import os
import sys
import tempfile

import torch

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Dict=()				,  __UpperCamelCase					: str=None				,  __UpperCamelCase					: List[str]="no"				,  __UpperCamelCase					: Optional[int]="29500"			)							->      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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Dict=()				,  __UpperCamelCase					: int=2			)							->      List[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"""			)

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


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

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
