






"""simple docstring"""


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{'vocab_file': 'tokenizer.model'}

lowercase_								=	{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
lowercase_								=	{
    'hf-internal-testing/llama-tokenizer': 2048,
}
lowercase_								=	'▁'

lowercase_	,						lowercase_								=	'[INST]', '[/INST]'
lowercase_	,						lowercase_								=	'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowercase_								=	'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					VOCAB_FILES_NAMES
   A_		:      List[Any]							=					PRETRAINED_VOCAB_FILES_MAP
   A_		:      Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_		:      Tuple							=					["input_ids", "attention_mask"]


   def __init__(      self					:	str,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Tuple="<unk>",			_lowerCamelCase					:	Dict="<s>",			_lowerCamelCase					:	int="</s>",			_lowerCamelCase					:	Tuple=None,			_lowerCamelCase					:	Optional[Dict[str, Any]] = None,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	str=False,			_lowerCamelCase					:	Any=False,			_lowerCamelCase					:	str=True,			**_lowerCamelCase					:	Dict,			):

     '''simple docstring'''
     __A		     =					{} if sp_model_kwargs is None else sp_model_kwargs
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else bos_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else eos_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else unk_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else pad_token
     super().__init__(
         bos_token=_lowerCamelCase,			eos_token=_lowerCamelCase,			unk_token=_lowerCamelCase,			pad_token=_lowerCamelCase,			add_bos_token=_lowerCamelCase,			add_eos_token=_lowerCamelCase,			sp_model_kwargs=self.sp_model_kwargs,			clean_up_tokenization_spaces=_lowerCamelCase,			legacy=_lowerCamelCase,			**_lowerCamelCase,			)
     if legacy:
       logger.warning_once(
           f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
           ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''    )
     __A		     =					legacy
     __A		     =					vocab_file
     __A		     =					add_bos_token
     __A		     =					add_eos_token
     __A		     =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.Load(_lowerCamelCase    )


   def __getstate__(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.__dict__.copy()
     __A		     =					None
     __A		     =					self.sp_model.serialized_model_proto()
     return state


   def __setstate__(      self					:	Tuple,			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					d
     __A		     =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     return self.sp_model.get_piece_size()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					{self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
     vocab.update(self.added_tokens_encoder    )
     return vocab


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	Dict,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
     # the beginning of the text
     if not self.legacy:
       __A		     =					SPIECE_UNDERLINE + text.replace(_lowerCamelCase,			''' '''    )
     return super().tokenize(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     if not self.legacy:
       __A		     =					text.startswith(_lowerCamelCase    )
       if is_first:
         __A		     =					text[1:]

     __A		     =					self.sp_model.encode(_lowerCamelCase,			out_type=_lowerCamelCase    )

     if not self.legacy and not is_first and not text.startswith(''' '''    ) and tokens[0].startswith(_lowerCamelCase    ):
       __A		     =					([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
     return tokens


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     return self.sp_model.piece_to_id(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.sp_model.IdToPiece(_lowerCamelCase    )
     return token


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					[]
     __A		     =					''''''
     __A		     =					False
     for i, token in enumerate(_lowerCamelCase    ):
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special and i != 0:
           out_string += " "
         out_string += self.sp_model.decode(_lowerCamelCase    ) + token
         __A		     =					True
         __A		     =					[]
       else:
         current_sub_tokens.append(_lowerCamelCase    )
         __A		     =					False
     out_string += self.sp_model.decode(_lowerCamelCase    )
     return out_string


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[str] = None    ):

     '''simple docstring'''
     if not os.path.isdir(_lowerCamelCase    ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
       return
     __A		     =					os.path.join(
         _lowerCamelCase,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

     if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
       copyfile(self.vocab_file,			_lowerCamelCase    )
     elif not os.path.isfile(self.vocab_file    ):
       with open(_lowerCamelCase,			'''wb'''    ) as fi:
         __A		     =					self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase    )

     return (out_vocab_file,)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	str=None    ):

     '''simple docstring'''
     __A		     =					[self.bos_token_id] if self.add_bos_token else []
     __A		     =					[self.eos_token_id] if self.add_eos_token else []

     __A		     =					bos_token_id + token_ids_a + eos_token_id

     if token_ids_a is not None:
       __A		     =					output + bos_token_id + token_ids_a + eos_token_id

     return output


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None,			_lowerCamelCase					:	bool = False    ):

     '''simple docstring'''
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase,			token_ids_a=_lowerCamelCase,			already_has_special_tokens=_lowerCamelCase    )

     __A		     =					[1] if self.add_bos_token else []
     __A		     =					[1] if self.add_eos_token else []

     if token_ids_a is None:
       return bos_token_id + ([0] * len(_lowerCamelCase    )) + eos_token_id
     return (
         bos_token_id
         + ([0] * len(_lowerCamelCase    ))
         + eos_token_id
         + bos_token_id
         + ([0] * len(_lowerCamelCase    ))
         + eos_token_id
     )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None    ):

     '''simple docstring'''
     __A		     =					[self.bos_token_id] if self.add_bos_token else []
     __A		     =					[self.eos_token_id] if self.add_eos_token else []

     __A		     =					[0] * len(bos_token_id + token_ids_a + eos_token_id    )

     if token_ids_a is not None:
       output += [1] * len(bos_token_id + token_ids_a + eos_token_id    )

     return output






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	"Conversation"    ):

     '''simple docstring'''
     __A		     =					list(conversation.iter_texts()    )
     if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
         [not is_user for is_user, msg in dialogue[1::2]]    ):
       raise ValueError(
           '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''    )

     __A		     =					[]
     if len(conversation.past_user_inputs    ) > 0:
       if not conversation.past_user_inputs[0].startswith(_lowerCamelCase    ) or E_SYS not in conversation.past_user_inputs[0]:
         __A		     =					(
             B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
         )
     elif not dialogue[0][1].startswith(_lowerCamelCase    ) or E_SYS not in dialogue[0][1]:
       __A		     =					(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

     dialog_tokens += sum(
         [
             [self.bos_token_id]
             + self.encode(
                 f'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ',			add_special_tokens=_lowerCamelCase    )
             + [self.eos_token_id]
             for prompt, answer in zip(dialogue[::2],			dialogue[1::2]    )
         ],			[],			)
     if not (dialogue[-1][0]):
       raise ValueError(f'Last message must be from user, got {dialogue[-1]["role"]}'    )
     dialog_tokens += [self.bos_token_id] + self.encode(
         f'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}',			add_special_tokens=_lowerCamelCase    )
     return dialog_tokens








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}"""    )

