

'''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        UpperCAmelCase_     (		_A   ,	_A=()   ,	_A=None   ,	_A="no"   ,	_A="29500"       ):





     '''simple docstring'''




     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'''   ,	_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:
               SCREAMING_SNAKE_CASE__					=					8

          SCREAMING_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       ):
                    SCREAMING_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():
                    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(*_A       )







def        UpperCAmelCase_     (		_A   ,	_A=()   ,	_A=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=_A   ,	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(_A   ,	debug=_A       )
               start_processes(_A   ,	args=_A   ,	nprocs=_A   ,	start_method='''fork'''       )





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


