


'''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,
    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


__A   :    List[Any]											= logging.get_logger(__name__)






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   ['pixel_values']

    def __init__(  self       :    int       ,		lowerCamelCase       :    bool = True       ,		lowerCamelCase       :    Dict[str, int] = None       ,		lowerCamelCase       :    PILImageResampling = PILImageResampling.BILINEAR       ,		lowerCamelCase       :    bool = True       ,		lowerCamelCase       :    Union[int, float] = 1 / 2_55       ,		lowerCamelCase       :    bool = True       ,		lowerCamelCase       :    Dict[str, int] = None       ,		lowerCamelCase       :    bool = True       ,		**lowerCamelCase       :    Optional[Any]       ,		)  ->    None:
      super().__init__(**lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  size if size is not None else {"""shortest_edge""": 2_24}
      lowerCAmelCase_     : List[str]     =  get_size_dict(lowerCamelCase       ,		default_to_square=lowerCamelCase						)
      lowerCAmelCase_     : List[str]     =  crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
      lowerCAmelCase_     : str     =  get_size_dict(lowerCamelCase       ,		param_name="""crop_size"""						)

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

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    np.ndarray       ,		lowerCamelCase       :    Dict[str, int]       ,		lowerCamelCase       :    PILImageResampling = PIL.Image.BILINEAR       ,		lowerCamelCase       :    Optional[Union[str, ChannelDimension]] = None       ,		**lowerCamelCase       :    List[Any]       ,		)  ->    np.ndarray:
      lowerCAmelCase_     : Optional[int]     =  get_size_dict(lowerCamelCase       ,		default_to_square=lowerCamelCase						)
      if "shortest_edge" not in size:
        raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
      lowerCAmelCase_     : Optional[int]     =  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 __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    np.ndarray       ,		lowerCamelCase       :    Dict[str, int]       ,		lowerCamelCase       :    Optional[Union[str, ChannelDimension]] = None       ,		**lowerCamelCase       :    Any       ,		)  ->    np.ndarray:
      lowerCAmelCase_     : List[str]     =  get_size_dict(lowerCamelCase						)
      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(lowerCamelCase       ,		size=(size["""height"""], size["""width"""])       ,		data_format=lowerCamelCase       ,		**lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    np.ndarray       ,		lowerCamelCase       :    Union[int, float]       ,		lowerCamelCase       :    Optional[Union[str, ChannelDimension]] = None       ,		**lowerCamelCase       :    Dict       ,		)  ->    List[Any]:
      return rescale(lowerCamelCase       ,		scale=lowerCamelCase       ,		data_format=lowerCamelCase       ,		**lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    np.ndarray       ,		lowerCamelCase       :    Optional[Union[str, ChannelDimension]] = None						)  ->    np.ndarray:
      return flip_channel_order(lowerCamelCase       ,		data_format=lowerCamelCase						)

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    ImageInput       ,		lowerCamelCase       :    bool = None       ,		lowerCamelCase       :    Dict[str, int] = None       ,		lowerCamelCase       :    PILImageResampling = None       ,		lowerCamelCase       :    bool = None       ,		lowerCamelCase       :    float = None       ,		lowerCamelCase       :    bool = None       ,		lowerCamelCase       :    Dict[str, int] = None       ,		lowerCamelCase       :    bool = None       ,		lowerCamelCase       :    Optional[Union[str, TensorType]] = None       ,		lowerCamelCase       :    ChannelDimension = ChannelDimension.FIRST       ,		**lowerCamelCase       :    int       ,		)  ->    PIL.Image.Image:
      lowerCAmelCase_     : Any     =  do_resize if do_resize is not None else self.do_resize
      lowerCAmelCase_     : Tuple     =  resample if resample is not None else self.resample
      lowerCAmelCase_     : Optional[int]     =  do_rescale if do_rescale is not None else self.do_rescale
      lowerCAmelCase_     : Any     =  rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCAmelCase_     : Any     =  do_center_crop if do_center_crop is not None else self.do_center_crop
      lowerCAmelCase_     : Union[str, Any]     =  (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      lowerCAmelCase_     : List[str]     =  size if size is not None else self.size
      lowerCAmelCase_     : List[str]     =  get_size_dict(lowerCamelCase       ,		default_to_square=lowerCamelCase						)
      lowerCAmelCase_     : List[Any]     =  crop_size if crop_size is not None else self.crop_size
      lowerCAmelCase_     : Optional[int]     =  get_size_dict(lowerCamelCase       ,		param_name="""crop_size"""						)

      lowerCAmelCase_     : 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_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.
      lowerCAmelCase_     : Optional[Any]     =  [to_numpy_array(lowerCamelCase						) for image in images]

      if do_resize:
        lowerCAmelCase_     : Dict     =  [self.resize(image=lowerCamelCase       ,		size=lowerCamelCase       ,		resample=lowerCamelCase						) for image in images]

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

      if do_rescale:
        lowerCAmelCase_     : Tuple     =  [self.rescale(image=lowerCamelCase       ,		scale=lowerCamelCase						) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
        lowerCAmelCase_     : List[str]     =  [self.flip_channel_order(image=lowerCamelCase						) for image in images]

      lowerCAmelCase_     : Tuple     =  [to_channel_dimension_format(lowerCamelCase       ,		lowerCamelCase						) for image in images]

      lowerCAmelCase_     : Union[str, Any]     =  {"""pixel_values""": images}
      return BatchFeature(data=lowerCamelCase       ,		tensor_type=lowerCamelCase						)





    def __lowercase  (  self       :    Dict       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    List[Tuple] = None						)  ->    int:
      lowerCAmelCase_     : 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						):
          lowerCAmelCase_     : Dict     =  target_sizes.numpy()

        lowerCAmelCase_     : List[str]     =  []

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

      return semantic_segmentation







'''simple docstring'''






from collections.abc import Callable






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Callable | None = None						)  ->    None:
      # Stores actual heap items.
      lowerCAmelCase_     : list     =  []
      # Stores indexes of each item for supporting updates and deletion.
      lowerCAmelCase_     : dict     =  {}
      # Stores current size of heap.
      lowerCAmelCase_     : List[Any]     =  0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      lowerCAmelCase_     : Tuple     =  key or (lambda lowerCamelCase						: x)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      return int((i - 1) / 2						) if i > 0 else None

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[str]     =  int(2 * i + 1						)
      return left if 0 < left < self.size else None

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[Any]     =  int(2 * i + 2						)
      return right if 0 < right < self.size else None

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  self.arr[j], self.arr[i]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    bool:
      return self.arr[i][1] < self.arr[j][1]

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self._left(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  self._right(lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  i

      if left is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : int     =  left
      if right is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  right

      return valid_parent

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Tuple     =  self._parent(lowerCamelCase						)
      while parent is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : str     =  parent, self._parent(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Optional[Any]     =  self._get_valid_parent(lowerCamelCase						)
      while valid_parent != index:
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  valid_parent, self._get_valid_parent(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : Dict     =  self.pos_map[item]
      lowerCAmelCase_     : Dict     =  [item, self.key(lowerCamelCase						)]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(lowerCamelCase						)
      self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : List[str]     =  self.pos_map[item]
      del self.pos_map[item]
      lowerCAmelCase_     : Tuple     =  self.arr[self.size - 1]
      lowerCAmelCase_     : List[str]     =  index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
        self._heapify_up(lowerCamelCase						)
        self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Any     =  len(self.arr						)
      if arr_len == self.size:
        self.arr.append([item, self.key(lowerCamelCase						)]						)
      else:
        lowerCAmelCase_     : str     =  [item, self.key(lowerCamelCase						)]
      lowerCAmelCase_     : Optional[Any]     =  self.size
      self.size += 1
      self._heapify_up(self.size - 1						)

    def __lowercase  (  self       :    str						)  ->    tuple | None:
      return self.arr[0] if self.size else None





    def __lowercase  (  self       :    Optional[Any]						)  ->    tuple | None:
      lowerCAmelCase_     : int     =  self.get_top()
      if top_item_tuple:
        self.delete_item(top_item_tuple[0]						)
      return top_item_tuple







def 							UpperCamelCase_     (       ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



