


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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a     =			logging.get_logger(__name__)






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       ['''pixel_values''']





      def __init__(		self						:					Any					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Optional[Dict[str, int]] = None					,       __lowerCamelCase						:					PILImageResampling = PILImageResampling.BICUBIC					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Union[int, float] = 1 / 2_5_5					,       __lowerCamelCase						:					Dict[str, int] = None					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       **__lowerCamelCase						:					int					,       )    ->   None:






         """simple docstring"""







         super().__init__(**__lowerCamelCase       )
         UpperCAmelCase							=     size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         UpperCAmelCase							=     get_size_dict(__lowerCamelCase       )
         UpperCAmelCase							=     crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         UpperCAmelCase							=     get_size_dict(__lowerCamelCase					,       default_to_square=__lowerCamelCase					,       param_name="""crop_size"""       )

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





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					Dict[str, int]					,       __lowerCamelCase						:					PILImageResampling = PILImageResampling.BILINEAR					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					str					,       )    ->   np.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     get_size_dict(__lowerCamelCase       )

         if "shortest_edge" in size:
            UpperCAmelCase							=     get_resize_output_image_size(__lowerCamelCase					,       size=size["""shortest_edge"""]					,       default_to_square=__lowerCamelCase       )
            # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
            UpperCAmelCase							=     (size["""height"""], size["""width"""])
         else:
            raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""       )
         return resize(__lowerCamelCase					,       size=__lowerCamelCase					,       resample=__lowerCamelCase					,       data_format=__lowerCamelCase					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					Dict[str, int]					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					Tuple					,       )    ->   np.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     get_size_dict(__lowerCamelCase       )
         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(__lowerCamelCase					,       size=(size["""height"""], size["""width"""])					,       data_format=__lowerCamelCase					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					float					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					Tuple       )    ->   np.ndarray:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					Union[float, List[float]]					,       __lowerCamelCase						:					Union[float, List[float]]					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					List[str]					,       )    ->   np.ndarray:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					ImageInput					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					Dict[str, int] = None					,       __lowerCamelCase						:					PILImageResampling = None					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					int = None					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					Optional[float] = None					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					Optional[Union[str, TensorType]] = None					,       __lowerCamelCase						:					Union[str, ChannelDimension] = ChannelDimension.FIRST					,       **__lowerCamelCase						:					Optional[int]					,       )    ->   BatchFeature:






         """simple docstring"""







         UpperCAmelCase							=     do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase							=     do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase							=     do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase							=     do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase							=     crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase							=     get_size_dict(__lowerCamelCase					,       param_name="""crop_size"""					,       default_to_square=__lowerCamelCase       )
         UpperCAmelCase							=     resample if resample is not None else self.resample
         UpperCAmelCase							=     rescale_factor if rescale_factor is not None else self.rescale_factor
         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(__lowerCamelCase       )

         if not is_batched(__lowerCamelCase       ):
            UpperCAmelCase							=     [images]

         if not valid_images(__lowerCamelCase       ):
            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."""       )

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

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

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

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

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

         UpperCAmelCase							=     [to_channel_dimension_format(__lowerCamelCase					,       __lowerCamelCase       ) for image in images]

         UpperCAmelCase							=     {"""pixel_values""": images}
         return BatchFeature(data=__lowerCamelCase					,       tensor_type=__lowerCamelCase       )







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     F"""const stableVersion = \"v{version}\"\n"""

   # Then update the dictionary
   while not lines[index].startswith("""const versionMapping = {"""   ):
      index += 1

   # We go until the end
   while not lines[index].startswith("""}"""   ):
      index += 1
   # We add the new version at the end
   lines[index - 1] += F"""    \"v{version}\": \"v{version}\",\n"""

   with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 parser.add_argument("""--version""", help="""Release version.""")
 __a     =			parser.parse_args()
 update_custom_js(args.version)



