

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


lowercase__				:      int	   = logging.get_logger(__name__)




class       a__	( UpperCamelCase__				):
     a		:      List[str]		 =     ["""pixel_values"""]







     def __init__(     self  ,		A = True  ,		A = None  ,		A = 0.9  ,		A = PILImageResampling.BICUBIC  ,		A = True  ,		A = None  ,		A = 1 / 255  ,		A = True  ,		A = True  ,		A = None  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          super().__init__(**A			)
          a     =				size if size is not None else {"shortest_edge": 224}
          a     =				get_size_dict(A  ,		default_to_square=A			)
          a     =				crop_size if crop_size is not None else {"height": 224, "width": 224}
          a     =				get_size_dict(A  ,		param_name="crop_size"			)

          a     =				do_resize
          a     =				size
          a     =				crop_pct
          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  ,		A  ,		A  ,		A = None  ,		A = PILImageResampling.BICUBIC  ,		A = None  ,		**A  ,		)  ->   np.ndarray:



          '''simple docstring'''



          a     =				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:
                    a     =				int(size["shortest_edge"] / crop_pct			)
               elif "height" in size and "width" in size:
                    if size["height"] == size["width"]:
                         a     =				int(size["height"] / crop_pct			)
                    else:
                         a     =				(int(size["height"] / crop_pct			), int(size["width"] / crop_pct			))
               else:
                    raise ValueError("Invalid size for resize: {}".format(A			)			)

               a     =				get_resize_output_image_size(A  ,		size=A  ,		default_to_square=A			)
          else:
               if "shortest_edge" in size:
                    a     =				get_resize_output_image_size(A  ,		size=size["shortest_edge"]  ,		default_to_square=A			)
               elif "height" in size and "width" in size:
                    a     =				(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       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   np.ndarray:



          '''simple docstring'''



          a     =				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       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   int:



          '''simple docstring'''



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







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



          '''simple docstring'''



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







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



          '''simple docstring'''



          a     =				do_resize if do_resize is not None else self.do_resize
          a     =				crop_pct if crop_pct is not None else self.crop_pct
          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(A  ,		default_to_square=A			)
          a     =				crop_size if crop_size is not None else self.crop_size
          a     =				get_size_dict(A  ,		param_name="crop_size"			)

          a     =				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.
          a     =				[to_numpy_array(A			) for image in images]

          if do_resize:
               a     =				[self.resize(image=A  ,		size=A  ,		crop_pct=A  ,		resample=A			) for image in images]

          if do_center_crop:
               a     =				[self.center_crop(image=A  ,		size=A			) for image in images]

          if do_rescale:
               a     =				[self.rescale(image=A  ,		scale=A			) for image in images]

          if do_normalize:
               a     =				[self.normalize(image=A  ,		mean=A  ,		std=A			) for image in images]

          a     =				[to_channel_dimension_format(A  ,		A			) for image in images]

          a     =				{"pixel_values": images}
          return BatchFeature(data=A  ,		tensor_type=A			)







import math
import sys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     if number != int(__UpperCamelCase):
          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
     a     =				[-1] * (number + 1)
     a     =				0
     for i in range(1		, number + 1):
          a     =				sys.maxsize
          a     =				int(math.sqrt(__UpperCamelCase))
          for j in range(1		, root + 1):
               a     =				1 + answers[i - (j**2)]
               a     =				min(__UpperCamelCase		, __UpperCamelCase)
          a     =				answer
     return answers[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

