





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

    try:
        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			8

        _SCREAMING_SNAKE_CASE									=			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    ):
                _SCREAMING_SNAKE_CASE									=			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():
                _SCREAMING_SNAKE_CASE									=			"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 lowerCamelCase						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Union[str, Any]=()					,       __lowerCamelCase :    Any=2    )						->Any:
    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"""					,       ):
            _SCREAMING_SNAKE_CASE									=			PrepareForLaunch(__lowerCamelCase					,       debug=__lowerCamelCase    )
            start_processes(__lowerCamelCase					,       args=__lowerCamelCase					,       nprocs=__lowerCamelCase					,       start_method="""fork"""    )




def       _snake_case	(   lowerCAmelCase							:   list	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    len(lowerCAmelCase	)
		for i in range(1   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		int				      =    collection[i]
				SCREAMING_SNAKE_CASE_  :		Any				      =    0
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    i - 1

				while low <= high:
						SCREAMING_SNAKE_CASE_  :		int				      =    (low + high) // 2
						if val < collection[mid]:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    mid - 1
						else:
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    mid + 1
				for j in range(lowerCAmelCase   , lowerCAmelCase   , -1	):
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    collection[j - 1]
				SCREAMING_SNAKE_CASE_  :		int				      =    val
		return collection


if __name__ == "__main__":
		__lowerCamelCase					:	Dict       =  input('''Enter numbers separated by a comma:\n''').strip()
		__lowerCamelCase					:	List[str]       =  [int(item) for item in user_input.split(''',''')]
		print(binary_insertion_sort(unsorted))
