'''simple docstring'''




import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


__snake_case   :      Optional[int]						    =  logging.getLogger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      str:
    random.seed(_UpperCamelCase   )
    np.random.seed(_UpperCamelCase   )
    torch.manual_seed(_UpperCamelCase   )
    torch.cuda.manual_seed_all(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[int]   )	->      Optional[Any]:
    model.eval()
    A_     =   next(model.parameters()   ).device
    A_     =   1
    A_     =   torch.zeros((eval_batch_size, context_len),   dtype=torch.long,   device=_UpperCamelCase   )
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )
    A_     =   torch.zeros(1,   device=_UpperCamelCase   )
    A_     =   0
    for batch in eval_dataloader:
        batch.to(_UpperCamelCase   )
        # pad
        context.zero_()
        for i in range(_UpperCamelCase   ):
            A_     =   batch[i]
        A_     =   model(_UpperCamelCase,   labels=_UpperCamelCase   )
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0   )
    A_     =   eval_loss / nb_eval_examples
    A_     =   torch.exp(_UpperCamelCase   )
    model.train()
    return perplexity





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any="gpt2"   )	->      Optional[int]:
    A_     =   GPTaLMHeadModel.from_pretrained(_UpperCamelCase,   output_hidden_states=_UpperCamelCase   )
    torch.save(model.state_dict(),   model_name + '''local.pt'''   )
    return model





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	str,   _UpperCamelCase    :	List[str]   )	->      Tuple:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    model.to(_UpperCamelCase   )

    A_     =   ['''bias''', '''LayerNorm.weight''']
    A_     =   [
        {
            '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
            '''weight_decay''': 0.0,
        },
        {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )], '''weight_decay''': 0.0},
    ]
    A_     =   AdamW(_UpperCamelCase,   lr=5E-5,   eps=1E-8   )
    A_     =   get_linear_schedule_with_warmup(_UpperCamelCase,   0,   _UpperCamelCase   )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    A_     =   np.array(_UpperCamelCase   ).mean()
    A_     =   np.array(_UpperCamelCase   ).std()
    A_     =   (real_perps - avg) / std
    A_     =   list(zip(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )   )
    joblib.dump(_UpperCamelCase,   _UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	str="dev.jbl",   _UpperCamelCase    :	Dict=recopy_gpta,   )	->      List[Any]:
    A_     =   []
    A_     =   []
    A_     =   []

    # Initialize the transformer model
    A_     =   copy.deepcopy(_UpperCamelCase   )
    orig_model.to(device='''cpu'''   )
    torch.cuda.empty_cache()

    # Compute perplexity of initial transformer model for comparison
    model.train()
    A_   ,A_   ,A_     =   recopy_model(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    for step in tqdm(range(_UpperCamelCase   )   ):
        A_     =   torch.zeros((1, context_len),   dtype=torch.long,   device=_UpperCamelCase   )
        A_     =   random.choice(_UpperCamelCase   )
        A_     =   random.randint(0,   len(story[0]   ) - context_len - 1   )
        A_     =   story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        A_     =   model(_UpperCamelCase,   labels=_UpperCamelCase   )
        A_     =   outputs[0]
        A_     =   compute_perplexity(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
        model.train()
        lm_loss.backward()
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters(),   3.0   )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule

        # Compute perplexity after back-propagating on the selected context
        A_     =   compute_perplexity(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 0 and step > 1:
            intermittent_save(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        A_   ,A_   ,A_     =   recopy_model(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        past_perps.append(past_perp.item()   )
        real_perps.append(orig_perp - real_perp.item()   )
        contexts.append(np.array(context.cpu()   )   )

    intermittent_save(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any,   _UpperCamelCase    :	List[Any]="data/tokenized_stories_train_wikitext103.jbl",   _UpperCamelCase    :	List[str]=1_00,   _UpperCamelCase    :	Optional[int]=10_26,   _UpperCamelCase    :	Dict=True   )	->      Optional[Any]:
    A_     =   joblib.load(_UpperCamelCase   )
    print('''data loaded'''   )
    A_     =   []
    if trim:
        for i, example in enumerate(_UpperCamelCase   ):
            if len(example[0]   ) > min_len:
                A_     =   random.randint(0,   len(example[0]   ) - context_len - 1   )
                objective_set.append(example[0, start : start + context_len]   )
            if len(_UpperCamelCase   ) >= number:
                break
        A_     =   []
        for j in range(i + 1,   len(_UpperCamelCase   )   ):
            if len(data[j][0]   ) > min_len:
                train_data.append(data[j]   )
    else:
        A_     =   data[0:number]
        A_     =   data[number:]

    joblib.dump(_UpperCamelCase,   '''objective_set.jbl'''   )
    print('''objective set saved'''   )
    return train_data, objective_set





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Union[str, Any]=50,   _UpperCamelCase    :	Tuple="secondary_learner.pt"   )	->      List[Any]:
    A_     =   torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
    # We will use the first 512 pairs from our dataset as a test set for
    # our secondary learner and the rest to train
    A_     =   train_dataset[:5_12]
    A_     =   train_dataset[5_12:]
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )

    # secondary learner model set up
    A_     =   nn.MSELoss()
    A_     =   nn.MSELoss(reduction='''sum'''   )
    secondary_learner.to(_UpperCamelCase   )
    A_     =   torch.optim.Adam(secondary_learner.parameters(),   lr=0.0_0_0_0_1   )
    secondary_learner.train()

    # TODO in original code this is written as number of actual batches seen
    # not number of items seen but other places it is number of items instead.
    # improve consistency! changed this to epochs for clarity
    A_     =   float('''inf'''   )
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(_UpperCamelCase   )   ):
        A_     =   0.0
        secondary_learner.train()
        for step, batch in enumerate(_UpperCamelCase   ):
            A_     =   batch[0].to(_UpperCamelCase   )
            A_     =   batch[1].to(_UpperCamelCase   )
            A_     =   secondary_learner(_UpperCamelCase   )
            q_optimizer.zero_grad()
            A_     =   loss(_UpperCamelCase,   real_q.float()   )
            q_loss.backward()
            q_optimizer.step()
            tr_q_loss += q_loss.item()

            # model trains fairly quickly so we won't wait for a full epoch
            # eval is triggered at eval_freq and end of epochs
            if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_UpperCamelCase   )):
                A_     =   tr_q_loss / (step + 1)

                secondary_learner.eval()
                A_     =   0.0
                A_     =   0.0
                A_     =   []
                A_     =   []
                # Compute performance of the secondary learner after this batch
                for stepa, batcha in enumerate(_UpperCamelCase   ):
                    A_     =   batcha[0].to(_UpperCamelCase   )
                    A_     =   batcha[1].to(_UpperCamelCase   )
                    A_     =   secondary_learner(_UpperCamelCase   )
                    q_lossa += test_loss(_UpperCamelCase,   _UpperCamelCase   ).item()
                    sum_qa += torch.sum(_UpperCamelCase   ).item()
                    for ei, i in enumerate(predicted_qa.cpu().detach().numpy()   ):
                        predicted.append(i.item()   )
                    for ei, i in enumerate(real_qa.cpu().detach().numpy()   ):
                        actual.append(i.item()   )

                q_lossa /= len(_UpperCamelCase   )
                print(
                    '''Epoch: ''',   _UpperCamelCase,   '''step: ''',   _UpperCamelCase,   '''Avg. q:''',   sum_qa / len(_UpperCamelCase   ),   '''Train Loss: ''',   _UpperCamelCase,   '''Test Loss: ''',   _UpperCamelCase,   )
                if q_lossa < best_test_loss:
                    joblib.dump((predicted, actual),   '''pred_vs_actual.jbl'''   )
                    torch.save(secondary_learner.state_dict(),   _UpperCamelCase   )
                    A_     =   q_lossa

            secondary_learner.train()
    return secondary_learner




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           super(_SCREAMING_SNAKE_CASE    ,     self ).__init__()
           A_     =   model.transformer.wte
           A_     =   copy.deepcopy(model.transformer.wte.weight )
           A_     =   nn.Convad(self.embeddings.weight.size(1 )    ,     256    ,     3    ,     padding=1 )
           A_     =   nn.Sequential(nn.Linear(256    ,     32 )    ,     nn.Dropout(p=0.1 )    ,     nn.Linear(32    ,     32 )    ,     nn.Linear(32    ,     1 ) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           A_     =   torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE ).squeeze(1 ).transpose(1    ,     2 ) )    ,     2 )[0]
           A_     =   self.fc(_SCREAMING_SNAKE_CASE )
           return qs.squeeze(1 )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   cls(_SCREAMING_SNAKE_CASE )  # this calls __init__
           A_     =   torch.load(_SCREAMING_SNAKE_CASE )
           secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE )
           A_     =   model.transformer.wte
           A_     =   copy.deepcopy(model.transformer.wte.weight )
           return secondary_learner














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			 =						"""\
"""

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

snake_case			 =						"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

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        SCREAMING_SNAKE_CASE	(	tr.AbstractTransform    ):
        '''simple docstring'''


        def __init__( self				:		Dict    ,       UpperCAmelCase_				:		str = " "					):
          SCREAMING_SNAKE_CASE				:					List[Any]		  =			sentence_delimiter


        def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		str					):
          return list(UpperCAmelCase_					)






        def  _A				( self				:		str    ,       UpperCAmelCase_				:		List[str]					):
          SCREAMING_SNAKE_CASE				:					Tuple		  =			[]
          for sent_idx, sentence in enumerate(UpperCAmelCase_					):
            chars.extend(self.process_string(UpperCAmelCase_					)					)
            if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase_					) - 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      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  return float((preds == labels).mean() )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  return {
      "f1": float(fa_score(y_true=lowercase  ,   y_pred=lowercase  ,   average="macro" ) ),
      "accuracy": simple_accuracy(lowercase  ,   lowercase ),
  }


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase="exp"  ,   lowercase=None  ,   lowercase=False  ,   lowercase=False  ,   lowercase=None  ,   lowercase=False  ,   ):

  """simple docstring"""






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


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  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				:					Tuple		  =			compute_measures(lowercase  ,   lowercase )["wer"]

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

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

    return {"wer": compute_score(lowercase  ,   lowercase  ,   "wer" ), "cer": compute_score(lowercase  ,   lowercase  ,   "cer" )}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION    )
class        SCREAMING_SNAKE_CASE	(	datasets.Metric    ):
     '''simple docstring'''


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

       SCREAMING_SNAKE_CASE				:					Tuple		  =			"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(UpperCAmelCase_					), "references": datasets.Value(UpperCAmelCase_					)}					)    ,       codebase_urls=[]    ,       reference_urls=[]    ,       format="numpy"    ,       )






     def  _A				( self				:		Optional[Any]    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Union[str, Any]    ,       UpperCAmelCase_				:		str=None    ,       UpperCAmelCase_				:		Dict=None					):
       SCREAMING_SNAKE_CASE				:					str		  =			bleu_kwargs if bleu_kwargs is not None else {}
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			wer_kwargs if wer_kwargs is not None else {}

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






