
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]=None							)	->     Tuple:
							if subparsers is not None:
														lowerCamelCase_					=       subparsers.add_parser("""test"""							)
							else:
														lowerCamelCase_					=       argparse.ArgumentParser("""Accelerate test command"""							)

							parser.add_argument(
							    """--config_file"""      ,					default=UpperCAmelCase__      ,					help=(
							        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
							        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
							        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
							        """with 'huggingface'."""
							    )      ,					)

							if subparsers is not None:
														parser.set_defaults(func=UpperCAmelCase__							)
							return parser



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int							)	->     str:
							lowerCamelCase_					=       os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]							)

							if args.config_file is None:
														lowerCamelCase_					=       script_name
							else:
														lowerCamelCase_					=       F'''--config_file={args.config_file} {script_name}'''

							lowerCamelCase_					=       ["""accelerate-launch"""] + test_args.split()
							lowerCamelCase_					=       execute_subprocess_async(UpperCAmelCase__      ,					env=os.environ.copy()							)
							if result.returncode == 0:
														print("""Test is a success! You are ready for your distributed training!"""							)



def   __lowerCAmelCase    (				)	->     Tuple:
							lowerCamelCase_					=       test_command_parser()
							lowerCamelCase_					=       parser.parse_args()
							test_command(UpperCAmelCase__							)


if __name__ == "__main__":
		main()







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


lowercase   =	logging.get_logger(__name__)  # pylint: disable=invalid-name



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							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							):
														lowerCamelCase_					=       [image]

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

														lowerCamelCase_					=       [np.array(i.resize((w, h)      ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       np.array(UpperCAmelCase__							).astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       image.transpose(0      ,					3      ,					1      ,					2							)
														lowerCamelCase_					=       2.0 * image - 1.0
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(image[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



					@torch.no_grad()
					def __call__(						self       :  List[str] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  int = 2_5_0 ,       __UpperCamelCase       :  float = 0.0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       __UpperCamelCase       :  Optional[str] = "pil" ,       __UpperCamelCase       :  bool = True ,       ):
												lowerCamelCase_					=       image

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       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.''' )

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

												lowerCamelCase_					=       self.scheduler.timesteps[0] + 1
												lowerCamelCase_					=       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
																										lowerCamelCase_					=       self.unet(__UpperCamelCase ,       __UpperCamelCase ).sample
																										# compute previous image: x_t -> x_t-1
																										lowerCamelCase_					=       self.scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ).prev_sample

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

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

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





