

__UpperCAmelCase    				=     [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    0
     while number:
          # Increased Speed Slightly by checking every 5 digits together.
          sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
          number //= 100000

     return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
__UpperCAmelCase    				=     [None] * 1_0_0_0_0_0_0_0
__UpperCAmelCase    				=     True
__UpperCAmelCase    				=     False







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      bool:

     """simple docstring"""







     if CHAINS[number - 1] is not None:
          return CHAINS[number - 1]  # type: ignore

     _lowerCAmelCase   :					Union[str, Any]        =    chain(next_number(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    number_chain

     while number < 10000000:
          _lowerCAmelCase   :					str        =    number_chain
          number *= 10

     return number_chain







def 	_snake_case		(    SCREAMING_SNAKE_CASE = 10000000				)       ->      int:

     """simple docstring"""







     for i in range(1		,							SCREAMING_SNAKE_CASE				):
          if CHAINS[i] is None:
               chain(i + 1				)

     return CHAINS[:number].count(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(f'''{solution() = }''')









from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						['''pixel_values''']

 def __init__(		self :	Union[str, Any]     ,						A_ :	bool = True     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	Optional[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else {"shortest_edge": 2_5_6}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Dict        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Tuple        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Dict        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					Union[str, Any]        =    resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop
      _lowerCAmelCase   :					str        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					str        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	int     ,						A_ :	np.ndarray     ,						A_ :	float     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[str]     ,						A_ :	ImageInput     ,						A_ :	Optional[bool] = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[float] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	Union[str, ChannelDimension] = ChannelDimension.FIRST     ,						**A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else self.size
      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Tuple        =    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   :					List[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Union[str, Any]        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[str]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    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   :					Any        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					str        =    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:
           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   :					Any        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					str        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					List[Any]        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Dict        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Tuple        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(A_       ) != len(A_       ):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					List[str]        =    target_sizes.numpy()

           _lowerCAmelCase   :					Tuple        =    []

           for idx in range(len(A_       )       ):
                _lowerCAmelCase   :					Tuple        =    torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0       )     ,						size=target_sizes[idx]     ,						mode="bilinear"     ,						align_corners=A_       )
                _lowerCAmelCase   :					str        =    resized_logits[0].argmax(dim=0       )
                semantic_segmentation.append(A_       )
      else:
           _lowerCAmelCase   :					List[Any]        =    logits.argmax(dim=1       )
           _lowerCAmelCase   :					str        =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation






