






'''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)
      SCREAMING_SNAKE_CASE						=	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')

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

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

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

      SCREAMING_SNAKE_CASE						=	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

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











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



