





'''simple docstring'''

import os
import sys
import tempfile

import torch

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







def    a__							(							lowercase   :	Dict,   lowercase   :	Dict=(),   lowercase   :	Union[str, Any]=None,   lowercase   :	List[Any]="no",   lowercase   :	str="29500" )						->						List[Any]:





     """simple docstring"""

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

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

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







def    a__							(							lowercase   :	Union[str, Any],   lowercase   :	int=(),   lowercase   :	int=2 )						->						int:





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










'''simple docstring'''

def    a__							(							lowercase   :	str )						->						int:





     """simple docstring"""

     assert column_title.isupper()
     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1
     _UpperCamelCase							       =			0

     while index >= 0:
          _UpperCamelCase							       =			(ord(column_title[index] ) - 64) * pow(26,   lowercase )
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()



