


"""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

__snake_case		=       imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__snake_case		=       cvtColor(img, COLOR_BGR2GRAY)
def       A_   (   ):






     """simple docstring"""

     _a    	=				cn.convert_to_negative(__snake_case       )
     # assert negative_img array for at least one True
     assert negative_img.any()
def       A_   (   ):






     """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(__snake_case,     1_10       )       ).startswith(
              '''<PIL.Image.Image image mode=RGB size=100x100 at'''       )
def       A_   (   ):






     """simple docstring"""

     _a    	=				canny.gen_gaussian_kernel(9,     sigma=1.4       )
     # Assert ambiguous array
     assert resp.all()
def       A_   (   ):






     """simple docstring"""

     _a    	=				imread('''digital_image_processing/image_data/lena_small.jpg''',     0       )
     # assert ambiguous array for all == True
     assert canny_img.all()
     _a    	=				canny.canny(__snake_case       )
     # assert canny array for at least one True
     assert canny_array.any()
def       A_   (   ):






     """simple docstring"""

     assert gg.gaussian_filter(__snake_case,     5,     sigma=0.9       ).all()
def       A_   (   ):






     """simple docstring"""

     _a    	=				array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]]       )
     _a    	=				conv.img_convolve(__snake_case,     __snake_case       ).astype(__snake_case       )
     assert res.any()
def       A_   (   ):






     """simple docstring"""

     assert med.median_filter(__snake_case,     3       ).any()
def       A_   (   ):






     """simple docstring"""

     _a	,      _a    	=				sob.sobel_filter(__snake_case       )
     assert grad.any() and theta.any()
def       A_   (   ):






     """simple docstring"""

     _a    	=				sp.make_sepia(__snake_case,     20       )
     assert sepia.all()
def       A_   (   _lowerCAmelCase     :					int = "digital_image_processing/image_data/lena_small.jpg"       ):






     """simple docstring"""

     _a    	=				bs.Burkes(imread(__snake_case,     1       ),     1_20       )
     burkes.process()
     assert burkes.output_img.any()
def       A_   (   _lowerCAmelCase     :					int = "digital_image_processing/image_data/lena_small.jpg",     ):






     """simple docstring"""

     _a    	=				rs.NearestNeighbour(imread(__snake_case,     1       ),     4_00,     2_00       )
     nn.process()
     assert nn.output.any()

def       A_   (   ):






     """simple docstring"""

     _a    	=				'''digital_image_processing/image_data/lena.jpg'''

     # Reading the image and converting it to grayscale.
     _a    	=				imread(__snake_case,     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(
         __snake_case,     __snake_case,     __snake_case,     __snake_case       )

     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(__snake_case,     __snake_case,     __snake_case       )

     assert lbp_image.any()









"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case		=       {
    '''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
    '''tokenization_lxmert''': ['''LxmertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''LxmertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''LxmertEncoder''',
       '''LxmertForPreTraining''',
       '''LxmertForQuestionAnswering''',
       '''LxmertModel''',
       '''LxmertPreTrainedModel''',
       '''LxmertVisualFeatureEncoder''',
       '''LxmertXLayer''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLxmertForPreTraining''',
       '''TFLxmertMainLayer''',
       '''TFLxmertModel''',
       '''TFLxmertPreTrainedModel''',
       '''TFLxmertVisualFeatureEncoder''',
   ]


if TYPE_CHECKING:
   from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
   from .tokenization_lxmert import LxmertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_lxmert_fast import LxmertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lxmert import (
          LxmertEncoder,
          LxmertForPreTraining,
          LxmertForQuestionAnswering,
          LxmertModel,
          LxmertPreTrainedModel,
          LxmertVisualFeatureEncoder,
          LxmertXLayer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_lxmert import (
          TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLxmertForPreTraining,
          TFLxmertMainLayer,
          TFLxmertModel,
          TFLxmertPreTrainedModel,
          TFLxmertVisualFeatureEncoder,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)