



"""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


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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		warnings.warn(
		    "The preprocess method is deprecated and will be removed in a future version. Please"
		    " use VaeImageProcessor.preprocess instead", _lowerCamelCase, )
		if isinstance(_lowerCamelCase, torch.Tensor	):
				return image
		elif isinstance(_lowerCamelCase, PIL.Image.Image	):
				a_     :      int 						=     [image]

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

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:





		if isinstance(_lowerCamelCase, torch.Tensor	):
				return mask
		elif isinstance(_lowerCamelCase, PIL.Image.Image	):
				a_     :      Any 						=     [mask]

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




class 			snake_case_						(						__lowercase				):
							__lowerCAmelCase    					=					4_2
							__lowerCAmelCase    					=					4_2






							def __init__(   self			,     a_			,     a_      ):
									super().__init__()
									self.register_modules(unet=a_			,     scheduler=a_      )






							@torch.no_grad()
							def __call__(   self			,     a_			,     a_			,     a_ = 2_5_0			,     a_ = 0.0			,     a_ = 1_0			,     a_ = 1_0			,     a_ = None			,     a_ = "pil"			,     a_ = True			,     ):
									a_     :      Tuple 						=     image

									a_     :      Optional[Any] 						=     _preprocess_image(a_      )
									a_     :      Optional[int] 						=     original_image.to(device=self.device			,     dtype=self.unet.dtype      )
									a_     :      Dict 						=     _preprocess_mask(a_      )
									a_     :      Any 						=     mask_image.to(device=self.device			,     dtype=self.unet.dtype      )

									a_     :      str 						=     original_image.shape[0]

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

									a_     :      List[str] 						=     original_image.shape
									a_     :      List[Any] 						=     randn_tensor(a_			,     generator=a_			,     device=self.device			,     dtype=self.unet.dtype      )

									# set step values
									self.scheduler.set_timesteps(a_			,     a_			,     a_			,     self.device      )
									a_     :      Tuple 						=     eta

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

											else:
													# compute the reverse: x_t-1 -> x_t
													a_     :      List[str] 						=     self.scheduler.undo_step(a_			,     a_			,     a_      )
											a_     :      Dict 						=     t

									a_     :      Union[str, Any] 						=     (image / 2 + 0.5).clamp(0			,     1      )
									a_     :      Tuple 						=     image.cpu().permute(0			,     2			,     3			,     1      ).numpy()
									if output_type == "pil":
											a_     :      str 						=     self.numpy_to_pil(a_      )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=a_      )




"""simple docstring"""
# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, 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'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)