






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       __a  (   _SCREAMING_SNAKE_CASE	,       unittest.TestCase	):
 # FIXME: add fast tests
 pass







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







 @property
 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






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







 @property
 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  ort.SessionOptions()
      UpperCamelCase__      :       Tuple	    =  False
      return options







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






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

      UpperCamelCase__      :       List[Any]	    =  "A red cat sitting on a park bench"

      UpperCamelCase__      :       Optional[int]	    =  np.random.RandomState(0						)
      UpperCamelCase__      :       Union[str, Any]	    =  pipe(
          prompt=SCREAMING_SNAKE_CASE							,       image=SCREAMING_SNAKE_CASE							,       mask_image=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=10							,       generator=SCREAMING_SNAKE_CASE							,       output_type="np"							,       )
      UpperCamelCase__      :       Dict	    =  output.images
      UpperCamelCase__      :       Dict	    =  images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Tuple	    =  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 :       Any						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Dict	    =  "A red cat sitting on a park bench"

      UpperCamelCase__      :       List[Any]	    =  np.random.RandomState(0						)
      UpperCamelCase__      :       Optional[int]	    =  pipe(
          prompt=SCREAMING_SNAKE_CASE							,       image=SCREAMING_SNAKE_CASE							,       mask_image=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=20							,       generator=SCREAMING_SNAKE_CASE							,       output_type="np"							,       )
      UpperCamelCase__      :       Optional[int]	    =  output.images
      UpperCamelCase__      :       List[Any]	    =  images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Dict	    =  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







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4