
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


lowerCamelCase      =logging.get_logger(__name__)


if is_vision_available():
    import PIL




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				size if size is not None else {'''shortest_edge''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   str					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE						,						param_name='''crop_size'''  )

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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           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(__SCREAMING_SNAKE_CASE						,						size=(size['''height'''], size['''width'''])						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[Any]:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







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

           UpperCamelCase__    :   int					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 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:
                 UpperCamelCase__    :   int					=				[convert_to_rgb(__SCREAMING_SNAKE_CASE  ) for image in images]

           # All transformations expect numpy arrays.
           UpperCamelCase__    :   str					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_resize:
                 UpperCamelCase__    :   int					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_center_crop:
                 UpperCamelCase__    :   Optional[int]					=				[self.center_crop(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   Optional[int]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   List[Any]					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   str					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE						,						tensor_type=__SCREAMING_SNAKE_CASE  )




import math


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   List[str]					=				math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

      return exponent == int(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 1 / 1_2_3_4_5		):
      UpperCamelCase__    :   Dict					=				0
      UpperCamelCase__    :   Tuple					=				0

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


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


