






"""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 ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Any											=						c_float


    @property
    def 		A__      (      self			):
     return sizeof(self.value			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 0			)    ->int:
 # < 0 is possible if using a dynamic axis
 if dimension <= 0:
  UpperCAmelCase__ 						=				fixed_dimension

 dimension -= num_token_to_add
 return dimension




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->int:
 return num_parameters * dtype.size




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE			)
 except (ValueError, OSError, KeyError):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				None, None
  try:
   UpperCAmelCase__ 						=				AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)
  except (OSError, KeyError):
   pass
  try:
   UpperCAmelCase__ 						=				AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE			)
  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


a  :						Dict					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Any:
 UpperCAmelCase__ 						=				ArgumentParser()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				parser.parse_known_args()
 for arg in unknown:
  if arg.startswith(("""-""", """--""")			):
   parser.add_argument(arg.split("""="""			)[0]			)

 return parser.parse_args()




def       snake_case__		(   )    ->List[Any]:
 UpperCAmelCase__ 						=				parse_args()
 UpperCAmelCase__ 						=				8_8_8_8
 UpperCAmelCase__ 						=				int(os.environ["""SM_NUM_GPUS"""]			)
 UpperCAmelCase__ 						=				json.loads(os.environ["""SM_HOSTS"""]			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				os.environ["""SM_CURRENT_HOST"""]
 UpperCAmelCase__ 						=				hosts.index(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				"""INFO"""

 if num_nodes > 1:
  UpperCAmelCase__ 						=				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:
  UpperCAmelCase__ 						=				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(_SCREAMING_SNAKE_CASE       ,	shell=_SCREAMING_SNAKE_CASE			)
 except Exception as e:
  logger.info(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 main()



