



'''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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=()     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase="no"     ,   _lowerCAmelCase="29500"		)		-> int:
      snake_case__  :    List[str]    						=      False
      snake_case__  :    Tuple    						=      False
      if any(key.startswith("""KAGGLE"""		) for key in os.environ.keys()		):
            snake_case__  :    List[str]    						=      True
      elif "IPython" in sys.modules:
            snake_case__  :    int    						=      """google.colab""" in str(sys.modules["""IPython"""].get_ipython()		)

      try:
            snake_case__  :    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"""     ,   _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:
                  snake_case__  :    Union[str, Any]    						=      8

            snake_case__  :    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		):
                        snake_case__  :    Optional[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():
                        snake_case__  :    Tuple    						=      """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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=()     ,   _lowerCAmelCase=2		)		-> Tuple:
      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"""     ,   ):
                  snake_case__  :    Tuple    						=      PrepareForLaunch(_lowerCAmelCase     ,   debug=_lowerCAmelCase		)
                  start_processes(_lowerCAmelCase     ,   args=_lowerCAmelCase     ,   nprocs=_lowerCAmelCase     ,   start_method="""fork"""		)

from __future__ import annotations

from math import pi, sqrt




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   float	):
		"""simple docstring"""







		if inductance <= 0:
				raise ValueError("Inductance cannot be 0 or negative"	)

		elif capacitance <= 0:
				raise ValueError("Capacitance cannot be 0 or negative"	)

		else:
				return (
				    "Resonant frequency",
				    float(1 / (2 * pi * (sqrt(inductance * capacitance	)))	),
				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
