





import os
import sys
import tempfile

import torch

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




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

       try:
              _lowerCamelCase				:  Optional[Any]						=		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:
                     _lowerCamelCase				:  Any						=		8

              _lowerCamelCase				:  List[Any]						=		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_   ):
                            _lowerCamelCase				:  Optional[Any]						=		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():
                            _lowerCamelCase				:  List[Any]						=		'''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  __UpperCAmelCase(   lowercase_			,     lowercase_=()			,     lowercase_=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=lowercase_			,     master_addr='''127.0.01'''			,     master_port='''29500'''			,     accelerate_mixed_precision='''no'''			,     accelerate_debug_rdv_file=tmp_file.name			,     accelerate_use_cpu='''yes'''			,     ):
                     _lowerCamelCase				:  Tuple						=		PrepareForLaunch(lowercase_			,     debug=lowercase_   )
                     start_processes(lowercase_			,     args=lowercase_			,     nprocs=lowercase_			,     start_method='''fork'''   )








import os




def  __UpperCAmelCase(   ):
       with open(os.path.dirname(lowercase_   ) + '''/p022_names.txt'''   ) as file:
              _lowerCamelCase				:  Optional[int]						=		str(file.readlines()[0]   )
              _lowerCamelCase				:  List[Any]						=		names.replace('''"'''			,     ''''''   ).split(''','''   )

       names.sort()

       _lowerCamelCase				:  Optional[int]						=		0
       _lowerCamelCase				:  Tuple						=		0

       for i, name in enumerate(lowercase_   ):
              for letter in name:
                     name_score += ord(lowercase_   ) - 64

              total_score += (i + 1) * name_score
              _lowerCamelCase				:  Optional[int]						=		0
       return total_score


if __name__ == "__main__":
       print(solution())

