'''simple docstring'''


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple[int, int]:
  def constraint_to_multiple_of(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=0 ,	SCREAMING_SNAKE_CASE_=None						):
    lowercase_     =       round(val / multiple						) * multiple

    if max_val is not None and x > max_val:
      lowercase_     =       math.floor(val / multiple						) * multiple

    if x < min_val:
      lowercase_     =       math.ceil(val / multiple						) * multiple

    return x

  lowercase_     =       (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) else output_size

  lowercase_     ,	lowercase_     =       get_image_size(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       output_size

  # determine new height and width
  lowercase_     =       output_height / input_height
  lowercase_     =       output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width						) < abs(1 - scale_height						):
      # fit width
      lowercase_     =       scale_width
    else:
      # fit height
      lowercase_     =       scale_height

  lowercase_     =       constraint_to_multiple_of(scale_height * input_height ,	multiple=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       constraint_to_multiple_of(scale_width * input_width ,	multiple=SCREAMING_SNAKE_CASE_						)

  return (new_height, new_width)





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




  def __init__(		self      :       Dict   ,				lowercase_      :       bool = True   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       bool = False   ,				lowercase_      :       int = 1   ,				lowercase_      :       bool = True   ,				lowercase_      :       Union[int, float] = 1 / 255   ,				lowercase_      :       bool = True   ,				lowercase_      :       Optional[Union[float, List[float]]] = None   ,				lowercase_      :       Optional[Union[float, List[float]]] = None   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 384, """width""": 384}
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       keep_aspect_ratio
    lowercase_     =       ensure_multiple_of
    lowercase_     =       resample
    lowercase_     =       do_rescale
    lowercase_     =       rescale_factor
    lowercase_     =       do_normalize
    lowercase_     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    lowercase_     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       bool = False   ,				lowercase_      :       int = 1   ,				lowercase_      :       PILImageResampling = PILImageResampling.BICUBIC   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       List[Any]   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    if "height" not in size or "width" not in size:
      raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""      )
    lowercase_     =       get_resize_output_image_size(
        lowercase_   ,				output_size=(size["""height"""], size["""width"""])   ,				keep_aspect_ratio=lowercase_   ,				multiple=lowercase_   ,				)
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )




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







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Union[float, List[float]]   ,				lowercase_      :       Union[float, List[float]]   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       Optional[int]   ,				):







    '''simple docstring'''


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




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







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
    lowercase_     =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       do_rescale if do_rescale is not None else self.do_rescale
    lowercase_     =       rescale_factor if rescale_factor is not None else self.rescale_factor
    lowercase_     =       do_normalize if do_normalize is not None else self.do_normalize
    lowercase_     =       image_mean if image_mean is not None else self.image_mean
    lowercase_     =       image_std if image_std is not None else self.image_std

    lowercase_     =       make_list_of_images(lowercase_      )

    if not valid_images(lowercase_      ):
      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_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_     =       [to_numpy_array(lowercase_      ) for image in images]

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

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

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

    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

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





  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       List[Tuple] = None      ):







    '''simple docstring'''


    lowercase_     =       outputs.logits

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

      if is_torch_tensor(lowercase_      ):
        lowercase_     =       target_sizes.numpy()

      lowercase_     =       []

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

    return semantic_segmentation


'''simple docstring'''


import math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

