
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    List[Any]=False)      ->					int:






       '''simple docstring'''





       try:
              __UpperCamelCase			:     List[str]         =      os.environ[key]
       except KeyError:
              # KEY isn't set, default to `default`.
              __UpperCamelCase			:     Optional[int]         =      default
       else:
              # KEY is set, convert it to True or False.
              try:
                     __UpperCamelCase			:     List[Any]         =      strtobool(_lowerCamelCase)
              except ValueError:
                     # More values are supported, but let's keep the message simple.
                     raise ValueError(F'If set, {key} must be yes or no.')
       return _value


lowercase						:      Union[str, Any]									= parse_flag_from_env('RUN_SLOW', default=False)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					List[str]:






       '''simple docstring'''





       return unittest.skip("Test was skipped")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






       '''simple docstring'''





       return unittest.skipUnless(_run_slow_tests		,						"test is slow")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[Any]:






       '''simple docstring'''





       return unittest.skipUnless(not torch.cuda.is_available()		,						"test requires only a CPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple)      ->					Tuple:






       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.is_available()		,						"test requires a GPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[Any]:






       '''simple docstring'''





       return unittest.skipUnless(is_xpu_available()		,						"test requires a XPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					List[str]:






       '''simple docstring'''





       return unittest.skipUnless(is_mps_available()		,						"test requires a `mps` backend support in `torch`")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






       '''simple docstring'''





       return unittest.skipUnless(
           is_transformers_available() and is_datasets_available()		,						"test requires the Hugging Face suite")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Any:






       '''simple docstring'''





       return unittest.skipUnless(is_bnb_available()		,						"test requires the bitsandbytes library")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					Any:






       '''simple docstring'''





       return unittest.skipUnless(is_tpu_available()		,						"test requires TPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Optional[Any]:






       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.device_count() == 1		,						"test requires a GPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple)      ->					Union[str, Any]:






       '''simple docstring'''





       return unittest.skipUnless(torch.xpu.device_count() == 1		,						"test requires a XPU")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					int:






       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.device_count() > 1		,						"test requires multiple GPUs")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Tuple:






       '''simple docstring'''





       return unittest.skipUnless(torch.xpu.device_count() > 1		,						"test requires multiple XPUs")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Dict:






       '''simple docstring'''





       return unittest.skipUnless(is_safetensors_available()		,						"test requires safetensors")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Any:






       '''simple docstring'''





       return unittest.skipUnless(is_deepspeed_available()		,						"test requires DeepSpeed")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Dict:






       '''simple docstring'''





       return unittest.skipUnless(is_torch_version(">="		,						"1.12.0")		,						"test requires torch version >= 1.12.0")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]=None		,						_lowerCamelCase				:    Optional[int]=None)      ->					Dict:






       '''simple docstring'''





       if test_case is None:
              return partial(_lowerCamelCase		,						version=_lowerCamelCase)
       return unittest.skipUnless(is_torch_version(">="		,						_lowerCamelCase)		,						F'test requires torch version >= {version}')(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Union[str, Any]:






       '''simple docstring'''





       return unittest.skipUnless(is_tensorboard_available()		,						"test requires Tensorboard")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					Any:






       '''simple docstring'''





       return unittest.skipUnless(is_wandb_available()		,						"test requires wandb")(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					Union[str, Any]:






       '''simple docstring'''





       return unittest.skipUnless(is_comet_ml_available()		,						"test requires comet_ml")(_lowerCamelCase)


lowercase						:      int									= (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str])      ->					Union[str, Any]:






       '''simple docstring'''





       return unittest.skipUnless(
           _atleast_one_tracker_available		,						"test requires at least one tracker to be available and for `comet_ml` to not be installed"		,						)(_lowerCamelCase)




class 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 _A			=  True







 @classmethod
 def      _lowerCamelCase       (      cls	:Union[str, Any]		)	->				Tuple:
        __UpperCamelCase			:     Optional[Any]         =      tempfile.mkdtemp()







 @classmethod
 def      _lowerCamelCase       (      cls	:List[str]		)	->				Tuple:
        if os.path.exists(cls.tmpdir		):
               shutil.rmtree(cls.tmpdir		)







 def      _lowerCamelCase       (      self	:Dict		)	->				Dict:
        if self.clear_on_setup:
               for path in Path(self.tmpdir		).glob("**/*"		):
                      if path.is_file():
                             path.unlink()
                      elif path.is_dir():
                             shutil.rmtree(_SCREAMING_SNAKE_CASE		)




class 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 def      _lowerCamelCase       (      self	:Dict		)	->				Any:
        super().tearDown()
        # Reset the state of the AcceleratorState singleton.
        AcceleratorState._reset_state()
        PartialState._reset_state()




class 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 def      _lowerCamelCase       (      self	:List[Any]  ,     a	:Union[mock.Mock, List[mock.Mock]]		)	->				int:
        __UpperCamelCase			:     Optional[int]         =      mocks if isinstance(_SCREAMING_SNAKE_CASE  ,     (tuple, list)		) else [mocks]
        for m in self.mocks:
               m.start()
               self.addCleanup(m.stop		)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					List[str]:






       '''simple docstring'''





       __UpperCamelCase			:     Union[str, Any]         =      AcceleratorState()
       __UpperCamelCase			:     Tuple         =      tensor[None].clone().to(state.device)
       __UpperCamelCase			:     Optional[Any]         =      gather(_lowerCamelCase).cpu()
       __UpperCamelCase			:     List[Any]         =      tensor[0].cpu()
       for i in range(tensors.shape[0]):
              if not torch.equal(tensors[i]		,						_lowerCamelCase):
                     return False
       return True




class 				lowerCamelCase__							:




 '''simple docstring'''




 def __init__(      self	:List[Any]  ,     a	:Dict  ,     a	:List[str]  ,     a	:Any		)	->				Tuple:
        __UpperCamelCase			:     Tuple         =      returncode
        __UpperCamelCase			:     List[str]         =      stdout
        __UpperCamelCase			:     Any         =      stderr




async def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Any)      ->					str:






       '''simple docstring'''





       while True:
              __UpperCamelCase			:     Optional[int]         =      await stream.readline()
              if line:
                     callback(_lowerCamelCase)
              else:
                     break




async def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str]=None		,						_lowerCamelCase				:    Union[str, Any]=None		,						_lowerCamelCase				:    List[str]=None		,						_lowerCamelCase				:    Tuple=False		,						_lowerCamelCase				:    int=False)      ->					int:






       '''simple docstring'''





       if echo:
              print("\nRunning: "		,						" ".join(_lowerCamelCase))

       __UpperCamelCase			:     int         =      await asyncio.create_subprocess_exec(
           cmd[0]		,						*cmd[1:]		,						stdin=_lowerCamelCase		,						stdout=asyncio.subprocess.PIPE		,						stderr=asyncio.subprocess.PIPE		,						env=_lowerCamelCase		,						)

       # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
       # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
       #
       # If it starts hanging, will need to switch to the following code. The problem is that no data
       # will be seen until it's done and if it hangs for example there will be no debug info.
       # out, err = await p.communicate()
       # return _RunOutput(p.returncode, out, err)

       __UpperCamelCase			:     List[Any]         =      []
       __UpperCamelCase			:     Dict         =      []

       def tee(_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Any=""):
              __UpperCamelCase			:     int         =      line.decode("utf-8").rstrip()
              sink.append(_lowerCamelCase)
              if not quiet:
                     print(_lowerCamelCase		,						_lowerCamelCase		,						file=_lowerCamelCase)

    # XXX: the timeout doesn't seem to make any difference here
       await asyncio.wait(
           [
               asyncio.create_task(_read_stream(p.stdout		,						lambda _lowerCamelCase: tee(_lowerCamelCase		,						_lowerCamelCase		,						sys.stdout		,						label="stdout:"))),
               asyncio.create_task(_read_stream(p.stderr		,						lambda _lowerCamelCase: tee(_lowerCamelCase		,						_lowerCamelCase		,						sys.stderr		,						label="stderr:"))),
           ]		,						timeout=_lowerCamelCase		,						)
       return _RunOutput(await p.wait()		,						_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[str]=None		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    Union[str, Any]=180		,						_lowerCamelCase				:    List[Any]=False		,						_lowerCamelCase				:    Optional[int]=True)      ->					Union[str, Any]:






       '''simple docstring'''





       __UpperCamelCase			:     str         =      asyncio.get_event_loop()
       __UpperCamelCase			:     Any         =      loop.run_until_complete(
           _stream_subprocess(_lowerCamelCase		,						env=_lowerCamelCase		,						stdin=_lowerCamelCase		,						timeout=_lowerCamelCase		,						quiet=_lowerCamelCase		,						echo=_lowerCamelCase))

       __UpperCamelCase			:     Tuple         =      " ".join(_lowerCamelCase)
       if result.returncode > 0:
              __UpperCamelCase			:     Dict         =      "\n".join(result.stderr)
              raise RuntimeError(
                  F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
                  F'The combined stderr from workers follows:\n{stderr}')

       return result




class 				lowerCamelCase__							(       snake_case__):




 '''simple docstring'''




 pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Dict=False)      ->					Any:






       '''simple docstring'''





       try:
              __UpperCamelCase			:     List[Any]         =      subprocess.check_output(_lowerCamelCase		,						stderr=subprocess.STDOUT)
              if return_stdout:
                     if hasattr(_lowerCamelCase		,						"decode"):
                            __UpperCamelCase			:     str         =      output.decode("utf-8")
                     return output
       except subprocess.CalledProcessError as e:
              raise SubprocessCallException(
                  F'Command `{" ".join(_lowerCamelCase)}` failed with the following error:\n\n{e.output.decode()}') from e

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							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):
														__UpperCamelCase			:     int         =      [image]

							if isinstance(image[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      image[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (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			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
														__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
														__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(image[0]		,						torch.Tensor):
														__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





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

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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







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

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

								__UpperCamelCase			:     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.'		)

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     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		)
								__UpperCamelCase			:     Tuple         =      eta

								__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
								__UpperCamelCase			:     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
																						__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
																						# compute previous image: x_t -> x_t-1
																						__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

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

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)