

"""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    snake_case     (    __SCREAMING_SNAKE_CASE  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  ["pixel_values"]

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    int = True					,  UpperCamelCase__							:    Optional[int] = None					,  UpperCamelCase__							:    str = PILImageResampling.BICUBIC					,  UpperCamelCase__							:    str = True					,  UpperCamelCase__							:    List[Any] = None					,  UpperCamelCase__							:    Optional[Any] = True					,  UpperCamelCase__							:    str = 1 / 2_5_5					,  UpperCamelCase__							:    Optional[Any] = True					,  UpperCamelCase__							:    Optional[Any] = IMAGENET_DEFAULT_MEAN					,  UpperCamelCase__							:    str = IMAGENET_DEFAULT_STD					,  **UpperCamelCase__							:    str					,  )->       Optional[Any]:



						'''simple docstring'''


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

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

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Optional[int] = PILImageResampling.BICUBIC					,  UpperCamelCase__							:    str = None					,  **UpperCamelCase__							:    Optional[int]					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      get_size_dict(_snake_case					,  default_to_square=_snake_case)
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
								__lowerCAmelCase:	List[str]   				=      int((2_5_6 / 2_2_4) * size["shortest_edge"])
								__lowerCAmelCase:	List[Any]   				=      get_resize_output_image_size(_snake_case					,  size=_snake_case					,  default_to_square=_snake_case)
								__lowerCAmelCase:	Union[str, Any]   				=      {"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(
						    _snake_case					,  size=(size_dict["height"], size_dict["width"])					,  resample=_snake_case					,  data_format=_snake_case					,  **_snake_case)

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    str = None					,  **UpperCamelCase__							:    Union[str, Any]					,  )->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      get_size_dict(_snake_case)
						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(_snake_case					,  size=(size["height"], size["width"])					,  data_format=_snake_case					,  **_snake_case)

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int = None					,  **UpperCamelCase__							:    Optional[int]					,  )->       Tuple:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Tuple = None					,  **UpperCamelCase__							:    List[str]					,  )->       Union[str, Any]:



						'''simple docstring'''


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




				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str] = None					,  UpperCamelCase__							:    List[Any] = None					,  UpperCamelCase__							:    Any = None					,  UpperCamelCase__							:    Union[str, Any] = None					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Tuple = None					,  UpperCamelCase__							:    Union[str, Any] = None					,  UpperCamelCase__							:    List[Any] = None					,  UpperCamelCase__							:    int = None					,  UpperCamelCase__							:    str = None					,  UpperCamelCase__							:    Optional[int] = ChannelDimension.FIRST					,  **UpperCamelCase__							:    List[str]					,  )->       Optional[int]:



						'''simple docstring'''


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

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

						__lowerCAmelCase:	Any   				=      make_list_of_images(_snake_case)

						if not valid_images(_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.")

						# All transformations expect numpy arrays.
						__lowerCAmelCase:	Optional[Any]   				=      [to_numpy_array(_snake_case) for image in images]

						if do_resize:
								__lowerCAmelCase:	Optional[int]   				=      [self.resize(_snake_case					,  _snake_case					,  _snake_case) for image in images]

						if do_center_crop:
								__lowerCAmelCase:	Dict   				=      [self.center_crop(_snake_case					,  _snake_case) for image in images]

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

						if do_normalize:
								__lowerCAmelCase:	List[str]   				=      [self.normalize(_snake_case					,  _snake_case					,  _snake_case) for image in images]

						__lowerCAmelCase:	List[Any]   				=      [to_channel_dimension_format(_snake_case					,  _snake_case) for image in images]

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





"""simple docstring"""


import math
import sys






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if number != int(__SCREAMING_SNAKE_CASE			):
				raise ValueError("the value of input must be a natural number"			)
		if number < 0:
				raise ValueError("the value of input must not be a negative number"			)
		if number == 0:
				return 1
		__lowerCAmelCase:	int   				=      [-1] * (number + 1)
		__lowerCAmelCase:	Optional[Any]   				=      0
		for i in range(1 ,			number + 1			):
				__lowerCAmelCase:	List[str]   				=      sys.maxsize
				__lowerCAmelCase:	int   				=      int(math.sqrt(__SCREAMING_SNAKE_CASE			)			)
				for j in range(1 ,			root + 1			):
						__lowerCAmelCase:	Optional[Any]   				=      1 + answers[i - (j**2)]
						__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[Any]   				=      answer
		return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


