






'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
 import torch

if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)
@dataclass
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(	self			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a					  =     deprecated_arg[3:]
               setattr(self			, lowerCamelCase			, not kwargs.pop(lowerCamelCase   )   )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""   )

         __a					  =     kwargs.pop('torchscript'			, self.torchscript   )
         __a					  =     kwargs.pop('torch_xla_tpu_print_metrics'			, self.torch_xla_tpu_print_metrics   )
         __a					  =     kwargs.pop('fp16_opt_level'			, self.fpaa_opt_level   )
         super().__init__(**lowerCamelCase   )

      __a	       =field(default=_lowerCAmelCase   ,   metadata={"help": "Trace the models using torchscript"}  )
      __a	       =field(default=_lowerCAmelCase   ,   metadata={"help": "Print Xla/PyTorch tpu metrics"}  )
      __a	       =field(
          default="O1"   ,   metadata={
              "help": (
                  "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                  "See details at https://nvidia.github.io/apex/amp.html"
              )
          }   ,   )




      @cached_property
      def 			__UpperCamelCase				(	self   )				->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self			, ['torch']   )
         logger.info('PyTorch: setting up devices'   )
         if not self.cuda:
            __a					  =     torch.device('cpu'   )
            __a					  =     0
         elif is_torch_tpu_available():
            __a					  =     xm.xla_device()
            __a					  =     0
         else:
            __a					  =     torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )
            __a					  =     torch.cuda.device_count()
         return device, n_gpu




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         requires_backends(self			, ['torch']   )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def 			__UpperCamelCase				(	self   )				->"torch.device":



         '''simple docstring'''
         requires_backends(self			, ['torch']   )
         return self._setup_devices[0]




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         requires_backends(self			, ['torch']   )
         return self._setup_devices[1]






      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         return self.n_gpu > 0







'''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"""