





"""simple docstring"""




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase_	 ="""\
@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},
}
"""

UpperCamelCase_	 ="""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


UpperCamelCase_	 ="""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[str]      =       [rgram for rgrams in rgramslist for rgram in rgrams]
						_UpperCamelCase     :   List[Any]      =       Counter(_lowercase  )

						_UpperCamelCase     :   Tuple      =       Counter(_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       Counter()
						for sgram, scount in sgramcounter.items():
												_UpperCamelCase     :   List[Any]      =       scount * numref

						_UpperCamelCase     :   Union[str, Any]      =       Counter(_lowercase  )
						_UpperCamelCase     :   Any      =       Counter()
						for cgram, ccount in cgramcounter.items():
												_UpperCamelCase     :   str      =       ccount * numref

						# KEEP
						_UpperCamelCase     :   List[str]      =       sgramcounter_rep & cgramcounter_rep
						_UpperCamelCase     :   Optional[int]      =       keepgramcounter_rep & rgramcounter
						_UpperCamelCase     :   Optional[int]      =       sgramcounter_rep & rgramcounter

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

						# DELETION
						_UpperCamelCase     :   int      =       sgramcounter_rep - cgramcounter_rep
						_UpperCamelCase     :   str      =       delgramcounter_rep - rgramcounter
						_UpperCamelCase     :   Optional[Any]      =       sgramcounter_rep - rgramcounter
						_UpperCamelCase     :   List[Any]      =       0
						_UpperCamelCase     :   Optional[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     :   List[Any]      =       1
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   Any      =       deltmpscorea / len(_lowercase  )

						# ADDITION
						_UpperCamelCase     :   Optional[Any]      =       set(_lowercase  ) - set(_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       set(_lowercase  ) & set(_lowercase  )
						_UpperCamelCase     :   Any      =       set(_lowercase  ) - set(_lowercase  )

						_UpperCamelCase     :   Optional[int]      =       0
						for addgram in addgramcountergood:
												addtmpscore += 1

						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						_UpperCamelCase     :   List[Any]      =       1
						_UpperCamelCase     :   List[str]      =       1
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   List[str]      =       addtmpscore / len(_lowercase  )
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   str      =       addtmpscore / len(_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       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 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[Any]      =       len(_lowercase  )

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

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

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





def 			a_							(				_lowercase			,		_lowercase = True			,		_lowercase = "13a"			,		_lowercase = 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     :   Optional[Any]      =       sentence.lower()

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

						if not return_str:
												_UpperCamelCase     :   Any      =       normalized_sent.split()

						return normalized_sent






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






				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
										        {
										            '''sources''': datasets.Value('''string''',					id='''sequence'''				),
										            '''predictions''': datasets.Value('''string''',					id='''sequence'''				),
										            '''references''': datasets.Sequence(datasets.Value('''string''',					id='''sequence'''				),					id='''references'''				),
										        }				),					codebase_urls=[
										        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
										        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
										    ],					reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf'''],					)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str]				)      -> List[str]:




										'''simple docstring'''



										if not (len(lowerCAmelCase__				) == len(lowerCAmelCase__				) == len(lowerCAmelCase__				)):
																raise ValueError('''Sources length must match predictions and references lengths.'''				)
										_UpperCamelCase     :   Union[str, Any]      =       0
										for src, pred, refs in zip(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				):
																sari_score += SARIsent(normalize(lowerCAmelCase__				),					normalize(lowerCAmelCase__				),					[normalize(lowerCAmelCase__				) for sent in refs]				)
										_UpperCamelCase     :   Dict      =       sari_score / len(lowerCAmelCase__				)
										return {"sari": 1_0_0 * sari_score}







"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__a      			= logging.get_logger(__name__)

__a      			= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a      			= {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a      			= {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__a      			= {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE              =   PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE              =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE              =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
							SCREAMING_SNAKE_CASE              =   DebertaTokenizer






							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Dict="replace"		,			lowerCamelCase_			:  List[Any]="[CLS]"		,			lowerCamelCase_			:  Optional[Any]="[SEP]"		,			lowerCamelCase_			:  List[Any]="[SEP]"		,			lowerCamelCase_			:  str="[CLS]"		,			lowerCamelCase_			:  List[str]="[UNK]"		,			lowerCamelCase_			:  Any="[PAD]"		,			lowerCamelCase_			:  Tuple="[MASK]"		,			lowerCamelCase_			:  List[str]=False		,			**lowerCamelCase_			:  List[str]		,			):
													super().__init__(
													    lowerCamelCase_		,			lowerCamelCase_		,			tokenizer_file=lowerCamelCase_		,			errors=lowerCamelCase_		,			bos_token=lowerCamelCase_		,			eos_token=lowerCamelCase_		,			unk_token=lowerCamelCase_		,			sep_token=lowerCamelCase_		,			cls_token=lowerCamelCase_		,			pad_token=lowerCamelCase_		,			mask_token=lowerCamelCase_		,			add_prefix_space=lowerCamelCase_		,			**lowerCamelCase_		,			)
													UpperCAmelCase__												=     kwargs.pop('''add_bos_token'''		,			lowerCamelCase_					)

													UpperCAmelCase__												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''		,			lowerCamelCase_					) != add_prefix_space:
																			UpperCAmelCase__												=     getattr(lowerCamelCase_		,			pre_tok_state.pop('''type'''					)					)
																			UpperCAmelCase__												=     add_prefix_space
																			UpperCAmelCase__												=     pre_tok_class(**lowerCamelCase_					)

													UpperCAmelCase__												=     add_prefix_space






							@property
							def        lowerCAmelCase  (    self			:  int					):
													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''					)
																			return None
													return str(self._mask_token					)






							@mask_token.setter
							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     AddedToken(lowerCamelCase_		,			lstrip=lowerCamelCase_		,			rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_		,			lowerCamelCase_					) else value
													UpperCAmelCase__												=     value






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]
													UpperCAmelCase__												=     [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													UpperCAmelCase__												=     [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  Optional[int]		,			**lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  str		,			**lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[str] = None					):
													UpperCAmelCase__												=     self._tokenizer.model.save(lowerCamelCase_		,			name=lowerCamelCase_					)
													return tuple(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  "Conversation"					):
													UpperCAmelCase__												=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		,			add_special_tokens=lowerCamelCase_					) + [self.eos_token_id]					)

													if len(lowerCamelCase_					) > self.model_max_length:
																			UpperCAmelCase__												=     input_ids[-self.model_max_length :]
													return input_ids