


'''simple docstring'''







from scipy.stats import spearmanr

import datasets


A				:					str    						=			'''
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				:					str    						=			'''
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				:					str    						=			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 			__lowerCamelCase			(	datasets.Metric	):
    """simple docstring"""


    def        A   (  self      :							Tuple):
           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        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str=False):
           _A					:     Union[str, Any]     = spearmanr(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           if return_pvalue:
                  return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
           else:
                  return {"spearmanr": results[0]}










'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


A				:					Tuple    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "longformer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[List[int], int] = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 30522				,							SCREAMING_SNAKE_CASE      :							int = 768				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 3072				,							SCREAMING_SNAKE_CASE      :							str = "gelu"				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							float = 1e-12				,							SCREAMING_SNAKE_CASE      :							bool = False				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           super().__init__(pad_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = attention_window
           _A					:     int     = sep_token_id
           _A					:     Tuple     = bos_token_id
           _A					:     Any     = eos_token_id
           _A					:     List[str]     = vocab_size
           _A					:     Any     = hidden_size
           _A					:     Optional[int]     = num_hidden_layers
           _A					:     int     = num_attention_heads
           _A					:     Dict     = hidden_act
           _A					:     List[Any]     = intermediate_size
           _A					:     int     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     int     = max_position_embeddings
           _A					:     Any     = type_vocab_size
           _A					:     Dict     = initializer_range
           _A					:     Any     = layer_norm_eps
           _A					:     List[Any]     = onnx_export




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							"PretrainedConfig"				,							SCREAMING_SNAKE_CASE      :							str = "default"				,							SCREAMING_SNAKE_CASE      :							"List[PatchingSpec]" = None):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = True


    @property
    def        A   (  self      :							List[str]):
           if self.task == "multiple-choice":
                  _A					:     Tuple     = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  _A					:     List[Any]     = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])


    @property
    def        A   (  self      :							str):
           _A					:     int     = super().outputs
           if self.task == "default":
                  _A					:     str     = {0: 'batch'}
           return outputs


    @property
    def        A   (  self      :							List[Any]):
           return 1e-4


    @property
    def        A   (  self      :							Dict):
           # needs to be >= 14 to support tril operator
           return max(super().default_onnx_opset				,							14)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							"PreTrainedTokenizerBase"				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[TensorType] = None				,							):
           _A					:     Union[str, Any]     = super().generate_dummy_inputs(
               preprocessor=SCREAMING_SNAKE_CASE				,							batch_size=SCREAMING_SNAKE_CASE				,							seq_length=SCREAMING_SNAKE_CASE				,							is_pair=SCREAMING_SNAKE_CASE				,							framework=SCREAMING_SNAKE_CASE)
           import torch

           # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
           # makes the export fail randomly
           _A					:     Tuple     = torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _A					:     Dict     = 1

           return inputs






