



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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

						"""simple docstring"""


						lowercase__					:   str													=					set()
						# keep track of all the paths to be checked
						lowercase__					:   List[str]													=					[[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
												lowercase__					:   Optional[int]													=					queue.pop(0						)
												# get the last node from the path
												lowercase__					:   Optional[Any]													=					path[-1]
												if node not in explored:
																		lowercase__					:   Tuple													=					graph[node]
																		# go through all neighbour nodes, construct a new path and
																		# push it into the queue
																		for neighbour in neighbours:
																								lowercase__					:   int													=					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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

						"""simple docstring"""


						if not graph or start not in graph or target not in graph:
												return -1
						if start == target:
												return 0
						lowercase__					:   List[str]													=					[start]
						lowercase__					:   Tuple													=					set(lowerCamelCase__						)
						# Keep tab on distances from `start` node.
						lowercase__					:   List[str]													=					{start: 0, target: -1}
						while queue:
												lowercase__					:   List[str]													=					queue.pop(0						)
												if node == target:
																		lowercase__					:   Union[str, Any]													=					(
																		    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__						)
																								lowercase__					:   Optional[int]													=					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 unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@property
					def 			snake_case					(							self :     Any					):
									torch.manual_seed(0					)
									lowercase__					:   Tuple													=					UNetaDModel(
									    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=("DownBlock2D", "AttnDownBlock2D")				,       up_block_types=("AttnUpBlock2D", "UpBlock2D")				,       )
									return model







					@property
					def 			snake_case					(							self :     List[str]					):
									torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					VQModel(
									    block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=3				,       )
									return model







					@property
					def 			snake_case					(							self :     Dict					):
									torch.manual_seed(0					)
									lowercase__					:   str													=					CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_000				,       )
									return CLIPTextModel(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   Any													=					self.dummy_uncond_unet
									lowercase__					:   Dict													=					DDIMScheduler()
									lowercase__					:   Optional[Any]													=					self.dummy_vq_model

									lowercase__					:   Union[str, Any]													=					LDMPipeline(unet=SCREAMING_SNAKE_CASE				,       vqvae=SCREAMING_SNAKE_CASE				,       scheduler=SCREAMING_SNAKE_CASE					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"					).images

									lowercase__					:   str													=					torch.manual_seed(0					)
									lowercase__					:   List[Any]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"				,       return_dict=SCREAMING_SNAKE_CASE					)[0]

									lowercase__					:   Any													=					image[0, -3:, -3:, -1]
									lowercase__					:   Optional[Any]													=					image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__					:   List[Any]													=					np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]					)
									lowercase__					:   Optional[Any]													=					1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice					).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < tolerance






@slow
@require_torch
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					torch.manual_seed(0					)
									lowercase__					:   Tuple													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=5				,       output_type="numpy"					).images

									lowercase__					:   Tuple													=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									lowercase__					:   Optional[Any]													=					np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]					)
									lowercase__					:   int													=					1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice					).max() < tolerance




