

'''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


lowerCAmelCase__      		=			logging.get_logger(__name__)






@dataclass
class     lowercase_			:




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()   )}   )
  SCREAMING_SNAKE_CASE					:						str           =    field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
  SCREAMING_SNAKE_CASE					:						int           =    field(
      default=1_2_8       , metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }       , )
  SCREAMING_SNAKE_CASE					:						bool           =    field(
      default=lowerCamelCase__       , metadata={'help': 'Overwrite the cached training and evaluation sets'}   )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.task_name.lower()






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Any           =    'train'
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    'dev'
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    'test'






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						GlueDataTrainingArguments
  SCREAMING_SNAKE_CASE					:						str
  SCREAMING_SNAKE_CASE					:						List[InputFeatures]






  def __init__(   self     :  List[Any]      ,lowercase__     :  GlueDataTrainingArguments      ,lowercase__     :  PreTrainedTokenizerBase      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  Union[str, Split] = Split.train      ,lowercase__     :  Optional[str] = None      ,):
   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'''      ,lowercase__      ,)
   __lowercase													=				args
   __lowercase													=				glue_processors[args.task_name]()
   __lowercase													=				glue_output_modes[args.task_name]
   if isinstance(lowercase__      ,lowercase__    ):
    try:
     __lowercase													=				Split[mode]
    except KeyError:
     raise KeyError('''mode is not a valid split name'''    )
        # Load data features from cache or dataset file
   __lowercase													=				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}"      ,)
   __lowercase													=				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)
    __lowercase						,			__lowercase													=				label_list[2], label_list[1]
   __lowercase													=				label_list

   # Make sure only the first process in distributed training processes the dataset,
   # and the others will use the cache.
   __lowercase													=				cached_features_file + '''.lock'''
   with FileLock(lowercase__    ):
    if os.path.exists(lowercase__    ) and not args.overwrite_cache:
     __lowercase													=				time.time()
     __lowercase													=				torch.load(lowercase__    )
     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:
      __lowercase													=				self.processor.get_dev_examples(args.data_dir    )
     elif mode == Split.test:
      __lowercase													=				self.processor.get_test_examples(args.data_dir    )
     else:
      __lowercase													=				self.processor.get_train_examples(args.data_dir    )
     if limit_length is not None:
      __lowercase													=				examples[:limit_length]
     __lowercase													=				glue_convert_examples_to_features(
         lowercase__      ,lowercase__      ,max_length=args.max_seq_length      ,label_list=lowercase__      ,output_mode=self.output_mode      ,)
     __lowercase													=				time.time()
     torch.save(self.features      ,lowercase__    )
     # ^ 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     :  int    ):
   return len(self.features    )






  def __getitem__(   self     :  Tuple      ,lowercase__     :  Optional[Any]    ):
   return self.features[i]






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   return self.label_list




'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






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




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

