

import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


lowerCAmelCase_       	=	logging.getLogger(__name__)





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				"summarization"
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				["loss"]
			SCREAMING_SNAKE_CASE     :   List[Any]									=				ROUGE_KEYS
			SCREAMING_SNAKE_CASE     :   List[Any]									=				"rouge2"







			def __init__(     self :				int							,   _UpperCamelCase :				Any							,   **_UpperCamelCase :				List[Any]							)      ->List[Any]:
									if hparams.sortish_sampler and hparams.gpus > 1:
															snake_case_   =	False
									elif hparams.max_tokens_per_batch is not None:
															if hparams.gpus > 1:
																					raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''							)
															if hparams.sortish_sampler:
																					raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''							)

									super().__init__(_UpperCamelCase							,   num_labels=_UpperCamelCase							,   mode=self.mode							,   **_UpperCamelCase							)
									use_task_specific_params(self.model							,   '''summarization'''							)
									save_git_info(self.hparams.output_dir							)
									snake_case_   =	Path(self.output_dir							) / '''metrics.json'''
									snake_case_   =	Path(self.output_dir							) / '''hparams.pkl'''
									pickle_save(self.hparams							,   self.hparams_save_path							)
									snake_case_   =	0
									snake_case_   =	defaultdict(_UpperCamelCase							)
									snake_case_   =	self.config.model_type
									snake_case_   =	self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

									snake_case_   =	{
									    "data_dir": self.hparams.data_dir,
									    "max_source_length": self.hparams.max_source_length,
									    "prefix": self.model.config.prefix or "",
									}
									snake_case_   =	{
									    '''train''': self.hparams.n_train,
									    '''val''': self.hparams.n_val,
									    '''test''': self.hparams.n_test,
									}
									snake_case_   =	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

									snake_case_   =	{
									    '''train''': self.hparams.max_target_length,
									    '''val''': self.hparams.val_max_target_length,
									    '''test''': self.hparams.test_max_target_length,
									}
									assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
									assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
									if self.hparams.freeze_embeds:
															freeze_embeds(self.model							)
									if self.hparams.freeze_encoder:
															freeze_params(self.model.get_encoder()							)
															assert_all_frozen(self.model.get_encoder()							)

									snake_case_   =	get_git_info()['''repo_sha''']
									snake_case_   =	hparams.num_workers
									snake_case_   =	None  # default to config
									if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,   _UpperCamelCase							):
															snake_case_   =	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
															snake_case_   =	self.decoder_start_token_id
									snake_case_   =	(
									    SeqaSeqDataset if hasattr(self.tokenizer							,   '''prepare_seq2seq_batch'''							) else LegacySeqaSeqDataset
									)
									snake_case_   =	False
									snake_case_   =	self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
									if self.hparams.eval_max_gen_length is not None:
															snake_case_   =	self.hparams.eval_max_gen_length
									else:
															snake_case_   =	self.model.config.max_length
									snake_case_   =	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Dict[str, torch.Tensor]							)      ->Dict[str, List[str]]:
									snake_case_   =	{
									    k: self.tokenizer.batch_decode(v.tolist()							) if '''mask''' not in k else v.shape for k, v in batch.items()
									}
									save_json(_UpperCamelCase							,   Path(self.output_dir							) / '''text_batch.json'''							)
									save_json({k: v.tolist() for k, v in batch.items()}							,   Path(self.output_dir							) / '''tok_batch.json'''							)

									snake_case_   =	True
									return readable_batch







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							,   **_UpperCamelCase :				List[str]							)      ->List[Any]:
									return self.model(_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[int]							)      ->List[Any]:
									snake_case_   =	self.tokenizer.batch_decode(
									    _UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)
									return lmap(str.strip							,   _UpperCamelCase							)







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				dict							)      ->Tuple:
									snake_case_   =	self.tokenizer.pad_token_id
									snake_case_,    snake_case_   =	batch['''input_ids'''], batch['''attention_mask''']
									snake_case_   =	batch['''labels''']
									if isinstance(self.model							,   _UpperCamelCase							):
															snake_case_   =	self.model._shift_right(_UpperCamelCase							)
									else:
															snake_case_   =	shift_tokens_right(_UpperCamelCase							,   _UpperCamelCase							)
									if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
															snake_case_   =	decoder_input_ids
															self.save_readable_batch(_UpperCamelCase							)

									snake_case_   =	self(_UpperCamelCase							,   attention_mask=_UpperCamelCase							,   decoder_input_ids=_UpperCamelCase							,   use_cache=_UpperCamelCase							)
									snake_case_   =	outputs['''logits''']
									if self.hparams.label_smoothing == 0:
															# Same behavior as modeling_bart.py, besides ignoring pad_token_id
															snake_case_   =	nn.CrossEntropyLoss(ignore_index=_UpperCamelCase							)

															assert lm_logits.shape[-1] == self.vocab_size
															snake_case_   =	ce_loss_fct(lm_logits.view(-1							,   lm_logits.shape[-1]							)							,   tgt_ids.view(-1							)							)
									else:
															snake_case_   =	nn.functional.log_softmax(_UpperCamelCase							,   dim=-1							)
															snake_case_,    snake_case_   =	label_smoothed_nll_loss(
															    _UpperCamelCase							,   _UpperCamelCase							,   self.hparams.label_smoothing							,   ignore_index=_UpperCamelCase							)
									return (loss,)







			@property
			def 				snake_case__(     self :				Tuple							)      ->int:
									return self.tokenizer.pad_token_id







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_   =	self._step(_UpperCamelCase							)

									snake_case_   =	dict(zip(self.loss_names							,   _UpperCamelCase							)							)
									# tokens per batch
									snake_case_   =	batch['''input_ids'''].ne(self.pad							).sum() + batch['''labels'''].ne(self.pad							).sum()
									snake_case_   =	batch['''input_ids'''].shape[0]
									snake_case_   =	batch['''input_ids'''].eq(self.pad							).sum()
									snake_case_   =	batch['''input_ids'''].eq(self.pad							).float().mean()
									# TODO(SS): make a wandb summary metric for this
									return {"loss": loss_tensors[0], "log": logs}







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				Dict							)      ->Dict:
									return self._generative_step(_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[Any]="val"							)      ->Dict:
									self.step_count += 1
									snake_case_   =	{k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
									snake_case_   =	losses['''loss''']
									snake_case_   =	{
									    k: np.array([x[k] for x in outputs]							).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
									}
									snake_case_   =	(
									    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
									)
									snake_case_   =	torch.tensor(_UpperCamelCase							).type_as(_UpperCamelCase							)
									generative_metrics.update({k: v.item() for k, v in losses.items()}							)
									losses.update(_UpperCamelCase							)
									snake_case_   =	{f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
									snake_case_   =	self.step_count
									self.metrics[prefix].append(_UpperCamelCase							)  # callback writes this to self.metrics_save_path
									snake_case_   =	flatten_list([x['''preds'''] for x in outputs]							)
									return {
									    "log": all_metrics,
									    "preds": preds,
									    f'''{prefix}_loss''': loss,
									    f'''{prefix}_{self.val_metric}''': metric_tensor,
									}







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				List[str]							)      ->Dict:
									return calculate_rouge(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				dict							)      ->dict:
									snake_case_   =	time.time()

									# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
									snake_case_   =	self.model.generate(
									    batch['''input_ids''']							,   attention_mask=batch['''attention_mask''']							,   use_cache=_UpperCamelCase							,   decoder_start_token_id=self.decoder_start_token_id							,   num_beams=self.eval_beams							,   max_length=self.eval_max_length							,   )
									snake_case_   =	(time.time() - ta) / batch['''input_ids'''].shape[0]
									snake_case_   =	self.ids_to_clean_text(_UpperCamelCase							)
									snake_case_   =	self.ids_to_clean_text(batch['''labels''']							)
									snake_case_   =	self._step(_UpperCamelCase							)
									snake_case_   =	dict(zip(self.loss_names							,   _UpperCamelCase							)							)
									snake_case_   =	self.calc_generative_metrics(_UpperCamelCase							,   _UpperCamelCase							)
									snake_case_   =	np.mean(lmap(_UpperCamelCase							,   _UpperCamelCase							)							)
									base_metrics.update(gen_time=_UpperCamelCase							,   gen_len=_UpperCamelCase							,   preds=_UpperCamelCase							,   target=_UpperCamelCase							,   **_UpperCamelCase							)
									return base_metrics







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Dict							)      ->Optional[int]:
									return self._generative_step(_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[int]							)      ->List[Any]:
									return self.validation_epoch_end(_UpperCamelCase							,   prefix='''test'''							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Any							)      ->SeqaSeqDataset:
									snake_case_   =	self.n_obs[type_path]
									snake_case_   =	self.target_lens[type_path]
									snake_case_   =	self.dataset_class(
									    self.tokenizer							,   type_path=_UpperCamelCase							,   n_obs=_UpperCamelCase							,   max_target_length=_UpperCamelCase							,   **self.dataset_kwargs							,   )
									return dataset







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				int							,   _UpperCamelCase :				bool = False							)      ->DataLoader:
									snake_case_   =	self.get_dataset(_UpperCamelCase							)

									if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
															snake_case_   =	dataset.make_sortish_sampler(_UpperCamelCase							,   distributed=self.hparams.gpus > 1							)
															return DataLoader(
															    _UpperCamelCase							,   batch_size=_UpperCamelCase							,   collate_fn=dataset.collate_fn							,   shuffle=_UpperCamelCase							,   num_workers=self.num_workers							,   sampler=_UpperCamelCase							,   )

									elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
															snake_case_   =	dataset.make_dynamic_sampler(
															    self.hparams.max_tokens_per_batch							,   distributed=self.hparams.gpus > 1							)
															return DataLoader(
															    _UpperCamelCase							,   batch_sampler=_UpperCamelCase							,   collate_fn=dataset.collate_fn							,   num_workers=self.num_workers							,   )
									else:
															return DataLoader(
															    _UpperCamelCase							,   batch_size=_UpperCamelCase							,   collate_fn=dataset.collate_fn							,   shuffle=_UpperCamelCase							,   num_workers=self.num_workers							,   sampler=_UpperCamelCase							,   )







			def 				snake_case__(     self :				List[str]							)      ->DataLoader:
									snake_case_   =	self.get_dataloader('''train'''							,   batch_size=self.hparams.train_batch_size							,   shuffle=_UpperCamelCase							)
									return dataloader







			def 				snake_case__(     self :				Union[str, Any]							)      ->DataLoader:
									return self.get_dataloader('''val'''							,   batch_size=self.hparams.eval_batch_size							)







			def 				snake_case__(     self :				Tuple							)      ->DataLoader:
									return self.get_dataloader('''test'''							,   batch_size=self.hparams.eval_batch_size							)







			@staticmethod
			def 				snake_case__(     _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									BaseTransformer.add_model_specific_args(_UpperCamelCase							,   _UpperCamelCase							)
									add_generic_args(_UpperCamelCase							,   _UpperCamelCase							)
									parser.add_argument(
									    '''--max_source_length'''							,   default=1_0_2_4							,   type=_UpperCamelCase							,   help=(
									        '''The maximum total input sequence length after tokenization. Sequences longer '''
									        '''than this will be truncated, sequences shorter will be padded.'''
									    )							,   )
									parser.add_argument(
									    '''--max_target_length'''							,   default=5_6							,   type=_UpperCamelCase							,   help=(
									        '''The maximum total input sequence length after tokenization. Sequences longer '''
									        '''than this will be truncated, sequences shorter will be padded.'''
									    )							,   )
									parser.add_argument(
									    '''--val_max_target_length'''							,   default=1_4_2							,   type=_UpperCamelCase							,   help=(
									        '''The maximum total input sequence length after tokenization. Sequences longer '''
									        '''than this will be truncated, sequences shorter will be padded.'''
									    )							,   )
									parser.add_argument(
									    '''--test_max_target_length'''							,   default=1_4_2							,   type=_UpperCamelCase							,   help=(
									        '''The maximum total input sequence length after tokenization. Sequences longer '''
									        '''than this will be truncated, sequences shorter will be padded.'''
									    )							,   )
									parser.add_argument('''--freeze_encoder'''							,   action='''store_true'''							)
									parser.add_argument('''--freeze_embeds'''							,   action='''store_true'''							)
									parser.add_argument('''--sortish_sampler'''							,   action='''store_true'''							,   default=_UpperCamelCase							)
									parser.add_argument('''--overwrite_output_dir'''							,   action='''store_true'''							,   default=_UpperCamelCase							)
									parser.add_argument('''--max_tokens_per_batch'''							,   type=_UpperCamelCase							,   default=_UpperCamelCase							)
									parser.add_argument('''--logger_name'''							,   type=_UpperCamelCase							,   choices=['''default''', '''wandb''', '''wandb_shared''']							,   default='''default'''							)
									parser.add_argument('''--n_train'''							,   type=_UpperCamelCase							,   default=-1							,   required=_UpperCamelCase							,   help='''# examples. -1 means use all.'''							)
									parser.add_argument('''--n_val'''							,   type=_UpperCamelCase							,   default=5_0_0							,   required=_UpperCamelCase							,   help='''# examples. -1 means use all.'''							)
									parser.add_argument('''--n_test'''							,   type=_UpperCamelCase							,   default=-1							,   required=_UpperCamelCase							,   help='''# examples. -1 means use all.'''							)
									parser.add_argument(
									    '''--task'''							,   type=_UpperCamelCase							,   default='''summarization'''							,   required=_UpperCamelCase							,   help='''# examples. -1 means use all.'''							)
									parser.add_argument('''--label_smoothing'''							,   type=_UpperCamelCase							,   default=0.0							,   required=_UpperCamelCase							)
									parser.add_argument('''--src_lang'''							,   type=_UpperCamelCase							,   default=''''''							,   required=_UpperCamelCase							)
									parser.add_argument('''--tgt_lang'''							,   type=_UpperCamelCase							,   default=''''''							,   required=_UpperCamelCase							)
									parser.add_argument('''--eval_beams'''							,   type=_UpperCamelCase							,   default=_UpperCamelCase							,   required=_UpperCamelCase							)
									parser.add_argument(
									    '''--val_metric'''							,   type=_UpperCamelCase							,   default=_UpperCamelCase							,   required=_UpperCamelCase							,   choices=['''bleu''', '''rouge2''', '''loss''', None]							)
									parser.add_argument('''--eval_max_gen_length'''							,   type=_UpperCamelCase							,   default=_UpperCamelCase							,   help='''never generate more than n tokens'''							)
									parser.add_argument('''--save_top_k'''							,   type=_UpperCamelCase							,   default=1							,   required=_UpperCamelCase							,   help='''How many checkpoints to save'''							)
									parser.add_argument(
									    '''--early_stopping_patience'''							,   type=_UpperCamelCase							,   default=-1							,   required=_UpperCamelCase							,   help=(
									        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
									        ''' val_check_interval will effect it.'''
									    )							,   )
									return parser






class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				"translation"
			SCREAMING_SNAKE_CASE     :   Dict									=				["loss"]
			SCREAMING_SNAKE_CASE     :   str									=				["bleu"]
			SCREAMING_SNAKE_CASE     :   List[str]									=				"bleu"







			def __init__(     self :				Optional[Any]							,   _UpperCamelCase :				Any							,   **_UpperCamelCase :				Any							)      ->Optional[int]:
									super().__init__(_UpperCamelCase							,   **_UpperCamelCase							)
									snake_case_   =	hparams.src_lang
									snake_case_   =	hparams.tgt_lang







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->dict:
									return calculate_bleu(_UpperCamelCase							,   _UpperCamelCase							)






def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None       ):
						Path(args.output_dir       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
						check_output_dir(SCREAMING_SNAKE_CASE__  ,		expected_items=3       )

						if model is None:
												if "summarization" in args.task:
																		snake_case_   =	SummarizationModule(SCREAMING_SNAKE_CASE__       )
												else:
																		snake_case_   =	TranslationModule(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	Path(args.data_dir       ).name
						if (
						    args.logger_name == "default"
						    or args.fast_dev_run
						    or str(args.output_dir       ).startswith('''/tmp'''       )
						    or str(args.output_dir       ).startswith('''/var'''       )
						):
												snake_case_   =	True  # don't pollute wandb logs unnecessarily
						elif args.logger_name == "wandb":
												from pytorch_lightning.loggers import WandbLogger

												snake_case_   =	os.environ.get('''WANDB_PROJECT'''  ,		SCREAMING_SNAKE_CASE__       )
												snake_case_   =	WandbLogger(name=model.output_dir.name  ,		project=SCREAMING_SNAKE_CASE__       )

						elif args.logger_name == "wandb_shared":
												from pytorch_lightning.loggers import WandbLogger

												snake_case_   =	WandbLogger(name=model.output_dir.name  ,		project=F'''hf_{dataset}'''       )

						if args.early_stopping_patience >= 0:
												snake_case_   =	get_early_stopping_callback(model.val_metric  ,		args.early_stopping_patience       )
						else:
												snake_case_   =	False

						snake_case_   =	args.val_metric == '''loss'''
						snake_case_   =	generic_train(
						    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		logging_callback=SeqaSeqLoggingCallback()  ,		checkpoint_callback=get_checkpoint_callback(
						        args.output_dir  ,		model.val_metric  ,		args.save_top_k  ,		SCREAMING_SNAKE_CASE__       )  ,		early_stopping_callback=SCREAMING_SNAKE_CASE__  ,		logger=SCREAMING_SNAKE_CASE__  ,		)
						pickle_save(model.hparams  ,		model.output_dir / '''hparams.pkl'''       )
						if not args.do_predict:
												return model

						snake_case_   =	''''''
						snake_case_   =	sorted(glob.glob(os.path.join(args.output_dir  ,		'''*.ckpt'''       )  ,		recursive=SCREAMING_SNAKE_CASE__       )       )
						if checkpoints:
												snake_case_   =	checkpoints[-1]
												snake_case_   =	checkpoints[-1]
						trainer.logger.log_hyperparams(model.hparams       )

						# test() without a model tests using the best checkpoint automatically
						trainer.test()
						return model


if __name__ == "__main__":
					lowerCAmelCase_       	=	argparse.ArgumentParser()
					lowerCAmelCase_       	=	pl.Trainer.add_argparse_args(parser)
					lowerCAmelCase_       	=	SummarizationModule.add_model_specific_args(parser, os.getcwd())

					lowerCAmelCase_       	=	parser.parse_args()

					main(args)


from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_       	=	'''\
@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}
}
'''

lowerCAmelCase_       	=	'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowerCAmelCase_       	=	'''
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 	snake_case_   (		datasets.Metric					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[int]							)      ->List[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 				snake_case__(     self :				List[Any]							)      ->Optional[int]:
									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 				snake_case__(     self :				int							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				Optional[int]="uniform_average"							,   _UpperCamelCase :				Tuple=True							)      ->Tuple:
									snake_case_   =	mean_squared_error(
									    _UpperCamelCase							,   _UpperCamelCase							,   sample_weight=_UpperCamelCase							,   multioutput=_UpperCamelCase							,   squared=_UpperCamelCase							)

									return {"mse": mse}