






"""simple docstring"""




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : str    =      c_float


 @property
 def _lowercase			(  self	:		str							)      -> int:
   return sizeof(self.value							)







def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__ = 0       ):



  '''simple docstring'''
  # < 0 is possible if using a dynamic axis
  if dimension <= 0:
    _a						: List[str]    =			fixed_dimension

  dimension -= num_token_to_add
  return dimension

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  return num_parameters * dtype.size



def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

  try:
    return AutoProcessor.from_pretrained(UpperCamelCase__       )
  except (ValueError, OSError, KeyError):
    _a						: Optional[Any]    =			None, None
    try:
      _a						: Any    =			AutoTokenizer.from_pretrained(UpperCamelCase__       )
    except (OSError, KeyError):
      pass
    try:
      _a						: List[str]    =			AutoFeatureExtractor.from_pretrained(UpperCamelCase__       )
    except (OSError, KeyError):
      pass

    if tokenizer is not None and feature_extractor is not None:
      raise ValueError(
          F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""       )
    elif tokenizer is None and feature_extractor is None:
      return None
    elif tokenizer is not None:
      return tokenizer
    else:
      return feature_extractor









"""simple docstring"""




import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_snake_case					   =     logging.getLogger(__name__)

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Any    =			ArgumentParser()
  _a   ,						_a						: str    =			parser.parse_known_args()
  for arg in unknown:
    if arg.startswith(("""-""", """--""")       ):
      parser.add_argument(arg.split("""="""       )[0]       )

  return parser.parse_args()

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[Any]    =			parse_args()
  _a						: Any    =			8_8_8_8
  _a						: List[str]    =			int(os.environ["""SM_NUM_GPUS"""]       )
  _a						: Union[str, Any]    =			json.loads(os.environ["""SM_HOSTS"""]       )
  _a						: Tuple    =			len(UpperCamelCase__       )
  _a						: Optional[int]    =			os.environ["""SM_CURRENT_HOST"""]
  _a						: Optional[Any]    =			hosts.index(UpperCamelCase__       )
  _a						: List[str]    =			"""INFO"""

  if num_nodes > 1:
    _a						: int    =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  else:
    _a						: Any    =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  try:
    subprocess.run(UpperCamelCase__  ,     shell=UpperCamelCase__       )
  except Exception as e:
    logger.info(UpperCamelCase__       )


if __name__ == "__main__":
 main()

