



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

  try:
    __lowerCamelCase       :       Union[str, 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:
      __lowerCamelCase       :       Union[str, Any]           = 8

    __lowerCamelCase       :       Union[str, Any]           = 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       :       Union[str, 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       :       Union[str, 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       )      ->      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'       ,):
      __lowerCamelCase       :       Union[str, Any]           = PrepareForLaunch(_lowerCAmelCase       ,debug=_lowerCAmelCase       )
      start_processes(_lowerCAmelCase       ,args=_lowerCAmelCase       ,nprocs=_lowerCAmelCase       ,start_method='fork'       )











'''simple docstring'''





from __future__ import annotations

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






