


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


lowerCAmelCase__      		= False







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''
     pass







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




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[Any]				    = 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      :       int				    = "A painting of a squirrel eating a burger "
            _A      :       Any				    = torch.manual_seed(0)
            _A      :       str				    = 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      :       List[str]				    = VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase)
            pipe.to(__lowerCamelCase)
            pipe.set_progress_bar_config(disable=__lowerCamelCase)

            _A      :       Union[str, Any]				    = generator.manual_seed(0)
            _A      :       Union[str, Any]				    = 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  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Union[str, Any]				    = VersatileDiffusionTextToImagePipeline.from_pretrained(
                "shi-labs/versatile-diffusion" ,						torch_dtype=torch.floataa)
            pipe.to(__lowerCamelCase)
            pipe.set_progress_bar_config(disable=__lowerCamelCase)

            _A      :       Optional[int]				    = "A painting of a squirrel eating a burger "
            _A      :       List[Any]				    = torch.manual_seed(0)
            _A      :       Optional[Any]				    = pipe(
                prompt=__lowerCamelCase ,						generator=__lowerCamelCase ,						guidance_scale=7.5 ,						num_inference_steps=5_0 ,						output_type="numpy").images

            _A      :       str				    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            _A      :       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-2







a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





