



'''simple docstring'''






# Copyright 2021 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

from accelerate.test_utils import execute_subprocess_async
def 				lowerCamelCase__	(						_A=None    ):







  if subparsers is not None:
    a						: Optional[Any]					       =					subparsers.add_parser('test'    )
  else:
    a						: Optional[Any]					       =					argparse.ArgumentParser('Accelerate test command'    )

  parser.add_argument(
      '--config_file'			,      default=_lowercase			,      help=(
          'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )			,      )

  if subparsers is not None:
    parser.set_defaults(func=_lowercase    )
  return parser
def 				lowerCamelCase__	(						_A    ):
  a						: Optional[int]					       =					os.path.sep.join(__file__.split(os.path.sep    )[:-2] + ['test_utils', 'scripts', 'test_script.py']    )

  if args.config_file is None:
    a						: Dict					       =					script_name
  else:
    a						: Optional[int]					       =					f"""--config_file={args.config_file} {script_name}"""

  a						: List[str]					       =					['accelerate-launch'] + test_args.split()
  a						: List[str]					       =					execute_subprocess_async(_lowercase			,      env=os.environ.copy()    )
  if result.returncode == 0:
    print('Test is a success! You are ready for your distributed training!'    )
def 				lowerCamelCase__	(						):
  a						: int					       =					test_command_parser()
  a						: Union[str, Any]					       =					parser.parse_args()
  test_command(_lowercase    )


if __name__ == "__main__":
     main()




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )