



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort







class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=ort.SessionOptions()
      a_       		=False
      return options




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png")
      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
      a_       		=OnnxStableDiffusionInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,  revision="onnx"							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		="A red cat sitting on a park bench"

      a_       		=np.random.RandomState(0)
      a_       		=pipe(
          prompt=lowerCAmelCase_							,  image=lowerCAmelCase_							,  mask_image=lowerCAmelCase_							,  guidance_scale=7.5							,  num_inference_steps=1_0							,  generator=lowerCAmelCase_							,  output_type="np"							,  )
      a_       		=output.images
      a_       		=images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png")
      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
      a_       		=LMSDiscreteScheduler.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,  subfolder="scheduler"							,  revision="onnx")
      a_       		=OnnxStableDiffusionInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,  revision="onnx"							,  scheduler=lowerCAmelCase_							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		="A red cat sitting on a park bench"

      a_       		=np.random.RandomState(0)
      a_       		=pipe(
          prompt=lowerCAmelCase_							,  image=lowerCAmelCase_							,  mask_image=lowerCAmelCase_							,  guidance_scale=7.5							,  num_inference_steps=2_0							,  generator=lowerCAmelCase_							,  output_type="np"							,  )
      a_       		=output.images
      a_       		=images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     print(F"""Vertex\tShortest Distance from vertex {src}"""       )
     for i, d in enumerate(lowercase__       ):
          print(F"""{i}\t\t{d}"""       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for j in range(lowercase__       ):
          a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])
          if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
               return True
     return False






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[float("inf"       )] * vertex_count
     a_       		=0.0

     for _ in range(vertex_count - 1       ):
          for j in range(lowercase__       ):
               a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])

               if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
                    a_       		=distance[u] + w

     a_       		=check_negative_cycle(lowercase__       ,	lowercase__       ,	lowercase__       )
     if negative_cycle_exists:
          raise Exception("Negative cycle found"       )

     return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowercase							       =			int(input('''Enter number of vertices: ''').strip())
  lowercase							       =			int(input('''Enter number of edges: ''').strip())

  lowercase							       =			[{} for _ in range(E)]

  for i in range(E):
    print('''Edge ''', i + 1)
    lowercase       ,      lowercase       ,      lowercase							       =			(
        int(x)
        for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
    )
    lowercase							       =			{'''src''': src, '''dst''': dest, '''weight''': weight}

  lowercase							       =			int(input('''\nEnter shortest path source:''').strip())
  lowercase							       =			bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)


