
import os
import sys
import tempfile

import torch

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





def     _a					(						lowerCamelCase,	lowerCamelCase=(),	lowerCamelCase=None,	lowerCamelCase="no",	lowerCamelCase="29500"							):
 lowerCamelCase		:	Tuple							=      False
 lowerCamelCase		:	List[str]							=      False
 if any(key.startswith("""KAGGLE"""							) for key in os.environ.keys()							):
  lowerCamelCase		:	str							=      True
 elif "IPython" in sys.modules:
  lowerCamelCase		:	str							=      """google.colab""" in str(sys.modules["""IPython"""].get_ipython()							)

 try:
  lowerCamelCase		:	List[str]							=      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:
   lowerCamelCase		:	Tuple							=      8

  lowerCamelCase		:	List[str]							=      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							):
    lowerCamelCase		:	Any							=      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():
    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(*lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase=(),	lowerCamelCase=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""",	):
   lowerCamelCase		:	Optional[int]							=      PrepareForLaunch(lowerCamelCase,	debug=lowerCamelCase							)
   start_processes(lowerCamelCase,	args=lowerCamelCase,	nprocs=lowerCamelCase,	start_method="""fork"""							)






def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




