

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


a_						     =    logging.get_logger(__name__)


if is_vision_available():
			import PIL






class _UpperCamelCase							(							lowercase__   ):
		'''simple docstring'''







		lowerCamelCase__           =['pixel_values']
		def __init__(			self							: Dict      ,      a							: bool = True      ,      a							: Dict[str, int] = None      ,      a							: PILImageResampling = PILImageResampling.BICUBIC      ,      a							: bool = True      ,      a							: Dict[str, int] = None      ,      a							: bool = True      ,      a							: Union[int, float] = 1 / 255      ,      a							: bool = True      ,      a							: Optional[Union[float, List[float]]] = None      ,      a							: Optional[Union[float, List[float]]] = None      ,      a							: bool = True      ,      **a							: Tuple      ,      )							->       None:

			"""simple docstring"""

			super().__init__(**a	)
			SCREAMING_SNAKE_CASE   :      str				     =       size if size is not None else {"shortest_edge": 224}
			SCREAMING_SNAKE_CASE   :      Dict				     =       get_size_dict(a      ,      default_to_square=a	)
			SCREAMING_SNAKE_CASE   :      int				     =       crop_size if crop_size is not None else {"height": 224, "width": 224}
			SCREAMING_SNAKE_CASE   :      int				     =       get_size_dict(a      ,      default_to_square=a      ,      param_name="crop_size"	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       do_resize
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       size
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       resample
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       do_center_crop
			SCREAMING_SNAKE_CASE   :      Any				     =       crop_size
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       do_rescale
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rescale_factor
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       do_normalize
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
			SCREAMING_SNAKE_CASE   :      Any				     =       image_std if image_std is not None else OPENAI_CLIP_STD
			SCREAMING_SNAKE_CASE   :      Tuple				     =       do_convert_rgb
		def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: np.ndarray      ,      a							: Dict[str, int]      ,      a							: PILImageResampling = PILImageResampling.BICUBIC      ,      a							: Optional[Union[str, ChannelDimension]] = None      ,      **a							: Any      ,      )							->       np.ndarray:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      str				     =       get_size_dict(a      ,      default_to_square=a	)
			if "shortest_edge" not in size:
				raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
			SCREAMING_SNAKE_CASE   :      int				     =       get_resize_output_image_size(a      ,      size=size["shortest_edge"]      ,      default_to_square=a	)
			return resize(a      ,      size=a      ,      resample=a      ,      data_format=a      ,      **a	)
		def 							__UpperCamelCase (			self							: Tuple      ,      a							: np.ndarray      ,      a							: Dict[str, int]      ,      a							: Optional[Union[str, ChannelDimension]] = None      ,      **a							: List[Any]      ,      )							->       np.ndarray:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       get_size_dict(a	)
			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(a      ,      size=(size["height"], size["width"])      ,      data_format=a      ,      **a	)
		def 							__UpperCamelCase (			self							: str      ,      a							: np.ndarray      ,      a							: Union[int, float]      ,      a							: Optional[Union[str, ChannelDimension]] = None      ,      **a							: Any      ,      )							->       Dict:

			"""simple docstring"""

			return rescale(a      ,      scale=a      ,      data_format=a      ,      **a	)
		def 							__UpperCamelCase (			self							: Dict      ,      a							: np.ndarray      ,      a							: Union[float, List[float]]      ,      a							: Union[float, List[float]]      ,      a							: Optional[Union[str, ChannelDimension]] = None      ,      **a							: List[Any]      ,      )							->       np.ndarray:

			"""simple docstring"""

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






		def 							__UpperCamelCase (			self							: int      ,      a							: ImageInput      ,      a							: bool = None      ,      a							: Dict[str, int] = None      ,      a							: PILImageResampling = None      ,      a							: bool = None      ,      a							: int = None      ,      a							: bool = None      ,      a							: float = None      ,      a							: bool = None      ,      a							: Optional[Union[float, List[float]]] = None      ,      a							: Optional[Union[float, List[float]]] = None      ,      a							: bool = None      ,      a							: Optional[Union[str, TensorType]] = None      ,      a							: Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **a							: Union[str, Any]      ,      )							->       PIL.Image.Image:

			"""simple docstring"""

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

			SCREAMING_SNAKE_CASE   :      List[Any]				     =       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:
				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:
				SCREAMING_SNAKE_CASE   :      Tuple				     =       [convert_to_rgb(a	) for image in images]

			# All transformations expect numpy arrays.
			SCREAMING_SNAKE_CASE   :      List[str]				     =       [to_numpy_array(a	) for image in images]

			if do_resize:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [self.resize(image=a      ,      size=a      ,      resample=a	) for image in images]

			if do_center_crop:
				SCREAMING_SNAKE_CASE   :      Dict				     =       [self.center_crop(image=a      ,      size=a	) for image in images]

			if do_rescale:
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [self.rescale(image=a      ,      scale=a	) for image in images]

			if do_normalize:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [self.normalize(image=a      ,      mean=a      ,      std=a	) for image in images]

			SCREAMING_SNAKE_CASE   :      Dict				     =       [to_channel_dimension_format(a      ,      a	) for image in images]

			SCREAMING_SNAKE_CASE   :      Tuple				     =       {"pixel_values": images}
			return BatchFeature(data=a      ,      tensor_type=a	)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, 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_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

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

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

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    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
			a_     ,	a_						     =    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)