



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

UpperCAmelCase__       =	imread(r"digital_image_processing/image_data/lena_small.jpg")
UpperCAmelCase__       =	cvtColor(img, COLOR_BGR2GRAY)





def 							A     (					)    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       cn.convert_to_negative(__a   )
			# assert negative_img array for at least one True
			assert negative_img.any()





def 							A     (					)    ->			Tuple:







			'''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(__a							,       110   )   ).startswith(
						    '<PIL.Image.Image image mode=RGB size=100x100 at'   )





def 							A     (					)    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       canny.gen_gaussian_kernel(9							,       sigma=1.4   )
			# Assert ambiguous array
			assert resp.all()





def 							A     (					)    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       imread('digital_image_processing/image_data/lena_small.jpg'							,       0   )
			# assert ambiguous array for all == True
			assert canny_img.all()
			_UpperCAmelCase			=       canny.canny(__a   )
			# assert canny array for at least one True
			assert canny_array.any()





def 							A     (					)    ->			Optional[int]:







			'''simple docstring'''
			assert gg.gaussian_filter(__a							,       5							,       sigma=0.9   ).all()





def 							A     (					)    ->			List[str]:







			'''simple docstring'''
			# laplace diagonals
			_UpperCAmelCase			=       array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]   )
			_UpperCAmelCase			=       conv.img_convolve(__a							,       __a   ).astype(__a   )
			assert res.any()





def 							A     (					)    ->			Union[str, Any]:







			'''simple docstring'''
			assert med.median_filter(__a							,       3   ).any()





def 							A     (					)    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       sob.sobel_filter(__a   )
			assert grad.any() and theta.any()





def 							A     (					)    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       sp.make_sepia(__a							,       20   )
			assert sepia.all()





def 							A     (					_UpperCAmelCase    :       Dict = "digital_image_processing/image_data/lena_small.jpg"   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       bs.Burkes(imread(__a							,       1   )							,       120   )
			burkes.process()
			assert burkes.output_img.any()





def 							A     (					_UpperCAmelCase    :       List[Any] = "digital_image_processing/image_data/lena_small.jpg"							,       )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       rs.NearestNeighbour(imread(__a							,       1   )							,       400							,       200   )
			nn.process()
			assert nn.output.any()





def 							A     (					)    ->			Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       'digital_image_processing/image_data/lena.jpg'

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

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

			_UpperCAmelCase			=       lbp.get_neighbors_pixel(
			    __a							,       __a							,       __a							,       __a   )

			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
			_UpperCAmelCase			=       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]   ):
									_UpperCAmelCase			=       lbp.local_binary_value(__a							,       __a							,       __a   )

			assert lbp_image.any()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MegaForCausalLM",
							    "MegaForMaskedLM",
							    "MegaForMultipleChoice",
							    "MegaForQuestionAnswering",
							    "MegaForSequenceClassification",
							    "MegaForTokenClassification",
							    "MegaModel",
							    "MegaPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mega import (
														    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MegaForCausalLM,
														    MegaForMaskedLM,
														    MegaForMultipleChoice,
														    MegaForQuestionAnswering,
														    MegaForSequenceClassification,
														    MegaForTokenClassification,
														    MegaModel,
														    MegaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
