code: 
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def __init__(		self    :    str					,			lowerCamelCase    :    Dict	):
  							lowerCamelCase_  :    Optional[Any]   	=			vertices
  							lowerCamelCase_  :    int   	=			[[0] * vertices for _ in range(lowercase_	)]
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    Dict	):
  							print('Vertex \t Distance from Source'	)
  							for vertex in range(self.vertices	):
  									print(lowercase_					,			'\t\t'					,			distances_from_source[vertex]	)
  
  
  
  					def      __a    (		self    :    Optional[Any]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    int	):
  							lowerCamelCase_  :    List[Any]   	=			1E7
  							lowerCamelCase_  :    Tuple   	=			0
  
  							# Search not nearest vertex not in the shortest path tree
  							for vertex in range(self.vertices	):
  									if distances_from_source[vertex] < minimum and visited[vertex] is False:
  											lowerCamelCase_  :    Dict   	=			distances_from_source[vertex]
  											lowerCamelCase_  :    int   	=			vertex
  							return min_index
  
  
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    List[Any]	):
  							lowerCamelCase_  :    List[Any]   	=			[int(1E7	)] * self.vertices  # distances from the source
  							lowerCamelCase_  :    List[Any]   	=			0
  							lowerCamelCase_  :    List[str]   	=			[False] * self.vertices
  
  							for _ in range(self.vertices	):
  									lowerCamelCase_  :    List[Any]   	=			self.minimum_distance(lowercase_					,			lowercase_	)
  									lowerCamelCase_  :    List[str]   	=			True
  
  									# Update dist value of the adjacent vertices
  									# of the picked vertex only if the current
  									# distance is greater than new distance and
  									# the vertex in not in the shortest path tree
  									for v in range(self.vertices	):
  											if (
  											    self.graph[u][v] > 0
  											    and visited[v] is False
  											    and distances[v] > distances[u] + self.graph[u][v]
  											):
  													lowerCamelCase_  :    Any   	=			distances[u] + self.graph[u][v]
  
  							self.print_solution(lowercase_	)
  
  
  if __name__ == "__main__":
  		_lowercase		:     int 					=Graph(9)
  		_lowercase		:     Union[str, Any] 					=[
  		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
  		    [4, 0, 8, 0, 0, 0, 0, 11, 0],
  		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
  		    [0, 0, 7, 0, 9, 14, 0, 0, 0],
  		    [0, 0, 0, 9, 0, 10, 0, 0, 0],
  		    [0, 0, 4, 14, 10, 0, 2, 0, 0],
  		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
  		    [8, 11, 0, 0, 0, 0, 1, 0, 7],
  		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
  		]
  		graph.dijkstra(0)
  
style_context: 
  
  
  
  
  
  import unittest
  
  import requests
  
  from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      is_torch_available,
      require_tf,
      require_torch,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_torch_available():
  		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
  		_lowercase		:     Dict 					=False
  
  
  if is_vision_available():
  		from PIL import Image
  else:
  
  
  
  
  
  
  		class 	UpperCamelCase_     :
  
  
  
  							@staticmethod
  							def      __a    (		*lowerCamelCase    :    Any					,			**lowerCamelCase    :    List[Any]	):
  									pass
  
  
  
  
  
  
  @is_pipeline_test
  @require_vision
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  					_a			:	int							=				MODEL_FOR_VISION_2_SEQ_MAPPING
  					_a			:	int							=				TF_MODEL_FOR_VISION_2_SEQ_MAPPING
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model=lowerCamelCase					,			tokenizer=lowerCamelCase					,			image_processor=lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			[
  							    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
  							    './tests/fixtures/tests_samples/COCO/000000039769.png',
  							]
  							return pipe, examples
  
  
  
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]	):
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': ANY(lowerCamelCase	)}],
  							        [{'generated_text': ANY(lowerCamelCase	)}],
  							    ]					,			)
  
  
  
  					@require_tf
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    Tuple   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'					,			framework='tf'	)
  							lowerCamelCase_  :    Optional[int]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        {
  							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							        },
  							    ]					,			)
  
  							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							    ]					,			)
  
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			max_new_tokens=1	)
  							self.assertEqual(
  							    lowerCamelCase					,			[{'generated_text': 'growth'}]					,			)
  
  
  
  					@require_torch
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'	)
  							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        {
  							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							        },
  							    ]					,			)
  
  							lowerCamelCase_  :    Tuple   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							    ]					,			)
  
  
  
  					@require_torch
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Any   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'	)
  							lowerCamelCase_  :    List[Any]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  							lowerCamelCase_  :    List[Any]   	=			'a photo of'
  
  							lowerCamelCase_  :    Dict   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertTrue(outputs[0]['generated_text'].startswith(lowerCamelCase	)	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'	)
  							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							    ]					,			)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    int   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
  							lowerCamelCase_  :    Tuple   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
  							lowerCamelCase_  :    List[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Optional[Any]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
  							lowerCamelCase_  :    str   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
  							lowerCamelCase_  :    Dict   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cartoon of a purple character.'}]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    List[Any]   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
  							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    List[str]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    int   	=			'a photography of'
  
  							lowerCamelCase_  :    Tuple   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photography of a volcano'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[int]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
  							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    Optional[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[int]   	=			'a photo of a'
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    List[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  					@unittest.skipIf(
  					    not is_torch_greater_or_equal_than_1_11					,			reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'	)
  					@slow
  					@require_torch
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='google/pix2struct-ai2d-base'	)
  							lowerCamelCase_  :    List[str]   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    Tuple   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    str   	=			'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'
  
  							lowerCamelCase_  :    Union[str, Any]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'ash cloud'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    Optional[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  
  
  					@slow
  					@require_tf
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'					,			framework='tf'	)
  							lowerCamelCase_  :    Dict   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)
  
  							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							    ]					,			)
  