"""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(					a ,  a=() ,  a=None ,  a="no" ,  a="29500"							):
       __a     				=  False
       __a     				=  False
       if any(key.startswith("KAGGLE"							) for key in os.environ.keys()							):
              __a     				=  True
       elif "IPython" in sys.modules:
              __a     				=  "google.colab" in str(sys.modules["IPython"].get_ipython()							)

       try:
              __a     				=  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:
                     __a     				=  8

              __a     				=  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_							):
                            __a     				=  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():
                            __a     				=  "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 					_lowerCamelCase(					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" ,  ):
                     __a     				=  PrepareForLaunch(a_ ,  debug=a_							)
                     start_processes(a_ ,  args=a_ ,  nprocs=a_ ,  start_method="fork"							)

"""simple docstring"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)






class 				snake_case__     :
 _snake_case   :			List[str]           =		None



@experimental
def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a ,  a ,  a ,  a ,  a ,  a ,  a							)

       return _map_with_joblib(a ,  a ,  a ,  a ,  a ,  a ,  a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       __a     				=  num_proc if num_proc <= len(a							) else len(a							)
       __a     				=  []  # We organize the splits ourselve (contiguous splits)
       for index in range(a							):
              __a     				=  len(a							) // num_proc
              __a     				=  len(a							) % num_proc
              __a     				=  div * index + min(a ,  a							)
              __a     				=  start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

       if len(a							) != sum(len(i[1]							) for i in split_kwds							):
              raise ValueError(
                  F"Error dividing inputs iterable among processes. "
                  F"Total number of objects {len(a							)}, "
                  F"length: {sum(len(i[1]							) for i in split_kwds							)}"							)

       logger.info(
           F"Spawning {num_proc} processes for {len(a							)} objects in slices of {[len(i[1]							) for i in split_kwds]}"							)
       __a				, __a     				=  None, None
       if not disable_tqdm:
              __a				, __a     				=  (RLock(),), tqdm.set_lock
       with Pool(a ,  initargs=a ,  initializer=a							) as pool:
              __a     				=  pool.map(a ,  a							)
       logger.info(F"Finished {num_proc} processes"							)
       __a     				=  [obj for proc_res in mapped for obj in proc_res]
       logger.info(F"Unpacked {len(a							)} objects"							)

       return mapped



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
       # and it requires monkey-patching joblib internal classes which is subject to change
       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name ,  n_jobs=a							):
              return joblib.Parallel()(
                  joblib.delayed(a							)((function, obj, types, None, True, None)							) for obj in iterable							)




@experimental
@contextlib.contextmanager
def 					_lowerCamelCase(					a							):
       __a     				=  backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              __a     				=  None
