code: 
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  __lowerCamelCase       :       List[str]				   =			"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"
  
  __lowerCamelCase       :       Tuple				   =			"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"
  
  
  __lowerCamelCase       :       int				   =			"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							str:
  				A__     :							int   				=[rgram for rgrams in rgramslist for rgram in rgrams]
  				A__     :							Any   				=Counter(snake_case_	)
  
  				A__     :							Optional[Any]   				=Counter(snake_case_	)
  				A__     :							List[str]   				=Counter()
  				for sgram, scount in sgramcounter.items():
  								A__     :							Any   				=scount * numref
  
  				A__     :							int   				=Counter(snake_case_	)
  				A__     :							str   				=Counter()
  				for cgram, ccount in cgramcounter.items():
  								A__     :							Union[str, Any]   				=ccount * numref
  
  				# KEEP
  				A__     :							Tuple   				=sgramcounter_rep & cgramcounter_rep
  				A__     :							Tuple   				=keepgramcounter_rep & rgramcounter
  				A__     :							Any   				=sgramcounter_rep & rgramcounter
  
  				A__     :							Optional[int]   				=0
  				A__     :							List[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.
  				A__     :							Tuple   				=1
  				A__     :							str   				=1
  				if len(snake_case_	) > 0:
  								A__     :							Optional[Any]   				=keeptmpscorea / len(snake_case_	)
  				if len(snake_case_	) > 0:
  								# Fix an alleged bug [2] in the keep score computation.
  								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  								A__     :							str   				=keeptmpscorea / sum(keepgramcounterall_rep.values()	)
  				A__     :							List[Any]   				=0
  				if keepscore_precision > 0 or keepscore_recall > 0:
  								A__     :							List[Any]   				=2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
  				# DELETION
  				A__     :							int   				=sgramcounter_rep - cgramcounter_rep
  				A__     :							Dict   				=delgramcounter_rep - rgramcounter
  				A__     :							int   				=sgramcounter_rep - rgramcounter
  				A__     :							Optional[int]   				=0
  				A__     :							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.
  				A__     :							int   				=1
  				if len(snake_case_	) > 0:
  								A__     :							List[str]   				=deltmpscorea / len(snake_case_	)
  
  				# ADDITION
  				A__     :							Dict   				=set(snake_case_	) - set(snake_case_	)
  				A__     :							Optional[Any]   				=set(snake_case_	) & set(snake_case_	)
  				A__     :							Any   				=set(snake_case_	) - set(snake_case_	)
  
  				A__     :							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.
  				A__     :							List[str]   				=1
  				A__     :							Tuple   				=1
  				if len(snake_case_	) > 0:
  								A__     :							Dict   				=addtmpscore / len(snake_case_	)
  				if len(snake_case_	) > 0:
  								A__     :							Tuple   				=addtmpscore / len(snake_case_	)
  				A__     :							Union[str, Any]   				=0
  				if addscore_precision > 0 or addscore_recall > 0:
  								A__     :							List[Any]   				=2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
  				return (keepscore, delscore_precision, addscore)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							List[str]:
  				A__     :							Tuple   				=len(snake_case_	)
  
  				A__     :							Optional[int]   				=ssent.split(''' '''	)
  				A__     :							Dict   				=csent.split(''' '''	)
  				A__     :							str   				=[]
  				A__     :							Dict   				=[]
  				A__     :							str   				=[]
  				A__     :							Optional[int]   				=[]
  				A__     :							Any   				=[]
  				A__     :							Union[str, Any]   				=[]
  
  				A__     :							int   				=[]
  				A__     :							int   				=[]
  				A__     :							List[str]   				=[]
  				A__     :							Optional[Any]   				=[]
  				for rsent in rsents:
  								A__     :							Dict   				=rsent.split(''' '''	)
  								A__     :							Union[str, Any]   				=[]
  								A__     :							Union[str, Any]   				=[]
  								A__     :							List[str]   				=[]
  								ragramslist.append(snake_case_	)
  								for i in range(0,	len(snake_case_	) - 1	):
  												if i < len(snake_case_	) - 1:
  																A__     :							Optional[int]   				=ragrams[i] + ''' ''' + ragrams[i + 1]
  																ragrams.append(snake_case_	)
  												if i < len(snake_case_	) - 2:
  																A__     :							Tuple   				=ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
  																ragrams.append(snake_case_	)
  												if i < len(snake_case_	) - 3:
  																A__     :							Dict   				=ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
  																ragrams.append(snake_case_	)
  								ragramslist.append(snake_case_	)
  								ragramslist.append(snake_case_	)
  								ragramslist.append(snake_case_	)
  
  				for i in range(0,	len(snake_case_	) - 1	):
  								if i < len(snake_case_	) - 1:
  												A__     :							Dict   				=sagrams[i] + ''' ''' + sagrams[i + 1]
  												sagrams.append(snake_case_	)
  								if i < len(snake_case_	) - 2:
  												A__     :							Union[str, Any]   				=sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
  												sagrams.append(snake_case_	)
  								if i < len(snake_case_	) - 3:
  												A__     :							Optional[int]   				=sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
  												sagrams.append(snake_case_	)
  
  				for i in range(0,	len(snake_case_	) - 1	):
  								if i < len(snake_case_	) - 1:
  												A__     :							List[str]   				=cagrams[i] + ''' ''' + cagrams[i + 1]
  												cagrams.append(snake_case_	)
  								if i < len(snake_case_	) - 2:
  												A__     :							Dict   				=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
  												cagrams.append(snake_case_	)
  								if i < len(snake_case_	) - 3:
  												A__     :							List[Any]   				=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
  												cagrams.append(snake_case_	)
  
  				((A__)				,  (A__)				,  (A__))     :							List[str]   				=SARIngram(snake_case_,	snake_case_,	snake_case_,	snake_case_	)
  				((A__)				,  (A__)				,  (A__))     :							List[Any]   				=SARIngram(snake_case_,	snake_case_,	snake_case_,	snake_case_	)
  				((A__)				,  (A__)				,  (A__))     :							List[Any]   				=SARIngram(snake_case_,	snake_case_,	snake_case_,	snake_case_	)
  				((A__)				,  (A__)				,  (A__))     :							Optional[Any]   				=SARIngram(snake_case_,	snake_case_,	snake_case_,	snake_case_	)
  				A__     :							List[str]   				=sum([keepascore, keepascore, keepascore, keepascore]	) / 4
  				A__     :							Dict   				=sum([delascore, delascore, delascore, delascore]	) / 4
  				A__     :							Union[str, Any]   				=sum([addascore, addascore, addascore, addascore]	) / 4
  				A__     :							Optional[int]   				=(avgkeepscore + avgdelscore + avgaddscore) / 3
  				return finalscore
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_ = True,	snake_case_ = "13a",	snake_case_ = True	)			->							List[Any]:
  				# 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:
  								A__     :							Any   				=sentence.lower()
  
  				if tokenizer in ["13a", "intl"]:
  								if version.parse(sacrebleu.__version__	).major >= 2:
  												A__     :							List[str]   				=sacrebleu.metrics.bleu._get_tokenizer(snake_case_	)()(snake_case_	)
  								else:
  												A__     :							str   				=sacrebleu.TOKENIZERS[tokenizer]()(snake_case_	)
  				elif tokenizer == "moses":
  								A__     :							List[Any]   				=sacremoses.MosesTokenizer().tokenize(snake_case_,	return_str=snake_case_,	escape=snake_case_	)
  				elif tokenizer == "penn":
  								A__     :							Union[str, Any]   				=sacremoses.MosesTokenizer().penn_tokenize(snake_case_,	return_str=snake_case_	)
  				else:
  								A__     :							List[Any]   				=sentence
  
  				if not return_str:
  								A__     :							Any   				=normalized_sent.split()
  
  				return normalized_sent
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,_KWARGS_DESCRIPTION   )
  class 			a      (      datasets.Metric   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						if not (len(__UpperCamelCase							) == len(__UpperCamelCase							) == len(__UpperCamelCase							)):
  										raise ValueError('''Sources length must match predictions and references lengths.'''							)
  						A__     :							List[str]   				=0
  						for src, pred, refs in zip(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							):
  										sari_score += SARIsent(normalize(__UpperCamelCase							)	,     normalize(__UpperCamelCase							)	,     [normalize(__UpperCamelCase							) for sent in refs]							)
  						A__     :							Tuple   				=sari_score / len(__UpperCamelCase							)
  						return {"sari": 1_00 * sari_score}
  
  
  
  
style_context: 
  
  
  import argparse
  import collections
  from pathlib import Path
  
  import torch
  from torch.serialization import default_restore_location
  
  from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
  
  
  __lowerCamelCase       :       Optional[Any]				   =			collections.namedtuple(
      "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
  )
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							CheckpointState:
  				print(f'Reading saved model from {model_file}'	)
  				A__     :							Optional[int]   				=torch.load(snake_case_,	map_location=lambda snake_case_,	snake_case_	: default_restore_location(snake_case_,	'''cpu'''	)	)
  				return CheckpointState(**snake_case_	)
  
  
  
  class 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=src_file
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						raise NotImplementedError
  
  
  
  
  
  
  		@staticmethod
  		def       lowerCAmelCase_  (  __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			"DPRState":
  						'''simple docstring'''
  
  
  						if comp_type.startswith('''c'''							):
  										return DPRContextEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						if comp_type.startswith('''q'''							):
  										return DPRQuestionEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						if comp_type.startswith('''r'''							):
  										return DPRReaderState(*__UpperCamelCase	,     **__UpperCamelCase							)
  						else:
  										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR biencoder from {self.src_file}'							)
  						A__     :							Optional[int]   				=load_states_from_checkpoint(self.src_file							)
  						A__				,  A__     :							List[str]   				=model.ctx_encoder, '''ctx_model.'''
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[int]   				={'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith(__UpperCamelCase							):
  														A__     :							int   				=key[len(__UpperCamelCase							) :]
  														if not key.startswith('''encode_proj.'''							):
  																		A__     :							Any   				='''bert_model.''' + key
  														A__     :							List[str]   				=value
  						encoder.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR biencoder from {self.src_file}'							)
  						A__     :							List[str]   				=load_states_from_checkpoint(self.src_file							)
  						A__				,  A__     :							Optional[int]   				=model.question_encoder, '''question_model.'''
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[Any]   				={'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith(__UpperCamelCase							):
  														A__     :							List[Any]   				=key[len(__UpperCamelCase							) :]
  														if not key.startswith('''encode_proj.'''							):
  																		A__     :							Tuple   				='''bert_model.''' + key
  														A__     :							str   				=value
  						encoder.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
  						print(F'Loading DPR reader from {self.src_file}'							)
  						A__     :							Dict   				=load_states_from_checkpoint(self.src_file							)
  						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  						A__     :							Optional[Any]   				={
  						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
  						}
  						for key, value in saved_state.model_dict.items():
  										if key.startswith('''encoder.'''							) and not key.startswith('''encoder.encode_proj'''							):
  														A__     :							List[Any]   				='''encoder.bert_model.''' + key[len('''encoder.'''							) :]
  										A__     :							Optional[int]   				=value
  						model.span_predictor.load_state_dict(__UpperCamelCase							)
  						return model
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
  				A__     :							Optional[int]   				=Path(snake_case_	)
  				dest_dir.mkdir(exist_ok=snake_case_	)
  
  				A__     :							Tuple   				=DPRState.from_type(snake_case_,	src_file=snake_case_	)
  				A__     :							str   				=dpr_state.load_dpr_model()
  				model.save_pretrained(snake_case_	)
  				model.from_pretrained(snake_case_	)  # sanity check
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Optional[Any]				   =			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  						)
  						parser.add_argument(
  						    "--src",
  						    type=str,
  						    help=(
  						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
  						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
  						        " 'retriever' checkpoints."
  						    ),
  						)
  						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
  						__lowerCamelCase       :       Tuple				   =			parser.parse_args()
  
  						__lowerCamelCase       :       Optional[Any]				   =			Path(args.src)
  						__lowerCamelCase       :       Optional[int]				   =			F"converted-{src_file.name}" if args.dest is None else args.dest
  						__lowerCamelCase       :       Optional[Any]				   =			Path(dest_dir)
  						assert src_file.exists()
  						assert (
  						    args.type is not None
  						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  						convert(args.type, src_file, dest_dir)
  
  
  
  