

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()


_lowerCamelCase       :     Optional[Any]           =							logging.getLogger(__name__)





@dataclass
class       __UpperCAmelCase  :
   UpperCamelCase       =  field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   UpperCamelCase       =  field(
       default=lowerCamelCase__    ,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
   UpperCamelCase       =  field(
       default="""NER"""    ,						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}		)
   UpperCamelCase       =  field(
       default=lowerCamelCase__    ,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
   UpperCamelCase       =  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.
   UpperCamelCase       =  field(
       default=lowerCamelCase__    ,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,						)






@dataclass
class       __UpperCAmelCase  :
   UpperCamelCase       =  field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}		)
   UpperCamelCase       =  field(
       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}		)
   UpperCamelCase       =  field(
       default=1_2_8    ,						metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }    ,						)
   UpperCamelCase       =  field(
       default=lowerCamelCase__    ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)






def     a__  (	)						->		List[Any]:
  # 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.
  UpperCAmelCase :							Tuple								=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
  UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							Union[str, Any]								=				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.'''    )

  UpperCAmelCase :							Optional[int]								=				import_module('''tasks'''    )

  try:
    UpperCAmelCase :							Optional[int]								=				getattr(UpperCAmelCase       ,				model_args.task_type    )
    UpperCAmelCase :							TokenClassificationTask								=				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'''       ,				UpperCAmelCase    )

  # Prepare Token Classification task
  UpperCAmelCase :							Optional[int]								=				token_classification_task.get_labels(data_args.labels    )
  UpperCAmelCase :							Dict[int, str]								=				dict(enumerate(UpperCAmelCase    )    )
  UpperCAmelCase :							List[str]								=				len(UpperCAmelCase    )

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

  UpperCAmelCase :							Dict								=				AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,				num_labels=UpperCAmelCase       ,				idalabel=UpperCAmelCase       ,				labelaid={label: i for i, label in enumerate(UpperCAmelCase    )}       ,				cache_dir=model_args.cache_dir       ,				)
  UpperCAmelCase :							Optional[Any]								=				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():
    UpperCAmelCase :							Any								=				TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path       ,				from_pt=bool('''.bin''' in model_args.model_name_or_path    )       ,				config=UpperCAmelCase       ,				cache_dir=model_args.cache_dir       ,				)

  # Get datasets
  UpperCAmelCase :							Optional[Any]								=				(
      TFTokenClassificationDataset(
          token_classification_task=UpperCAmelCase       ,				data_dir=data_args.data_dir       ,				tokenizer=UpperCAmelCase       ,				labels=UpperCAmelCase       ,				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
  )
  UpperCAmelCase :							Optional[Any]								=				(
      TFTokenClassificationDataset(
          token_classification_task=UpperCAmelCase       ,				data_dir=data_args.data_dir       ,				tokenizer=UpperCAmelCase       ,				labels=UpperCAmelCase       ,				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(UpperCAmelCase							:    np.ndarray       ,				UpperCAmelCase							:    np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase :							str								=				np.argmax(UpperCAmelCase       ,				axis=2    )
    UpperCAmelCase				,		UpperCAmelCase :							Dict								=				preds.shape
    UpperCAmelCase :							List[Any]								=				[[] for _ in range(UpperCAmelCase    )]
    UpperCAmelCase :							int								=				[[] for _ in range(UpperCAmelCase    )]

    for i in range(UpperCAmelCase    ):
      for j in range(UpperCAmelCase    ):
        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(UpperCAmelCase							:    EvalPrediction    ) -> Dict:
    UpperCAmelCase				,		UpperCAmelCase :							Optional[Any]								=				align_predictions(p.predictions       ,				p.label_ids    )

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

  # Initialize our Trainer
  UpperCAmelCase :							Dict								=				TFTrainer(
      model=UpperCAmelCase       ,				args=UpperCAmelCase       ,				train_dataset=train_dataset.get_dataset() if train_dataset else None       ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,				compute_metrics=UpperCAmelCase       ,				)

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

  # Evaluation
  UpperCAmelCase :							Any								=				{}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''    )

    UpperCAmelCase :							int								=				trainer.evaluate()
    UpperCAmelCase :							Tuple								=				os.path.join(training_args.output_dir       ,				'''eval_results.txt'''    )

    with open(UpperCAmelCase       ,				'''w'''    ) as writer:
      logger.info('''***** Eval results *****'''    )

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

      results.update(UpperCAmelCase    )

    # Predict
  if training_args.do_predict:
    UpperCAmelCase :							Optional[int]								=				TFTokenClassificationDataset(
        token_classification_task=UpperCAmelCase       ,				data_dir=data_args.data_dir       ,				tokenizer=UpperCAmelCase       ,				labels=UpperCAmelCase       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.test       ,				)

    UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							int								=				trainer.predict(test_dataset.get_dataset()    )
    UpperCAmelCase				,		UpperCAmelCase :							Optional[Any]								=				align_predictions(UpperCAmelCase       ,				UpperCAmelCase    )
    UpperCAmelCase :							List[Any]								=				classification_report(UpperCAmelCase       ,				UpperCAmelCase    )

    logger.info('''\n%s'''       ,				UpperCAmelCase    )

    UpperCAmelCase :							Optional[int]								=				os.path.join(training_args.output_dir       ,				'''test_results.txt'''    )

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

    # Save predictions
    UpperCAmelCase :							Any								=				os.path.join(training_args.output_dir       ,				'''test_predictions.txt'''    )

    with open(UpperCAmelCase       ,				'''w'''    ) as writer:
      with open(os.path.join(data_args.data_dir       ,				'''test.txt'''    )       ,				'''r'''    ) as f:
        UpperCAmelCase :							str								=				0

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

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            UpperCAmelCase :							Optional[int]								=				line.split()[0] + ''' ''' + preds_list[example_id].pop(0    ) + '''\n'''

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

  return results


if __name__ == "__main__":
     main()








def     a__  (	UpperCAmelCase							:    str    )						->		str:
  return " ".join([word[::-1] for word in input_str.split()]    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





