'''simple docstring'''



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    ( _lowerCamelCase	, _lowerCamelCase	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			str     =    IFInpaintingPipeline
	a      :			List[str]     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	a      :			Optional[Any]     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	a      :			List[str]     =    PipelineTesterMixin.required_optional_params - {"latents"}


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				return self._get_dummy_components()


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=0	)							->							Dict:







				'''simple docstring'''





				if str(lowercase_	).startswith('''mps'''	):
							__lowercase			  =							torch.manual_seed(lowercase_	)
				else:
							__lowercase			  =							torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

				__lowercase			  =							floats_tensor((1, 3, 32, 32)       ,rng=random.Random(lowercase_	)	).to(lowercase_	)
				__lowercase			  =							floats_tensor((1, 3, 32, 32)       ,rng=random.Random(lowercase_	)	).to(lowercase_	)

				__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   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				self._test_save_load_optional_components()


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







				'''simple docstring'''





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


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







				'''simple docstring'''





				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2	)


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







				'''simple docstring'''





				self._test_save_load_local()


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







				'''simple docstring'''





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












'''simple docstring'''



_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 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[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			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							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_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    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			  =							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





