




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,
    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_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase				:		str     =   logging.get_logger(__name__)







class   A(						_SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						["pixel_values"]

 def __init__(				self					:  List[Any]						,							A_					:  List[Any] = True						,							A_					:  Optional[Any] = None						,							A_					:  str = None						,							A_					:  Any = PILImageResampling.BILINEAR						,							A_					:  List[str] = True						,							A_					:  Dict = 1 / 255						,							A_					:  int = True						,							A_					:  Optional[Any] = None						,							A_					:  Optional[int] = None						,							**A_					:  Union[str, Any]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 384}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase_		     =    crop_pct if crop_pct is not None else 224 / 256
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 				a__ (				self					:  Any						,							A_					:  int						,							A_					:  List[Any]						,							A_					:  Optional[Any]						,							A_					:  Dict = PILImageResampling.BICUBIC						,							A_					:  Tuple = None						,							**A_					:  Any						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""  )
       lowerCamelCase_		     =    size['shortest_edge']

       if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             lowerCamelCase_		     =    int(shortest_edge / crop_pct  )
             lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=A_						,							default_to_square=A_  )
             lowerCamelCase_		     =    resize(image=A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=A_						,							size=(shortest_edge, shortest_edge)						,							data_format=A_						,							**A_  )
       else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 A_						,							size=(shortest_edge, shortest_edge)						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  List[str]						,							A_					:  str						,							A_					:  Tuple						,							A_					:  Union[str, Any] = None						,							**A_					:  Dict						,							)   ->	Dict:

       """simple docstring"""






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

 def 				a__ (				self					:  Optional[int]						,							A_					:  Optional[Any]						,							A_					:  Union[str, Any]						,							A_					:  str						,							A_					:  List[Any] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






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





 def 				a__ (				self					:  Union[str, Any]						,							A_					:  Union[str, Any]						,							A_					:  Optional[Any] = None						,							A_					:  List[str] = None						,							A_					:  Optional[Any] = None						,							A_					:  List[Any] = None						,							A_					:  int = None						,							A_					:  Any = None						,							A_					:  Any = None						,							A_					:  Tuple = None						,							A_					:  Tuple = None						,							A_					:  Optional[Any] = None						,							A_					:  int = ChannelDimension.FIRST						,							**A_					:  str						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std

       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             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_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'  )

       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_		     =    [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							crop_pct=A_						,							resample=A_  ) for image in images]

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

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )





def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
