
"""simple docstring"""




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
snake_case			       =		'\\n'

snake_case			       =		'\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

snake_case			       =		'\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

snake_case			       =		['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
snake_case			       =		''

try:
 from jiwer import transforms as tr

 snake_case			       =		True
except ImportError:
 snake_case			       =		False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):




 class 					UpperCamelCase     (	tr.AbstractTransform				):

     """simple docstring"""



     def __init__(    self		,     lowercase__ = " "      )   ->   Dict:
          """simple docstring"""







          SCREAMING_SNAKE_CASE           =     sentence_delimiter


     def 							A (    self		,     lowercase__      )   ->   List[str]:
          """simple docstring"""







          return list(lowercase_      )




     def 							A (    self		,     lowercase__      )   ->   Dict:
          """simple docstring"""







          SCREAMING_SNAKE_CASE           =     []
          for sent_idx, sentence in enumerate(lowercase_      ):
               chars.extend(self.process_string(lowercase_      )      )
               if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase_      ) - 1:
                    chars.append(self.sentence_delimiter      )
          return chars






 snake_case			       =		tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
elif _jiwer_available:
 snake_case			       =		tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )
else:
 snake_case			       =		None

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     return float((preds == labels).mean()      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     return {
         "f1": float(fa_score(y_true=SCREAMING_SNAKE_CASE__,					y_pred=SCREAMING_SNAKE_CASE__,					average='macro'      )      ),
         "accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__      ),
     }

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="exp",					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=False,					):
     SCREAMING_SNAKE_CASE           =     [[label] for label in labels]
     SCREAMING_SNAKE_CASE           =     list(SCREAMING_SNAKE_CASE__      )
     try:
          import sacrebleu as scb
     except ImportError:
          raise ValueError(
              'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
              'You can install it via `pip install sacrebleu`.'      )

     if version.parse(scb.__version__      ) < version.parse('1.4.12'      ):
          raise ImportWarning(
              'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
              'You can install it with `pip install "sacrebleu>=1.4.12"`.'      )

     SCREAMING_SNAKE_CASE           =     len(labels[0]      )
     if any(len(SCREAMING_SNAKE_CASE__      ) != references_per_prediction for refs in labels      ):
          raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
     SCREAMING_SNAKE_CASE           =     [[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE__      )]
     SCREAMING_SNAKE_CASE           =     scb.corpus_bleu(
         SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					smooth_method=SCREAMING_SNAKE_CASE__,					smooth_value=SCREAMING_SNAKE_CASE__,					force=SCREAMING_SNAKE_CASE__,					lowercase=SCREAMING_SNAKE_CASE__,					use_effective_order=SCREAMING_SNAKE_CASE__,					**({'tokenize': tokenize} if tokenize else {}),					)
     return {"bleu": output.score}

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):





     try:
          from jiwer import compute_measures
     except ImportError:
          raise ValueError(
              f'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
              'You can install it via `pip install jiwer`.'      )

     if concatenate_texts:
          SCREAMING_SNAKE_CASE           =     compute_measures(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__      )['wer']

          SCREAMING_SNAKE_CASE           =     compute_measures(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					truth_transform=SCREAMING_SNAKE_CASE__,					hypothesis_transform=SCREAMING_SNAKE_CASE__      )['wer']
          return {"wer": wer, "cer": cer}
     else:

          def compute_score(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="wer"      ):
               SCREAMING_SNAKE_CASE           =     0
               SCREAMING_SNAKE_CASE           =     0
               for prediction, reference in zip(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__      ):
                    if score_type == "wer":
                         SCREAMING_SNAKE_CASE           =     compute_measures(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__      )
                    elif score_type == "cer":
                         SCREAMING_SNAKE_CASE           =     compute_measures(
                             SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					truth_transform=SCREAMING_SNAKE_CASE__,					hypothesis_transform=SCREAMING_SNAKE_CASE__      )
                    incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                    total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total

          return {"wer": compute_score(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					'wer'      ), "cer": compute_score(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					'cer'      )}




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

    """simple docstring"""



    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







         if self.config_name not in _CONFIG_NAMES:
              raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''      )

         SCREAMING_SNAKE_CASE           =     'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

         return datasets.MetricInfo(
             description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
                 {'predictions': datasets.Value(lowercase_      ), 'references': datasets.Value(lowercase_      )}      )		,     codebase_urls=[]		,     reference_urls=[]		,     format='numpy'		,     )




    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__=None		,     lowercase__=None      )   ->   Dict:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     bleu_kwargs if bleu_kwargs is not None else {}
         SCREAMING_SNAKE_CASE           =     wer_kwargs if wer_kwargs is not None else {}

         if self.config_name == "fleurs-lang_id":
              return {"accuracy": simple_accuracy(lowercase_		,     lowercase_      )}
         elif self.config_name == "minds14":
              return fa_and_simple_accuracy(lowercase_		,     lowercase_      )
         elif self.config_name == "covost2":
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('smooth_method'		,     'exp'      )
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('smooth_value'		,     lowercase_      )
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('force'		,     lowercase_      )
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('lowercase'		,     lowercase_      )
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('tokenize'		,     lowercase_      )
              SCREAMING_SNAKE_CASE           =     bleu_kwargs.pop('use_effective_order'		,     lowercase_      )
              return bleu(
                  preds=lowercase_		,     labels=lowercase_		,     smooth_method=lowercase_		,     smooth_value=lowercase_		,     force=lowercase_		,     lowercase=lowercase_		,     tokenize=lowercase_		,     use_effective_order=lowercase_		,     )
         elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
              SCREAMING_SNAKE_CASE           =     wer_kwargs.pop('concatenate_texts'		,     lowercase_      )
              return wer_and_cer(lowercase_		,     lowercase_		,     lowercase_		,     self.config_name      )
         else:
              raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''      )


"""simple docstring"""




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


snake_case			       =		logging.get_logger(__name__)




@dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				Optional[str]						  =    None
				UpperCAmelCase_    :				Optional[str]						  =    None


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									return json.dumps(dataclasses.asdict(self      )		,     indent=2      ) + "\n"






@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				List[int]
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[Union[int, float]]						  =    None


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									return json.dumps(dataclasses.asdict(self      )      ) + "\n"






class 					UpperCamelCase     :

				"""simple docstring"""



				def 							A (    self		,     lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   int:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									if len(self.get_labels()      ) > 1:
														SCREAMING_SNAKE_CASE           =     self.get_labels()[int(example.label      )]
									return example




				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None      )   ->   Dict:
									"""simple docstring"""







									with open(lowercase__		,     'r'		,     encoding='utf-8-sig'      ) as f:
														return list(csv.reader(lowercase__		,     delimiter='\t'		,     quotechar=lowercase__      )      )






class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				def __init__(    self		,     lowercase__=None		,     lowercase__=None		,     lowercase__="classification"		,     lowercase__=False      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     [] if labels is None else labels
									SCREAMING_SNAKE_CASE           =     [] if examples is None else examples
									SCREAMING_SNAKE_CASE           =     mode
									SCREAMING_SNAKE_CASE           =     verbose


				def __len__(    self      )   ->   Optional[int]:
									"""simple docstring"""







									return len(self.examples      )


				def __getitem__(    self		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







									if isinstance(lowercase__		,     lowercase__      ):
														return SingleSentenceClassificationProcessor(labels=self.labels		,     examples=self.examples[idx]      )
									return self.examples[idx]


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     **lowercase__      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples_from_csv(
									    lowercase__		,     split_name=lowercase__		,     column_label=lowercase__		,     column_text=lowercase__		,     column_id=lowercase__		,     skip_first_row=lowercase__		,     overwrite_labels=lowercase__		,     overwrite_examples=lowercase__		,     )
									return processor


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None		,     **lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples(lowercase__		,     labels=lowercase__      )
									return processor


				def 							A (    self		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._read_tsv(lowercase__      )
									if skip_first_row:
														SCREAMING_SNAKE_CASE           =     lines[1:]
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for i, line in enumerate(lowercase__      ):
														texts.append(line[column_text]      )
														labels.append(line[column_label]      )
														if column_id is not None:
																			ids.append(line[column_id]      )
														else:
																			SCREAMING_SNAKE_CASE           =     f'''{split_name}-{i}''' if split_name else str(lowercase__      )
																			ids.append(lowercase__      )

									return self.add_examples(
									    lowercase__		,     lowercase__		,     lowercase__		,     overwrite_labels=lowercase__		,     overwrite_examples=lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False      )   ->   str:
									"""simple docstring"""







									if labels is not None and len(lowercase__      ) != len(lowercase__      ):
														raise ValueError(
														    f'''Text and labels have mismatched lengths {len(lowercase__      )} and {len(lowercase__      )}'''      )
									if ids is not None and len(lowercase__      ) != len(lowercase__      ):
														raise ValueError(f'''Text and ids have mismatched lengths {len(lowercase__      )} and {len(lowercase__      )}'''      )
									if ids is None:
														SCREAMING_SNAKE_CASE           =     [None] * len(lowercase__      )
									if labels is None:
														SCREAMING_SNAKE_CASE           =     [None] * len(lowercase__      )
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     set()
									for text_or_text_and_label, label, guid in zip(lowercase__		,     lowercase__		,     lowercase__      ):
														if isinstance(lowercase__		,     (tuple, list)      ) and label is None:
																			SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     text_or_text_and_label
														else:
																			SCREAMING_SNAKE_CASE           =     text_or_text_and_label
														added_labels.add(lowercase__      )
														examples.append(InputExample(guid=lowercase__		,     text_a=lowercase__		,     text_b=lowercase__		,     label=lowercase__      )      )

									# Update examples
									if overwrite_examples:
														SCREAMING_SNAKE_CASE           =     examples
									else:
														self.examples.extend(lowercase__      )

									# Update labels
									if overwrite_labels:
														SCREAMING_SNAKE_CASE           =     list(lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     list(set(self.labels      ).union(lowercase__      )      )

									return self.examples




				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=False		,     lowercase__=0		,     lowercase__=True		,     lowercase__=None		,     )   ->   Optional[int]:
									"""simple docstring"""







									if max_length is None:
														SCREAMING_SNAKE_CASE           =     tokenizer.max_len

									SCREAMING_SNAKE_CASE           =     {label: i for i, label in enumerate(self.labels      )}

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, example in enumerate(self.examples      ):
														if ex_index % 10000 == 0:
																			logger.info(f'''Tokenizing example {ex_index}'''      )

														SCREAMING_SNAKE_CASE           =     tokenizer.encode(
														    example.text_a		,     add_special_tokens=lowercase__		,     max_length=min(lowercase__		,     tokenizer.max_len      )		,     )
														all_input_ids.append(lowercase__      )

									SCREAMING_SNAKE_CASE           =     max(len(lowercase__      ) for input_ids in all_input_ids      )

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, (input_ids, example) in enumerate(zip(lowercase__		,     self.examples      )      ):
														if ex_index % 10000 == 0:
																			logger.info(f'''Writing example {ex_index}/{len(self.examples      )}'''      )
														# The mask has 1 for real tokens and 0 for padding tokens. Only real
														# tokens are attended to.
														SCREAMING_SNAKE_CASE           =     [1 if mask_padding_with_zero else 0] * len(lowercase__      )

														# Zero-pad up to the sequence length.
														SCREAMING_SNAKE_CASE           =     batch_length - len(lowercase__      )
														if pad_on_left:
																			SCREAMING_SNAKE_CASE           =     ([pad_token] * padding_length) + input_ids
																			SCREAMING_SNAKE_CASE           =     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																			SCREAMING_SNAKE_CASE           =     input_ids + ([pad_token] * padding_length)
																			SCREAMING_SNAKE_CASE           =     attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

														if len(lowercase__      ) != batch_length:
																			raise ValueError(f'''Error with input length {len(lowercase__      )} vs {batch_length}'''      )
														if len(lowercase__      ) != batch_length:
																			raise ValueError(f'''Error with input length {len(lowercase__      )} vs {batch_length}'''      )

														if self.mode == "classification":
																			SCREAMING_SNAKE_CASE           =     label_map[example.label]
														elif self.mode == "regression":
																			SCREAMING_SNAKE_CASE           =     float(example.label      )
														else:
																			raise ValueError(self.mode      )

														if ex_index < 5 and self.verbose:
																			logger.info('*** Example ***'      )
																			logger.info(f'''guid: {example.guid}'''      )
																			logger.info(f'''input_ids: {' '.join([str(lowercase__      ) for x in input_ids]      )}'''      )
																			logger.info(f'''attention_mask: {' '.join([str(lowercase__      ) for x in attention_mask]      )}'''      )
																			logger.info(f'''label: {example.label} (id = {label})'''      )

														features.append(InputFeatures(input_ids=lowercase__		,     attention_mask=lowercase__		,     label=lowercase__      )      )

									if return_tensors is None:
														return features
									elif return_tensors == "tf":
														if not is_tf_available():
																			raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
														import tensorflow as tf

														def gen():
																			for ex in features:
																								yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

														SCREAMING_SNAKE_CASE           =     tf.data.Dataset.from_generator(
														    lowercase__		,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)		,     ({'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )}, tf.TensorShape([]      ))		,     )
														return dataset
									elif return_tensors == "pt":
														if not is_torch_available():
																			raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
														import torch
														from torch.utils.data import TensorDataset

														SCREAMING_SNAKE_CASE           =     torch.tensor([f.input_ids for f in features]		,     dtype=torch.long      )
														SCREAMING_SNAKE_CASE           =     torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long      )
														if self.mode == "classification":
																			SCREAMING_SNAKE_CASE           =     torch.tensor([f.label for f in features]		,     dtype=torch.long      )
														elif self.mode == "regression":
																			SCREAMING_SNAKE_CASE           =     torch.tensor([f.label for f in features]		,     dtype=torch.float      )

														SCREAMING_SNAKE_CASE           =     TensorDataset(lowercase__		,     lowercase__		,     lowercase__      )
														return dataset
									else:
														raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )
