





"""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}





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
	if is_torch_available():
		import torch
	if is_tf_available():
		import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__a							  =						logging.get_logger(__name__)

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

__a							  =						{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

__a							  =						{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}

@lru_cache()
def    UpperCamelCase_ (	)     ->List[Any]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Union[str, Any]:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask"]







	def __init__(      self				:			Optional[int]							,			snake_case__				:			List[Any]							,			snake_case__				:			int							,			snake_case__				:			Tuple="replace"							,			snake_case__				:			List[str]="<|endoftext|>"							,			snake_case__				:			Tuple="<|endoftext|>"							,			snake_case__				:			Union[str, Any]="<|endoftext|>"							,			snake_case__				:			int=None							,			snake_case__				:			Optional[int]=False							,			snake_case__				:			Tuple=False							,			**snake_case__				:			Optional[Any]							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token
			super().__init__(
			    errors=snake_case__							,			unk_token=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			pad_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			Any							,			snake_case__				:			Optional[Any]=None ):
			"""simple docstring"""





			if self.add_bos_token:
					A				=[self.bos_token_id]
			else:
					A				=[]

			A				=bos_token_ids + token_ids_a

			if token_ids_a is None:
					return output

			return output + bos_token_ids + token_ids_a







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".join(
					    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			Tuple							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			str							,			snake_case__				:			Dict ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







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





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			List[str]							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as f:
					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__ ) + "\n" )

			A				=0
			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as writer:
					writer.write("#version: 0.2\n" )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda snake_case__ : kv[1] ):
							if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			str							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[int]=False							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if is_split_into_words or add_prefix_space:
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			str							,			snake_case__				:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,			snake_case__				:			bool = False							,			snake_case__				:			bool = None							,			snake_case__				:			Optional[List[str]] = None							,			**snake_case__				:			Union[str, Any]							,			):
			"""simple docstring"""





			A				=super()._decode(
			    token_ids=snake_case__							,			skip_special_tokens=snake_case__							,			clean_up_tokenization_spaces=snake_case__							,			**snake_case__							,			)

			if truncate_before_pattern is not None and len(snake_case__ ) > 0:
					A				=self.truncate(snake_case__							,			snake_case__ )

			return decoded_text







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





			def find_re(snake_case__				:			Dict							,			snake_case__				:			Tuple							,			snake_case__				:			int ):
					A				=pattern.search(snake_case__							,			snake_case__ )
					return m.start() if m else -1

			A				=[re.compile(snake_case__							,			re.MULTILINE ) for pattern in truncate_before_pattern]

			A				=list(re.finditer("^print"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: prints[1].start()]

			A				=list(re.finditer("^def"							,			snake_case__							,			re.MULTILINE ) )

			if len(snake_case__ ) > 1:
					A				=completion[: defs[1].start()]

			A				=0

			A				=[
			    pos for pos in [find_re(snake_case__							,			snake_case__							,			snake_case__ ) for terminal in terminals] if pos != -1
			]

			if len(snake_case__ ) > 0:
					return completion[: min(snake_case__ )]
			else:
					return completion




