
"""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)
   _A     :			Tuple  						=			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')

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

   _A     :			Optional[int]  						=			tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

   _A     :			List[str]  						=			train_datagen.flow_from_directory(
       """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
   )

   _A     :			List[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

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




"""simple docstring"""




import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_A     :			str  						=			False
try:
   _A     :			Dict  						=			_is_package_available("""google.colab""")
except ModuleNotFoundError:
   pass

@input.register
class 						a__		:
   def __init__(     self      ,       _a = None      ,       _a = []							):
          lowercase       :							Union[str, Any]									=				0
          lowercase       :							str									=				choices
          lowercase       :							List[Any]									=				prompt
          if sys.platform == "win32":
                 lowercase       :							Union[str, Any]									=				"*"
          else:
                 lowercase       :							int									=				"➔ "
   def      __magic_name__     (     self      ,       _a      ,       _a = ""							):
          if sys.platform != "win32":
                 writeColor(self.choices[index]      ,       32      ,       _a							)
          else:
                 forceWrite(self.choices[index]      ,       _a							)
   def      __magic_name__     (     self      ,       _a							):
          if index == self.position:
                 forceWrite(f""" {self.arrow_char} """							)
                 self.write_choice(_a							)
          else:
                 forceWrite(f"""    {self.choices[index]}"""							)
          reset_cursor()
   def      __magic_name__     (     self      ,       _a      ,       _a = 1							):
          lowercase       :							Optional[int]									=				self.position
          if direction == Direction.DOWN:
                 if self.position + 1 >= len(self.choices							):
                        return
                 self.position += num_spaces
          else:
                 if self.position - 1 < 0:
                        return
                 self.position -= num_spaces
          clear_line()
          self.print_choice(_a							)
          move_cursor(_a      ,       direction.name							)
          self.print_choice(self.position							)
   @input.mark(KEYMAP["up"]							)
   def      __magic_name__     (     self							):
          self.move_direction(Direction.UP							)
   @input.mark(KEYMAP["down"]							)
   def      __magic_name__     (     self							):
          self.move_direction(Direction.DOWN							)
   @input.mark(KEYMAP["newline"]							)
   def      __magic_name__     (     self							):
          move_cursor(len(self.choices							) - self.position      ,       "DOWN"							)
          return self.position
   @input.mark(KEYMAP["interrupt"]							)
   def      __magic_name__     (     self							):
          move_cursor(len(self.choices							) - self.position      ,       "DOWN"							)
          raise KeyboardInterrupt
   @input.mark_multiple(*[KEYMAP[str(_a							)] for number in range(10							)]							)
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				int(chr(self.current_selection							)							)
          lowercase       :							Union[str, Any]									=				index - self.position
          if index == self.position:
                 return
          if index < len(self.choices							):
                 if self.position > index:
                        self.move_direction(Direction.UP      ,       -movement							)
                 elif self.position < index:
                        self.move_direction(Direction.DOWN      ,       _a							)
                 else:
                        return
          else:
                 return

   def      __magic_name__     (     self      ,       _a = 0							):
          if self.prompt:
                 linebreak()
                 forceWrite(self.prompt      ,       "\n"							)
                 if in_colab:
                        forceWrite("Please input a choice index (starting from 0), and press enter"      ,       "\n"							)
                 else:
                        forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"      ,       "\n"							)
          lowercase       :							Tuple									=				default_choice
          for i in range(len(self.choices							)							):
                 self.print_choice(_a							)
                 forceWrite("\n"							)
          move_cursor(len(self.choices							) - self.position      ,       "UP"							)
          with cursor.hide():
                 while True:
                        if in_colab:
                               try:
                                      lowercase       :							Tuple									=				int(builtins.input()							)
                               except ValueError:
                                      lowercase       :							Optional[int]									=				default_choice
                        else:
                               lowercase       :							int									=				self.handle_input()
                        if choice is not None:
                               reset_cursor()
                               for _ in range(len(self.choices							) + 1							):
                                      move_cursor(1      ,       "UP"							)
                                      clear_line()
                               self.write_choice(_a      ,       "\n"							)
                               return choice


