






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE     :   List[Any] 							=						'\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'

SCREAMING_SNAKE_CASE     :   int 							=						'\\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'


SCREAMING_SNAKE_CASE     :   List[Any] 							=						'\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      _lowerCamelCase	(						__A    :							Optional[int] )      ->					int:

    def remove_articles(__A    :							List[str] ):
        _UpperCAmelCase				:      List[str]			  =				re.compile(r'''\b(a|an|the)\b'''		,		re.UNICODE )
        return re.sub(__A		,		''' '''		,		__A )

    def white_space_fix(__A    :							Dict ):
        return " ".join(text.split() )

    def remove_punc(__A    :							Any ):
        _UpperCAmelCase				:      Tuple			  =				set(string.punctuation )
        return "".join(ch for ch in text if ch not in exclude )

    def lower(__A    :							Optional[Any] ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							int )      ->					Optional[Any]:
    return int(normalize_answer(__A ) == normalize_answer(__A ) )

def      _lowerCamelCase	(						__A    :							str		,		__A    :							List[Any] )      ->					str:
    _UpperCAmelCase				:      Dict			  =				[any(compute_exact(__A		,		__A ) for ref in refs ) for pred, refs in zip(__A		,		__A )]
    return (sum(__A ) / len(__A )) * 100

def      _lowerCamelCase	(						__A    :							Dict		,		__A    :							Any		,		__A    :							Tuple		,		__A    :							Dict )      ->					Any:
    _UpperCAmelCase				:      Optional[int]			  =				[rgram for rgrams in rgramslist for rgram in rgrams]
    _UpperCAmelCase				:      List[Any]			  =				Counter(__A )

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

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

    # KEEP
    _UpperCAmelCase				:      List[str]			  =				sgramcounter_rep & cgramcounter_rep
    _UpperCAmelCase				:      Optional[int]			  =				keepgramcounter_rep & rgramcounter
    _UpperCAmelCase				:      Union[str, Any]			  =				sgramcounter_rep & rgramcounter

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

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

    # ADDITION
    _UpperCAmelCase				:      int			  =				set(__A ) - set(__A )
    _UpperCAmelCase				:      str			  =				set(__A ) & set(__A )
    _UpperCAmelCase				:      Optional[int]			  =				set(__A ) - set(__A )

    _UpperCAmelCase				:      List[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.
    _UpperCAmelCase				:      Dict			  =				1
    _UpperCAmelCase				:      Tuple			  =				1
    if len(__A ) > 0:
        _UpperCAmelCase				:      Union[str, Any]			  =				addtmpscore / len(__A )
    if len(__A ) > 0:
        _UpperCAmelCase				:      int			  =				addtmpscore / len(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    if addscore_precision > 0 or addscore_recall > 0:
        _UpperCAmelCase				:      str			  =				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)

def      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							Optional[Any]		,		__A    :							int )      ->					Union[str, Any]:
    _UpperCAmelCase				:      List[str]			  =				len(__A )

    _UpperCAmelCase				:      Optional[Any]			  =				ssent.split(''' ''' )
    _UpperCAmelCase				:      Optional[int]			  =				csent.split(''' ''' )
    _UpperCAmelCase				:      Optional[int]			  =				[]
    _UpperCAmelCase				:      Optional[int]			  =				[]
    _UpperCAmelCase				:      List[Any]			  =				[]
    _UpperCAmelCase				:      List[Any]			  =				[]
    _UpperCAmelCase				:      List[str]			  =				[]
    _UpperCAmelCase				:      Optional[int]			  =				[]

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

    for i in range(0		,		len(__A ) - 1 ):
        if i < len(__A ) - 1:
            _UpperCAmelCase				:      Tuple			  =				sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__A )
        if i < len(__A ) - 2:
            _UpperCAmelCase				:      List[Any]			  =				sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__A )
        if i < len(__A ) - 3:
            _UpperCAmelCase				:      Any			  =				sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__A )

    for i in range(0		,		len(__A ) - 1 ):
        if i < len(__A ) - 1:
            _UpperCAmelCase				:      Union[str, Any]			  =				cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__A )
        if i < len(__A ) - 2:
            _UpperCAmelCase				:      Dict			  =				cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__A )
        if i < len(__A ) - 3:
            _UpperCAmelCase				:      Tuple			  =				cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__A )

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

def      _lowerCamelCase	(						__A    :							int		,		__A    :							bool = True		,		__A    :							str = "13a"		,		__A    :							bool = 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:
        _UpperCAmelCase				:      Union[str, Any]			  =				sentence.lower()

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

    if not return_str:
        _UpperCAmelCase				:      Union[str, Any]			  =				normalized_sent.split()

    return normalized_sent

def      _lowerCamelCase	(						__A    :							str		,		__A    :							List[Any]		,		__A    :							Dict )      ->					Any:
    if not (len(__A ) == len(__A ) == len(__A )):
        raise ValueError('''Sources length must match predictions and references lengths.''' )
    _UpperCAmelCase				:      List[Any]			  =				0
    for src, pred, refs in zip(__A		,		__A		,		__A ):
        sari_score += SARIsent(normalize(__A )		,		normalize(__A )		,		[normalize(__A ) for sent in refs] )
    _UpperCAmelCase				:      List[str]			  =				sari_score / len(__A )
    return 100 * sari_score

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[Any]		,		__A    :							Any="exp"		,		__A    :							List[str]=None		,		__A    :							List[str]=False		,		__A    :							str=False		,		__A    :							List[str]=False		,		)      ->					Tuple:
    _UpperCAmelCase				:      Dict			  =				len(references[0] )
    if any(len(__A ) != references_per_prediction for refs in references ):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
    _UpperCAmelCase				:      str			  =				[[refs[i] for refs in references] for i in range(__A )]
    _UpperCAmelCase				:      Union[str, Any]			  =				sacrebleu.corpus_bleu(
        __A		,		__A		,		smooth_method=__A		,		smooth_value=__A		,		force=__A		,		lowercase=__A		,		use_effective_order=__A		,		)
    return output.score


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






 '''simple docstring'''




 def  snake_case__				(	self) ->		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							,  _A							,  _A							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				{}
     result.update({'''sari''': compute_sari(sources=_A							,  predictions=_A							,  references=_A)})
     result.update({'''sacrebleu''': compute_sacrebleu(predictions=_A							,  references=_A)})
     result.update({'''exact''': compute_em(predictions=_A							,  references=_A)})
     return result












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 							A_    (						pl.LightningModule			):






 '''simple docstring'''




 def __init__(	self							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				LongformerForQuestionAnswering.from_pretrained(__A )

    # transfer weights
    longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
    longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
    longformer_for_qa.eval()

    # save model
    longformer_for_qa.save_pretrained(__A )

    print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--longformer_model',
      default=None,
      type=str,
      required=True,
      help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
  )
  parser.add_argument(
      '--longformer_question_answering_ckpt_path',
      default=None,
      type=str,
      required=True,
      help='Path the official PyTorch Lightning Checkpoint.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




