from sklearn.metrics import mean_squared_error

import datasets


lowercase__			:				int		=	'''\\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__			:				Optional[int]		=	'''\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'''


lowercase__			:				Union[str, Any]		=	'''\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    lowercase_  (  datasets.Metric						):



	"""simple docstring"""





	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[Any]:
								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 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								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 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE="uniform_average"		,			__SCREAMING_SNAKE_CASE=True				)						->Any:
								lowerCAmelCase			=		mean_squared_error(
								    UpperCAmelCase__		,			UpperCAmelCase__		,			sample_weight=UpperCAmelCase__		,			multioutput=UpperCAmelCase__		,			squared=UpperCAmelCase__				)

								return {"mse": mse}






"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

   __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   lightning_model.load_state_dict(ckpt["state_dict"])

   # init longformer question answering model
   __lowercase								=						LongformerForQuestionAnswering.from_pretrained(UpperCamelCase_)

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(UpperCamelCase_)

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--longformer_model',
       default=None,
       type=str,
       required=True,
       help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
   )
   parser.add_argument(
       '--longformer_question_answering_ckpt_path',
       default=None,
       type=str,
       required=True,
       help='Path the official PyTorch Lightning Checkpoint.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




