


from sklearn.metrics import mean_absolute_error

import datasets


SCREAMING_SNAKE_CASE		:Optional[int]											=       '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

SCREAMING_SNAKE_CASE		:int											=       '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


SCREAMING_SNAKE_CASE		:List[Any]											=       '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,					_KWARGS_DESCRIPTION   )
class       __lowerCAmelCase       (	datasets.Metric   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	int:




     """simple docstring"""
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,			citation=_CITATION      ,			inputs_description=_KWARGS_DESCRIPTION      ,			features=datasets.Features(self._get_feature_types()					)      ,			reference_urls=[
             "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
         ]      ,			)



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Any:




     """simple docstring"""
     if self.config_name == "multilist":
        return {
            "predictions": datasets.Sequence(datasets.Value("float"					)					),
            "references": datasets.Sequence(datasets.Value("float"					)					),
        }
     else:
        return {
            "predictions": datasets.Value("float"					),
            "references": datasets.Value("float"					),
        }



  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    int=None      ,			_lowerCAmelCase    :    Dict="uniform_average"					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   mean_absolute_error(_lowerCAmelCase      ,			_lowerCAmelCase      ,			sample_weight=_lowerCAmelCase      ,			multioutput=_lowerCAmelCase					)

     return {"mae": mae_score}








import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


SCREAMING_SNAKE_CASE		:Tuple											=       logging.get_logger(__name__)


class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Any					)				->	List[str]:




     """simple docstring"""
     super().__init__()
     if version.parse(torch.__version__					) < version.parse("1.12.0"					):
        raise ImportError(
            F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
            "PytorchGELUTanh. Please upgrade torch."					)



  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return nn.functional.gelu(_lowerCAmelCase      ,			approximate="tanh"					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.044_715 * torch.pow(_lowerCAmelCase      ,			3.0					))					))






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    int      ,			_lowerCAmelCase    :    bool = False					)				->	int:




     """simple docstring"""
     super().__init__()
     if use_gelu_python:
        snake_case_         =   self._gelu_python
     else:
        snake_case_         =   nn.functional.gelu



  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0					)					))



  def    lowerCAmelCase__					(       self    :    Dict      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return self.act(_lowerCAmelCase					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)					))






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return input * torch.sigmoid(1.702 * input					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    List[str]      ,			_lowerCAmelCase    :    float      ,			_lowerCAmelCase    :    float					)				->	Any:




     """simple docstring"""
     if min > max:
        raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''					)

     super().__init__()
     snake_case_         =   min
     snake_case_         =   max



  def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return torch.clip(gelu(_lowerCAmelCase					)      ,			self.min      ,			self.max					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Any					)				->	str:




     """simple docstring"""
     super().__init__()
     snake_case_         =   math.sqrt(2 / math.pi					)



  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(_lowerCAmelCase      ,			3					))					))






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return nn.functional.silu(_lowerCAmelCase					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Optional[int]					)				->	List[Any]:




     """simple docstring"""
     super().__init__()
     if version.parse(torch.__version__					) < version.parse("1.9.0"					):
        snake_case_         =   self._mish_python
     else:
        snake_case_         =   nn.functional.mish



  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return input * torch.tanh(nn.functional.softplus(_lowerCAmelCase					)					)



  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return self.act(_lowerCAmelCase					)






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    List[str]      ,			_lowerCAmelCase    :    Tensor					)				->	Tensor:




     """simple docstring"""
     return input






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Union[str, Any]=0.707_107      ,			_lowerCAmelCase    :    int=0.282_095					)				->	List[str]:




     """simple docstring"""
     snake_case_         =   (input - mu).div(sigma * math.sqrt(2.0					)					)
     return 0.5 * (1.0 + torch.erf(_lowerCAmelCase					))






class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Any      ,			_lowerCAmelCase    :    Union[str, Any]					)				->	Any:




     """simple docstring"""
     snake_case_         =   nn.functional.relu(_lowerCAmelCase					)
     snake_case_         =   torch.square(_lowerCAmelCase					)
     return squared






class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  def __getitem__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    str					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   super().__getitem__(_lowerCAmelCase					)
     snake_case_      ,      snake_case_         =   content if isinstance(_lowerCAmelCase      ,			_lowerCAmelCase					) else (content, {})
     return cls(**_lowerCAmelCase					)






SCREAMING_SNAKE_CASE		:Optional[int]											=       {
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
SCREAMING_SNAKE_CASE		:Optional[int]											=       ClassInstantier(ACTaCLS)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]							)->Union[str, Any]:
   '''simple docstring'''






   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}'''							)


# For backwards compatibility with: from activations import gelu_python
SCREAMING_SNAKE_CASE		:Tuple											=       get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE		:int											=       get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE		:List[Any]											=       get_activation('''gelu''')
SCREAMING_SNAKE_CASE		:int											=       get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE		:Tuple											=       get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE		:Tuple											=       get_activation('''silu''')
SCREAMING_SNAKE_CASE		:Any											=       get_activation('''mish''')



SCREAMING_SNAKE_CASE		:Optional[Any]											=       get_activation('''linear''')




