





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


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



class    _a     (				_UpperCAmelCase							):






							"""simple docstring"""

							snake_case_							 =						['pixel_values']

							def __init__(      self				:     Optional[int]							,	a				:     bool = True							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	**a				:     List[str]							,	)	->None:
													super().__init__(**__UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size if size is not None else {"shortest_edge": 2_56}
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_size_dict(__UpperCamelCase							,	default_to_square=__UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_size_dict(__UpperCamelCase							,	param_name="crop_size"    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				Dict   =					size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				str   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				str   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Any   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std if image_std is not None else IMAGENET_STANDARD_STD

							def      A_  (      self				:     List[str]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[str]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				int   =					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()}"""    )
													SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Tuple							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_size_dict(__UpperCamelCase    )
													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(__UpperCamelCase							,	size=(size["height"], size["width"])							,	data_format=__UpperCamelCase							,	**__UpperCamelCase    )

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

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[str]							,	)	->np.ndarray:
													return normalize(__UpperCamelCase							,	mean=__UpperCamelCase							,	std=__UpperCamelCase							,	data_format=__UpperCamelCase							,	**__UpperCamelCase    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Optional[int]							,	)	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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__     :				List[str]   =					get_size_dict(__UpperCamelCase							,	default_to_square=__UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Any   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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__     :				Dict   =					get_size_dict(__UpperCamelCase							,	param_name="crop_size"    )
													SCREAMING_SNAKE_CASE__     :				str   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Optional[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.
													SCREAMING_SNAKE_CASE__     :				Any   =					[to_numpy_array(__UpperCamelCase    ) for image in images]

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

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[self.center_crop(image=__UpperCamelCase							,	size=__UpperCamelCase    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.rescale(image=__UpperCamelCase							,	scale=__UpperCamelCase    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				int   =					[self.normalize(image=__UpperCamelCase							,	mean=__UpperCamelCase							,	std=__UpperCamelCase    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				int   =					[to_channel_dimension_format(__UpperCamelCase							,	__UpperCamelCase    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"pixel_values": images}
													return BatchFeature(data=__UpperCamelCase							,	tensor_type=__UpperCamelCase    )






							def      A_  (      self				:     Optional[int]							,	a				:     List[Any]							,	a				:     List[Tuple] = None    )	->int:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					outputs.logits

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

																			if is_torch_tensor(__UpperCamelCase    ):
																									SCREAMING_SNAKE_CASE__     :				str   =					target_sizes.numpy()

																			SCREAMING_SNAKE_CASE__     :				int   =					[]

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

													return semantic_segmentation






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  int | None = None			,   _lowerCamelCase      :  int | None = None     ):





						'''simple docstring'''


						if start is None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0

						if end is None:
												SCREAMING_SNAKE_CASE__     :				Any   =					len(_lowerCamelCase     ) - 1

						if start >= end:
												return

						SCREAMING_SNAKE_CASE__     :				List[str]   =					(start + end) // 2

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						slowsort(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )

						if sequence[end] < sequence[mid]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sequence[mid], sequence[end]

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   end - 1     )


if __name__ == "__main__":
				from doctest import testmod

				testmod()