





from __future__ import annotations

from collections import Counter
from random import random




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self ):
        A__											=						{}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						{}



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        if nodea not in self.connections:
               self.add_node(__lowerCamelCase )
        if nodea not in self.connections:
               self.add_node(__lowerCamelCase )
        A__											=						probability



 def 				UpperCamelCase			(							self ):
        return list(self.connections )




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						0
        A__											=						random()

        for dest in self.connections[node]:
               current_probability += self.connections[node][dest]
               if current_probability > random_value:
                      return dest
        return ""


def 	UpperCamelCase__(       UpperCamelCase__			:       str ,     UpperCamelCase__			:       list[tuple[str, str, float]] ,     UpperCamelCase__			:       int  )->dict[str, int]:
       A__											=						MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						Counter(graph.get_nodes()  )
       A__											=						start

       for _ in range(UpperCamelCase__  ):
              A__											=						graph.transition(UpperCamelCase__  )
              visited[node] += 1

       return visited


if __name__ == "__main__":
       import doctest

       doctest.testmod()












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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




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



 def 				UpperCamelCase			(							self ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

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

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,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 ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

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

        assert image.shape == (1, 512, 512, 3)
        A__											=						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





