

'''simple docstring'''




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_a			:	List[str]								=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_a			:	Dict								=  {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_a			:	int								=  {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_a			:	Any								=  sorted(arg_to_scheduler.keys())
_a			:	Dict								=  '{' + ', '.join(arg_to_scheduler_choices) + '}'



class     lowercase_		(						pl.LightningModule	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=None				,	a_="base"				,	a_=None				,	a_=None				,	a_=None				,	**a_				,	)							->			Any:


    """simple docstring"""
    super().__init__()
    # TODO: move to self.save_hyperparameters()
    # self.save_hyperparameters()
    # can also expand arguments into trainer signature for easier reading

    self.save_hyperparameters(a_       )
    UpperCAmelCase  							=		0
    UpperCAmelCase  							=		Path(self.hparams.output_dir       )
    UpperCAmelCase  							=		self.hparams.cache_dir if self.hparams.cache_dir else None
    if config is None:
      UpperCAmelCase  							=		AutoConfig.from_pretrained(
          self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,	**({'num_labels': num_labels} if num_labels is not None else {})				,	cache_dir=a_				,	**a_				,	)
    else:
      UpperCAmelCase  							=		config

    UpperCAmelCase  							=		('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
    for p in extra_model_params:
      if getattr(self.hparams				,	a_				,	a_       ):
        assert hasattr(self.config				,	a_       ), F'''model config doesn\'t have a `{p}` attribute'''
        setattr(self.config				,	a_				,	getattr(self.hparams				,	a_       )       )

    if tokenizer is None:
      UpperCAmelCase  							=		AutoTokenizer.from_pretrained(
          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,	cache_dir=a_				,	)
    else:
      UpperCAmelCase  							=		tokenizer
    UpperCAmelCase  							=		MODEL_MODES[mode]
    if model is None:
      UpperCAmelCase  							=		self.model_type.from_pretrained(
          self.hparams.model_name_or_path				,	from_tf=bool('.ckpt' in self.hparams.model_name_or_path       )				,	config=self.config				,	cache_dir=a_				,	)
    else:
      UpperCAmelCase  							=		model
  def   snake_case_     (       self				,	*a_				,	**a_       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_type.from_pretrained(*a_				,	**a_       )
  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		arg_to_scheduler[self.hparams.lr_scheduler]
    UpperCAmelCase  							=		get_schedule_func(
        self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps()       )
    UpperCAmelCase  							=		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
    return scheduler
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model
    UpperCAmelCase  							=		['bias', 'LayerNorm.weight']
    UpperCAmelCase  							=		[
        {
            'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
            'weight_decay': self.hparams.weight_decay,
        },
        {
            'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )],
            'weight_decay': 0.0,
        },
    ]
    if self.hparams.adafactor:
      UpperCAmelCase  							=		Adafactor(
          a_				,	lr=self.hparams.learning_rate				,	scale_parameter=a_				,	relative_step=a_       )

    else:
      UpperCAmelCase  							=		AdamW(
          a_				,	lr=self.hparams.learning_rate				,	eps=self.hparams.adam_epsilon       )
    UpperCAmelCase  							=		optimizer

    UpperCAmelCase  							=		self.get_lr_scheduler()

    return [optimizer], [scheduler]
  def   snake_case_     (       self				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    return self.validation_step(a_				,	a_       )
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    return self.validation_end(a_       )
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		max(1				,	self.hparams.gpus       )  # TODO: consider num_tpu_cores
    UpperCAmelCase  							=		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
    return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    if stage == "test":
      UpperCAmelCase  							=		len(self.test_dataloader().dataset       )
    else:
      UpperCAmelCase  							=		self.get_dataloader('train'				,	self.hparams.train_batch_size				,	shuffle=a_       )
      UpperCAmelCase  							=		len(self.train_dataloader().dataset       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = False       )							->			str:


    """simple docstring"""
    raise NotImplementedError('You must implement this for your task'       )
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    return self.train_loader
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    return self.get_dataloader('dev'				,	self.hparams.eval_batch_size				,	shuffle=a_       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return self.get_dataloader('test'				,	self.hparams.eval_batch_size				,	shuffle=a_       )
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    return os.path.join(
        self.hparams.data_dir				,	'cached_{}_{}_{}'.format(
            a_				,	list(filter(a_				,	self.hparams.model_name_or_path.split('/'       )       )       ).pop()				,	str(self.hparams.max_seq_length       )				,	)				,	)
  @pl.utilities.rank_zero_only
  def   snake_case_     (       self				,	a_       )							->			None:


    """simple docstring"""
    UpperCAmelCase  							=		self.output_dir.joinpath('best_tfmr'       )
    UpperCAmelCase  							=		self.step_count
    self.model.save_pretrained(a_       )
    self.tokenizer.save_pretrained(a_       )





  @staticmethod
  def   snake_case_     (       a_				,	a_       )							->			Optional[int]:


    """simple docstring"""
    parser.add_argument(
        '--model_name_or_path'				,	default=a_				,	type=a_				,	required=a_				,	help='Path to pretrained model or model identifier from huggingface.co/models'				,	)
    parser.add_argument(
        '--config_name'				,	default=''				,	type=a_				,	help='Pretrained config name or path if not the same as model_name'       )
    parser.add_argument(
        '--tokenizer_name'				,	default=a_				,	type=a_				,	help='Pretrained tokenizer name or path if not the same as model_name'				,	)
    parser.add_argument(
        '--cache_dir'				,	default=''				,	type=a_				,	help='Where do you want to store the pre-trained models downloaded from huggingface.co'				,	)
    parser.add_argument(
        '--encoder_layerdrop'				,	type=a_				,	help='Encoder layer dropout probability (Optional). Goes into model.config'				,	)
    parser.add_argument(
        '--decoder_layerdrop'				,	type=a_				,	help='Decoder layer dropout probability (Optional). Goes into model.config'				,	)
    parser.add_argument(
        '--dropout'				,	type=a_				,	help='Dropout probability (Optional). Goes into model.config'				,	)
    parser.add_argument(
        '--attention_dropout'				,	type=a_				,	help='Attention dropout probability (Optional). Goes into model.config'				,	)
    parser.add_argument('--learning_rate'				,	default=5E-5				,	type=a_				,	help='The initial learning rate for Adam.'       )
    parser.add_argument(
        '--lr_scheduler'				,	default='linear'				,	choices=a_				,	metavar=a_				,	type=a_				,	help='Learning rate scheduler'				,	)
    parser.add_argument('--weight_decay'				,	default=0.0				,	type=a_				,	help='Weight decay if we apply some.'       )
    parser.add_argument('--adam_epsilon'				,	default=1E-8				,	type=a_				,	help='Epsilon for Adam optimizer.'       )
    parser.add_argument('--warmup_steps'				,	default=0				,	type=a_				,	help='Linear warmup over warmup_steps.'       )
    parser.add_argument('--num_workers'				,	default=4				,	type=a_				,	help='kwarg passed to DataLoader'       )
    parser.add_argument('--num_train_epochs'				,	dest='max_epochs'				,	default=3				,	type=a_       )
    parser.add_argument('--train_batch_size'				,	default=3_2				,	type=a_       )
    parser.add_argument('--eval_batch_size'				,	default=3_2				,	type=a_       )
    parser.add_argument('--adafactor'				,	action='store_true'       )




class     lowercase_		(						pl.Callback	):

  '''simple docstring'''





  def   snake_case_     (       self				,	a_				,	a_       )							->			List[str]:


    """simple docstring"""
    if (
        trainer.is_global_zero and trainer.global_rank == 0
    ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
      pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class     lowercase_		(						pl.Callback	):

  '''simple docstring'''





  def   snake_case_     (       self				,	a_				,	a_       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		trainer.lr_schedulers[0]['scheduler']
    UpperCAmelCase  							=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
    pl_module.logger.log_metrics(a_       )
  def   snake_case_     (       self				,	a_				,	a_       )							->			int:


    """simple docstring"""
    rank_zero_info('***** Validation results *****'       )
    UpperCAmelCase  							=		trainer.callback_metrics
    # Log results
    for key in sorted(a_       ):
      if key not in ["log", "progress_bar"]:
        rank_zero_info('{} = {}\n'.format(a_				,	str(metrics[key]       )       )       )





  def   snake_case_     (       self				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    rank_zero_info('***** Test results *****'       )
    UpperCAmelCase  							=		trainer.callback_metrics
    # Log and save results to file
    UpperCAmelCase  							=		os.path.join(pl_module.hparams.output_dir				,	'test_results.txt'       )
    with open(a_				,	'w'       ) as writer:
      for key in sorted(a_       ):
        if key not in ["log", "progress_bar"]:
          rank_zero_info('{} = {}\n'.format(a_				,	str(metrics[key]       )       )       )
          writer.write('{} = {}\n'.format(a_				,	str(metrics[key]       )       )       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				List[Any]  ):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      '--output_dir'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				help='The output directory where the model predictions and checkpoints will be written.'    ,				)
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)

  parser.add_argument(
      '--fp16_opt_level'    ,				type=SCREAMING_SNAKE_CASE    ,				default='O2'    ,				help=(
          'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
          'See details at https://nvidia.github.io/apex/amp.html'
      )    ,				)
  parser.add_argument('--n_tpu_cores'    ,				dest='tpu_cores'    ,				type=SCREAMING_SNAKE_CASE  )
  parser.add_argument('--max_grad_norm'    ,				dest='gradient_clip_val'    ,				default=1.0    ,				type=SCREAMING_SNAKE_CASE    ,				help='Max gradient norm'  )
  parser.add_argument('--do_train'    ,				action='store_true'    ,				help='Whether to run training.'  )
  parser.add_argument('--do_predict'    ,				action='store_true'    ,				help='Whether to run predictions on the test set.'  )
  parser.add_argument(
      '--gradient_accumulation_steps'    ,				dest='accumulate_grad_batches'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1    ,				help='Number of updates steps to accumulate before performing a backward/update pass.'    ,				)
  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument(
      '--data_dir'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'    ,				)



def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				BaseTransformer    ,				SCREAMING_SNAKE_CASE :				argparse.Namespace    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=None    ,				SCREAMING_SNAKE_CASE :				int=True    ,				SCREAMING_SNAKE_CASE :				Dict=None    ,				SCREAMING_SNAKE_CASE :				Any=[]    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				**SCREAMING_SNAKE_CASE :				int    ,				):
  pl.seed_everything(args.seed  )

  # init model
  UpperCAmelCase  							=		Path(model.hparams.output_dir  )
  odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE  )

  # add custom checkpoints
  if checkpoint_callback is None:
    UpperCAmelCase  							=		pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir    ,				prefix='checkpoint'    ,				monitor='val_loss'    ,				mode='min'    ,				save_top_k=1  )
  if early_stopping_callback:
    extra_callbacks.append(SCREAMING_SNAKE_CASE  )
  if logging_callback is None:
    UpperCAmelCase  							=		LoggingCallback()

  UpperCAmelCase  							=		{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    UpperCAmelCase  							=		16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    UpperCAmelCase  							=		'auto'  # "ddp"
    UpperCAmelCase  							=		'ddp'

  UpperCAmelCase  							=		args.accumulate_grad_batches
  UpperCAmelCase  							=		None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  UpperCAmelCase  							=		'auto'

  UpperCAmelCase  							=		pl.Trainer.from_argparse_args(
      SCREAMING_SNAKE_CASE    ,				weights_summary=SCREAMING_SNAKE_CASE    ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,				logger=SCREAMING_SNAKE_CASE    ,				**SCREAMING_SNAKE_CASE    ,				)

  if args.do_train:
    trainer.fit(SCREAMING_SNAKE_CASE  )

  return trainer





'''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)
_a			:	List[Any]								=  '\\n'

_a			:	Optional[Any]								=  '\\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'

_a			:	Dict								=  '\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'

_a			:	Dict								=  ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_a			:	Dict								=  ''

try:
  from jiwer import transforms as tr

  _a			:	Optional[Any]								=  True
except ImportError:
  _a			:	List[Any]								=  False

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



  class     lowercase_		(						tr.AbstractTransform	):

    '''simple docstring'''





    def __init__(       self				,	a_ = " "       )							->			str:


      """simple docstring"""
      UpperCAmelCase  							=		sentence_delimiter
    def   snake_case_     (       self				,	a_       )							->			Optional[int]:


      """simple docstring"""
      return list(a_       )





    def   snake_case_     (       self				,	a_       )							->			List[str]:


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




  _a			:	List[Any]								=  tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
elif _jiwer_available:
  _a			:	int								=  tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )
else:
  _a			:	Optional[int]								=  None


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
  return float((preds == labels).mean()  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				Optional[Any]  ):
  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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				str="exp"    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]=None    ,				SCREAMING_SNAKE_CASE :				Any=False    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=False    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				SCREAMING_SNAKE_CASE :				List[Any]=False    ,				):
  # xtreme-s can only have one label
  UpperCAmelCase  							=		[[label] for label in labels]
  UpperCAmelCase  							=		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"`.'  )

  UpperCAmelCase  							=		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'  )
  UpperCAmelCase  							=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE  )]
  UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  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:
    UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )['wer']

    UpperCAmelCase  							=		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 :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				List[str]="wer"  ):
      UpperCAmelCase  							=		0
      UpperCAmelCase  							=		0
      for prediction, reference in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
        if score_type == "wer":
          UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
        elif score_type == "cer":
          UpperCAmelCase  							=		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     lowercase_		(						datasets.Metric	):

  '''simple docstring'''





  def   snake_case_     (       self       )							->			Tuple:


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

    UpperCAmelCase  							=		'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(a_       ), 'references': datasets.Value(a_       )}       )				,	codebase_urls=[]				,	reference_urls=[]				,	format='numpy'				,	)





  def   snake_case_     (       self				,	a_				,	a_				,	a_=None				,	a_=None       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		bleu_kwargs if bleu_kwargs is not None else {}
    UpperCAmelCase  							=		wer_kwargs if wer_kwargs is not None else {}

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


