



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


__lowerCamelCase     :							Any  							=				"Run commands across TPU VMs for initial setup before running `accelerate launch`."

def    lowerCamelCase_(lowerCamelCase_=None    ) ->       List[Any]:






						if subparsers is not None:
												UpperCAmelCase         =     subparsers.add_parser("tpu-config"    ,   description=_description    )
						else:
												UpperCAmelCase         =     argparse.ArgumentParser("Accelerate tpu-config command"    ,   description=_description    )
						# Core arguments
						UpperCAmelCase         =     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."    ,   )
						UpperCAmelCase         =     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_(lowerCamelCase_    ) ->       List[str]:
						UpperCAmelCase         =     None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(UpperCAmelCase__    ):
												UpperCAmelCase         =     load_config_from_file(args.config_file    )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		UpperCAmelCase         =     defaults.command_file
												if not args.command and defaults.commands is not None:
																		UpperCAmelCase         =     defaults.commands
												if not args.tpu_name:
																		UpperCAmelCase         =     defaults.tpu_name
												if not args.tpu_zone:
																		UpperCAmelCase         =     defaults.tpu_zone
						if args.accelerate_version == "dev":
												UpperCAmelCase         =     "git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												UpperCAmelCase         =     "accelerate -U"
						elif isinstance(parse(args.accelerate_version    )    ,   UpperCAmelCase__    ):
												UpperCAmelCase         =     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:
																		UpperCAmelCase         =     [f.read().splitlines()]

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

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						UpperCAmelCase         =     ["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_() ->       List[str]:
						UpperCAmelCase         =     tpu_command_parser()
						UpperCAmelCase         =     parser.parse_args()

						tpu_command_launcher(UpperCAmelCase__    )








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


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          =['''pixel_values''']






		def __init__(						self      :     Any				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BILINEAR				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Union[int, float] = 1 / 2_55				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		**UpperCamelCase__      :     List[str]				,		)				->					None:
								'''simple docstring'''







								super().__init__(**UpperCamelCase__      )
								UpperCAmelCase         =     size if size is not None else {"shortest_edge": 3_84}
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase         =     crop_pct if crop_pct is not None else 2_24 / 2_56
								UpperCAmelCase         =     resample
								UpperCAmelCase         =     do_rescale
								UpperCAmelCase         =     rescale_factor
								UpperCAmelCase         =     do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase         =     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Dict[str, int]				,		UpperCamelCase__      :     float				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BICUBIC				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					np.ndarray:
								'''simple docstring'''







								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
								if "shortest_edge" not in size:
														raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
								UpperCAmelCase         =     size["shortest_edge"]

								if shortest_edge < 3_84:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														UpperCAmelCase         =     int(shortest_edge / crop_pct      )
														UpperCAmelCase         =     get_resize_output_image_size(UpperCamelCase__				,		size=UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
														UpperCAmelCase         =     resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
								else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[int, float]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					List[str]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					np.ndarray:
								'''simple docstring'''







								return normalize(UpperCamelCase__				,		mean=UpperCamelCase__				,		std=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     ImageInput				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     ChannelDimension = ChannelDimension.FIRST				,		**UpperCamelCase__      :     Any				,		)				->					PIL.Image.Image:
								'''simple docstring'''







								UpperCAmelCase         =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase         =     crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase         =     resample if resample is not None else self.resample
								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_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase         =     image_std if image_std is not None else self.image_std

								UpperCAmelCase         =     size if size is not None else self.size
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								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_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"] < 3_84 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.
								UpperCAmelCase         =     [to_numpy_array(UpperCamelCase__      ) for image in images]

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

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

								if do_normalize:
														UpperCAmelCase         =     [self.normalize(image=UpperCamelCase__				,		mean=UpperCamelCase__				,		std=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__      )



