





'''simple docstring'''



import flax.linen as nn
import jax
import jax.numpy as jnp






class 							__lowercase       (      nn.Module						):







	'''simple docstring'''

	a    :				Tuple											=       42
	a    :				Union[str, Any]											=       jnp.floataa


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							nn.Conv(
				    self.out_channels     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)


	def __call__(self     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							hidden_states.shape
				__lowercase										=							jax.image.resize(
				    __A     ,shape=(batch, height * 2, width * 2, channels)     ,method='''nearest'''     ,)
				__lowercase										=							self.conv(__A )
				return hidden_states






class 							__lowercase       (      nn.Module						):







	'''simple docstring'''

	a    :				Any											=       42
	a    :				Union[str, Any]											=       jnp.floataa


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							nn.Conv(
				    self.out_channels     ,kernel_size=(3, 3)     ,strides=(2, 2)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)


	def __call__(self     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							self.conv(__A )
				return hidden_states






class 							__lowercase       (      nn.Module						):







	'''simple docstring'''

	a    :				Union[str, Any]											=       42
	a    :				str											=       None
	a    :				List[str]											=       0.0
	a    :				Optional[int]											=       None
	a    :				Dict											=       jnp.floataa


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							self.in_channels if self.out_channels is None else self.out_channels

				__lowercase										=							nn.GroupNorm(num_groups=32     ,epsilon=1E-5 )
				__lowercase										=							nn.Conv(
				    __A     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)

				__lowercase										=							nn.Dense(__A     ,dtype=self.dtype )

				__lowercase										=							nn.GroupNorm(num_groups=32     ,epsilon=1E-5 )
				__lowercase										=							nn.Dropout(self.dropout_prob )
				__lowercase										=							nn.Conv(
				    __A     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)

				__lowercase										=							self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

				__lowercase										=							None
				if use_nin_shortcut:
							__lowercase										=							nn.Conv(
							    __A     ,kernel_size=(1, 1)     ,strides=(1, 1)     ,padding='''VALID'''     ,dtype=self.dtype     ,)


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase=True )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							hidden_states
				__lowercase										=							self.norma(__A )
				__lowercase										=							nn.swish(__A )
				__lowercase										=							self.conva(__A )

				__lowercase										=							self.time_emb_proj(nn.swish(__A ) )
				__lowercase										=							jnp.expand_dims(jnp.expand_dims(__A     ,1 )     ,1 )
				__lowercase										=							hidden_states + temb

				__lowercase										=							self.norma(__A )
				__lowercase										=							nn.swish(__A )
				__lowercase										=							self.dropout(__A     ,__A )
				__lowercase										=							self.conva(__A )

				if self.conv_shortcut is not None:
							__lowercase										=							self.conv_shortcut(__A )

				return hidden_states + residual












'''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(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')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

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

	_SCREAMING_SNAKE_CASE					=    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

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_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'''





