




'''simple docstring'''






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     str			   =      CONFIG_NAME



 @register_to_config
 def __init__(  self   ,		A = True   ,		A = 8   ,		A = "lanczos"   ,		A = True   ,		A = False   ,		):






        '''simple docstring'''




        super().__init__()



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Optional[Any]						=      images[None, ...]
        lowercase  :		List[str]						=      (images * 255).round().astype('''uint8''' )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               lowercase  :		Optional[int]						=      [Image.fromarray(image.squeeze()   ,		mode='''L''' ) for image in images]
        else:
               lowercase  :		Tuple						=      [Image.fromarray(A ) for image in images]

        return pil_images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if not isinstance(A   ,		A ):
               lowercase  :		Dict						=      [images]
        lowercase  :		List[str]						=      [np.array(A ).astype(np.floataa ) / 2_5_5.0 for image in images]
        lowercase  :		Union[str, Any]						=      np.stack(A   ,		axis=0 )

        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		str						=      images[..., None]

        lowercase  :		int						=      torch.from_numpy(images.transpose(0   ,		3   ,		1   ,		2 ) )
        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      images.cpu().permute(0   ,		2   ,		3   ,		1 ).float().numpy()
        return images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return 2.0 * images - 1.0



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return (images / 2 + 0.5).clamp(0   ,		1 )



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      image.convert('''RGB''' )
        return image



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None   ,		A = None   ,		):






        '''simple docstring'''




        if height is None:
               lowercase  :		List[str]						=      image.height
        if width is None:
               lowercase  :		str						=      image.width

        lowercase,     lowercase  :		str						=      (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        lowercase  :		Union[str, Any]						=      image.resize((width, height)   ,		resample=PIL_INTERPOLATION[self.config.resample] )
        return image



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(A   ,		A ):
               lowercase  :		Dict						=      [image]
        elif not (isinstance(A   ,		A ) and all(isinstance(A   ,		A ) for i in image )):
               raise ValueError(
                   f"""Input is in incorrect format: {[type(A ) for i in image]}. Currently, we only support {', '.join(A )}""" )

        if isinstance(image[0]   ,		PIL.Image.Image ):
               if self.config.do_convert_rgb:
                      lowercase  :		Dict						=      [self.convert_to_rgb(A ) for i in image]
               if self.config.do_resize:
                      lowercase  :		Tuple						=      [self.resize(A   ,		A   ,		A ) for i in image]
               lowercase  :		List[Any]						=      self.pil_to_numpy(A )  # to np
               lowercase  :		List[str]						=      self.numpy_to_pt(A )  # to pt

        elif isinstance(image[0]   ,		np.ndarray ):
               lowercase  :		Optional[int]						=      np.concatenate(A   ,		axis=0 ) if image[0].ndim == 4 else np.stack(A   ,		axis=0 )
               lowercase  :		Optional[Any]						=      self.numpy_to_pt(A )
               lowercase,     lowercase,     lowercase,     lowercase  :		Optional[Any]						=      image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                          f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        elif isinstance(image[0]   ,		torch.Tensor ):
               lowercase  :		Optional[Any]						=      torch.cat(A   ,		axis=0 ) if image[0].ndim == 4 else torch.stack(A   ,		axis=0 )
               lowercase,     lowercase,     lowercase,     lowercase  :		Dict						=      image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                      return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                          f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        # expected range [0,1], normalize to [-1,1]
        lowercase  :		Any						=      self.config.do_normalize
        if image.min() < 0:
               warnings.warn(
                   '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                   f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   ,		A   ,		)
               lowercase  :		List[Any]						=      False

        if do_normalize:
               lowercase  :		str						=      self.normalize(A )

        return image





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = "pil"   ,		A = None   ,		):






        '''simple docstring'''




        if not isinstance(A   ,		torch.Tensor ):
               raise ValueError(
                   f"""Input for postprocessing is in incorrect format: {type(A )}. We only support pytorch tensor""" )
        if output_type not in ["latent", "pt", "np", "pil"]:
               lowercase  :		List[Any]						=      (
                   f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                   '''`pil`, `np`, `pt`, `latent`'''
               )
               deprecate('''Unsupported output_type'''   ,		'''1.0.0'''   ,		A   ,		standard_warn=A )
               lowercase  :		Optional[int]						=      '''np'''

        if output_type == "latent":
               return image

        if do_denormalize is None:
               lowercase  :		List[Any]						=      [self.config.do_normalize] * image.shape[0]

        lowercase  :		Optional[Any]						=      torch.stack(
            [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

        if output_type == "pt":
               return image

        lowercase  :		List[Any]						=      self.pt_to_numpy(A )

        if output_type == "np":
               return image

        if output_type == "pil":
               return self.numpy_to_pil(A )






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Tuple			   =      CONFIG_NAME



 @register_to_config
 def __init__(  self   ,		A = True   ,		A = 8   ,		A = "lanczos"   ,		A = True   ,		):






        '''simple docstring'''




        super().__init__()



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		int						=      images[None, ...]
        lowercase  :		List[Any]						=      (images * 255).round().astype('''uint8''' )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               lowercase  :		str						=      [Image.fromarray(image.squeeze()   ,		mode='''L''' ) for image in images]
        else:
               lowercase  :		Any						=      [Image.fromarray(image[:, :, :3] ) for image in images]

        return pil_images



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A ):






        '''simple docstring'''




        return image[:, :, 1] * 2**8 + image[:, :, 2]



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if images.ndim == 3:
               lowercase  :		Tuple						=      images[None, ...]
        lowercase  :		Any						=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
               lowercase  :		Optional[Any]						=      (images_depth * 255).round().astype('''uint8''' )
               lowercase  :		List[str]						=      [
                   Image.fromarray(self.rgblike_to_depthmap(A )   ,		mode='''I;16''' ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               lowercase  :		Any						=      (images_depth * 6_5_5_3_5.0).astype(np.uintaa )
               lowercase  :		List[str]						=      [Image.fromarray(A   ,		mode='''I;16''' ) for image_depth in images_depth]
        else:
               raise Exception('''Not supported''' )

        return pil_images





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = "pil"   ,		A = None   ,		):






        '''simple docstring'''




        if not isinstance(A   ,		torch.Tensor ):
               raise ValueError(
                   f"""Input for postprocessing is in incorrect format: {type(A )}. We only support pytorch tensor""" )
        if output_type not in ["latent", "pt", "np", "pil"]:
               lowercase  :		Union[str, Any]						=      (
                   f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                   '''`pil`, `np`, `pt`, `latent`'''
               )
               deprecate('''Unsupported output_type'''   ,		'''1.0.0'''   ,		A   ,		standard_warn=A )
               lowercase  :		int						=      '''np'''

        if do_denormalize is None:
               lowercase  :		Any						=      [self.config.do_normalize] * image.shape[0]

        lowercase  :		Any						=      torch.stack(
            [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

        lowercase  :		Tuple						=      self.pt_to_numpy(A )

        if output_type == "np":
               if image.shape[-1] == 6:
                      lowercase  :		List[Any]						=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]   ,		axis=0 )
               else:
                      lowercase  :		Optional[int]						=      image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

        if output_type == "pil":
               return self.numpy_to_pil(A ), self.numpy_to_depth(A )
        else:
               raise Exception(f"""This type {output_type} is not supported""" )










'''simple docstring'''






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->list[tuple[int, int, int]]:






       '''simple docstring'''




       lowercase  :		Dict						=      sorted(lowerCamelCase   ,				key=lambda lowerCamelCase      : edge[2]      )

       lowercase  :		str						=      list(range(lowerCamelCase      )      )

       def find_parent(lowerCamelCase      ):
              if i != parent[i]:
                     lowercase  :		Optional[int]						=      find_parent(parent[i]      )
              return parent[i]

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

       for edge in edges:
              lowercase  :		Optional[Any]						=      find_parent(edge[0]      )
              lowercase  :		Any						=      find_parent(edge[1]      )
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(lowerCamelCase      )
                     lowercase  :		Optional[int]						=      parent_b

       return minimum_spanning_tree


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

      for _ in range(num_edges):
            lowercase	,	lowercase	,	lowercase	:      Optional[Any]				=       (int(x) for x in input().strip().split())
            edges.append((nodea, nodea, cost))

      kruskal(num_nodes, edges)




