




"""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,
    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, logging


a_        =       logging.get_logger(__name__)

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=["pixel_values"]
		def __init__(						self				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = PILImageResampling.BILINEAR				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = True				,						UpperCamelCase_ = 1 / 2_55				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       None:
					super().__init__(**UpperCamelCase_       )
					__lowercase			:  List[str]								=       size if size is not None else {'''shortest_edge''': 2_56}
					__lowercase			:  Dict								=       get_size_dict(UpperCamelCase_				,						default_to_square=UpperCamelCase_       )
					__lowercase			:  Optional[Any]								=       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__lowercase			:  Dict								=       get_size_dict(UpperCamelCase_       )
					__lowercase			:  Dict								=       do_resize
					__lowercase			:  Optional[Any]								=       size
					__lowercase			:  List[Any]								=       resample
					__lowercase			:  Dict								=       do_center_crop
					__lowercase			:  Any								=       crop_size
					__lowercase			:  List[str]								=       do_rescale
					__lowercase			:  List[str]								=       rescale_factor
					__lowercase			:  Optional[Any]								=       do_normalize
					__lowercase			:  Any								=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__lowercase			:  List[str]								=       image_std if image_std is not None else IMAGENET_STANDARD_STD
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = PILImageResampling.BICUBIC				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					__lowercase			:  List[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()}"""       )
					__lowercase			:  List[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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					__lowercase			:  Union[str, Any]								=       get_size_dict(UpperCamelCase_       )
					return center_crop(UpperCamelCase_				,						size=(size['''height'''], size['''width'''])				,						data_format=UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_       )						->       np.ndarray:
					return rescale(UpperCamelCase_				,						scale=UpperCamelCase_				,						data_format=UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					return normalize(UpperCamelCase_				,						mean=UpperCamelCase_				,						std=UpperCamelCase_				,						data_format=UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						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_				,						)						->       Optional[Any]:
					__lowercase			:  Union[str, Any]								=       do_resize if do_resize is not None else self.do_resize
					__lowercase			:  Tuple								=       size if size is not None else self.size
					__lowercase			:  Optional[Any]								=       get_size_dict(UpperCamelCase_				,						default_to_square=UpperCamelCase_       )
					__lowercase			:  int								=       resample if resample is not None else self.resample
					__lowercase			:  Tuple								=       do_center_crop if do_center_crop is not None else self.do_center_crop
					__lowercase			:  List[str]								=       crop_size if crop_size is not None else self.crop_size
					__lowercase			:  List[str]								=       get_size_dict(UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       do_rescale if do_rescale is not None else self.do_rescale
					__lowercase			:  Union[str, Any]								=       rescale_factor if rescale_factor is not None else self.rescale_factor
					__lowercase			:  Dict								=       do_normalize if do_normalize is not None else self.do_normalize
					__lowercase			:  Tuple								=       image_mean if image_mean is not None else self.image_mean
					__lowercase			:  Any								=       image_std if image_std is not None else self.image_std

					__lowercase			:  Any								=       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.
					__lowercase			:  Optional[int]								=       [to_numpy_array(UpperCamelCase_       ) for image in images]

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

					if do_center_crop:
								__lowercase			:  Any								=       [self.center_crop(image=UpperCamelCase_				,						size=UpperCamelCase_       ) for image in images]

					if do_rescale:
								__lowercase			:  str								=       [self.rescale(image=UpperCamelCase_				,						scale=UpperCamelCase_       ) for image in images]

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

					__lowercase			:  str								=       [to_channel_dimension_format(UpperCamelCase_				,						UpperCamelCase_       ) for image in images]

					__lowercase			:  Optional[Any]								=       {'''pixel_values''': images}
					return BatchFeature(data=UpperCamelCase_				,						tensor_type=UpperCamelCase_       )





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)






