



import os
import sys
import tempfile

import torch

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Dict=() ,					UpperCAmelCase__ :Any=None ,					UpperCAmelCase__ :Union[str, Any]="no" ,					UpperCAmelCase__ :int="29500"					):






 '''simple docstring'''

 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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Dict=() ,					UpperCAmelCase__ :int=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" ,					):
   a						=		PrepareForLaunch(__A ,					debug=__A					)
   start_processes(__A ,					args=__A ,					nprocs=__A ,					start_method="fork"					)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:     Any        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''lilt'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]=3_0522							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[Any]=12							,   __lowerCAmelCase			:       List[Any]=3072							,   __lowerCAmelCase			:       Dict="gelu"							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Optional[int]=2							,   __lowerCAmelCase			:       int=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       List[Any]="absolute"							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Dict=4							,   __lowerCAmelCase			:       Dict=1024							,   **__lowerCAmelCase			:       Dict							,   )       ->				int:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		classifier_dropout
    a						=		channel_shrink_ratio
    a						=		max_ad_position_embeddings

