



'''simple docstring'''




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any]      ):
		__SCREAMING_SNAKE_CASE :	str					 =	filter(lambda _lowerCamelCase      : p.requires_grad ,   model.parameters()      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	sum([np.prod(p.size()      ) for p in model_parameters]      )
		return params


UpperCamelCase__ :			Dict												=				logging.getLogger(__name__)


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Union[str, Any]      ):
		if metric == "rouge2":
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	"""{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				__SCREAMING_SNAKE_CASE :	Dict					 =	"""{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "em":
				__SCREAMING_SNAKE_CASE :	int					 =	"""{val_avg_em:.4f}-{step_count}"""
		elif metric == "loss":
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	"""{val_avg_loss:.4f}-{step_count}"""
		else:
				raise NotImplementedError(
				    F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
				    """ function."""      )

		__SCREAMING_SNAKE_CASE :	int					 =	ModelCheckpoint(
		    dirpath=_lowerCamelCase ,   filename=_lowerCamelCase ,   monitor=F"val_{metric}" ,   mode="""max""" ,   save_top_k=1 ,   every_n_epochs=1 ,   )

		return checkpoint_callback


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     List[Any]      ):
		return EarlyStopping(
		    monitor=F"val_{metric}" ,   mode="""min""" if """loss""" in metric else """max""" ,   patience=_lowerCamelCase ,   verbose=_lowerCamelCase ,   )






class 		_UpperCamelCase						(							pl.Callback					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
					pl_module.logger.log_metrics(lowerCAmelCase__		)

			@rank_zero_only
			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				pl.Trainer							,  lowerCAmelCase__  :				pl.LightningModule							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				str=True		):





					"""simple docstring"""






					logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	trainer.callback_metrics
					trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
					# Log results
					__SCREAMING_SNAKE_CASE :	List[str]					 =	Path(pl_module.hparams.output_dir		)
					if type_path == "test":
							__SCREAMING_SNAKE_CASE :	Any					 =	od / """test_results.txt"""
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	od / """test_generations.txt"""
					else:
							# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
							# If people want this it will be easy enough to add back.
							__SCREAMING_SNAKE_CASE :	Dict					 =	od / F"{type_path}_results/{trainer.global_step:05d}.txt"
							__SCREAMING_SNAKE_CASE :	str					 =	od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
							results_file.parent.mkdir(exist_ok=lowerCAmelCase__		)
							generations_file.parent.mkdir(exist_ok=lowerCAmelCase__		)
					with open(lowerCAmelCase__							,  """a+"""		) as writer:
							for key in sorted(lowerCAmelCase__		):
									if key in ["log", "progress_bar", "preds"]:
											continue
									__SCREAMING_SNAKE_CASE :	List[str]					 =	metrics[key]
									if isinstance(lowerCAmelCase__							,  torch.Tensor		):
											__SCREAMING_SNAKE_CASE :	Optional[int]					 =	val.item()
									__SCREAMING_SNAKE_CASE :	Dict					 =	F"{key}: {val:.6f}\n"
									writer.write(lowerCAmelCase__		)

					if not save_generations:
							return

					if "preds" in metrics:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	"""\n""".join(metrics["""preds"""]		)
							generations_file.open("""w+"""		).write(lowerCAmelCase__		)

			@rank_zero_only
			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					try:
							__SCREAMING_SNAKE_CASE :	Dict					 =	pl_module.model.model.num_parameters()
					except AttributeError:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	pl_module.model.num_parameters()

					__SCREAMING_SNAKE_CASE :	int					 =	count_trainable_parameters(lowerCAmelCase__		)
					# mp stands for million parameters
					trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)

			@rank_zero_only
			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				pl.Trainer							,  lowerCAmelCase__  :				pl.LightningModule		):





					"""simple docstring"""






					save_json(pl_module.metrics							,  pl_module.metrics_save_path		)
					return self._write_logs(lowerCAmelCase__							,  lowerCAmelCase__							,  """test"""		)



			@rank_zero_only
			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				pl.Trainer							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					save_json(pl_module.metrics							,  pl_module.metrics_save_path		)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			Optional[Any]												=				'''\
@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}
}
'''

UpperCamelCase__ :			List[str]												=				'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCamelCase__ :			Dict												=				'''
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.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

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

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''






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


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				str		):





					"""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 UpperCamelCase__      (							self  :				Optional[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 UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}