






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_		     =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		     =			['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		     =			[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   A_		     =			_LazyModule(__name__, globals()['__file__'], _import_structure)







import os
import sys
import tempfile

import torch

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

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

  try:
    UpperCamelCase_: Optional[int]   				=    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'     ,   UpperCAmelCase__  ) 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:
      UpperCamelCase_: List[str]   				=    8

    UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='TPU'  )
    print(F'''Launching a training on {num_processes} TPU cores.'''  )
    xmp.spawn(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   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(*UpperCAmelCase__  )
  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=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port=UpperCAmelCase__     ,   mixed_precision=UpperCAmelCase__  ):
        UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='MULTI_GPU'  )
        print(F'''Launching training on {num_processes} GPUs.'''  )
        try:
          start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   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():
        UpperCamelCase_: Tuple   				=    '1'
        print('Launching training on MPS.'  )
      elif torch.cuda.is_available():
        print('Launching training on one GPU.'  )
      else:
        print('Launching training on CPU.'  )
      function(*UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=()     ,   UpperCAmelCase__=2  )	->    Optional[int]:
  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=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port='29500'     ,   accelerate_mixed_precision='no'     ,   accelerate_debug_rdv_file=tmp_file.name     ,   accelerate_use_cpu='yes'     ,   ):
      UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   debug=UpperCAmelCase__  )
      start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )