






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class       __a  :
 _lowerCAmelCase	:	int
 _lowerCAmelCase	:	int







class       __a  :







 def __init__(		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       list[list[Edge]]	    =  [[] for _ in range(SCREAMING_SNAKE_CASE						)]
      UpperCamelCase__      :       List[Any]	    =  size







 def __getitem__(		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      return iter(self._graph[vertex]						)







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return self._size







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      if weight not in (0, 1):
           raise ValueError("Edge weight must be either 0 or 1."						)

      if to_vertex < 0 or to_vertex >= self.size:
           raise ValueError("Vertex indexes must be in [0; size)."						)

      self._graph[from_vertex].append(Edge(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)						)







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  deque([start_vertex]						)
      UpperCamelCase__      :       list[int | None]	    =  [None] * self.size
      UpperCamelCase__      :       Optional[int]	    =  0

      while queue:
           UpperCamelCase__      :       List[str]	    =  queue.popleft()
           UpperCamelCase__      :       Optional[Any]	    =  distances[current_vertex]
           if current_distance is None:
                continue

           for edge in self[current_vertex]:
                UpperCamelCase__      :       List[Any]	    =  current_distance + edge.weight
                UpperCamelCase__      :       Union[str, Any]	    =  distances[edge.destination_vertex]
                if (
                    isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
                    and new_distance >= dest_vertex_distance
                ):
                     continue
                UpperCamelCase__      :       Optional[int]	    =  new_distance
                if edge.weight == 0:
                     queue.appendleft(edge.destination_vertex						)
                else:
                     queue.append(edge.destination_vertex						)

      if distances[finish_vertex] is None:
           raise ValueError("No path from start_vertex to finish_vertex."						)

      return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)