


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

lowerCAmelCase	=							imread(R'digital_image_processing/image_data/lena_small.jpg')
lowerCAmelCase	=							cvtColor(img, COLOR_BGR2GRAY)
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				cn.convert_to_negative(SCREAMING_SNAKE_CASE					)
		# assert negative_img array for at least one True
		assert negative_img.any()
def       _a			(				):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__						=				canny.gen_gaussian_kernel(9						,    sigma=1.4					)
		# Assert ambiguous array
		assert resp.all()
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0					)
		# assert ambiguous array for all == True
		assert canny_img.all()
		lowercase__						=				canny.canny(SCREAMING_SNAKE_CASE					)
		# assert canny array for at least one True
		assert canny_array.any()
def       _a			(				):


		"""simple docstring"""




		assert gg.gaussian_filter(SCREAMING_SNAKE_CASE						,    5						,    sigma=0.9					).all()
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
		lowercase__						=				conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					).astype(SCREAMING_SNAKE_CASE					)
		assert res.any()
def       _a			(				):


		"""simple docstring"""




		assert med.median_filter(SCREAMING_SNAKE_CASE						,    3					).any()
def       _a			(				):


		"""simple docstring"""




		lowercase__					,		lowercase__						=				sob.sobel_filter(SCREAMING_SNAKE_CASE					)
		assert grad.any() and theta.any()
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				sp.make_sepia(SCREAMING_SNAKE_CASE						,    20					)
		assert sepia.all()
def       _a			(				SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"					):


		"""simple docstring"""




		lowercase__						=				bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1					)						,    1_20					)
		burkes.process()
		assert burkes.output_img.any()
def       _a			(				SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):


		"""simple docstring"""




		lowercase__						=				rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1					)						,    4_00						,    2_00					)
		nn.process()
		assert nn.output.any()
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				'''digital_image_processing/image_data/lena.jpg'''

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

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

		lowercase__						=				lbp.get_neighbors_pixel(
		    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		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
		lowercase__						=				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]					):
						lowercase__						=				lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		assert lbp_image.any()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


