



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__ =						"\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"

UpperCamelCase__ =						"\\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"


UpperCamelCase__ =						"\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__    (    lowercase__   ) ->      Dict:

						def remove_articles(lowercase__   ):
												__lowercase					=				re.compile(r"""\b(a|an|the)\b"""						,       re.UNICODE   )
												return re.sub(lowercase__						,       """ """						,       lowercase__   )

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(lowercase__   )   )   )   )






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      List[Any]:
						return int(normalize_answer(lowercase__   ) == normalize_answer(lowercase__   )   )






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						__lowercase					=				[any(compute_exact(lowercase__						,       lowercase__   ) for ref in refs   ) for pred, refs in zip(lowercase__						,       lowercase__   )]
						return (sum(lowercase__   ) / len(lowercase__   )) * 100






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   ) ->      Tuple:
						__lowercase					=				[rgram for rgrams in rgramslist for rgram in rgrams]
						__lowercase					=				Counter(lowercase__   )

						__lowercase					=				Counter(lowercase__   )
						__lowercase					=				Counter()
						for sgram, scount in sgramcounter.items():
												__lowercase					=				scount * numref

						__lowercase					=				Counter(lowercase__   )
						__lowercase					=				Counter()
						for cgram, ccount in cgramcounter.items():
												__lowercase					=				ccount * numref

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

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

						# DELETION
						__lowercase					=				sgramcounter_rep - cgramcounter_rep
						__lowercase					=				delgramcounter_rep - rgramcounter
						__lowercase					=				sgramcounter_rep - rgramcounter
						__lowercase					=				0
						__lowercase					=				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.
						__lowercase					=				1
						if len(lowercase__   ) > 0:
												__lowercase					=				deltmpscorea / len(lowercase__   )

						# ADDITION
						__lowercase					=				set(lowercase__   ) - set(lowercase__   )
						__lowercase					=				set(lowercase__   ) & set(lowercase__   )
						__lowercase					=				set(lowercase__   ) - set(lowercase__   )

						__lowercase					=				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.
						__lowercase					=				1
						__lowercase					=				1
						if len(lowercase__   ) > 0:
												__lowercase					=				addtmpscore / len(lowercase__   )
						if len(lowercase__   ) > 0:
												__lowercase					=				addtmpscore / len(lowercase__   )
						__lowercase					=				0
						if addscore_precision > 0 or addscore_recall > 0:
												__lowercase					=				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      Tuple:
						__lowercase					=				len(lowercase__   )

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

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

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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__ = True						,       lowercase__ = "13a"						,       lowercase__ = True   ) ->      List[str]:
						# 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:
												__lowercase					=				sentence.lower()

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

						if not return_str:
												__lowercase					=				normalized_sent.split()

						return normalized_sent






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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__="exp"						,       lowercase__=None						,       lowercase__=False						,       lowercase__=False						,       lowercase__=False						,       ) ->      List[Any]:
						__lowercase					=				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"""   )
						__lowercase					=				[[refs[i] for refs in references] for i in range(lowercase__   )]
						__lowercase					=				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 			_lowerCAmelCase   ( datasets.Metric			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					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 		snake_case__			(				self	:       str			,     lowercase	:       Dict			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				{}
									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










from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ =						logging.get_logger(__name__)

# TODO Update this
UpperCamelCase__ =						{
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""esm"""





			def __init__(				self	:       Any			,     lowercase	:       Optional[Any]=None			,     lowercase	:       Optional[int]=None			,     lowercase	:       List[Any]=None			,     lowercase	:       Optional[int]=768			,     lowercase	:       str=12			,     lowercase	:       Union[str, Any]=12			,     lowercase	:       Dict=3_072			,     lowercase	:       Optional[int]=0.1			,     lowercase	:       str=0.1			,     lowercase	:       Dict=1_026			,     lowercase	:       Tuple=0.02			,     lowercase	:       str=1E-1_2			,     lowercase	:       Dict="absolute"			,     lowercase	:       Optional[Any]=True			,     lowercase	:       int=None			,     lowercase	:       int=False			,     lowercase	:       List[str]=False			,     lowercase	:       Tuple=None			,     lowercase	:       Tuple=None			,     **lowercase	:       Union[str, Any]			,     )						->					int:
									"""simple docstring"""







									super().__init__(pad_token_id=lowercase			,     mask_token_id=lowercase			,     **lowercase   )

									__lowercase					=				vocab_size
									__lowercase					=				hidden_size
									__lowercase					=				num_hidden_layers
									__lowercase					=				num_attention_heads
									__lowercase					=				intermediate_size
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				max_position_embeddings
									__lowercase					=				initializer_range
									__lowercase					=				layer_norm_eps
									__lowercase					=				position_embedding_type
									__lowercase					=				use_cache
									__lowercase					=				emb_layer_norm_before
									__lowercase					=				token_dropout
									__lowercase					=				is_folding_model
									if is_folding_model:
															if esmfold_config is None:
																					logger.info("""No esmfold_config supplied for folding model, using default values."""   )
																					__lowercase					=				EsmFoldConfig()
															elif isinstance(lowercase			,     lowercase   ):
																					__lowercase					=				EsmFoldConfig(**lowercase   )
															__lowercase					=				esmfold_config
															if vocab_list is None:
																					logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""   )
																					__lowercase					=				get_default_vocab_list()
															else:
																					__lowercase					=				vocab_list
									else:
															__lowercase					=				None
															__lowercase					=				None
									if self.esmfold_config is not None and getattr(self.esmfold_config			,     """use_esm_attn_map"""			,     lowercase   ):
															raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""   )







			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				super().to_dict()
									if isinstance(self.esmfold_config			,     lowercase   ):
															__lowercase					=				self.esmfold_config.to_dict()
									return output





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  str      =			None
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False
			lowercase__      :  bool      =			False
			lowercase__      :  bool      =			False
			lowercase__      :  float      =			0

			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False

			lowercase__      :  int      =			128
			lowercase__      :  "TrunkConfig"      =			None





			def 		snake_case__			(				self	:       List[str]   )						->					Any:
									"""simple docstring"""







									if self.trunk is None:
															__lowercase					=				TrunkConfig()
									elif isinstance(self.trunk			,     lowercase   ):
															__lowercase					=				TrunkConfig(**self.trunk   )







			def 		snake_case__			(				self	:       Dict   )						->					Any:
									"""simple docstring"""







									__lowercase					=				asdict(self   )
									__lowercase					=				self.trunk.to_dict()
									return output





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  int      =			48
			lowercase__      :  int      =			1_024
			lowercase__      :  int      =			128
			lowercase__      :  int      =			32
			lowercase__      :  int      =			32
			lowercase__      :  int      =			32
			lowercase__      :  float      =			0
			lowercase__      :  float      =			0
			lowercase__      :  bool      =			False
			lowercase__      :  int      =			4
			lowercase__      :  Optional[int]      =			128
			lowercase__      :  "StructureModuleConfig"      =			None





			def 		snake_case__			(				self	:       Tuple   )						->					str:
									"""simple docstring"""







									if self.structure_module is None:
															__lowercase					=				StructureModuleConfig()
									elif isinstance(self.structure_module			,     lowercase   ):
															__lowercase					=				StructureModuleConfig(**self.structure_module   )

									if self.max_recycles <= 0:
															raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}."   )
									if self.sequence_state_dim % self.sequence_state_dim != 0:
															raise ValueError(
															    """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
															    F" {self.sequence_state_dim} and {self.sequence_state_dim}."   )
									if self.pairwise_state_dim % self.pairwise_state_dim != 0:
															raise ValueError(
															    """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
															    F" {self.pairwise_state_dim} and {self.pairwise_state_dim}."   )

									__lowercase					=				self.sequence_state_dim // self.sequence_head_width
									__lowercase					=				self.pairwise_state_dim // self.pairwise_head_width

									if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
															raise ValueError(
															    """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
															    F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."   )
									if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
															raise ValueError(
															    """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
															    F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."   )
									if self.pairwise_state_dim % 2 != 0:
															raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."   )

									if self.dropout >= 0.4:
															raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}."   )







			def 		snake_case__			(				self	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				asdict(self   )
									__lowercase					=				self.structure_module.to_dict()
									return output





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  int      =			384
			lowercase__      :  int      =			128
			lowercase__      :  int      =			16
			lowercase__      :  int      =			128
			lowercase__      :  int      =			12
			lowercase__      :  int      =			4
			lowercase__      :  int      =			8
			lowercase__      :  float      =			0.1
			lowercase__      :  int      =			8
			lowercase__      :  int      =			1
			lowercase__      :  int      =			2
			lowercase__      :  int      =			7
			lowercase__      :  int      =			10
			lowercase__      :  float      =			1E-8
			lowercase__      :  float      =			1E5





			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									return asdict(self   )






def  UpperCAmelCase__    (    ) ->      List[Any]:
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)





