

from __future__ import annotations

from typing import Any




class   UpperCamelCase			:

    def __init__(      self			:   List[Any]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      num_of_nodes
     SCREAMING_SNAKE_CASE		    =      []
     SCREAMING_SNAKE_CASE		    =      {}

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   int				,     snake_case__			:   int				,     snake_case__			:   int     ):

     """simple docstring"""






     self.m_edges.append([u_node, v_node, weight]     )

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   int     ):

     """simple docstring"""






     if self.m_component[u_node] == u_node:
      return u_node
     return self.find_component(self.m_component[u_node]     )

    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   int     ):

     """simple docstring"""






     if self.m_component[u_node] != u_node:
      for k in self.m_component:
       SCREAMING_SNAKE_CASE		    =      self.find_component(snake_case__     )

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   list[int]				,     snake_case__			:   int				,     snake_case__			:   int     ):

     """simple docstring"""






     if component_size[u_node] <= component_size[v_node]:
      SCREAMING_SNAKE_CASE		    =      v_node
      component_size[v_node] += component_size[u_node]
      self.set_component(snake_case__     )

     elif component_size[u_node] >= component_size[v_node]:
      SCREAMING_SNAKE_CASE		    =      self.find_component(snake_case__     )
      component_size[u_node] += component_size[v_node]
      self.set_component(snake_case__     )




    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     SCREAMING_SNAKE_CASE		    =      0

     SCREAMING_SNAKE_CASE		    =      [-1] * self.m_num_of_nodes

     # A list of components (initialized to all of the nodes)
     for node in range(self.m_num_of_nodes     ):
      self.m_component.update({node: node}     )
      component_size.append(1     )

     SCREAMING_SNAKE_CASE		    =      self.m_num_of_nodes

     while num_of_components > 1:
      for edge in self.m_edges:
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      edge

       SCREAMING_SNAKE_CASE		    =      self.m_component[u]
       SCREAMING_SNAKE_CASE		    =      self.m_component[v]

       if u_component != v_component:

        for component in (u_component, v_component):
         if (
             minimum_weight_edge[component] == -1
             or minimum_weight_edge[component][2] > w
         ):
          SCREAMING_SNAKE_CASE		    =      [u, v, w]

      for edge in minimum_weight_edge:
       if isinstance(snake_case__				,     snake_case__     ):
        SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      edge

        SCREAMING_SNAKE_CASE		    =      self.m_component[u]
        SCREAMING_SNAKE_CASE		    =      self.m_component[v]

        if u_component != v_component:
         mst_weight += w
         self.union(snake_case__				,     snake_case__				,     snake_case__     )
         print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""     )
         num_of_components -= 1

      SCREAMING_SNAKE_CASE		    =      [-1] * self.m_num_of_nodes
     print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""     )


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    @property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      1
     SCREAMING_SNAKE_CASE		    =      3
     SCREAMING_SNAKE_CASE		    =      (3_2, 3_2)

     SCREAMING_SNAKE_CASE		    =      floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0     )     ).to(snake_case__     )
     return image

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         block_out_channels=(3_2, 3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=7				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=3_2				,     attention_head_dim=8				,     use_linear_projection=snake_case__				,     only_cross_attention=(True, True, False)				,     num_class_embeds=1_0_0				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoencoderKL(
         block_out_channels=[3_2, 3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']				,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']				,     latent_channels=4				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      CLIPTextConfig(
         bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     hidden_act='gelu'				,     projection_dim=5_1_2				,     )
     return CLIPTextModel(snake_case__     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      output.images

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     return_dict=snake_case__				,     )[0]

     SCREAMING_SNAKE_CASE		    =      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE		    =      image_from_tuple[0, -3:, -3:, -1]

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE		    =      np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]     )

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

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         2 * [prompt]				,     image=2 * [low_res_image]				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_images_per_prompt=2				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2




    @unittest.skipIf(torch_device != 'cuda'				,     'This test requires a GPU'     )
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # put models in fp16, except vae as it overflows in fp16
     SCREAMING_SNAKE_CASE		    =      unet.half()
     SCREAMING_SNAKE_CASE		    =      text_encoder.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=2				,     output_type='np'				,     ).images

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)




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

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 1E-3

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat_fp16.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=5				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 1_0**9

