





'''simple docstring'''





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_							=			get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class        snake_case_		(			__lowerCAmelCase						,					unittest.TestCase):
		lowerCamelCase    :List[str]						=     PegasusTokenizer
		lowerCamelCase    :Tuple						=     PegasusTokenizerFast
		lowerCamelCase    :List[Any]						=     True
		lowerCamelCase    :Tuple						=     True







		def 			__lowercase   (		self   )				->   List[str]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				lowerCamelCase   : List[Any]								=PegasusTokenizer(_UpperCamelCase   )
				tokenizer.save_pretrained(self.tmpdirname   )







		@cached_property
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				return PegasusTokenizer.from_pretrained('''google/pegasus-large'''   )







		def 			__lowercase   (		self			,     **__lowercase   )				->   PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname			,     **_UpperCamelCase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   Dict:
				return ("This is a test", "This is a test")







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Optional[int]								="""</s>"""
				lowerCamelCase   : Dict								=1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase   )			,     _UpperCamelCase   )
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase   )			,     _UpperCamelCase   )







		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : Dict								=list(self.get_tokenizer().get_vocab().keys()   )

				self.assertEqual(vocab_keys[0]			,     '''<pad>'''   )
				self.assertEqual(vocab_keys[1]			,     '''</s>'''   )
				self.assertEqual(vocab_keys[-1]			,     '''v'''   )
				self.assertEqual(len(_UpperCamelCase   )			,     1_1_0_3   )







		def 			__lowercase   (		self   )				->   List[str]:
				self.assertEqual(self.get_tokenizer().vocab_size			,     1_1_0_3   )







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : str								=self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
				lowerCamelCase   : Tuple								=self.tokenizer_class.from_pretrained(self.tmpdirname   )
				lowerCamelCase   : Optional[int]								=(
				    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
				    """ </s> <pad> <pad> <pad>"""
				)
				lowerCamelCase   : List[Any]								=rust_tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   ).input_ids[0]
				lowerCamelCase   : List[Any]								=py_tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   ).input_ids[0]
				self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : List[Any]								=self._large_tokenizer
				# <mask_1> masks whole sentence while <mask_2> masks single word
				lowerCamelCase   : str								="""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
				lowerCamelCase   : Optional[int]								=[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
				lowerCamelCase   : List[Any]								=tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase   ).input_ids[0]
				self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : int								=self._large_tokenizer
				# The tracebacks for the following asserts are **better** without messages or self.assertEqual
				assert tokenizer.vocab_size == 9_6_1_0_3
				assert tokenizer.pad_token_id == 0
				assert tokenizer.eos_token_id == 1
				assert tokenizer.offset == 1_0_3
				assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
				assert tokenizer.unk_token == "<unk>"
				assert tokenizer.model_max_length == 1_0_2_4
				lowerCamelCase   : Any								="""To ensure a smooth flow of bank resolutions."""
				lowerCamelCase   : Optional[int]								=[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
				lowerCamelCase   : Any								=tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase   ).input_ids[0]
				self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
				assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







		@require_torch
		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : Optional[int]								=["""This is going to be way too long.""" * 1_5_0, """short example"""]
				lowerCamelCase   : Dict								=["""not super long but more than 5 tokens""", """tiny"""]
				lowerCamelCase   : List[Any]								=self._large_tokenizer(_UpperCamelCase			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     return_tensors='''pt'''   )
				lowerCamelCase   : int								=self._large_tokenizer(
				    text_target=_UpperCamelCase			,     max_length=5			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     return_tensors='''pt'''   )

				assert batch.input_ids.shape == (2, 1_0_2_4)
				assert batch.attention_mask.shape == (2, 1_0_2_4)
				assert targets["input_ids"].shape == (2, 5)
				assert len(_UpperCamelCase   ) == 2  # input_ids, attention_mask.







		@slow
		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : List[str]								={"""input_ids""": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_UpperCamelCase			,     model_name='''google/bigbird-pegasus-large-arxiv'''			,     revision='''ba85d0851d708441f91440d509690f1ab6353415'''			,     )

@require_sentencepiece
@require_tokenizers
class        snake_case_		(			__lowerCAmelCase						,					unittest.TestCase):
		lowerCamelCase    :Union[str, Any]						=     PegasusTokenizer
		lowerCamelCase    :Optional[int]						=     PegasusTokenizerFast
		lowerCamelCase    :str						=     True
		lowerCamelCase    :str						=     True







		def 			__lowercase   (		self   )				->   Tuple:
				super().setUp()

				# We have a SentencePiece fixture for testing
				lowerCamelCase   : Union[str, Any]								=PegasusTokenizer(_UpperCamelCase			,     offset=0			,     mask_token_sent=_UpperCamelCase			,     mask_token='''[MASK]'''   )
				tokenizer.save_pretrained(self.tmpdirname   )







		@cached_property
		def 			__lowercase   (		self   )				->   Optional[Any]:
				return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''   )







		def 			__lowercase   (		self			,     **__lowercase   )				->   PegasusTokenizer:
				return PegasusTokenizer.from_pretrained(self.tmpdirname			,     **_UpperCamelCase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   List[Any]:
				return ("This is a test", "This is a test")







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : Any								=self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
				lowerCamelCase   : List[str]								=self.tokenizer_class.from_pretrained(self.tmpdirname   )
				lowerCamelCase   : int								=(
				    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
				    """ <pad> <pad> <pad>"""
				)
				lowerCamelCase   : Union[str, Any]								=rust_tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   ).input_ids[0]
				lowerCamelCase   : str								=py_tokenizer([raw_input_str]			,     return_tensors=_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   ).input_ids[0]
				self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )







		@require_torch
		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : List[Any]								=["""This is going to be way too long.""" * 1_0_0_0, """short example"""]
				lowerCamelCase   : Tuple								=["""not super long but more than 5 tokens""", """tiny"""]
				lowerCamelCase   : Optional[int]								=self._large_tokenizer(_UpperCamelCase			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     return_tensors='''pt'''   )
				lowerCamelCase   : List[str]								=self._large_tokenizer(
				    text_target=_UpperCamelCase			,     max_length=5			,     padding=_UpperCamelCase			,     truncation=_UpperCamelCase			,     return_tensors='''pt'''   )

				assert batch.input_ids.shape == (2, 4_0_9_6)
				assert batch.attention_mask.shape == (2, 4_0_9_6)
				assert targets["input_ids"].shape == (2, 5)
				assert len(_UpperCamelCase   ) == 2  # input_ids, attention_mask.







		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : Tuple								=(
				    """This is an example string that is used to test the original TF implementation against the HF"""
				    """ implementation"""
				)

				lowerCamelCase   : int								=self._large_tokenizer(_UpperCamelCase   ).input_ids

				self.assertListEqual(
				    _UpperCamelCase			,     [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]			,     )








import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_							=			'''
@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",
}
'''

snake_case_							=			'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


snake_case_							=			'''
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__	(       SCREAMING_SNAKE_CASE_		)   ->     Tuple:

		def remove_articles(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : Tuple								=re.compile(R'''\b(a|an|the)\b'''							,       re.UNICODE		)
				return re.sub(SCREAMING_SNAKE_CASE_							,       ''' '''							,       SCREAMING_SNAKE_CASE_		)

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_		)		)		)		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		return int(normalize_answer(SCREAMING_SNAKE_CASE_		) == normalize_answer(SCREAMING_SNAKE_CASE_		)		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		lowerCamelCase   : Union[str, Any]								=[any(compute_exact(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) for ref in refs		) for pred, refs in zip(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)]
		return (sum(SCREAMING_SNAKE_CASE_		) / len(SCREAMING_SNAKE_CASE_		)) * 1_0_0


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Any								=[rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCamelCase   : int								=Counter(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Dict								=Counter(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Any								=Counter()
		for sgram, scount in sgramcounter.items():
				lowerCamelCase   : Tuple								=scount * numref

		lowerCamelCase   : Optional[int]								=Counter(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Tuple								=Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCamelCase   : Tuple								=ccount * numref

		# KEEP
		lowerCamelCase   : str								=sgramcounter_rep & cgramcounter_rep
		lowerCamelCase   : Union[str, Any]								=keepgramcounter_rep & rgramcounter
		lowerCamelCase   : Optional[Any]								=sgramcounter_rep & rgramcounter

		lowerCamelCase   : Optional[Any]								=0
		lowerCamelCase   : List[Any]								=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.
		lowerCamelCase   : Tuple								=1
		lowerCamelCase   : int								=1
		if len(SCREAMING_SNAKE_CASE_		) > 0:
				lowerCamelCase   : Tuple								=keeptmpscorea / len(SCREAMING_SNAKE_CASE_		)
		if len(SCREAMING_SNAKE_CASE_		) > 0:
				# Fix an alleged bug [2] in the keep score computation.
				# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
				lowerCamelCase   : Any								=keeptmpscorea / sum(keepgramcounterall_rep.values()		)
		lowerCamelCase   : Optional[Any]								=0
		if keepscore_precision > 0 or keepscore_recall > 0:
				lowerCamelCase   : Optional[int]								=2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

		# DELETION
		lowerCamelCase   : int								=sgramcounter_rep - cgramcounter_rep
		lowerCamelCase   : Dict								=delgramcounter_rep - rgramcounter
		lowerCamelCase   : Dict								=sgramcounter_rep - rgramcounter
		lowerCamelCase   : Optional[int]								=0
		lowerCamelCase   : List[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.
		lowerCamelCase   : str								=1
		if len(SCREAMING_SNAKE_CASE_		) > 0:
				lowerCamelCase   : Optional[int]								=deltmpscorea / len(SCREAMING_SNAKE_CASE_		)

		# ADDITION
		lowerCamelCase   : List[Any]								=set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : int								=set(SCREAMING_SNAKE_CASE_		) & set(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : int								=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.
		lowerCamelCase   : int								=1
		lowerCamelCase   : List[Any]								=1
		if len(SCREAMING_SNAKE_CASE_		) > 0:
				lowerCamelCase   : str								=addtmpscore / len(SCREAMING_SNAKE_CASE_		)
		if len(SCREAMING_SNAKE_CASE_		) > 0:
				lowerCamelCase   : List[str]								=addtmpscore / len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=0
		if addscore_precision > 0 or addscore_recall > 0:
				lowerCamelCase   : Optional[Any]								=2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Dict:
		lowerCamelCase   : Optional[int]								=len(SCREAMING_SNAKE_CASE_		)

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

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

		for i in range(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		):
				if i < len(SCREAMING_SNAKE_CASE_		) - 1:
						lowerCamelCase   : Optional[int]								=sagrams[i] + ''' ''' + sagrams[i + 1]
						sagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 2:
						lowerCamelCase   : List[Any]								=sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
						sagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 3:
						lowerCamelCase   : Optional[int]								=sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
						sagrams.append(SCREAMING_SNAKE_CASE_		)

		for i in range(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		):
				if i < len(SCREAMING_SNAKE_CASE_		) - 1:
						lowerCamelCase   : Optional[int]								=cagrams[i] + ''' ''' + cagrams[i + 1]
						cagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 2:
						lowerCamelCase   : List[str]								=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
						cagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 3:
						lowerCamelCase   : str								=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
						cagrams.append(SCREAMING_SNAKE_CASE_		)

		((lowerCamelCase)		,   (lowerCamelCase)		,   (lowerCamelCase))   : Any								=SARIngram(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		((lowerCamelCase)		,   (lowerCamelCase)		,   (lowerCamelCase))   : Optional[Any]								=SARIngram(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		((lowerCamelCase)		,   (lowerCamelCase)		,   (lowerCamelCase))   : List[Any]								=SARIngram(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		((lowerCamelCase)		,   (lowerCamelCase)		,   (lowerCamelCase))   : List[str]								=SARIngram(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=sum([keepascore, keepascore, keepascore, keepascore]		) / 4
		lowerCamelCase   : List[str]								=sum([delascore, delascore, delascore, delascore]		) / 4
		lowerCamelCase   : int								=sum([addascore, addascore, addascore, addascore]		) / 4
		lowerCamelCase   : Any								=(avgkeepscore + avgdelscore + avgaddscore) / 3
		return finalscore


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = "13a"							,       SCREAMING_SNAKE_CASE_ = True		)   ->     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:
				lowerCamelCase   : Union[str, Any]								=sentence.lower()

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

		if not return_str:
				lowerCamelCase   : Union[str, Any]								=normalized_sent.split()

		return normalized_sent


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:
		if not (len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		)):
				raise ValueError('''Sources length must match predictions and references lengths.'''		)
		lowerCamelCase   : Dict								=0
		for src, pred, refs in zip(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
				sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE_		)							,       normalize(SCREAMING_SNAKE_CASE_		)							,       [normalize(SCREAMING_SNAKE_CASE_		) for sent in refs]		)
		lowerCamelCase   : str								=sari_score / len(SCREAMING_SNAKE_CASE_		)
		return 1_0_0 * sari_score


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_="exp"							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=False							,       )   ->     Dict:
		lowerCamelCase   : Optional[int]								=len(references[0]		)
		if any(len(SCREAMING_SNAKE_CASE_		) != references_per_prediction for refs in references		):
				raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
		lowerCamelCase   : Optional[int]								=[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_		)]
		lowerCamelCase   : Union[str, Any]								=sacrebleu.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_							,       )
		return output.score



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







		def 			__lowercase   (		self   )				->   List[str]:
				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 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Tuple:
				lowerCamelCase   : str								={}
				result.update({'''sari''': compute_sari(sources=__lowercase			,     predictions=__lowercase			,     references=__lowercase   )}   )
				result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowercase			,     references=__lowercase   )}   )
				result.update({'''exact''': compute_em(predictions=__lowercase			,     references=__lowercase   )}   )
				return result

