


'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
      import PIL


A__		:					Dict												=  logging.get_logger(__name__)





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					str							     =			['pixel_values']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = 1 / 2_55			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         super().__init__(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		size if size is not None else {'height': 2_56, 'width': 2_56}
         __lowerCamelCase   :	Any								=		get_size_dict(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
         __lowerCamelCase   :	Optional[int]								=		get_size_dict(SCREAMING_SNAKE_CASE_			,     param_name='crop_size'	)

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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     np.ndarray:
         __lowerCamelCase   :	Union[str, Any]								=		get_size_dict(SCREAMING_SNAKE_CASE_	)
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'	)
         return resize(
             SCREAMING_SNAKE_CASE_			,     size=(size['height'], size['width'])			,     resample=SCREAMING_SNAKE_CASE_			,     data_format=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     np.ndarray:
         __lowerCamelCase   :	List[str]								=		get_size_dict(SCREAMING_SNAKE_CASE_	)
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. 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			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     str:
         return rescale(SCREAMING_SNAKE_CASE_			,     scale=SCREAMING_SNAKE_CASE_			,     data_format=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE_			,     mean=SCREAMING_SNAKE_CASE_			,     std=SCREAMING_SNAKE_CASE_			,     data_format=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST			,     **SCREAMING_SNAKE_CASE_			,     )				->     PIL.Image.Image:
         __lowerCamelCase   :	Tuple								=		do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase   :	int								=		resample if resample is not None else self.resample
         __lowerCamelCase   :	Any								=		do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase   :	Optional[Any]								=		do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase   :	Union[str, Any]								=		rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase   :	Optional[Any]								=		do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase   :	List[str]								=		image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase   :	Dict								=		image_std if image_std is not None else self.image_std

         __lowerCamelCase   :	Optional[Any]								=		size if size is not None else self.size
         __lowerCamelCase   :	Optional[Any]								=		get_size_dict(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase   :	Tuple								=		get_size_dict(SCREAMING_SNAKE_CASE_			,     param_name='crop_size'	)

         __lowerCamelCase   :	Union[str, 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_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.'	)

         # All transformations expect numpy arrays.
         __lowerCamelCase   :	List[Any]								=		[to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

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

         if do_center_crop:
              __lowerCamelCase   :	List[str]								=		[self.center_crop(image=SCREAMING_SNAKE_CASE_			,     size=SCREAMING_SNAKE_CASE_	) for image in images]

         if do_rescale:
              __lowerCamelCase   :	List[str]								=		[self.rescale(image=SCREAMING_SNAKE_CASE_			,     scale=SCREAMING_SNAKE_CASE_	) for image in images]

         if do_normalize:
              __lowerCamelCase   :	Optional[int]								=		[self.normalize(image=SCREAMING_SNAKE_CASE_			,     mean=SCREAMING_SNAKE_CASE_			,     std=SCREAMING_SNAKE_CASE_	) for image in images]

         __lowerCamelCase   :	int								=		[to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) for image in images]

         __lowerCamelCase   :	Dict								=		{'pixel_values': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE_			,     tensor_type=SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



import unittest

from knapsack import knapsack as k





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






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

         __lowerCamelCase   :	List[str]								=		[60]
         __lowerCamelCase   :	Union[str, Any]								=		[10]
         __lowerCamelCase   :	List[Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Optional[int]								=		3
         __lowerCamelCase   :	int								=		[1, 2, 3]
         __lowerCamelCase   :	str								=		[3, 2, 1]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     5	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Optional[int]								=		50
         __lowerCamelCase   :	List[str]								=		[60, 1_00, 1_20]
         __lowerCamelCase   :	List[str]								=		[10, 20, 30]
         __lowerCamelCase   :	List[str]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     2_20	)


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






