





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	:					Dict			  =					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=128, 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	:					Union[str, Any]			  =					tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
    )

    __lowerCamelCase	:					int			  =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

    __lowerCamelCase	:					Optional[int]			  =					train_datagen.flow_from_directory(
        """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
    )

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase	:					Tuple			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :List[Any]						   =       'timm_backbone'





      def __init__(			self	,							A_=None	,							A_=3	,							A_=True	,							A_=True	,							A_=None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Tuple	     =       backbone
       UpperCamelCase     : Dict	     =       num_channels
       UpperCamelCase     : Tuple	     =       features_only
       UpperCamelCase     : Optional[int]	     =       use_pretrained_backbone
       UpperCamelCase     : Dict	     =       True
       UpperCamelCase     : List[str]	     =       out_indices if out_indices is not None else (-1,)
