





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
     import PIL

class   A							(						__lowercase    ):
      _snake_case				  =['''pixel_values''']



      def __init__(						self:    Union[str, Any]		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Dict[str, int] = None		,	_lowerCAmelCase:    PILImageResampling = PILImageResampling.BICUBIC		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Dict[str, int] = None		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Union[int, float] = 1 / 255		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Optional[Union[float, List[float]]] = None		,	_lowerCAmelCase:    Optional[Union[float, List[float]]] = None		,	_lowerCAmelCase:    bool = True		,	**_lowerCAmelCase:    str		,	)    ->     None:
         '''simple docstring'''

         super().__init__(**_lowerCAmelCase							)
         UpperCAmelCase_           =size if size is not None else {"shortest_edge": 224}
         UpperCAmelCase_           =get_size_dict(_lowerCAmelCase		,	default_to_square=_lowerCAmelCase							)
         UpperCAmelCase_           =crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_           =get_size_dict(_lowerCAmelCase		,	default_to_square=_lowerCAmelCase		,	param_name="crop_size"							)

         UpperCAmelCase_           =do_resize
         UpperCAmelCase_           =size
         UpperCAmelCase_           =resample
         UpperCAmelCase_           =do_center_crop
         UpperCAmelCase_           =crop_size
         UpperCAmelCase_           =do_rescale
         UpperCAmelCase_           =rescale_factor
         UpperCAmelCase_           =do_normalize
         UpperCAmelCase_           =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         UpperCAmelCase_           =image_std if image_std is not None else OPENAI_CLIP_STD
         UpperCAmelCase_           =do_convert_rgb



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    np.ndarray		,	_lowerCAmelCase:    Dict[str, int]		,	_lowerCAmelCase:    PILImageResampling = PILImageResampling.BICUBIC		,	_lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None		,	**_lowerCAmelCase:    Union[str, Any]		,	)    ->     np.ndarray:
         '''simple docstring'''

         UpperCAmelCase_           =get_size_dict(_lowerCAmelCase		,	default_to_square=_lowerCAmelCase							)
         if "shortest_edge" not in size:
            raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'							)
         UpperCAmelCase_           =get_resize_output_image_size(_lowerCAmelCase		,	size=size["shortest_edge"]		,	default_to_square=_lowerCAmelCase							)
         return resize(_lowerCAmelCase		,	size=_lowerCAmelCase		,	resample=_lowerCAmelCase		,	data_format=_lowerCAmelCase		,	**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    np.ndarray		,	_lowerCAmelCase:    Dict[str, int]		,	_lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None		,	**_lowerCAmelCase:    Optional[int]		,	)    ->     np.ndarray:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    np.ndarray		,	_lowerCAmelCase:    Union[int, float]		,	_lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None		,	**_lowerCAmelCase:    Optional[Any]		,	)    ->     Optional[int]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    np.ndarray		,	_lowerCAmelCase:    Union[float, List[float]]		,	_lowerCAmelCase:    Union[float, List[float]]		,	_lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None		,	**_lowerCAmelCase:    Optional[int]		,	)    ->     np.ndarray:
         '''simple docstring'''

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






      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    ImageInput		,	_lowerCAmelCase:    bool = None		,	_lowerCAmelCase:    Dict[str, int] = None		,	_lowerCAmelCase:    PILImageResampling = None		,	_lowerCAmelCase:    bool = None		,	_lowerCAmelCase:    int = None		,	_lowerCAmelCase:    bool = None		,	_lowerCAmelCase:    float = None		,	_lowerCAmelCase:    bool = None		,	_lowerCAmelCase:    Optional[Union[float, List[float]]] = None		,	_lowerCAmelCase:    Optional[Union[float, List[float]]] = None		,	_lowerCAmelCase:    bool = None		,	_lowerCAmelCase:    Optional[Union[str, TensorType]] = None		,	_lowerCAmelCase:    Optional[ChannelDimension] = ChannelDimension.FIRST		,	**_lowerCAmelCase:    Tuple		,	)    ->     PIL.Image.Image:
         '''simple docstring'''

         UpperCAmelCase_           =do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_           =size if size is not None else self.size
         UpperCAmelCase_           =get_size_dict(_lowerCAmelCase		,	param_name="size"		,	default_to_square=_lowerCAmelCase							)
         UpperCAmelCase_           =resample if resample is not None else self.resample
         UpperCAmelCase_           =do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_           =crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_           =get_size_dict(_lowerCAmelCase		,	param_name="crop_size"		,	default_to_square=_lowerCAmelCase							)
         UpperCAmelCase_           =do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_           =rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_           =do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_           =image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_           =image_std if image_std is not None else self.image_std
         UpperCAmelCase_           =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCAmelCase_           =make_list_of_images(_lowerCAmelCase							)

         if not valid_images(_lowerCAmelCase							):
            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."							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
            UpperCAmelCase_           =[convert_to_rgb(_lowerCAmelCase							) for image in images]

         # All transformations expect numpy arrays.
         UpperCAmelCase_           =[to_numpy_array(_lowerCAmelCase							) for image in images]

         if do_resize:
            UpperCAmelCase_           =[self.resize(image=_lowerCAmelCase		,	size=_lowerCAmelCase		,	resample=_lowerCAmelCase							) for image in images]

         if do_center_crop:
            UpperCAmelCase_           =[self.center_crop(image=_lowerCAmelCase		,	size=_lowerCAmelCase							) for image in images]

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

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

         UpperCAmelCase_           =[to_channel_dimension_format(_lowerCAmelCase		,	_lowerCAmelCase							) for image in images]

         UpperCAmelCase_           ={"pixel_values": images}
         return BatchFeature(data=_lowerCAmelCase		,	tensor_type=_lowerCAmelCase							)






def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      lowercase__	: str     		=   mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )
    else:
      lowercase__	: List[str]     		=   max(
          mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )			,			mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			j - wt[i - 1] ) + val[i - 1]			,			)
    lowercase__	: List[Any]     		=   val
  return f[i][j]

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Any     		=   [[0] * (w + 1) for _ in range(n + 1 )]

  for i in range(1			,			n + 1 ):
    for w_ in range(1			,			w + 1 ):
      if wt[i - 1] <= w_:
        lowercase__	: List[Any]     		=   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,			dp[i - 1][w_] )
      else:
        lowercase__	: Tuple     		=   dp[i - 1][w_]

  return dp[n][w_], dp

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  if not (isinstance(lowercase_			,			(list, tuple) ) and isinstance(lowercase_			,			(list, tuple) )):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples""" )

  lowercase__	: str     		=   len(lowercase_ )
  if num_items != len(lowercase_ ):
    lowercase__	: Optional[int]     		=   (
        """The number of weights must be the same as the number of values.\n"""
        F'But got {num_items} weights and {len(lowercase_ )} values'
    )
    raise ValueError(lowercase_ )
  for i in range(lowercase_ ):
    if not isinstance(wt[i]			,			lowercase_ ):
      lowercase__	: int     		=   (
          """All weights must be integers but got weight of """
          F'type {type(wt[i] )} at index {i}'
      )
      raise TypeError(lowercase_ )

  lowercase__		,     lowercase__	: Tuple     		=   knapsack(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: set     		=   set()
  _construct_solution(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  return optimal_val, example_optional_set

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			lowercase_			,			lowercase_ )
    else:
      optimal_set.add(lowercase_ )
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			j - wt[i - 1]			,			lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		Dict        = [3, 2, 4, 4]
     lowerCamelCase__     :		List[Any]        = [4, 3, 2, 3]
     lowerCamelCase__     :		Optional[int]        = 4
     lowerCamelCase__     :		Dict        = 6
     lowerCamelCase__     :		Optional[int]        = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowerCamelCase__   ,    lowerCamelCase__     :		int        = knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowerCamelCase__   ,    lowerCamelCase__     :		Optional[int]        = knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("""optimal_value = """, optimal_solution)
     print("""An optimal subset corresponding to the optimal value""", optimal_subset)






