


"""simple docstring"""






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_lowerCamelCase     =  '\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

_lowerCamelCase     =  '\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

_lowerCamelCase     =  '\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'


def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      int  ):
				return float((preds == labels).mean()  )


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				simple_accuracy(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				float(fa_score(y_true=lowerCamelCase_						,					y_pred=lowerCamelCase_  )  )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}


def __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Any  ):
				SCREAMING_SNAKE_CASE__     							=				float(pearsonr(lowerCamelCase_						,					lowerCamelCase_  )[0]  )
				SCREAMING_SNAKE_CASE__     							=				float(spearmanr(lowerCamelCase_						,					lowerCamelCase_  )[0]  )
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}


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





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):





								if self.config_name not in [
								    "sst2",
								    "mnli",
								    "mnli_mismatched",
								    "mnli_matched",
								    "cola",
								    "stsb",
								    "mrpc",
								    "qqp",
								    "qnli",
								    "rte",
								    "wnli",
								    "hans",
								]:
												raise KeyError(
												    "You should supply a configuration name selected in "
												    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
												    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"     )
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"     ),
								            "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"     ),
								        }     )       ,		codebase_urls=[]       ,		reference_urls=[]       ,		format="numpy"       ,		)






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):





								if self.config_name == "cola":
												return {"matthews_correlation": matthews_corrcoef(_lowerCamelCase       ,		_lowerCamelCase     )}
								elif self.config_name == "stsb":
												return pearson_and_spearman(_lowerCamelCase       ,		_lowerCamelCase     )
								elif self.config_name in ["mrpc", "qqp"]:
												return acc_and_fa(_lowerCamelCase       ,		_lowerCamelCase     )
								elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
												return {"accuracy": simple_accuracy(_lowerCamelCase       ,		_lowerCamelCase     )}
								else:
												raise KeyError(
												    "You should supply a configuration name selected in "
												    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
												    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"     )




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
