



import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def       __lowerCAmelCase      (						A       ,     A       ,     A=1024       ,     A=1024       ,     A=False       ,     **A    ):
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained(A    )
	UpperCAmelCase_       				=			SeqaSeqDataset(A       ,     A       ,     A       ,     A       ,     type_path="train"       ,     **A    )
	UpperCAmelCase_       				=			tok.pad_token_id

	def get_lens(A    ):
		UpperCAmelCase_       				=			tqdm(
		    DataLoader(A       ,     batch_size=512       ,     num_workers=8       ,     shuffle=A       ,     collate_fn=ds.collate_fn    )       ,     desc=str(ds.len_file    )       ,     )
		UpperCAmelCase_       				=			[]
		for batch in dl:
			UpperCAmelCase_       				=			batch["input_ids"].ne(A    ).sum(1    ).tolist()
			UpperCAmelCase_       				=			batch["labels"].ne(A    ).sum(1    ).tolist()
			if consider_target:
				for src, tgt in zip(A       ,     A    ):
					max_lens.append(max(A       ,     A    )    )
			else:
				max_lens.extend(A    )
		return max_lens

	UpperCAmelCase_       				=			get_lens(A    )
	UpperCAmelCase_       				=			SeqaSeqDataset(A       ,     A       ,     A       ,     A       ,     type_path="val"       ,     **A    )
	UpperCAmelCase_       				=			get_lens(A    )
	pickle_save(A       ,     train_ds.len_file    )
	pickle_save(A       ,     val_ds.len_file    )


if __name__ == "__main__":
				fire.Fire(save_len_file)


from sklearn.metrics import mean_squared_error

import datasets


_a:    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}
}
"""

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


_a:    List[str] 							=			"""
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						):




	def       __A					(		self	:						List[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       __A					(		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       __A					(		self	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						str="uniform_average"		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			mean_squared_error(
		    lowerCAmelCase		,	lowerCAmelCase		,	sample_weight=lowerCAmelCase		,	multioutput=lowerCAmelCase		,	squared=lowerCAmelCase							)

		return {"mse": mse}