
'''simple docstring'''

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


__UpperCAmelCase												=   getLogger(__name__)


__UpperCAmelCase												=   4
__UpperCAmelCase												=   7

__UpperCAmelCase												=   {
    'float16': torch.floataa,
    'float32': torch.floataa,
}



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict							)       ->	List[Any]:
 if isinstance(snake_case_   ,     torch.Tensor							):
  return image
 elif isinstance(snake_case_   ,     PIL.Image.Image							):
  SCREAMING_SNAKE_CASE  :  Any								=		[image]

 if isinstance(image[0]   ,     PIL.Image.Image							):
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image[0].size
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

  SCREAMING_SNAKE_CASE  :  Any								=		[np.array(i.resize((w, h)							)							)[None, :] for i in image]
  SCREAMING_SNAKE_CASE  :  Any								=		np.concatenate(snake_case_   ,     axis=0							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		np.array(snake_case_							).astype(np.floataa							) / 255.0
  SCREAMING_SNAKE_CASE  :  List[Any]								=		image.transpose(0   ,     3   ,     1   ,     2							)
  SCREAMING_SNAKE_CASE  :  Dict								=		2.0 * image - 1.0
  SCREAMING_SNAKE_CASE  :  Tuple								=		torch.from_numpy(snake_case_							)
 elif isinstance(image[0]   ,     torch.Tensor							):
  SCREAMING_SNAKE_CASE  :  str								=		torch.cat(snake_case_   ,     dim=0							)

 return image





class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 350    ,):
    super().__init__(
        vae=__SCREAMING_SNAKE_CASE    ,text_encoder=__SCREAMING_SNAKE_CASE    ,tokenizer=__SCREAMING_SNAKE_CASE    ,unet=__SCREAMING_SNAKE_CASE    ,low_res_scheduler=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE    ,safety_checker=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE    ,watermarker=__SCREAMING_SNAKE_CASE    ,max_noise_level=__SCREAMING_SNAKE_CASE    ,)






   def __call__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 75    ,__SCREAMING_SNAKE_CASE = 9.0    ,__SCREAMING_SNAKE_CASE = 20    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,__SCREAMING_SNAKE_CASE = 0.0    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "pil"    ,__SCREAMING_SNAKE_CASE = True    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = 1    ,):
    self.check_inputs(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    # 2. Define call parameters
    if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		1
    elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Dict								=		len(__SCREAMING_SNAKE_CASE      )
    else:
     SCREAMING_SNAKE_CASE  :  Dict								=		prompt_embeds.shape[0]

    SCREAMING_SNAKE_CASE  :  Tuple								=		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.
    SCREAMING_SNAKE_CASE  :  Tuple								=		guidance_scale > 1.0

    # 3. Encode input prompt
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self._encode_prompt(
        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,prompt_embeds=__SCREAMING_SNAKE_CASE    ,negative_prompt_embeds=__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

    # 4. Preprocess image
    SCREAMING_SNAKE_CASE  :  int								=		preprocess(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image.cpu()

    # 5. set timesteps
    self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    ,device=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		self.scheduler.timesteps

    # 5. Add noise to image
    SCREAMING_SNAKE_CASE  :  int								=		torch.tensor([noise_level]    ,dtype=torch.long    ,device=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.randn(image.shape    ,generator=__SCREAMING_SNAKE_CASE    ,device=__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		self.low_res_scheduler.add_noise(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		2 if do_classifier_free_guidance else 1
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
    SCREAMING_SNAKE_CASE  :  Dict								=		np.concatenate([noise_level] * image.shape[0]      )

    # 6. Prepare latent variables
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		image.shape[2:]
    SCREAMING_SNAKE_CASE  :  Any								=		self.prepare_latents(
        batch_size * num_images_per_prompt    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,)

    # 7. Check that sizes of image and latents match
    SCREAMING_SNAKE_CASE  :  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
    SCREAMING_SNAKE_CASE  :  str								=		self.prepare_extra_step_kwargs(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		next(
        (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')    ,'tensor(float)'      )
    SCREAMING_SNAKE_CASE  :  str								=		ORT_TO_NP_TYPE[timestep_dtype]

    # 9. Denoising loop
    SCREAMING_SNAKE_CASE  :  Any								=		len(__SCREAMING_SNAKE_CASE      ) - num_inference_steps * self.scheduler.order
    with self.progress_bar(total=__SCREAMING_SNAKE_CASE      ) as progress_bar:
     for i, t in enumerate(__SCREAMING_SNAKE_CASE      ):
      # expand the latents if we are doing classifier free guidance
      SCREAMING_SNAKE_CASE  :  Any								=		np.concatenate([latents] * 2      ) if do_classifier_free_guidance else latents

      # concat latents, mask, masked_image_latents in the channel dimension
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE  :  Tuple								=		np.concatenate([latent_model_input, image]    ,axis=1      )

      # timestep to tensor
      SCREAMING_SNAKE_CASE  :  int								=		np.array([t]    ,dtype=__SCREAMING_SNAKE_CASE      )

      # predict the noise residual
      SCREAMING_SNAKE_CASE  :  Dict								=		self.unet(
          sample=__SCREAMING_SNAKE_CASE    ,timestep=__SCREAMING_SNAKE_CASE    ,encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,class_labels=noise_level.astype(np.intaa      )    ,)[0]

      # perform guidance
      if do_classifier_free_guidance:
       SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		np.split(__SCREAMING_SNAKE_CASE    ,2      )
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

      # compute the previous noisy sample x_t -> x_t-1
      SCREAMING_SNAKE_CASE  :  List[str]								=		self.scheduler.step(
          torch.from_numpy(__SCREAMING_SNAKE_CASE      )    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ).prev_sample

      # call the callback, if provided
      if i == len(__SCREAMING_SNAKE_CASE      ) - 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(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

        # 10. Post-processing
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.decode_latents(latents.float()      )

    # 11. Convert to PIL
    if output_type == "pil":
     SCREAMING_SNAKE_CASE  :  int								=		self.numpy_to_pil(__SCREAMING_SNAKE_CASE      )

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		1 / 0.0_8333 * latents
    SCREAMING_SNAKE_CASE  :  Any								=		self.vae(latent_sample=__SCREAMING_SNAKE_CASE      )[0]
    SCREAMING_SNAKE_CASE  :  str								=		np.clip(image / 2 + 0.5    ,0    ,1      )
    SCREAMING_SNAKE_CASE  :  Any								=		image.transpose((0, 2, 3, 1)      )
    return image






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = None    ,):
    if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  int								=		1
    elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Tuple								=		len(__SCREAMING_SNAKE_CASE      )
    else:
     SCREAMING_SNAKE_CASE  :  str								=		prompt_embeds.shape[0]

    if prompt_embeds is None:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.tokenizer(
         __SCREAMING_SNAKE_CASE    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,truncation=__SCREAMING_SNAKE_CASE    ,return_tensors='pt'    ,)
     SCREAMING_SNAKE_CASE  :  List[str]								=		text_inputs.input_ids
     SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer(__SCREAMING_SNAKE_CASE    ,padding='longest'    ,return_tensors='pt'      ).input_ids

     if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  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
     SCREAMING_SNAKE_CASE  :  List[str]								=		self.text_encoder(
         input_ids=text_input_ids.int().to(__SCREAMING_SNAKE_CASE      )    ,)
     SCREAMING_SNAKE_CASE  :  List[str]								=		prompt_embeds[0]

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		prompt_embeds.repeat(1    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,__SCREAMING_SNAKE_CASE    ,-1      )

    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
     SCREAMING_SNAKE_CASE  :  List[str]
     if negative_prompt is None:
      SCREAMING_SNAKE_CASE  :  Tuple								=		[''] * batch_size
     elif type(__SCREAMING_SNAKE_CASE      ) is not type(__SCREAMING_SNAKE_CASE      ):
      raise TypeError(
          f"""`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE      )} !="""
          f""" {type(__SCREAMING_SNAKE_CASE      )}."""      )
     elif isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  List[str]								=		[negative_prompt]
     elif batch_size != len(__SCREAMING_SNAKE_CASE      ):
      raise ValueError(
          f"""`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE      )}, but `prompt`:"""
          f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
          ' the batch size of `prompt`.'      )
     else:
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		negative_prompt

     SCREAMING_SNAKE_CASE  :  int								=		text_input_ids.shape[-1]
     SCREAMING_SNAKE_CASE  :  Any								=		self.tokenizer(
         __SCREAMING_SNAKE_CASE    ,padding='max_length'    ,max_length=__SCREAMING_SNAKE_CASE    ,truncation=__SCREAMING_SNAKE_CASE    ,return_tensors='pt'    ,)

     # if hasattr(uncond_input, "attention_mask"):
     #     attention_mask = uncond_input.attention_mask.to(device)
     # else:
     #     attention_mask = None

     SCREAMING_SNAKE_CASE  :  Any								=		self.text_encoder(
         input_ids=uncond_input.input_ids.int().to(__SCREAMING_SNAKE_CASE      )    ,)
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		uncond_embeddings[0]

    if do_classifier_free_guidance:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		uncond_embeddings.shape[1]
     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		uncond_embeddings.repeat(1    ,__SCREAMING_SNAKE_CASE      )
     SCREAMING_SNAKE_CASE  :  int								=		uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,__SCREAMING_SNAKE_CASE    ,-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
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.concatenate([uncond_embeddings, prompt_embeds]      )

    return prompt_embeds




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	list[tuple[int, int, int]]							)       ->	list[tuple[int, int, int]]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		sorted(snake_case_   ,     key=lambda snake_case_							: edge[2]							)

 SCREAMING_SNAKE_CASE  :  str								=		list(range(snake_case_							)							)

 def find_parent(snake_case_				:	Union[str, Any]							):
  if i != parent[i]:
   SCREAMING_SNAKE_CASE  :  int								=		find_parent(parent[i]							)
  return parent[i]

 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		0
 SCREAMING_SNAKE_CASE  :  Any								=		[]

 for edge in edges:
  SCREAMING_SNAKE_CASE  :  Dict								=		find_parent(edge[0]							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		find_parent(edge[1]							)
  if parent_a != parent_b:
   minimum_spanning_tree_cost += edge[2]
   minimum_spanning_tree.append(snake_case_							)
   SCREAMING_SNAKE_CASE  :  Tuple								=		parent_b

 return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
   __UpperCAmelCase						,						__UpperCAmelCase												=   list(map(int, input().strip().split()))
   __UpperCAmelCase												=   []

   for _ in range(num_edges):
      __UpperCAmelCase						,						__UpperCAmelCase						,						__UpperCAmelCase												=   (int(x) for x in input().strip().split())
      edges.append((nodea, nodea, cost))

   kruskal(num_nodes, edges)


