code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  import os
  import tempfile
  
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
  	import numpy as np
  	import torch
  
  if is_vision_available():
  	from PIL import Image
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase=False      ,			lowercase=False      ,			lowercase=False			)      ->      int:
  			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      		=       __lowerCAmelCase      		=       image_processor_tester.max_resolution
  						else:
  									# To avoid getting image width/height 0
  									__lowerCAmelCase      		=       image_processor_tester.min_resolution
  									if getattr(lowercase      ,			"""size_divisor"""      ,			lowercase			):
  												# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
  												__lowerCAmelCase      		=       max(image_processor_tester.size_divisor      ,			lowercase			)
  									__lowerCAmelCase		, __lowerCAmelCase      		=       np.random.choice(np.arange(lowercase      ,			image_processor_tester.max_resolution			)      ,			2			)
  						image_inputs.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 image in image_inputs]
  
  			if torchify:
  						__lowerCAmelCase      		=       [torch.from_numpy(lowercase			) for image in image_inputs]
  
  			return image_inputs
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase=10      ,			lowercase=10      ,			lowercase=False      ,			lowercase=False			)      ->      int:
  			__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      _lowerCAmelCase		(		lowercase      ,			lowercase=False      ,			lowercase=False      ,			lowercase=False			)      ->      Optional[Any]:
  			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      		=       __lowerCAmelCase      		=       image_processor_tester.max_resolution
  						else:
  									__lowerCAmelCase		, __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 					_UpperCAmelCase							:
  		a     :     Dict     						=None
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.image_processing_class(**self.image_processor_dict    )
  					__lowerCAmelCase      		=       json.loads(image_processor.to_json_string()    )
  					for key, value in self.image_processor_dict.items():
  								self.assertEqual(obj[key],__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.image_processing_class(**self.image_processor_dict    )
  
  					with tempfile.TemporaryDirectory() as tmpdirname:
  								__lowerCAmelCase      		=       os.path.join(__SCREAMING_SNAKE_CASE,"""image_processor.json"""    )
  								image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE    )
  
  					self.assertEqual(image_processor_second.to_dict(),image_processor_first.to_dict()    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.image_processing_class(**self.image_processor_dict    )
  
  					with tempfile.TemporaryDirectory() as tmpdirname:
  								__lowerCAmelCase      		=       image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE    )[0]
  								check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE    )
  
  					self.assertEqual(image_processor_second.to_dict(),image_processor_first.to_dict()    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.image_processing_class()
  					self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		@require_torch
  		@require_vision
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if self.test_cast_dtype is not None:
  								# Initialize image_processor
  								__lowerCAmelCase      		=       self.image_processing_class(**self.image_processor_dict    )
  
  								# create random PyTorch tensors
  								__lowerCAmelCase      		=       prepare_image_inputs(self.image_processor_tester,equal_resolution=__SCREAMING_SNAKE_CASE,torchify=__SCREAMING_SNAKE_CASE    )
  
  								__lowerCAmelCase      		=       image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    )
  								# for layoutLM compatiblity
  								self.assertEqual(encoding.pixel_values.device,torch.device("""cpu"""    )    )
  								self.assertEqual(encoding.pixel_values.dtype,torch.floataa    )
  
  								__lowerCAmelCase      		=       image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(torch.floataa    )
  								self.assertEqual(encoding.pixel_values.device,torch.device("""cpu"""    )    )
  								self.assertEqual(encoding.pixel_values.dtype,torch.floataa    )
  
  								__lowerCAmelCase      		=       image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to("""cpu""",torch.bfloataa    )
  								self.assertEqual(encoding.pixel_values.device,torch.device("""cpu"""    )    )
  								self.assertEqual(encoding.pixel_values.dtype,torch.bfloataa    )
  
  								with self.assertRaises(__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(torch.bfloataa,"""cpu"""    )
  
  								# Try with text + image feature
  								__lowerCAmelCase      		=       image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    )
  								encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]    )}    )
  								__lowerCAmelCase      		=       encoding.to(torch.floataa    )
  
  								self.assertEqual(encoding.pixel_values.device,torch.device("""cpu"""    )    )
  								self.assertEqual(encoding.pixel_values.dtype,torch.floataa    )
  								self.assertEqual(encoding.input_ids.dtype,torch.long    )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import math
  import sys
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       0
  					__lowerCAmelCase      		=       []
  					__lowerCAmelCase      		=       {}  # To store the pos of node in array
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self.cur_size == 0
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.left(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       self.right(__SCREAMING_SNAKE_CASE    )
  					if lc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE    )[0] < self.array(__SCREAMING_SNAKE_CASE    )[0]:
  								__lowerCAmelCase      		=       lc
  					else:
  								__lowerCAmelCase      		=       idx
  					if rc < self.cur_size and self.array(__SCREAMING_SNAKE_CASE    )[0] < self.array(__SCREAMING_SNAKE_CASE    )[0]:
  								__lowerCAmelCase      		=       rc
  					if smallest != idx:
  								self.swap(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								self.min_heapify(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_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 						lowerCamelCase__    (						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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return 2 * i + 1
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return 2 * i + 2
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return math.floor(i / 2    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       j
  					__lowerCAmelCase      		=       i
  					__lowerCAmelCase      		=       self.array[i]
  					__lowerCAmelCase      		=       self.array[j]
  					__lowerCAmelCase      		=       temp
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_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(__SCREAMING_SNAKE_CASE    )][0] > self.array[idx][0]:
  								self.swap(__SCREAMING_SNAKE_CASE,self.par(__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       self.par(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self,__SCREAMING_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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_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 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					for u in self.adjList:
  								print(__SCREAMING_SNAKE_CASE,"""->""",""" -> """.join(str(f'{v}({w})'    ) for v, w in self.adjList[u]    )    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_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),__SCREAMING_SNAKE_CASE    )
  														__lowerCAmelCase      		=       new_dist
  														__lowerCAmelCase      		=       u
  
          # Show the shortest distances from src
  					self.show_distances(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					__lowerCAmelCase      		=       0
  					__lowerCAmelCase      		=       dest
  					# Backtracking from dest to src
  					while self.par[temp] != -1:
  								path.append(__SCREAMING_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(__SCREAMING_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: """,__SCREAMING_SNAKE_CASE    )
  
  
  if __name__ == "__main__":
  	_a      :				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, 1_1)
  	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, 1_4)
  	graph.add_edge(4, 5, 1_0)
  	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
  
  