from pathlib import Path

import numpy as np
from PIL import Image
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a ,				__a ,				__a		 =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def 							lowerCAmelCase(     __lowerCamelCase						):
					return (gray > 127) & (gray <= 255)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       np.zeros_like(__lowerCamelCase						)
					__a		 =       np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

					# Copy image to padded image
					__a		 =       image

					# Iterate over image & apply kernel
					for x in range(image.shape[1]						):
										for y in range(image.shape[0]						):
															__a		 =       (
															    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
															).sum()
															__a		 =       int(summation > 0						)
					return output


if __name__ == "__main__":
	# read original image
	lowerCamelCase_    :		Optional[int]							=	Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
	lowerCamelCase_    :		Union[str, Any]							=	np.array(Image.open(lena_path))
	# kernel to be applied
	lowerCamelCase_    :		Optional[Any]							=	np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	lowerCamelCase_    :		Any							=	dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	lowerCamelCase_    :		Optional[int]							=	Image.fromarray(output).convert("""RGB""")
	pil_img.save("""result_dilation.png""")





import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Any:
								warnings.warn(
								    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
								    'instead.'				,						UpperCAmelCase				,						)
								super().__init__(args=UpperCAmelCase				,						**UpperCAmelCase   )




