"""simple docstring"""




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


_a      :		str           =   logging.get_logger(__name__)

_a      :		int           =   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			str							=		"mra"






     def __init__(		self				,     a__=50265				,     a__=768				,     a__=12				,     a__=12				,     a__=3072				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=1				,     a__=0.0_2				,     a__=1e-5				,     a__="absolute"				,     a__=4				,     a__="full"				,     a__=0				,     a__=0				,     a__=1				,     a__=0				,     a__=2				,     **a__				,     ):
       super().__init__(pad_token_id=a__				,     bos_token_id=a__				,     eos_token_id=a__				,     **a__  )

       _lowerCAmelCase						:       List[Any]  			=     vocab_size
       _lowerCAmelCase						:       str  			=     max_position_embeddings
       _lowerCAmelCase						:       Any  			=     hidden_size
       _lowerCAmelCase						:       Tuple  			=     num_hidden_layers
       _lowerCAmelCase						:       Dict  			=     num_attention_heads
       _lowerCAmelCase						:       Tuple  			=     intermediate_size
       _lowerCAmelCase						:       List[Any]  			=     hidden_act
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     type_vocab_size
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     position_embedding_type
       _lowerCAmelCase						:       Dict  			=     block_per_row
       _lowerCAmelCase						:       int  			=     approx_mode
       _lowerCAmelCase						:       Dict  			=     initial_prior_first_n_blocks
       _lowerCAmelCase						:       Any  			=     initial_prior_diagonal_n_blocks





"""simple docstring"""




from scipy.stats import pearsonr

import datasets


_a      :		str           =   '\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      :		List[str]           =   '\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      :		List[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 						__A							( datasets.Metric		):






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




