






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["pixel_values"]

	def __init__(							self       ,    UpperCAmelCase = True       ,    UpperCAmelCase = 1 / 255       ,    UpperCAmelCase = True       ,    UpperCAmelCase = 8       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase    )

							_UpperCAmelCase									=			do_rescale
							_UpperCAmelCase									=			rescale_factor
							_UpperCAmelCase									=			do_pad
							_UpperCAmelCase									=			pad_size

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return rescale(UpperCAmelCase       ,    scale=UpperCAmelCase       ,    data_format=UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			get_image_size(UpperCAmelCase    )
							_UpperCAmelCase									=			(old_height // size + 1) * size - old_height
							_UpperCAmelCase									=			(old_width // size + 1) * size - old_width

							return pad(UpperCAmelCase       ,    ((0, pad_height), (0, pad_width))       ,    mode='symmetric'       ,    data_format=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = ChannelDimension.FIRST       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			do_rescale if do_rescale is not None else self.do_rescale
							_UpperCAmelCase									=			rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCAmelCase									=			do_pad if do_pad is not None else self.do_pad
							_UpperCAmelCase									=			pad_size if pad_size is not None else self.pad_size

							_UpperCAmelCase									=			make_list_of_images(UpperCAmelCase    )

							if not valid_images(UpperCAmelCase    ):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

							if do_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

							# All transformations expect numpy arrays.
							_UpperCAmelCase									=			[to_numpy_array(UpperCAmelCase    ) for image in images]

							if do_rescale:
													_UpperCAmelCase									=			[self.rescale(image=UpperCAmelCase       ,    scale=UpperCAmelCase    ) for image in images]

							if do_pad:
													_UpperCAmelCase									=			[self.pad(UpperCAmelCase       ,    size=UpperCAmelCase    ) for image in images]

							_UpperCAmelCase									=			[to_channel_dimension_format(UpperCAmelCase       ,    UpperCAmelCase    ) for image in images]

							_UpperCAmelCase									=			{'pixel_values': images}
							return BatchFeature(data=UpperCAmelCase       ,    tensor_type=UpperCAmelCase    )







'''simple docstring'''

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
      lowercase_					= 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""")
      lowercase_				,			lowercase_					= 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""")
            lowercase_					= rh.cluster(
                name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
            )
      else:
            lowercase_					= rh.cluster(
                name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      lowercase_					= 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)



