


'''simple docstring'''
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_			:  Union[str, Any]										=   False

class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''
 pass


@nightly
@require_torch_gpu
class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
  # remove text_unet
  pipe.remove_unused_weights()
  pipe.to(__lowercase    )
  pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		Optional[int]	  =      'A painting of a squirrel eating a burger '
  __A	:		Optional[int]	  =      torch.manual_seed(0    )
  __A	:		Tuple	  =      pipe(
      prompt=__lowercase   ,   generator=__lowercase   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type='numpy'    ).images

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(__lowercase    )
   __A	:		str	  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowercase    )
  pipe.to(__lowercase    )
  pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		List[Any]	  =      generator.manual_seed(0    )
  __A	:		List[Any]	  =      pipe(
      prompt=__lowercase   ,   generator=__lowercase   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type='numpy'    ).images

  assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
      'shi-labs/versatile-diffusion'   ,   torch_dtype=torch.floataa    )
  pipe.to(__lowercase    )
  pipe.set_progress_bar_config(disable=__lowercase    )

  __A	:		List[str]	  =      'A painting of a squirrel eating a burger '
  __A	:		Union[str, Any]	  =      torch.manual_seed(0    )
  __A	:		int	  =      pipe(
      prompt=__lowercase   ,   generator=__lowercase   ,   guidance_scale=7.5   ,   num_inference_steps=50   ,   output_type='numpy'    ).images

  __A	:		Dict	  =      image[0, 253:256, 253:256, -1]

  assert image.shape == (1, 512, 512, 3)
  __A	:		Union[str, Any]	  =      np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

  assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2










'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Dict	  =      set()
 # edges = list of graph's edges
 __A	:		Any	  =      get_edges(UpperCamelCase__      )

 # While there are still elements in edges list, take an arbitrary edge
 # (from_node, to_node) and add his extremity to chosen_vertices and then
 # remove all arcs adjacent to the from_node and to_node
 while edges:
  __A  ,__A	:		List[Any]	  =      edges.pop()
  chosen_vertices.add(UpperCamelCase__      )
  chosen_vertices.add(UpperCamelCase__      )
  for edge in edges.copy():
   if from_node in edge or to_node in edge:
    edges.discard(UpperCamelCase__      )
 return chosen_vertices


def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Any	  =      set()
 for from_node, to_nodes in graph.items():
  for to_node in to_nodes:
   edges.add((from_node, to_node)      )
 return edges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






