'''simple docstring'''




import gc
import tempfile
import unittest

import numpy as np
import torch

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


_a   :	Optional[int] 							=   False






class 		__A       (unittest.TestCase   ):
       pass






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






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"				,     torch_dtype=torch.floataa      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[int]       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )

              __UpperCAmelCase  :				List[Any]       				=		torch.manual_seed(0      )
              __UpperCAmelCase  :				Dict       				=		pipe.dual_guided(
                  prompt="first prompt"				,     image=UpperCamelCase_				,     text_to_image_strength=0.7_5				,     generator=UpperCamelCase_				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type="numpy"				,     ).images

              with tempfile.TemporaryDirectory() as tmpdirname:
                     pipe.save_pretrained(UpperCamelCase_      )
                     __UpperCAmelCase  :				Union[str, Any]       				=		VersatileDiffusionPipeline.from_pretrained(UpperCamelCase_				,     torch_dtype=torch.floataa      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				List[str]       				=		generator.manual_seed(0      )
              __UpperCAmelCase  :				str       				=		pipe.dual_guided(
                  prompt="first prompt"				,     image=UpperCamelCase_				,     text_to_image_strength=0.7_5				,     generator=UpperCamelCase_				,     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 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"				,     torch_dtype=torch.floataa      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				int       				=		"cyberpunk 2077"
              __UpperCAmelCase  :				Tuple       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
              __UpperCAmelCase  :				List[str]       				=		torch.manual_seed(0      )
              __UpperCAmelCase  :				Any       				=		pipe.dual_guided(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     text_to_image_strength=0.7_5				,     generator=UpperCamelCase_				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="numpy"				,     ).images

              __UpperCAmelCase  :				Union[str, Any]       				=		image[0, 2_53:2_56, 2_53:2_56, -1]

              assert image.shape == (1, 5_12, 5_12, 3)
              __UpperCAmelCase  :				List[Any]       				=		np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]      )

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

              __UpperCAmelCase  :				List[str]       				=		"A painting of a squirrel eating a burger "
              __UpperCAmelCase  :				int       				=		torch.manual_seed(0      )
              __UpperCAmelCase  :				Any       				=		pipe.text_to_image(
                  prompt=UpperCamelCase_				,     generator=UpperCamelCase_				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="numpy"      ).images

              __UpperCAmelCase  :				List[str]       				=		image[0, 2_53:2_56, 2_53:2_56, -1]

              assert image.shape == (1, 5_12, 5_12, 3)
              __UpperCAmelCase  :				Optional[Any]       				=		np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]      )

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

              __UpperCAmelCase  :				Optional[int]       				=		pipe.image_variation(UpperCamelCase_				,     generator=UpperCamelCase_				,     output_type="numpy"      ).images

              __UpperCAmelCase  :				Union[str, Any]       				=		image[0, 2_53:2_56, 2_53:2_56, -1]

              assert image.shape == (1, 5_12, 5_12, 3)
              __UpperCAmelCase  :				Optional[Any]       				=		np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]      )

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







'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              if src_parent == dst_parent:
                     return False

              if self.ranks[dst_parent] >= self.ranks[src_parent]:
                     self.set_counts[dst_parent] += self.set_counts[src_parent]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






