





'''simple docstring'''




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

a__					:			Tuple          =     imread(r'''digital_image_processing/image_data/lena_small.jpg''')
a__					:			Dict          =     cvtColor(img, COLOR_BGR2GRAY)




def 						__lowerCamelCase			(	)   ->Dict:
	snake_case__	       =   cn.convert_to_negative(UpperCAmelCase_							)
	# assert negative_img array for at least one True
	assert negative_img.any()




def 						__lowerCamelCase			(	)   ->int:
	with Image.open('digital_image_processing/image_data/lena_small.jpg'							) as img:
		# Work around assertion for response
		assert str(cc.change_contrast(UpperCAmelCase_					,				1_10							)							).startswith(
		    '<PIL.Image.Image image mode=RGB size=100x100 at'							)




def 						__lowerCamelCase			(	)   ->str:
	snake_case__	       =   canny.gen_gaussian_kernel(9					,				sigma=1.4							)
	# Assert ambiguous array
	assert resp.all()




def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__	       =   imread('digital_image_processing/image_data/lena_small.jpg'					,				0							)
	# assert ambiguous array for all == True
	assert canny_img.all()
	snake_case__	       =   canny.canny(UpperCAmelCase_							)
	# assert canny array for at least one True
	assert canny_array.any()




def 						__lowerCamelCase			(	)   ->int:
	assert gg.gaussian_filter(UpperCAmelCase_					,				5					,				sigma=0.9							).all()




def 						__lowerCamelCase			(	)   ->str:
	# laplace diagonals
	snake_case__	       =   array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]							)
	snake_case__	       =   conv.img_convolve(UpperCAmelCase_					,				UpperCAmelCase_							).astype(UpperCAmelCase_							)
	assert res.any()




def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	assert med.median_filter(UpperCAmelCase_					,				3							).any()




def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__       ,  snake_case__	       =   sob.sobel_filter(UpperCAmelCase_							)
	assert grad.any() and theta.any()




def 						__lowerCamelCase			(	)   ->List[Any]:
	snake_case__	       =   sp.make_sepia(UpperCAmelCase_					,				20							)
	assert sepia.all()




def 						__lowerCamelCase			(	UpperCAmelCase_ = "digital_image_processing/image_data/lena_small.jpg"							)   ->str:
	snake_case__	       =   bs.Burkes(imread(UpperCAmelCase_					,				1							)					,				1_20							)
	burkes.process()
	assert burkes.output_img.any()




def 						__lowerCamelCase			(	UpperCAmelCase_ = "digital_image_processing/image_data/lena_small.jpg"					,				)   ->int:
	snake_case__	       =   rs.NearestNeighbour(imread(UpperCAmelCase_					,				1							)					,				4_00					,				2_00							)
	nn.process()
	assert nn.output.any()







def 						__lowerCamelCase			(	)   ->List[Any]:
	snake_case__	       =   'digital_image_processing/image_data/lena.jpg'

	# Reading the image and converting it to grayscale.
	snake_case__	       =   imread(UpperCAmelCase_					,				0							)

	# Test for get_neighbors_pixel function() return not None
	snake_case__	       =   0
	snake_case__	       =   0
	snake_case__	       =   image[x_coordinate][y_coordinate]

	snake_case__	       =   lbp.get_neighbors_pixel(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	assert neighbors_pixels is not None

	# Test for local_binary_pattern function()
	# Create a numpy array as the same height and width of read image
	snake_case__	       =   np.zeros((image.shape[0], image.shape[1])							)

	# Iterating through the image and calculating the local binary pattern value
	# for each pixel.
	for i in range(0					,				image.shape[0]							):
		for j in range(0					,				image.shape[1]							):
			snake_case__	       =   lbp.local_binary_value(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	assert lbp_image.any()










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



