






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


snake_case			 =						logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						torch.FloatTensor
     UpperCamelCase_							:						Optional[torch.FloatTensor]								=		None





def      lowerCamelCase__	(   lowercase  ,   lowercase=0.999  ,   lowercase="cosine"  ,   ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase ):
      return math.exp(t * -12.0 )

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

  SCREAMING_SNAKE_CASE				:					List[Any]		  =			[]
  for i in range(lowercase ):
    SCREAMING_SNAKE_CASE				:					Any		  =			i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE				:					Tuple		  =			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase ) / alpha_bar_fn(lowercase )  ,   lowercase ) )
  return torch.tensor(lowercase  ,   dtype=torch.floataa )


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Optional[Any]								=		[e.name for e in KarrasDiffusionSchedulers]
     UpperCamelCase_							:						int								=		1


     @register_to_config
     def __init__( self				:		List[str]    ,       UpperCAmelCase_				:		int = 1000    ,       UpperCAmelCase_				:		float = 0.0_001    ,       UpperCAmelCase_				:		float = 0.02    ,       UpperCAmelCase_				:		str = "linear"    ,       UpperCAmelCase_				:		Optional[Union[np.ndarray, List[float]]] = None    ,       UpperCAmelCase_				:		str = "epsilon"    ,       UpperCAmelCase_				:		str = "linspace"    ,       UpperCAmelCase_				:		int = 0    ,       ):
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			torch.tensor(UpperCAmelCase_    ,       dtype=torch.floataa					)
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			torch.linspace(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       dtype=torch.floataa					)
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE				:					Any		  =			(
             torch.linspace(beta_start**0.5    ,       beta_end**0.5    ,       UpperCAmelCase_    ,       dtype=torch.floataa					) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE				:					Dict		  =			betas_for_alpha_bar(UpperCAmelCase_					)
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

       SCREAMING_SNAKE_CASE				:					Dict		  =			1.0 - self.betas
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			torch.cumprod(self.alphas    ,       dim=0					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       SCREAMING_SNAKE_CASE				:					Any		  =			np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			torch.from_numpy(UpperCAmelCase_					)

       # setable values
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			None
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.linspace(0    ,       num_train_timesteps - 1    ,       UpperCAmelCase_    ,       dtype=UpperCAmelCase_					)[::-1].copy()
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			torch.from_numpy(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			False


     @property
     def  _A				( self				:		Tuple					):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5


     def  _A				( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       UpperCAmelCase_				:		Union[float, torch.FloatTensor]					):
       if isinstance(UpperCAmelCase_    ,       torch.Tensor					):
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			timestep.to(self.timesteps.device					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			(self.timesteps == timestep).nonzero().item()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.sigmas[step_index]
       SCREAMING_SNAKE_CASE				:					Tuple		  =			sample / ((sigma**2 + 1) ** 0.5)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			True
       return sample


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Union[str, torch.device] = None					):
       SCREAMING_SNAKE_CASE				:					str		  =			num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
         SCREAMING_SNAKE_CASE				:					Dict		  =			np.linspace(0    ,       self.config.num_train_timesteps - 1    ,       UpperCAmelCase_    ,       dtype=UpperCAmelCase_					)[
             ::-1
         ].copy()
       elif self.config.timestep_spacing == "leading":
         SCREAMING_SNAKE_CASE				:					Any		  =			self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			(np.arange(0    ,       UpperCAmelCase_					) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_					)
         timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.config.num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         SCREAMING_SNAKE_CASE				:					Any		  =			(np.arange(self.config.num_train_timesteps    ,       0    ,       -step_ratio					)).round().copy().astype(UpperCAmelCase_					)
         timesteps -= 1
       else:
         raise ValueError(
             f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

       SCREAMING_SNAKE_CASE				:					Any		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			np.interp(UpperCAmelCase_    ,       np.arange(0    ,       len(UpperCAmelCase_					)					)    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)
       if str(UpperCAmelCase_					).startswith("mps"					):
         # mps does not support float64
         SCREAMING_SNAKE_CASE				:					Dict		  =			torch.from_numpy(UpperCAmelCase_					).to(UpperCAmelCase_    ,       dtype=torch.floataa					)
       else:
         SCREAMING_SNAKE_CASE				:					Any		  =			torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_					)


     def  _A				( self				:		List[str]    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       UpperCAmelCase_				:		Union[float, torch.FloatTensor]    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       UpperCAmelCase_				:		Optional[torch.Generator] = None    ,       UpperCAmelCase_				:		bool = True    ,       ):

       if (
           isinstance(UpperCAmelCase_    ,       UpperCAmelCase_					)
           or isinstance(UpperCAmelCase_    ,       torch.IntTensor					)
           or isinstance(UpperCAmelCase_    ,       torch.LongTensor					)
       ):
         raise ValueError(
             (
                 "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                 " one of the `scheduler.timesteps` as a timestep."
             )    ,       )

       if not self.is_scale_input_called:
         logger.warning(
             "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
             "See `StableDiffusionPipeline` for a usage example."					)

       if isinstance(UpperCAmelCase_    ,       torch.Tensor					):
         SCREAMING_SNAKE_CASE				:					int		  =			timestep.to(self.timesteps.device					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			(self.timesteps == timestep).nonzero().item()
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         SCREAMING_SNAKE_CASE				:					Dict		  =			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         SCREAMING_SNAKE_CASE				:					str		  =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
         raise NotImplementedError("prediction_type not implemented yet: sample"					)
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

       SCREAMING_SNAKE_CASE				:					Dict		  =			self.sigmas[step_index]
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.sigmas[step_index + 1]
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			(sigma_to**2 - sigma_up**2) ** 0.5

       # 2. Convert to an ODE derivative
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			(sample - pred_original_sample) / sigma

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sigma_down - sigma

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sample + derivative * dt

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			model_output.device
       SCREAMING_SNAKE_CASE				:					List[str]		  =			randn_tensor(model_output.shape    ,       dtype=model_output.dtype    ,       device=UpperCAmelCase_    ,       generator=UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			prev_sample + noise * sigma_up

       if not return_dict:
         return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=UpperCAmelCase_    ,       pred_original_sample=UpperCAmelCase_					)


     def  _A				( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       UpperCAmelCase_				:		torch.FloatTensor    ,       ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.sigmas.to(device=original_samples.device    ,       dtype=original_samples.dtype					)
       if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_					):
         # mps does not support float64
         SCREAMING_SNAKE_CASE				:					Any		  =			self.timesteps.to(original_samples.device    ,       dtype=torch.floataa					)
         SCREAMING_SNAKE_CASE				:					Dict		  =			timesteps.to(original_samples.device    ,       dtype=torch.floataa					)
       else:
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.timesteps.to(original_samples.device					)
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			timesteps.to(original_samples.device					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			sigmas[step_indices].flatten()
       while len(sigma.shape					) < len(original_samples.shape					):
         SCREAMING_SNAKE_CASE				:					Dict		  =			sigma.unsqueeze(-1					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			original_samples + noise * sigma
       return noisy_samples






     def __len__( self				:		Dict					):
       return self.config.num_train_timesteps














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}'''					)






