code: 
  
  
  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        lowercase_				( SCREAMING_SNAKE_CASE__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		c_float
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return sizeof(self.value		)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__ = 0		):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if dimension <= 0:
  												a_					=		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_			,   a_					=		None, None
  												try:
  																		a_					=		AutoTokenizer.from_pretrained(_UpperCamelCase		)
  												except (OSError, KeyError):
  																		pass
  												try:
  																		a_					=		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
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		ArgumentParser(
  						    description=(
  						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
  						    )      )
  
  						# Optional arguments for the launch helper
  						parser.add_argument("""--num_cores"""       ,    type=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    help=(
  						        """The full path to the single TPU training """
  						        """program/script to be launched in parallel, """
  						        """followed by all the arguments for the """
  						        """training script"""
  						    )       ,    )
  
  						# rest from the training program
  						parser.add_argument("""training_script_args"""       ,    nargs=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]
  
  						xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores      )
  
  
  if __name__ == "__main__":
  	main()