





import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_A					:				List[str]				=			get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.plbart.modeling_plbart import shift_tokens_right

_A					:				List[str]				=			5_00_03
_A					:				int				=			5_00_02



@require_sentencepiece
@require_tokenizers
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	PLBartTokenizer
    lowerCamelCase__    :		Optional[int]				=	None
    lowerCamelCase__    :		Union[str, Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # We have a SentencePiece fixture for testing
        SCREAMING_SNAKE_CASE__											=			PLBartTokenizer(A_   ,				language_codes='''base'''   ,				keep_accents=A_     )
        tokenizer.save_pretrained(self.tmpdirname     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			PLBartTokenizer(A_   ,				language_codes='''base'''   ,				keep_accents=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize('''This is a test'''     )
        self.assertListEqual(A_   ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(A_     )   ,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,				)

        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
        self.assertListEqual(
            A_   ,				[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''9''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''é''',
                '''.''',
            ]   ,				)
        SCREAMING_SNAKE_CASE__											=			tokenizer.convert_tokens_to_ids(A_     )
        self.assertListEqual(
            A_   ,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]   ,				)

        SCREAMING_SNAKE_CASE__											=			tokenizer.convert_ids_to_tokens(A_     )
        self.assertListEqual(
            A_   ,				[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''<unk>''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''<unk>''',
                '''.''',
            ]   ,				)

        SCREAMING_SNAKE_CASE__											=			tokenizer.vocab_size
        SCREAMING_SNAKE_CASE__											=			[tokenizer.convert_ids_to_tokens(A_     ) for x in range(end - 4   ,				A_     )]

        self.assertListEqual(A_   ,				['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']     )

        SCREAMING_SNAKE_CASE__											=			'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     ).input_ids
        self.assertEqual(
            tokenizer.decode(A_   ,				skip_special_tokens=A_   ,				clean_up_tokenization_spaces=A_     )   ,				A_   ,				)




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			PLBartTokenizer(A_   ,				language_codes='''multi'''   ,				keep_accents=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize('''This is a test'''     )
        self.assertListEqual(A_   ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(A_     )   ,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,				)

        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
        self.assertListEqual(
            A_   ,				[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''9''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''é''',
                '''.''',
            ]   ,				)
        SCREAMING_SNAKE_CASE__											=			tokenizer.convert_tokens_to_ids(A_     )
        self.assertListEqual(
            A_   ,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]   ,				)

        SCREAMING_SNAKE_CASE__											=			tokenizer.convert_ids_to_tokens(A_     )
        self.assertListEqual(
            A_   ,				[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''<unk>''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''<unk>''',
                '''.''',
            ]   ,				)
        SCREAMING_SNAKE_CASE__											=			tokenizer.vocab_size
        SCREAMING_SNAKE_CASE__											=			[tokenizer.convert_ids_to_tokens(A_     ) for x in range(end - 7   ,				A_     )]

        self.assertListEqual(
            A_   ,				['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']     )
        SCREAMING_SNAKE_CASE__											=			'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     ).input_ids
        self.assertEqual(
            tokenizer.decode(A_   ,				skip_special_tokens=A_   ,				clean_up_tokenization_spaces=A_     )   ,				A_   ,				)





@require_torch
@require_sentencepiece
@require_tokenizers
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	"""uclanlp/plbart-python-en_XX"""
    lowerCamelCase__    :		int				=	[
        """def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
        """def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
    ]
    lowerCamelCase__    :		Tuple				=	[
        """Returns the maximum value of a b c.""",
        """Sums the values of a b c.""",
    ]
    lowerCamelCase__    :		int				=	[
        1_3_4,
        5_4_5_2,
        3_3_4_6_0,
        3_3_4_4_1,
        3_3_4_6_3,
        3_3_4_6_5,
        3_3_4_6_3,
        3_3_4_4_9,
        9_8_8,
        2_0,
        3_3_4_5_6,
        1_9,
        3_3_4_5_6,
        7_7_1,
        3_9,
        4_2_5_8,
        8_8_9,
        3_3_1_8,
        3_3_4_4_1,
        3_3_4_6_3,
        3_3_4_6_5,
        3_3_4_6_3,
        3_3_4_4_9,
        2_4_7_1,
        2,
        PYTHON_CODE,
    ]



    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			PLBartTokenizer.from_pretrained(
            cls.checkpoint_name   ,				language_codes='''base'''   ,				src_lang='''python'''   ,				tgt_lang='''en_XX'''     )
        SCREAMING_SNAKE_CASE__											=			1
        return cls



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']   ,				5_00_01     )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']   ,				5_00_02     )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']   ,				5_00_03     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.assertIn(A_   ,				self.tokenizer.all_special_ids     )
        SCREAMING_SNAKE_CASE__											=			[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
        SCREAMING_SNAKE_CASE__											=			self.tokenizer.decode(A_   ,				skip_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			self.tokenizer.decode(generated_ids[1:]   ,				skip_special_tokens=A_     )
        self.assertEqual(A_   ,				A_     )
        self.assertNotIn(self.tokenizer.eos_token   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
        self.assertIsInstance(src_text[0]   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			self.tokenizer(A_   ,				max_length=A_   ,				truncation=A_     ).input_ids[0]
        self.assertEqual(ids[-2]   ,				2     )
        self.assertEqual(ids[-1]   ,				A_     )
        self.assertEqual(len(A_     )   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']     )   ,				[5_00_04, 5_00_01]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE__											=			self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(A_     )
        SCREAMING_SNAKE_CASE__											=			PLBartTokenizer.from_pretrained(A_     )
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,				A_     )



    @require_torch
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.tokenizer(self.src_text   ,				text_target=self.tgt_text   ,				padding=A_   ,				return_tensors='''pt'''     )
        SCREAMING_SNAKE_CASE__											=			shift_tokens_right(batch['''labels''']   ,				self.tokenizer.pad_token_id     )

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        self.assertEqual(batch.input_ids[1][-2:].tolist()   ,				[2, PYTHON_CODE]     )
        self.assertEqual(batch.decoder_input_ids[1][0]   ,				A_     )
        self.assertEqual(batch.decoder_input_ids[1][-1]   ,				2     )
        self.assertEqual(batch.labels[1][-2:].tolist()   ,				[2, EN_CODE]     )



    @require_torch
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.tokenizer(
            self.src_text   ,				text_target=self.tgt_text   ,				padding=A_   ,				truncation=A_   ,				max_length=len(self.expected_src_tokens     )   ,				return_tensors='''pt'''   ,				)
        SCREAMING_SNAKE_CASE__											=			shift_tokens_right(batch['''labels''']   ,				self.tokenizer.pad_token_id     )

        self.assertIsInstance(A_   ,				A_     )

        self.assertEqual((2, 26)   ,				batch.input_ids.shape     )
        self.assertEqual((2, 26)   ,				batch.attention_mask.shape     )
        SCREAMING_SNAKE_CASE__											=			batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens   ,				A_     )
        self.assertEqual(2   ,				batch.decoder_input_ids[0, -1]     )  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens   ,				[]     )
        self.assertEqual(self.tokenizer.suffix_tokens   ,				[self.tokenizer.eos_token_id, PYTHON_CODE]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.tokenizer(self.src_text   ,				padding=A_   ,				truncation=A_   ,				max_length=3   ,				return_tensors='''pt'''     )
        SCREAMING_SNAKE_CASE__											=			self.tokenizer(
            text_target=self.tgt_text   ,				padding=A_   ,				truncation=A_   ,				max_length=10   ,				return_tensors='''pt'''     )
        SCREAMING_SNAKE_CASE__											=			targets['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			shift_tokens_right(A_   ,				self.tokenizer.pad_token_id     )

        self.assertEqual(batch.input_ids.shape[1]   ,				3     )
        self.assertEqual(batch.decoder_input_ids.shape[1]   ,				10     )




    @require_torch
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.tokenizer._build_translation_inputs(
            '''A test'''   ,				return_tensors='''pt'''   ,				src_lang='''en_XX'''   ,				tgt_lang='''java'''     )

        self.assertEqual(
            nested_simplify(A_     )   ,				{
                # A, test, EOS, en_XX
                '''input_ids''': [[1_50, 2_42, 2, 5_00_03]],
                '''attention_mask''': [[1, 1, 1, 1]],
                # java
                '''forced_bos_token_id''': 5_00_01,
            }   ,				)

"""simple docstring"""




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


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

__lowerCAmelCase     :				str    				=    "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase     :				int    				=    "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		def remove_articles(lowerCamelCase__  ):
				lowerCAmelCase__   = 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__  ):
				lowerCAmelCase__   = 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 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return int(normalize_answer(lowerCamelCase__  ) == normalize_answer(lowerCamelCase__  )  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [any(compute_exact(lowerCamelCase__      ,	lowerCamelCase__  ) for ref in refs  ) for pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__  )]
		return (sum(lowerCamelCase__  ) / len(lowerCamelCase__  )) * 100







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase__   = Counter(lowerCamelCase__  )

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

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

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

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

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

		# ADDITION
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) & set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )

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

		return (keepscore, delscore_precision, addscore)







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = len(lowerCamelCase__  )

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

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

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__ = True      ,	lowerCamelCase__ = "13a"      ,	lowerCamelCase__ = True  ):





		"""simple docstring"""




		if lowercase:
				lowerCAmelCase__   = sentence.lower()

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

		if not return_str:
				lowerCAmelCase__   = normalized_sent.split()

		return normalized_sent







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if not (len(lowerCamelCase__  ) == len(lowerCamelCase__  ) == len(lowerCamelCase__  )):
				raise ValueError("""Sources length must match predictions and references lengths."""  )
		lowerCAmelCase__   = 0
		for src, pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):
				sari_score += SARIsent(normalize(lowerCamelCase__  )      ,	normalize(lowerCamelCase__  )      ,	[normalize(lowerCamelCase__  ) for sent in refs]  )
		lowerCAmelCase__   = sari_score / len(lowerCamelCase__  )
		return 100 * sari_score







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__="exp"      ,	lowerCamelCase__=None      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = 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"""  )
		lowerCAmelCase__   = [[refs[i] for refs in references] for i in range(lowerCamelCase__  )]
		lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							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     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Dict	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = {}
							result.update({"""sari""": compute_sari(sources=snake_case__	,    predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""sacrebleu""": compute_sacrebleu(predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""exact""": compute_em(predictions=snake_case__	,    references=snake_case__  )}  )
							return result




