





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

lowercase :			Union[str, Any]     		=    logging.get_logger(__name__)



def A_      (						A__							)    ->						int:
       if isinstance(snake_case__						, (list, tuple)							) and isinstance(videos[0]						, (list, tuple)							) and is_valid_image(videos[0][0]							):
              return videos

       elif isinstance(snake_case__						, (list, tuple)							) and is_valid_image(videos[0]							):
              return [videos]

       elif is_valid_image(snake_case__							):
              return [[videos]]

       raise ValueError(F'Could not make batched video from {videos}'							)





class 					A__       (				__UpperCAmelCase      ):




   """simple docstring"""



   __A  :							Optional[Any]			    =    ['pixel_values']

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







          '''simple docstring'''

          super().__init__(**SCREAMING_SNAKE_CASE_)
          a__		:      Tuple					=							size if size is not None else {'shortest_edge': 256}
          a__		:      List[str]					=							get_size_dict(SCREAMING_SNAKE_CASE_   ,    default_to_square=SCREAMING_SNAKE_CASE_)
          a__		:      Union[str, Any]					=							crop_size if crop_size is not None else {'height': 224, 'width': 224}
          a__		:      Optional[int]					=							get_size_dict(SCREAMING_SNAKE_CASE_   ,    param_name='crop_size')

          a__		:      Any					=							do_resize
          a__		:      Union[str, Any]					=							size
          a__		:      Optional[Any]					=							do_center_crop
          a__		:      List[str]					=							crop_size
          a__		:      Union[str, Any]					=							resample
          a__		:      int					=							do_rescale
          a__		:      List[str]					=							rescale_factor
          a__		:      int					=							offset
          a__		:      List[str]					=							do_normalize
          a__		:      str					=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a__		:      Tuple					=							image_std if image_std is not None else IMAGENET_STANDARD_STD

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







          '''simple docstring'''

          a__		:      Optional[Any]					=							get_size_dict(SCREAMING_SNAKE_CASE_   ,    default_to_square=SCREAMING_SNAKE_CASE_)
          if "shortest_edge" in size:
                 a__		:      List[str]					=							get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,    size['shortest_edge']   ,    default_to_square=SCREAMING_SNAKE_CASE_)
          elif "height" in size and "width" in size:
                 a__		:      Optional[int]					=							(size['height'], size['width'])
          else:
                 raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
          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   ,    )     ->		np.ndarray:







          '''simple docstring'''

          a__		:      int					=							get_size_dict(SCREAMING_SNAKE_CASE_)
          if "height" not in size or "width" not in size:
                 raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
          return center_crop(SCREAMING_SNAKE_CASE_   ,    size=(size['height'], size['width'])   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase = True   ,    lowercase = None   ,    **lowercase   ,    )     ->		Tuple:







          '''simple docstring'''

          a__		:      str					=							image.astype(np.floataa)
          if offset:
                 a__		:      int					=							image - (scale / 2)
          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   ,    )     ->		np.ndarray:







          '''simple docstring'''

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

          if offset and not do_rescale:
                 raise ValueError('For offset, do_rescale must also be set to True.')

          # All transformations expect numpy arrays.
          a__		:      Optional[Any]					=							to_numpy_array(SCREAMING_SNAKE_CASE_)

          if do_resize:
                 a__		:      Any					=							self.resize(image=SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_   ,    resample=SCREAMING_SNAKE_CASE_)

          if do_center_crop:
                 a__		:      List[str]					=							self.center_crop(SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_)

          if do_rescale:
                 a__		:      Tuple					=							self.rescale(image=SCREAMING_SNAKE_CASE_   ,    scale=SCREAMING_SNAKE_CASE_   ,    offset=SCREAMING_SNAKE_CASE_)

          if do_normalize:
                 a__		:      str					=							self.normalize(image=SCREAMING_SNAKE_CASE_   ,    mean=SCREAMING_SNAKE_CASE_   ,    std=SCREAMING_SNAKE_CASE_)

          a__		:      Optional[int]					=							to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_)
          return image

   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 = None   ,    lowercase = ChannelDimension.FIRST   ,    **lowercase   ,    )     ->		PIL.Image.Image:







          '''simple docstring'''

          a__		:      List[str]					=							do_resize if do_resize is not None else self.do_resize
          a__		:      Tuple					=							resample if resample is not None else self.resample
          a__		:      Optional[Any]					=							do_center_crop if do_center_crop is not None else self.do_center_crop
          a__		:      List[str]					=							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__		:      str					=							offset if offset is not None else self.offset
          a__		:      List[Any]					=							do_normalize if do_normalize is not None else self.do_normalize
          a__		:      str					=							image_mean if image_mean is not None else self.image_mean
          a__		:      Dict					=							image_std if image_std is not None else self.image_std

          a__		:      Dict					=							size if size is not None else self.size
          a__		:      Optional[int]					=							get_size_dict(SCREAMING_SNAKE_CASE_   ,    default_to_square=SCREAMING_SNAKE_CASE_)
          a__		:      Dict					=							crop_size if crop_size is not None else self.crop_size
          a__		:      str					=							get_size_dict(SCREAMING_SNAKE_CASE_   ,    param_name='crop_size')

          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.')

          a__		:      str					=							make_batched(SCREAMING_SNAKE_CASE_)

          a__		:      Optional[Any]					=							[
              [
                  self._preprocess_image(
                      image=SCREAMING_SNAKE_CASE_   ,    do_resize=SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_   ,    resample=SCREAMING_SNAKE_CASE_   ,    do_center_crop=SCREAMING_SNAKE_CASE_   ,    crop_size=SCREAMING_SNAKE_CASE_   ,    do_rescale=SCREAMING_SNAKE_CASE_   ,    rescale_factor=SCREAMING_SNAKE_CASE_   ,    offset=SCREAMING_SNAKE_CASE_   ,    do_normalize=SCREAMING_SNAKE_CASE_   ,    image_mean=SCREAMING_SNAKE_CASE_   ,    image_std=SCREAMING_SNAKE_CASE_   ,    data_format=SCREAMING_SNAKE_CASE_   ,    )
                  for img in video
              ]
              for video in videos
          ]

          a__		:      Dict					=							{'pixel_values': videos}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,    tensor_type=SCREAMING_SNAKE_CASE_)




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()




