



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  _lowercase				(__lowerCamelCase ,				__lowerCamelCase ):
				'''simple docstring'''







				def __init__(	self      ):

									'''simple docstring'''


									super().__init__()

									self.register_buffer("watermark_image"       ,	torch.zeros((62, 62, 4)      )      )
									UpperCamelCase_											=				None

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__=None      ):

									'''simple docstring'''


									UpperCamelCase_											=				images[0].height
									UpperCamelCase_											=				images[0].width

									UpperCamelCase_											=				sample_size or h

									UpperCamelCase_											=				min(h / sample_size       ,	w / sample_size      )
									UpperCamelCase_					,      UpperCamelCase_											=				(int(h / coef      ), int(w / coef      )) if coef < 1 else (h, w)

									UpperCamelCase_					,      UpperCamelCase_											=				1024**2, img_w * img_h
									UpperCamelCase_											=				(Sa / Sa) ** 0.5
									UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				int(K * 62      ), img_w - int(14 * K      ), img_h - int(14 * K      )

									if self.watermark_image_as_pil is None:
														UpperCamelCase_											=				self.watermark_image.to(torch.uinta      ).cpu().numpy()
														UpperCamelCase_											=				Image.fromarray(_UpperCamelCase       ,	mode="RGBA"      )
														UpperCamelCase_											=				watermark_image

									UpperCamelCase_											=				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





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
