






"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_   : Any   			= logging.get_logger(__name__)


if is_vision_available():
  import PIL




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  Dict						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  bool = True						,	**lowercase_     :  Any						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		size if size is not None else {'''shortest_edge''': 224}
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_						,	param_name='''crop_size''')

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		size
          SCREAMING_SNAKE_CASE_      :  str    						=		resample
          SCREAMING_SNAKE_CASE_      :  Any    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  Any    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  Any    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  int    						=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          SCREAMING_SNAKE_CASE_      :  str    						=		image_std if image_std is not None else OPENAI_CLIP_STD
          SCREAMING_SNAKE_CASE_      :  str    						=		do_convert_rgb
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Optional[Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          if "shortest_edge" not in size:
              raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_resize_output_image_size(lowercase_						,	size=size['''shortest_edge''']						,	default_to_square=lowercase_)
          return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_size_dict(lowercase_)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}')
          return center_crop(lowercase_						,	size=(size['''height'''], size['''width'''])						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[int, float]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[Any]						,	):




          '''simple docstring'''


          return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  int = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	lowercase_     :  Optional[ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_     :  Union[str, Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :  int    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  Any    						=		get_size_dict(lowercase_						,	param_name='''size'''						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :  Any    						=		do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_size_dict(lowercase_						,	param_name='''crop_size'''						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_std if image_std is not None else self.image_std
          SCREAMING_SNAKE_CASE_      :  str    						=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		make_list_of_images(lowercase_)

          if not valid_images(lowercase_):
              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:
              raise ValueError('''Size 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.''')

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
              SCREAMING_SNAKE_CASE_      :  int    						=		[convert_to_rgb(lowercase_) for image in images]

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[to_numpy_array(lowercase_) for image in images]

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_) for image in images]

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  str    						=		[self.center_crop(image=lowercase_						,	size=lowercase_) for image in images]

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.rescale(image=lowercase_						,	scale=lowercase_) for image in images]

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		[self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[to_channel_dimension_format(lowercase_						,	lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		{'''pixel_values''': images}
          return BatchFeature(data=lowercase_						,	tensor_type=lowercase_)









"""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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def      __lowerCamelCase   (							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ArgumentParser('''Accelerate CLI tool''' ,   usage='''accelerate <command> [<args>]''' ,   allow_abbrev=a_							)
      __SCREAMING_SNAKE_CASE   :str						=  parser.add_subparsers(help='''accelerate command helpers'''							)

      # Register commands
      get_config_parser(subparsers=a_							)
      env_command_parser(subparsers=a_							)
      launch_command_parser(subparsers=a_							)
      tpu_command_parser(subparsers=a_							)
      test_command_parser(subparsers=a_							)

      # Let's go
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      if not hasattr(a_ ,   '''func'''							):
            parser.print_help()
            exit(1							)

      # Run
      args.func(a_							)


if __name__ == "__main__":
  main()