


'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowerCAmelCase     :							Tuple    =				"""
@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\",
}
"""

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


_lowerCAmelCase     :							Union[str, Any]    =				"""
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			(			snake_case__    :			Any				):

	def remove_articles(snake_case__    :			Optional[Any]				):
		snake_case__			:			Any             =		re.compile(R'''\b(a|an|the)\b'''						,						re.UNICODE				)
		return re.sub(__lowercase						,						''' '''						,						__lowercase				)

	def white_space_fix(snake_case__    :			List[Any]				):
		return " ".join(text.split()				)

	def remove_punc(snake_case__    :			str				):
		snake_case__			:			Union[str, Any]             =		set(string.punctuation				)
		return "".join(ch for ch in text if ch not in exclude				)

	def lower(snake_case__    :			int				):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(__lowercase				)				)				)				)






def 					_A			(			snake_case__    :			Union[str, Any]						,						snake_case__    :			int				):
	return int(normalize_answer(__lowercase				) == normalize_answer(__lowercase				)				)






def 					_A			(			snake_case__    :			List[Any]						,						snake_case__    :			Tuple				):
	snake_case__			:			Any             =		[any(compute_exact(__lowercase						,						__lowercase				) for ref in refs				) for pred, refs in zip(__lowercase						,						__lowercase				)]
	return (sum(__lowercase				) / len(__lowercase				)) * 1_00






def 					_A			(			snake_case__    :			int						,						snake_case__    :			Any						,						snake_case__    :			Union[str, Any]						,						snake_case__    :			Optional[int]				):
	snake_case__			:			Optional[Any]             =		[rgram for rgrams in rgramslist for rgram in rgrams]
	snake_case__			:			Any             =		Counter(__lowercase				)

	snake_case__			:			str             =		Counter(__lowercase				)
	snake_case__			:			Tuple             =		Counter()
	for sgram, scount in sgramcounter.items():
		snake_case__			:			List[str]             =		scount * numref

	snake_case__			:			Any             =		Counter(__lowercase				)
	snake_case__			:			Dict             =		Counter()
	for cgram, ccount in cgramcounter.items():
		snake_case__			:			Optional[Any]             =		ccount * numref

	# KEEP
	snake_case__			:			Tuple             =		sgramcounter_rep & cgramcounter_rep
	snake_case__			:			List[Any]             =		keepgramcounter_rep & rgramcounter
	snake_case__			:			Optional[Any]             =		sgramcounter_rep & rgramcounter

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

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

	# ADDITION
	snake_case__			:			Union[str, Any]             =		set(__lowercase				) - set(__lowercase				)
	snake_case__			:			Dict             =		set(__lowercase				) & set(__lowercase				)
	snake_case__			:			List[Any]             =		set(__lowercase				) - set(__lowercase				)

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

	return (keepscore, delscore_precision, addscore)






def 					_A			(			snake_case__    :			Any						,						snake_case__    :			int						,						snake_case__    :			Any				):
	snake_case__			:			Optional[Any]             =		len(__lowercase				)

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

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

	for i in range(0						,						len(__lowercase				) - 1				):
		if i < len(__lowercase				) - 1:
			snake_case__			:			Union[str, Any]             =		sagrams[i] + """ """ + sagrams[i + 1]
			sagrams.append(__lowercase				)
		if i < len(__lowercase				) - 2:
			snake_case__			:			Optional[int]             =		sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
			sagrams.append(__lowercase				)
		if i < len(__lowercase				) - 3:
			snake_case__			:			List[str]             =		sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
			sagrams.append(__lowercase				)

	for i in range(0						,						len(__lowercase				) - 1				):
		if i < len(__lowercase				) - 1:
			snake_case__			:			List[Any]             =		cagrams[i] + """ """ + cagrams[i + 1]
			cagrams.append(__lowercase				)
		if i < len(__lowercase				) - 2:
			snake_case__			:			List[Any]             =		cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
			cagrams.append(__lowercase				)
		if i < len(__lowercase				) - 3:
			snake_case__			:			Optional[int]             =		cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
			cagrams.append(__lowercase				)

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






def 					_A			(			snake_case__    :			Union[str, Any]						,						snake_case__    :			Union[str, Any] = True						,						snake_case__    :			int = "13a"						,						snake_case__    :			Optional[int] = 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:
		snake_case__			:			Any             =		sentence.lower()

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

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

	return normalized_sent






def 					_A			(			snake_case__    :			Any						,						snake_case__    :			Optional[int]						,						snake_case__    :			List[str]				):
	if not (len(__lowercase				) == len(__lowercase				) == len(__lowercase				)):
		raise ValueError('''Sources length must match predictions and references lengths.'''				)
	snake_case__			:			List[Any]             =		0
	for src, pred, refs in zip(__lowercase						,						__lowercase						,						__lowercase				):
		sari_score += SARIsent(normalize(__lowercase				)						,						normalize(__lowercase				)						,						[normalize(__lowercase				) for sent in refs]				)
	snake_case__			:			Any             =		sari_score / len(__lowercase				)
	return 1_00 * sari_score






def 					_A			(			snake_case__    :			Optional[Any]						,						snake_case__    :			List[Any]						,						snake_case__    :			List[str]="exp"						,						snake_case__    :			str=None						,						snake_case__    :			Dict=False						,						snake_case__    :			Optional[int]=False						,						snake_case__    :			Any=False						,						):
	snake_case__			:			Tuple             =		len(references[0]				)
	if any(len(__lowercase				) != references_per_prediction for refs in references				):
		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''				)
	snake_case__			:			List[str]             =		[[refs[i] for refs in references] for i in range(__lowercase				)]
	snake_case__			:			Optional[int]             =		sacrebleu.corpus_bleu(
	    __lowercase						,						__lowercase						,						smooth_method=__lowercase						,						smooth_value=__lowercase						,						force=__lowercase						,						lowercase=__lowercase						,						use_effective_order=__lowercase						,						)
	return output.score





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

				"""simple docstring"""




				def    lowercase__							(	self )    ->      List[Any]:







					"""simple docstring"""






					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       ,    lowerCamelCase       ,    lowerCamelCase       ,    lowerCamelCase )    ->      Tuple:







					"""simple docstring"""






					snake_case__			:			Dict             =		{}
					result.update({'''sari''': compute_sari(sources=__a       ,    predictions=__a       ,    references=__a )} )
					result.update({'''sacrebleu''': compute_sacrebleu(predictions=__a       ,    references=__a )} )
					result.update({'''exact''': compute_em(predictions=__a       ,    references=__a )} )
					return result











'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Dict      =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					"""deta"""
     lowerCAmelCase_  :     Any          =					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(				self							,__a=None							,__a=900							,__a=2_048							,__a=6							,__a=2_048							,__a=8							,__a=6							,__a=1_024							,__a=8							,__a=0.0							,__a=True							,__a="relu"							,__a=256							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=1.0							,__a=True							,__a=False							,__a="sine"							,__a=5							,__a=4							,__a=4							,__a=True							,__a=300							,__a=True							,__a=True							,__a=1							,__a=5							,__a=2							,__a=1							,__a=1							,__a=5							,__a=2							,__a=0.1							,__a=0.25							,**__a							,)      ->				int:
          if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               snake_case			:      int        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
          else:
               if isinstance(__a							,__a							):
                    snake_case			:      str        =			backbone_config.pop("""model_type"""							)
                    snake_case			:      Optional[Any]        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[Any]        =			config_class.from_dict(__a							)

          snake_case			:      Any        =			backbone_config
          snake_case			:      Optional[Any]        =			num_queries
          snake_case			:      Dict        =			max_position_embeddings
          snake_case			:      int        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			encoder_attention_heads
          snake_case			:      int        =			decoder_ffn_dim
          snake_case			:      List[Any]        =			decoder_layers
          snake_case			:      Dict        =			decoder_attention_heads
          snake_case			:      Dict        =			dropout
          snake_case			:      Dict        =			attention_dropout
          snake_case			:      List[Any]        =			activation_dropout
          snake_case			:      Dict        =			activation_function
          snake_case			:      List[Any]        =			init_std
          snake_case			:      Any        =			init_xavier_std
          snake_case			:      List[Any]        =			encoder_layerdrop
          snake_case			:      Optional[Any]        =			auxiliary_loss
          snake_case			:      Optional[int]        =			position_embedding_type
          # deformable attributes
          snake_case			:      Tuple        =			num_feature_levels
          snake_case			:      List[Any]        =			encoder_n_points
          snake_case			:      Dict        =			decoder_n_points
          snake_case			:      int        =			two_stage
          snake_case			:      Any        =			two_stage_num_proposals
          snake_case			:      List[str]        =			with_box_refine
          snake_case			:      Tuple        =			assign_first_stage
          if two_stage is True and with_box_refine is False:
               raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
          # Hungarian matcher
          snake_case			:      Optional[int]        =			class_cost
          snake_case			:      int        =			bbox_cost
          snake_case			:      Optional[int]        =			giou_cost
          # Loss coefficients
          snake_case			:      str        =			mask_loss_coefficient
          snake_case			:      Any        =			dice_loss_coefficient
          snake_case			:      List[Any]        =			bbox_loss_coefficient
          snake_case			:      List[Any]        =			giou_loss_coefficient
          snake_case			:      List[str]        =			eos_coefficient
          snake_case			:      Optional[Any]        =			focal_alpha
          super().__init__(is_encoder_decoder=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[str]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      List[Any]        =			self.__class__.model_type
          return output



