






"""simple docstring"""





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						logging.get_logger(__name__)







def      lowerCamelCase__       (							_lowerCamelCase       :		np.ndarray		,				_lowerCamelCase       :		Union[int, Iterable[int]]		,				_lowerCamelCase       :		bool		,				_lowerCamelCase       :		int )      ->				Tuple[int, int]:
  def constraint_to_multiple_of(_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Optional[int]=0		,				_lowerCamelCase       :		Optional[Any]=None ):
    lowerCamelCase_         =						round(val / multiple ) * multiple

    if max_val is not None and x > max_val:
      lowerCamelCase_         =						math.floor(val / multiple ) * multiple

    if x < min_val:
      lowerCamelCase_         =						math.ceil(val / multiple ) * multiple

    return x

  lowerCamelCase_         =						(output_size, output_size) if isinstance(_lowerCamelCase		,				_lowerCamelCase ) else output_size

  lowerCamelCase_     ,	lowerCamelCase_         =						get_image_size(_lowerCamelCase )
  lowerCamelCase_     ,	lowerCamelCase_         =						output_size

  # determine new height and width
  lowerCamelCase_         =						output_height / input_height
  lowerCamelCase_         =						output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width ) < abs(1 - scale_height ):
      # fit width
      lowerCamelCase_         =						scale_width
    else:
      # fit height
      lowerCamelCase_         =						scale_height

  lowerCamelCase_         =						constraint_to_multiple_of(scale_height * input_height		,				multiple=_lowerCamelCase )
  lowerCamelCase_         =						constraint_to_multiple_of(scale_width * input_width		,				multiple=_lowerCamelCase )

  return (new_height, new_width)






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Tuple						= ["""pixel_values"""]






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Dict[str, int] = None       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = PILImageResampling.BILINEAR       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Union[int, float] = 1 / 255       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[float, List[float]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[float, List[float]]] = None       ,						**__SCREAMING_SNAKE_CASE     :							List[str]       ,						)     ->		None:
      super().__init__(**__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						size if size is not None else {'height': 384, 'width': 384}
      lowerCamelCase_         =						get_size_dict(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size
      lowerCamelCase_         =						keep_aspect_ratio
      lowerCamelCase_         =						ensure_multiple_of
      lowerCamelCase_         =						resample
      lowerCamelCase_         =						do_rescale
      lowerCamelCase_         =						rescale_factor
      lowerCamelCase_         =						do_normalize
      lowerCamelCase_         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      lowerCamelCase_         =						image_std if image_std is not None else IMAGENET_STANDARD_STD






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							np.ndarray       ,						__SCREAMING_SNAKE_CASE     :							Dict[str, int]       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = PILImageResampling.BICUBIC       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, ChannelDimension]] = None       ,						**__SCREAMING_SNAKE_CASE     :							Dict       ,						)     ->		np.ndarray:
      lowerCamelCase_         =						get_size_dict(__SCREAMING_SNAKE_CASE					)
      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()}'''					)
      lowerCamelCase_         =						get_resize_output_image_size(
          __SCREAMING_SNAKE_CASE       ,						output_size=(size['height'], size['width'])       ,						keep_aspect_ratio=__SCREAMING_SNAKE_CASE       ,						multiple=__SCREAMING_SNAKE_CASE       ,						)
      return resize(__SCREAMING_SNAKE_CASE       ,						size=__SCREAMING_SNAKE_CASE       ,						resample=__SCREAMING_SNAKE_CASE       ,						data_format=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






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






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






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							ImageInput       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							int = None       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							int = None       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = None       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							float = None       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[float, List[float]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[float, List[float]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, TensorType]] = None       ,						__SCREAMING_SNAKE_CASE     :							ChannelDimension = ChannelDimension.FIRST       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]       ,						)     ->		PIL.Image.Image:
      lowerCamelCase_         =						do_resize if do_resize is not None else self.do_resize
      lowerCamelCase_         =						size if size is not None else self.size
      lowerCamelCase_         =						get_size_dict(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
      lowerCamelCase_         =						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
      lowerCamelCase_         =						resample if resample is not None else self.resample
      lowerCamelCase_         =						do_rescale if do_rescale is not None else self.do_rescale
      lowerCamelCase_         =						rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCamelCase_         =						do_normalize if do_normalize is not None else self.do_normalize
      lowerCamelCase_         =						image_mean if image_mean is not None else self.image_mean
      lowerCamelCase_         =						image_std if image_std is not None else self.image_std

      lowerCamelCase_         =						make_list_of_images(__SCREAMING_SNAKE_CASE					)

      if not valid_images(__SCREAMING_SNAKE_CASE					):
        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_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.
      lowerCamelCase_         =						[to_numpy_array(__SCREAMING_SNAKE_CASE					) for image in images]

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

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

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

      lowerCamelCase_         =						[to_channel_dimension_format(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					) for image in images]

      lowerCamelCase_         =						{'pixel_values': images}
      return BatchFeature(data=__SCREAMING_SNAKE_CASE       ,						tensor_type=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							List[Tuple] = None					)     ->		Optional[int]:
      lowerCamelCase_         =						outputs.logits

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

        if is_torch_tensor(__SCREAMING_SNAKE_CASE					):
          lowerCamelCase_         =						target_sizes.numpy()

        lowerCamelCase_         =						[]

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

      return semantic_segmentation












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						TypeVar('''T''')






class      a						(					Generic[T]		):






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						self
      lowerCamelCase_         =						0






class      a						(					Generic[T]		):






    def __init__(       self     :							Any					)     ->		None:
      # map from node name to the node object
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # create a new set with x as its member
      lowerCamelCase_         =						DisjointSetTreeNode(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		DisjointSetTreeNode[T]:
      # find the set x belongs to (with path-compression)
      lowerCamelCase_         =						self.map[data]
      if elem_ref != elem_ref.parent:
        lowerCamelCase_         =						self.find_set(elem_ref.parent.data					)
      return elem_ref.parent






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]					)     ->		None:
      # helper function for union operation
      if nodea.rank > nodea.rank:
        lowerCamelCase_         =						nodea
      else:
        lowerCamelCase_         =						nodea
        if nodea.rank == nodea.rank:
          nodea.rank += 1






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # merge 2 disjoint sets
      self.link(self.find_set(__SCREAMING_SNAKE_CASE					)       ,						self.find_set(__SCREAMING_SNAKE_CASE					)					)






class      a						(					Generic[T]		):






    def __init__(       self     :							Optional[int]					)     ->		None:
      # connections: map from the node to the neighbouring nodes (with weights)
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # add a node ONLY if its not present in the graph
      if node not in self.connections:
        lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		None:
      # add an edge with the given weight
      self.add_node(__SCREAMING_SNAKE_CASE					)
      self.add_node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						weight
      lowerCamelCase_         =						weight






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		GraphUndirectedWeighted[T]:
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						set()
      for start in self.connections:
        for end in self.connections[start]:
          if (start, end) not in seen:
            seen.add((end, start)					)
            edges.append((start, end, self.connections[start][end])					)
      edges.sort(key=lambda __SCREAMING_SNAKE_CASE					: x[2]					)

      # creating the disjoint set
      lowerCamelCase_         =						DisjointSetTree[T]()
      for node in self.connections:
        disjoint_set.make_set(__SCREAMING_SNAKE_CASE					)

      # MST generation
      lowerCamelCase_         =						0
      lowerCamelCase_         =						0
      lowerCamelCase_         =						GraphUndirectedWeighted[T]()
      while num_edges < len(self.connections					) - 1:
        lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						edges[index]
        index += 1
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        if parent_u != parent_v:
          num_edges += 1
          graph.add_edge(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          disjoint_set.union(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      return graph




