





"""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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__A											=						logging.get_logger(__name__)






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:int						    =   ["""pixel_values"""]






       def __init__(       self       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = PILImageResampling.BILINEAR       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = 1 / 2_5_5       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )
         lowerCAmelCase__     :int											=				size if size is not None else {'shortest_edge': 2_5_6}
         lowerCAmelCase__     :Optional[int]											=				get_size_dict(__UpperCAmelCase       ,					default_to_square=__UpperCAmelCase )
         lowerCAmelCase__     :str											=				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         lowerCAmelCase__     :Optional[int]											=				get_size_dict(__UpperCAmelCase       ,					param_name='crop_size' )
         lowerCAmelCase__     :Optional[Any]											=				do_resize
         lowerCAmelCase__     :Dict											=				size
         lowerCAmelCase__     :Tuple											=				resample
         lowerCAmelCase__     :Any											=				do_center_crop
         lowerCAmelCase__     :Tuple											=				crop_size
         lowerCAmelCase__     :List[Any]											=				do_rescale
         lowerCAmelCase__     :Any											=				rescale_factor
         lowerCAmelCase__     :Tuple											=				do_normalize
         lowerCAmelCase__     :Optional[Any]											=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__     :List[str]											=				image_std if image_std is not None else IMAGENET_STANDARD_STD






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = PILImageResampling.BICUBIC       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				get_size_dict(__UpperCAmelCase       ,					default_to_square=__UpperCAmelCase )
         if "shortest_edge" not in size:
           raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
         lowerCAmelCase__     :Any											=				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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				get_size_dict(__UpperCAmelCase )
         if "height" not in size or "width" not in size:
           raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
         return center_crop(__UpperCAmelCase       ,					size=(size['height'], size['width'])       ,					data_format=__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = ChannelDimension.FIRST       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__     :int											=				size if size is not None else self.size
         lowerCAmelCase__     :Optional[Any]											=				get_size_dict(__UpperCAmelCase       ,					default_to_square=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				resample if resample is not None else self.resample
         lowerCAmelCase__     :List[str]											=				do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__     :List[str]											=				crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__     :Union[str, Any]											=				get_size_dict(__UpperCAmelCase       ,					param_name='crop_size' )
         lowerCAmelCase__     :List[str]											=				do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__     :List[str]											=				rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__     :List[Any]											=				do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__     :Union[str, Any]											=				image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__     :Optional[int]											=				image_std if image_std is not None else self.image_std

         lowerCAmelCase__     :List[str]											=				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_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__     :Optional[int]											=				[to_numpy_array(__UpperCAmelCase ) for image in images]

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

         if do_center_crop:
           lowerCAmelCase__     :Union[str, Any]											=				[self.center_crop(image=__UpperCAmelCase       ,					size=__UpperCAmelCase ) for image in images]

         if do_rescale:
           lowerCAmelCase__     :Union[str, Any]											=				[self.rescale(image=__UpperCAmelCase       ,					scale=__UpperCAmelCase ) for image in images]

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

         lowerCAmelCase__     :Dict											=				[to_channel_dimension_format(__UpperCAmelCase       ,					__UpperCAmelCase ) for image in images]

         lowerCAmelCase__     :List[str]											=				{'pixel_values': images}
         return BatchFeature(data=__UpperCAmelCase       ,					tensor_type=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				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 ):
             lowerCAmelCase__     :Optional[Any]											=				target_sizes.numpy()

           lowerCAmelCase__     :List[str]											=				[]

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

         return semantic_segmentation



"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


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




