




"""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


a							      =     logging.get_logger(__name__)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Tuple     =      ['''pixel_values''']

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BICUBIC       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Union[int, float] = 1 / 255       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN       ,				_UpperCAmelCase			:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD       ,				**_UpperCAmelCase			:			Any       ,				):
       super().__init__(**_UpperCAmelCase						)
       _A     =  size if size is not None else {'shortest_edge': 224}
       _A     =  get_size_dict(_UpperCAmelCase       ,				default_to_square=_UpperCAmelCase						)
       _A     =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
       _A     =  get_size_dict(_UpperCAmelCase       ,				param_name='crop_size'						)

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

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Dict[str, int]       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BICUBIC       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Dict       ,				):
       _A     =  get_size_dict(_UpperCAmelCase       ,				default_to_square=_UpperCAmelCase						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
             _A     =  int((256 / 224) * size['shortest_edge']						)
             _A     =  get_resize_output_image_size(_UpperCAmelCase       ,				size=_UpperCAmelCase       ,				default_to_square=_UpperCAmelCase						)
             _A     =  {'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(
           _UpperCAmelCase       ,				size=(size_dict['height'], size_dict['width'])       ,				resample=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Dict[str, int]       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  get_size_dict(_UpperCAmelCase						)
       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(_UpperCAmelCase       ,				size=(size['height'], size['width'])       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Union[int, float]       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Optional[int]       ,				):
       return rescale(_UpperCAmelCase       ,				scale=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Union[float, List[float]]       ,				_UpperCAmelCase			:			Union[float, List[float]]       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			int       ,				):
       return normalize(_UpperCAmelCase       ,				mean=_UpperCAmelCase       ,				std=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			ImageInput       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Dict[str, int]] = None       ,				_UpperCAmelCase			:			PILImageResampling = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Dict[str, int]] = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[float] = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Union[float, Iterable[float]]] = None       ,				_UpperCAmelCase			:			Optional[Union[float, Iterable[float]]] = None       ,				_UpperCAmelCase			:			Optional[TensorType] = None       ,				_UpperCAmelCase			:			ChannelDimension = ChannelDimension.FIRST       ,				**_UpperCAmelCase			:			Dict       ,				):
       _A     =  do_resize if do_resize is not None else self.do_resize
       _A     =  resample if resample is not None else self.resample
       _A     =  do_center_crop if do_center_crop is not None else self.do_center_crop
       _A     =  do_rescale if do_rescale is not None else self.do_rescale
       _A     =  rescale_factor if rescale_factor is not None else self.rescale_factor
       _A     =  do_normalize if do_normalize is not None else self.do_normalize
       _A     =  image_mean if image_mean is not None else self.image_mean
       _A     =  image_std if image_std is not None else self.image_std

       _A     =  size if size is not None else self.size
       _A     =  get_size_dict(_UpperCAmelCase       ,				default_to_square=_UpperCAmelCase						)
       _A     =  crop_size if crop_size is not None else self.crop_size
       _A     =  get_size_dict(_UpperCAmelCase       ,				param_name='crop_size'						)

       _A     =  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.'						)

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

       if do_resize:
             _A     =  [self.resize(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

       if do_center_crop:
             _A     =  [self.center_crop(_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

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

       if do_normalize:
             _A     =  [self.normalize(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

       _A     =  [to_channel_dimension_format(_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

       _A     =  {'pixel_values': images}
       return BatchFeature(data=_UpperCAmelCase       ,				tensor_type=_UpperCAmelCase						)







"""simple docstring"""

import argparse


a							      =     '''docs/source/_static/js/custom.js'''






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Any:






      '''simple docstring'''


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

      # First let's put the right version
      while not lines[index].startswith('const stableVersion ='				):
            index += 1
      _A     =  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__":
       a							      =     argparse.ArgumentParser()
       parser.add_argument('''--version''', help='''Release version.''')
       a							      =     parser.parse_args()
       update_custom_js(args.version)

