



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

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



def 		lowerCAmelCase__    (					)->  List[Any]:
    A__		 =					cn.convert_to_negative(UpperCamelCase_	)
    # assert negative_img array for at least one True
    assert negative_img.any()



def 		lowerCAmelCase__    (					)->  Dict:
    with Image.open('''digital_image_processing/image_data/lena_small.jpg'''	) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(UpperCamelCase_ ,       1_1_0	)	).startswith(
            '''<PIL.Image.Image image mode=RGB size=100x100 at'''	)



def 		lowerCAmelCase__    (					)->  Optional[Any]:
    A__		 =					canny.gen_gaussian_kernel(9 ,       sigma=1.4	)
    # Assert ambiguous array
    assert resp.all()



def 		lowerCAmelCase__    (					)->  Tuple:
    A__		 =					imread('''digital_image_processing/image_data/lena_small.jpg''' ,       0	)
    # assert ambiguous array for all == True
    assert canny_img.all()
    A__		 =					canny.canny(UpperCamelCase_	)
    # assert canny array for at least one True
    assert canny_array.any()



def 		lowerCAmelCase__    (					)->  str:
    assert gg.gaussian_filter(UpperCamelCase_ ,       5 ,       sigma=0.9	).all()



def 		lowerCAmelCase__    (					)->  str:
    # laplace diagonals
    A__		 =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]	)
    A__		 =					conv.img_convolve(UpperCamelCase_ ,       UpperCamelCase_	).astype(UpperCamelCase_	)
    assert res.any()



def 		lowerCAmelCase__    (					)->  Tuple:
    assert med.median_filter(UpperCamelCase_ ,       3	).any()



def 		lowerCAmelCase__    (					)->  Dict:
    A__ ,	A__		 =					sob.sobel_filter(UpperCamelCase_	)
    assert grad.any() and theta.any()



def 		lowerCAmelCase__    (					)->  List[str]:
    A__		 =					sp.make_sepia(UpperCamelCase_ ,       2_0	)
    assert sepia.all()



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str = "digital_image_processing/image_data/lena_small.jpg"	)->  List[Any]:
    A__		 =					bs.Burkes(imread(UpperCamelCase_ ,       1	) ,       1_2_0	)
    burkes.process()
    assert burkes.output_img.any()



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str = "digital_image_processing/image_data/lena_small.jpg" ,       )->  Optional[int]:
    A__		 =					rs.NearestNeighbour(imread(UpperCamelCase_ ,       1	) ,       4_0_0 ,       2_0_0	)
    nn.process()
    assert nn.output.any()



def 		lowerCAmelCase__    (					)->  List[str]:
    A__		 =					'''digital_image_processing/image_data/lena.jpg'''

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

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

    A__		 =					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
    A__		 =					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]	):
            A__		 =					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,
    is_vision_available,
)


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

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