0]   ,   PIL.Image.Image					):
				lowercase__      ,     lowercase__					      =		mask[0].size
				lowercase__      ,     lowercase__					      =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowercase__					      =		[np.array(m.convert("""L"""					).resize((w, h)   ,   resample=PIL_INTERPOLATION["""nearest"""]					)					)[None, :] for m in mask]
				lowercase__					      =		np.concatenate(__magic_name__   ,   axis=0					)
				lowercase__					      =		mask.astype(np.floataa					) / 255.0
				lowercase__					      =		0
				lowercase__					      =		1
				lowercase__					      =		torch.from_numpy(__magic_name__					)
		elif isinstance(mask[0]   ,   torch.Tensor					):
				lowercase__					      =		torch.cat(__magic_name__   ,   dim=0					)
		return mask







class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Tuple				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						List[Any]		)						->       Tuple:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Union[str, Any]				, _UpperCAmelCase   :						Union[torch.Tensor, PIL.Image.Image]				, _UpperCAmelCase   :						Union[torch.Tensor, PIL.Image.Image]				, _UpperCAmelCase   :						int = 250				, _UpperCAmelCase   :						float = 0.0				, _UpperCAmelCase   :						int = 10				, _UpperCAmelCase   :						int = 10				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		image

							lowercase__					      =		_preprocess_image(_UpperCAmelCase		)
							lowercase__					      =		original_image.to(device=self.device				, dtype=self.unet.dtype		)
							lowercase__					      =		_preprocess_mask(_UpperCAmelCase		)
							lowercase__					      =		mask_image.to(device=self.device				, dtype=self.unet.dtype		)

							lowercase__					      =		original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(_UpperCAmelCase				, _UpperCAmelCase		) and len(_UpperCAmelCase		) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(_UpperCAmelCase		)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

							lowercase__					      =		original_image.shape
							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase				, device=self.device				, dtype=self.unet.dtype		)

							# set step values
							self.scheduler.set_timesteps(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, self.device		)
							lowercase__					      =		eta

							lowercase__					      =		self.scheduler.timesteps[0] + 1
							lowercase__					      =		generator[0] if isinstance(_UpperCAmelCase				, _UpperCAmelCase		) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									if t < t_last:
											# predict the noise residual
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											# compute previous image: x_t -> x_t-1
											lowercase__					      =		self.scheduler.step(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		).prev_sample

									else:
											# compute the reverse: x_t-1 -> x_t
											lowercase__					      =		self.scheduler.undo_step(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)
									lowercase__					      =		t

							lowercase__					      =		(image / 2 + 0.5).clamp(0				, 1		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)









import pytest


A	:		Optional[Any]    =       '__dummy_dataset1__'

A	:		Tuple    =       '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'






@pytest.fixture
def 			UpperCamelCase (     )       ->  Any:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




		return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		dataset_loading_script_name
		lowercase__					      =		tmp_path / """datasets""" / script_name
		script_dir.mkdir(parents=__magic_name__					)
		lowercase__					      =		script_dir / f'''{script_name}.py'''
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)

