
'''simple docstring'''





from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[Any]     =	""
     UpperCAmelCase__       :							Dict     =	"hf-legacy"  # "hf://"" is reserved for hffs





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[DatasetInfo] = None       ,					_A   :					Optional[str] = None       ,					**_A   :					int       ,					)		->		Optional[int]:
         super().__init__(self       ,					**_A     )
         snake_case				  =      repo_info
         snake_case				  =      token
         snake_case				  =      None





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[Any]:
         if self.dir_cache is None:
             snake_case				  =      {}
             for hf_file in self.repo_info.siblings:
                 # TODO(QL): add sizes
                 snake_case				  =      {
                     "name": hf_file.rfilename,
                     "size": None,
                     "type": "file",
                 }
                 self.dir_cache.update(
                     {
                         str(_A     ): {"name": str(_A     ), "size": None, "type": "directory"}
                         for d in list(PurePosixPath(hf_file.rfilename     ).parents     )[:-1]
                     }     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					str       ,					_A   :					str = "rb"       ,					**_A   :					List[str]       ,					)		->		Optional[Any]:
         if not isinstance(self.repo_info       ,					_A     ):
             raise NotImplementedError(f'Open is only implemented for dataset repositories, but got {self.repo_info}'     )
         snake_case				  =      hf_hub_url(self.repo_info.id       ,					_A       ,					revision=self.repo_info.sha     )
         return fsspec.open(
             _A       ,					mode=_A       ,					headers=get_authentication_headers_for_url(_A       ,					use_auth_token=self.token     )       ,					client_kwargs={"trust_env": True}       ,					).open()





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					Tuple       ,					**_A   :					Optional[Any]     )		->		Optional[Any]:
         self._get_dirs()
         snake_case				  =      self._strip_protocol(_A     )
         if path in self.dir_cache:
             return self.dir_cache[path]
         else:
             raise FileNotFoundError(_A     )





     def 	UpperCAmelCase(self   :					int       ,					_A   :					Optional[int]       ,					_A   :					Tuple=False       ,					**_A   :					Dict     )		->		Union[str, Any]:
         self._get_dirs()
         snake_case				  =      PurePosixPath(path.strip("/"     )     )
         snake_case				  =      {}
         for p, f in self.dir_cache.items():
             snake_case				  =      PurePosixPath(p.strip("/"     )     )
             snake_case				  =      p.parent
             if root == path:
                 snake_case				  =      f
         snake_case				  =      list(paths.values()     )
         if detail:
             return out
         else:
             return sorted(f["name"] for f in out     )



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"	)							->							Optional[int]:
    """simple docstring"""

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

    try:
        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"				,	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				  =      8

        snake_case				  =      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				  =      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				  =      "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	)							->							Dict:
    """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=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				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

