




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


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Any			=      ['''pixel_values''']






  def __init__(  self		:     str				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     int = 0.9				,						_A		:     PILImageResampling = PILImageResampling.BICUBIC				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     Union[int, float] = 1 / 255				,						_A		:     bool = True				,						_A		:     bool = True				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						**_A		:     Tuple				,						)		->   None:






       """simple docstring"""







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

       lowercase :       List[str]       =			do_resize
       lowercase :       Optional[Any]       =			size
       lowercase :       List[Any]       =			crop_pct
       lowercase :       Any       =			resample
       lowercase :       Union[str, Any]       =			do_center_crop
       lowercase :       List[Any]       =			crop_size
       lowercase :       int       =			do_rescale
       lowercase :       Optional[Any]       =			rescale_factor
       lowercase :       Union[str, Any]       =			do_normalize
       lowercase :       List[str]       =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowercase :       Optional[Any]       =			image_std if image_std is not None else IMAGENET_DEFAULT_STD






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






       """simple docstring"""







       lowercase :       Tuple       =			get_size_dict(_A				,						default_to_square=_A				)
       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:
                 lowercase :       int       =			int(size['''shortest_edge'''] / crop_pct				)
            elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                      lowercase :       Tuple       =			int(size['''height'''] / crop_pct				)
                 else:
                      lowercase :       str       =			(int(size['''height'''] / crop_pct				), int(size['''width'''] / crop_pct				))
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(_A				)				)

            lowercase :       str       =			get_resize_output_image_size(_A				,						size=_A				,						default_to_square=_A				)
       else:
            if "shortest_edge" in size:
                 lowercase :       Optional[int]       =			get_resize_output_image_size(_A				,						size=size['''shortest_edge''']				,						default_to_square=_A				)
            elif "height" in size and "width" in size:
                 lowercase :       List[str]       =			(size['''height'''], size['''width'''])
            else:
                 raise ValueError('''Invalid size for resize: {}'''.format(_A				)				)

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






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






       """simple docstring"""







       lowercase :       int       =			get_size_dict(_A				)
       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(_A				,						size=(size['''height'''], size['''width'''])				,						data_format=_A				,						**_A				)






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






       """simple docstring"""







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






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






       """simple docstring"""







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






  def  __a      (  self		:     str				,						_A		:     ImageInput				,						_A		:     bool = None				,						_A		:     Dict[str, int] = None				,						_A		:     int = None				,						_A		:     PILImageResampling = None				,						_A		:     bool = None				,						_A		:     Dict[str, int] = None				,						_A		:     bool = None				,						_A		:     float = None				,						_A		:     bool = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[str, TensorType]] = None				,						_A		:     ChannelDimension = ChannelDimension.FIRST				,						**_A		:     List[str]				,						)		->   PIL.Image.Image:






       """simple docstring"""







       lowercase :       Any       =			do_resize if do_resize is not None else self.do_resize
       lowercase :       Tuple       =			crop_pct if crop_pct is not None else self.crop_pct
       lowercase :       Tuple       =			resample if resample is not None else self.resample
       lowercase :       Optional[int]       =			do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase :       List[str]       =			do_rescale if do_rescale is not None else self.do_rescale
       lowercase :       List[str]       =			rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase :       str       =			do_normalize if do_normalize is not None else self.do_normalize
       lowercase :       Any       =			image_mean if image_mean is not None else self.image_mean
       lowercase :       Dict       =			image_std if image_std is not None else self.image_std

       lowercase :       List[str]       =			size if size is not None else self.size
       lowercase :       Optional[int]       =			get_size_dict(_A				,						default_to_square=_A				)
       lowercase :       Union[str, Any]       =			crop_size if crop_size is not None else self.crop_size
       lowercase :       int       =			get_size_dict(_A				,						param_name='''crop_size'''				)

       lowercase :       List[Any]       =			make_list_of_images(_A				)

       if not valid_images(_A				):
            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.
       lowercase :       int       =			[to_numpy_array(_A				) for image in images]

       if do_resize:
            lowercase :       Dict       =			[self.resize(image=_A				,						size=_A				,						crop_pct=_A				,						resample=_A				) for image in images]

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

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

       if do_normalize:
            lowercase :       Dict       =			[self.normalize(image=_A				,						mean=_A				,						std=_A				) for image in images]

       lowercase :       str       =			[to_channel_dimension_format(_A				,						_A				) for image in images]

       lowercase :       List[Any]       =			{'''pixel_values''': images}
       return BatchFeature(data=_A				,						tensor_type=_A				)





import math






def  snake_case(    __magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

     return exponent == int(__magic_name__   )






def  snake_case(    __magic_name__ = 1 / 1_23_45   )			->							int:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			0
     lowercase :       str       =			0

     lowercase :       Optional[int]       =			3
     while True:
          lowercase :       Any       =			(integer**2 - 1) / 4
          # if candidate is an integer, then there is a partition for k
          if partition_candidate == int(__magic_name__   ):
               lowercase :       Any       =			int(__magic_name__   )
               total_partitions += 1
               if check_partition_perfect(__magic_name__   ):
                    perfect_partitions += 1
          if perfect_partitions > 0:
               if perfect_partitions / total_partitions < max_proportion:
                    return int(__magic_name__   )
          integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')