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       __lowerCAmelCase      (						)    ->  List[str]:
  __UpperCAmelCase						    =       cn.convert_to_negative(A_					)
  # assert negative_img array for at least one True
  assert negative_img.any()




def       __lowerCAmelCase      (						)    ->  str:



  with Image.open("digital_image_processing/image_data/lena_small.jpg"					) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(A_						,	1_10					)					).startswith(
        "<PIL.Image.Image image mode=RGB size=100x100 at"					)




def       __lowerCAmelCase      (						)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       canny.gen_gaussian_kernel(9						,	sigma=1.4					)
  # Assert ambiguous array
  assert resp.all()




def       __lowerCAmelCase      (						)    ->  Optional[Any]:
  __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       __lowerCAmelCase      (						)    ->  Optional[Any]:
  assert gg.gaussian_filter(A_						,	5						,	sigma=0.9					).all()




def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __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       __lowerCAmelCase      (						)    ->  Optional[Any]:
  assert med.median_filter(A_						,	3					).any()




def       __lowerCAmelCase      (						)    ->  Any:
  __UpperCAmelCase						    =       sob.sobel_filter(A_					)
  assert grad.any() and theta.any()




def       __lowerCAmelCase      (						)    ->  Optional[Any]:
  __UpperCAmelCase						    =       sp.make_sepia(A_						,	20					)
  assert sepia.all()




def       __lowerCAmelCase      (						A_   : str = "digital_image_processing/image_data/lena_small.jpg"					)    ->  Tuple:
  __UpperCAmelCase						    =       bs.Burkes(imread(A_						,	1					)						,	1_20					)
  burkes.process()
  assert burkes.output_img.any()




def       __lowerCAmelCase      (						A_   : str = "digital_image_processing/image_data/lena_small.jpg"						,	)    ->  Dict:
  __UpperCAmelCase						    =       rs.NearestNeighbour(imread(A_						,	1					)						,	4_00						,	2_00					)
  nn.process()
  assert nn.output.any()





def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

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



