




"""simple docstring"""






import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowercase__			=		"""Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""







def 	_snake_case     (							):
				_lowerCamelCase	:     Union[str, Any]       =       _ask_options(
				    'In which compute environment are you running?'				,       ['This machine', 'AWS (Amazon SageMaker)']				,       _convert_compute_environment				,       )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								_lowerCamelCase	:     Union[str, Any]       =       get_sagemaker_input()
				else:
								_lowerCamelCase	:     int       =       get_cluster_input()
				return config







def 	_snake_case     (							lowercase__=None						):

				if subparsers is not None:
								_lowerCamelCase	:     int       =       subparsers.add_parser('config'				,       description=a_						)
				else:
								_lowerCamelCase	:     Optional[int]       =       argparse.ArgumentParser('Accelerate config command'				,       description=a_						)

				parser.add_argument(
				    '--config_file'				,       default=a_				,       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=a_						)
				return parser







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     List[str]       =       get_user_input()
				if args.config_file is not None:
								_lowerCamelCase	:     Optional[int]       =       args.config_file
				else:
								if not os.path.isdir(a_						):
												os.makedirs(a_						)
								_lowerCamelCase	:     List[Any]       =       default_yaml_config_file

				if config_file.endswith('.json'						):
								config.to_json_file(a_						)
				else:
								config.to_yaml_file(a_						)
				print(f'''accelerate configuration saved at {config_file}'''						)







def 	_snake_case     (							):
				_lowerCamelCase	:     List[Any]       =       config_command_parser()
				_lowerCamelCase	:     List[Any]       =       parser.parse_args()
				config_command(a_						)


if __name__ == "__main__":
			main()







'''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)
      UpperCamelCase      					=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')

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      UpperCamelCase      					=train_datagen.flow_from_directory(
          "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      UpperCamelCase      					=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

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






