





from collections import deque

from .hash_table import HashTable



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: List[str]					,   *_SCREAMING_SNAKE_CASE: Any					,   **_SCREAMING_SNAKE_CASE: Any		)       -> Any:




              """simple docstring"""


              super().__init__(*_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  deque([]		) if self.values[key] is None else self.values[key]
              self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.values[key]

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              return (
                  sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
                  / self.size_table
                  * self.charge_factor
              )



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str=None		)       -> List[Any]:




              """simple docstring"""


              if not (
                  len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
              ):
                     return key
              return super()._collision_resolution(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








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)
     _UpperCAmelCase        =				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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

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

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (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_2_8, 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')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

