





'''simple docstring'''


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

@dataclass
class  lowerCAmelCase_		:
 __lowerCamelCase    :				str								=       field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()		)}		)
 __lowerCamelCase    :				str								=       field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}		)
 __lowerCamelCase    :				int								=       field(
     default=128    ,metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }    ,)
 __lowerCamelCase    :				bool								=       field(
     default=__magic_name__    ,metadata={"help": "Overwrite the cached training and evaluation sets"}		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.task_name.lower()







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "train"
 __lowerCamelCase    :				Tuple								=       "dev"
 __lowerCamelCase    :				int								=       "test"







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				GlueDataTrainingArguments
 __lowerCamelCase    :				str
 __lowerCamelCase    :				List[InputFeatures]




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = Split.train      ,  _lowerCAmelCase = None      ,  )     ->				Union[str, Any]:
     warnings.warn(
         "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
         "library. You can have a look at this example script for pointers: "
         "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"      ,  _lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   args
     _lowerCAmelCase						      =   glue_processors[args.task_name]()
     _lowerCAmelCase						      =   glue_output_modes[args.task_name]
     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         try:
             _lowerCAmelCase						      =   Split[mode]
         except KeyError:
             raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
     _lowerCAmelCase						      =   os.path.join(
         cache_dir if cache_dir is not None else args.data_dir      ,  f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''      ,  )
     _lowerCAmelCase						      =   self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   label_list[2], label_list[1]
     _lowerCAmelCase						      =   label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     _lowerCAmelCase						      =   cached_features_file + ".lock"
     with FileLock(_lowerCAmelCase		):
         if os.path.exists(_lowerCAmelCase		) and not args.overwrite_cache:
             _lowerCAmelCase						      =   time.time()
             _lowerCAmelCase						      =   torch.load(_lowerCAmelCase		)
             logger.info(
                 f'''Loading features from cached file {cached_features_file} [took %.3f s]'''      ,  time.time() - start		)
         else:
             logger.info(f'''Creating features from dataset file at {args.data_dir}'''		)

             if mode == Split.dev:
                 _lowerCAmelCase						      =   self.processor.get_dev_examples(args.data_dir		)
             elif mode == Split.test:
                 _lowerCAmelCase						      =   self.processor.get_test_examples(args.data_dir		)
             else:
                 _lowerCAmelCase						      =   self.processor.get_train_examples(args.data_dir		)
             if limit_length is not None:
                 _lowerCAmelCase						      =   examples[:limit_length]
             _lowerCAmelCase						      =   glue_convert_examples_to_features(
                 _lowerCAmelCase      ,  _lowerCAmelCase      ,  max_length=args.max_seq_length      ,  label_list=_lowerCAmelCase      ,  output_mode=self.output_mode      ,  )
             _lowerCAmelCase						      =   time.time()
             torch.save(self.features      ,  _lowerCAmelCase		)
             # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
             logger.info(
                 f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''		)




 def __len__(						self		)     ->				List[str]:
     return len(self.features		)




 def __getitem__(						self      ,  _lowerCAmelCase		)     ->				InputFeatures:
     return self.features[i]





 def 				_snake_case (						self		)     ->				Optional[Any]:
     return self.label_list









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\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"

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


_SCREAMING_SNAKE_CASE									=				"\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  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				Dict:
     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		)     ->				Tuple:
     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      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


