
import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]=()  ,   lowerCAmelCase__							:				str=None  ,   lowerCAmelCase__							:				Union[str, Any]="no"  ,   lowerCAmelCase__							:				int="29500"  ):
 __a :  List[str]						=  False
 __a :  Optional[Any]						=  False
 if any(key.startswith('''KAGGLE'''  ) for key in os.environ.keys()  ):
  __a :  int						=  True
 elif "IPython" in sys.modules:
  __a :  List[Any]						=  '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()  )

 try:
  __a :  Dict						=  PrecisionType(mixed_precision.lower()  )
 except ValueError:
  raise ValueError(
      f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."  )

 if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''  ,   lowerCAmelCase__  ) is not None):
  # TPU launch
  import torch_xla.distributed.xla_multiprocessing as xmp

  if len(AcceleratorState._shared_state  ) > 0:
   raise ValueError(
       '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
       '''your training function. Restart your notebook and make sure no cells initializes an '''
       '''`Accelerator`.'''  )
  if num_processes is None:
   __a :  List[Any]						=  8

  __a :  Optional[int]						=  PrepareForLaunch(lowerCAmelCase__  ,   distributed_type='''TPU'''  )
  print(f"Launching a training on {num_processes} TPU cores."  )
  xmp.spawn(lowerCAmelCase__  ,   args=lowerCAmelCase__  ,   nprocs=lowerCAmelCase__  ,   start_method='''fork'''  )
 elif in_colab:
  # No need for a distributed launch otherwise as it's either CPU or one GPU.
  if torch.cuda.is_available():
   print('''Launching training on one GPU.'''  )
  else:
   print('''Launching training on one CPU.'''  )
  function(*lowerCAmelCase__  )
 else:
  if num_processes is None:
   raise ValueError(
       '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''  )

  if num_processes > 1:
   # Multi-GPU launch
   from torch.multiprocessing import start_processes
   from torch.multiprocessing.spawn import ProcessRaisedException

   if len(AcceleratorState._shared_state  ) > 0:
    raise ValueError(
        '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
        '''inside your training function. Restart your notebook and make sure no cells initializes an '''
        '''`Accelerator`.'''  )

   if torch.cuda.is_initialized():
    raise ValueError(
        '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
        '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
        '''function.'''  )

   # torch.distributed will expect a few environment variable to be here. We set the ones common to each
   # process here (the other ones will be set be the launcher).
   with patch_environment(
       world_size=lowerCAmelCase__  ,   master_addr='''127.0.01'''  ,   master_port=lowerCAmelCase__  ,   mixed_precision=lowerCAmelCase__  ):
    __a :  Dict						=  PrepareForLaunch(lowerCAmelCase__  ,   distributed_type='''MULTI_GPU'''  )
    print(f"Launching training on {num_processes} GPUs."  )
    try:
     start_processes(lowerCAmelCase__  ,   args=lowerCAmelCase__  ,   nprocs=lowerCAmelCase__  ,   start_method='''fork'''  )
    except ProcessRaisedException as e:
     if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
      raise RuntimeError(
          '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
          '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
          '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
          '''which one is problematic.'''  ) from e

  else:
   # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
   if is_mps_available():
    __a :  Optional[int]						=  '''1'''
    print('''Launching training on MPS.'''  )
   elif torch.cuda.is_available():
    print('''Launching training on one GPU.'''  )
   else:
    print('''Launching training on CPU.'''  )
   function(*lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Tuple=()  ,   lowerCAmelCase__							:				Optional[Any]=2  ):
 from torch.multiprocessing import start_processes

 with tempfile.NamedTemporaryFile() as tmp_file:
  # torch.distributed will expect a few environment variable to be here. We set the ones common to each
  # process here (the other ones will be set be the launcher).
  with patch_environment(
      world_size=lowerCAmelCase__  ,   master_addr='''127.0.01'''  ,   master_port='''29500'''  ,   accelerate_mixed_precision='''no'''  ,   accelerate_debug_rdv_file=tmp_file.name  ,   accelerate_use_cpu='''yes'''  ,   ):
   __a :  Dict						=  PrepareForLaunch(lowerCAmelCase__  ,   debug=lowerCAmelCase__  )
   start_processes(lowerCAmelCase__  ,   args=lowerCAmelCase__  ,   nprocs=lowerCAmelCase__  ,   start_method='''fork'''  )


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-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   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  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
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  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

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





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
