"""simple docstring"""
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


lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""pixel_values"""]



      def __init__(						self   ,      lowercase = True   ,      lowercase = None   ,      lowercase = PILImageResampling.BILINEAR   ,      lowercase = True   ,      lowercase = 1 / 2_55   ,      lowercase = True   ,      lowercase = None   ,      lowercase = True   ,      **lowercase   ,      )     ->      None:





        '''simple docstring'''





        super().__init__(**lowercase)
        a__:					Dict      							=     size if size is not None else {'shortest_edge': 2_24}
        a__:					str      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        a__:					str      							=     crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
        a__:					List[Any]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = PIL.Image.BILINEAR   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					Dict      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        if "shortest_edge" not in size:
          raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
        a__:					Any      							=     get_resize_output_image_size(lowercase   ,      size=size['shortest_edge']   ,      default_to_square=lowercase)
        return resize(lowercase   ,      size=lowercase   ,      resample=lowercase   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					List[str]      							=     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()}')
        return center_crop(lowercase   ,      size=(size['height'], size['width'])   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      str:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      np.ndarray:





        '''simple docstring'''





        return flip_channel_order(lowercase   ,      data_format=lowercase)



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





        '''simple docstring'''





        a__:					List[Any]      							=     do_resize if do_resize is not None else self.do_resize
        a__:					Dict      							=     resample if resample is not None else self.resample
        a__:					Tuple      							=     do_rescale if do_rescale is not None else self.do_rescale
        a__:					Dict      							=     rescale_factor if rescale_factor is not None else self.rescale_factor
        a__:					Union[str, Any]      							=     do_center_crop if do_center_crop is not None else self.do_center_crop
        a__:					List[Any]      							=     (
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        a__:					Optional[Any]      							=     size if size is not None else self.size
        a__:					List[str]      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        a__:					Any      							=     crop_size if crop_size is not None else self.crop_size
        a__:					Union[str, Any]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					Any      							=     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:
          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__:					Optional[Any]      							=     [to_numpy_array(lowercase) for image in images]

        if do_resize:
          a__:					Union[str, Any]      							=     [self.resize(image=lowercase   ,      size=lowercase   ,      resample=lowercase) for image in images]

        if do_center_crop:
          a__:					int      							=     [self.center_crop(image=lowercase   ,      size=lowercase) for image in images]

        if do_rescale:
          a__:					List[Any]      							=     [self.rescale(image=lowercase   ,      scale=lowercase) for image in images]

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

        a__:					Tuple      							=     [to_channel_dimension_format(lowercase   ,      lowercase) for image in images]

        a__:					Any      							=     {'pixel_values': images}
        return BatchFeature(data=lowercase   ,      tensor_type=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     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):
            a__:					Any      							=     target_sizes.numpy()

          a__:					int      							=     []

          for idx in range(len(lowercase)):
            a__:					Dict      							=     torch.nn.functional.interpolate(
                logits[idx].unsqueeze(dim=0)   ,      size=target_sizes[idx]   ,      mode='bilinear'   ,      align_corners=lowercase)
            a__:					Optional[Any]      							=     resized_logits[0].argmax(dim=0)
            semantic_segmentation.append(lowercase)
        else:
          a__:					Optional[int]      							=     logits.argmax(dim=1)
          a__:					Tuple      							=     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  a__:					Tuple      							=     0

  for i in range(1		,					n + 1       ):
    a__:					List[str]      							=     max_rev[i]
    for j in range(1		,					i + 1       ):
      a__:					Tuple      							=     max(_SCREAMING_SNAKE_CASE		,					prices[j - 1] + max_rev[i - j]       )

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a__:					Any      							=     36

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()


