
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCamelCase__    :		Optional[Any]          =   get_tests_dir("""fixtures""")



class       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      mock.Mock()
   lowercase__  :   Union[str, Any]    		=      5_00
   lowercase__  :   Union[str, Any]    		=      {}
   lowercase__  :   Any    		=      HTTPError
   lowercase__  :   str    		=      {}

   # Download this model to make sure it's in the cache.
   lowercase__  :   Optional[int]    		=      WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2""")
   # Under the mock environment we get a 500 error when trying to reach the model.
   with mock.patch("""requests.Session.request"""					,  return_value=SCREAMING_SNAKE_CASE_) as mock_head:
     lowercase__  :   int    		=      WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2""")
     # This check we did call the fake head request
     mock_head.assert_called()



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      WavaVecaFeatureExtractor.from_pretrained(
       """https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json""")



@is_staging_test
class       _snake_case   (	unittest.TestCase							):


 @classmethod
 def      lowercase__     (					cls):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      TOKEN
   HfFolder.save_token(SCREAMING_SNAKE_CASE_)


 @classmethod
 def      lowercase__     (					cls):


   '''simple docstring'''




   try:
     delete_repo(token=cls._token					,  repo_id="""test-feature-extractor""")
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token					,  repo_id="""valid_org/test-feature-extractor-org""")
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token					,  repo_id="""test-dynamic-feature-extractor""")
   except HTTPError:
     pass


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_)
   feature_extractor.push_to_hub("""test-feature-extractor"""					,  use_auth_token=self._token)

   lowercase__  :   Tuple    		=      WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor')
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_))

   # Reset repo
   delete_repo(token=self._token					,  repo_id="""test-feature-extractor""")

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         SCREAMING_SNAKE_CASE_					,  repo_id="""test-feature-extractor"""					,  push_to_hub=SCREAMING_SNAKE_CASE_					,  use_auth_token=self._token)

   lowercase__  :   int    		=      WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor')
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_))


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_)
   feature_extractor.push_to_hub("""valid_org/test-feature-extractor"""					,  use_auth_token=self._token)

   lowercase__  :   str    		=      WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor""")
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_))

   # Reset repo
   delete_repo(token=self._token					,  repo_id="""valid_org/test-feature-extractor""")

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         SCREAMING_SNAKE_CASE_					,  repo_id="""valid_org/test-feature-extractor-org"""					,  push_to_hub=SCREAMING_SNAKE_CASE_					,  use_auth_token=self._token)

   lowercase__  :   str    		=      WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org""")
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_))



 def      lowercase__     (					self):


   '''simple docstring'''




   CustomFeatureExtractor.register_for_auto_class()
   lowercase__  :   Optional[int]    		=      CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_)

   feature_extractor.push_to_hub("""test-dynamic-feature-extractor"""					,  use_auth_token=self._token)

   # This has added the proper auto_map field to the config
   self.assertDictEqual(
       feature_extractor.auto_map					,  {"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor"""}					,  )

   lowercase__  :   Optional[Any]    		=      AutoFeatureExtractor.from_pretrained(
       f'{USER}/test-dynamic-feature-extractor'					,  trust_remote_code=SCREAMING_SNAKE_CASE_)
   # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
   self.assertEqual(new_feature_extractor.__class__.__name__					,  """CustomFeatureExtractor""")








from scipy.stats import spearmanr

import datasets


lowerCamelCase__    :		Union[str, Any]          =   """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

lowerCamelCase__    :		int          =   """
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

lowerCamelCase__    :		str          =   R"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""



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


 def      lowercase__     (					self):


   '''simple docstring'''




   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.spearmanr.html"""]					,  )



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      spearmanr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   if return_pvalue:
     return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
     return {"spearmanr": results[0]}






