

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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["pixel_values"]


						def __init__(    self  ,   A_ = True  ,   A_ = None  ,   A_ = PILImageResampling.BILINEAR  ,   A_ = True  ,   A_ = 1 / 255  ,   A_ = True  ,   A_ = None  ,   A_ = True  ,   **A_  ,   ):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase    :						Dict							=      size if size is not None else {"shortest_edge": 224}
										_UpperCAmelCase    :						int							=      get_size_dict(A_  ,   default_to_square=A_		)
										_UpperCAmelCase    :						Optional[int]							=      crop_size if crop_size is not None else {"height": 256, "width": 256}
										_UpperCAmelCase    :						str							=      get_size_dict(A_  ,   param_name="crop_size"		)

										_UpperCAmelCase    :						int							=      do_resize
										_UpperCAmelCase    :						Optional[Any]							=      size
										_UpperCAmelCase    :						List[Any]							=      resample
										_UpperCAmelCase    :						Union[str, Any]							=      do_rescale
										_UpperCAmelCase    :						Tuple							=      rescale_factor
										_UpperCAmelCase    :						Dict							=      do_center_crop
										_UpperCAmelCase    :						Any							=      crop_size
										_UpperCAmelCase    :						List[Any]							=      do_flip_channel_order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = PIL.Image.BILINEAR  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      get_size_dict(A_  ,   default_to_square=A_		)
										if "shortest_edge" not in size:
														raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'		)
										_UpperCAmelCase    :						List[str]							=      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  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      get_size_dict(A_		)
										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 center_crop(A_  ,   size=(size["height"], size["width"])  ,   data_format=A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										return flip_channel_order(A_  ,   data_format=A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = ChannelDimension.FIRST  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase    :						Optional[Any]							=      resample if resample is not None else self.resample
										_UpperCAmelCase    :						List[Any]							=      do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase    :						str							=      rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase    :						int							=      do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase    :						List[str]							=      (
										    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
										)

										_UpperCAmelCase    :						str							=      size if size is not None else self.size
										_UpperCAmelCase    :						Dict							=      get_size_dict(A_  ,   default_to_square=A_		)
										_UpperCAmelCase    :						int							=      crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase    :						Dict							=      get_size_dict(A_  ,   param_name="crop_size"		)

										_UpperCAmelCase    :						int							=      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_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."		)

										if do_center_crop and crop_size is None:
														raise ValueError("Crop size must be specified if do_center_crop is True."		)

										# All transformations expect numpy arrays.
										_UpperCAmelCase    :						List[Any]							=      [to_numpy_array(A_		) for image in images]

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

										if do_center_crop:
														_UpperCAmelCase    :						Union[str, Any]							=      [self.center_crop(image=A_  ,   size=A_		) for image in images]

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

										# the pretrained checkpoints assume images are BGR, not RGB
										if do_flip_channel_order:
														_UpperCAmelCase    :						Tuple							=      [self.flip_channel_order(image=A_		) for image in images]

										_UpperCAmelCase    :						Union[str, Any]							=      [to_channel_dimension_format(A_  ,   A_		) for image in images]

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





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      outputs.logits

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

														if is_torch_tensor(A_		):
																		_UpperCAmelCase    :						int							=      target_sizes.numpy()

														_UpperCAmelCase    :						Tuple							=      []

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

										return semantic_segmentation





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


