
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   =			imread(R"digital_image_processing/image_data/lena_small.jpg")
_A   =			cvtColor(img, COLOR_BGR2GRAY)



def 						lowercase_      (       )							->							str:
    """simple docstring"""

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



def 						lowercase_      (       )							->							Union[str, Any]:
    """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 						lowercase_      (       )							->							Union[str, Any]:
    """simple docstring"""

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



def 						lowercase_      (       )							->							Union[str, Any]:
    """simple docstring"""

    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(A__		)
    # assert canny array for at least one True
    assert canny_array.any()



def 						lowercase_      (       )							->							Union[str, Any]:
    """simple docstring"""

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



def 						lowercase_      (       )							->							Union[str, Any]:
    """simple docstring"""

    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(A__			,		A__		).astype(A__		)
    assert res.any()



def 						lowercase_      (       )							->							str:
    """simple docstring"""

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



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    snake_case					,							snake_case								= sob.sobel_filter(A__		)
    assert grad.any() and theta.any()



def 						lowercase_      (       )							->							Tuple:
    """simple docstring"""

    snake_case								= sp.make_sepia(A__			,		20		)
    assert sepia.all()



def 						lowercase_      (       A__ = "digital_image_processing/image_data/lena_small.jpg"		)							->							Tuple:
    """simple docstring"""

    snake_case								= bs.Burkes(imread(A__			,		1		)			,		120		)
    burkes.process()
    assert burkes.output_img.any()



def 						lowercase_      (       A__ = "digital_image_processing/image_data/lena_small.jpg"			,		)							->							Union[str, Any]:
    """simple docstring"""

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





def 						lowercase_      (       )							->							List[Any]:
    """simple docstring"""

    snake_case								= "digital_image_processing/image_data/lena.jpg"

    # Reading the image and converting it to grayscale.
    snake_case								= imread(A__			,		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(
        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
    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(A__			,		A__			,		A__		)

    assert lbp_image.any()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

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

