






"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

A__					: Optional[int]=	logging.get_logger(__name__)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	List[List[ImageInput]]:
    """simple docstring"""

    if isinstance(SCREAMING_SNAKE_CASE ,	(list, tuple)					) and isinstance(videos[0] ,	(list, tuple)					) and is_valid_image(videos[0][0]					):
        return videos

    elif isinstance(SCREAMING_SNAKE_CASE ,	(list, tuple)					) and is_valid_image(videos[0]					):
        return [videos]

    elif is_valid_image(SCREAMING_SNAKE_CASE					):
        return [[videos]]

    raise ValueError(F'Could not make batched video from {videos}'					)





class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=["""pixel_values"""]

   def __init__(					self ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = PILImageResampling.BILINEAR ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = True ,				snake_case_ = 1 / 255 ,				snake_case_ = True ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = None ,				**snake_case_ ,				)		->       None:
       super().__init__(**snake_case_      )
       UpperCamelCase__         =    size if size is not None else {'shortest_edge': 256}
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				default_to_square=snake_case_      )
       UpperCamelCase__         =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				param_name='crop_size'      )

       UpperCamelCase__         =    do_resize
       UpperCamelCase__         =    size
       UpperCamelCase__         =    do_center_crop
       UpperCamelCase__         =    crop_size
       UpperCamelCase__         =    resample
       UpperCamelCase__         =    do_rescale
       UpperCamelCase__         =    rescale_factor
       UpperCamelCase__         =    offset
       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 ,				snake_case_ ,				snake_case_ ,				snake_case_ = PILImageResampling.BILINEAR ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				default_to_square=snake_case_      )
       if "shortest_edge" in size:
           UpperCamelCase__         =    get_resize_output_image_size(snake_case_ ,				size['shortest_edge'] ,				default_to_square=snake_case_      )
       elif "height" in size and "width" in size:
           UpperCamelCase__         =    (size['height'], size['width'])
       else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
       return resize(snake_case_ ,				size=snake_case_ ,				resample=snake_case_ ,				data_format=snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:
       UpperCamelCase__         =    get_size_dict(snake_case_      )
       if "height" not in size or "width" not in size:
           raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'      )
       return center_crop(snake_case_ ,				size=(size['height'], size['width']) ,				data_format=snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = True ,				snake_case_ = None ,				**snake_case_ ,				)		->       str:
       UpperCamelCase__         =    image.astype(np.floataa      )
       if offset:
           UpperCamelCase__         =    image - (scale / 2)
       return rescale(snake_case_ ,				scale=snake_case_ ,				data_format=snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:
       return normalize(snake_case_ ,				mean=snake_case_ ,				std=snake_case_ ,				data_format=snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = ChannelDimension.FIRST ,				)		->       np.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_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'      )

       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.'      )

       if offset and not do_rescale:
           raise ValueError('For offset, do_rescale must also be set to True.'      )

       # All transformations expect numpy arrays.
       UpperCamelCase__         =    to_numpy_array(snake_case_      )

       if do_resize:
           UpperCamelCase__         =    self.resize(image=snake_case_ ,				size=snake_case_ ,				resample=snake_case_      )

       if do_center_crop:
           UpperCamelCase__         =    self.center_crop(snake_case_ ,				size=snake_case_      )

       if do_rescale:
           UpperCamelCase__         =    self.rescale(image=snake_case_ ,				scale=snake_case_ ,				offset=snake_case_      )

       if do_normalize:
           UpperCamelCase__         =    self.normalize(image=snake_case_ ,				mean=snake_case_ ,				std=snake_case_      )

       UpperCamelCase__         =    to_channel_dimension_format(snake_case_ ,				snake_case_      )
       return image







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = ChannelDimension.FIRST ,				**snake_case_ ,				)		->       PIL.Image.Image:
       UpperCamelCase__         =    do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__         =    resample if resample is not None else self.resample
       UpperCamelCase__         =    do_center_crop if do_center_crop is not None else self.do_center_crop
       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__         =    offset if offset is not None else self.offset
       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(snake_case_ ,				default_to_square=snake_case_      )
       UpperCamelCase__         =    crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				param_name='crop_size'      )

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

       UpperCamelCase__         =    make_batched(snake_case_      )

       UpperCamelCase__         =    [
           [
               self._preprocess_image(
                   image=snake_case_ ,				do_resize=snake_case_ ,				size=snake_case_ ,				resample=snake_case_ ,				do_center_crop=snake_case_ ,				crop_size=snake_case_ ,				do_rescale=snake_case_ ,				rescale_factor=snake_case_ ,				offset=snake_case_ ,				do_normalize=snake_case_ ,				image_mean=snake_case_ ,				image_std=snake_case_ ,				data_format=snake_case_ ,				)
               for img in video
           ]
           for video in videos
       ]

       UpperCamelCase__         =    {'pixel_values': videos}
       return BatchFeature(data=snake_case_ ,				tensor_type=snake_case_      )












"""simple docstring"""





# 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


A__					: Optional[Any]=	"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Dict:
    """simple docstring"""

    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=SCREAMING_SNAKE_CASE ,	default=SCREAMING_SNAKE_CASE ,	help='Path to the config file to use for accelerate.' ,	)
    config_args.add_argument(
        '--tpu_name' ,	default=SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE					)
    return parser







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE					):
        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					) ,	SCREAMING_SNAKE_CASE					):
        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] ,	SCREAMING_SNAKE_CASE					):
        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(SCREAMING_SNAKE_CASE					)

    # 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(SCREAMING_SNAKE_CASE					)}'					)
        return
    subprocess.run(SCREAMING_SNAKE_CASE					)
    print('Successfully setup pod.'					)







def   lowerCAmelCase_( )					->	int:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE					)




