






'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a_					=	'__DUMMY_TRANSFORMERS_USER__'
a_					=	'Dummy User'
a_					=	'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'

a_					=	'https://hub-ci.huggingface.co'
a_					=	CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
a_					=	CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
a_					=	Path('~/.huggingface/hub_ci_token').expanduser()





@pytest.fixture
def 	_a(					UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      monkeypatch.setattr(
          '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''',		UpperCamelCase__							)





@pytest.fixture
def 	_a(					UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      monkeypatch.setattr('''datasets.config.HF_ENDPOINT''',		UpperCamelCase__							)
      monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''',		UpperCamelCase__							)





@pytest.fixture
def 	_a(					UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''',		UpperCamelCase__							)





@pytest.fixture
def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      HfFolder.save_token(UpperCamelCase__							)
      yield
      HfFolder.delete_token()





@pytest.fixture(scope='''session'''							)
def 	_a(					):




      '''simple docstring'''






      return HfApi(endpoint=UpperCamelCase__							)





@pytest.fixture(scope='''session'''							)
def 	_a(					UpperCamelCase__			:  HfApi							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=HfFolder.get_token()
      HfFolder.save_token(UpperCamelCase__							)
      yield CI_HUB_USER_TOKEN
      if previous_token is not None:
            HfFolder.save_token(UpperCamelCase__							)





@pytest.fixture
def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      def _cleanup_repo(UpperCamelCase__			:  List[Any]							):
            hf_api.delete_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset'''							)

      return _cleanup_repo





@pytest.fixture
def 	_a(					UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      @contextmanager
      def _temporary_repo(UpperCamelCase__			:  Optional[int]							):
            try:
                  yield repo_id
            finally:
                  cleanup_repo(UpperCamelCase__							)

      return _temporary_repo





@pytest.fixture(scope='''session'''							)
def 	_a(					UpperCamelCase__			:  HfApi,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=f"repo_txt_data-{int(time.time() * 10e3							)}"
      SCREAMING_SNAKE_CASE__       :			Any													=f"{CI_HUB_USER}/{repo_name}"
      hf_api.create_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset''',		private=UpperCamelCase__							)
      hf_api.upload_file(
          token=UpperCamelCase__,		path_or_fileobj=str(UpperCamelCase__							),		path_in_repo='''data/text_data.txt''',		repo_id=UpperCamelCase__,		repo_type='''dataset''',		)
      yield repo_id
      try:
            hf_api.delete_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset'''							)
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass





@pytest.fixture()
def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  int,		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      return hf_private_dataset_repo_txt_data_





@pytest.fixture(scope='''session'''							)
def 	_a(					UpperCamelCase__			:  HfApi,		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=f"repo_zipped_txt_data-{int(time.time() * 10e3							)}"
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=f"{CI_HUB_USER}/{repo_name}"
      hf_api.create_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset''',		private=UpperCamelCase__							)
      hf_api.upload_file(
          token=UpperCamelCase__,		path_or_fileobj=str(UpperCamelCase__							),		path_in_repo='''data.zip''',		repo_id=UpperCamelCase__,		repo_type='''dataset''',		)
      yield repo_id
      try:
            hf_api.delete_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset'''							)
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass





@pytest.fixture()
def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      return hf_private_dataset_repo_zipped_txt_data_





@pytest.fixture(scope='''session'''							)
def 	_a(					UpperCamelCase__			:  HfApi,		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[int]													=f"repo_zipped_img_data-{int(time.time() * 10e3							)}"
      SCREAMING_SNAKE_CASE__       :			Any													=f"{CI_HUB_USER}/{repo_name}"
      hf_api.create_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset''',		private=UpperCamelCase__							)
      hf_api.upload_file(
          token=UpperCamelCase__,		path_or_fileobj=str(UpperCamelCase__							),		path_in_repo='''data.zip''',		repo_id=UpperCamelCase__,		repo_type='''dataset''',		)
      yield repo_id
      try:
            hf_api.delete_repo(UpperCamelCase__,		token=UpperCamelCase__,		repo_type='''dataset'''							)
      except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
            pass







@pytest.fixture()
def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      return hf_private_dataset_repo_zipped_img_data_









'''simple docstring'''
from scipy.stats import pearsonr

import datasets


a_					=	'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


a_					=	'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


a_					=	'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'






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




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       return datasets.MetricInfo(
           description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float''' ),
                   '''references''': datasets.Value('''float''' ),
               } )   , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']   , )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=False )		->  int:
       if return_pvalue:
             SCREAMING_SNAKE_CASE__       :			List[str]													=pearsonr(__lowercase   , __lowercase )
             return {"pearsonr": results[0], "p-value": results[1]}
       else:
             return {"pearsonr": float(pearsonr(__lowercase   , __lowercase )[0] )}