





"""simple docstring"""
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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import TvltImageProcessor

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Tuple     ,    _UpperCAmelCase				:						Optional[int]=10     ,    _UpperCAmelCase				:						Optional[Any]=10     ,    _UpperCAmelCase				:						Optional[Any]=False     ,    _UpperCAmelCase				:						Any=False ):
 lowerCAmelCase													=   []
 for i in range(image_processor_tester.num_frames ):
  video.append(np.random.randint(255     ,    size=(image_processor_tester.num_channels, width, height)     ,    dtype=np.uinta ) )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  lowerCAmelCase													=   [Image.fromarray(np.moveaxis(lowercase_     ,    0     ,    -1 ) ) for frame in video]

 if torchify:
  lowerCAmelCase													=   [torch.from_numpy(lowercase_ ) for frame in video]

 return video

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[str]     ,    _UpperCAmelCase				:						Optional[Any]=False     ,    _UpperCAmelCase				:						Tuple=False     ,    _UpperCAmelCase				:						Union[str, Any]=False ):
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 lowerCAmelCase													=   []
 for i in range(image_processor_tester.batch_size ):
  if equal_resolution:
   lowerCAmelCase													=   image_processor_tester.max_resolution
  else:
   lowerCAmelCase													=   np.random.choice(
       np.arange(image_processor_tester.min_resolution     ,    image_processor_tester.max_resolution )     ,    2 )
   lowerCAmelCase													=   prepare_video(
       image_processor_tester=lowercase_     ,    width=lowercase_     ,    height=lowercase_     ,    numpify=lowercase_     ,    torchify=lowercase_     ,    )
  video_inputs.append(lowercase_ )

 return video_inputs







class 			a       (      unittest.TestCase     ):



 def __init__(    self							,					_snake_case							,					_snake_case=7							,					_snake_case=3							,					_snake_case=4							,					_snake_case=18							,					_snake_case=30							,					_snake_case=4_00							,					_snake_case=True							,					_snake_case=None							,					_snake_case=True							,					_snake_case=[0.5, 0.5, 0.5]							,					_snake_case=[0.5, 0.5, 0.5]							,					_snake_case=True							,					_snake_case=None							,					):







  """simple docstring"""






  lowerCAmelCase													=   size if size is not None else {'shortest_edge': 18}
  lowerCAmelCase													=   crop_size if crop_size is not None else {'height': 18, 'width': 18}

  lowerCAmelCase													=   parent
  lowerCAmelCase													=   batch_size
  lowerCAmelCase													=   num_channels
  lowerCAmelCase													=   num_frames
  lowerCAmelCase													=   image_size
  lowerCAmelCase													=   min_resolution
  lowerCAmelCase													=   max_resolution
  lowerCAmelCase													=   do_resize
  lowerCAmelCase													=   size
  lowerCAmelCase													=   do_normalize
  lowerCAmelCase													=   image_mean
  lowerCAmelCase													=   image_std
  lowerCAmelCase													=   do_center_crop
  lowerCAmelCase													=   crop_size



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
  }







@require_torch
@require_vision
class 			a       (      a_      ,   unittest.TestCase     ):
 snake_case__								=					TvltImageProcessor if is_vision_available() else None



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   TvltImageProcessorTester(self    )



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return self.image_processor_tester.prepare_image_processor_dict()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_processing_class(**self.image_processor_dict    )
  self.assertTrue(hasattr(_snake_case							,					'image_mean'    )    )
  self.assertTrue(hasattr(_snake_case							,					'image_std'    )    )
  self.assertTrue(hasattr(_snake_case							,					'do_normalize'    )    )
  self.assertTrue(hasattr(_snake_case							,					'do_resize'    )    )
  self.assertTrue(hasattr(_snake_case							,					'do_center_crop'    )    )
  self.assertTrue(hasattr(_snake_case							,					'size'    )    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_processing_class(**self.image_processor_dict    )
  # create random PIL videos
  lowerCAmelCase													=   prepare_video_inputs(self.image_processor_tester							,					equal_resolution=_snake_case    )
  for video in video_inputs:
   self.assertIsInstance(_snake_case							,					_snake_case    )
   self.assertIsInstance(video[0]							,					Image.Image    )

  # Test not batched input
  lowerCAmelCase													=   image_processor(video_inputs[0]							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)

  # Test batched
  lowerCAmelCase													=   image_processor(_snake_case							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_processing_class(**self.image_processor_dict    )
  # create random numpy tensors
  lowerCAmelCase													=   prepare_video_inputs(self.image_processor_tester							,					equal_resolution=_snake_case							,					numpify=_snake_case    )
  for video in video_inputs:
   self.assertIsInstance(_snake_case							,					_snake_case    )
   self.assertIsInstance(video[0]							,					np.ndarray    )

  # Test not batched input
  lowerCAmelCase													=   image_processor(video_inputs[0]							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)

  # Test batched
  lowerCAmelCase													=   image_processor(_snake_case							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_processing_class(**self.image_processor_dict    )
  # create random PyTorch tensors
  lowerCAmelCase													=   prepare_video_inputs(self.image_processor_tester							,					equal_resolution=_snake_case							,					torchify=_snake_case    )
  for video in video_inputs:
   self.assertIsInstance(_snake_case							,					_snake_case    )
   self.assertIsInstance(video[0]							,					torch.Tensor    )

  # Test not batched input
  lowerCAmelCase													=   image_processor(video_inputs[0]							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)

  # Test batched
  lowerCAmelCase													=   image_processor(_snake_case							,					return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape							,					(
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )							,					)












"""simple docstring"""
import math
import sys







class 			a       :



 def __init__(    self    ):







  """simple docstring"""






  lowerCAmelCase													=   0
  lowerCAmelCase													=   []
  lowerCAmelCase													=   {}  # To store the pos of node in array



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return self.cur_size == 0



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.left(_snake_case    )
  lowerCAmelCase													=   self.right(_snake_case    )
  if lc < self.cur_size and self.array(_snake_case    )[0] < self.array(_snake_case    )[0]:
   lowerCAmelCase													=   lc
  else:
   lowerCAmelCase													=   idx
  if rc < self.cur_size and self.array(_snake_case    )[0] < self.array(_snake_case    )[0]:
   lowerCAmelCase													=   rc
  if smallest != idx:
   self.swap(_snake_case							,					_snake_case    )
   self.min_heapify(_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.cur_size
  self.cur_size += 1
  self.array.append((sys.maxsize, tup[1])    )
  self.decrease_key((sys.maxsize, tup[1])							,					tup[0]    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.array[0][1]
  lowerCAmelCase													=   self.array[self.cur_size - 1]
  self.cur_size -= 1
  self.min_heapify(1    )
  del self.pos[min_node]
  return min_node



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  return 2 * i + 1



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  return 2 * i + 2



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  return math.floor(i / 2    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   j
  lowerCAmelCase													=   i
  lowerCAmelCase													=   self.array[i]
  lowerCAmelCase													=   self.array[j]
  lowerCAmelCase													=   temp



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.pos[tup[1]]
  # assuming the new_d is atmost old_d
  lowerCAmelCase													=   (new_d, tup[1])
  while idx > 0 and self.array[self.par(_snake_case    )][0] > self.array[idx][0]:
   self.swap(_snake_case							,					self.par(_snake_case    )    )
   lowerCAmelCase													=   self.par(_snake_case    )







class 			a       :



 def __init__(    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   {}  # To store graph: u -> (v,w)
  lowerCAmelCase													=   num  # Number of nodes in graph
  # To store the distance from source vertex
  lowerCAmelCase													=   [0] * self.num_nodes
  lowerCAmelCase													=   [-1] * self.num_nodes  # To store the path



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  if u in self.adjList:
   self.adjList[u].append((v, w)    )
  else:
   lowerCAmelCase													=   [(v, w)]

  # Assuming undirected graph
  if v in self.adjList:
   self.adjList[v].append((u, w)    )
  else:
   lowerCAmelCase													=   [(u, w)]



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  for u in self.adjList:
   print(_snake_case							,					'->'							,					' -> '.join(str(F'{v}({w})'    ) for v, w in self.adjList[u]    )    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   [-1] * self.num_nodes
  # src is the source node
  lowerCAmelCase													=   0
  lowerCAmelCase													=   PriorityQueue()
  q.insert((0, src)    )  # (dist from src, node)
  for u in self.adjList:
   if u != src:
    lowerCAmelCase													=   sys.maxsize  # Infinity
    lowerCAmelCase													=   -1

  while not q.is_empty():
   lowerCAmelCase													=   q.extract_min()  # Returns node with the min dist from source
   # Update the distance of all the neighbours of u and
   # if their prev dist was INFINITY then push them in Q
   for v, w in self.adjList[u]:
    lowerCAmelCase													=   self.dist[u] + w
    if self.dist[v] > new_dist:
     if self.dist[v] == sys.maxsize:
      q.insert((new_dist, v)    )
     else:
      q.decrease_key((self.dist[v], v)							,					_snake_case    )
     lowerCAmelCase													=   new_dist
     lowerCAmelCase													=   u

        # Show the shortest distances from src
  self.show_distances(_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  print(F'Distance from node: {src}'    )
  for u in range(self.num_nodes    ):
   print(F'Node {u} has distance: {self.dist[u]}'    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   []
  lowerCAmelCase													=   0
  lowerCAmelCase													=   dest
  # Backtracking from dest to src
  while self.par[temp] != -1:
   path.append(_snake_case    )
   if temp != src:
    for v, w in self.adjList[temp]:
     if v == self.par[temp]:
      cost += w
      break
   lowerCAmelCase													=   self.par[temp]
  path.append(_snake_case    )
  path.reverse()

  print(F'----Path to reach {dest} from {src}----'    )
  for u in path:
   print(F'{u}'							,					end=' '    )
   if u != dest:
    print('-> '							,					end=''    )

  print('\nTotal cost of path: '							,					_snake_case    )


if __name__ == "__main__":
       __UpperCamelCase    :	str						=     Graph(9)
       graph.add_edge(0, 1, 4)
       graph.add_edge(0, 7, 8)
       graph.add_edge(1, 2, 8)
       graph.add_edge(1, 7, 11)
       graph.add_edge(2, 3, 7)
       graph.add_edge(2, 8, 2)
       graph.add_edge(2, 5, 4)
       graph.add_edge(3, 4, 9)
       graph.add_edge(3, 5, 14)
       graph.add_edge(4, 5, 10)
       graph.add_edge(5, 6, 2)
       graph.add_edge(6, 7, 1)
       graph.add_edge(6, 8, 6)
       graph.add_edge(7, 8, 7)
       graph.show_graph()
       graph.dijkstra(0)
       graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21





