





import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




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



 def 				UpperCamelCase			(							self ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
        A__											=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
        sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

        sd_pipe.set_scheduler('''sample_euler''' )

        A__											=						'''A painting of a squirrel eating a burger'''
        A__											=						torch.manual_seed(0 )
        A__											=						sd_pipe([prompt],generator=__SCREAMING_SNAKE_CASE,guidance_scale=9.0,num_inference_steps=20,output_type='''np''' )

        A__											=						output.images

        A__											=						image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

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



 def 				UpperCamelCase			(							self ):
        A__											=						StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
        A__											=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
        sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

        sd_pipe.set_scheduler('''sample_euler''' )

        A__											=						'''A painting of a squirrel eating a burger'''
        A__											=						torch.manual_seed(0 )
        A__											=						sd_pipe([prompt],generator=__SCREAMING_SNAKE_CASE,guidance_scale=9.0,num_inference_steps=20,output_type='''np''' )

        A__											=						output.images

        A__											=						image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1




 def 				UpperCamelCase			(							self ):
        A__											=						StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
        A__											=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
        sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

        sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

        A__											=						'''A painting of a squirrel eating a burger'''
        A__											=						torch.manual_seed(0 )
        A__											=						sd_pipe(
            [prompt],generator=__SCREAMING_SNAKE_CASE,guidance_scale=7.5,num_inference_steps=15,output_type='''np''',use_karras_sigmas=__SCREAMING_SNAKE_CASE,)

        A__											=						output.images

        A__											=						image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array(
            [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

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







'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

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







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

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

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



