

"""simple docstring"""






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


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



def 	lowercase_						(     _lowercase							:		Union[List, PIL.Image.Image, torch.Tensor]						):




					'''simple docstring'''






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

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

										UpperCAmelCase      : Any   =					[np.array(i.resize((w, h)				,   resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
										UpperCAmelCase      : Optional[int]   =					np.concatenate(UpperCamelCase__				,   axis=0						)
										UpperCAmelCase      : Any   =					np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
										UpperCAmelCase      : Dict   =					image.transpose(0				,   3				,   1				,   2						)
										UpperCAmelCase      : Any   =					2.0 * image - 1.0
										UpperCAmelCase      : Any   =					torch.from_numpy(UpperCamelCase__						)
					elif isinstance(image[0]				,   torch.Tensor						):
										UpperCAmelCase      : Tuple   =					torch.cat(UpperCamelCase__				,   dim=0						)
					return image



def 	lowercase_						(     _lowercase							:		Union[List, PIL.Image.Image, torch.Tensor]						):




					'''simple docstring'''






					if isinstance(UpperCamelCase__				,   torch.Tensor						):
										return mask
					elif isinstance(UpperCamelCase__				,   PIL.Image.Image						):
										UpperCAmelCase      : int   =					[mask]

					if isinstance(mask[0]				,   PIL.Image.Image						):
										UpperCAmelCase      : str   =					mask[0].size
										UpperCAmelCase      : Optional[int]   =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
										UpperCAmelCase      : str   =					[np.array(m.convert("L"						).resize((w, h)				,   resample=PIL_INTERPOLATION["nearest"]						)						)[None, :] for m in mask]
										UpperCAmelCase      : Dict   =					np.concatenate(UpperCamelCase__				,   axis=0						)
										UpperCAmelCase      : Union[str, Any]   =					mask.astype(np.floataa						) / 2_55.0
										UpperCAmelCase      : Optional[int]   =					0
										UpperCAmelCase      : Dict   =					1
										UpperCAmelCase      : List[str]   =					torch.from_numpy(UpperCamelCase__						)
					elif isinstance(mask[0]				,   torch.Tensor						):
										UpperCAmelCase      : Tuple   =					torch.cat(UpperCamelCase__				,   dim=0						)
					return mask







class    snake_case__	(			__lowercase  ):
			SCREAMING_SNAKE_CASE__  					=    42
			SCREAMING_SNAKE_CASE__  					=    42







			def __init__(      self      :			Any      ,	lowercase      :			Union[str, Any]      ,	lowercase      :			List[str]	):





								'''simple docstring'''



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







			@torch.no_grad()
			def __call__(      self      :			str      ,	lowercase      :			Tuple      ,	lowercase      :			Union[str, Any]      ,	lowercase      :			Optional[Any] = 2_50      ,	lowercase      :			Optional[int] = 0.0      ,	lowercase      :			Optional[Any] = 10      ,	lowercase      :			List[str] = 10      ,	lowercase      :			str = None      ,	lowercase      :			Tuple = "pil"      ,	lowercase      :			Dict = True      ,	):





								'''simple docstring'''



								UpperCAmelCase      : Union[str, Any]   =					image

								UpperCAmelCase      : List[str]   =					_preprocess_image(_lowerCAmelCase	)
								UpperCAmelCase      : Tuple   =					original_image.to(device=self.device      ,	dtype=self.unet.dtype	)
								UpperCAmelCase      : Dict   =					_preprocess_mask(_lowerCAmelCase	)
								UpperCAmelCase      : List[str]   =					mask_image.to(device=self.device      ,	dtype=self.unet.dtype	)

								UpperCAmelCase      : int   =					original_image.shape[0]

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

								UpperCAmelCase      : List[str]   =					original_image.shape
								UpperCAmelCase      : Optional[int]   =					randn_tensor(_lowerCAmelCase      ,	generator=_lowerCAmelCase      ,	device=self.device      ,	dtype=self.unet.dtype	)

								# set step values
								self.scheduler.set_timesteps(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	self.device	)
								UpperCAmelCase      : Optional[int]   =					eta

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

													else:
																		# compute the reverse: x_t-1 -> x_t
																		UpperCAmelCase      : str   =					self.scheduler.undo_step(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase	)
													UpperCAmelCase      : Any   =					t

								UpperCAmelCase      : int   =					(image / 2 + 0.5).clamp(0      ,	1	)
								UpperCAmelCase      : Optional[Any]   =					image.cpu().permute(0      ,	2      ,	3      ,	1	).numpy()
								if output_type == "pil":
													UpperCAmelCase      : Dict   =					self.numpy_to_pil(_lowerCAmelCase	)

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=_lowerCAmelCase	)









import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase							:       Dict					 =	"""\
    Text data.
    Second line of data."""

lowerCAmelCase							:       str					 =	"""file"""






@pytest.fixture(scope='''session'''					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   tmp_path_factory.mktemp('''data'''					) / (FILE_PATH + '''.zstd''')
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   bytes(UpperCamelCase__       ,			'''utf-8'''					)
						with zstd.open(UpperCamelCase__       ,			'''wb'''					) as f:
												f.write(UpperCamelCase__					)
						return path






@pytest.fixture
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    int:

						"""simple docstring"""





						with open(os.path.join(tmpfs.local_root_dir       ,			UpperCamelCase__					)       ,			'''w'''					) as f:
												f.write(UpperCamelCase__					)
						return FILE_PATH






@pytest.mark.parametrize('''compression_format'''       ,			['''gzip''', '''xz''', '''zstd''']					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   List[Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
						__SCREAMING_SNAKE_CASE:				List[str]           =   input_paths[compression_format]
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''cache'''
						__SCREAMING_SNAKE_CASE:				List[str]           =   DownloadConfig(cache_dir=UpperCamelCase__       ,			extract_compressed_file=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   f.read()
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Dict           =   f.read()
						assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''       ,			[True, False]					)
@pytest.mark.parametrize('''default_cache_dir'''       ,			[True, False]					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Optional[Any]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   '''custom_cache'''
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''custom_extracted_dir'''
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''custom_extracted_path'''
						if default_extracted:
												__SCREAMING_SNAKE_CASE:				int           =   ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
						else:
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''       ,			UpperCamelCase__					)
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''       ,			str(UpperCamelCase__					)					)
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

						__SCREAMING_SNAKE_CASE:				Dict           =   xz_file
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   (
						    DownloadConfig(extract_compressed_file=UpperCamelCase__					)
						    if default_cache_dir
						    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir       ,			extract_compressed_file=UpperCamelCase__					)
						)
						__SCREAMING_SNAKE_CASE:				int           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						assert Path(UpperCamelCase__					).parent.parts[-2:] == expected






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   str(Path(UpperCamelCase__					).resolve()					)
						assert cached_path(UpperCamelCase__					) == text_file
						# relative path
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   str(Path(UpperCamelCase__					).resolve().relative_to(Path(os.getcwd()					)					)					)
						assert cached_path(UpperCamelCase__					) == text_file






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   str(tmp_path.resolve() / '''__missing_file__.txt'''					)
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)
						# relative path
						__SCREAMING_SNAKE_CASE:				int           =   '''./__missing_file__.txt'''
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   get_from_cache(F"""tmp://{tmpfs_file}"""					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Tuple           =   f.read()
						assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





						with pytest.raises(UpperCamelCase__					):
												cached_path('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												http_get('''https://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												http_head('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    List[str]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												ftp_get('''ftp://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												ftp_head('''ftp://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												fsspec_get('''s3://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												fsspec_head('''s3://huggingface.co'''					)



