





'''simple docstring'''






from typing import Dict, Iterable, 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, logging


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''pixel_values''']

		def __init__(   self					:   Any  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BICUBIC  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[int, float] = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN  ,      __magic_name__					:   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD  ,      **__magic_name__					:   Union[str, Any]  ,      )					->    None:


					"""simple docstring"""

					super().__init__(**__magic_name__			)
					__snake_case					:      Dict							      =     size if size is not None else {"""shortest_edge""": 2_24}
					__snake_case					:      str							      =     get_size_dict(__magic_name__  ,      default_to_square=__magic_name__			)
					__snake_case					:      int							      =     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					__snake_case					:      Optional[Any]							      =     get_size_dict(__magic_name__  ,      param_name="""crop_size"""			)

					__snake_case					:      List[Any]							      =     do_resize
					__snake_case					:      Dict							      =     size
					__snake_case					:      str							      =     resample
					__snake_case					:      Any							      =     do_center_crop
					__snake_case					:      Optional[Any]							      =     crop_size
					__snake_case					:      Optional[Any]							      =     do_rescale
					__snake_case					:      Optional[int]							      =     rescale_factor
					__snake_case					:      Tuple							      =     do_normalize
					__snake_case					:      str							      =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					__snake_case					:      Any							      =     image_std if image_std is not None else IMAGENET_DEFAULT_STD

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Dict[str, int]  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BICUBIC  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   str  ,      )					->    np.ndarray:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     get_size_dict(__magic_name__  ,      default_to_square=__magic_name__			)
					# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
					if "shortest_edge" in size:
								__snake_case					:      List[Any]							      =     int((2_56 / 2_24) * size["""shortest_edge"""]			)
								__snake_case					:      int							      =     get_resize_output_image_size(__magic_name__  ,      size=__magic_name__  ,      default_to_square=__magic_name__			)
								__snake_case					:      List[str]							      =     {"""height""": output_size[0], """width""": output_size[1]}
					if "height" not in size_dict or "width" not in size_dict:
								raise ValueError(
								    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''			)
					return resize(
					    __magic_name__  ,      size=(size_dict["""height"""], size_dict["""width"""])  ,      resample=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Dict[str, int]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   str  ,      )					->    np.ndarray:


					"""simple docstring"""

					__snake_case					:      str							      =     get_size_dict(__magic_name__			)
					if "height" not in size or "width" not in size:
								raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''			)
					return center_crop(__magic_name__  ,      size=(size["""height"""], size["""width"""])  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[int, float]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   int  ,      )					->    np.ndarray:


					"""simple docstring"""

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

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


					"""simple docstring"""

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

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


					"""simple docstring"""

					__snake_case					:      str							      =     do_resize if do_resize is not None else self.do_resize
					__snake_case					:      List[str]							      =     resample if resample is not None else self.resample
					__snake_case					:      Optional[int]							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
					__snake_case					:      Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
					__snake_case					:      Optional[int]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case					:      List[str]							      =     do_normalize if do_normalize is not None else self.do_normalize
					__snake_case					:      List[str]							      =     image_mean if image_mean is not None else self.image_mean
					__snake_case					:      Optional[Any]							      =     image_std if image_std is not None else self.image_std

					__snake_case					:      Optional[int]							      =     size if size is not None else self.size
					__snake_case					:      Optional[int]							      =     get_size_dict(__magic_name__  ,      default_to_square=__magic_name__			)
					__snake_case					:      Tuple							      =     crop_size if crop_size is not None else self.crop_size
					__snake_case					:      Tuple							      =     get_size_dict(__magic_name__  ,      param_name="""crop_size"""			)

					__snake_case					:      int							      =     make_list_of_images(__magic_name__			)

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

					# All transformations expect numpy arrays.
					__snake_case					:      Optional[int]							      =     [to_numpy_array(__magic_name__			) for image in images]

					if do_resize:
								__snake_case					:      Any							      =     [self.resize(__magic_name__  ,      __magic_name__  ,      __magic_name__			) for image in images]

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

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

					if do_normalize:
								__snake_case					:      List[Any]							      =     [self.normalize(__magic_name__  ,      __magic_name__  ,      __magic_name__			) for image in images]

					__snake_case					:      List[str]							      =     [to_channel_dimension_format(__magic_name__  ,      __magic_name__			) for image in images]

					__snake_case					:      Optional[Any]							      =     {"""pixel_values""": images}
					return BatchFeature(data=__magic_name__  ,      tensor_type=__magic_name__			)





import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       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(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


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




