




import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
      import torch_xla.core.xla_model as xm  # noqa: F401

      __SCREAMING_SNAKE_CASE    :   List[str]       =True
except ImportError:
      __SCREAMING_SNAKE_CASE    :   List[Any]       =False


# Cache this result has it's a C FFI call which can be pretty time-consuming
__SCREAMING_SNAKE_CASE    :   str       =torch.distributed.is_available()





def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				importlib.util.find_spec(lowerCamelCase						) is not None
      if package_exists:
            try:
                  lowerCamelCase__        =				importlib.metadata.metadata(lowerCamelCase						)
                  return True
            except importlib.metadata.PackageNotFoundError:
                  return False





def 			lowerCAmelCase_			(  )					->			bool:


      '''simple docstring'''




      return _torch_distributed_available





def 			lowerCAmelCase_			(  )					->			Optional[Any]:


      '''simple docstring'''




      try:
            pass
      except ImportError:
            print(
                'Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'
                ' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'
                ' Bindings for PyTorch*.'						)
      return (
          importlib.util.find_spec('torch_ccl'						) is not None
          or importlib.util.find_spec('oneccl_bindings_for_pytorch'						) is not None
      )





def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      return importlib.metadata.version('oneccl_bind_pt'						)





def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('transformer_engine'						)





@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       Any=True						)					->			Dict:


      '''simple docstring'''




      if torch.cuda.is_available():
            return False
      if _tpu_available and check_device:
            try:
                  # Will raise a RuntimeError if no XLA configuration is found
                  lowerCamelCase__        =				xm.xla_device()
                  return True
            except RuntimeError:
                  return False
      return _tpu_available





def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      return _is_package_available('deepspeed'						)





def 			lowerCAmelCase_			(  lowerCamelCase:       Dict=False						)					->			Union[str, Any]:


      '''simple docstring'''




      if is_tpu_available():
            return not ignore_tpu
      if torch.cuda.is_available():
            return torch.cuda.is_bfaa_supported()
      if is_npu_available():
            return False
      return True





def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				_is_package_available('bitsandbytes'						)
      if package_exists:
            lowerCamelCase__        =				version.parse(importlib.metadata.version('bitsandbytes'						)						)
            return compare_versions(lowerCamelCase     ,				'>='     ,				'0.39.0'						)
      return False





def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				_is_package_available('bitsandbytes'						)
      if package_exists:
            lowerCamelCase__        =				version.parse(importlib.metadata.version('bitsandbytes'						)						)
            return compare_versions(lowerCamelCase     ,				'>='     ,				'0.37.2'						)
      return False





def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('bitsandbytes'						)





def 			lowerCAmelCase_			(  )					->			str:


      '''simple docstring'''




      if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'     ,				'False'						)						) == 1:
            lowerCamelCase__        =				importlib.util.find_spec('megatron'						) is not None
            if package_exists:
                  try:
                        lowerCamelCase__        =				parse(importlib.metadata.version('megatron-lm'						)						)
                        return compare_versions(lowerCamelCase     ,				'>='     ,				'2.2.0'						)
                  except Exception as e:
                        warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""						)
                        return False





def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      return _is_package_available('safetensors'						)





def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      return _is_package_available('transformers'						)





def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      return _is_package_available('datasets'						)





def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('aim'						)





def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      return _is_package_available('tensorboard'						) or _is_package_available('tensorboardX'						)





def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      return _is_package_available('wandb'						)





def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      return _is_package_available('comet_ml'						)





def 			lowerCAmelCase_			(  )					->			Dict:


      '''simple docstring'''




      return _is_package_available('boto3'						)





def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      if _is_package_available('rich'						):
            if "ACCELERATE_DISABLE_RICH" in os.environ:
                  warnings.warn(
                      '`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'						)
                  return not parse_flag_from_env('ACCELERATE_DISABLE_RICH'     ,				lowerCamelCase						)
            return parse_flag_from_env('ACCELERATE_ENABLE_RICH'     ,				lowerCamelCase						)
      return False





def 			lowerCAmelCase_			(  )					->			Tuple:


      '''simple docstring'''




      return _is_package_available('sagemaker'						)





def 			lowerCAmelCase_			(  )					->			int:


      '''simple docstring'''




      return _is_package_available('tqdm'						)





def 			lowerCAmelCase_			(  )					->			Tuple:


      '''simple docstring'''




      return _is_package_available('mlflow'						)





def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return is_torch_version('>='     ,				'1.12'						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()





def 			lowerCAmelCase_			(  )					->			Dict:


      '''simple docstring'''




      def get_major_and_minor_from_version(lowerCamelCase:       List[str]						):
            return str(version.parse(lowerCamelCase						).major						) + "." + str(version.parse(lowerCamelCase						).minor						)

      lowerCamelCase__        =				importlib.metadata.version('torch'						)
      if importlib.util.find_spec('intel_extension_for_pytorch'						) is None:
            return False
      lowerCamelCase__        =				'N/A'
      try:
            lowerCamelCase__        =				importlib.metadata.version('intel_extension_for_pytorch'						)
      except importlib.metadata.PackageNotFoundError:
            return False
      lowerCamelCase__        =				get_major_and_minor_from_version(_torch_version						)
      lowerCamelCase__        =				get_major_and_minor_from_version(_ipex_version						)
      if torch_major_and_minor != ipex_major_and_minor:
            warnings.warn(
                f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
            return False
      return True





@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       int=False						)					->			str:


      '''simple docstring'''




      if importlib.util.find_spec('torch'						) is None or importlib.util.find_spec('torch_npu'						) is None:
            return False

      import torch
      import torch_npu  # noqa: F401

      if check_device:
            try:
                  # Will raise a RuntimeError if no NPU is found
                  lowerCamelCase__        =				torch.npu.device_count()
                  return torch.npu.is_available()
            except RuntimeError:
                  return False
      return hasattr(lowerCamelCase     ,				'npu'						) and torch.npu.is_available()





@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       Any=False						)					->			List[Any]:


      '''simple docstring'''




      if not parse_flag_from_env('ACCELERATE_USE_XPU'     ,				default=lowerCamelCase						):
            return False
      if is_ipex_available():
            import torch

            if is_torch_version('<='     ,				'1.12'						):
                  return False
      else:
            return False

      import intel_extension_for_pytorch  # noqa: F401

      if check_device:
            try:
                  # Will raise a RuntimeError if no XPU  is found
                  lowerCamelCase__        =				torch.xpu.device_count()
                  return torch.xpu.is_available()
            except RuntimeError:
                  return False
      return hasattr(lowerCamelCase     ,				'xpu'						) and torch.xpu.is_available()





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


      '''simple docstring'''




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

      lowerCamelCase__        =				list(range(lowerCamelCase						)						)

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

      lowerCamelCase__        =				0
      lowerCamelCase__        =				[]

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

      return minimum_spanning_tree


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

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

      kruskal(num_nodes, edges)