






"""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)
 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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='''relu''')
 )

 # Step 2 - Pooling
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(3_2, (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_2_8, 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_	:    int            =      tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCAmelCase_	:    Union[str, Any]            =      tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

 lowerCAmelCase_	:    Dict            =      train_datagen.flow_from_directory(
     '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
 )

 lowerCAmelCase_	:    Dict            =      test_datagen.flow_from_directory(
     '''dataset/test_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
 )

 classifier.save('''cnn.h5''')

 # Part 3 - Making new predictions

 lowerCAmelCase_	:    Optional[int]            =      tf.keras.preprocessing.image.load_img(
     '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
 )
 lowerCAmelCase_	:    Dict            =      tf.keras.preprocessing.image.img_to_array(test_image)
 lowerCAmelCase_	:    Optional[int]            =      np.expand_dims(test_image, axis=0)
 lowerCAmelCase_	:    Any            =      classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  lowerCAmelCase_	:    Any            =      '''Normal'''
 if result[0][0] == 1:
  lowerCAmelCase_	:    str            =      '''Abnormality detected'''








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Any            =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'canine'



  def __init__(      self ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=1_63_84 ,		snake_case__=16 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=0 ,		snake_case__=0xE_0_0_0 ,		snake_case__=0xE_0_0_1 ,		snake_case__=4 ,		snake_case__=4 ,		snake_case__=8 ,		snake_case__=1_63_84 ,		snake_case__=1_28 ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)

         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps

         # Character config:
         UpperCAmelCase	    =			downsampling_rate
         UpperCAmelCase	    =			upsampling_kernel_size
         UpperCAmelCase	    =			num_hash_functions
         UpperCAmelCase	    =			num_hash_buckets
         UpperCAmelCase	    =			local_transformer_stride
