





"""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 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							IFInpaintingPipeline
					UpperCamelCase      =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
					UpperCamelCase      =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCamelCase      =							PipelineTesterMixin.required_optional_params - {'''latents'''}


					def 	lowerCamelCase       (					self			:List[str]		):
												return self._get_dummy_components()


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Dict=0		):
												if str(__UpperCamelCase		).startswith("mps"		):
																			A		=  torch.manual_seed(__UpperCamelCase		)
												else:
																			A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

												A		=  floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
												A		=  floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

												A		=  {
												    "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 	lowerCamelCase       (					self			:Optional[int]		):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												self._test_save_load_optional_components()


					@unittest.skipIf(torch_device != "cuda"     ,       reason="float16 requires CUDA"		)
					def 	lowerCamelCase       (					self			:Any		):
												# 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 	lowerCamelCase       (					self			:Any		):
												self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)


					def 	lowerCamelCase       (					self			:Tuple		):
												self._test_save_load_local()


					def 	lowerCamelCase       (					self			:str		):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1e-2     ,       )









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


