

import os
import sys
import tempfile

import torch

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	int=()		,				_UpperCamelCase		:	Union[str, Any]=None		,				_UpperCamelCase		:	List[str]="no"		,				_UpperCamelCase		:	str="29500"						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      False
 SCREAMING_SNAKE_CASE		    =      False
 if any(key.startswith('KAGGLE'						) for key in os.environ.keys()						):
  SCREAMING_SNAKE_CASE		    =      True
 elif "IPython" in sys.modules:
  SCREAMING_SNAKE_CASE		    =      'google.colab' in str(sys.modules['IPython'].get_ipython()						)

 try:
  SCREAMING_SNAKE_CASE		    =      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'		,				_UpperCamelCase						) 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:
   SCREAMING_SNAKE_CASE		    =      8

  SCREAMING_SNAKE_CASE		    =      PrepareForLaunch(_UpperCamelCase		,				distributed_type='TPU'						)
  print(f"""Launching a training on {num_processes} TPU cores."""						)
  xmp.spawn(_UpperCamelCase		,				args=_UpperCamelCase		,				nprocs=_UpperCamelCase		,				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(*_UpperCamelCase						)
 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=_UpperCamelCase		,				master_addr='127.0.01'		,				master_port=_UpperCamelCase		,				mixed_precision=_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      PrepareForLaunch(_UpperCamelCase		,				distributed_type='MULTI_GPU'						)
    print(f"""Launching training on {num_processes} GPUs."""						)
    try:
     start_processes(_UpperCamelCase		,				args=_UpperCamelCase		,				nprocs=_UpperCamelCase		,				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():
    SCREAMING_SNAKE_CASE		    =      '1'
    print('Launching training on MPS.'						)
   elif torch.cuda.is_available():
    print('Launching training on one GPU.'						)
   else:
    print('Launching training on CPU.'						)
   function(*_UpperCamelCase						)




def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Tuple=()		,				_UpperCamelCase		:	Optional[Any]=2						)		->				str:
 '''simple docstring'''


 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=_UpperCamelCase		,				master_addr='127.0.01'		,				master_port='29500'		,				accelerate_mixed_precision='no'		,				accelerate_debug_rdv_file=tmp_file.name		,				accelerate_use_cpu='yes'		,				):
   SCREAMING_SNAKE_CASE		    =      PrepareForLaunch(_UpperCamelCase		,				debug=_UpperCamelCase						)
   start_processes(_UpperCamelCase		,				args=_UpperCamelCase		,				nprocs=_UpperCamelCase		,				start_method='fork'						)




from graphs.minimum_spanning_tree_kruskal import kruskal


def 							__lowerCAmelCase     (							)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      9
 SCREAMING_SNAKE_CASE		    =      [
     [0, 1, 4],
     [0, 7, 8],
     [1, 2, 8],
     [7, 8, 7],
     [7, 6, 1],
     [2, 8, 2],
     [8, 6, 6],
     [2, 3, 7],
     [2, 5, 4],
     [6, 5, 2],
     [3, 5, 14],
     [3, 4, 9],
     [5, 4, 10],
     [1, 7, 11],
 ]

 SCREAMING_SNAKE_CASE		    =      kruskal(_UpperCamelCase		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      [
     [7, 6, 1],
     [2, 8, 2],
     [6, 5, 2],
     [0, 1, 4],
     [2, 5, 4],
     [2, 3, 7],
     [0, 7, 8],
     [3, 4, 9],
 ]

 assert sorted(_UpperCamelCase						) == sorted(_UpperCamelCase						)

