


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase   	=  '''\
@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},
}
'''

__lowerCamelCase   	=  '''\
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
'''


__lowerCamelCase   	=  '''
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      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  int				      =     [rgram for rgrams in rgramslist for rgram in rgrams]
     a_     :  Tuple				      =     Counter(__UpperCamelCase			)

     a_     :  Tuple				      =     Counter(__UpperCamelCase			)
     a_     :  List[Any]				      =     Counter()
     for sgram, scount in sgramcounter.items():
          a_     :  Any				      =     scount * numref

     a_     :  Tuple				      =     Counter(__UpperCamelCase			)
     a_     :  int				      =     Counter()
     for cgram, ccount in cgramcounter.items():
          a_     :  str				      =     ccount * numref

     # KEEP
     a_     :  Any				      =     sgramcounter_rep & cgramcounter_rep
     a_     :  Tuple				      =     keepgramcounter_rep & rgramcounter
     a_     :  Union[str, Any]				      =     sgramcounter_rep & rgramcounter

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

     # DELETION
     a_     :  Tuple				      =     sgramcounter_rep - cgramcounter_rep
     a_     :  Union[str, Any]				      =     delgramcounter_rep - rgramcounter
     a_     :  Any				      =     sgramcounter_rep - rgramcounter
     a_     :  Optional[int]				      =     0
     a_     :  int				      =     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_     :  Any				      =     1
     if len(__UpperCamelCase			) > 0:
          a_     :  Tuple				      =     deltmpscorea / len(__UpperCamelCase			)

     # ADDITION
     a_     :  List[str]				      =     set(__UpperCamelCase			) - set(__UpperCamelCase			)
     a_     :  Any				      =     set(__UpperCamelCase			) & set(__UpperCamelCase			)
     a_     :  Dict				      =     set(__UpperCamelCase			) - set(__UpperCamelCase			)

     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[Any]				      =     1
     a_     :  int				      =     1
     if len(__UpperCamelCase			) > 0:
          a_     :  Dict				      =     addtmpscore / len(__UpperCamelCase			)
     if len(__UpperCamelCase			) > 0:
          a_     :  Tuple				      =     addtmpscore / len(__UpperCamelCase			)
     a_     :  Any				      =     0
     if addscore_precision > 0 or addscore_recall > 0:
          a_     :  Optional[Any]				      =     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  int				      =     len(__UpperCamelCase			)

     a_     :  Any				      =     ssent.split(""" """			)
     a_     :  str				      =     csent.split(""" """			)
     a_     :  Optional[int]				      =     []
     a_     :  Any				      =     []
     a_     :  Tuple				      =     []
     a_     :  str				      =     []
     a_     :  Optional[int]				      =     []
     a_     :  Optional[int]				      =     []

     a_     :  Optional[Any]				      =     []
     a_     :  Optional[Any]				      =     []
     a_     :  Dict				      =     []
     a_     :  Optional[int]				      =     []
     for rsent in rsents:
          a_     :  Union[str, Any]				      =     rsent.split(""" """			)
          a_     :  Optional[int]				      =     []
          a_     :  List[Any]				      =     []
          a_     :  Union[str, Any]				      =     []
          ragramslist.append(__UpperCamelCase			)
          for i in range(0	,	len(__UpperCamelCase			) - 1			):
               if i < len(__UpperCamelCase			) - 1:
                    a_     :  Optional[int]				      =     ragrams[i] + """ """ + ragrams[i + 1]
                    ragrams.append(__UpperCamelCase			)
               if i < len(__UpperCamelCase			) - 2:
                    a_     :  str				      =     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_     :  List[str]				      =     sagrams[i] + """ """ + sagrams[i + 1]
               sagrams.append(__UpperCamelCase			)
          if i < len(__UpperCamelCase			) - 2:
               a_     :  str				      =     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_     :  Any				      =     cagrams[i] + """ """ + cagrams[i + 1]
               cagrams.append(__UpperCamelCase			)
          if i < len(__UpperCamelCase			) - 2:
               a_     :  Optional[int]				      =     cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
               cagrams.append(__UpperCamelCase			)
          if i < len(__UpperCamelCase			) - 3:
               a_     :  Optional[int]				      =     cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
               cagrams.append(__UpperCamelCase			)

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



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase = True	,	__UpperCamelCase = "13a"	,	__UpperCamelCase = 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:
          a_     :  Union[str, Any]				      =     sentence.lower()

     if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__			).major >= 2:
               a_     :  Tuple				      =     sacrebleu.metrics.bleu._get_tokenizer(__UpperCamelCase			)()(__UpperCamelCase			)
          else:
               a_     :  Any				      =     sacrebleu.TOKENIZERS[tokenizer]()(__UpperCamelCase			)
     elif tokenizer == "moses":
          a_     :  str				      =     sacremoses.MosesTokenizer().tokenize(__UpperCamelCase	,	return_str=__UpperCamelCase	,	escape=__UpperCamelCase			)
     elif tokenizer == "penn":
          a_     :  Tuple				      =     sacremoses.MosesTokenizer().penn_tokenize(__UpperCamelCase	,	return_str=__UpperCamelCase			)
     else:
          a_     :  Optional[int]				      =     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     UpperCAmelCase( self   :  Tuple     ):
            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     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  str     ):
            if not (len(lowerCamelCase_     ) == len(lowerCamelCase_     ) == len(lowerCamelCase_     )):
                 raise ValueError("""Sources length must match predictions and references lengths."""     )
            a_     :  int				      =     0
            for src, pred, refs in zip(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     ):
                 sari_score += SARIsent(normalize(lowerCamelCase_     )   ,				normalize(lowerCamelCase_     )   ,				[normalize(lowerCamelCase_     ) for sent in refs]     )
            a_     :  Union[str, Any]				      =     sari_score / len(lowerCamelCase_     )
            return {"sari": 1_0_0 * sari_score}











'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__A   :							List[str]     	=						logging.getLogger(__name__)






@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
     _snake_case							   =	field(
         default='NER'			,    metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
     _snake_case							   =	field(default=lowerCamelCase			,    metadata={'help': 'Set this flag to use fast tokenization.'}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,    )







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}    )
     _snake_case							   =	field(
         metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    )
     _snake_case							   =	field(
         default=128			,    metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }			,    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def     _UpperCamelCase				(  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
      __A		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A ,					__A ,					__A		 =   parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir				)
          and os.listdir(training_args.output_dir				)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                " --overwrite_output_dir to overcome."				)

      __A		 =   import_module("tasks"				)

      try:
            __A		 =   getattr(snake_case__				,      model_args.task_type				)
            __A		 =   token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"				)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,      datefmt="%m/%d/%Y %H:%M:%S"				,      level=logging.INFO				,      )
      logger.info(
          "n_replicas: %s, distributed training: %s, 16-bits training: %s"				,      training_args.n_replicas				,      bool(training_args.n_replicas > 1				)				,      training_args.fpaa				,      )
      logger.info("Training/evaluation parameters %s"				,      snake_case__				)

      # Prepare Token Classification task
      __A		 =   token_classification_task.get_labels(data_args.labels				)
      __A		 =   dict(enumerate(snake_case__				)				)
      __A		 =   len(snake_case__				)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      __A		 =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,      num_labels=snake_case__				,      idalabel=snake_case__				,      labelaid={label: i for i, label in enumerate(snake_case__				)}				,      cache_dir=model_args.cache_dir				,      )
      __A		 =   AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,      cache_dir=model_args.cache_dir				,      use_fast=model_args.use_fast				,      )

      with training_args.strategy.scope():
            __A		 =   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool(".bin" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

      # Get datasets
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.train				,      )
          if training_args.do_train
          else None
      )
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.dev				,      )
          if training_args.do_eval
          else None
      )

      def align_predictions(snake_case__				,      snake_case__				) -> Tuple[List[int], List[int]]:
            __A		 =   np.argmax(snake_case__				,      axis=2				)
            __A ,					__A		 =   preds.shape
            __A		 =   [[] for _ in range(snake_case__				)]
            __A		 =   [[] for _ in range(snake_case__				)]

            for i in range(snake_case__				):
                  for j in range(snake_case__				):
                        if label_ids[i, j] != -100:
                              out_label_list[i].append(label_map[label_ids[i][j]]				)
                              preds_list[i].append(label_map[preds[i][j]]				)

            return preds_list, out_label_list

      def compute_metrics(snake_case__				) -> Dict:
            __A ,					__A		 =   align_predictions(p.predictions				,      p.label_ids				)

            return {
                "precision": precision_score(snake_case__				,      snake_case__				),
                "recall": recall_score(snake_case__				,      snake_case__				),
                "f1": fa_score(snake_case__				,      snake_case__				),
            }

      # Initialize our Trainer
      __A		 =   TFTrainer(
          model=snake_case__				,      args=snake_case__				,      train_dataset=train_dataset.get_dataset() if train_dataset else None				,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,      compute_metrics=snake_case__				,      )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

      # Evaluation
      __A		 =   {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)

            __A		 =   trainer.evaluate()
            __A		 =   os.path.join(training_args.output_dir				,      "eval_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  logger.info("***** Eval results *****"				)

                  for key, value in result.items():
                        logger.info("  %s = %s"				,      snake_case__				,      snake_case__				)
                        writer.write("%s = %s\n" % (key, value)				)

                  results.update(snake_case__				)

    # Predict
      if training_args.do_predict:
            __A		 =   TFTokenClassificationDataset(
                token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.test				,      )

            __A ,					__A ,					__A		 =   trainer.predict(test_dataset.get_dataset()				)
            __A ,					__A		 =   align_predictions(snake_case__				,      snake_case__				)
            __A		 =   classification_report(snake_case__				,      snake_case__				)

            logger.info("\n%s"				,      snake_case__				)

            __A		 =   os.path.join(training_args.output_dir				,      "test_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  writer.write("%s\n" % report				)

            # Save predictions
            __A		 =   os.path.join(training_args.output_dir				,      "test_predictions.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  with open(os.path.join(data_args.data_dir				,      "test.txt"				)				,      "r"				) as f:
                        __A		 =   0

                        for line in f:
                              if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                                    writer.write(snake_case__				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A		 =   line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"

                                    writer.write(snake_case__				)
                              else:
                                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."				,      line.split()[0]				)

      return results


if __name__ == "__main__":
       main()


