



"""simple docstring"""







import math
from collections import OrderedDict

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

from .utils import logging


A    =	logging.get_logger(__name__)

class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]		)       ->				str:
									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 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return nn.functional.gelu(_lowercase      ,  approximate="tanh"		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044715 * torch.pow(_lowercase      ,  3.0		))		))



class 			_a     (   nn.Module):



			def __init__( self       :							Tuple      ,  _lowercase       :							bool = False		)       ->				Optional[Any]:
									super().__init__()
									if use_gelu_python:
															snake_case      : Tuple							=   self._gelu_python
									else:
															snake_case      : Any							=   nn.functional.gelu



			def 							__lowercase				( self       :							Any      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0		)		))



			def 							__lowercase				( self       :							Optional[int]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return self.act(_lowercase		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Dict      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * torch.sigmoid(1.702 * input		)



class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]      ,  _lowercase       :							float      ,  _lowercase       :							float		)       ->				Union[str, Any]:
									if min > max:
															raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''		)

									super().__init__()
									snake_case      : str							=   min
									snake_case      : Dict							=   max



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return torch.clip(gelu(_lowercase		)      ,  self.min      ,  self.max		)



class 			_a     (   nn.Module):



			def __init__( self       :							str		)       ->				Tuple:
									super().__init__()
									snake_case      : int							=   math.sqrt(2 / math.pi		)



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(_lowercase      ,  3		))		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							int      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return nn.functional.silu(_lowercase		)



class 			_a     (   nn.Module):



			def __init__( self       :							Union[str, Any]		)       ->				Optional[Any]:
									super().__init__()
									if version.parse(torch.__version__		) < version.parse("1.9.0"		):
															snake_case      : Union[str, Any]							=   self._mish_python
									else:
															snake_case      : Union[str, Any]							=   nn.functional.mish



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input * torch.tanh(nn.functional.softplus(_lowercase		)		)



			def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return self.act(_lowercase		)



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							List[Any]      ,  _lowercase       :							Tensor		)       ->				Tensor:
									return input



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Optional[Any]      ,  _lowercase       :							List[str]=0.707107      ,  _lowercase       :							Dict=0.282095		)       ->				int:
									snake_case      : Any							=   (input - mu).div(sigma * math.sqrt(2.0		)		)
									return 0.5 * (1.0 + torch.erf(_lowercase		))



class 			_a     (   nn.Module):



			def 							__lowercase				( self       :							str      ,  _lowercase       :							List[str]		)       ->				str:
									snake_case      : Optional[Any]							=   nn.functional.relu(_lowercase		)
									snake_case      : List[str]							=   torch.square(_lowercase		)
									return squared



class 			_a     (   SCREAMING_SNAKE_CASE__):



			def __getitem__( self       :							List[str]      ,  _lowercase       :							Any		)       ->				int:
									snake_case      : List[str]							=   super().__getitem__(_lowercase		)
									snake_case						, snake_case      : Optional[Any]							=   content if isinstance(_lowercase      ,  _lowercase		) else (content, {})
									return cls(**_lowercase		)



A    =	{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    '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,
}
A    =	ClassInstantier(ACTaCLS)

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Tuple   ):
						"""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
A    =	get_activation('gelu_python')
A    =	get_activation('gelu_new')
A    =	get_activation('gelu')
A    =	get_activation('gelu_fast')
A    =	get_activation('quick_gelu')
A    =	get_activation('silu')
A    =	get_activation('mish')
A    =	get_activation('linear')








"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


A    =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


A    =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


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

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



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32"		)		),
									            "references": datasets.Sequence(datasets.Value("int32"		)		),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32"		),
									            "references": datasets.Value("int32"		),
									        }		)      ,  reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,  )



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Union[str, Any]=True      ,  _lowercase       :							int=None		)       ->				Any:
									return {
									    "accuracy": float(
									        accuracy_score(_lowercase      ,  _lowercase      ,  normalize=_lowercase      ,  sample_weight=_lowercase		)		)
									}



