





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)







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 '}],
							    ]					,			)
