






'''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

SCREAMING_SNAKE_CASE__     =       imread(R'digital_image_processing/image_data/lena_small.jpg')
SCREAMING_SNAKE_CASE__     =       cvtColor(img, COLOR_BGR2GRAY)







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = cn.convert_to_negative(__UpperCamelCase   )
      # assert negative_img array for at least one True
      assert negative_img.any()







def    lowercase__			(				)->			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		,						110   )   ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""   )







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







def    lowercase__			(				)->			Union[str, 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(__UpperCamelCase   )
      # assert canny array for at least one True
      assert canny_array.any()







def    lowercase__			(				)->			Tuple:
      assert gg.gaussian_filter(__UpperCamelCase		,						5		,						sigma=0.9   ).all()







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







def    lowercase__			(				)->			Optional[int]:
      assert med.median_filter(__UpperCamelCase		,						3   ).any()







def    lowercase__			(				)->			int:
      UpperCamelCase  ,UpperCamelCase      = sob.sobel_filter(__UpperCamelCase   )
      assert grad.any() and theta.any()







def    lowercase__			(				)->			Tuple:
      UpperCamelCase      = sp.make_sepia(__UpperCamelCase		,						20   )
      assert sepia.all()







def    lowercase__			(				__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"   )->			Any:
      UpperCamelCase      = bs.Burkes(imread(__UpperCamelCase		,						1   )		,						120   )
      burkes.process()
      assert burkes.output_img.any()







def    lowercase__			(				__UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,						)->			int:
      UpperCamelCase      = rs.NearestNeighbour(imread(__UpperCamelCase		,						1   )		,						400		,						200   )
      nn.process()
      assert nn.output.any()







def    lowercase__			(				)->			Optional[Any]:
      UpperCamelCase      = """digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      UpperCamelCase      = imread(__UpperCamelCase		,						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(
          __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      = 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(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )

      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,
)


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

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