


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A		={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TableTransformerForObjectDetection',
		    'TableTransformerModel',
		    'TableTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TableTransformerConfig,
		    TableTransformerOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_table_transformer import (
				    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TableTransformerForObjectDetection,
				    TableTransformerModel,
				    TableTransformerPreTrainedModel,
				)

else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''simple docstring'''



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A		='\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'

__A		='\\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'


__A		='\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 (						UpperCamelCase__   ):

			def remove_articles(UpperCamelCase__   ):
						UpperCAmelCase__					:   Tuple       				=       re.compile(R"""\b(a|an|the)\b"""	, re.UNICODE   )
						return re.sub(UpperCamelCase__	, """ """	, UpperCamelCase__   )

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__   )   )   )   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			return int(normalize_answer(UpperCamelCase__   ) == normalize_answer(UpperCamelCase__   )   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       [any(compute_exact(UpperCamelCase__	, UpperCamelCase__   ) for ref in refs   ) for pred, refs in zip(UpperCamelCase__	, UpperCamelCase__   )]
			return (sum(UpperCamelCase__   ) / len(UpperCamelCase__   )) * 1_0_0







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[Any]       				=       [rgram for rgrams in rgramslist for rgram in rgrams]
			UpperCAmelCase__					:   List[Any]       				=       Counter(UpperCamelCase__   )

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

			UpperCAmelCase__					:   int       				=       Counter(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[int]       				=       Counter()
			for cgram, ccount in cgramcounter.items():
						UpperCAmelCase__					:   Union[str, Any]       				=       ccount * numref

			# KEEP
			UpperCAmelCase__					:   str       				=       sgramcounter_rep & cgramcounter_rep
			UpperCAmelCase__					:   List[Any]       				=       keepgramcounter_rep & rgramcounter
			UpperCAmelCase__					:   Dict       				=       sgramcounter_rep & rgramcounter

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

			# DELETION
			UpperCAmelCase__					:   str       				=       sgramcounter_rep - cgramcounter_rep
			UpperCAmelCase__					:   Optional[Any]       				=       delgramcounter_rep - rgramcounter
			UpperCAmelCase__					:   List[str]       				=       sgramcounter_rep - rgramcounter
			UpperCAmelCase__					:   str       				=       0
			UpperCAmelCase__					:   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.
			UpperCAmelCase__					:   Union[str, Any]       				=       1
			if len(UpperCamelCase__   ) > 0:
						UpperCAmelCase__					:   Optional[Any]       				=       deltmpscorea / len(UpperCamelCase__   )

			# ADDITION
			UpperCAmelCase__					:   Tuple       				=       set(UpperCamelCase__   ) - set(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       set(UpperCamelCase__   ) & set(UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       set(UpperCamelCase__   ) - set(UpperCamelCase__   )

			UpperCAmelCase__					:   str       				=       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__					:   List[Any]       				=       1
			UpperCAmelCase__					:   List[Any]       				=       1
			if len(UpperCamelCase__   ) > 0:
						UpperCAmelCase__					:   Optional[int]       				=       addtmpscore / len(UpperCamelCase__   )
			if len(UpperCamelCase__   ) > 0:
						UpperCAmelCase__					:   int       				=       addtmpscore / len(UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       0
			if addscore_precision > 0 or addscore_recall > 0:
						UpperCAmelCase__					:   int       				=       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Dict       				=       len(UpperCamelCase__   )

			UpperCAmelCase__					:   Tuple       				=       ssent.split(""" """   )
			UpperCAmelCase__					:   Optional[int]       				=       csent.split(""" """   )
			UpperCAmelCase__					:   Union[str, Any]       				=       []
			UpperCAmelCase__					:   Tuple       				=       []
			UpperCAmelCase__					:   Any       				=       []
			UpperCAmelCase__					:   Optional[Any]       				=       []
			UpperCAmelCase__					:   Any       				=       []
			UpperCAmelCase__					:   Tuple       				=       []

			UpperCAmelCase__					:   Optional[Any]       				=       []
			UpperCAmelCase__					:   Union[str, Any]       				=       []
			UpperCAmelCase__					:   Dict       				=       []
			UpperCAmelCase__					:   List[Any]       				=       []
			for rsent in rsents:
						UpperCAmelCase__					:   List[str]       				=       rsent.split(""" """   )
						UpperCAmelCase__					:   Dict       				=       []
						UpperCAmelCase__					:   str       				=       []
						UpperCAmelCase__					:   Dict       				=       []
						ragramslist.append(UpperCamelCase__   )
						for i in range(0	, len(UpperCamelCase__   ) - 1   ):
									if i < len(UpperCamelCase__   ) - 1:
												UpperCAmelCase__					:   Optional[int]       				=       ragrams[i] + """ """ + ragrams[i + 1]
												ragrams.append(UpperCamelCase__   )
									if i < len(UpperCamelCase__   ) - 2:
												UpperCAmelCase__					:   Union[str, Any]       				=       ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
												ragrams.append(UpperCamelCase__   )
									if i < len(UpperCamelCase__   ) - 3:
												UpperCAmelCase__					:   Any       				=       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__					:   Optional[int]       				=       sagrams[i] + """ """ + sagrams[i + 1]
									sagrams.append(UpperCamelCase__   )
						if i < len(UpperCamelCase__   ) - 2:
									UpperCAmelCase__					:   Dict       				=       sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
									sagrams.append(UpperCamelCase__   )
						if i < len(UpperCamelCase__   ) - 3:
									UpperCAmelCase__					:   str       				=       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__					:   Dict       				=       cagrams[i] + """ """ + cagrams[i + 1]
									cagrams.append(UpperCamelCase__   )
						if i < len(UpperCamelCase__   ) - 2:
									UpperCAmelCase__					:   int       				=       cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
									cagrams.append(UpperCamelCase__   )
						if i < len(UpperCamelCase__   ) - 3:
									UpperCAmelCase__					:   List[Any]       				=       cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
									cagrams.append(UpperCamelCase__   )

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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = True	, UpperCamelCase__ = "13a"	, UpperCamelCase__ = True   ):
			# 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__					:   List[str]       				=       sentence.lower()

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

			if not return_str:
						UpperCAmelCase__					:   List[str]       				=       normalized_sent.split()

			return normalized_sent







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			if not (len(UpperCamelCase__   ) == len(UpperCamelCase__   ) == len(UpperCamelCase__   )):
						raise ValueError("""Sources length must match predictions and references lengths."""   )
			UpperCAmelCase__					:   Optional[int]       				=       0
			for src, pred, refs in zip(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
						sari_score += SARIsent(normalize(UpperCamelCase__   )	, normalize(UpperCamelCase__   )	, [normalize(UpperCamelCase__   ) for sent in refs]   )
			UpperCAmelCase__					:   Optional[int]       				=       sari_score / len(UpperCamelCase__   )
			return 1_0_0 * sari_score







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__="exp"	, UpperCamelCase__=None	, UpperCamelCase__=False	, UpperCamelCase__=False	, UpperCamelCase__=False	, ):
			UpperCAmelCase__					:   int       				=       len(references[0]   )
			if any(len(UpperCamelCase__   ) != references_per_prediction for refs in references   ):
						raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
			UpperCAmelCase__					:   int       				=       [[refs[i] for refs in references] for i in range(UpperCamelCase__   )]
			UpperCAmelCase__					:   int       				=       sacrebleu.corpus_bleu(
			    UpperCamelCase__	, UpperCamelCase__	, smooth_method=UpperCamelCase__	, smooth_value=UpperCamelCase__	, force=UpperCamelCase__	, lowercase=UpperCamelCase__	, use_effective_order=UpperCamelCase__	, )
			return output.score


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




					def 			snake_case__  ( self):
								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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       {}
								result.update({"""sari""": compute_sari(sources=_lowerCamelCase							,							predictions=_lowerCamelCase							,							references=_lowerCamelCase)})
								result.update({"""sacrebleu""": compute_sacrebleu(predictions=_lowerCamelCase							,							references=_lowerCamelCase)})
								result.update({"""exact""": compute_em(predictions=_lowerCamelCase							,							references=_lowerCamelCase)})
								return result