



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=()				,					_lowerCAmelCase=None				,					_lowerCAmelCase="no"				,					_lowerCAmelCase="29500"		)	->  List[str]:



    """simple docstring"""






    A			:	List[Any]										=       False
    A			:	Any										=       False
    if any(key.startswith("""KAGGLE"""		) for key in os.environ.keys()		):
        A			:	Any										=       True
    elif "IPython" in sys.modules:
        A			:	str										=       """google.colab""" in str(sys.modules["""IPython"""].get_ipython()		)

    try:
        A			:	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:
            A			:	Tuple										=       8

        A			:	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		):
                A			:	Tuple										=       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():
                A			:	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(*_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=()				,					_lowerCAmelCase=2		)	->  Any:



    """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=_lowerCAmelCase				,					master_addr="""127.0.01"""				,					master_port="""29500"""				,					accelerate_mixed_precision="""no"""				,					accelerate_debug_rdv_file=tmp_file.name				,					accelerate_use_cpu="""yes"""				,					):
            A			:	List[str]										=       PrepareForLaunch(_lowerCAmelCase				,					debug=_lowerCAmelCase		)
            start_processes(_lowerCAmelCase				,					args=_lowerCAmelCase				,					nprocs=_lowerCAmelCase				,					start_method="""fork"""		)







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


