




'''simple docstring'''





from scipy.stats import spearmanr

import datasets


_SCREAMING_SNAKE_CASE				:  List[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.
"""

_SCREAMING_SNAKE_CASE				:  Any 						=					"""
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
"""

_SCREAMING_SNAKE_CASE				:  List[Any] 						=					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   lowerCAmelCase__							(   self				)							->							Optional[Any]:

										'''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   lowerCAmelCase__							(   self							,				a__							,				a__							,				a__=False				)							->							int:

										'''simple docstring'''




										snake_case_         = spearmanr(UpperCAmelCase__							,				UpperCAmelCase__				)
										if return_pvalue:
																	return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
																	return {"spearmanr": results[0]}










from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      :						str							=  logging.get_logger(__name__)

_lowerCamelCase      :						List[str]							=  {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''openai-gpt'''
    UpperCAmelCase__				    = {
        '''max_position_embeddings''': '''n_positions''',
        '''hidden_size''': '''n_embd''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }


    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Dict=40_478   ,					UpperCAmelCase__ :      str=512   ,					UpperCAmelCase__ :      Union[str, Any]=768   ,					UpperCAmelCase__ :      Optional[Any]=12   ,					UpperCAmelCase__ :      Any=12   ,					UpperCAmelCase__ :      Optional[Any]="gelu"   ,					UpperCAmelCase__ :      Any=0.1   ,					UpperCAmelCase__ :      List[str]=0.1   ,					UpperCAmelCase__ :      Tuple=0.1   ,					UpperCAmelCase__ :      List[str]=1e-5   ,					UpperCAmelCase__ :      int=0.02   ,					UpperCAmelCase__ :      Any="cls_index"   ,					UpperCAmelCase__ :      Optional[Any]=True   ,					UpperCAmelCase__ :      List[Any]=None   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Optional[Any]=0.1   ,					**UpperCAmelCase__ :      Dict   ,					)		->Any:





           '''simple docstring'''

           A__ 					=       vocab_size
           A__ 					=       n_positions
           A__ 					=       n_embd
           A__ 					=       n_layer
           A__ 					=       n_head
           A__ 					=       afn
           A__ 					=       resid_pdrop
           A__ 					=       embd_pdrop
           A__ 					=       attn_pdrop
           A__ 					=       layer_norm_epsilon
           A__ 					=       initializer_range
           A__ 					=       summary_type
           A__ 					=       summary_use_proj
           A__ 					=       summary_activation
           A__ 					=       summary_first_dropout
           A__ 					=       summary_proj_to_labels
           super().__init__(**UpperCAmelCase__)


