



"""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:   Any								=		imread(R"digital_image_processing/image_data/lena_small.jpg")
A:   Optional[int]								=		cvtColor(img, COLOR_BGR2GRAY)

def       _snake_case						(						):
 UpperCAmelCase	:					Optional[int]	=	cn.convert_to_negative(UpperCamelCase						)
 # assert negative_img array for at least one True
 assert negative_img.any()

def       _snake_case						(						):
 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       _snake_case						(						):
 UpperCAmelCase	:					str	=	canny.gen_gaussian_kernel(9			,		sigma=1.4						)
 # Assert ambiguous array
 assert resp.all()

def       _snake_case						(						):
 UpperCAmelCase	:					int	=	imread("""digital_image_processing/image_data/lena_small.jpg"""			,		0						)
 # assert ambiguous array for all == True
 assert canny_img.all()
 UpperCAmelCase	:					Any	=	canny.canny(UpperCamelCase						)
 # assert canny array for at least one True
 assert canny_array.any()

def       _snake_case						(						):
 assert gg.gaussian_filter(UpperCamelCase			,		5			,		sigma=0.9						).all()

def       _snake_case						(						):
 # laplace diagonals
 UpperCAmelCase	:					Any	=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]						)
 UpperCAmelCase	:					Dict	=	conv.img_convolve(UpperCamelCase			,		UpperCamelCase						).astype(UpperCamelCase						)
 assert res.any()

def       _snake_case						(						):
 assert med.median_filter(UpperCamelCase			,		3						).any()

def       _snake_case						(						):
 UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	sob.sobel_filter(UpperCamelCase						)
 assert grad.any() and theta.any()

def       _snake_case						(						):
 UpperCAmelCase	:					str	=	sp.make_sepia(UpperCamelCase			,		20						)
 assert sepia.all()

def       _snake_case						(						UpperCamelCase						:		str = "digital_image_processing/image_data/lena_small.jpg"						):
 UpperCAmelCase	:					List[str]	=	bs.Burkes(imread(UpperCamelCase			,		1						)			,		120						)
 burkes.process()
 assert burkes.output_img.any()

def       _snake_case						(						UpperCamelCase						:		str = "digital_image_processing/image_data/lena_small.jpg"			,		):
 UpperCAmelCase	:					List[str]	=	rs.NearestNeighbour(imread(UpperCamelCase			,		1						)			,		400			,		200						)
 nn.process()
 assert nn.output.any()

def       _snake_case						(						):
 UpperCAmelCase	:					Optional[Any]	=	"""digital_image_processing/image_data/lena.jpg"""

 # Reading the image and converting it to grayscale.
 UpperCAmelCase	:					Optional[Any]	=	imread(UpperCamelCase			,		0						)

 # Test for get_neighbors_pixel function() return not None
 UpperCAmelCase	:					Optional[Any]	=	0
 UpperCAmelCase	:					Union[str, Any]	=	0
 UpperCAmelCase	:					Any	=	image[x_coordinate][y_coordinate]

 UpperCAmelCase	:					Dict	=	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
 UpperCAmelCase	:					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]						):
   UpperCAmelCase	:					List[Any]	=	lbp.local_binary_value(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 assert lbp_image.any()





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
