from scipy.stats import pearsonr

import datasets


__lowerCamelCase					:	List[Any]       =  """
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


__lowerCamelCase					:	Optional[int]       =  """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__lowerCamelCase					:	str       =  """
@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, Ilhan 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, Antonio 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 		a__						(					datasets.Metric					):

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""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.pearsonr.html"],)






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Optional[int],_A      :       Optional[Any],_A      :       Any=False    ):







							"""simple docstring"""






							if return_pvalue:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    pearsonr(__lowerCAmelCase,__lowerCAmelCase    )
									return {"pearsonr": results[0], "p-value": results[1]}
							else:
									return {"pearsonr": float(pearsonr(__lowerCAmelCase,__lowerCAmelCase    )[0]    )}

"""simple docstring"""






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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """simple docstring"""



            if self.rope_scaling is None:
                  return

            if not isinstance(self.rope_scaling		,       __lowerCAmelCase						) or len(self.rope_scaling						) != 2:
                  raise ValueError(
                      """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                      f'got {self.rope_scaling}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	self.rope_scaling.get("""factor"""		,       __lowerCAmelCase						)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                  raise ValueError(
                      f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
            if rope_scaling_factor is None or not isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) or rope_scaling_factor <= 1.0:
                  raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)



