



import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
      from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
      UpperCamelCase_									=   True
      from torch.cuda.amp import autocast


UpperCamelCase_									=   logging.getLogger(__name__)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : str       =						field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__      : Optional[str]       =						field(
       default=__snake_case ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,							)
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Whether to log verbose messages or not."} ,							)







def       lowerCamelCase_  (    _a       :						ModelArguments   ,		_a       :						TrainingArguments    ):



    '''simple docstring'''




    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,		datefmt="""%m/%d/%Y %H:%M:%S"""   ,		handlers=[logging.StreamHandler(sys.stdout    )]   ,		)
    UpperCAmelCase_     :       List[Any]  		=					logging.WARNING
    if model_args.verbose_logging:
        UpperCAmelCase_     :       int  		=					logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank    ):
        UpperCAmelCase_     :       Union[str, Any]  		=					logging.INFO
    logger.setLevel(_a    )







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : str       =						field(
       default=__snake_case ,							metadata={"help": "The name of the dataset to use (via the datasets library)."} )
   A__      : Optional[str]       =						field(
       default=__snake_case ,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
   A__      : Optional[str]       =						field(
       default="train" ,							metadata={
           "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
       } ,							)
   A__      : Optional[str]       =						field(
       default="validation" ,							metadata={
           "help": (
               "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
           )
       } ,							)
   A__      : Optional[str]       =						field(
       default="text" ,							metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"} ,							)
   A__      : Optional[str]       =						field(
       default="file" ,							metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,							)
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."} ,							)
   A__      : Optional[float]       =						field(
       default=__snake_case ,							metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."} ,							)
   A__      : Optional[str]       =						field(
       default="librispeech" ,							metadata={
           "help": (
               "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
               " 'buckwalter'."
           )
       } ,							)
   A__      : bool       =						field(
       default=__snake_case ,							metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
   A__      : Optional[int]       =						field(
       default=__snake_case ,							metadata={"help": "The number of processes to use for the preprocessing."} ,							)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : bool       =						False
   A__      : Optional[str]       =						None
   A__      : Optional[str]       =						"|"
   A__      : Optional[Dict[str, str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Set[str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Callable[[str], str]]       =						None


   @classmethod
   def       A__    (    cls:     List[Any]  ,lowerCamelCase_:     str							)	->      Union[str, Any]:
       if name == "librispeech":
           return cls()
       if name == "timit":
           return cls(
               do_lower_case=lowerCamelCase_  ,translation_table=str.maketrans({"""-""": """ """}							)  ,)
       if name == "buckwalter":
           UpperCAmelCase_     :       Dict  		=					{
               """-""": """ """,  # sometimes used to represent pauses
               """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
           }
           return cls(
               vocab_file=pathlib.Path(lowerCamelCase_							).parent.joinpath("""vocab/buckwalter.json"""							)  ,word_delimiter_token="""/"""  ,translation_table=str.maketrans(lowerCamelCase_							)  ,words_to_remove={"""sil"""}  ,untransliterator=arabic.buckwalter.untransliterate  ,)
       raise ValueError(F'''Unsupported orthography: \'{name}\'.'''							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     str							)	->      str:
       # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
       if len(self.translation_table							) > 0:
           UpperCAmelCase_     :       Optional[int]  		=					text.translate(self.translation_table							)
       if len(self.words_to_remove							) == 0:
           UpperCAmelCase_     :       List[Any]  		=					""" """.join(text.split()							)  # clean up whitespaces
       else:
           UpperCAmelCase_     :       List[str]  		=					""" """.join(w for w in text.split() if w not in self.words_to_remove							)  # and clean up whilespaces
       return text






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     ModelArguments							)	->      WavaVecaProcessor:
       UpperCAmelCase_     :       List[Any]  		=					WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path  ,cache_dir=model_args.cache_dir							)
       if self.vocab_file:
           UpperCAmelCase_     :       Tuple  		=					WavaVecaCTCTokenizer(
               self.vocab_file  ,cache_dir=model_args.cache_dir  ,do_lower_case=self.do_lower_case  ,word_delimiter_token=self.word_delimiter_token  ,)
       else:
           UpperCAmelCase_     :       Tuple  		=					WavaVecaCTCTokenizer.from_pretrained(
               model_args.model_name_or_path  ,cache_dir=model_args.cache_dir  ,do_lower_case=self.do_lower_case  ,word_delimiter_token=self.word_delimiter_token  ,)
       return WavaVecaProcessor(lowerCamelCase_  ,lowerCamelCase_							)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : WavaVecaProcessor
   A__      : Union[bool, str]       =						True
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None


   def __call__(    self:     Tuple  ,lowerCamelCase_:     List[Dict[str, Union[List[int], torch.Tensor]]]							)	->      Dict[str, torch.Tensor]:
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       UpperCAmelCase_     :       Union[str, Any]  		=					[{"""input_values""": feature["""input_values"""]} for feature in features]
       UpperCAmelCase_     :       List[Any]  		=					[{"""input_ids""": feature["""labels"""]} for feature in features]

       UpperCAmelCase_     :       List[str]  		=					self.processor.pad(
           lowerCamelCase_  ,padding=self.padding  ,max_length=self.max_length  ,pad_to_multiple_of=self.pad_to_multiple_of  ,return_tensors="""pt"""  ,)
       UpperCAmelCase_     :       Optional[Any]  		=					self.processor.pad(
           labels=lowerCamelCase_  ,padding=self.padding  ,max_length=self.max_length_labels  ,pad_to_multiple_of=self.pad_to_multiple_of_labels  ,return_tensors="""pt"""  ,)

       # replace padding with -100 to ignore loss correctly
       UpperCAmelCase_     :       Any  		=					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1							)  ,-100							)

       UpperCAmelCase_     :       List[str]  		=					labels

       return batch







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Any  ,lowerCamelCase_:     nn.Module  ,lowerCamelCase_:     Dict[str, Union[torch.Tensor, Any]]							)	->      torch.Tensor:
       model.train()
       UpperCAmelCase_     :       Optional[int]  		=					self._prepare_inputs(lowerCamelCase_							)

       if self.use_amp:
           with autocast():
               UpperCAmelCase_     :       int  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Tuple  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)

       if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
               UpperCAmelCase_     :       List[str]  		=					loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
               UpperCAmelCase_     :       List[str]  		=					loss.sum() / (inputs["""labels"""] >= 0).sum()
           else:
               raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''							)

       if self.args.gradient_accumulation_steps > 1:
           UpperCAmelCase_     :       Union[str, Any]  		=					loss / self.args.gradient_accumulation_steps

       if self.use_amp:
           self.scaler.scale(lowerCamelCase_							).backward()
       elif self.use_apex:
           with amp.scale_loss(lowerCamelCase_  ,self.optimizer							) as scaled_loss:
               scaled_loss.backward()
       elif self.deepspeed:
           self.deepspeed.backward(lowerCamelCase_							)
       else:
           loss.backward()

       return loss.detach()







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					parser.parse_args_into_dataclasses()
    configure_logger(_a   ,		_a    )

    UpperCAmelCase_     :       Tuple  		=					Orthography.from_name(data_args.orthography.lower()    )
    UpperCAmelCase_     :       int  		=					orthography.create_processor(_a    )
    UpperCAmelCase_     :       Dict  		=					WavaVecaForCTC.from_pretrained(
        model_args.model_name_or_path   ,		cache_dir=model_args.cache_dir   ,		gradient_checkpointing=training_args.gradient_checkpointing   ,		vocab_size=len(processor.tokenizer    )   ,		)

    UpperCAmelCase_     :       Dict  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.train_split_name    )
    UpperCAmelCase_     :       int  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.validation_split_name    )

    UpperCAmelCase_     :       Optional[Any]  		=					datasets.load_metric("""wer"""    )
    UpperCAmelCase_     :       str  		=					processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
    UpperCAmelCase_     :       Optional[Any]  		=					"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(_a    ) == 1    )
    UpperCAmelCase_     :       Optional[Any]  		=					re.compile(  # remove characters not in vocabulary
        rF'''[^\s{re.escape(_a    )}]'''   ,		flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0   ,		)
    UpperCAmelCase_     :       int  		=					[]

    def prepare_example(_a       :						Dict    ):  # TODO(elgeish) make use of multiprocessing?
        UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					librosa.load(example[data_args.speech_file_column]   ,		sr=_a    )
        if data_args.max_duration_in_seconds is not None:
            UpperCAmelCase_     :       Dict  		=					len(example["""speech"""]    ) / example["""sampling_rate"""]
        # Normalize and clean up text; order matters!
        UpperCAmelCase_     :       Optional[int]  		=					orthography.preprocess_for_training(example[data_args.target_text_column]    )
        UpperCAmelCase_     :       int  		=					vocabulary_text_cleaner.sub(""""""   ,		_a    )
        if updated_text != example[data_args.target_text_column]:
            text_updates.append((example[data_args.target_text_column], updated_text)    )
            UpperCAmelCase_     :       int  		=					updated_text
        return example

    UpperCAmelCase_     :       Any  		=					train_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )

    if data_args.max_duration_in_seconds is not None:

        def filter_by_max_duration(_a       :						List[Any]    ):
            return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

        UpperCAmelCase_     :       Union[str, Any]  		=					len(_a    )
        UpperCAmelCase_     :       int  		=					len(_a    )
        UpperCAmelCase_     :       Optional[Any]  		=					train_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        UpperCAmelCase_     :       Tuple  		=					val_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        if len(_a    ) > old_train_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_train_size} train example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
        if len(_a    ) > old_val_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_val_size} validation example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
    logger.info(F'''Split sizes: {len(_a    )} train and {len(_a    )} validation.'''    )

    logger.warning(F'''Updated {len(_a    )} transcript(s) using \'{data_args.orthography}\' orthography rules.'''    )
    if logger.isEnabledFor(logging.DEBUG    ):
        for original_text, updated_text in text_updates:
            logger.debug(F'''Updated text: "{original_text}" -> "{updated_text}"'''    )
    UpperCAmelCase_     :       Dict  		=					None

    def prepare_dataset(_a       :						Optional[int]    ):
        # check that all files have the correct sampling rate
        assert (
            len(set(batch["""sampling_rate"""]    )    ) == 1
        ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

        UpperCAmelCase_     :       List[str]  		=					processor(
            audio=batch["""speech"""]   ,		text=batch[data_args.target_text_column]   ,		sampling_rate=batch["""sampling_rate"""][0]    )
        batch.update(_a    )
        return batch

    UpperCAmelCase_     :       int  		=					train_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)

    UpperCAmelCase_     :       Optional[int]  		=					DataCollatorCTCWithPadding(processor=_a   ,		padding=_a    )

    def compute_metrics(_a       :						int    ):
        UpperCAmelCase_     :       List[Any]  		=					pred.predictions
        UpperCAmelCase_     :       Optional[int]  		=					np.argmax(_a   ,		axis=-1    )

        UpperCAmelCase_     :       List[str]  		=					processor.tokenizer.pad_token_id

        UpperCAmelCase_     :       str  		=					processor.batch_decode(_a    )
        # we do not want to group tokens when computing the metrics
        UpperCAmelCase_     :       List[Any]  		=					processor.batch_decode(pred.label_ids   ,		group_tokens=_a    )
        if logger.isEnabledFor(logging.DEBUG    ):
            for reference, predicted in zip(_a   ,		_a    ):
                logger.debug(F'''reference: "{reference}"'''    )
                logger.debug(F'''predicted: "{predicted}"'''    )
                if orthography.untransliterator is not None:
                    logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(_a    )}"'''    )
                    logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(_a    )}"'''    )

        UpperCAmelCase_     :       Dict  		=					wer_metric.compute(predictions=_a   ,		references=_a    )

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    UpperCAmelCase_     :       Union[str, Any]  		=					CTCTrainer(
        model=_a   ,		data_collator=_a   ,		args=_a   ,		compute_metrics=_a   ,		train_dataset=_a   ,		eval_dataset=_a   ,		tokenizer=processor.feature_extractor   ,		)

    trainer.train()


if __name__ == "__main__":
      main()








"""simple docstring"""



from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
						import pyximport

						pyximport.install(setup_args={"include_dirs": np.get_include()})
						from . import algos_graphormer  # noqa E402




def     lowerCamelCase_					(	__A	:		List[str]      ,						__A	:		int = 512      ):
					lowerCamelCase__					=x.shape[1] if len(x.shape      ) > 1 else 1
					lowerCamelCase__					=1 + np.arange(0      ,						feature_num * offset      ,						__A      ,						dtype=np.intaa      )
					lowerCamelCase__					=x + feature_offset
					return x




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		Optional[Any]=True      ):
					requires_backends(__A      ,						['cython']      )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										lowerCamelCase__					=np.asarray(item['edge_attr']      ,						dtype=np.intaa      )
					else:
										lowerCamelCase__					=np.ones((len(item['edge_index'][0]      ), 1)      ,						dtype=np.intaa      )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										lowerCamelCase__					=np.asarray(item['node_feat']      ,						dtype=np.intaa      )
					else:
										lowerCamelCase__					=np.ones((item['num_nodes'], 1)      ,						dtype=np.intaa      )  # same embedding for all

					lowerCamelCase__					=np.asarray(item['edge_index']      ,						dtype=np.intaa      )

					lowerCamelCase__					=convert_to_single_emb(__A      ) + 1
					lowerCamelCase__					=item['num_nodes']

					if len(edge_attr.shape      ) == 1:
										lowerCamelCase__					=edge_attr[:, None]
					lowerCamelCase__					=np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,						dtype=np.intaa      )
					lowerCamelCase__					=convert_to_single_emb(__A      ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					lowerCamelCase__					=np.zeros([num_nodes, num_nodes]      ,						dtype=__A      )
					lowerCamelCase__					=True

					lowerCamelCase__					,  lowerCamelCase__					=algos_graphormer.floyd_warshall(__A      )
					lowerCamelCase__					=np.amax(__A      )

					lowerCamelCase__					=algos_graphormer.gen_edge_input(__A      ,						__A      ,						__A      )
					lowerCamelCase__					=np.zeros([num_nodes + 1, num_nodes + 1]      ,						dtype=np.single      )  # with graph token

					# combine
					lowerCamelCase__					=input_nodes + 1  # we shift all indices by one for padding
					lowerCamelCase__					=attn_bias
					lowerCamelCase__					=attn_edge_type
					lowerCamelCase__					=shortest_path_result.astype(np.intaa      ) + 1  # we shift all indices by one for padding
					lowerCamelCase__					=np.sum(__A      ,						axis=1      ).reshape(-1      ) + 1  # we shift all indices by one for padding
					lowerCamelCase__					=item['in_degree']  # for undirected graph
					lowerCamelCase__					=input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										lowerCamelCase__					=item['y']

					return item




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE=20     ,    _SCREAMING_SNAKE_CASE=False)      ->	Any:




											"""simple docstring"""





											if not is_cython_available():
																raise ImportError('Graphormer preprocessing needs Cython (pyximport)')

											lowerCamelCase__					=spatial_pos_max
											lowerCamelCase__					=on_the_fly_processing






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Dict[str, Any]:




											"""simple docstring"""





											if self.on_the_fly_processing:
																lowerCamelCase__					=[preprocess_item(_SCREAMING_SNAKE_CASE) for i in features]

											if not isinstance(features[0]     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=[vars(_SCREAMING_SNAKE_CASE) for f in features]
											lowerCamelCase__					={}

											lowerCamelCase__					=max(len(i['input_nodes']) for i in features)
											lowerCamelCase__					=len(features[0]['input_nodes'][0])
											lowerCamelCase__					=len(features[0]['attn_edge_type'][0][0])
											lowerCamelCase__					=max(len(i['input_edges'][0][0]) for i in features)
											lowerCamelCase__					=len(features[0]['input_edges'][0][0][0])
											lowerCamelCase__					=len(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    max_node_num + 1     ,    max_node_num + 1     ,    dtype=torch.float)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(
											    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)

											for ix, f in enumerate(_SCREAMING_SNAKE_CASE):
																for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																					lowerCamelCase__					=torch.tensor(f[k])

																if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]) > 0:
																					lowerCamelCase__					=float('-inf')

																lowerCamelCase__					=f['attn_bias']
																lowerCamelCase__					=f[
																    'attn_edge_type'
																]
																lowerCamelCase__					=f['spatial_pos']
																lowerCamelCase__					=f['in_degree']
																lowerCamelCase__					=f['input_nodes']
																lowerCamelCase__					=f['input_edges']

											lowerCamelCase__					=batch['in_degree']

											lowerCamelCase__					=features[0]['labels']
											if len(_SCREAMING_SNAKE_CASE) == 1:  # one task
																if isinstance(sample[0]     ,    _SCREAMING_SNAKE_CASE):  # regression
																					lowerCamelCase__					=torch.from_numpy(np.concatenate([i['labels'] for i in features]))
																else:  # binary classification
																					lowerCamelCase__					=torch.from_numpy(np.concatenate([i['labels'] for i in features]))
											else:  # multi task classification, left to float to keep the NaNs
																lowerCamelCase__					=torch.from_numpy(np.stack([i['labels'] for i in features]     ,    axis=0))

											return batch