"""simple docstring"""







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase_    :    List[str]											=						"""\
@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},
}
"""

UpperCamelCase_    :    str											=						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


UpperCamelCase_    :    Tuple											=						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""


def 					a__ (						UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Any     )->		List[Any]:
						UpperCAmelCase__   		=						[rgram for rgrams in rgramslist for rgram in rgrams]
						UpperCAmelCase__   		=						Counter(UpperCAmelCase__     )

						UpperCAmelCase__   		=						Counter(UpperCAmelCase__     )
						UpperCAmelCase__   		=						Counter()
						for sgram, scount in sgramcounter.items():
												UpperCAmelCase__   		=						scount * numref

						UpperCAmelCase__   		=						Counter(UpperCAmelCase__     )
						UpperCAmelCase__   		=						Counter()
						for cgram, ccount in cgramcounter.items():
												UpperCAmelCase__   		=						ccount * numref

						# KEEP
						UpperCAmelCase__   		=						sgramcounter_rep & cgramcounter_rep
						UpperCAmelCase__   		=						keepgramcounter_rep & rgramcounter
						UpperCAmelCase__   		=						sgramcounter_rep & rgramcounter

						UpperCAmelCase__   		=						0
						UpperCAmelCase__   		=						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__   		=						1
						UpperCAmelCase__   		=						1
						if len(UpperCAmelCase__     ) > 0:
												UpperCAmelCase__   		=						keeptmpscorea / len(UpperCAmelCase__     )
						if len(UpperCAmelCase__     ) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												UpperCAmelCase__   		=						keeptmpscorea / sum(keepgramcounterall_rep.values()     )
						UpperCAmelCase__   		=						0
						if keepscore_precision > 0 or keepscore_recall > 0:
												UpperCAmelCase__   		=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						UpperCAmelCase__   		=						sgramcounter_rep - cgramcounter_rep
						UpperCAmelCase__   		=						delgramcounter_rep - rgramcounter
						UpperCAmelCase__   		=						sgramcounter_rep - rgramcounter
						UpperCAmelCase__   		=						0
						UpperCAmelCase__   		=						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__   		=						1
						if len(UpperCAmelCase__     ) > 0:
												UpperCAmelCase__   		=						deltmpscorea / len(UpperCAmelCase__     )

						# ADDITION
						UpperCAmelCase__   		=						set(UpperCAmelCase__     ) - set(UpperCAmelCase__     )
						UpperCAmelCase__   		=						set(UpperCAmelCase__     ) & set(UpperCAmelCase__     )
						UpperCAmelCase__   		=						set(UpperCAmelCase__     ) - set(UpperCAmelCase__     )

						UpperCAmelCase__   		=						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__   		=						1
						UpperCAmelCase__   		=						1
						if len(UpperCAmelCase__     ) > 0:
												UpperCAmelCase__   		=						addtmpscore / len(UpperCAmelCase__     )
						if len(UpperCAmelCase__     ) > 0:
												UpperCAmelCase__   		=						addtmpscore / len(UpperCAmelCase__     )
						UpperCAmelCase__   		=						0
						if addscore_precision > 0 or addscore_recall > 0:
												UpperCAmelCase__   		=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)


def 					a__ (						UpperCAmelCase__    :     Optional[int]						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     int     )->		Optional[int]:
						UpperCAmelCase__   		=						len(UpperCAmelCase__     )

						UpperCAmelCase__   		=						ssent.split(""" """     )
						UpperCAmelCase__   		=						csent.split(""" """     )
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]

						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						for rsent in rsents:
												UpperCAmelCase__   		=						rsent.split(""" """     )
												UpperCAmelCase__   		=						[]
												UpperCAmelCase__   		=						[]
												UpperCAmelCase__   		=						[]
												ragramslist.append(UpperCAmelCase__     )
												for i in range(0						,     len(UpperCAmelCase__     ) - 1     ):
																		if i < len(UpperCAmelCase__     ) - 1:
																								UpperCAmelCase__   		=						ragrams[i] + """ """ + ragrams[i + 1]
																								ragrams.append(UpperCAmelCase__     )
																		if i < len(UpperCAmelCase__     ) - 2:
																								UpperCAmelCase__   		=						ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																								ragrams.append(UpperCAmelCase__     )
																		if i < len(UpperCAmelCase__     ) - 3:
																								UpperCAmelCase__   		=						ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																								ragrams.append(UpperCAmelCase__     )
												ragramslist.append(UpperCAmelCase__     )
												ragramslist.append(UpperCAmelCase__     )
												ragramslist.append(UpperCAmelCase__     )

						for i in range(0						,     len(UpperCAmelCase__     ) - 1     ):
												if i < len(UpperCAmelCase__     ) - 1:
																		UpperCAmelCase__   		=						sagrams[i] + """ """ + sagrams[i + 1]
																		sagrams.append(UpperCAmelCase__     )
												if i < len(UpperCAmelCase__     ) - 2:
																		UpperCAmelCase__   		=						sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																		sagrams.append(UpperCAmelCase__     )
												if i < len(UpperCAmelCase__     ) - 3:
																		UpperCAmelCase__   		=						sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
																		sagrams.append(UpperCAmelCase__     )

						for i in range(0						,     len(UpperCAmelCase__     ) - 1     ):
												if i < len(UpperCAmelCase__     ) - 1:
																		UpperCAmelCase__   		=						cagrams[i] + """ """ + cagrams[i + 1]
																		cagrams.append(UpperCAmelCase__     )
												if i < len(UpperCAmelCase__     ) - 2:
																		UpperCAmelCase__   		=						cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																		cagrams.append(UpperCAmelCase__     )
												if i < len(UpperCAmelCase__     ) - 3:
																		UpperCAmelCase__   		=						cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																		cagrams.append(UpperCAmelCase__     )

						((UpperCAmelCase__)		, (UpperCAmelCase__)		, (UpperCAmelCase__))   		=						SARIngram(UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__     )
						((UpperCAmelCase__)		, (UpperCAmelCase__)		, (UpperCAmelCase__))   		=						SARIngram(UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__     )
						((UpperCAmelCase__)		, (UpperCAmelCase__)		, (UpperCAmelCase__))   		=						SARIngram(UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__     )
						((UpperCAmelCase__)		, (UpperCAmelCase__)		, (UpperCAmelCase__))   		=						SARIngram(UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__     )
						UpperCAmelCase__   		=						sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
						UpperCAmelCase__   		=						sum([delascore, delascore, delascore, delascore]     ) / 4
						UpperCAmelCase__   		=						sum([addascore, addascore, addascore, addascore]     ) / 4
						UpperCAmelCase__   		=						(avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore


def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     bool = True						,     UpperCAmelCase__    :     str = "13a"						,     UpperCAmelCase__    :     bool = True     )->		List[str]:
						# 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__   		=						sentence.lower()

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

						if not return_str:
												UpperCAmelCase__   		=						normalized_sent.split()

						return normalized_sent







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

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										return datasets.MetricInfo(
										    description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
										        {
										            """sources""": datasets.Value("""string"""    ,      id="""sequence"""       ),
										            """predictions""": datasets.Value("""string"""    ,      id="""sequence"""       ),
										            """references""": datasets.Sequence(datasets.Value("""string"""    ,      id="""sequence"""       )    ,      id="""references"""       ),
										        }       )    ,      codebase_urls=[
										        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
										        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
										    ]    ,      reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]    ,      )

				def     A	(       self    :      Optional[Any]    ,      _lowercase    :      int    ,      _lowercase    :      List[Any]    ,      _lowercase    :      List[str]       ):


										'''simple docstring'''
										if not (len(_lowercase       ) == len(_lowercase       ) == len(_lowercase       )):
																raise ValueError("""Sources length must match predictions and references lengths."""       )
										UpperCAmelCase__   		=						0
										for src, pred, refs in zip(_lowercase    ,      _lowercase    ,      _lowercase       ):
																sari_score += SARIsent(normalize(_lowercase       )    ,      normalize(_lowercase       )    ,      [normalize(_lowercase       ) for sent in refs]       )
										UpperCAmelCase__   		=						sari_score / len(_lowercase       )
										return {"sari": 1_0_0 * sari_score}


"""simple docstring"""







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
						)


from PIL import Image  # noqa: E402


class      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

										# Pad with silence if necessary.
										if len(self.audio       ) < self.x_res * self.hop_length:
																UpperCAmelCase__   		=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

