'''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


UpperCamelCase__ :			Optional[Any]												=				'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int]=None      ):
		if subparsers is not None:
				__SCREAMING_SNAKE_CASE :	int					 =	subparsers.add_parser("""tpu-config""" ,   description=_description      )
		else:
				__SCREAMING_SNAKE_CASE :	str					 =	argparse.ArgumentParser("""Accelerate tpu-config command""" ,   description=_description      )
		# Core arguments
		__SCREAMING_SNAKE_CASE :	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.""" ,   )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	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        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	None

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

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

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	["""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        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	int					 =	tpu_command_parser()
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parser.parse_args()

		tpu_command_launcher(_lowerCamelCase      )




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			Optional[Any]												=				'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

UpperCamelCase__ :			List[str]												=				'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCamelCase__ :			Dict												=				'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,      _KWARGS_DESCRIPTION					)
class 		_UpperCamelCase						(							datasets.Metric					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					return datasets.MetricInfo(
					    description=_DESCRIPTION							,  citation=_CITATION							,  inputs_description=_KWARGS_DESCRIPTION							,  features=datasets.Features(self._get_feature_types()		)							,  reference_urls=[
					        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
					    ]							,  )

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					if self.config_name == "multilist":
							return {
							    "predictions": datasets.Sequence(datasets.Value("""float"""		)		),
							    "references": datasets.Sequence(datasets.Value("""float"""		)		),
							}
					else:
							return {
							    "predictions": datasets.Value("""float"""		),
							    "references": datasets.Value("""float"""		),
							}



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}