from typing import TYPE_CHECKING

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


__a  :     List[Any]       =  {
    "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[Any]       =  ["ConvNextFeatureExtractor"]
		__a  :     Optional[int]       =  ["ConvNextImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConvNextForImageClassification",
		    "ConvNextModel",
		    "ConvNextPreTrainedModel",
		    "ConvNextBackbone",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    "TFConvNextForImageClassification",
		    "TFConvNextModel",
		    "TFConvNextPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_convnext import ConvNextFeatureExtractor
				from .image_processing_convnext import ConvNextImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convnext import (
				    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvNextBackbone,
				    ConvNextForImageClassification,
				    ConvNextModel,
				    ConvNextPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
		import sys

		__a  :     Optional[Any]       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)



"""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     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

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

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

			__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			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]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




