


"""simple docstring"""







class 				__A    :






 """simple docstring"""




 def __init__(		self						) ->					Union[str, Any]:
     a					=0
     a					=0
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     if vertex not in self.adjacency:
         a					={}
         self.num_vertices += 1


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     self.add_vertex(__A						)
     self.add_vertex(__A						)

     if head == tail:
         return

     a					=weight
     a					=weight


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.get_edges()
     for edge in edges:
         a		,				a		,				a					=edge
         edges.remove((tail, head, weight)						)
     for i in range(len(__A						)						):
         a					=list(edges[i]						)

     edges.sort(key=lambda __A						: e[2]						)
     for i in range(len(__A						) - 1						):
         if edges[i][2] >= edges[i + 1][2]:
             a					=edges[i][2] + 1
     for edge in edges:
         a		,				a		,				a					=edge
         a					=weight
         a					=weight


 def __str__(		self						) ->					str:
     a					=''''''
     for tail in self.adjacency:
         for head in self.adjacency[tail]:
             a					=self.adjacency[head][tail]
             string += f'''{head} -> {tail} == {weight}\n'''
     return string.rstrip('''\n'''						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=[]
     for tail in self.adjacency:
         for head in self.adjacency[tail]:
             output.append((tail, head, self.adjacency[head][tail])						)
     return output


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     return self.adjacency.keys()


 @staticmethod
 def 				SCREAMING_SNAKE_CASE							(		__A=None			,   __A=None						) ->					Optional[Any]:
     a					=Graph()
     if vertices is None:
         a					=[]
     if edges is None:
         a					=[]
     for vertex in vertices:
         g.add_vertex(__A						)
     for edge in edges:
         g.add_edge(*__A						)
     return g






 class 				__A    :






  """simple docstring"""




  def __init__(		self						) ->					Dict:
      a					={}
      a					={}


  def __len__(		self						) ->					List[Any]:
      return len(self.parent						)


  def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
      if item in self.parent:
          return self.find(__A						)

      a					=item
      a					=0
      return item


  def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Optional[Any]:
      if item not in self.parent:
          return self.make_set(__A						)
      if item != self.parent[item]:
          a					=self.find(self.parent[item]						)
      return self.parent[item]





  def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Optional[Any]:
      a					=self.find(__A						)
      a					=self.find(__A						)

      if roota == roota:
          return roota

      if self.rank[roota] > self.rank[roota]:
          a					=roota
          return roota

      if self.rank[roota] < self.rank[roota]:
          a					=roota
          return roota

      if self.rank[roota] == self.rank[roota]:
          self.rank[roota] += 1
          a					=roota
          return roota
      return None


 @staticmethod
 def 				SCREAMING_SNAKE_CASE							(		__A						) ->					Optional[Any]:
     a					=graph.num_vertices

     a					=Graph.UnionFind()
     a					=[]
     while num_components > 1:
         a					={}
         for vertex in graph.get_vertices():
             a					=-1

         a					=graph.get_edges()
         for edge in edges:
             a		,				a		,				a					=edge
             edges.remove((tail, head, weight)						)
         for edge in edges:
             a		,				a		,				a					=edge
             a					=union_find.find(__A						)
             a					=union_find.find(__A						)
             if seta != seta:
                 if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                     a					=[head, tail, weight]

                 if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                     a					=[head, tail, weight]
         for vertex in cheap_edge:
             if cheap_edge[vertex] != -1:
                 a		,				a		,				a					=cheap_edge[vertex]
                 if union_find.find(__A						) != union_find.find(__A						):
                     union_find.union(__A			,   __A						)
                     mst_edges.append(cheap_edge[vertex]						)
                     a					=num_components - 1
     a					=Graph.build(edges=__A						)
     return mst



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  IFInpaintingSuperResolutionPipeline
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}      )
 __lowerCAmelCase			 =  PipelineTesterMixin.required_optional_params - {"latents"}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     return self._get_superresolution_dummy_components()


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0						) ->					Optional[int]:
     if str(__A						).startswith('''mps'''						):
         a					=torch.manual_seed(__A						)
     else:
         a					=torch.Generator(device=__A						).manual_seed(__A						)

     a					=floats_tensor((1, 3, 16, 16)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)

     a					={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda'''			,   reason='''float16 requires CUDA'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # 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 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_save_load_local()





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,   )