import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case			      =    False







@nightly
@require_torch_gpu
class 			__snake_case   (     unittest.TestCase   ):
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





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

       UpperCAmelCase    : str      =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)

       UpperCAmelCase    : Tuple      =torch.manual_seed(0						)
       UpperCAmelCase    : str      =pipe(
           prompt='''first prompt'''		,   image=snake_case__		,   text_to_image_strength=0.75		,   generator=snake_case__		,   guidance_scale=7.5		,   num_inference_steps=2		,   output_type='''numpy'''		,   ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(snake_case__						)
             UpperCAmelCase    : Optional[int]      =VersatileDiffusionDualGuidedPipeline.from_pretrained(snake_case__						)

       pipe.to(snake_case__						)
       pipe.set_progress_bar_config(disable=snake_case__						)

       UpperCAmelCase    : Optional[int]      =generator.manual_seed(0						)
       UpperCAmelCase    : Union[str, Any]      =pipe(
           prompt='''first prompt'''		,   image=snake_case__		,   text_to_image_strength=0.75		,   generator=snake_case__		,   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 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
       pipe.remove_unused_weights()
       pipe.to(snake_case__						)
       pipe.set_progress_bar_config(disable=snake_case__						)

       UpperCAmelCase    : str      ='''cyberpunk 2077'''
       UpperCAmelCase    : Dict      =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
       UpperCAmelCase    : Dict      =torch.manual_seed(0						)
       UpperCAmelCase    : Tuple      =pipe(
           prompt=snake_case__		,   image=snake_case__		,   text_to_image_strength=0.75		,   generator=snake_case__		,   guidance_scale=7.5		,   num_inference_steps=50		,   output_type='''numpy'''		,   ).images

       UpperCAmelCase    : str      =image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase    : List[str]      =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]						)

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

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
