
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)
		lowercase						:      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(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')

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

		lowercase						:      List[str]									= tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

		lowercase						:      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

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

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)