code: 
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  snake_case				:	Tuple							   =						getLogger(__name__)
  
  
  snake_case				:	Any							   =						4
  snake_case				:	Optional[int]							   =						7
  
  snake_case				:	str							   =						{
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Union[str, Any]     )       ->	Optional[int]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   if isinstance(_snake_case   ,     torch.Tensor     ):
    return image
   elif isinstance(_snake_case   ,     PIL.Image.Image     ):
    __magic_name__	:	Tuple			     =  [image]
  
   if isinstance(image[0]   ,     PIL.Image.Image     ):
    __magic_name__	:	Tuple			     =  image[0].size
    __magic_name__	:	Union[str, Any]			     =  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
    __magic_name__	:	str			     =  [np.array(i.resize((w, h)     )     )[None, :] for i in image]
    __magic_name__	:	str			     =  np.concatenate(_snake_case   ,     axis=0     )
    __magic_name__	:	Any			     =  np.array(_snake_case     ).astype(np.floataa     ) / 255.0
    __magic_name__	:	Union[str, Any]			     =  image.transpose(0   ,     3   ,     1   ,     2     )
    __magic_name__	:	Tuple			     =  2.0 * image - 1.0
    __magic_name__	:	Any			     =  torch.from_numpy(_snake_case     )
   elif isinstance(image[0]   ,     torch.Tensor     ):
    __magic_name__	:	Optional[Any]			     =  torch.cat(_snake_case   ,     dim=0     )
  
   return image
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
     def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = 350		,     ):
      super().__init__(
          vae=_a		,     text_encoder=_a		,     tokenizer=_a		,     unet=_a		,     low_res_scheduler=_a		,     scheduler=_a		,     safety_checker=_a		,     feature_extractor=_a		,     watermarker=_a		,     max_noise_level=_a		,     )
  
  
  
  
  
  
  
     def __call__(       self		,     _a		,     _a		,     _a = 75		,     _a = 9.0		,     _a = 20		,     _a = None		,     _a = 1		,     _a = 0.0		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = "pil"		,     _a = True		,     _a = None		,     _a = 1		,     ):
      self.check_inputs(_a		,     _a		,     _a		,     _a	)
  
      # 2. Define call parameters
      if prompt is not None and isinstance(_a		,     _a	):
       __magic_name__	:	Union[str, Any]			     =  1
      elif prompt is not None and isinstance(_a		,     _a	):
       __magic_name__	:	List[Any]			     =  len(_a	)
      else:
       __magic_name__	:	str			     =  prompt_embeds.shape[0]
  
      __magic_name__	:	Optional[Any]			     =  self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      __magic_name__	:	str			     =  guidance_scale > 1.0
  
      # 3. Encode input prompt
      __magic_name__	:	Tuple			     =  self._encode_prompt(
          _a		,     _a		,     _a		,     _a		,     _a		,     prompt_embeds=_a		,     negative_prompt_embeds=_a		,     )
  
      __magic_name__	:	int			     =  ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]
  
      # 4. Preprocess image
      __magic_name__	:	Tuple			     =  preprocess(_a	)
      __magic_name__	:	int			     =  image.cpu()
  
      # 5. set timesteps
      self.scheduler.set_timesteps(_a		,     device=_a	)
      __magic_name__	:	Union[str, Any]			     =  self.scheduler.timesteps
  
      # 5. Add noise to image
      __magic_name__	:	Optional[int]			     =  torch.tensor([noise_level]		,     dtype=torch.long		,     device=_a	)
      __magic_name__	:	List[Any]			     =  torch.randn(image.shape		,     generator=_a		,     device=_a		,     dtype=_a	)
      __magic_name__	:	Union[str, Any]			     =  self.low_res_scheduler.add_noise(_a		,     _a		,     _a	)
  
      __magic_name__	:	Optional[Any]			     =  2 if do_classifier_free_guidance else 1
      __magic_name__	:	Dict			     =  np.concatenate([image] * batch_multiplier * num_images_per_prompt	)
      __magic_name__	:	str			     =  np.concatenate([noise_level] * image.shape[0]	)
  
      # 6. Prepare latent variables
      __magic_name__	:	List[Any]			     =  image.shape[2:]
      __magic_name__	:	str			     =  self.prepare_latents(
          batch_size * num_images_per_prompt		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     )
  
      # 7. Check that sizes of image and latents match
      __magic_name__	:	Union[str, Any]			     =  image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
       raise ValueError(
           "Incorrect configuration settings! The config of `pipeline.unet` expects"
           f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
           f''' `num_channels_image`: {num_channels_image} '''
           f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
           " `pipeline.unet` or your `image` input."	)
  
      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      __magic_name__	:	List[str]			     =  self.prepare_extra_step_kwargs(_a		,     _a	)
  
      __magic_name__	:	Optional[int]			     =  next(
          (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")		,     "tensor(float)"	)
      __magic_name__	:	Tuple			     =  ORT_TO_NP_TYPE[timestep_dtype]
  
      # 9. Denoising loop
      __magic_name__	:	int			     =  len(_a	) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=_a	) as progress_bar:
       for i, t in enumerate(_a	):
        # expand the latents if we are doing classifier free guidance
        __magic_name__	:	Tuple			     =  np.concatenate([latents] * 2	) if do_classifier_free_guidance else latents
  
        # concat latents, mask, masked_image_latents in the channel dimension
        __magic_name__	:	Optional[int]			     =  self.scheduler.scale_model_input(_a		,     _a	)
        __magic_name__	:	Dict			     =  np.concatenate([latent_model_input, image]		,     axis=1	)
  
        # timestep to tensor
        __magic_name__	:	Union[str, Any]			     =  np.array([t]		,     dtype=_a	)
  
        # predict the noise residual
        __magic_name__	:	List[str]			     =  self.unet(
            sample=_a		,     timestep=_a		,     encoder_hidden_states=_a		,     class_labels=noise_level.astype(np.intaa	)		,     )[0]
  
        # perform guidance
        if do_classifier_free_guidance:
         __magic_name__	:	Any			     =  np.split(_a		,     2	)
        __magic_name__	:	Optional[Any]			     =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
        # compute the previous noisy sample x_t -> x_t-1
        __magic_name__	:	int			     =  self.scheduler.step(
            torch.from_numpy(_a	)		,     _a		,     _a		,     **_a	).prev_sample
  
        # call the callback, if provided
        if i == len(_a	) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
         progress_bar.update()
         if callback is not None and i % callback_steps == 0:
          callback(_a		,     _a		,     _a	)
  
          # 10. Post-processing
      __magic_name__	:	Tuple			     =  self.decode_latents(latents.float()	)
  
      # 11. Convert to PIL
      if output_type == "pil":
       __magic_name__	:	List[str]			     =  self.numpy_to_pil(_a	)
  
      if not return_dict:
       return (image,)
  
      return ImagePipelineOutput(images=_a	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
      __magic_name__	:	Union[str, Any]			     =  1 / 0.0_83_33 * latents
      __magic_name__	:	Any			     =  self.vae(latent_sample=_a	)[0]
      __magic_name__	:	List[str]			     =  np.clip(image / 2 + 0.5		,     0		,     1	)
      __magic_name__	:	Any			     =  image.transpose((0, 2, 3, 1)	)
      return image
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     _a = None		,     ):
      if prompt is not None and isinstance(_a		,     _a	):
       __magic_name__	:	List[Any]			     =  1
      elif prompt is not None and isinstance(_a		,     _a	):
       __magic_name__	:	str			     =  len(_a	)
      else:
       __magic_name__	:	List[Any]			     =  prompt_embeds.shape[0]
  
      if prompt_embeds is None:
       __magic_name__	:	int			     =  self.tokenizer(
           _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     truncation=_a		,     return_tensors="pt"		,     )
       __magic_name__	:	Any			     =  text_inputs.input_ids
       __magic_name__	:	Optional[int]			     =  self.tokenizer(_a		,     padding="longest"		,     return_tensors="pt"	).input_ids
  
       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
           _a		,     _a	):
        __magic_name__	:	List[Any]			     =  self.tokenizer.batch_decode(
            untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
        logger.warning(
            "The following part of your input was truncated because CLIP can only handle sequences up to"
            f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''	)
  
       # no positional arguments to text_encoder
       __magic_name__	:	Tuple			     =  self.text_encoder(
           input_ids=text_input_ids.int().to(_a	)		,     )
       __magic_name__	:	int			     =  prompt_embeds[0]
  
      __magic_name__	:	Optional[Any]			     =  prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      __magic_name__	:	List[Any]			     =  prompt_embeds.repeat(1		,     _a	)
      __magic_name__	:	Any			     =  prompt_embeds.reshape(bs_embed * num_images_per_prompt		,     _a		,     -1	)
  
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
       __magic_name__	:	List[str]
       if negative_prompt is None:
        __magic_name__	:	int			     =  [""] * batch_size
       elif type(_a	) is not type(_a	):
        raise TypeError(
            f'''`negative_prompt` should be the same type to `prompt`, but got {type(_a	)} !='''
            f''' {type(_a	)}.'''	)
       elif isinstance(_a		,     _a	):
        __magic_name__	:	Optional[int]			     =  [negative_prompt]
       elif batch_size != len(_a	):
        raise ValueError(
            f'''`negative_prompt`: {negative_prompt} has batch size {len(_a	)}, but `prompt`:'''
            f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
            " the batch size of `prompt`."	)
       else:
        __magic_name__	:	Union[str, Any]			     =  negative_prompt
  
       __magic_name__	:	str			     =  text_input_ids.shape[-1]
       __magic_name__	:	Union[str, Any]			     =  self.tokenizer(
           _a		,     padding="max_length"		,     max_length=_a		,     truncation=_a		,     return_tensors="pt"		,     )
  
       # if hasattr(uncond_input, "attention_mask"):
       #     attention_mask = uncond_input.attention_mask.to(device)
       # else:
       #     attention_mask = None
  
       __magic_name__	:	Optional[int]			     =  self.text_encoder(
           input_ids=uncond_input.input_ids.int().to(_a	)		,     )
       __magic_name__	:	int			     =  uncond_embeddings[0]
  
      if do_classifier_free_guidance:
       __magic_name__	:	Optional[Any]			     =  uncond_embeddings.shape[1]
       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       __magic_name__	:	Optional[Any]			     =  uncond_embeddings.repeat(1		,     _a	)
       __magic_name__	:	Optional[Any]			     =  uncond_embeddings.reshape(batch_size * num_images_per_prompt		,     _a		,     -1	)
  
       # For classifier free guidance, we need to do two forward passes.
       # Here we concatenate the unconditional and text embeddings into a single batch
       # to avoid doing two forward passes
       __magic_name__	:	str			     =  np.concatenate([uncond_embeddings, prompt_embeds]	)
  
      return prompt_embeds
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  