

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


lowerCAmelCase__							=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCAmelCase__							=			{
    "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
lowerCAmelCase__							=			{
    "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
}
lowerCAmelCase__							=			sorted(arg_to_scheduler.keys())
lowerCAmelCase__							=			"{" + ", ".join(arg_to_scheduler_choices) + "}"






class     __magic_name__     (							pl.LightningModule     ):


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			argparse.Namespace      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			List[Any]="base"      ,    lowerCAmelCase__		:			List[Any]=None      ,    lowerCAmelCase__		:			int=None      ,    lowerCAmelCase__		:			int=None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   Tuple:
   super().__init__()
   # TODO: move to self.save_hyperparameters()
   # self.save_hyperparameters()
   # can also expand arguments into trainer signature for easier reading

   self.save_hyperparameters(_SCREAMING_SNAKE_CASE     )
   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=_SCREAMING_SNAKE_CASE      ,    **_SCREAMING_SNAKE_CASE      ,    )
   else:
     UpperCAmelCase          =	config

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

   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=_SCREAMING_SNAKE_CASE      ,    )
   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=_SCREAMING_SNAKE_CASE      ,    )
   else:
     UpperCAmelCase          =	model


 def _UpperCamelCase						(    self		:			Dict      ,    *lowerCAmelCase__		:			List[Any]      ,    **lowerCAmelCase__		:			Any     )     ->   List[str]:
   UpperCAmelCase          =	self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE      ,    **_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			int     )     ->   Dict:
   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 _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   List[Any]:
   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(
         _SCREAMING_SNAKE_CASE      ,    lr=self.hparams.learning_rate      ,    scale_parameter=_SCREAMING_SNAKE_CASE      ,    relative_step=_SCREAMING_SNAKE_CASE     )

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

   UpperCAmelCase          =	self.get_lr_scheduler()

   return [optimizer], [scheduler]


 def _UpperCamelCase						(    self		:			Tuple      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   Dict:
   return self.validation_step(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Optional[int]     )     ->   Dict:
   return self.validation_end(_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			List[str]     )     ->   int:
   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 _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			str     )     ->   List[str]:
   if mode == "test":
     UpperCAmelCase          =	len(self.test_dataloader().dataset     )
   else:
     UpperCAmelCase          =	self.get_dataloader("train"      ,    self.hparams.train_batch_size      ,    shuffle=_SCREAMING_SNAKE_CASE     )
     UpperCAmelCase          =	len(self.train_dataloader().dataset     )


 def _UpperCamelCase						(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			bool = False     )     ->   List[Any]:
   raise NotImplementedError("You must implement this for your task"     )


 def _UpperCamelCase						(    self		:			Dict     )     ->   Any:
   return self.train_loader


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   List[Any]:
   return self.get_dataloader("dev"      ,    self.hparams.eval_batch_size      ,    shuffle=_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			List[str]     )     ->   str:
   return self.get_dataloader("test"      ,    self.hparams.eval_batch_size      ,    shuffle=_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			Optional[int]      ,    lowerCAmelCase__		:			int     )     ->   Any:
   return os.path.join(
       self.hparams.data_dir      ,    "cached_{}_{}_{}".format(
           _SCREAMING_SNAKE_CASE      ,    list(filter(_SCREAMING_SNAKE_CASE      ,    self.hparams.model_name_or_path.split("/"     )     )     ).pop()      ,    str(self.hparams.max_seq_length     )      ,    )      ,    )


 @pl.utilities.rank_zero_only
 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			Dict[str, Any]     )     ->   None:
   UpperCAmelCase          =	self.output_dir.joinpath("best_tfmr"     )
   UpperCAmelCase          =	self.step_count
   self.model.save_pretrained(_SCREAMING_SNAKE_CASE     )
   self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE     )


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






class     __magic_name__     (							pl.Callback     ):


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   Union[str, Any]:
   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(_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			Tuple      ,    lowerCAmelCase__		:			pl.Trainer      ,    lowerCAmelCase__		:			pl.LightningModule     )     ->   List[Any]:
   rank_zero_info("***** Validation results *****"     )
   UpperCAmelCase          =	trainer.callback_metrics
   # Log results
   for key in sorted(_SCREAMING_SNAKE_CASE     ):
     if key not in ["log", "progress_bar"]:
       rank_zero_info("{} = {}\n".format(_SCREAMING_SNAKE_CASE      ,    str(metrics[key]     )     )     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pl.Trainer      ,    lowerCAmelCase__		:			pl.LightningModule     )     ->   str:
   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(_SCREAMING_SNAKE_CASE      ,    "w"     ) as writer:
     for key in sorted(_SCREAMING_SNAKE_CASE     ):
       if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(_SCREAMING_SNAKE_CASE      ,    str(metrics[key]     )     )     )
         writer.write("{} = {}\n".format(_SCREAMING_SNAKE_CASE      ,    str(metrics[key]     )     )     )

def   _lowerCAmelCase(						__A ,							__A      ):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      "--output_dir" ,							default=__A ,							type=__A ,							required=__A ,							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=__A ,							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=__A      )
  parser.add_argument("--max_grad_norm" ,							dest="gradient_clip_val" ,							default=1.0 ,							type=__A ,							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=__A ,							default=1 ,							help="Number of updates steps to accumulate before performing a backward/update pass." ,							)
  parser.add_argument("--seed" ,							type=__A ,							default=42 ,							help="random seed for initialization"      )
  parser.add_argument(
      "--data_dir" ,							default=__A ,							type=__A ,							required=__A ,							help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,							)





def   _lowerCAmelCase(						__A ,							__A ,							__A=None ,							__A=True ,							__A=[] ,							__A=None ,							__A=None ,							**__A ,							):
  pl.seed_everything(args.seed      )

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

  # 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(__A      )
  if logging_callback is None:
    UpperCAmelCase          =	LoggingCallback()

  UpperCAmelCase          =	{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    UpperCAmelCase          =	16
    UpperCAmelCase          =	args.fpaa_opt_level

  if args.gpus > 1:
    UpperCAmelCase          =	"ddp"

  UpperCAmelCase          =	args.accumulate_grad_batches
  UpperCAmelCase          =	extra_train_kwargs.get("accelerator" ,							__A      )
  UpperCAmelCase          =	extra_train_kwargs.get("profiler" ,							__A      )

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

  if args.do_train:
    trainer.fit(__A      )

  return trainer











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)
lowerCAmelCase_     =			'\\n'

lowerCAmelCase_     =			'\\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'

lowerCAmelCase_     =			'\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'

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

try:
					from jiwer import transforms as tr

					lowerCAmelCase_     =			True
except ImportError:
					lowerCAmelCase_     =			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		:							Any    ,_SCREAMING_SNAKE_CASE		:							str = " "		)     -> Dict:


											'''simple docstring'''
											A									=   sentence_delimiter
							def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> int:


											'''simple docstring'''
											return list(_SCREAMING_SNAKE_CASE		)







							def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Tuple:


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


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







def   snake_case		(				UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Tuple ):
				return float((preds == labels).mean() )







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							Tuple ):
				return {
				    "f1": float(fa_score(y_true=UpperCAmelCase,    y_pred=UpperCAmelCase,    average='macro' ) ),
				    "accuracy": simple_accuracy(UpperCAmelCase,    UpperCAmelCase ),
				}







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[str],    UpperCAmelCase						:							List[Any]="exp",    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							int=False,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							str=False,    ):
				# xtreme-s can only have one label
				A									=   [[label] for label in labels]
				A									=   list(UpperCAmelCase )
				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"`.' )

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







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							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:
								A									=   compute_measures(UpperCAmelCase,    UpperCAmelCase )['wer']

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

								def compute_score(UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[Any]="wer" ):
												A									=   0
												A									=   0
												for prediction, reference in zip(UpperCAmelCase,    UpperCAmelCase ):
																if score_type == "wer":
																				A									=   compute_measures(UpperCAmelCase,    UpperCAmelCase )
																elif score_type == "cer":
																				A									=   compute_measures(
																				    UpperCAmelCase,    UpperCAmelCase,    truth_transform=UpperCAmelCase,    hypothesis_transform=UpperCAmelCase )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total

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



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







		"""simple docstring"""



		def    A(	self		:							Any		)     -> str:


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

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







		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Dict=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None		)     -> Tuple:


						'''simple docstring'''
						A									=   bleu_kwargs if bleu_kwargs is not None else {}
						A									=   wer_kwargs if wer_kwargs is not None else {}

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





