"""simple docstring"""



import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


SCREAMING_SNAKE_CASE__:Tuple			=	'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''



def 					_lowerCamelCase(					a=None							):
       if subparsers is not None:
              __a     				=  subparsers.add_parser("tpu-config" ,  description=_description							)
       else:
              __a     				=  argparse.ArgumentParser("Accelerate tpu-config command" ,  description=_description							)
       # Core arguments
       __a     				=  parser.add_argument_group(
           "Config Arguments" ,  "Arguments that can be configured through `accelerate config`."							)
       config_args.add_argument(
           "--config_file" ,  type=_UpperCAmelCase ,  default=_UpperCAmelCase ,  help="Path to the config file to use for accelerate." ,  )
       config_args.add_argument(
           "--tpu_name" ,  default=_UpperCAmelCase ,  help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." ,  )
       config_args.add_argument(
           "--tpu_zone" ,  default=_UpperCAmelCase ,  help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." ,  )
       __a     				=  parser.add_argument_group("TPU Arguments" ,  "Arguments for options ran inside the TPU."							)
       pod_args.add_argument(
           "--use_alpha" ,  action="store_true" ,  help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." ,  )
       pod_args.add_argument(
           "--command_file" ,  default=_UpperCAmelCase ,  help="The path to the file containing the commands to run on the pod on startup." ,  )
       pod_args.add_argument(
           "--command" ,  action="append" ,  nargs="+" ,  help="A command to run on the pod. Can be passed multiple times." ,  )
       pod_args.add_argument(
           "--install_accelerate" ,  action="store_true" ,  help="Whether to install accelerate on the pod. Defaults to False." ,  )
       pod_args.add_argument(
           "--accelerate_version" ,  default="latest" ,  help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub." ,  )
       pod_args.add_argument(
           "--debug" ,  action="store_true" ,  help="If set, will print the command that would be run instead of running it."							)

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



def 					_lowerCamelCase(					a							):
       __a     				=  None

       # Get the default from the config file if it exists.
       if args.config_file is not None or os.path.isfile(_UpperCAmelCase							):
              __a     				=  load_config_from_file(args.config_file							)
              if not args.command_file and defaults.command_file is not None and not args.command:
                     __a     				=  defaults.command_file
              if not args.command and defaults.commands is not None:
                     __a     				=  defaults.commands
              if not args.tpu_name:
                     __a     				=  defaults.tpu_name
              if not args.tpu_zone:
                     __a     				=  defaults.tpu_zone
       if args.accelerate_version == "dev":
              __a     				=  'git+https://github.com/huggingface/accelerate.git'
       elif args.accelerate_version == "latest":
              __a     				=  'accelerate -U'
       elif isinstance(parse(args.accelerate_version							) ,  _UpperCAmelCase							):
              __a     				=  F"accelerate=={args.accelerate_version}"

       if not args.command_file and not args.command:
              raise ValueError("You must specify either a command file or a command to run on the pod."							)

       if args.command_file:
              with open(args.command_file ,  "r"							) as f:
                     __a     				=  [f.read().splitlines()]

    # To turn list of lists into list of strings
       if isinstance(args.command[0] ,  _UpperCAmelCase							):
              __a     				=  [line for cmd in args.command for line in cmd]
       # Default to the shared folder and install accelerate
       __a     				=  ['cd /usr/share']
       if args.install_accelerate:
              new_cmd += [F"pip install {args.accelerate_version}"]
       new_cmd += args.command
       __a     				=  '; '.join(_UpperCAmelCase							)

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       __a     				=  ['gcloud']
       if args.use_alpha:
              cmd += ["alpha"]
       cmd += [
           "compute",
           "tpus",
           "tpu-vm",
           "ssh",
           args.tpu_name,
           "--zone",
           args.tpu_zone,
           "--command",
           args.command,
           "--worker",
           "all",
       ]
       if args.debug:
              print(F"Running {' '.join(_UpperCAmelCase							)}"							)
              return
       subprocess.run(_UpperCAmelCase							)
       print("Successfully setup pod."							)




def 					_lowerCamelCase(					):
       __a     				=  tpu_command_parser()
       __a     				=  parser.parse_args()

       tpu_command_launcher(_UpperCAmelCase							)

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

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

        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

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

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

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

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

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

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
