

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


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		["pixel_values"]







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE = True     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,						_SCREAMING_SNAKE_CASE = True     ,						_SCREAMING_SNAKE_CASE = 1 / 255     ,						_SCREAMING_SNAKE_CASE = True     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							None:
													super().__init__(**_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=size if size is not None else {"""height""": 224, """width""": 224}
													lowerCamelCase_								=get_size_dict(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=do_resize
													lowerCamelCase_								=do_rescale
													lowerCamelCase_								=do_normalize
													lowerCamelCase_								=size
													lowerCamelCase_								=resample
													lowerCamelCase_								=rescale_factor
													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,						_SCREAMING_SNAKE_CASE = None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							np.ndarray:
													lowerCamelCase_								=get_size_dict(_SCREAMING_SNAKE_CASE							)
													if "height" not in size or "width" not in size:
																				raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
													return resize(
													    _SCREAMING_SNAKE_CASE     ,						size=(size["""height"""], size["""width"""])     ,						resample=_SCREAMING_SNAKE_CASE     ,						data_format=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						**_SCREAMING_SNAKE_CASE							)->							np.ndarray:
													return rescale(_SCREAMING_SNAKE_CASE     ,						scale=_SCREAMING_SNAKE_CASE     ,						data_format=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							np.ndarray:
													return normalize(_SCREAMING_SNAKE_CASE     ,						mean=_SCREAMING_SNAKE_CASE     ,						std=_SCREAMING_SNAKE_CASE     ,						data_format=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Dict:
													lowerCamelCase_								=do_resize if do_resize is not None else self.do_resize
													lowerCamelCase_								=do_rescale if do_rescale is not None else self.do_rescale
													lowerCamelCase_								=do_normalize if do_normalize is not None else self.do_normalize
													lowerCamelCase_								=resample if resample is not None else self.resample
													lowerCamelCase_								=rescale_factor if rescale_factor is not None else self.rescale_factor
													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(_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=make_list_of_images(_SCREAMING_SNAKE_CASE							)

													if not valid_images(_SCREAMING_SNAKE_CASE							):
																				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_rescale and rescale_factor is None:
																				raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

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

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

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

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

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

													lowerCamelCase_								={"""pixel_values""": images}
													return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,						tensor_type=_SCREAMING_SNAKE_CASE							)



from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
