




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A							    =	"""\
@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},
}
"""

_A							    =	"""\
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
"""


_A							    =	"""
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 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			int:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: int	     =[rgram for rgrams in rgramslist for rgram in rgrams]
							SCREAMING_SNAKE_CASE			: str	     =Counter(__a )

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

							SCREAMING_SNAKE_CASE			: Tuple	     =Counter(__a )
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Counter()
							for cgram, ccount in cgramcounter.items():
														SCREAMING_SNAKE_CASE			: int	     =ccount * numref

							# KEEP
							SCREAMING_SNAKE_CASE			: Optional[int]	     =sgramcounter_rep & cgramcounter_rep
							SCREAMING_SNAKE_CASE			: str	     =keepgramcounter_rep & rgramcounter
							SCREAMING_SNAKE_CASE			: int	     =sgramcounter_rep & rgramcounter

							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =0
							SCREAMING_SNAKE_CASE			: Dict	     =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.
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =1
							SCREAMING_SNAKE_CASE			: Dict	     =1
							if len(__a ) > 0:
														SCREAMING_SNAKE_CASE			: Tuple	     =keeptmpscorea / len(__a )
							if len(__a ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														SCREAMING_SNAKE_CASE			: str	     =keeptmpscorea / sum(keepgramcounterall_rep.values() )
							SCREAMING_SNAKE_CASE			: str	     =0
							if keepscore_precision > 0 or keepscore_recall > 0:
														SCREAMING_SNAKE_CASE			: Any	     =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							SCREAMING_SNAKE_CASE			: Any	     =sgramcounter_rep - cgramcounter_rep
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =delgramcounter_rep - rgramcounter
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =sgramcounter_rep - rgramcounter
							SCREAMING_SNAKE_CASE			: Dict	     =0
							SCREAMING_SNAKE_CASE			: Dict	     =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.
							SCREAMING_SNAKE_CASE			: Optional[int]	     =1
							if len(__a ) > 0:
														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =deltmpscorea / len(__a )

							# ADDITION
							SCREAMING_SNAKE_CASE			: List[str]	     =set(__a ) - set(__a )
							SCREAMING_SNAKE_CASE			: List[str]	     =set(__a ) & set(__a )
							SCREAMING_SNAKE_CASE			: Any	     =set(__a ) - set(__a )

							SCREAMING_SNAKE_CASE			: Tuple	     =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.
							SCREAMING_SNAKE_CASE			: str	     =1
							SCREAMING_SNAKE_CASE			: List[str]	     =1
							if len(__a ) > 0:
														SCREAMING_SNAKE_CASE			: Any	     =addtmpscore / len(__a )
							if len(__a ) > 0:
														SCREAMING_SNAKE_CASE			: str	     =addtmpscore / len(__a )
							SCREAMING_SNAKE_CASE			: str	     =0
							if addscore_precision > 0 or addscore_recall > 0:
														SCREAMING_SNAKE_CASE			: List[Any]	     =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[int]	     =len(__a )

							SCREAMING_SNAKE_CASE			: List[str]	     =ssent.split(''' ''' )
							SCREAMING_SNAKE_CASE			: Any	     =csent.split(''' ''' )
							SCREAMING_SNAKE_CASE			: List[Any]	     =[]
							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
							SCREAMING_SNAKE_CASE			: str	     =[]
							SCREAMING_SNAKE_CASE			: Dict	     =[]
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]

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

							for i in range(0	,	len(__a ) - 1 ):
														if i < len(__a ) - 1:
																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(__a )
														if i < len(__a ) - 2:
																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(__a )
														if i < len(__a ) - 3:
																					SCREAMING_SNAKE_CASE			: Tuple	     =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																					sagrams.append(__a )

							for i in range(0	,	len(__a ) - 1 ):
														if i < len(__a ) - 1:
																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(__a )
														if i < len(__a ) - 2:
																					SCREAMING_SNAKE_CASE			: Any	     =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(__a )
														if i < len(__a ) - 3:
																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(__a )

							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Any	     =SARIngram(__a	,	__a	,	__a	,	__a )
							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Dict	     =SARIngram(__a	,	__a	,	__a	,	__a )
							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: List[Any]	     =SARIngram(__a	,	__a	,	__a	,	__a )
							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Any	     =SARIngram(__a	,	__a	,	__a	,	__a )
							SCREAMING_SNAKE_CASE			: int	     =sum([keepascore, keepascore, keepascore, keepascore] ) / 4
							SCREAMING_SNAKE_CASE			: Any	     =sum([delascore, delascore, delascore, delascore] ) / 4
							SCREAMING_SNAKE_CASE			: Dict	     =sum([addascore, addascore, addascore, addascore] ) / 4
							SCREAMING_SNAKE_CASE			: Dict	     =(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore
def 						lowerCAmelCase_       (	__a	,	__a = True	,	__a = "13a"	,	__a = True )       ->			Any:





							"""simple docstring"""





							if lowercase:
														SCREAMING_SNAKE_CASE			: str	     =sentence.lower()

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

							if not return_str:
														SCREAMING_SNAKE_CASE			: Optional[int]	     =normalized_sent.split()

							return normalized_sent


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




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












import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_A							    =	logging.get_logger(__name__)

_A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A							    =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):




				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token

											super().__init__(
											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)

											SCREAMING_SNAKE_CASE			: Dict	     =3
											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
											SCREAMING_SNAKE_CASE			: int	     =remove_space
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True

											try:
																		import jieba
											except ModuleNotFoundError as error:
																		raise error.__class__(
																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
											SCREAMING_SNAKE_CASE			: str	     =jieba
											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]

											if token_ids_a is None:
																		return len(token_ids_a + sep							) * [0] + cls_segment_id
											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''							)

											if not os.path.isdir(snake_case_							):
																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																		return
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																		copyfile(self.vocab_file		,		snake_case_							)

											return (out_vocab_file,)




				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
											SCREAMING_SNAKE_CASE			: int	     =[
											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
											    for text in batch_text_or_text_pairs
											]
											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)







				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
											return text






