


"""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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[Any]    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = True    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 224}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				param_name='''crop_size'''							)

          _a    	=				do_resize
          _a    	=				size
          _a    	=				resample
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_center_crop
          _a    	=				crop_size
          _a    	=				do_flip_channel_order


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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase							)
          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(__UpperCAmelCase    ,				size=(size['''height'''], size['''width'''])    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							str:
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							PIL.Image.Image:
          _a    	=				do_resize if do_resize is not None else self.do_resize
          _a    	=				resample if resample is not None else self.resample
          _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_center_crop if do_center_crop is not None else self.do_center_crop
          _a    	=				(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          _a    	=				size if size is not None else self.size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				crop_size if crop_size is not None else self.crop_size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				param_name='''crop_size'''							)

          _a    	=				make_list_of_images(__UpperCAmelCase							)

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

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

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

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

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

          _a    	=				[to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase							) for image in images]

          _a    	=				{'''pixel_values''': images}
          return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Dict:
          _a    	=				outputs.logits

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

               if is_torch_tensor(__UpperCAmelCase							):
                    _a    	=				target_sizes.numpy()

               _a    	=				[]

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

          return semantic_segmentation



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()