"""simple docstring"""




import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


_a      :		Tuple           =   '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict=None	)       ->      List[str]:


  if subparsers is not None:
    _lowerCAmelCase						:       int  			=     subparsers.add_parser("""tpu-config"""			,description=_description	)
  else:
    _lowerCAmelCase						:       Tuple  			=     argparse.ArgumentParser("""Accelerate tpu-config command"""			,description=_description	)
  # Core arguments
  _lowerCAmelCase						:       Union[str, Any]  			=     parser.add_argument_group(
      """Config Arguments"""			,"""Arguments that can be configured through `accelerate config`."""	)
  config_args.add_argument(
      """--config_file"""			,type=lowerCAmelCase_			,default=lowerCAmelCase_			,help="""Path to the config file to use for accelerate."""			,)
  config_args.add_argument(
      """--tpu_name"""			,default=lowerCAmelCase_			,help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file."""			,)
  config_args.add_argument(
      """--tpu_zone"""			,default=lowerCAmelCase_			,help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file."""			,)
  _lowerCAmelCase						:       Tuple  			=     parser.add_argument_group("""TPU Arguments"""			,"""Arguments for options ran inside the TPU."""	)
  pod_args.add_argument(
      """--use_alpha"""			,action="""store_true"""			,help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."""			,)
  pod_args.add_argument(
      """--command_file"""			,default=lowerCAmelCase_			,help="""The path to the file containing the commands to run on the pod on startup."""			,)
  pod_args.add_argument(
      """--command"""			,action="""append"""			,nargs="""+"""			,help="""A command to run on the pod. Can be passed multiple times."""			,)
  pod_args.add_argument(
      """--install_accelerate"""			,action="""store_true"""			,help="""Whether to install accelerate on the pod. Defaults to False."""			,)
  pod_args.add_argument(
      """--accelerate_version"""			,default="""latest"""			,help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."""			,)
  pod_args.add_argument(
      """--debug"""			,action="""store_true"""			,help="""If set, will print the command that would be run instead of running it."""	)

  if subparsers is not None:
    parser.set_defaults(func=lowerCAmelCase_	)
  return parser





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Any:
  _lowerCAmelCase						:       List[Any]  			=     None

  # Get the default from the config file if it exists.
  if args.config_file is not None or os.path.isfile(lowerCAmelCase_	):
    _lowerCAmelCase						:       Union[str, Any]  			=     load_config_from_file(args.config_file	)
    if not args.command_file and defaults.command_file is not None and not args.command:
      _lowerCAmelCase						:       Union[str, Any]  			=     defaults.command_file
    if not args.command and defaults.commands is not None:
      _lowerCAmelCase						:       Union[str, Any]  			=     defaults.commands
    if not args.tpu_name:
      _lowerCAmelCase						:       List[str]  			=     defaults.tpu_name
    if not args.tpu_zone:
      _lowerCAmelCase						:       int  			=     defaults.tpu_zone
  if args.accelerate_version == "dev":
    _lowerCAmelCase						:       Union[str, Any]  			=     """git+https://github.com/huggingface/accelerate.git"""
  elif args.accelerate_version == "latest":
    _lowerCAmelCase						:       str  			=     """accelerate -U"""
  elif isinstance(parse(args.accelerate_version	)			,lowerCAmelCase_	):
    _lowerCAmelCase						:       Dict  			=     f"accelerate=={args.accelerate_version}"

  if not args.command_file and not args.command:
    raise ValueError("""You must specify either a command file or a command to run on the pod."""	)

  if args.command_file:
    with open(args.command_file			,"""r"""	) as f:
      _lowerCAmelCase						:       int  			=     [f.read().splitlines()]

    # To turn list of lists into list of strings
  if isinstance(args.command[0]			,lowerCAmelCase_	):
    _lowerCAmelCase						:       int  			=     [line for cmd in args.command for line in cmd]
  # Default to the shared folder and install accelerate
  _lowerCAmelCase						:       Dict  			=     ["""cd /usr/share"""]
  if args.install_accelerate:
    new_cmd += [f"pip install {args.accelerate_version}"]
  new_cmd += args.command
  _lowerCAmelCase						:       int  			=     """; """.join(lowerCAmelCase_	)

  # Then send it to gcloud
  # Eventually try to use google-api-core to do this instead of subprocess
  _lowerCAmelCase						:       Tuple  			=     ["""gcloud"""]
  if args.use_alpha:
    cmd += ["alpha"]
  cmd += [
      "compute",
      "tpus",
      "tpu-vm",
      "ssh",
      args.tpu_name,
      "--zone",
      args.tpu_zone,
      "--command",
      args.command,
      "--worker",
      "all",
  ]
  if args.debug:
    print(f"Running {' '.join(lowerCAmelCase_	)}"	)
    return
  subprocess.run(lowerCAmelCase_	)
  print("""Successfully setup pod."""	)






def 							SCREAMING_SNAKE_CASE     (			)       ->      List[Any]:
  _lowerCAmelCase						:       int  			=     tpu_command_parser()
  _lowerCAmelCase						:       Optional[int]  			=     parser.parse_args()

  tpu_command_launcher(lowerCAmelCase_	)








"""simple docstring"""







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


a__				:							Any						=     (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_   )
					return {
					    "acc": acc,
					    "f1": fa,
					    "acc_and_f1": (acc + fa) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					__SCREAMING_SNAKE_CASE						   =				spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					    "corr": (pearson_corr + spearman_corr) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""
					if task_name == "cola":
										return {"mcc": matthews_corrcoef(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "sst-2":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mrpc":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "sts-b":
										return pearson_and_spearman(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "qqp":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "mnli":
										return {"mnli/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mnli-mm":
										return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "qnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "rte":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "wnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "hans":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
										raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""   )
					if task_name == "xnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )
