






'''simple docstring'''







from typing import TYPE_CHECKING

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


_A     :      int   					={
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :      Any   					=["DebertaTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :      int   					=[
       "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaForMaskedLM",
       "DebertaForQuestionAnswering",
       "DebertaForSequenceClassification",
       "DebertaForTokenClassification",
       "DebertaModel",
       "DebertaPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :      Union[str, Any]   					=[
       "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaForMaskedLM",
       "TFDebertaForQuestionAnswering",
       "TFDebertaForSequenceClassification",
       "TFDebertaForTokenClassification",
       "TFDebertaModel",
       "TFDebertaPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   _A     :      Optional[Any]   					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





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





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





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





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	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
							lowerCamelCase__ :     List[str]	  =	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]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

