






'''simple docstring'''



from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCamelCase   :							Dict         =   1.0_5_4_5_7_1_8_1_7E-3_4  # unit of ℏ : J * s

__UpperCamelCase   :							Tuple         =   3E8  # unit of c : m * s^-1



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						float,       SCREAMING_SNAKE_CASE__:						float,       SCREAMING_SNAKE_CASE__:						float     )	->      dict[str, float]:

   """simple docstring"""

   if (force, area, distance).count(0     ) != 1:
      raise ValueError('One and only one argument must be 0'     )
   if force < 0:
      raise ValueError('Magnitude of force can not be negative'     )
   if distance < 0:
      raise ValueError('Distance can not be negative'     )
   if area < 0:
      raise ValueError('Area can not be negative'     )
   if force == 0:
      __a					  =     (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
          240 * (distance) ** 4
      )
      return {"force": force}
   elif area == 0:
      __a					  =     (240 * force * (distance) ** 4) / (
          REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
      )
      return {"area": area}
   elif distance == 0:
      __a					  =     (
          (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
      ) ** (1 / 4)
      return {"distance": distance}
   raise ValueError('One and only one argument must be 0'     )


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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)
 __UpperCamelCase   :							Union[str, Any]         =   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')

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

 __UpperCamelCase   :							str         =   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

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