


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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
    import PIL

if is_torch_available():
    import torch


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["pixel_values"]


     def __init__(						self ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = PILImageResampling.BILINEAR ,						__lowerCamelCase = True ,						__lowerCamelCase = 1 / 2_5_5 ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						**__lowerCamelCase ,						)			->				None:
            super().__init__(**__lowerCamelCase)
            _A      :       Tuple				    = size if size is not None else {"shortest_edge": 2_2_4}
            _A      :       Dict				    = get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase)
            _A      :       List[str]				    = crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
            _A      :       Optional[int]				    = get_size_dict(__lowerCamelCase ,						param_name="crop_size")

            _A      :       List[str]				    = do_resize
            _A      :       Optional[int]				    = size
            _A      :       List[str]				    = resample
            _A      :       int				    = do_rescale
            _A      :       Optional[int]				    = rescale_factor
            _A      :       Optional[int]				    = do_center_crop
            _A      :       int				    = crop_size
            _A      :       Optional[int]				    = do_flip_channel_order


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = PIL.Image.BILINEAR ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				np.ndarray:
            _A      :       List[Any]				    = get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase)
            if "shortest_edge" not in size:
                   raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
            _A      :       Union[str, Any]				    = get_resize_output_image_size(__lowerCamelCase ,						size=size["shortest_edge"] ,						default_to_square=__lowerCamelCase)
            return resize(__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				np.ndarray:
            _A      :       int				    = get_size_dict(__lowerCamelCase)
            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()}")
            return center_crop(__lowerCamelCase ,						size=(size["height"], size["width"]) ,						data_format=__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				List[str]:
            return rescale(__lowerCamelCase ,						scale=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				np.ndarray:
            return flip_channel_order(__lowerCamelCase ,						data_format=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						**__lowerCamelCase ,						)			->				PIL.Image.Image:
            _A      :       Tuple				    = do_resize if do_resize is not None else self.do_resize
            _A      :       Dict				    = resample if resample is not None else self.resample
            _A      :       int				    = do_rescale if do_rescale is not None else self.do_rescale
            _A      :       Union[str, Any]				    = rescale_factor if rescale_factor is not None else self.rescale_factor
            _A      :       Dict				    = do_center_crop if do_center_crop is not None else self.do_center_crop
            _A      :       Tuple				    = (
                do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
            )

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

            _A      :       int				    = make_list_of_images(__lowerCamelCase)

            if not valid_images(__lowerCamelCase):
                   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_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True.")

            if do_center_crop and crop_size is None:
                   raise ValueError("Crop size must be specified if do_center_crop is True.")

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

            if do_resize:
                   _A      :       List[Any]				    = [self.resize(image=__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase) for image in images]

            if do_center_crop:
                   _A      :       Dict				    = [self.center_crop(image=__lowerCamelCase ,						size=__lowerCamelCase) for image in images]

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

            # the pretrained checkpoints assume images are BGR, not RGB
            if do_flip_channel_order:
                   _A      :       int				    = [self.flip_channel_order(image=__lowerCamelCase) for image in images]

            _A      :       Tuple				    = [to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase) for image in images]

            _A      :       List[str]				    = {"pixel_values": images}
            return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				int:
            _A      :       Tuple				    = outputs.logits

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

                   if is_torch_tensor(__lowerCamelCase):
                          _A      :       Tuple				    = target_sizes.numpy()

                   _A      :       Dict				    = []

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

            return semantic_segmentation

"""simple docstring"""


from math import pow






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,):







  """simple docstring"""
  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  _UpperCAmelCase         =					int(pow(lowercase      ,lowercase						)						)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
  return current_sum, solutions_count






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
    raise ValueError(
        """Invalid input\n"""
        """needed_sum must be between 1 and 1000, power between 2 and 10."""						)

  return backtrack(lowercase      ,lowercase      ,1      ,0      ,0						)[1]  # Return the solutions_count


if __name__ == "__main__":
    import doctest

    doctest.testmod()
