




from scipy.stats import pearsonr

import datasets


lowerCamelCase__			:       Any         =							"\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"


lowerCamelCase__			:       Dict         =							"\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"


lowerCamelCase__			:       Union[str, Any]         =							"\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 	lowerCamelCase_							(				datasets.Metric ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		str						):
							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 				lowerCAmelCase_  (							self  :		List[str]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		int=False						):
							if return_pvalue:
													SCREAMING_SNAKE_CASE_     						=      pearsonr(__A    ,    __A						)
													return {"pearsonr": results[0], "p-value": results[1]}
							else:
													return {"pearsonr": float(pearsonr(__A    ,    __A						)[0]						)}





import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase__			:       str         =							get_tests_dir('fixtures/dummy-config.json')





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      0



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       self.assertIsNotNone(transformers.models.auto.__spec__						)
       self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto'						)						)



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('bert-base-uncased'						)
       self.assertIsInstance(_lowerCAmelCase    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase						)
       self.assertIsInstance(_lowerCAmelCase    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase						)
       self.assertIsInstance(_lowerCAmelCase    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      AutoConfig.for_model('roberta'						)
       self.assertIsInstance(_lowerCAmelCase    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       with tempfile.TemporaryDirectory() as tmp_dir:
             # This model name contains bert and roberta, but roberta ends up being picked.
             SCREAMING_SNAKE_CASE_     						=      os.path.join(_lowerCAmelCase    ,    'fake-roberta'						)
             os.makedirs(_lowerCAmelCase    ,    exist_ok=_lowerCAmelCase						)
             with open(os.path.join(_lowerCAmelCase    ,    'config.json'						)    ,    'w'						) as f:
                   f.write(json.dumps({}						)						)
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase						)
             self.assertEqual(type(_lowerCAmelCase						)    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       try:
             AutoConfig.register('custom'    ,    _lowerCAmelCase						)
             # Wrong model type will raise an error
             with self.assertRaises(_lowerCAmelCase						):
                   AutoConfig.register('model'    ,    _lowerCAmelCase						)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_lowerCAmelCase						):
                   AutoConfig.register('bert'    ,    _lowerCAmelCase						)

             # Now that the config is registered, it can be used as any other config with the auto-API
             SCREAMING_SNAKE_CASE_     						=      CustomConfig()
             with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase						)
                   self.assertIsInstance(_lowerCAmelCase    ,    _lowerCAmelCase						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       with self.assertRaisesRegex(
           _lowerCAmelCase    ,    'bert-base is not a local folder and is not a valid model identifier'						):
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('bert-base'						)



 def 				lowerCAmelCase_  (							self  :		int						):
       with self.assertRaisesRegex(
           _lowerCAmelCase    ,    R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase    ,    revision='aaaaaa'						)



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       with self.assertRaisesRegex(
           _lowerCAmelCase    ,    'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.'    ,    ):
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo'						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'						)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    ,    trust_remote_code=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    ,    trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(config.__class__.__name__    ,    'NewModelConfig'						)

       # Test config can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(_lowerCAmelCase    ,    trust_remote_code=_lowerCAmelCase						)
       self.assertEqual(reloaded_config.__class__.__name__    ,    'NewModelConfig'						)




 def 				lowerCAmelCase_  (							self  :		Any						):





       class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


        '''simple docstring'''







        lowercase_       =   "new-model"

       try:
             AutoConfig.register('new-model'    ,    _lowerCAmelCase						)
             # If remote code is not set, the default is to use local
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'						)
             self.assertEqual(config.__class__.__name__    ,    'NewModelConfigLocal'						)

             # If remote code is disabled, we load the local one.
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    ,    trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(config.__class__.__name__    ,    'NewModelConfigLocal'						)

             # If remote is enabled, we load from the Hub
             SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model'    ,    trust_remote_code=_lowerCAmelCase						)
             self.assertEqual(config.__class__.__name__    ,    'NewModelConfig'						)

       finally:
             if "new-model" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["new-model"]