
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


_UpperCAmelCase					: str      =       logging.get_logger(__name__)


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Tuple	     =				["pixel_values"]



		def __init__( self				,      A_ = True				,      A_ = None				,      A_ = None				,      A_ = PILImageResampling.BILINEAR				,      A_ = True				,      A_ = 1 / 255				,      A_ = True				,      A_ = None				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				super().__init__(**A_       )
				UpperCamelCase		=     size if size is not None else {'shortest_edge': 384}
				UpperCamelCase		=     get_size_dict(A_				,      default_to_square=A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_ = PILImageResampling.BICUBIC				,      A_ = None				,      **A_				,      )  ->					np.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     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()}'''       )
				UpperCamelCase		=     size['shortest_edge']

				if shortest_edge < 384:
						# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
						UpperCamelCase		=     int(shortest_edge / crop_pct       )
						UpperCamelCase		=     get_resize_output_image_size(A_				,      size=A_				,      default_to_square=A_       )
						UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_ = None				,      **A_				,      )  ->					Dict:
				"""simple docstring"""
				return rescale(A_				,      scale=A_				,      data_format=A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_ = None				,      **A_				,      )  ->					np.ndarray:
				"""simple docstring"""
				return normalize(A_				,      mean=A_				,      std=A_				,      data_format=A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = None				,      A_ = ChannelDimension.FIRST				,      **A_				,      )  ->					PIL.Image.Image:
				"""simple docstring"""
				UpperCamelCase		=     do_resize if do_resize is not None else self.do_resize
				UpperCamelCase		=     crop_pct if crop_pct is not None else self.crop_pct
				UpperCamelCase		=     resample if resample is not None else self.resample
				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		=     size if size is not None else self.size
				UpperCamelCase		=     get_size_dict(A_				,      default_to_square=A_       )

				UpperCamelCase		=     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.
				UpperCamelCase		=     [to_numpy_array(A_       ) for image in images]

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

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

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

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

				UpperCamelCase		=     {'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()
