
import colorsys

from PIL import Image  # type: ignore


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Union[str, Any]					=				x
      UpperCamelCase__    :   int					=				y
      for step in range(UpperCAmelCase__		):  # noqa: B007
            UpperCamelCase__    :   Tuple					=				a * a - b * b + x
            UpperCamelCase__    :   int					=				2 * a * b + y
            UpperCamelCase__    :   List[str]					=				a_new

            # divergence happens for all complex number with an absolute value
            # greater than 4
            if a * a + b * b > 4:
                  break
      return step / (max_step - 1)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_5_5, 2_5_5, 2_5_5)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ ,		1 ,		1		)		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 8_0_0 ,		UpperCamelCase__ = 6_0_0 ,		UpperCamelCase__ = -0.6 ,		UpperCamelCase__ = 0 ,		UpperCamelCase__ = 3.2 ,		UpperCamelCase__ = 5_0 ,		UpperCamelCase__ = True ,		):
      UpperCamelCase__    :   str					=				Image.new('''RGB''' ,		(image_width, image_height)		)
      UpperCamelCase__    :   int					=				img.load()

      # loop through the image-coordinates
      for image_x in range(UpperCAmelCase__		):
            for image_y in range(UpperCAmelCase__		):
                  # determine the figure-coordinates based on the image-coordinates
                  UpperCamelCase__    :   Dict					=				figure_width / image_width * image_height
                  UpperCamelCase__    :   Any					=				figure_center_x + (image_x / image_width - 0.5) * figure_width
                  UpperCamelCase__    :   Optional[int]					=				figure_center_y + (image_y / image_height - 0.5) * figure_height

                  UpperCamelCase__    :   List[Any]					=				get_distance(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__		)

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        UpperCamelCase__    :   Union[str, Any]					=				get_color_coded_rgb(UpperCAmelCase__		)
                  else:
                        UpperCamelCase__    :   Optional[Any]					=				get_black_and_white_rgb(UpperCAmelCase__		)

      return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCamelCase      =get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    lowerCamelCase      =argparse.ArgumentParser()
    parser.add_argument("--user", type=str, default="ubuntu")
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--key_path", type=str, default=None)
    parser.add_argument("--instance", type=str, default="V100:1")
    parser.add_argument("--provider", type=str, default="cheapest")
    parser.add_argument("--use_spot", type=bool, default=False)
    parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
    lowerCamelCase   ,     lowerCamelCase      =parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError("Cannot specify both BYO and on-demand cluster args")
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
        )
    else:
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    lowerCamelCase      =args.example.rsplit("/", 1)[0]

    # Set up remote environment
    cluster.install_packages(["pip:./"])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
    cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([F'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)


