






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__	:			List[str]			   = {
    """configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
    """feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
    """processing_whisper""": ["""WhisperProcessor"""],
    """tokenization_whisper""": ["""WhisperTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Dict			   = ["""WhisperTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Union[str, Any]			   = [
							    """WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """WhisperForConditionalGeneration""",
							    """WhisperModel""",
							    """WhisperPreTrainedModel""",
							    """WhisperForAudioClassification""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Dict			   = [
							    """TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFWhisperForConditionalGeneration""",
							    """TFWhisperModel""",
							    """TFWhisperPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Any			   = [
							    """FlaxWhisperForConditionalGeneration""",
							    """FlaxWhisperModel""",
							    """FlaxWhisperPreTrainedModel""",
							    """FlaxWhisperForAudioClassification""",
							]


if TYPE_CHECKING:
							from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
							from .feature_extraction_whisper import WhisperFeatureExtractor
							from .processing_whisper import WhisperProcessor
							from .tokenization_whisper import WhisperTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_whisper_fast import WhisperTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_whisper import (
														    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WhisperForAudioClassification,
														    WhisperForConditionalGeneration,
														    WhisperModel,
														    WhisperPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_whisper import (
														    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFWhisperForConditionalGeneration,
														    TFWhisperModel,
														    TFWhisperPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_whisper import (
														    FlaxWhisperForAudioClassification,
														    FlaxWhisperForConditionalGeneration,
														    FlaxWhisperModel,
														    FlaxWhisperPreTrainedModel,
														)

else:
							import sys

							A__	:			Any			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase       :     List[Any]   =     """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase       :     Optional[int]   =     """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase       :     str   =     """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""



def 		A_				(     _lowerCAmelCase     )						-> str:

 def remove_articles(_lowerCAmelCase     ):
  UpperCamelCase	:					Tuple       	=   re.compile(r"\b(a|an|the)\b"							,   re.UNICODE     )
  return re.sub(_lowerCAmelCase							,   " "							,   _lowerCAmelCase     )

 def white_space_fix(_lowerCAmelCase     ):
  return " ".join(text.split()     )

 def remove_punc(_lowerCAmelCase     ):
  UpperCamelCase	:					int       	=   set(string.punctuation     )
  return "".join(ch for ch in text if ch not in exclude     )

 def lower(_lowerCAmelCase     ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 return int(normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   [any(compute_exact(_lowerCAmelCase							,   _lowerCAmelCase     ) for ref in refs     ) for pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase     )]
 return (sum(_lowerCAmelCase     ) / len(_lowerCAmelCase     )) * 100



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					Union[str, Any]       	=   [rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[int]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					List[Any]       	=   Counter()
 for sgram, scount in sgramcounter.items():
  UpperCamelCase	:					Tuple       	=   scount * numref

 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCamelCase	:					Dict       	=   ccount * numref

 # KEEP
 UpperCamelCase	:					List[Any]       	=   sgramcounter_rep & cgramcounter_rep
 UpperCamelCase	:					Union[str, Any]       	=   keepgramcounter_rep & rgramcounter
 UpperCamelCase	:					Dict       	=   sgramcounter_rep & rgramcounter

 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Tuple       	=   0
 for keepgram in keepgramcountergood_rep:
  keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
  # Fix an alleged bug [2] in the keep score computation.
  # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
  keeptmpscorea += keepgramcountergood_rep[keepgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 #      a target exactly.
 UpperCamelCase	:					Any       	=   1
 UpperCamelCase	:					Any       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   keeptmpscorea / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  UpperCamelCase	:					Union[str, Any]       	=   keeptmpscorea / sum(keepgramcounterall_rep.values()     )
 UpperCamelCase	:					Any       	=   0
 if keepscore_precision > 0 or keepscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 UpperCamelCase	:					Any       	=   sgramcounter_rep - cgramcounter_rep
 UpperCamelCase	:					str       	=   delgramcounter_rep - rgramcounter
 UpperCamelCase	:					Any       	=   sgramcounter_rep - rgramcounter
 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Union[str, Any]       	=   0
 for delgram in delgramcountergood_rep:
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Dict       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					str       	=   deltmpscorea / len(_lowerCAmelCase     )

 # ADDITION
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) & set(_lowerCAmelCase     )
 UpperCamelCase	:					Dict       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   0
 for addgram in addgramcountergood:
  addtmpscore += 1

 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Tuple       	=   1
 UpperCamelCase	:					Tuple       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   addtmpscore / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Tuple       	=   addtmpscore / len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   0
 if addscore_precision > 0 or addscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   ssent.split(" "     )
 UpperCamelCase	:					Dict       	=   csent.split(" "     )
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					str       	=   []

 UpperCamelCase	:					Dict       	=   []
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Optional[Any]       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for rsent in rsents:
  UpperCamelCase	:					List[Any]       	=   rsent.split(" "     )
  UpperCamelCase	:					List[str]       	=   []
  UpperCamelCase	:					int       	=   []
  UpperCamelCase	:					Tuple       	=   []
  ragramslist.append(_lowerCAmelCase     )
  for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
   if i < len(_lowerCAmelCase     ) - 1:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 2:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 3:
    UpperCamelCase	:					List[Any]       	=   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
    ragrams.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )
  ragramslist.append(_lowerCAmelCase     )

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					List[str]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Optional[int]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
   sagrams.append(_lowerCAmelCase     )

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					Optional[Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(_lowerCAmelCase     )

 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[Any]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					str       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
 UpperCamelCase	:					str       	=   sum([delascore, delascore, delascore, delascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   sum([addascore, addascore, addascore, addascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   (avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase = True							,   _lowerCAmelCase = "13a"							,   _lowerCAmelCase = True     )						-> Optional[Any]:
 # Normalization is requried for the ASSET dataset (one of the primary
 # datasets in sentence simplification) to allow using space
 # to split the sentence. Even though Wiki-Auto and TURK datasets,
 # do not require normalization, we do it for consistency.
 # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
 # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

 if lowercase:
  UpperCamelCase	:					Dict       	=   sentence.lower()

 if tokenizer in ["13a", "intl"]:
  if version.parse(sacrebleu.__version__     ).major >= 2:
   UpperCamelCase	:					str       	=   sacrebleu.metrics.bleu._get_tokenizer(_lowerCAmelCase     )()(_lowerCAmelCase     )
  else:
   UpperCamelCase	:					Dict       	=   sacrebleu.TOKENIZERS[tokenizer]()(_lowerCAmelCase     )
 elif tokenizer == "moses":
  UpperCamelCase	:					Union[str, Any]       	=   sacremoses.MosesTokenizer().tokenize(_lowerCAmelCase							,   return_str=_lowerCAmelCase							,   escape=_lowerCAmelCase     )
 elif tokenizer == "penn":
  UpperCamelCase	:					str       	=   sacremoses.MosesTokenizer().penn_tokenize(_lowerCAmelCase							,   return_str=_lowerCAmelCase     )
 else:
  UpperCamelCase	:					Union[str, Any]       	=   sentence

 if not return_str:
  UpperCamelCase	:					Tuple       	=   normalized_sent.split()

 return normalized_sent



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 if not (len(_lowerCAmelCase     ) == len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )):
  raise ValueError("Sources length must match predictions and references lengths."     )
 UpperCamelCase	:					Optional[Any]       	=   0
 for src, pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     ):
  sari_score += SARIsent(normalize(_lowerCAmelCase     )							,   normalize(_lowerCAmelCase     )							,   [normalize(_lowerCAmelCase     ) for sent in refs]     )
 UpperCamelCase	:					Optional[int]       	=   sari_score / len(_lowerCAmelCase     )
 return 100 * sari_score



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase="exp"							,   _lowerCAmelCase=None							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   )						-> List[str]:
 UpperCamelCase	:					Optional[Any]       	=   len(references[0]     )
 if any(len(_lowerCAmelCase     ) != references_per_prediction for refs in references     ):
  raise ValueError("Sacrebleu requires the same number of references for each prediction"     )
 UpperCamelCase	:					Optional[int]       	=   [[refs[i] for refs in references] for i in range(_lowerCAmelCase     )]
 UpperCamelCase	:					Tuple       	=   sacrebleu.corpus_bleu(
     _lowerCAmelCase							,   _lowerCAmelCase							,   smooth_method=_lowerCAmelCase							,   smooth_value=_lowerCAmelCase							,   force=_lowerCAmelCase							,   lowercase=_lowerCAmelCase							,   use_effective_order=_lowerCAmelCase							,   )
 return output.score



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION			)
class 					A__   (       datasets.Metric			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
               {
                   "predictions": datasets.Value("string"       ,							id="sequence"   ),
                   "references": datasets.Sequence(datasets.Value("string"       ,							id="sequence"   )       ,							id="references"   ),
               }   )       ,							codebase_urls=[
               "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
               "https://github.com/cocoxu/simplification/blob/master/SARI.py",
               "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
               "https://github.com/mjpost/sacreBLEU",
           ]       ,							reference_urls=[
               "https://www.aclweb.org/anthology/Q16-1029.pdf",
               "https://github.com/mjpost/sacreBLEU",
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {}
       result.update({"sari": compute_sari(sources=A_       ,							predictions=A_       ,							references=A_   )}   )
       result.update({"sacrebleu": compute_sacrebleu(predictions=A_       ,							references=A_   )}   )
       result.update({"exact": compute_em(predictions=A_       ,							references=A_   )}   )
       return result
