



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

      try:
            UpperCAmelCase					=			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'		,     _A						) 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:
                  UpperCAmelCase					=			8

            UpperCAmelCase					=			PrepareForLaunch(_A		,     distributed_type='TPU'						)
            print(F"""Launching a training on {num_processes} TPU cores."""						)
            xmp.spawn(_A		,     args=_A		,     nprocs=_A		,     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(*_A						)
      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=_A		,     master_addr='127.0.01'		,     master_port=_A		,     mixed_precision=_A						):
                        UpperCAmelCase					=			PrepareForLaunch(_A		,     distributed_type='MULTI_GPU'						)
                        print(F"""Launching training on {num_processes} GPUs."""						)
                        try:
                              start_processes(_A		,     args=_A		,     nprocs=_A		,     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():
                        UpperCAmelCase					=			'1'
                        print('Launching training on MPS.'						)
                  elif torch.cuda.is_available():
                        print('Launching training on one GPU.'						)
                  else:
                        print('Launching training on CPU.'						)
                  function(*_A						)






def 			_lowerCAmelCase				(       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=_A		,     master_addr='127.0.01'		,     master_port='29500'		,     accelerate_mixed_precision='no'		,     accelerate_debug_rdv_file=tmp_file.name		,     accelerate_use_cpu='yes'		,     ):
                  UpperCAmelCase					=			PrepareForLaunch(_A		,     debug=_A						)
                  start_processes(_A		,     args=_A		,     nprocs=_A		,     start_method='fork'						)










"""simple docstring"""


import math

def 			_lowerCAmelCase				(       lowercase_						):
						assert isinstance(lowercase_		,     lowercase_						) and (
						    number >= 0
						), "'number' must been an int and positive"

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or not number % 2:
												# Negatives, 0, 1 and all even numbers are not primes
												return False

						UpperCAmelCase					=			range(3		,     int(math.sqrt(lowercase_						) + 1						)		,     2						)
						return not any(not number % i for i in odd_numbers						)






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_=1		,     **lowercase_						):
						UpperCAmelCase					=			factor * value
						UpperCAmelCase					=			value

						while not is_prime(lowercase_						):
												value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

						if value == first_value_val:
												return next_prime(value + 1		,     **lowercase_						)
						return value





