






'''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   :					int   							=					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')

		__lowerCamelCase   :					List[str]   							=					tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		__lowerCamelCase   :					Optional[int]   							=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

		__lowerCamelCase   :					List[Any]   							=					train_datagen.flow_from_directory(
		    '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
		)

		__lowerCamelCase   :					Tuple   							=					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

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












'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	__UpperCAmelCase       ( __magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if subparsers is not None:
						snake_case_				:					List[str]   =       subparsers.add_parser("test"						)
			else:
						snake_case_				:					List[Any]   =       argparse.ArgumentParser("Accelerate test command"						)

			parser.add_argument(
			    "--config_file"     ,default=__magic_name__     ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
			        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with 'huggingface'."
			    )     ,)

			if subparsers is not None:
						parser.set_defaults(func=__magic_name__						)
			return parser

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ["test_utils", "scripts", "test_script.py"]						)

			if args.config_file is None:
						snake_case_				:					str   =       script_name
			else:
						snake_case_				:					Any   =       F'''--config_file={args.config_file} {script_name}'''

			snake_case_				:					Union[str, Any]   =       ["accelerate-launch"] + test_args.split()
			snake_case_				:					Optional[int]   =       execute_subprocess_async(__magic_name__     ,env=os.environ.copy()						)
			if result.returncode == 0:
						print("Test is a success! You are ready for your distributed training!"						)

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       test_command_parser()
			snake_case_				:					Dict   =       parser.parse_args()
			test_command(__magic_name__						)


if __name__ == "__main__":
		main()




