



"""simple docstring"""






from scipy.stats import pearsonr

import datasets


lowerCAmelCase__        =				'''
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__        =				'''
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__        =				'''
@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        __snake_case       (	datasets.Metric):




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """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 						SCREAMING_SNAKE_CASE				( self     :						Any						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Optional[Any]=False		):






         """simple docstring"""






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






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


UpperCAmelCase_      : Dict  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Union[str, Any]  					=       {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[str]        =						'''switch_transformers'''
      snake_case__				:     Optional[int]        =						['''past_key_values''']
      snake_case__				:     Optional[Any]        =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_2_1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=8     ,							SCREAMING_SNAKE_CASE__				:       Dict=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.01     ,							SCREAMING_SNAKE_CASE__				:       str="float32"     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Dict=1E-6     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.001     ,							SCREAMING_SNAKE_CASE__				:       Any=0.001     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1.0     ,							SCREAMING_SNAKE_CASE__				:       Any="relu"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1     ,							**SCREAMING_SNAKE_CASE__				:       Dict     ,							)		->			Optional[Any]:
             a_					:		Optional[int]							=  vocab_size
             a_					:		List[str]							=  d_model
             a_					:		Tuple							=  d_kv
             a_					:		Optional[Any]							=  d_ff

             a_					:		List[Any]							=  num_sparse_encoder_layers

             a_					:		Any							=  num_layers
             a_					:		str							=  (
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             a_					:		List[Any]							=  num_sparse_decoder_layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_encoder_layers > 0:
                    a_					:		Optional[int]							=  self.num_layers // self.num_sparse_encoder_layers
             else:
                    a_					:		List[Any]							=  self.num_layers  # HACK: this will create 0 sparse layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_decoder_layers > 0:
                    a_					:		Union[str, Any]							=  self.num_decoder_layers // self.num_sparse_decoder_layers
             else:
                    a_					:		List[str]							=  self.num_decoder_layers  # HACK: this will create 0 sparse layers

             a_					:		Dict							=  num_heads
             a_					:		str							=  num_experts
             a_					:		Any							=  expert_capacity
             a_					:		List[Any]							=  router_bias
             a_					:		str							=  router_jitter_noise
             if router_dtype not in ["float32", "float16", "bfloat16"]:
                    raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""     )
             a_					:		Optional[int]							=  router_dtype

             a_					:		int							=  router_ignore_padding_tokens
             a_					:		Any							=  relative_attention_num_buckets
             a_					:		List[str]							=  relative_attention_max_distance

             a_					:		Optional[Any]							=  dropout_rate
             a_					:		Tuple							=  layer_norm_epsilon
             a_					:		Dict							=  initializer_factor
             a_					:		Any							=  feed_forward_proj
             a_					:		Tuple							=  use_cache
             a_					:		str							=  add_router_probs

             a_					:		Optional[int]							=  router_z_loss_coef
             a_					:		List[str]							=  router_aux_loss_coef

             a_					:		int							=  self.feed_forward_proj.split('-'     )
             a_					:		int							=  act_info[-1]
             a_					:		Optional[int]							=  act_info[0] == 'gated'

             if len(SCREAMING_SNAKE_CASE__     ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__     ) > 2:
                    raise ValueError(
                        F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                        'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                        '\'gated-gelu\' or \'relu\''     )

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                    a_					:		Any							=  'gelu_new'

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							is_encoder_decoder=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)


