
'''simple docstring'''
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							IFImgaImgSuperResolutionPipeline
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""})
				lowerCAmelCase_     			=							PipelineTesterMixin.required_optional_params - {"""latents"""}







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											return self._get_superresolution_dummy_components()







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=0     )->		int:







											'''simple docstring'''






											if str(A_     ).startswith('mps'     ):
																		UpperCamelCase   			=     torch.manual_seed(A_     )
											else:
																		UpperCamelCase   			=     torch.Generator(device=A_     ).manual_seed(A_     )

											UpperCamelCase   			=     floats_tensor((1, 3, 32, 32)						,				rng=random.Random(A_     )     ).to(A_     )
											UpperCamelCase   			=     floats_tensor((1, 3, 16, 16)						,				rng=random.Random(A_     )     ).to(A_     )

											UpperCamelCase   			=     {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': image,
											    'original_image': original_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 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											self._test_save_load_optional_components()







				@unittest.skipIf(torch_device != 'cuda'						,				reason='float16 requires CUDA'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											super().test_save_load_floataa(expected_max_diff=1e-1     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											self._test_save_load_local()







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											self._test_inference_batch_single_identical(
											    expected_max_diff=1e-2						,				)






'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[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
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

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







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




