import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[str]=10		,    lowercase_:						Union[str, Any]=10		,    lowercase_:						Union[str, Any]=False		,    lowercase_:						Optional[int]=False					):
       _UpperCamelCase  :							Any	    =	[]
       for i in range(image_processor_tester.num_frames					):
              video.append(np.random.randint(255		,    size=(image_processor_tester.num_channels, width, height)		,    dtype=np.uinta					)					)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase  :							int	    =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_		,    0		,    -1					)					) for frame in video]

       if torchify:
              _UpperCamelCase  :							Union[str, Any]	    =	[torch.from_numpy(SCREAMING_SNAKE_CASE_					) for frame in video]

       return video


def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict		,    lowercase_:						List[Any]=False		,    lowercase_:						List[str]=False		,    lowercase_:						Optional[Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Optional[Any]	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							str	    =	image_processor_tester.max_resolution
              else:
                     _UpperCamelCase  :							Optional[int]	    =	np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,    image_processor_tester.max_resolution					)		,    2					)
                     _UpperCamelCase  :							Dict	    =	prepare_video(
                         image_processor_tester=SCREAMING_SNAKE_CASE_		,    width=SCREAMING_SNAKE_CASE_		,    height=SCREAMING_SNAKE_CASE_		,    numpify=SCREAMING_SNAKE_CASE_		,    torchify=SCREAMING_SNAKE_CASE_		,    )
              video_inputs.append(SCREAMING_SNAKE_CASE_					)

       return video_inputs




class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def __init__(      self							:		int							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Any=7							,      lowerCamelCase							:		str=3							,      lowerCamelCase							:		Any=4							,      lowerCamelCase							:		Union[str, Any]=1_8							,      lowerCamelCase							:		Optional[Any]=3_0							,      lowerCamelCase							:		Dict=4_0_0							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Dict=None							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Tuple=[0.5, 0.5, 0.5]							,      lowerCamelCase							:		int=[0.5, 0.5, 0.5]							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		Tuple=None							,      )			->				int:
        _UpperCamelCase  :							List[Any]	    =	size if size is not None else {'''shortest_edge''': 1_8}
        _UpperCamelCase  :							Union[str, Any]	    =	crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

        _UpperCamelCase  :							Optional[Any]	    =	parent
        _UpperCamelCase  :							List[Any]	    =	batch_size
        _UpperCamelCase  :							Union[str, Any]	    =	num_channels
        _UpperCamelCase  :							Optional[Any]	    =	num_frames
        _UpperCamelCase  :							Dict	    =	image_size
        _UpperCamelCase  :							Tuple	    =	min_resolution
        _UpperCamelCase  :							Dict	    =	max_resolution
        _UpperCamelCase  :							Tuple	    =	do_resize
        _UpperCamelCase  :							int	    =	size
        _UpperCamelCase  :							str	    =	do_normalize
        _UpperCamelCase  :							Optional[Any]	    =	image_mean
        _UpperCamelCase  :							Union[str, Any]	    =	image_std
        _UpperCamelCase  :							Tuple	    =	do_center_crop
        _UpperCamelCase  :							Dict	    =	crop_size

 def        __snake_case       (      self							:		int	)			->				Union[str, Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class  __lowerCAmelCase	(							_a,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			TvltImageProcessor if is_vision_available() else None

 def        __snake_case       (      self							:		Dict	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	TvltImageProcessorTester(self	)

 @property
 def        __snake_case       (      self							:		Tuple	)			->				List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()

 def        __snake_case       (      self							:		List[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'image_mean'	)	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'image_std'	)	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'do_normalize'	)	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'do_resize'	)	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'do_center_crop'	)	)
        self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,      'size'	)	)

 def        __snake_case       (      self							:		Tuple	)			->				Any:
        _UpperCamelCase  :							Dict	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PIL videos
        _UpperCamelCase  :							str	    =	prepare_video_inputs(self.image_processor_tester							,      equal_resolution=__SCREAMING_SNAKE_CASE	)
        for video in video_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)
               self.assertIsInstance(video[0]							,      Image.Image	)

        # Test not batched input
        _UpperCamelCase  :							Tuple	    =	image_processor(video_inputs[0]							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )

        # Test batched
        _UpperCamelCase  :							Union[str, Any]	    =	image_processor(__SCREAMING_SNAKE_CASE							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        _UpperCamelCase  :							str	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase  :							List[Any]	    =	prepare_video_inputs(self.image_processor_tester							,      equal_resolution=__SCREAMING_SNAKE_CASE							,      numpify=__SCREAMING_SNAKE_CASE	)
        for video in video_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)
               self.assertIsInstance(video[0]							,      np.ndarray	)

        # Test not batched input
        _UpperCamelCase  :							Union[str, Any]	    =	image_processor(video_inputs[0]							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )

        # Test batched
        _UpperCamelCase  :							Optional[int]	    =	image_processor(__SCREAMING_SNAKE_CASE							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )

 def        __snake_case       (      self							:		List[Any]	)			->				List[str]:
        _UpperCamelCase  :							Any	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase  :							Optional[int]	    =	prepare_video_inputs(self.image_processor_tester							,      equal_resolution=__SCREAMING_SNAKE_CASE							,      torchify=__SCREAMING_SNAKE_CASE	)
        for video in video_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)
               self.assertIsInstance(video[0]							,      torch.Tensor	)

        # Test not batched input
        _UpperCamelCase  :							str	    =	image_processor(video_inputs[0]							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )

        # Test batched
        _UpperCamelCase  :							Union[str, Any]	    =	image_processor(__SCREAMING_SNAKE_CASE							,      return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_videos.shape							,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,      )







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)
__UpperCAmelCase			:			Union[str, Any]			      =					"\\n"

__UpperCAmelCase			:			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"

__UpperCAmelCase			:			str			      =					"\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"

__UpperCAmelCase			:			Union[str, Any]			      =					["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__UpperCAmelCase			:			List[str]			      =					""

try:
  from jiwer import transforms as tr

  __UpperCAmelCase			:			Optional[int]			      =					True
except ImportError:
  __UpperCAmelCase			:			str			      =					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			,       __SCREAMING_SNAKE_CASE = " "    ):




                """simple docstring"""




                UpperCamelCase     :						List[str] 						=		sentence_delimiter






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                return list(__SCREAMING_SNAKE_CASE    )






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                UpperCamelCase     :						Dict 						=		[]
                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




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


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





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


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       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   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]="exp"				,      SCREAMING_SNAKE_CASE_   :		Any=None				,      SCREAMING_SNAKE_CASE_   :		Any=False				,      SCREAMING_SNAKE_CASE_   :		List[str]=False				,      SCREAMING_SNAKE_CASE_   :		Dict=None				,      SCREAMING_SNAKE_CASE_   :		Tuple=False				,      ):






       """simple docstring"""





       UpperCamelCase     :						str 						=		[[label] for label in labels]
       UpperCamelCase     :						int 						=		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     :						Optional[int] 						=		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     :						Optional[Any] 						=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE_							)]
       UpperCamelCase     :						Optional[Any] 						=		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   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """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:
              UpperCamelCase     :						List[Any] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)['''wer''']

              UpperCamelCase     :						List[str] 						=		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_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]="wer"							):
                     UpperCamelCase     :						int 						=		0
                     UpperCamelCase     :						Union[str, Any] 						=		0
                     for prediction, reference in zip(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							):
                            if score_type == "wer":
                                   UpperCamelCase     :						Optional[int] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)
                            elif score_type == "cer":
                                   UpperCamelCase     :						Union[str, Any] 						=		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  UpperCAmelCase_						(				datasets.Metric):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




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

              UpperCamelCase     :						Optional[Any] 						=		'''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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		bleu_kwargs if bleu_kwargs is not None else {}
              UpperCamelCase     :						Dict 						=		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":
                     UpperCamelCase     :						List[str] 						=		bleu_kwargs.pop('''smooth_method'''			,       '''exp'''    )
                     UpperCamelCase     :						Tuple 						=		bleu_kwargs.pop('''smooth_value'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		bleu_kwargs.pop('''force'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Union[str, Any] 						=		bleu_kwargs.pop('''lowercase'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[Any] 						=		bleu_kwargs.pop('''tokenize'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		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"]:
                     UpperCamelCase     :						List[Any] 						=		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}"""    )

