
A     :	Tuple    = {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Tuple:
				__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(lowerCAmelCase__  )
																new_path.append(lowerCAmelCase__  )
																queue.append(lowerCAmelCase__  )
																# return path if neighbour is goal
																if neighbour == goal:
																				return new_path

            # mark node as explored
												explored.add(lowerCAmelCase__  )

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


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Dict:
				if not graph or start not in graph or target not in graph:
								return -1
				if start == target:
								return 0
				__a										=    [start]
				__a										=    set(lowerCAmelCase__  )
				# 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(lowerCAmelCase__  )
																queue.append(lowerCAmelCase__  )
																__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

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




