import math


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Optional[Any]  ,     __magic_name__ :       List[str]=0   )  ->	List[str]:  # a graph with Node 0,1,...,N-1
        SCREAMING_SNAKE_CASE_          =  n
        SCREAMING_SNAKE_CASE_          =  [
            [math.inf for j in range(0  ,     __UpperCamelCase   )] for i in range(0  ,     __UpperCamelCase   )
        ]  # adjacency matrix for weight
        SCREAMING_SNAKE_CASE_          =  [
            [math.inf for j in range(0  ,     __UpperCamelCase   )] for i in range(0  ,     __UpperCamelCase   )
        ]  # dp[i][j] stores minimum distance from i to j







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[str]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  w







  def    __A     (							self :       Optional[int]   )  ->	List[Any]:
        for k in range(0  ,     self.n   ):
              for i in range(0  ,     self.n   ):
                    for j in range(0  ,     self.n   ):
                          SCREAMING_SNAKE_CASE_          =  min(self.dp[i][j]  ,     self.dp[i][k] + self.dp[k][j]   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[int]   )  ->	Dict:
        return self.dp[u][v]


if __name__ == "__main__":
      A     :  List[str]					     =       Graph(5)
      graph.add_edge(0, 2, 9)
      graph.add_edge(0, 4, 10)
      graph.add_edge(1, 3, 5)
      graph.add_edge(2, 3, 7)
      graph.add_edge(3, 0, 10)
      graph.add_edge(3, 1, 2)
      graph.add_edge(3, 2, 1)
      graph.add_edge(3, 4, 6)
      graph.add_edge(4, 1, 3)
      graph.add_edge(4, 2, 4)
      graph.add_edge(4, 3, 9)
      graph.floyd_warshall()
      graph.show_min(1, 4)
      graph.show_min(0, 3)



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


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     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    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=50  ,     output_type="numpy"   ).images

        SCREAMING_SNAKE_CASE_          =  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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


