



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

				try:
								snake_case_				:  Optional[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'''	,    _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:
												snake_case_				:  Any       =			8

								snake_case_				:  Any       =			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					):
																snake_case_				:  Tuple       =			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():
																snake_case_				:  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(*_a					)






def __lowercase    (							_a	,    _a=()	,    _a=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'''	,    ):
												snake_case_				:  Dict       =			PrepareForLaunch(_a	,    debug=_a					)
												start_processes(_a	,    args=_a	,    nprocs=_a	,    start_method='''fork'''					)










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





