





'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


A__							:  Union[str, Any]    =logging.get_logger(__name__)




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Dict			=  ['''pixel_values''']




      def __init__(     self     :					Dict     ,     __snake_case     :					bool = True     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					int = 0.9     ,     __snake_case     :					PILImageResampling = PILImageResampling.BICUBIC     ,     __snake_case     :					bool = True     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					Union[int, float] = 1 / 2_55     ,     __snake_case     :					bool = True     ,     __snake_case     :					bool = True     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     **__snake_case     :					Dict     ,     )							-> None:
            super().__init__(**__snake_case    )
            _lowerCAmelCase					=				size if size is not None else {"""shortest_edge""": 2_24}
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     default_to_square=__snake_case    )
            _lowerCAmelCase					=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     param_name="""crop_size"""    )

            _lowerCAmelCase					=				do_resize
            _lowerCAmelCase					=				size
            _lowerCAmelCase					=				crop_pct
            _lowerCAmelCase					=				resample
            _lowerCAmelCase					=				do_center_crop
            _lowerCAmelCase					=				crop_size
            _lowerCAmelCase					=				do_rescale
            _lowerCAmelCase					=				rescale_factor
            _lowerCAmelCase					=				do_normalize
            _lowerCAmelCase					=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            _lowerCAmelCase					=				image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Dict[str, int]     ,     __snake_case     :					Optional[float] = None     ,     __snake_case     :					PILImageResampling = PILImageResampling.BICUBIC     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					Optional[int]     ,     )							-> np.ndarray:
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     default_to_square=__snake_case    )
            if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                  raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"    )
            if crop_pct is not None:
                  if "shortest_edge" in size:
                        _lowerCAmelCase					=				int(size["""shortest_edge"""] / crop_pct    )
                  elif "height" in size and "width" in size:
                        if size["height"] == size["width"]:
                              _lowerCAmelCase					=				int(size["""height"""] / crop_pct    )
                        else:
                              _lowerCAmelCase					=				(int(size["""height"""] / crop_pct    ), int(size["""width"""] / crop_pct    ))
                  else:
                        raise ValueError("""Invalid size for resize: {}""".format(__snake_case    )    )

                  _lowerCAmelCase					=				get_resize_output_image_size(__snake_case     ,     size=__snake_case     ,     default_to_square=__snake_case    )
            else:
                  if "shortest_edge" in size:
                        _lowerCAmelCase					=				get_resize_output_image_size(__snake_case     ,     size=size["""shortest_edge"""]     ,     default_to_square=__snake_case    )
                  elif "height" in size and "width" in size:
                        _lowerCAmelCase					=				(size["""height"""], size["""width"""])
                  else:
                        raise ValueError("""Invalid size for resize: {}""".format(__snake_case    )    )

            return resize(__snake_case     ,     size=__snake_case     ,     resample=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Dict[str, int]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					List[Any]     ,     )							-> np.ndarray:
            _lowerCAmelCase					=				get_size_dict(__snake_case    )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"size must contain '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 							lowercase__       (     self     :					int     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Union[int, float]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					List[Any]     ,     )							-> Dict:
            return rescale(__snake_case     ,     scale=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Union[float, List[float]]     ,     __snake_case     :					Union[float, List[float]]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					Optional[int]     ,     )							-> np.ndarray:
            return normalize(__snake_case     ,     mean=__snake_case     ,     std=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					ImageInput     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					int = None     ,     __snake_case     :					PILImageResampling = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					float = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[str, TensorType]] = None     ,     __snake_case     :					ChannelDimension = ChannelDimension.FIRST     ,     **__snake_case     :					Optional[Any]     ,     )							-> PIL.Image.Image:
            _lowerCAmelCase					=				do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase					=				crop_pct if crop_pct is not None else self.crop_pct
            _lowerCAmelCase					=				resample if resample is not None else self.resample
            _lowerCAmelCase					=				do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase					=				do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase					=				rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase					=				do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase					=				image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase					=				image_std if image_std is not None else self.image_std

            _lowerCAmelCase					=				size if size is not None else self.size
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     default_to_square=__snake_case    )
            _lowerCAmelCase					=				crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     param_name="""crop_size"""    )

            _lowerCAmelCase					=				make_list_of_images(__snake_case    )

            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."""    )

            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_pct is None:
                  raise ValueError("""Crop_pct 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."""    )

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

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

            if do_center_crop:
                  _lowerCAmelCase					=				[self.center_crop(image=__snake_case     ,     size=__snake_case    ) for image in images]

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

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

            _lowerCAmelCase					=				[to_channel_dimension_format(__snake_case     ,     __snake_case    ) for image in images]

            _lowerCAmelCase					=				{"""pixel_values""": images}
            return BatchFeature(data=__snake_case     ,     tensor_type=__snake_case    )













'''simple docstring'''






import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__							:  Optional[int]    ='''.'''


if __name__ == "__main__":
      A__							:  int    =os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
      A__							:  Optional[int]    =[]
      A__							:  Tuple    =[]
      with open(doctest_file_path) as fp:
            for line in fp:
                  A__							:  Optional[Any]    =line.strip()
                  A__							:  Union[str, Any]    =os.path.join(REPO_PATH, line)
                  if not (os.path.isfile(path) or os.path.isdir(path)):
                        non_existent_paths.append(line)
                  all_paths.append(path)
      if len(non_existent_paths) > 0:
            A__							:  List[Any]    ='''\n'''.join(non_existent_paths)
            raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
      if all_paths != sorted(all_paths):
            raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






