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

        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	: 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 os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase__	:       Dict			      =      [(1,)]






def    __lowercase (       )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"






def    __lowercase (       )       ->  Optional[Any]:
      SCREAMING_SNAKE_CASE	: Dict					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"







@require_torch
class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @slow
  @require_torch_gpu
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        check_slow_torch_cuda()


  @require_torch_gpu
  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        check_slow_torch_cuda()




  @parameterized.expand(UpperCAmelCase__						)
  @slow
  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        check_slow()






@slow
@require_torch_gpu
def    __lowercase (       )       ->  Optional[int]:
      check_slow_torch_cuda()






@require_torch_gpu
@slow
def    __lowercase (       )       ->  int:
      check_slow_torch_cuda()






@slow
@pytest.mark.parametrize("""param"""  ,      [1]     )
def    __lowercase (       _A     )       ->  Optional[int]:
      check_slow()







@pytest.mark.parametrize("""param"""  ,      [1]     )
@slow
def    __lowercase (       _A     )       ->  str:
      check_slow()




