

"""simple docstring"""






import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
 # Initialising the CNN
 # (Sequential- Building the model layer by layer)
 __lowerCamelCase										=		models.Sequential()

 # Step 1 - Convolution
 # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
 # (3,3) is the kernel size (filter matrix)
 classifier.add(
     layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
 )

 # Step 2 - Pooling
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Step 3 - Flattening
 classifier.add(layers.Flatten())

 # Step 4 - Full connection
 classifier.add(layers.Dense(units=1_28, activation="relu"))
 classifier.add(layers.Dense(units=1, activation="sigmoid"))

 # Compiling the CNN
 classifier.compile(
     optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
 )

 # Part 2 - Fitting the CNN to the images

 # Load Trained model weights

 # from keras.models import load_model
 # regressor=load_model('cnn.h5')

 __lowerCamelCase										=		tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 __lowerCamelCase										=		tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

 __lowerCamelCase										=		train_datagen.flow_from_directory(
     "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
 )

 __lowerCamelCase										=		test_datagen.flow_from_directory(
     "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
 )

 classifier.save("cnn.h5")

 # Part 3 - Making new predictions

 __lowerCamelCase										=		tf.keras.preprocessing.image.load_img(
     "dataset/single_prediction/image.png", target_size=(64, 64)
 )
 __lowerCamelCase										=		tf.keras.preprocessing.image.img_to_array(test_image)
 __lowerCamelCase										=		np.expand_dims(test_image, axis=0)
 __lowerCamelCase										=		classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  __lowerCamelCase										=		"Normal"
 if result[0][0] == 1:
  __lowerCamelCase										=		"Abnormality detected"




"""simple docstring"""






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
 import bsa
 from bsa import BeautifulSoup


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):







     def __init__(      self ,						**UpperCamelCase_       ):
      requires_backends(self ,						['''bs4''']       )
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      []
      __magic_name__     =      []
      __magic_name__     =      element if element.name else element.parent
      for parent in child.parents:  # type: bs4.element.Tag
       __magic_name__     =      parent.find_all(child.name ,						recursive=UpperCamelCase_       )
       xpath_tags.append(child.name       )
       xpath_subscripts.append(
           0 if 1 == len(UpperCamelCase_       ) else next(i for i, s in enumerate(UpperCamelCase_ ,						1       ) if s is child       )       )
       __magic_name__     =      parent
      xpath_tags.reverse()
      xpath_subscripts.reverse()
      return xpath_tags, xpath_subscripts







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      BeautifulSoup(UpperCamelCase_ ,						'''html.parser'''       )

      __magic_name__     =      []
      __magic_name__     =      []
      __magic_name__     =      []

      for element in html_code.descendants:
       if type(UpperCamelCase_       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
         continue

        __magic_name__     =      html.unescape(UpperCamelCase_       ).strip()
        if not text_in_this_tag:
         continue

        all_doc_strings.append(UpperCamelCase_       )

        __magic_name__   ,						__magic_name__     =      self.xpath_soup(UpperCamelCase_       )
        stringaxtag_seq.append(UpperCamelCase_       )
        stringaxsubs_seq.append(UpperCamelCase_       )

      if len(UpperCamelCase_       ) != len(UpperCamelCase_       ):
       raise ValueError('''Number of doc strings and xtags does not correspond'''       )
      if len(UpperCamelCase_       ) != len(UpperCamelCase_       ):
       raise ValueError('''Number of doc strings and xsubs does not correspond'''       )

      return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      ''''''
      for tagname, subs in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       xpath += f'''/{tagname}'''
       if subs != 0:
        xpath += f'''[{subs}]'''
      return xpath







     def __call__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      False

      # Check that strings has a valid type
      if isinstance(UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      True
      elif isinstance(UpperCamelCase_ ,						(list, tuple)       ):
       if len(UpperCamelCase_       ) == 0 or isinstance(html_strings[0] ,						UpperCamelCase_       ):
        __magic_name__     =      True

      if not valid_strings:
       raise ValueError(
           '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
           f'''but is of type {type(UpperCamelCase_       )}.'''       )

      __magic_name__     =      bool(isinstance(UpperCamelCase_ ,						(list, tuple)       ) and (isinstance(html_strings[0] ,						UpperCamelCase_       ))       )

      if not is_batched:
       __magic_name__     =      [html_strings]

      # Get nodes + xpaths
      __magic_name__     =      []
      __magic_name__     =      []
      for html_string in html_strings:
       __magic_name__   ,						__magic_name__   ,						__magic_name__     =      self.get_three_from_single(UpperCamelCase_       )
       nodes.append(UpperCamelCase_       )
       __magic_name__     =      []
       for node, tag_list, sub_list in zip(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
        __magic_name__     =      self.construct_xpath(UpperCamelCase_ ,						UpperCamelCase_       )
        xpath_strings.append(UpperCamelCase_       )
       xpaths.append(UpperCamelCase_       )

      # return as Dict
      __magic_name__     =      {'''nodes''': nodes, '''xpaths''': xpaths}
      __magic_name__     =      BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

      return encoded_inputs

