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



def 		UpperCamelCase     (       )   ->			Any:






						'''simple docstring'''

						_lowercase       :	Dict           =   cn.convert_to_negative(_UpperCAmelCase  )
						# assert negative_img array for at least one True
						assert negative_img.any()



def 		UpperCamelCase     (       )   ->			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(_UpperCAmelCase ,			110  )  ).startswith(
												    "<PIL.Image.Image image mode=RGB size=100x100 at"  )



def 		UpperCamelCase     (       )   ->			int:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			int:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			Optional[Any]:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Tuple           =   array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
						_lowercase       :	Optional[Any]           =   conv.img_convolve(_UpperCAmelCase ,			_UpperCAmelCase  ).astype(_UpperCAmelCase  )
						assert res.any()



def 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			List[Any]:






						'''simple docstring'''

						_lowercase      ,    _lowercase       :	Any           =   sob.sobel_filter(_UpperCAmelCase  )
						assert grad.any() and theta.any()



def 		UpperCamelCase     (       )   ->			int:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   sp.make_sepia(_UpperCAmelCase ,			20  )
						assert sepia.all()



def 		UpperCamelCase     (       _UpperCAmelCase  :   str = "digital_image_processing/image_data/lena_small.jpg"  )   ->			Union[str, Any]:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   bs.Burkes(imread(_UpperCAmelCase ,			1  ) ,			120  )
						burkes.process()
						assert burkes.output_img.any()



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






						'''simple docstring'''

						_lowercase       :	Any           =   rs.NearestNeighbour(imread(_UpperCAmelCase ,			1  ) ,			400 ,			200  )
						nn.process()
						assert nn.output.any()



def 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Dict           =   "digital_image_processing/image_data/lena.jpg"

						# Reading the image and converting it to grayscale.
						_lowercase       :	List[str]           =   imread(_UpperCAmelCase ,			0  )

						# Test for get_neighbors_pixel function() return not None
						_lowercase       :	str           =   0
						_lowercase       :	Optional[int]           =   0
						_lowercase       :	Union[str, Any]           =   image[x_coordinate][y_coordinate]

						_lowercase       :	Optional[int]           =   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
						_lowercase       :	int           =   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       :	List[str]           =   lbp.local_binary_value(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						assert lbp_image.any()






from typing import TYPE_CHECKING

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


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




