

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







  """simple docstring"""




  def __init__(     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        super().__init__()

        self.register_buffer("""watermark_image"""			,			torch.zeros((6_2, 6_2, 4)						)						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				None




  def 	_lowercase (     self      :	List[Any]			,			UpperCAmelCase__      :	List[PIL.Image.Image]			,			UpperCAmelCase__      :	Tuple=None						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				images[0].height
        SCREAMING_SNAKE_CASE	: Dict					       =				images[0].width

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				sample_size or h

        SCREAMING_SNAKE_CASE	: int					       =				min(h / sample_size			,			w / sample_size						)
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				(int(h / coef						), int(w / coef						)) if coef < 1 else (h, w)

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				1_0_2_4**2, img_w * img_h
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(Sa / Sa) ** 0.5
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				int(K * 6_2						), img_w - int(1_4 * K						), img_h - int(1_4 * K						)

        if self.watermark_image_as_pil is None:
              SCREAMING_SNAKE_CASE	: str					       =				self.watermark_image.to(torch.uinta						).cpu().numpy()
              SCREAMING_SNAKE_CASE	: List[Any]					       =				Image.fromarray(UpperCAmelCase__			,			mode="""RGBA"""						)
              SCREAMING_SNAKE_CASE	: Dict					       =				watermark_image

        SCREAMING_SNAKE_CASE	: List[str]					       =				self.watermark_image_as_pil.resize(
            (wm_size, wm_size)			,			PIL_INTERPOLATION["""bicubic"""]			,			reducing_gap=UpperCAmelCase__						)

        for pil_img in images:
              pil_img.paste(UpperCAmelCase__			,			box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)			,			mask=wm_img.split()[-1]						)

        return images







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				inspect.getfile(accelerate.test_utils						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_sync.py"""]						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->str:
        """simple docstring"""







        debug_launcher(test_sync.main			,			num_processes=1						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        debug_launcher(test_sync.main						)


  @require_single_gpu
  def 	_lowercase (     self      :	str						)   ->Any:
        """simple docstring"""







        test_sync.main()




  @require_multi_gpu
  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(UpperCAmelCase__			,			env=os.environ.copy()						)




