






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase						= '\\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						= '\\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						= '\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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		List[Any]:







							"""simple docstring"""





							_a  :    int			=						[rgram for rgrams in rgramslist for rgram in rgrams]
							_a  :    List[str]			=						Counter(UpperCAmelCase     )

							_a  :    Union[str, Any]			=						Counter(UpperCAmelCase     )
							_a  :    Optional[Any]			=						Counter()
							for sgram, scount in sgramcounter.items():
														_a  :    Optional[Any]			=						scount * numref

							_a  :    List[Any]			=						Counter(UpperCAmelCase     )
							_a  :    Optional[Any]			=						Counter()
							for cgram, ccount in cgramcounter.items():
														_a  :    List[str]			=						ccount * numref

							# KEEP
							_a  :    Optional[Any]			=						sgramcounter_rep & cgramcounter_rep
							_a  :    Union[str, Any]			=						keepgramcounter_rep & rgramcounter
							_a  :    Dict			=						sgramcounter_rep & rgramcounter

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

							# DELETION
							_a  :    List[Any]			=						sgramcounter_rep - cgramcounter_rep
							_a  :    Optional[int]			=						delgramcounter_rep - rgramcounter
							_a  :    Union[str, Any]			=						sgramcounter_rep - rgramcounter
							_a  :    int			=						0
							_a  :    Dict			=						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(UpperCAmelCase     ) > 0:
														_a  :    Union[str, Any]			=						deltmpscorea / len(UpperCAmelCase     )

							# ADDITION
							_a  :    List[Any]			=						set(UpperCAmelCase     ) - set(UpperCAmelCase     )
							_a  :    Tuple			=						set(UpperCAmelCase     ) & set(UpperCAmelCase     )
							_a  :    int			=						set(UpperCAmelCase     ) - set(UpperCAmelCase     )

							_a  :    Dict			=						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  :    Optional[Any]			=						1
							_a  :    Tuple			=						1
							if len(UpperCAmelCase     ) > 0:
														_a  :    Tuple			=						addtmpscore / len(UpperCAmelCase     )
							if len(UpperCAmelCase     ) > 0:
														_a  :    Dict			=						addtmpscore / len(UpperCAmelCase     )
							_a  :    Union[str, Any]			=						0
							if addscore_precision > 0 or addscore_recall > 0:
														_a  :    Union[str, Any]			=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		int:







							"""simple docstring"""





							_a  :    List[Any]			=						len(UpperCAmelCase     )

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

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

							for i in range(0    ,	len(UpperCAmelCase     ) - 1     ):
														if i < len(UpperCAmelCase     ) - 1:
																					_a  :    str			=						sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(UpperCAmelCase     )
														if i < len(UpperCAmelCase     ) - 2:
																					_a  :    Optional[int]			=						sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(UpperCAmelCase     )
														if i < len(UpperCAmelCase     ) - 3:
																					_a  :    List[str]			=						sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																					sagrams.append(UpperCAmelCase     )

							for i in range(0    ,	len(UpperCAmelCase     ) - 1     ):
														if i < len(UpperCAmelCase     ) - 1:
																					_a  :    str			=						cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(UpperCAmelCase     )
														if i < len(UpperCAmelCase     ) - 2:
																					_a  :    str			=						cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(UpperCAmelCase     )
														if i < len(UpperCAmelCase     ) - 3:
																					_a  :    List[str]			=						cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(UpperCAmelCase     )

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






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase = True    ,	UpperCAmelCase = "13a"    ,	UpperCAmelCase = True     )     ->		Optional[Any]:







							"""simple docstring"""





							if lowercase:
														_a  :    List[str]			=						sentence.lower()

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

							if not return_str:
														_a  :    Optional[int]			=						normalized_sent.split()

							return normalized_sent







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




						def      snake_case__(    self       )			->						str:
													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		,	lowercase		,	lowercase		,	lowercase       )			->						Any:
													if not (len(lowercase       ) == len(lowercase       ) == len(lowercase       )):
																				raise ValueError('''Sources length must match predictions and references lengths.'''       )
													_a  :    Optional[Any]			=						0
													for src, pred, refs in zip(lowercase		,	lowercase		,	lowercase       ):
																				sari_score += SARIsent(normalize(lowercase       )		,	normalize(lowercase       )		,	[normalize(lowercase       ) for sent in refs]       )
													_a  :    str			=						sari_score / len(lowercase       )
													return {"sari": 100 * sari_score}







import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)