


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   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=1_0				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=1_0				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=False				,      SCREAMING_SNAKE_CASE_   :		Optional[int]=False							):






       """simple docstring"""





       UpperCamelCase     :						Any 						=		[]
       for i in range(image_processor_tester.num_frames							):
              video.append(np.random.randint(2_5_5				,      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   a						(				SCREAMING_SNAKE_CASE_   :		Dict				,      SCREAMING_SNAKE_CASE_   :		List[Any]=False				,      SCREAMING_SNAKE_CASE_   :		List[str]=False				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       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						,			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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=7			,       __SCREAMING_SNAKE_CASE=3			,       __SCREAMING_SNAKE_CASE=4			,       __SCREAMING_SNAKE_CASE=18			,       __SCREAMING_SNAKE_CASE=30			,       __SCREAMING_SNAKE_CASE=400			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None			,       ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		size if size is not None else {'''shortest_edge''': 18}
              UpperCamelCase     :						Union[str, Any] 						=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

              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 			_lowercase (  self    ):




              """simple docstring"""




              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  UpperCAmelCase_						(				_a,  unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       TvltImageProcessor if is_vision_available() else None






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		TvltImageProcessorTester(self    )






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.image_processor_tester.prepare_image_processor_dict()






       def 			_lowercase (  self    ):




              """simple docstring"""




              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 			_lowercase (  self    ):




              """simple docstring"""




              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 			_lowercase (  self    ):




              """simple docstring"""




              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 			_lowercase (  self    ):




              """simple docstring"""




              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}"""    )

