






from importlib import import_module

from .logging import get_logger


__lowerCamelCase	:		List[Any]										=   get_logger(__name__)







class  _lowercase   :
      def __init__(     self				,			a				,			a=None   ):
       snake_case__  :    str							       =attrs or []
       if module is not None:
        for key in module.__dict__:
         if key in attrs or not key.startswith("""__"""   ):
          setattr(self				,			snake_case_				,			getattr(snake_case_				,			snake_case_   )   )
       snake_case__  :    Tuple							       =module._original_module if isinstance(snake_case_				,			_PatchedModuleObj   ) else module







class  _lowercase   :
      _a							:						Any										=							[]
      def __init__(     self				,			a				,			a				,			a				,			a=None   ):
       snake_case__  :    List[str]							       =obj
       snake_case__  :    int							       =target
       snake_case__  :    Dict							       =new
       snake_case__  :    Tuple							       =target.split("""."""   )[0]
       snake_case__  :    Optional[int]							       ={}
       snake_case__  :    Optional[Any]							       =attrs or []
      def __enter__(     self   ):
       *snake_case__       ,   snake_case__  :    List[str]							       =self.target.split("""."""   )

       # Patch modules:
       # it's used to patch attributes of submodules like "os.path.join";
       # in this case we need to patch "os" and "os.path"

       for i in range(len(snake_case_   )   ):
        try:
         snake_case__  :    Any							       =import_module(""".""".join(submodules[: i + 1]   )   )
        except ModuleNotFoundError:
         continue
        # We iterate over all the globals in self.obj in case we find "os" or "os.path"
        for attr in self.obj.__dir__():
         snake_case__  :    Any							       =getattr(self.obj				,			snake_case_   )
         # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
         # This allows to patch renamed modules like "from os import path as ospath".
         if obj_attr is submodule or (
             (isinstance(snake_case_				,			_PatchedModuleObj   ) and obj_attr._original_module is submodule)
         ):
          snake_case__  :    Optional[int]							       =obj_attr
          # patch at top level
          setattr(self.obj				,			snake_case_				,			_PatchedModuleObj(snake_case_				,			attrs=self.attrs   )   )
          snake_case__  :    Tuple							       =getattr(self.obj				,			snake_case_   )
          # construct lower levels patches
          for key in submodules[i + 1 :]:
           setattr(snake_case_				,			snake_case_				,			_PatchedModuleObj(getattr(snake_case_				,			snake_case_				,			snake_case_   )				,			attrs=self.attrs   )   )
           snake_case__  :    Dict							       =getattr(snake_case_				,			snake_case_   )
          # finally set the target attribute
          setattr(snake_case_				,			snake_case_				,			self.new   )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

       if submodules:  # if it's an attribute of a submodule like "os.path.join"
        try:
         snake_case__  :    int							       =getattr(import_module(""".""".join(snake_case_   )   )				,			snake_case_   )
        except (AttributeError, ModuleNotFoundError):
         return
        # We iterate over all the globals in self.obj in case we find "os.path.join"
        for attr in self.obj.__dir__():
         # We don't check for the name of the global, but rather if its value *is* "os.path.join".
         # This allows to patch renamed attributes like "from os.path import join as pjoin".
         if getattr(self.obj				,			snake_case_   ) is attr_value:
          snake_case__  :    List[Any]							       =getattr(self.obj				,			snake_case_   )
          setattr(self.obj				,			snake_case_				,			self.new   )
       elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
        snake_case__  :    List[Any]							       =globals()["""__builtins__"""][target_attr]
        setattr(self.obj				,			snake_case_				,			self.new   )
       else:
        raise RuntimeError(F"Tried to patch attribute {target_attr} instead of a submodule."   )
      def __exit__(     self				,			*a   ):
       for attr in list(self.original   ):
        setattr(self.obj				,			snake_case_				,			self.original.pop(snake_case_   )   )
      def 						lowercase__					(     self   ):
       self.__enter__()
       self._active_patches.append(self   )



      def 						lowercase__					(     self   ):
       try:
        self._active_patches.remove(self   )
       except ValueError:
        # If the patch hasn't been started this will fail
        return None

       return self.__exit__()













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"""





