






from sklearn.metrics import mean_absolute_error

import datasets


lowercase  							= "\\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"

lowercase  							= "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase  							= "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"




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



	'''simple docstring'''




	def 							_UpperCamelCase      (	self )	->   Dict:
								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 							_UpperCamelCase      (	self )	->   List[str]:
								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 							_UpperCamelCase      (	self	,      a	,      a	,      a=None	,      a="uniform_average" )	->   Any:
								snake_case_           =							mean_absolute_error(a	,      a	,      sample_weight=a	,      multioutput=a )

								return {"mae": mae_score}













import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
	import transformer_engine.pytorch as te





def 						__UpperCAmelCase      (					a_							,					a_=True							,					a_=True							,					a_=True):
							if not is_fpa_available():
														raise ImportError('Using `convert_model` requires transformer_engine to be installed.')
							for name, module in model.named_children():
														if isinstance(a_							,					nn.Linear) and to_transformer_engine and _convert_linear:
																					# Return early if the linear layer weights are not multiples of 16
																					if any(p % 16 != 0 for p in module.weight.shape):
																												return
																					snake_case_           =							module.bias is not None
																					snake_case_           =							te.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					nn.LayerNorm) and to_transformer_engine and _convert_ln:
																					snake_case_           =							te.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.Linear) and not to_transformer_engine and _convert_linear:
																					snake_case_           =							module.bias is not None
																					snake_case_           =							nn.Linear(
																					    module.in_features							,					module.out_features							,					bias=a_							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					if has_bias:
																												snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														elif isinstance(a_							,					te.LayerNorm) and not to_transformer_engine and _convert_ln:
																					snake_case_           =							nn.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype)
																					snake_case_           =							module.weight.data.clone()
																					snake_case_           =							module.bias.data.clone()

																					setattr(a_							,					a_							,					a_)
														else:
																					convert_model(
																					    a_							,					to_transformer_engine=a_							,					_convert_linear=_convert_linear							,					_convert_ln=_convert_ln							,					)





def 						__UpperCAmelCase      (					a_):
							if not is_fpa_available():
														raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.')
							for m in model.modules():
														if isinstance(a_							,					(te.LayerNorm, te.Linear)):
																					return True
							return False





