





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


lowercase :			List[Any]     		=    logging.get_logger(__name__)



def A_      (						A__						, A__						, A__						, A__							)    ->						Tuple:
       def constraint_to_multiple_of(A__						, A__						, A__=0						, A__=None							):
              a__		:      List[Any]					=							round(val / multiple							) * multiple

              if max_val is not None and x > max_val:
                     a__		:      List[str]					=							math.floor(val / multiple							) * multiple

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

              return x

       a__		:      Union[str, Any]					=							(output_size, output_size) if isinstance(snake_case__						, snake_case__							) else output_size

       a__					,				a__		:      List[Any]					=							get_image_size(snake_case__							)
       a__					,				a__		:      List[Any]					=							output_size

       # determine new height and width
       a__		:      List[str]					=							output_height / input_height
       a__		:      List[Any]					=							output_width / input_width

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

       a__		:      Optional[Any]					=							constraint_to_multiple_of(scale_height * input_height						, multiple=snake_case__							)
       a__		:      List[str]					=							constraint_to_multiple_of(scale_width * input_width						, multiple=snake_case__							)

       return (new_height, new_width)





class 					A__       (				__UpperCAmelCase      ):




   """simple docstring"""



   __A  :							Union[str, Any]			    =    ['pixel_values']

   def __init__(   self   ,    lowercase = True   ,    lowercase = None   ,    lowercase = PILImageResampling.BILINEAR   ,    lowercase = False   ,    lowercase = 1   ,    lowercase = True   ,    lowercase = 1 / 255   ,    lowercase = True   ,    lowercase = None   ,    lowercase = None   ,    **lowercase   ,    )     ->		None:







          '''simple docstring'''

          super().__init__(**SCREAMING_SNAKE_CASE_)
          a__		:      Tuple					=							size if size is not None else {'height': 384, 'width': 384}
          a__		:      Optional[Any]					=							get_size_dict(SCREAMING_SNAKE_CASE_)
          a__		:      List[Any]					=							do_resize
          a__		:      Optional[int]					=							size
          a__		:      Tuple					=							keep_aspect_ratio
          a__		:      Union[str, Any]					=							ensure_multiple_of
          a__		:      Tuple					=							resample
          a__		:      Optional[Any]					=							do_rescale
          a__		:      Tuple					=							rescale_factor
          a__		:      Dict					=							do_normalize
          a__		:      Dict					=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a__		:      Union[str, Any]					=							image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase = False   ,    lowercase = 1   ,    lowercase = PILImageResampling.BICUBIC   ,    lowercase = None   ,    **lowercase   ,    )     ->		np.ndarray:







          '''simple docstring'''

          a__		:      Optional[Any]					=							get_size_dict(SCREAMING_SNAKE_CASE_)
          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()}')
          a__		:      int					=							get_resize_output_image_size(
              SCREAMING_SNAKE_CASE_   ,    output_size=(size['height'], size['width'])   ,    keep_aspect_ratio=SCREAMING_SNAKE_CASE_   ,    multiple=SCREAMING_SNAKE_CASE_   ,    )
          return resize(SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_   ,    resample=SCREAMING_SNAKE_CASE_   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase = None   ,    **lowercase   ,    )     ->		List[Any]:







          '''simple docstring'''

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

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







          '''simple docstring'''

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

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = None   ,    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__		:      int					=							size if size is not None else self.size
          a__		:      List[str]					=							get_size_dict(SCREAMING_SNAKE_CASE_)
          a__		:      Optional[int]					=							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
          a__		:      Dict					=							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
          a__		:      str					=							resample if resample is not None else self.resample
          a__		:      Optional[int]					=							do_rescale if do_rescale is not None else self.do_rescale
          a__		:      Optional[Any]					=							rescale_factor if rescale_factor is not None else self.rescale_factor
          a__		:      List[Any]					=							do_normalize if do_normalize is not None else self.do_normalize
          a__		:      int					=							image_mean if image_mean is not None else self.image_mean
          a__		:      List[Any]					=							image_std if image_std is not None else self.image_std

          a__		:      Any					=							make_list_of_images(SCREAMING_SNAKE_CASE_)

          if not valid_images(SCREAMING_SNAKE_CASE_):
                 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.
          a__		:      Optional[int]					=							[to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

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

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

          if do_normalize:
                 a__		:      List[str]					=							[self.normalize(image=SCREAMING_SNAKE_CASE_   ,    mean=SCREAMING_SNAKE_CASE_   ,    std=SCREAMING_SNAKE_CASE_) for image in images]

          a__		:      Union[str, Any]					=							[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_) for image in images]

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

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		Dict:







          '''simple docstring'''

          a__		:      int					=							outputs.logits

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

                 if is_torch_tensor(SCREAMING_SNAKE_CASE_):
                        a__		:      Any					=							target_sizes.numpy()

                 a__		:      Tuple					=							[]

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

          return semantic_segmentation




import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




