






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
    import transformers



def 		A_    (					_lowerCAmelCase					)						->			str:
 random.seed(_lowerCAmelCase					)
 np.random.seed(_lowerCAmelCase					)
 torch.manual_seed(_lowerCAmelCase					)
 torch.cuda.manual_seed_all(_lowerCAmelCase					)
 # ^^ safe to call this function even if cuda is not available



class  A__       :





      def __init__(			self	,							A_	,							A_ = 0.99_99	,							A_ = 0.0	,							A_ = 0	,							A_ = False	,							A_ = 1.0	,							A_ = 2 / 3	,							A_ = None	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       if isinstance(A_	,							torch.nn.Module       ):
        UpperCamelCase     : Optional[int]	     =       (
            "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
            "Please pass the parameters of the module instead."
        )
        deprecate(
            "passing a `torch.nn.Module` to `ExponentialMovingAverage`"	,							"1.0.0"	,							A_	,							standard_warn=A_	,							)
        UpperCamelCase     : List[str]	     =       parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        UpperCamelCase     : str	     =       True

       if kwargs.get("max_value"	,							A_       ) is not None:
        UpperCamelCase     : int	     =       "The `max_value` argument is deprecated. Please use `decay` instead."
        deprecate("max_value"	,							"1.0.0"	,							A_	,							standard_warn=A_       )
        UpperCamelCase     : Dict	     =       kwargs["max_value"]

       if kwargs.get("min_value"	,							A_       ) is not None:
        UpperCamelCase     : int	     =       "The `min_value` argument is deprecated. Please use `min_decay` instead."
        deprecate("min_value"	,							"1.0.0"	,							A_	,							standard_warn=A_       )
        UpperCamelCase     : Any	     =       kwargs["min_value"]

       UpperCamelCase     : Dict	     =       list(A_       )
       UpperCamelCase     : List[Any]	     =       [p.clone().detach() for p in parameters]

       if kwargs.get("device"	,							A_       ) is not None:
        UpperCamelCase     : str	     =       "The `device` argument is deprecated. Please use `to` instead."
        deprecate("device"	,							"1.0.0"	,							A_	,							standard_warn=A_       )
        self.to(device=kwargs["device"]       )

       UpperCamelCase     : Tuple	     =       None

       UpperCamelCase     : int	     =       decay
       UpperCamelCase     : Tuple	     =       min_decay
       UpperCamelCase     : Union[str, Any]	     =       update_after_step
       UpperCamelCase     : str	     =       use_ema_warmup
       UpperCamelCase     : Any	     =       inv_gamma
       UpperCamelCase     : str	     =       power
       UpperCamelCase     : Optional[int]	     =       0
       UpperCamelCase     : Optional[Any]	     =       None  # set in `step()`

       UpperCamelCase     : Tuple	     =       model_cls
       UpperCamelCase     : Dict	     =       model_config





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       model_cls.load_config(A_	,							return_unused_kwargs=A_       )
       UpperCamelCase     : Tuple	     =       model_cls.from_pretrained(A_       )

       UpperCamelCase     : Optional[Any]	     =       cls(model.parameters()	,							model_cls=A_	,							model_config=model.config       )

       ema_model.load_state_dict(A_       )
       return ema_model





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.model_cls is None:
        raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."       )

       if self.model_config is None:
        raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."       )

       UpperCamelCase     : Any	     =       self.model_cls.from_config(self.model_config       )
       UpperCamelCase     : str	     =       self.state_dict()
       state_dict.pop("shadow_params"	,							A_       )

       model.register_to_config(**A_       )
       self.copy_to(model.parameters()       )
       model.save_pretrained(A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       max(0	,							optimization_step - self.update_after_step - 1       )

       if step <= 0:
        return 0.0

       if self.use_ema_warmup:
        UpperCamelCase     : str	     =       1 - (1 + step / self.inv_gamma) ** -self.power
       else:
        UpperCamelCase     : Optional[Any]	     =       (1 + step) / (10 + step)

       UpperCamelCase     : int	     =       min(A_	,							self.decay       )
       # make sure decay is not smaller than min_decay
       UpperCamelCase     : Optional[Any]	     =       max(A_	,							self.min_decay       )
       return cur_decay_value





      @torch.no_grad()
      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if isinstance(A_	,							torch.nn.Module       ):
        UpperCamelCase     : Tuple	     =       (
            "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
            "Please pass the parameters of the module instead."
        )
        deprecate(
            "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"	,							"1.0.0"	,							A_	,							standard_warn=A_	,							)
        UpperCamelCase     : Dict	     =       parameters.parameters()

       UpperCamelCase     : Optional[int]	     =       list(A_       )

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       UpperCamelCase     : str	     =       self.get_decay(self.optimization_step       )
       UpperCamelCase     : Optional[Any]	     =       decay
       UpperCamelCase     : Dict	     =       1 - decay

       UpperCamelCase     : List[Any]	     =       contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

       for s_param, param in zip(self.shadow_params	,							A_       ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
         UpperCamelCase     : Tuple	     =       deepspeed.zero.GatheredParameters(A_	,							modifier_rank=A_       )

        with context_manager():
         if param.requires_grad:
          s_param.sub_(one_minus_decay * (s_param - param)       )
         else:
          s_param.copy_(A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       list(A_       )
       for s_param, param in zip(self.shadow_params	,							A_       ):
        param.data.copy_(s_param.to(param.device       ).data       )





      def 	__UpperCamelCase(			self	,							A_=None	,							A_=None       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       [
           p.to(device=A_	,							dtype=A_       ) if p.is_floating_point() else p.to(device=A_       )
           for p in self.shadow_params
       ]





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       [param.detach().cpu().clone() for param in parameters]





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.temp_stored_params is None:
        raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"       )
       for c_param, param in zip(self.temp_stored_params	,							A_       ):
        param.data.copy_(c_param.data       )

       # Better memory-wise.
       UpperCamelCase     : int	     =       None





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       copy.deepcopy(A_       )

       UpperCamelCase     : Dict	     =       state_dict.get("decay"	,							self.decay       )
       if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("Decay must be between 0 and 1"       )

       UpperCamelCase     : List[Any]	     =       state_dict.get("min_decay"	,							self.min_decay       )
       if not isinstance(self.min_decay	,							A_       ):
        raise ValueError("Invalid min_decay"       )

       UpperCamelCase     : Tuple	     =       state_dict.get("optimization_step"	,							self.optimization_step       )
       if not isinstance(self.optimization_step	,							A_       ):
        raise ValueError("Invalid optimization_step"       )

       UpperCamelCase     : List[str]	     =       state_dict.get("update_after_step"	,							self.update_after_step       )
       if not isinstance(self.update_after_step	,							A_       ):
        raise ValueError("Invalid update_after_step"       )

       UpperCamelCase     : int	     =       state_dict.get("use_ema_warmup"	,							self.use_ema_warmup       )
       if not isinstance(self.use_ema_warmup	,							A_       ):
        raise ValueError("Invalid use_ema_warmup"       )

       UpperCamelCase     : Tuple	     =       state_dict.get("inv_gamma"	,							self.inv_gamma       )
       if not isinstance(self.inv_gamma	,							(float, int)       ):
        raise ValueError("Invalid inv_gamma"       )

       UpperCamelCase     : str	     =       state_dict.get("power"	,							self.power       )
       if not isinstance(self.power	,							(float, int)       ):
        raise ValueError("Invalid power"       )

       UpperCamelCase     : int	     =       state_dict.get("shadow_params"	,							A_       )
       if shadow_params is not None:
        UpperCamelCase     : int	     =       shadow_params
        if not isinstance(self.shadow_params	,							A_       ):
         raise ValueError("shadow_params must be a list"       )
        if not all(isinstance(A_	,							torch.Tensor       ) for p in self.shadow_params       ):
         raise ValueError("shadow_params must all be Tensors"       )







from scipy.stats import spearmanr

import datasets


__lowerCamelCase	:					List[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.
"""

__lowerCamelCase	:					List[Any]			  =					"""
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
"""

__lowerCamelCase	:					Optional[int]			  =					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  A__       (	datasets.Metric      ):





      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	,							A_	,							A_	,							A_=False       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       spearmanr(A_	,							A_       )
       if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
       else:
        return {"spearmanr": results[0]}
