





'''simple docstring'''

import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:

			class    UpperCAmelCase			:




					@staticmethod
					def 					UpperCAmelCase__ (*A__   :						Any				,      **A__   :						Optional[int]					)       ->			Union[str, Any]:
									pass

@is_pipeline_test
@require_torch
@require_vision
class    UpperCAmelCase			(    unittest.TestCase	):
		UpperCAmelCase       :						Union[str, Any]			      =		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




		def 					UpperCAmelCase__ (self   :						List[Any]				,      A__   :						Union[str, Any]				,      A__   :						Tuple				,      A__   :						Union[str, Any]					)       ->			Dict:
						lowercase    =			pipeline("visual-question-answering"				,      model="hf-internal-testing/tiny-vilt-random-vqa"					)
						lowercase    =			[
						    {
						        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
						        "question": "How many cats are there?",
						    },
						    {
						        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
						        "question": "How many cats are there?",
						    },
						]
						return vqa_pipeline, examples




		def 					UpperCAmelCase__ (self   :						Optional[Any]				,      A__   :						Union[str, Any]				,      A__   :						List[str]					)       ->			Union[str, Any]:
						lowercase    =			vqa_pipeline(_snake_case				,      top_k=1					)
						self.assertEqual(
						    _snake_case				,      [
						        [{"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}],
						        [{"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}],
						    ]				,      )




		@require_torch
		def 					UpperCAmelCase__ (self   :						Any					)       ->			Optional[Any]:
						lowercase    =			pipeline("visual-question-answering"				,      model="hf-internal-testing/tiny-vilt-random-vqa"					)
						lowercase    =			"./tests/fixtures/tests_samples/COCO/000000039769.png"
						lowercase    =			"How many cats are there?"

						lowercase    =			vqa_pipeline(image=_snake_case				,      question="How many cats are there?"				,      top_k=2					)
						self.assertEqual(
						    _snake_case				,      [{"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}, {"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}]					)

						lowercase    =			vqa_pipeline({"image": image, "question": question}				,      top_k=2					)
						self.assertEqual(
						    _snake_case				,      [{"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}, {"score": ANY(_snake_case					), "answer": ANY(_snake_case					)}]					)




		@slow
		@require_torch
		def 					UpperCAmelCase__ (self   :						Any					)       ->			str:
						lowercase    =			pipeline("visual-question-answering"				,      model="dandelin/vilt-b32-finetuned-vqa"					)
						lowercase    =			"./tests/fixtures/tests_samples/COCO/000000039769.png"
						lowercase    =			"How many cats are there?"

						lowercase    =			vqa_pipeline(image=_snake_case				,      question=_snake_case				,      top_k=2					)
						self.assertEqual(
						    nested_simplify(_snake_case				,      decimals=4					)				,      [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]					)

						lowercase    =			vqa_pipeline({"image": image, "question": question}				,      top_k=2					)
						self.assertEqual(
						    nested_simplify(_snake_case				,      decimals=4					)				,      [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]					)

						lowercase    =			vqa_pipeline(
						    [{"image": image, "question": question}, {"image": image, "question": question}]				,      top_k=2					)
						self.assertEqual(
						    nested_simplify(_snake_case				,      decimals=4					)				,      [[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2				,      )





		@require_tf
		@unittest.skip("Visual question answering not implemented in TF"					)
		def 					UpperCAmelCase__ (self   :						Dict					)       ->			Optional[Any]:
						pass









SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




