
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

__snake_case					:							Optional[Any]           =							[(1,)]




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[int] 		=    bool(os.getenv('RUN_SLOW'     ,						0							)							)
			if run_slow:
						assert True
			else:
						assert False, "should have been skipped"




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    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 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						@slow
						@require_torch_gpu
						def    A__       (  self	)  ->Optional[Any]:
									check_slow_torch_cuda()


						@require_torch_gpu
						@slow
						def    A__       (  self	)  ->Optional[Any]:
									check_slow_torch_cuda()


						@parameterized.expand(lowercase_	)
						@slow
						def    A__       (  self  , A=None	)  ->List[str]:
									check_slow()




@slow
@require_torch_gpu
def 		A						(  ):




			"""simple docstring"""


			check_slow_torch_cuda()




@require_torch_gpu
@slow
def 		A						(  ):




			"""simple docstring"""


			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('param'     ,						[1]							)
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			check_slow()




@pytest.mark.parametrize('param'     ,						[1]							)
@slow
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			check_slow()







'''simple docstring'''
import numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


# kernel to be applied
a_           =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




