



'''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


lowerCAmelCase: Any							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  def constraint_to_multiple_of(_A			,      _A			,      _A=0			,      _A=None    ):
    a						: str					       =					round(val / multiple    ) * multiple

    if max_val is not None and x > max_val:
      a						: Union[str, Any]					       =					math.floor(val / multiple    ) * multiple

    if x < min_val:
      a						: Optional[int]					       =					math.ceil(val / multiple    ) * multiple

    return x

  a						: List[Any]					       =					(output_size, output_size) if isinstance(_A			,      _A    ) else output_size

  a						: Dict					       =					get_image_size(_A    )
  a						: Optional[int]					       =					output_size

  # determine new height and width
  a						: Dict					       =					output_height / input_height
  a						: List[Any]					       =					output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width    ) < abs(1 - scale_height    ):
      # fit width
      a						: Dict					       =					scale_width
    else:
      # fit height
      a						: Optional[int]					       =					scale_height

  a						: str					       =					constraint_to_multiple_of(scale_height * input_height			,      multiple=_A    )
  a						: str					       =					constraint_to_multiple_of(scale_width * input_width			,      multiple=_A    )

  return (new_height, new_width)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  Tuple						,      __snake_case			:  bool = True						,      __snake_case			:  Dict[str, int] = None						,      __snake_case			:  PILImageResampling = PILImageResampling.BILINEAR						,      __snake_case			:  bool = False						,      __snake_case			:  int = 1						,      __snake_case			:  bool = True						,      __snake_case			:  Union[int, float] = 1 / 2_55						,      __snake_case			:  bool = True						,      __snake_case			:  Optional[Union[float, List[float]]] = None						,      __snake_case			:  Optional[Union[float, List[float]]] = None						,      **__snake_case			:  Union[str, Any]						,      ):
      super().__init__(**__snake_case     )
      a						: List[Any]					       =					size if size is not None else {'height': 3_84, 'width': 3_84}
      a						: List[str]					       =					get_size_dict(__snake_case     )
      a						: Optional[int]					       =					do_resize
      a						: Tuple					       =					size
      a						: str					       =					keep_aspect_ratio
      a						: Optional[Any]					       =					ensure_multiple_of
      a						: int					       =					resample
      a						: Optional[Any]					       =					do_rescale
      a						: Tuple					       =					rescale_factor
      a						: Any					       =					do_normalize
      a						: Optional[Any]					       =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a						: Optional[Any]					       =					image_std if image_std is not None else IMAGENET_STANDARD_STD







    def       lowercase_	(				self			:  Any						,      __snake_case			:  np.ndarray						,      __snake_case			:  Dict[str, int]						,      __snake_case			:  bool = False						,      __snake_case			:  int = 1						,      __snake_case			:  PILImageResampling = PILImageResampling.BICUBIC						,      __snake_case			:  Optional[Union[str, ChannelDimension]] = None						,      **__snake_case			:  str						,      ):
      a						: Union[str, Any]					       =					get_size_dict(__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()}"""     )
      a						: Any					       =					get_resize_output_image_size(
          __snake_case						,      output_size=(size['height'], size['width'])						,      keep_aspect_ratio=__snake_case						,      multiple=__snake_case						,      )
      return resize(__snake_case						,      size=__snake_case						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  np.ndarray						,      __snake_case			:  Union[int, float]						,      __snake_case			:  Optional[Union[str, ChannelDimension]] = None						,      **__snake_case			:  int						,      ):
      return rescale(__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  np.ndarray						,      __snake_case			:  Union[float, List[float]]						,      __snake_case			:  Union[float, List[float]]						,      __snake_case			:  Optional[Union[str, ChannelDimension]] = None						,      **__snake_case			:  Optional[Any]						,      ):
      return normalize(__snake_case						,      mean=__snake_case						,      std=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







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

      a						: Any					       =					make_list_of_images(__snake_case     )

      if not valid_images(__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.
      a						: Dict					       =					[to_numpy_array(__snake_case     ) for image in images]

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

      if do_rescale:
        a						: int					       =					[self.rescale(image=__snake_case						,      scale=__snake_case     ) for image in images]

      if do_normalize:
        a						: Tuple					       =					[self.normalize(image=__snake_case						,      mean=__snake_case						,      std=__snake_case     ) for image in images]

      a						: List[str]					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: str					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[Tuple] = None     ):
      a						: Any					       =					outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
        if len(__snake_case     ) != len(__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(__snake_case     ):
          a						: List[str]					       =					target_sizes.numpy()

        a						: str					       =					[]

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

      return semantic_segmentation







'''simple docstring'''






import math

class 							a__:







    def __init__(				self			:  int						,      __snake_case			:  Any=0     ):  # a graph with Node 0,1,...,N-1
      a						: List[Any]					       =					n
      a						: Optional[Any]					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # adjacency matrix for weight
      a						: Dict					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # dp[i][j] stores minimum distance from i to j







    def       lowercase_	(				self			:  int						,      __snake_case			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  int     ):
      a						: str					       =					w







    def       lowercase_	(				self			:  int     ):
      for k in range(0						,      self.n     ):
        for i in range(0						,      self.n     ):
          for j in range(0						,      self.n     ):
            a						: str					       =					min(self.dp[i][j]						,      self.dp[i][k] + self.dp[k][j]     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Any						,      __snake_case			:  List[str]     ):
      return self.dp[u][v]


if __name__ == "__main__":
     lowerCAmelCase: Any							=						Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 1_0)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 1_0)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)