






def 			A__							(      _a  :					str	):





 '''simple docstring'''







 snake_case__  :    Union[str, Any]							       =""""""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups







def 			A__							(      _a  :					str	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =[chr(i + 65	) for i in range(26	)]
 # Remove duplicate characters from key
 snake_case__  :    Any							       =remove_duplicates(key.upper()	)
 snake_case__  :    Union[str, Any]							       =len(_a	)
 # First fill cipher with key characters
 snake_case__  :    Optional[Any]							       ={alphabet[i]: char for i, char in enumerate(_a	)}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_a	)		,  26	):
  snake_case__  :    Optional[int]							       =alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   snake_case__  :    Tuple							       =alphabet[i - offset]
  snake_case__  :    Union[str, Any]							       =char
 return cipher_alphabet







def 			A__							(      _a  :					str		,  _a  :					dict[str, str]	):





 '''simple docstring'''







 return "".join(cipher_map.get(_a		,  _a	) for ch in message.upper()	)







def 			A__							(      _a  :					str		,  _a  :					dict[str, str]	):





 '''simple docstring'''







 snake_case__  :    List[Any]							       ={v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_a		,  _a	) for ch in message.upper()	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Tuple							       =input("""Enter message to encode or decode: """	).strip()
 snake_case__  :    Optional[Any]							       =input("""Enter keyword: """	).strip()
 snake_case__  :    List[Any]							       =input("""Encipher or decipher? E/D:"""	).strip()[0].lower()
 try:
  snake_case__  :    Union[str, Any]							       ={"""e""": encipher, """d""": decipher}[option]
 except KeyError:
  raise KeyError("""invalid input option"""	)
 snake_case__  :    int							       =create_cipher_map(_a	)
 print(func(_a		,  _a	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()













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

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

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

       __lowerCamelCase	:		Optional[Any]										=   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	:		Tuple										=   np.expand_dims(test_image, axis=0)
       __lowerCamelCase	:		Optional[int]										=   classifier.predict(test_image)
       # training_set.class_indices
       if result[0][0] == 0:
              __lowerCamelCase	:		Dict										=   """Normal"""
       if result[0][0] == 1:
              __lowerCamelCase	:		List[Any]										=   """Abnormality detected"""





