



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






def       lowerCAmelCase__						(       lowerCamelCase_						:						Tuple):





				'''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):
								lowerCAmelCase__	:							Dict				=	[image]

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

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






def       lowerCAmelCase__						(       lowerCamelCase_						:						Dict):





				'''simple docstring'''

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

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



class 			lowerCamelCase__     (				_snake_case):




		'''simple docstring'''



		snake_case_      =42
		snake_case_      =42



		def __init__(self						,__lowerCamelCase						,__lowerCamelCase					)	->     Optional[int]:

						"""simple docstring"""






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



		@torch.no_grad()
		def __call__(self						,__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase = 2_50						,__lowerCamelCase = 0.0						,__lowerCamelCase = 10						,__lowerCamelCase = 10						,__lowerCamelCase = None						,__lowerCamelCase = "pil"						,__lowerCamelCase = True						,)	->     Union[ImagePipelineOutput, Tuple]:

						"""simple docstring"""






						lowerCAmelCase__	:							Optional[Any]				=	image

						lowerCAmelCase__	:							Tuple				=	_preprocess_image(UpperCamelCase__					)
						lowerCAmelCase__	:							Union[str, Any]				=	original_image.to(device=self.device						,dtype=self.unet.dtype					)
						lowerCAmelCase__	:							Optional[Any]				=	_preprocess_mask(UpperCamelCase__					)
						lowerCAmelCase__	:							Union[str, Any]				=	mask_image.to(device=self.device						,dtype=self.unet.dtype					)

						lowerCAmelCase__	:							List[Any]				=	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__	:							int				=	original_image.shape
						lowerCAmelCase__	:							int				=	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__	:							Dict				=	eta

						lowerCAmelCase__	:							Optional[Any]				=	self.scheduler.timesteps[0] + 1
						lowerCAmelCase__	:							Tuple				=	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__	:							Optional[int]				=	self.unet(UpperCamelCase__						,UpperCamelCase__					).sample
														# compute previous image: x_t -> x_t-1
														lowerCAmelCase__	:							Any				=	self.scheduler.step(UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__					).prev_sample

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

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

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=UpperCamelCase__					)




'''simple docstring'''







# Copyright 2022 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
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowerCamelCase									=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def    UpperCAmelCase__     (   UpperCAmelCase__=None					)      ->      List[str]:
       if subparsers is not None:
              A_						       =   subparsers.add_parser("""tpu-config""",							description=_description					)
       else:
              A_						       =   argparse.ArgumentParser("""Accelerate tpu-config command""",							description=_description					)
       # Core arguments
       A_						       =   parser.add_argument_group(
           """Config Arguments""",							"""Arguments that can be configured through `accelerate config`."""					)
       config_args.add_argument(
           """--config_file""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							help="""Path to the config file to use for accelerate.""",							)
       config_args.add_argument(
           """--tpu_name""",							default=UpperCAmelCase__,							help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""",							)
       config_args.add_argument(
           """--tpu_zone""",							default=UpperCAmelCase__,							help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""",							)
       A_						       =   parser.add_argument_group("""TPU Arguments""",							"""Arguments for options ran inside the TPU."""					)
       pod_args.add_argument(
           """--use_alpha""",							action="""store_true""",							help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""",							)
       pod_args.add_argument(
           """--command_file""",							default=UpperCAmelCase__,							help="""The path to the file containing the commands to run on the pod on startup.""",							)
       pod_args.add_argument(
           """--command""",							action="""append""",							nargs="""+""",							help="""A command to run on the pod. Can be passed multiple times.""",							)
       pod_args.add_argument(
           """--install_accelerate""",							action="""store_true""",							help="""Whether to install accelerate on the pod. Defaults to False.""",							)
       pod_args.add_argument(
           """--accelerate_version""",							default="""latest""",							help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""",							)
       pod_args.add_argument(
           """--debug""",							action="""store_true""",							help="""If set, will print the command that would be run instead of running it."""					)

       if subparsers is not None:
              parser.set_defaults(func=UpperCAmelCase__					)
       return parser
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   None

       # Get the default from the config file if it exists.
       if args.config_file is not None or os.path.isfile(UpperCAmelCase__					):
              A_						       =   load_config_from_file(args.config_file					)
              if not args.command_file and defaults.command_file is not None and not args.command:
                     A_						       =   defaults.command_file
              if not args.command and defaults.commands is not None:
                     A_						       =   defaults.commands
              if not args.tpu_name:
                     A_						       =   defaults.tpu_name
              if not args.tpu_zone:
                     A_						       =   defaults.tpu_zone
       if args.accelerate_version == "dev":
              A_						       =   """git+https://github.com/huggingface/accelerate.git"""
       elif args.accelerate_version == "latest":
              A_						       =   """accelerate -U"""
       elif isinstance(parse(args.accelerate_version					),							UpperCAmelCase__					):
              A_						       =   F'''accelerate=={args.accelerate_version}'''

       if not args.command_file and not args.command:
              raise ValueError("""You must specify either a command file or a command to run on the pod."""					)

       if args.command_file:
              with open(args.command_file,							"""r"""					) as f:
                     A_						       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
       if isinstance(args.command[0],							UpperCAmelCase__					):
              A_						       =   [line for cmd in args.command for line in cmd]
       # Default to the shared folder and install accelerate
       A_						       =   ["""cd /usr/share"""]
       if args.install_accelerate:
              new_cmd += [F'''pip install {args.accelerate_version}''']
       new_cmd += args.command
       A_						       =   """; """.join(UpperCAmelCase__					)

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       A_						       =   ["""gcloud"""]
       if args.use_alpha:
              cmd += ["alpha"]
       cmd += [
           "compute",
           "tpus",
           "tpu-vm",
           "ssh",
           args.tpu_name,
           "--zone",
           args.tpu_zone,
           "--command",
           args.command,
           "--worker",
           "all",
       ]
       if args.debug:
              print(F'''Running {" ".join(UpperCAmelCase__					)}'''					)
              return
       subprocess.run(UpperCAmelCase__					)
       print("""Successfully setup pod."""					)







def    UpperCAmelCase__     (   )      ->      int:
       A_						       =   tpu_command_parser()
       A_						       =   parser.parse_args()

       tpu_command_launcher(UpperCAmelCase__					)
