"""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)
 lowercase__					       =	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')

 lowercase__					       =	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowercase__					       =	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

 lowercase__					       =	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

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



"""simple docstring"""
from math import pow, sqrt







def 						__a   (	*_SCREAMING_SNAKE_CASE   )	->bool:
  a__:     Union[str, Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 0 and all(value > 0.0 for value in values   )
  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError('Input Error: Molar mass values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,     2   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,     2   ) / molar_mass		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )


