





'''simple docstring'''
import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				import PIL

				from transformers import FlavaImageProcessor
				from transformers.image_utils import PILImageResampling
				from transformers.models.flava.image_processing_flava import (
				    FLAVA_CODEBOOK_MEAN,
				    FLAVA_CODEBOOK_STD,
				    FLAVA_IMAGE_MEAN,
				    FLAVA_IMAGE_STD,
				)
else:
				a__							:							List[Any]			=None

class 					snake_case					(   unittest.TestCase	):




	"""simple docstring"""



	def __init__(	self	:							Dict							,     __A	:							List[Any]							,     __A	:							List[Any]=7							,     __A	:							str=3							,     __A	:							Optional[int]=3_0							,     __A	:							Optional[Any]=4_0_0							,     __A	:							Union[str, Any]=True							,     __A	:							Any=None							,     __A	:							Tuple=True							,     __A	:							List[str]=None							,     __A	:							Tuple=None							,     __A	:							str=True							,     __A	:							str=1 / 2_5_5							,     __A	:							Union[str, Any]=True							,     __A	:							Optional[int]=FLAVA_IMAGE_MEAN							,     __A	:							int=FLAVA_IMAGE_STD							,     __A	:							Tuple=1_4							,     __A	:							Tuple=7_5							,     __A	:							Union[str, Any]=None							,     __A	:							Optional[int]=1_6							,     __A	:							Optional[int]=0.3							,     __A	:							Tuple=None							,     __A	:							Dict=True							,     __A	:							str=None							,     __A	:							int=None							,     __A	:							str=True							,     __A	:							List[Any]=None							,     __A	:							Any=True							,     __A	:							Optional[int]=True							,     __A	:							Dict=FLAVA_CODEBOOK_MEAN							,     __A	:							Tuple=FLAVA_CODEBOOK_STD							,     ):
								__UpperCamelCase	=  size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__UpperCamelCase	=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__UpperCamelCase	=  codebook_size if codebook_size is not None else {'height': 1_1_2, 'width': 1_1_2}
								__UpperCamelCase	=  codebook_crop_size if codebook_crop_size is not None else {'height': 1_1_2, 'width': 1_1_2}

								__UpperCamelCase	=  parent
								__UpperCamelCase	=  batch_size
								__UpperCamelCase	=  num_channels
								__UpperCamelCase	=  do_resize
								__UpperCamelCase	=  do_rescale
								__UpperCamelCase	=  rescale_factor
								__UpperCamelCase	=  min_resolution
								__UpperCamelCase	=  max_resolution
								__UpperCamelCase	=  size
								__UpperCamelCase	=  resample if resample is not None else PILImageResampling.BICUBIC
								__UpperCamelCase	=  do_normalize
								__UpperCamelCase	=  image_mean
								__UpperCamelCase	=  image_std
								__UpperCamelCase	=  do_center_crop
								__UpperCamelCase	=  crop_size

								__UpperCamelCase	=  input_size_patches
								__UpperCamelCase	=  total_mask_patches
								__UpperCamelCase	=  mask_group_max_patches
								__UpperCamelCase	=  mask_group_min_patches
								__UpperCamelCase	=  mask_group_min_aspect_ratio
								__UpperCamelCase	=  mask_group_max_aspect_ratio

								__UpperCamelCase	=  codebook_do_resize
								__UpperCamelCase	=  codebook_size
								__UpperCamelCase	=  codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
								__UpperCamelCase	=  codebook_do_center_crop
								__UpperCamelCase	=  codebook_crop_size
								__UpperCamelCase	=  codebook_do_map_pixels
								__UpperCamelCase	=  codebook_do_normalize
								__UpperCamelCase	=  codebook_image_mean
								__UpperCamelCase	=  codebook_image_std






	def 	_lowerCamelCase (	self	:							int     ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "resample": self.resample,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "input_size_patches": self.input_size_patches,
								    "total_mask_patches": self.total_mask_patches,
								    "mask_group_max_patches": self.mask_group_max_patches,
								    "mask_group_min_patches": self.mask_group_min_patches,
								    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
								    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
								    "codebook_do_resize": self.codebook_do_resize,
								    "codebook_size": self.codebook_size,
								    "codebook_resample": self.codebook_resample,
								    "codebook_do_center_crop": self.codebook_do_center_crop,
								    "codebook_crop_size": self.codebook_crop_size,
								    "codebook_do_map_pixels": self.codebook_do_map_pixels,
								    "codebook_do_normalize": self.codebook_do_normalize,
								    "codebook_image_mean": self.codebook_image_mean,
								    "codebook_image_std": self.codebook_image_std,
								}






	def 	_lowerCamelCase (	self	:							Dict     ):
								return (self.size["height"], self.size["width"])






	def 	_lowerCamelCase (	self	:							int     ):
								return (
								    (self.input_size_patches, self.input_size_patches)
								    if not isinstance(self.input_size_patches							,     __A     )
								    else self.input_size_patches
								)






	def 	_lowerCamelCase (	self	:							str     ):
								return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class 					snake_case					(   A__      ,	unittest.TestCase	):




	"""simple docstring"""



	SCREAMING_SNAKE_CASE_     : Union[str, Any]      		=FlavaImageProcessor if is_vision_available() else None
	SCREAMING_SNAKE_CASE_     : List[str]      		=None






	def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
								__UpperCamelCase	=  FlavaImageProcessingTester(self     )






	@property
	def 	_lowerCamelCase (	self	:							int     ):
								return self.image_processor_tester.prepare_image_processor_dict()






	def 	_lowerCamelCase (	self	:							int     ):
								__UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(__A							,     'image_mean'     )     )
								self.assertTrue(hasattr(__A							,     'image_std'     )     )
								self.assertTrue(hasattr(__A							,     'do_normalize'     )     )
								self.assertTrue(hasattr(__A							,     'do_resize'     )     )
								self.assertTrue(hasattr(__A							,     'resample'     )     )
								self.assertTrue(hasattr(__A							,     'crop_size'     )     )
								self.assertTrue(hasattr(__A							,     'do_center_crop'     )     )
								self.assertTrue(hasattr(__A							,     'do_rescale'     )     )
								self.assertTrue(hasattr(__A							,     'rescale_factor'     )     )
								self.assertTrue(hasattr(__A							,     'masking_generator'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_do_resize'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_size'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_resample'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_do_center_crop'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_crop_size'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_do_map_pixels'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_do_normalize'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_image_mean'     )     )
								self.assertTrue(hasattr(__A							,     'codebook_image_std'     )     )






	def 	_lowerCamelCase (	self	:							Optional[int]     ):
								__UpperCamelCase	=  self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size							,     {'height': 2_2_4, 'width': 2_2_4}     )
								self.assertEqual(image_processor.crop_size							,     {'height': 2_2_4, 'width': 2_2_4}     )
								self.assertEqual(image_processor.codebook_size							,     {'height': 1_1_2, 'width': 1_1_2}     )
								self.assertEqual(image_processor.codebook_crop_size							,     {'height': 1_1_2, 'width': 1_1_2}     )

								__UpperCamelCase	=  self.image_processing_class.from_dict(
								    self.image_processor_dict							,     size=4_2							,     crop_size=8_4							,     codebook_size=3_3							,     codebook_crop_size=6_6     )
								self.assertEqual(image_processor.size							,     {'height': 4_2, 'width': 4_2}     )
								self.assertEqual(image_processor.crop_size							,     {'height': 8_4, 'width': 8_4}     )
								self.assertEqual(image_processor.codebook_size							,     {'height': 3_3, 'width': 3_3}     )
								self.assertEqual(image_processor.codebook_crop_size							,     {'height': 6_6, 'width': 6_6}     )






	def 	_lowerCamelCase (	self	:							str     ):
								pass






	def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
								__UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								__UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A     )
								for image in image_inputs:
															self.assertIsInstance(__A							,     PIL.Image.Image     )

								# Test not batched input
								__UpperCamelCase	=  image_processing(image_inputs[0]							,     return_tensors='pt'     )

								# Test no bool masked pos
								self.assertFalse('bool_masked_pos' in encoded_images     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()

								self.assertEqual(
								    encoded_images.pixel_values.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

								# Test batched
								__UpperCamelCase	=  image_processing(__A							,     return_tensors='pt'     )
								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()

								# Test no bool masked pos
								self.assertFalse('bool_masked_pos' in encoded_images     )

								self.assertEqual(
								    encoded_images.pixel_values.shape							,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,     )






	def 	_lowerCamelCase (	self	:							Tuple							,     __A	:							Dict							,     __A	:							Tuple     ):
								__UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
								# create random tensors
								__UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A							,     **__A     )
								for image in image_inputs:
															self.assertIsInstance(__A							,     __A     )

								# Test not batched input
								__UpperCamelCase	=  image_processing(image_inputs[0]							,     return_tensors='pt'     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

								__UpperCamelCase	=  image_processing(__A							,     return_image_mask=__A							,     return_tensors='pt'     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape							,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_mask_size()
								self.assertEqual(
								    encoded_images.bool_masked_pos.shape							,     (
								        self.image_processor_tester.batch_size,
								        expected_height,
								        expected_width,
								    )							,     )

								# Test batched
								__UpperCamelCase	=  image_processing(__A							,     return_tensors='pt'     ).pixel_values

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.shape							,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,     )

								# Test masking
								__UpperCamelCase	=  image_processing(__A							,     return_image_mask=__A							,     return_tensors='pt'     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape							,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,     )

								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_mask_size()
								self.assertEqual(
								    encoded_images.bool_masked_pos.shape							,     (
								        self.image_processor_tester.batch_size,
								        expected_height,
								        expected_width,
								    )							,     )






	def 	_lowerCamelCase (	self	:							str     ):
								self._test_call_framework(np.ndarray							,     prepare_kwargs={'numpify': True}     )






	def 	_lowerCamelCase (	self	:							Optional[int]     ):
								self._test_call_framework(torch.Tensor							,     prepare_kwargs={'torchify': True}     )






	def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
								random.seed(1_2_3_4     )
								__UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
								__UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A							,     torchify=__A     )

								# Test not batched input
								__UpperCamelCase	=  image_processing(image_inputs[0]							,     return_image_mask=__A							,     return_tensors='pt'     )
								self.assertEqual(encoded_images.bool_masked_pos.sum().item()							,     7_5     )






	def 	_lowerCamelCase (	self	:							Any     ):
								__UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								__UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A     )
								for image in image_inputs:
															self.assertIsInstance(__A							,     PIL.Image.Image     )

								# Test not batched input
								__UpperCamelCase	=  image_processing(image_inputs[0]							,     return_codebook_pixels=__A							,     return_tensors='pt'     )
								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_codebook_image_size()
								self.assertEqual(
								    encoded_images.codebook_pixel_values.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

								# Test batched
								__UpperCamelCase	=  image_processing(__A							,     return_codebook_pixels=__A							,     return_tensors='pt'     )
								__UpperCamelCase	,						__UpperCamelCase	=  self.image_processor_tester.get_expected_codebook_image_size()
								self.assertEqual(
								    encoded_images.codebook_pixel_values.shape							,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )							,     )







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

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


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

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
