





from scipy.stats import spearmanr

import datasets


a_			=      """
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.
"""

a_			=      """
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
"""

a_			=      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					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=False  ):



     '''simple docstring'''




     __A   :						Any									=					spearmanr(__lowerCamelCase ,						__lowerCamelCase  )
     if return_pvalue:
         return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
     else:
         return {"spearmanr": results[0]}











"""simple docstring"""



import functools
import operator

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


a_			=      logging.get_logger(__name__)

a_			=      {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""sew-d"""

 def __init__(   self ,						__lowerCamelCase=32 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase=2 ,						__lowerCamelCase=512 ,						__lowerCamelCase=256 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=("p2c", "c2p") ,						__lowerCamelCase="layer_norm" ,						__lowerCamelCase="gelu_python" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-7 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase="group" ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,						__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,						__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,						__lowerCamelCase=False ,						__lowerCamelCase=128 ,						__lowerCamelCase=16 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0_5 ,						__lowerCamelCase=10 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=10 ,						__lowerCamelCase=0 ,						__lowerCamelCase="mean" ,						__lowerCamelCase=False ,						__lowerCamelCase=False ,						__lowerCamelCase=256 ,						__lowerCamelCase=0 ,						__lowerCamelCase=1 ,						__lowerCamelCase=2 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase ,						pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase  )
     __A   :						str									=					hidden_size
     __A   :						List[Any]									=					feat_extract_norm
     __A   :						Tuple									=					feat_extract_activation
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						int									=					list(__lowerCamelCase  )
     __A   :						List[Any]									=					list(__lowerCamelCase  )
     __A   :						Any									=					conv_bias
     __A   :						List[Any]									=					num_conv_pos_embeddings
     __A   :						Any									=					num_conv_pos_embedding_groups
     __A   :						Optional[Any]									=					len(self.conv_dim  )
     __A   :						int									=					num_hidden_layers
     __A   :						Union[str, Any]									=					intermediate_size
     __A   :						Union[str, Any]									=					squeeze_factor
     __A   :						int									=					max_position_embeddings
     __A   :						Tuple									=					position_buckets
     __A   :						Tuple									=					share_att_key
     __A   :						List[str]									=					relative_attention
     __A   :						Optional[Any]									=					norm_rel_ebd
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						str									=					hidden_act
     __A   :						List[str]									=					num_attention_heads
     __A   :						Union[str, Any]									=					hidden_dropout
     __A   :						Optional[int]									=					attention_dropout
     __A   :						Optional[Any]									=					activation_dropout
     __A   :						List[str]									=					feat_proj_dropout
     __A   :						str									=					final_dropout
     __A   :						Tuple									=					layer_norm_eps
     __A   :						int									=					feature_layer_norm_eps
     __A   :						Optional[int]									=					initializer_range
     __A   :						Dict									=					vocab_size

     if (
         (len(self.conv_stride  ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
         or (len(self.conv_dim  ) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect.'''
             '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
             F"""but is `len(config.conv_dim) = {len(self.conv_dim  )}`, `len(config.conv_stride)"""
             F"""= {len(self.conv_stride  )}`, `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     __A   :						int									=					apply_spec_augment
     __A   :						Any									=					mask_time_prob
     __A   :						Optional[int]									=					mask_time_length
     __A   :						Any									=					mask_time_min_masks
     __A   :						int									=					mask_feature_prob
     __A   :						Tuple									=					mask_feature_length
     __A   :						Dict									=					mask_feature_min_masks

     # ctc loss
     __A   :						Tuple									=					ctc_loss_reduction
     __A   :						Union[str, Any]									=					ctc_zero_infinity

     # sequence classification
     __A   :						Tuple									=					use_weighted_layer_sum
     __A   :						List[str]									=					classifier_proj_size







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return functools.reduce(operator.mul ,						self.conv_stride ,						1  )




