


import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
a					:				Optional[int]       =      1e-10
a					:				Optional[int]       =      """This is incredible! I love it, this is the best chicken I have ever had."""
a					:				Dict       =      100

class   lowerCamelCase						(	nn.Module  ):

       def __init__(      self						,    a__						,    a__="gpt2-medium"						,    a__=False						,    a__="cpu"       ):






        '''simple docstring'''






        super().__init__()
        snake_case :			str							=   GPTaTokenizer.from_pretrained(lowerCAmelCase__       )
        snake_case :			str							=   GPTaLMHeadModel.from_pretrained(lowerCAmelCase__       )
        snake_case :			Any							=   self.encoder.transformer.config.hidden_size
        snake_case :			str							=   ClassificationHead(class_size=lowerCAmelCase__						,    embed_size=self.embed_size       )
        snake_case :			Dict							=   cached_mode
        snake_case :			Dict							=   device

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.classifier_head

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for param in self.encoder.parameters():
         snake_case :			str							=   False
        self.classifier_head.train()

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   x.ne(0       ).unsqueeze(2       ).repeat(1						,    1						,    self.embed_size       ).float().to(self.device       ).detach()
        snake_case :			Tuple							=   self.encoder.transformer(lowerCAmelCase__       )["""last_hidden_state"""]
        snake_case :			Any							=   hidden * mask
        snake_case :			List[Any]							=   torch.sum(lowerCAmelCase__						,    dim=1       ) / (torch.sum(lowerCAmelCase__						,    dim=1       ).detach() + EPSILON)
        return avg_hidden



       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        if self.cached_mode:
         snake_case :			List[Any]							=   x.to(self.device       )
        else:
         snake_case :			Dict							=   self.avg_representation(x.to(self.device       )       )

        snake_case :			int							=   self.classifier_head(lowerCAmelCase__       )
        snake_case :			Any							=   nn.functional.log_softmax(lowerCAmelCase__						,    dim=-1       )

        return probs







class   lowerCamelCase						(	data.Dataset  ):

       def __init__(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			List[str]							=   X
        snake_case :			Union[str, Any]							=   y

       def __len__(      self       ):






        '''simple docstring'''






        return len(self.X       )



       def __getitem__(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			str							=   {}
        snake_case :			Tuple							=   self.X[index]
        snake_case :			Tuple							=   self.y[index]
        return data







def  _UpperCAmelCase						( __lowerCamelCase							)		->Tuple:


 '''simple docstring'''







 def pad_sequences(__lowerCamelCase							):
  snake_case :			Optional[Any]							=   [len(__a							) for seq in sequences]

  snake_case :			Any							=   torch.zeros(len(__a							)			,  max(__a							)							).long()  # padding value = 0

  for i, seq in enumerate(__a							):
   snake_case :			Dict							=   lengths[i]
   snake_case :			List[Any]							=   seq[:end]

  return padded_sequences, lengths

 snake_case :			str							=   {}
 for key in data[0].keys():
  snake_case :			Union[str, Any]							=   [d[key] for d in data]

 snake_case :			int							=   pad_sequences(item_info['''X''']							)
 snake_case :			List[str]							=   torch.tensor(item_info['''y''']			,  dtype=torch.long							)

 return x_batch, y_batch






def  _UpperCAmelCase						( __lowerCamelCase							)		->str:


 '''simple docstring'''
 snake_case :			Union[str, Any]							=   {}
 for key in data[0].keys():
  snake_case :			Tuple							=   [d[key] for d in data]

 snake_case :			Union[str, Any]							=   torch.cat(item_info['''X''']			,  0							)
 snake_case :			Union[str, Any]							=   torch.tensor(item_info['''y''']			,  dtype=torch.long							)

 return x_batch, y_batch






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=0			,  __lowerCamelCase=10			,  __lowerCamelCase="cpu"							)		->List[Any]:


 '''simple docstring'''
 snake_case :			Any							=   0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(__a							):
  snake_case :			Optional[Any]							=   input_t.to(__a							), target_t.to(__a							)

  optimizer.zero_grad()

  snake_case :			Tuple							=   discriminator(__a							)
  snake_case :			Tuple							=   nn.functional.nll_loss(__a			,  __a							)
  loss.backward(retain_graph=__a							)
  optimizer.step()

  samples_so_far += len(__a							)

  if batch_idx % log_interval == 0:
   print(
       '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
           epoch + 1			,  __a			,  len(data_loader.dataset							)			,  100 * samples_so_far / len(data_loader.dataset							)			,  loss.item()			,  )							)






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase="cpu"							)		->Tuple:


 '''simple docstring'''
 discriminator.eval()
 snake_case :			Any							=   0
 snake_case :			str							=   0
 with torch.no_grad():
  for input_t, target_t in data_loader:
   snake_case :			Tuple							=   input_t.to(__a							), target_t.to(__a							)
   snake_case :			Any							=   discriminator(__a							)
   # sum up batch loss
   test_loss += nn.functional.nll_loss(__a			,  __a			,  reduction='''sum'''							).item()
   # get the index of the max log-probability
   snake_case :			Any							=   output_t.argmax(dim=1			,  keepdim=__a							)
   correct += pred_t.eq(target_t.view_as(__a							)							).sum().item()

 test_loss /= len(data_loader.dataset							)

 print(
     '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
         __a			,  __a			,  len(data_loader.dataset							)			,  100.0 * correct / len(data_loader.dataset							)							)							)






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase="cpu"							)		->int:


 '''simple docstring'''
 snake_case :			Union[str, Any]							=   model.tokenizer.encode(__a							)
 snake_case :			List[str]							=   torch.tensor([input_t]			,  dtype=torch.long			,  device=__a							)
 if cached:
  snake_case :			List[Any]							=   model.avg_representation(__a							)

 snake_case :			Union[str, Any]							=   model(__a							).data.cpu().numpy().flatten().tolist()
 print('''Input sentence:'''			,  __a							)
 print(
     '''Predictions:'''			,  ''', '''.join('''{}: {:.4f}'''.format(__a			,  math.exp(__a							)							) for c, log_prob in zip(__a			,  __a							)							)			,  )






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase="cpu"							)		->str:


 '''simple docstring'''
 snake_case :			List[Any]							=   torch.utils.data.DataLoader(dataset=__a			,  batch_size=__a			,  collate_fn=__a							)

 snake_case :			Dict							=   []
 snake_case :			Dict							=   []
 for batch_idx, (x, y) in enumerate(tqdm(__a			,  ascii=__a							)							):
  with torch.no_grad():
   snake_case :			List[str]							=   x.to(__a							)
   snake_case :			int							=   discriminator.avg_representation(__a							).cpu().detach()
   snake_case :			Optional[Any]							=   torch.unbind(avg_rep.unsqueeze(1							)							)
   xs += avg_rep_list
   ys += y.cpu().numpy().tolist()

 snake_case :			Optional[Any]							=   torch.utils.data.DataLoader(
     dataset=Dataset(__a			,  __a							)			,  batch_size=__a			,  shuffle=__a			,  collate_fn=__a							)

 return data_loader






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=None			,  __lowerCamelCase="gpt2-medium"			,  __lowerCamelCase=10			,  __lowerCamelCase=64			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  )		->str:


 '''simple docstring'''
 snake_case :			List[str]							=   """cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

 print('''Preprocessing {} dataset...'''.format(__a							)							)
 snake_case :			Optional[int]							=   time.time()

 if dataset == "SST":
  snake_case :			Dict							=   ["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
  snake_case :			str							=   {c: i for i, c in enumerate(__a							)}

  snake_case :			Optional[Any]							=   Discriminator(
      class_size=len(__a							)			,  pretrained_model=__a			,  cached_mode=__a			,  device=__a							).to(__a							)

  snake_case :			Dict							=   torchtext_data.Field()
  snake_case :			Union[str, Any]							=   torchtext_data.Field(sequential=__a							)
  snake_case :			Union[str, Any]							=   datasets.SST.splits(
      __a			,  __a			,  fine_grained=__a			,  train_subtrees=__a			,  )

  snake_case :			Union[str, Any]							=   []
  snake_case :			int							=   []
  for i in trange(len(__a							)			,  ascii=__a							):
   snake_case :			Union[str, Any]							=   TreebankWordDetokenizer().detokenize(vars(train_data[i]							)['''text''']							)
   snake_case :			List[str]							=   discriminator.tokenizer.encode(__a							)
   snake_case :			int							=   torch.tensor([50256] + seq			,  device=__a			,  dtype=torch.long							)
   x.append(__a							)
   y.append(classaidx[vars(train_data[i]							)['''label''']]							)
  snake_case :			Optional[int]							=   Dataset(__a			,  __a							)

  snake_case :			Optional[int]							=   []
  snake_case :			List[Any]							=   []
  for i in trange(len(__a							)			,  ascii=__a							):
   snake_case :			Dict							=   TreebankWordDetokenizer().detokenize(vars(test_data[i]							)['''text''']							)
   snake_case :			Optional[int]							=   discriminator.tokenizer.encode(__a							)
   snake_case :			Optional[Any]							=   torch.tensor([50256] + seq			,  device=__a			,  dtype=torch.long							)
   test_x.append(__a							)
   test_y.append(classaidx[vars(test_data[i]							)['''label''']]							)
  snake_case :			Optional[Any]							=   Dataset(__a			,  __a							)

  snake_case :			Dict							=   {
      """class_size""": len(__a							),
      """embed_size""": discriminator.embed_size,
      """pretrained_model""": pretrained_model,
      """class_vocab""": classaidx,
      """default_class""": 2,
  }

 elif dataset == "clickbait":
  snake_case :			Tuple							=   ["""non_clickbait""", """clickbait"""]
  snake_case :			List[Any]							=   {c: i for i, c in enumerate(__a							)}

  snake_case :			List[Any]							=   Discriminator(
      class_size=len(__a							)			,  pretrained_model=__a			,  cached_mode=__a			,  device=__a							).to(__a							)

  with open('''datasets/clickbait/clickbait_train_prefix.txt'''							) as f:
   snake_case :			Optional[Any]							=   []
   for i, line in enumerate(__a							):
    try:
     data.append(eval(__a							)							)
    except Exception:
     print('''Error evaluating line {}: {}'''.format(__a			,  __a							)							)
     continue
  snake_case :			str							=   []
  snake_case :			Optional[Any]							=   []
  with open('''datasets/clickbait/clickbait_train_prefix.txt'''							) as f:
   for i, line in enumerate(tqdm(__a			,  ascii=__a							)							):
    try:
     snake_case :			str							=   eval(__a							)
     snake_case :			Union[str, Any]							=   discriminator.tokenizer.encode(d['''text''']							)

     if len(__a							) < max_length_seq:
      snake_case :			Any							=   torch.tensor([50256] + seq			,  device=__a			,  dtype=torch.long							)
     else:
      print('''Line {} is longer than maximum length {}'''.format(__a			,  __a							)							)
      continue
     x.append(__a							)
     y.append(d['''label''']							)
    except Exception:
     print('''Error evaluating / tokenizing line {}, skipping it'''.format(__a							)							)
     pass

  snake_case :			List[Any]							=   Dataset(__a			,  __a							)
  snake_case :			str							=   int(0.9 * len(__a							)							)
  snake_case :			Optional[int]							=   len(__a							) - train_size
  snake_case :			Tuple							=   torch.utils.data.random_split(__a			,  [train_size, test_size]							)

  snake_case :			Union[str, Any]							=   {
      """class_size""": len(__a							),
      """embed_size""": discriminator.embed_size,
      """pretrained_model""": pretrained_model,
      """class_vocab""": classaidx,
      """default_class""": 1,
  }

 elif dataset == "toxic":
  snake_case :			Optional[int]							=   ["""non_toxic""", """toxic"""]
  snake_case :			str							=   {c: i for i, c in enumerate(__a							)}

  snake_case :			Dict							=   Discriminator(
      class_size=len(__a							)			,  pretrained_model=__a			,  cached_mode=__a			,  device=__a							).to(__a							)

  snake_case :			List[Any]							=   []
  snake_case :			Any							=   []
  with open('''datasets/toxic/toxic_train.txt'''							) as f:
   for i, line in enumerate(tqdm(__a			,  ascii=__a							)							):
    try:
     snake_case :			Union[str, Any]							=   eval(__a							)
     snake_case :			Optional[int]							=   discriminator.tokenizer.encode(d['''text''']							)

     if len(__a							) < max_length_seq:
      snake_case :			List[str]							=   torch.tensor([50256] + seq			,  device=__a			,  dtype=torch.long							)
     else:
      print('''Line {} is longer than maximum length {}'''.format(__a			,  __a							)							)
      continue
     x.append(__a							)
     y.append(int(np.sum(d['''label''']							) > 0							)							)
    except Exception:
     print('''Error evaluating / tokenizing line {}, skipping it'''.format(__a							)							)
     pass

  snake_case :			Dict							=   Dataset(__a			,  __a							)
  snake_case :			List[str]							=   int(0.9 * len(__a							)							)
  snake_case :			List[str]							=   len(__a							) - train_size
  snake_case :			List[Any]							=   torch.utils.data.random_split(__a			,  [train_size, test_size]							)

  snake_case :			int							=   {
      """class_size""": len(__a							),
      """embed_size""": discriminator.embed_size,
      """pretrained_model""": pretrained_model,
      """class_vocab""": classaidx,
      """default_class""": 0,
  }

 else:  # if dataset == "generic":
  # This assumes the input dataset is a TSV with the following structure:
  # class \t text

  if dataset_fp is None:
   raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''							)

  snake_case :			Optional[Any]							=   set()
  with open(__a							) as f:
   snake_case :			Dict							=   csv.reader(__a			,  delimiter='''\t'''							)
   for row in tqdm(__a			,  ascii=__a							):
    if row:
     classes.add(row[0]							)

  snake_case :			Optional[Any]							=   sorted(__a							)
  snake_case :			Optional[Any]							=   {c: i for i, c in enumerate(__a							)}

  snake_case :			Union[str, Any]							=   Discriminator(
      class_size=len(__a							)			,  pretrained_model=__a			,  cached_mode=__a			,  device=__a							).to(__a							)

  snake_case :			Any							=   []
  snake_case :			Optional[int]							=   []
  with open(__a							) as f:
   snake_case :			Union[str, Any]							=   csv.reader(__a			,  delimiter='''\t'''							)
   for i, row in enumerate(tqdm(__a			,  ascii=__a							)							):
    if row:
     snake_case :			Tuple							=   row[0]
     snake_case :			Optional[int]							=   row[1]

     try:
      snake_case :			Any							=   discriminator.tokenizer.encode(__a							)
      if len(__a							) < max_length_seq:
       snake_case :			Optional[int]							=   torch.tensor([50256] + seq			,  device=__a			,  dtype=torch.long							)

      else:
       print('''Line {} is longer than maximum length {}'''.format(__a			,  __a							)							)
       continue

      x.append(__a							)
      y.append(classaidx[label]							)

     except Exception:
      print('''Error tokenizing line {}, skipping it'''.format(__a							)							)
      pass

  snake_case :			str							=   Dataset(__a			,  __a							)
  snake_case :			str							=   int(0.9 * len(__a							)							)
  snake_case :			List[str]							=   len(__a							) - train_size
  snake_case :			Dict							=   torch.utils.data.random_split(__a			,  [train_size, test_size]							)

  snake_case :			Optional[int]							=   {
      """class_size""": len(__a							),
      """embed_size""": discriminator.embed_size,
      """pretrained_model""": pretrained_model,
      """class_vocab""": classaidx,
      """default_class""": 0,
  }

 snake_case :			Tuple							=   time.time()
 print('''Preprocessed {} data points'''.format(len(__a							) + len(__a							)							)							)
 print('''Data preprocessing took: {:.3f}s'''.format(end - start							)							)

 if cached:
  print('''Building representation cache...'''							)

  snake_case :			int							=   time.time()

  snake_case :			int							=   get_cached_data_loader(__a			,  __a			,  __a			,  shuffle=__a			,  device=__a							)

  snake_case :			Tuple							=   get_cached_data_loader(__a			,  __a			,  __a			,  device=__a							)

  snake_case :			Optional[Any]							=   time.time()
  print('''Building representation cache took: {:.3f}s'''.format(end - start							)							)

 else:
  snake_case :			Union[str, Any]							=   torch.utils.data.DataLoader(
      dataset=__a			,  batch_size=__a			,  shuffle=__a			,  collate_fn=__a							)
  snake_case :			str							=   torch.utils.data.DataLoader(dataset=__a			,  batch_size=__a			,  collate_fn=__a							)

 if save_model:
  with open('''{}_classifier_head_meta.json'''.format(__a							)			,  '''w'''							) as meta_file:
   json.dump(__a			,  __a							)

 snake_case :			Optional[int]							=   optim.Adam(discriminator.parameters()			,  lr=0.0001							)

 for epoch in range(__a							):
  snake_case :			int							=   time.time()
  print('''\nEpoch'''			,  epoch + 1							)

  train_epoch(
      discriminator=__a			,  data_loader=__a			,  optimizer=__a			,  epoch=__a			,  log_interval=__a			,  device=__a			,  )
  evaluate_performance(data_loader=__a			,  discriminator=__a			,  device=__a							)

  snake_case :			int							=   time.time()
  print('''Epoch took: {:.3f}s'''.format(end - start							)							)

  print('''\nExample prediction'''							)
  predict(__a			,  __a			,  __a			,  cached=__a			,  device=__a							)

  if save_model:
   # torch.save(discriminator.state_dict(),
   #           "{}_discriminator_{}.pt".format(
   #               args.dataset, epoch + 1
   #               ))
   torch.save(
       discriminator.get_classifier().state_dict()			,  '''{}_classifier_head_epoch_{}.pt'''.format(__a			,  epoch + 1							)			,  )


if __name__ == "__main__":
  a					:				int       =      argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
  parser.add_argument(
      '--dataset',
      type=str,
      default='SST',
      choices=('SST', 'clickbait', 'toxic', 'generic'),
      help=(
          'dataset to train the discriminator on.'
          'In case of generic, the dataset is expected'
          'to be a TSBV file with structure: class \\t text'
      ),
  )
  parser.add_argument(
      '--dataset_fp',
      type=str,
      default='',
      help='File path of the dataset to use. Needed only in case of generic datadset',
  )
  parser.add_argument(
      '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
  )
  parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
  parser.add_argument(
      '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
  )
  parser.add_argument(
      '--log_interval',
      type=int,
      default=10,
      metavar='N',
      help='how many batches to wait before logging training status',
  )
  parser.add_argument('--save_model', action='store_true', help='whether to save the model')
  parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
  parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
  a					:				Union[str, Any]       =      parser.parse_args()

  train_discriminator(**(vars(args)))







'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				Union[str, 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}
}
"""

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
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.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




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



   def  _UpperCamelCase		(    self     )				->     int:
        """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_absolute_error.html"""
            ]					,      )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """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					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}