





'''simple docstring'''



import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    '''compression_format, is_archive'''		,       [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]		,       )
def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       ):
			__lowercase										=							{
			    '''7z''': (seven_zip_file, SevenZipExtractor),
			    '''bz2''': (bza_file, BzipaExtractor),
			    '''gzip''': (gz_file, GzipExtractor),
			    '''lz4''': (lza_file, LzaExtractor),
			    '''tar''': (tar_file, TarExtractor),
			    '''xz''': (xz_file, XzExtractor),
			    '''zip''': (zip_file, ZipExtractor),
			    '''zstd''': (zstd_file, ZstdExtractor),
			}
			__lowercase						,		__lowercase										=							input_paths_and_base_extractors[compression_format]
			if input_path is None:
						__lowercase										=							f"for '{compression_format}' compression_format, "
						if compression_format == "7z":
									reason += require_pyazr.kwargs["reason"]
						elif compression_format == "lz4":
									reason += require_lza.kwargs["reason"]
						elif compression_format == "zstd":
									reason += require_zstandard.kwargs["reason"]
						pytest.skip(lowerCamelCase_				)
			assert base_extractor.is_extractable(lowerCamelCase_				)
			__lowercase										=							tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
			base_extractor.extract(lowerCamelCase_		,       lowerCamelCase_				)
			if is_archive:
						assert output_path.is_dir()
						for file_path in output_path.iterdir():
									assert file_path.name == text_file.name
									__lowercase										=							file_path.read_text(encoding='''utf-8'''				)
			else:
						__lowercase										=							output_path.read_text(encoding='''utf-8'''				)
			__lowercase										=							text_file.read_text(encoding='''utf-8'''				)
			assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    '''compression_format, is_archive'''		,       [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]		,       )
def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Any		,       ):
			__lowercase										=							{
			    '''7z''': seven_zip_file,
			    '''bz2''': bza_file,
			    '''gzip''': gz_file,
			    '''lz4''': lza_file,
			    '''tar''': tar_file,
			    '''xz''': xz_file,
			    '''zip''': zip_file,
			    '''zstd''': zstd_file,
			}
			__lowercase										=							input_paths[compression_format]
			if input_path is None:
						__lowercase										=							f"for '{compression_format}' compression_format, "
						if compression_format == "7z":
									reason += require_pyazr.kwargs["reason"]
						elif compression_format == "lz4":
									reason += require_lza.kwargs["reason"]
						elif compression_format == "zstd":
									reason += require_zstandard.kwargs["reason"]
						pytest.skip(lowerCamelCase_				)
			__lowercase										=							Extractor.infer_extractor_format(lowerCamelCase_				)
			assert extractor_format is not None
			__lowercase										=							tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
			Extractor.extract(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			if is_archive:
						assert output_path.is_dir()
						for file_path in output_path.iterdir():
									assert file_path.name == text_file.name
									__lowercase										=							file_path.read_text(encoding='''utf-8'''				)
			else:
						__lowercase										=							output_path.read_text(encoding='''utf-8'''				)
			__lowercase										=							text_file.read_text(encoding='''utf-8'''				)
			assert extracted_file_content == expected_file_content




@pytest.fixture
def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Any				):
			import tarfile

			__lowercase										=							tmp_path / '''data_dot_dot'''
			directory.mkdir()
			__lowercase										=							directory / '''tar_file_with_dot_dot.tar'''
			with tarfile.TarFile(lowerCamelCase_		,       '''w'''				) as f:
						f.add(lowerCamelCase_		,       arcname=os.path.join('''..'''		,       text_file.name				)				)
			return path




@pytest.fixture
def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]				):
			import tarfile

			__lowercase										=							tmp_path / '''data_sym_link'''
			directory.mkdir()
			__lowercase										=							directory / '''tar_file_with_sym_link.tar'''
			os.symlink('''..'''		,       directory / '''subdir'''		,       target_is_directory=lowerCamelCase_				)
			with tarfile.TarFile(lowerCamelCase_		,       '''w'''				) as f:
						f.add(str(directory / '''subdir'''				)		,       arcname='''subdir'''				)  # str required by os.readlink on Windows and Python < 3.8
			return path




@pytest.mark.parametrize(
    '''insecure_tar_file, error_log'''		,       [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')]		,       )
def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							{
			    '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
			    '''tar_file_with_sym_link''': tar_file_with_sym_link,
			}
			__lowercase										=							insecure_tar_files[insecure_tar_file]
			__lowercase										=							tmp_path / '''extracted'''
			TarExtractor.extract(lowerCamelCase_		,       lowerCamelCase_				)
			assert caplog.text
			for record in caplog.records:
						assert record.levelname == "ERROR"
						assert error_log in record.msg




def    _lowerCAmelCase						(				lowerCamelCase_			:  str				):
			# We should have less false positives than zipfile.is_zipfile
			# We do that by checking only the magic number
			__lowercase										=							tmpdir / '''not_a_zip_file'''
			# From: https://github.com/python/cpython/pull/5053
			__lowercase										=							(
			    b'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
			    b'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
			    b'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
			    b'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
			)
			with not_a_zip_file.open('''wb'''				) as f:
						f.write(lowerCamelCase_				)
			assert zipfile.is_zipfile(str(lowerCamelCase_				)				)  # is a false positive for `zipfile`
			assert not ZipExtractor.is_extractable(lowerCamelCase_				)  # but we're right












'''simple docstring'''



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

import datasets


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

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

_SCREAMING_SNAKE_CASE					=    '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''




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




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




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






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				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"]''' )





