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 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    List[Any]:




       '''simple docstring'''






       _A=						[rgram for rgrams in rgramslist for rgram in rgrams]
       _A=						Counter(lowerCAmelCase_ )

       _A=						Counter(lowerCAmelCase_ )
       _A=						Counter()
       for sgram, scount in sgramcounter.items():
              _A=						scount * numref

       _A=						Counter(lowerCAmelCase_ )
       _A=						Counter()
       for cgram, ccount in cgramcounter.items():
              _A=						ccount * numref

       # KEEP
       _A=						sgramcounter_rep & cgramcounter_rep
       _A=						keepgramcounter_rep & rgramcounter
       _A=						sgramcounter_rep & rgramcounter

       _A=						0
       _A=						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=						1
       _A=						1
       if len(lowerCAmelCase_ ) > 0:
              _A=						keeptmpscorea / len(lowerCAmelCase_ )
       if len(lowerCAmelCase_ ) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              _A=						keeptmpscorea / sum(keepgramcounterall_rep.values() )
       _A=						0
       if keepscore_precision > 0 or keepscore_recall > 0:
              _A=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       _A=						sgramcounter_rep - cgramcounter_rep
       _A=						delgramcounter_rep - rgramcounter
       _A=						sgramcounter_rep - rgramcounter
       _A=						0
       _A=						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=						1
       if len(lowerCAmelCase_ ) > 0:
              _A=						deltmpscorea / len(lowerCAmelCase_ )

       # ADDITION
       _A=						set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
       _A=						set(lowerCAmelCase_ ) & set(lowerCAmelCase_ )
       _A=						set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )

       _A=						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=						1
       _A=						1
       if len(lowerCAmelCase_ ) > 0:
              _A=						addtmpscore / len(lowerCAmelCase_ )
       if len(lowerCAmelCase_ ) > 0:
              _A=						addtmpscore / len(lowerCAmelCase_ )
       _A=						0
       if addscore_precision > 0 or addscore_recall > 0:
              _A=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						len(lowerCAmelCase_ )

       _A=						ssent.split(' ' )
       _A=						csent.split(' ' )
       _A=						[]
       _A=						[]
       _A=						[]
       _A=						[]
       _A=						[]
       _A=						[]

       _A=						[]
       _A=						[]
       _A=						[]
       _A=						[]
       for rsent in rsents:
              _A=						rsent.split(' ' )
              _A=						[]
              _A=						[]
              _A=						[]
              ragramslist.append(lowerCAmelCase_ )
              for i in range(0							,       len(lowerCAmelCase_ ) - 1 ):
                     if i < len(lowerCAmelCase_ ) - 1:
                            _A=						ragrams[i] + ' ' + ragrams[i + 1]
                            ragrams.append(lowerCAmelCase_ )
                     if i < len(lowerCAmelCase_ ) - 2:
                            _A=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                            ragrams.append(lowerCAmelCase_ )
                     if i < len(lowerCAmelCase_ ) - 3:
                            _A=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                            ragrams.append(lowerCAmelCase_ )
              ragramslist.append(lowerCAmelCase_ )
              ragramslist.append(lowerCAmelCase_ )
              ragramslist.append(lowerCAmelCase_ )

       for i in range(0							,       len(lowerCAmelCase_ ) - 1 ):
              if i < len(lowerCAmelCase_ ) - 1:
                     _A=						sagrams[i] + ' ' + sagrams[i + 1]
                     sagrams.append(lowerCAmelCase_ )
              if i < len(lowerCAmelCase_ ) - 2:
                     _A=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                     sagrams.append(lowerCAmelCase_ )
              if i < len(lowerCAmelCase_ ) - 3:
                     _A=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                     sagrams.append(lowerCAmelCase_ )

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

       ((_A),      (_A),      (_A))=						SARIngram(lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )
       ((_A),      (_A),      (_A))=						SARIngram(lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )
       ((_A),      (_A),      (_A))=						SARIngram(lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )
       ((_A),      (_A),      (_A))=						SARIngram(lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )
       _A=						sum([keepascore, keepascore, keepascore, keepascore] ) / 4
       _A=						sum([delascore, delascore, delascore, delascore] ) / 4
       _A=						sum([addascore, addascore, addascore, addascore] ) / 4
       _A=						(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ = True							,       lowerCAmelCase_ = "13a"							,       lowerCAmelCase_ = True )		->    List[str]:




       '''simple docstring'''






       if lowercase:
              _A=						sentence.lower()

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

       if not return_str:
              _A=						normalized_sent.split()

       return normalized_sent





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



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


UpperCAmelCase_    		=				collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    CheckpointState:




       '''simple docstring'''






       print(F"Reading saved model from {model_file}" )
       _A=						torch.load(lowerCAmelCase_							,       map_location=lambda lowerCAmelCase_							,       lowerCAmelCase_ : default_restore_location(lowerCAmelCase_							,       'cpu' ) )
       return CheckpointState(**lowerCAmelCase_ )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						src_file



     def       a__	(       self							):
            raise NotImplementedError





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   *lowerCAmelCase__					,   **lowerCAmelCase__							):
            if comp_type.startswith('c'							):
                   return DPRContextEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('q'							):
                   return DPRQuestionEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('r'							):
                   return DPRReaderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            else:
                   raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'							)





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.ctx_encoder, 'ctx_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'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(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.question_encoder, 'question_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'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(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR reader from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{
                '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=						'encoder.bert_model.' + key[len('encoder.'							) :]
                   _A=						value
            model.span_predictor.load_state_dict(lowerCAmelCase__							)
            return model


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						Path(lowerCAmelCase_ )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_ )

       _A=						DPRState.from_type(lowerCAmelCase_							,       src_file=lowerCAmelCase_ )
       _A=						dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_ )
       model.from_pretrained(lowerCAmelCase_ )  # sanity check


if __name__ == "__main__":
      UpperCAmelCase_    		=				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.''')
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				Path(args.src)
      UpperCAmelCase_    		=				F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCAmelCase_    		=				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)