





'''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


lowercase__      =   logging.get_logger(__name__)


if is_vision_available():
  import PIL


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	["pixel_values"]


     def __init__(						self						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = PILImageResampling.BICUBIC						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = True						,     _UpperCAmelCase = 1 / 255						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = True						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

          __A						:			Any								=	do_resize
          __A						:			Dict								=	size
          __A						:			Any								=	resample
          __A						:			Optional[int]								=	do_center_crop
          __A						:			List[str]								=	crop_size
          __A						:			List[Any]								=	do_rescale
          __A						:			Any								=	rescale_factor
          __A						:			Tuple								=	do_normalize
          __A						:			Dict								=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          __A						:			Optional[int]								=	image_std if image_std is not None else OPENAI_CLIP_STD
          __A						:			Optional[int]								=	do_convert_rgb


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = PILImageResampling.BICUBIC						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			List[Any]								=	get_size_dict(_UpperCAmelCase						,     default_to_square=_UpperCAmelCase)
          if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          __A						:			Optional[Any]								=	get_resize_output_image_size(_UpperCAmelCase						,     size=size['shortest_edge']						,     default_to_square=_UpperCAmelCase)
          return resize(_UpperCAmelCase						,     size=_UpperCAmelCase						,     resample=_UpperCAmelCase						,     data_format=_UpperCAmelCase						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			Dict								=	get_size_dict(_UpperCAmelCase)
          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(_UpperCAmelCase						,     size=(size['height'], size['width'])						,     data_format=_UpperCAmelCase						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = ChannelDimension.FIRST						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

          __A						:			Tuple								=	make_list_of_images(_UpperCAmelCase)

          if not valid_images(_UpperCAmelCase):
               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:
               __A						:			str								=	[convert_to_rgb(_UpperCAmelCase) for image in images]

          # All transformations expect numpy arrays.
          __A						:			Dict								=	[to_numpy_array(_UpperCAmelCase) for image in images]

          if do_resize:
               __A						:			List[str]								=	[self.resize(image=_UpperCAmelCase						,     size=_UpperCAmelCase						,     resample=_UpperCAmelCase) for image in images]

          if do_center_crop:
               __A						:			Union[str, Any]								=	[self.center_crop(image=_UpperCAmelCase						,     size=_UpperCAmelCase) for image in images]

          if do_rescale:
               __A						:			Any								=	[self.rescale(image=_UpperCAmelCase						,     scale=_UpperCAmelCase) for image in images]

          if do_normalize:
               __A						:			int								=	[self.normalize(image=_UpperCAmelCase						,     mean=_UpperCAmelCase						,     std=_UpperCAmelCase) for image in images]

          __A						:			Union[str, Any]								=	[to_channel_dimension_format(_UpperCAmelCase						,     _UpperCAmelCase) for image in images]

          __A						:			Union[str, Any]								=	{'pixel_values': images}
          return BatchFeature(data=_UpperCAmelCase						,     tensor_type=_UpperCAmelCase)






'''simple docstring'''




import argparse


lowercase__   :				Any      =   '''docs/source/_static/js/custom.js'''






def 	_lowerCAmelCase      (  __snake_case  :			Union[str, Any]      )					->					str:
     with open(__snake_case			,							encoding='utf-8'			,							newline='\n'      ) as f:
          __A						:			Optional[Any]								=	f.readlines()
     __A						:			List[str]								=	0

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


if __name__ == "__main__":
  lowercase__   :				Optional[Any]      =   argparse.ArgumentParser()
  parser.add_argument('''--version''', help='''Release version.''')
  lowercase__   :				List[str]      =   parser.parse_args()
  update_custom_js(args.version)