



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"						):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					False
							_lowerCAmelCase  :							Union[str, Any]				     =					False
							if any(key.startswith("KAGGLE"						) for key in os.environ.keys()						):
														_lowerCAmelCase  :							Dict				     =					True
							elif "IPython" in sys.modules:
														_lowerCAmelCase  :							Dict				     =					'google.colab' in str(sys.modules["IPython"].get_ipython()						)

							try:
														_lowerCAmelCase  :							Tuple				     =					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"       ,				__lowercase						) 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  :							List[Any]				     =					8

														_lowerCAmelCase  :							Optional[int]				     =					PrepareForLaunch(__lowercase       ,				distributed_type="TPU"						)
														print(F"Launching a training on {num_processes} TPU cores."						)
														xmp.spawn(__lowercase       ,				args=__lowercase       ,				nprocs=__lowercase       ,				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(*__lowercase						)
							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=__lowercase       ,				master_addr="127.0.01"       ,				master_port=__lowercase       ,				mixed_precision=__lowercase						):
																												_lowerCAmelCase  :							Tuple				     =					PrepareForLaunch(__lowercase       ,				distributed_type="MULTI_GPU"						)
																												print(F"Launching training on {num_processes} GPUs."						)
																												try:
																																			start_processes(__lowercase       ,				args=__lowercase       ,				nprocs=__lowercase       ,				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  :							Optional[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(*__lowercase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=()       ,				_lowerCamelCase=2						):





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











import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					BeautifulSoup(requests.get(_lowerCamelCase       ,				params=_lowerCamelCase						).content       ,				"html.parser"						)
							_lowerCAmelCase  :							Any				     =					soup.find("div"       ,				attrs={"class": "gs_ri"}						)
							_lowerCAmelCase  :							str				     =					div.find("div"       ,				attrs={"class": "gs_fl"}						).find_all("a"						)
							return anchors[2].get_text()


if __name__ == "__main__":
						_snake_case	=						{
						    "title": (
						        "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
						        "capacitance, volumetric capacitance, and energy density"
						    ),
						    "journal": "Chem. Mater.",
						    "volume": 30,
						    "pages": "3979-3990",
						    "year": 2018,
						    "hl": "en",
						}
						print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
