


from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase       =							'\\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'

lowerCAmelCase       =							'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase       =							'\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\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'


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

							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""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_squared_error.html'''
									    ]    ,						)

							def       lowerCamelCase_ (							self:	Tuple							)			->   List[str]:





									"""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]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	List[Any]=None    ,						UpperCamelCase_:	Optional[Any]="uniform_average"    ,						UpperCamelCase_:	Optional[int]=True							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      mean_squared_error(
									    UpperCamelCase_    ,						UpperCamelCase_    ,						sample_weight=UpperCamelCase_    ,						multioutput=UpperCamelCase_    ,						squared=UpperCamelCase_							)

									return {"mse": mse}






import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


