"""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       :			Optional[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(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       :			Any   	=tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       __lowerCAmelCase       :			Optional[int]   	=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

       __lowerCAmelCase       :			Optional[Any]   	=train_datagen.flow_from_directory(
           """dataset/training_set""", target_size=(6_4, 6_4), batch_size=3_2, class_mode="""binary"""
       )

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




"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



