'''simple docstring'''






from sklearn.metrics import mean_absolute_error

import datasets


_UpperCAmelCase						:    Optional[int]     					=					'''\
@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						:    Optional[int]     					=					'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_UpperCAmelCase						:    int     					=					'''
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.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

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

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''







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







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: Optional[Any]    )     -> int:
        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_absolute_error.html'
            ],   )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> List[Any]:
        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			: Optional[Any],   UpperCamelCase__			: str,   UpperCamelCase__			: str,   UpperCamelCase__			: str=None,   UpperCamelCase__			: Union[str, Any]="uniform_average"    )     -> Optional[Any]:
        _A								=     mean_absolute_error(UpperCamelCase__,   UpperCamelCase__,   sample_weight=UpperCamelCase__,   multioutput=UpperCamelCase__    )

        return {"mae": mae_score}




'''simple docstring'''






import argparse
import os
import sys
import urllib.request
import zipfile


_UpperCAmelCase						:    int     					=					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCAmelCase						:    Dict     					=					{
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

_UpperCAmelCase						:    Any     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCAmelCase						:    List[Any]     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Union[str, Any]							):
     print(F'Downloading and extracting {task}...'							)
     _A								=     F'{task}.zip'
     urllib.request.urlretrieve(TASKaPATH[task]      ,		__snake_case							)
     with zipfile.ZipFile(__snake_case							) as zip_ref:
          zip_ref.extractall(__snake_case							)
     os.remove(__snake_case							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : str							):
     print('Processing MRPC...'							)
     _A								=     os.path.join(__snake_case      ,		'MRPC'							)
     if not os.path.isdir(__snake_case							):
          os.mkdir(__snake_case							)
     if path_to_data:
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
     else:
          print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Train data not found at {mrpc_train_file}'							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Test data not found at {mrpc_test_file}'							)
     urllib.request.urlretrieve(TASKaPATH['MRPC']      ,		os.path.join(__snake_case      ,		'dev_ids.tsv'							)							)

     _A								=     []
     with open(os.path.join(__snake_case      ,		'dev_ids.tsv'							)      ,		encoding='utf8'							) as ids_fh:
          for row in ids_fh:
               dev_ids.append(row.strip().split('\t'							)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'train.tsv'							)      ,		'w'      ,		encoding='utf8'							) as train_fh, open(os.path.join(__snake_case      ,		'dev.tsv'							)      ,		'w'      ,		encoding='utf8'							) as dev_fh:
          _A								=     data_fh.readline()
          train_fh.write(__snake_case							)
          dev_fh.write(__snake_case							)
          for row in data_fh:
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               if [ida, ida] in dev_ids:
                    dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)
               else:
                    train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'test.tsv'							)      ,		'w'      ,		encoding='utf8'							) as test_fh:
          _A								=     data_fh.readline()
          test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'							)
          for idx, row in enumerate(__snake_case							):
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     print('Downloading and extracting diagnostic...'							)
     if not os.path.isdir(os.path.join(__snake_case      ,		'diagnostic'							)							):
          os.mkdir(os.path.join(__snake_case      ,		'diagnostic'							)							)
     _A								=     os.path.join(__snake_case      ,		'diagnostic'      ,		'diagnostic.tsv'							)
     urllib.request.urlretrieve(TASKaPATH['diagnostic']      ,		__snake_case							)
     print('\tCompleted!'							)
     return





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     task_names.split(','							)
     if "all" in task_names:
          _A								=     TASKS
     else:
          _A								=     []
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(F'Task {task_name} not found!'							)
               tasks.append(__snake_case							)
     return tasks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]							):
     _A								=     argparse.ArgumentParser()
     parser.add_argument('--data_dir'      ,		help='directory to save data to'      ,		type=__snake_case      ,		default='glue_data'							)
     parser.add_argument(
         '--tasks'      ,		help='tasks to download data for as a comma separated string'      ,		type=__snake_case      ,		default='all'							)
     parser.add_argument(
         '--path_to_mrpc'      ,		help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'      ,		type=__snake_case      ,		default=''      ,		)
     _A								=     parser.parse_args(__snake_case							)

     if not os.path.isdir(args.data_dir							):
          os.mkdir(args.data_dir							)
     _A								=     get_tasks(args.tasks							)

     for task in tasks:
          if task == "MRPC":
               format_mrpc(args.data_dir      ,		args.path_to_mrpc							)
          elif task == "diagnostic":
               download_diagnostic(args.data_dir							)
          else:
               download_and_extract(__snake_case      ,		args.data_dir							)


if __name__ == "__main__":
   sys.exit(main(sys.argv[1:]))



