



'''simple docstring'''




from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


UpperCamelCase__       :	Optional[Any]    = logging.get_logger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[str]			    =		['''pixel_values''']

			def __init__(							self     : int							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Optional[Dict[str, int]] = None							,     lowerCAmelCase__     : PILImageResampling = PILImageResampling.BILINEAR							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Union[int, float] = 1 / 2_5_5							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     **lowerCAmelCase__     : Optional[Any]							,     ):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		size if size is not None else {"""shortest_edge""": 2_5_6}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_size_dict(lowerCAmelCase__							,     default_to_square=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		get_size_dict(lowerCAmelCase__							,     param_name="""crop_size"""						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		do_resize
					__SCREAMING_SNAKE_CASE       :		Any           =		size
					__SCREAMING_SNAKE_CASE       :		Dict           =		resample
					__SCREAMING_SNAKE_CASE       :		Any           =		do_center_crop
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		crop_size
					__SCREAMING_SNAKE_CASE       :		Tuple           =		do_rescale
					__SCREAMING_SNAKE_CASE       :		Tuple           =		rescale_factor
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		do_normalize
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		image_std if image_std is not None else IMAGENET_STANDARD_STD

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Dict[str, int]							,     lowerCAmelCase__     : PILImageResampling = PILImageResampling.BICUBIC							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : List[str]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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()}"						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		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 UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Dict[str, int]							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Any							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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` and `width`. Got {size.keys()}"						)
					return center_crop(lowerCAmelCase__							,     size=(size["""height"""], size["""width"""])							,     data_format=lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : float							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Any						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : np.ndarray							,     lowerCAmelCase__     : Union[float, List[float]]							,     lowerCAmelCase__     : Union[float, List[float]]							,     lowerCAmelCase__     : Optional[Union[str, ChannelDimension]] = None							,     **lowerCAmelCase__     : Dict							,     ):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : ImageInput							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : PILImageResampling = None							,     lowerCAmelCase__     : bool = None							,     lowerCAmelCase__     : Dict[str, int] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : Optional[float] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[float, List[float]]] = None							,     lowerCAmelCase__     : Optional[Union[str, TensorType]] = None							,     lowerCAmelCase__     : Union[str, ChannelDimension] = ChannelDimension.FIRST							,     **lowerCAmelCase__     : Dict							,     ):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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."""						)

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

					if do_resize:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		[self.resize(image=lowerCAmelCase__							,     size=lowerCAmelCase__							,     resample=lowerCAmelCase__						) for image in images]

					if do_center_crop:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[self.center_crop(image=lowerCAmelCase__							,     size=lowerCAmelCase__						) for image in images]

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

					if do_normalize:
							__SCREAMING_SNAKE_CASE       :		Dict           =		[self.normalize(image=lowerCAmelCase__							,     mean=lowerCAmelCase__							,     std=lowerCAmelCase__						) for image in images]

					__SCREAMING_SNAKE_CASE       :		Any           =		[to_channel_dimension_format(lowerCAmelCase__							,     lowerCAmelCase__						) for image in images]

					__SCREAMING_SNAKE_CASE       :		Any           =		{"""pixel_values""": images}
					return BatchFeature(data=lowerCAmelCase__							,     tensor_type=lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[Tuple] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
							if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
									raise ValueError(
									    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""						)

							if is_torch_tensor(lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE       :		int           =		target_sizes.numpy()

							__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

							for idx in range(len(lowerCAmelCase__						)						):
									__SCREAMING_SNAKE_CASE       :		str           =		torch.nn.functional.interpolate(
									    logits[idx].unsqueeze(dim=0						)							,     size=target_sizes[idx]							,     mode="""bilinear"""							,     align_corners=lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		resized_logits[0].argmax(dim=0						)
									semantic_segmentation.append(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		logits.argmax(dim=1						)
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

					return semantic_segmentation




'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   list[int]       ,      _lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		list(range(len(_lowerCamelCase					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[v / w for v, w in zip(_lowerCamelCase       ,      _lowerCamelCase					)]
		index.sort(key=lambda _lowerCamelCase					: ratio[i]       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		float           =		0
		__SCREAMING_SNAKE_CASE       :		list[float]           =		[0] * len(_lowerCamelCase					)
		for i in index:
				if weight[i] <= capacity:
						__SCREAMING_SNAKE_CASE       :		str           =		1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__SCREAMING_SNAKE_CASE       :		int           =		capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
						import doctest

						doctest.testmod()