text: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Fine-tuning the library models for named entity recognition."""
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional, Tuple
  
  import numpy as np
  from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
  
  from transformers import (
      AutoConfig,
      AutoTokenizer,
      EvalPrediction,
      HfArgumentParser,
      TFAutoModelForTokenClassification,
      TFTrainer,
      TFTrainingArguments,
  )
  from utils_ner import Split, TFNerDataset, get_labels
  
  
  logger = logging.getLogger(__name__)
  
  
  @dataclass
  class ModelArguments:
      """
      Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
      """
  
      model_name_or_path: str = field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
      )
      config_name: Optional[str] = field(
          default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
      )
      tokenizer_name: Optional[str] = field(
          default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
      )
      use_fast: bool = field(default=False, 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.
      cache_dir: Optional[str] = field(
          default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
      )
  
  
  @dataclass
  class DataTrainingArguments:
      """
      Arguments pertaining to what data we are going to input our model for training and eval.
      """
  
      data_dir: str = field(
          metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}
      )
      labels: Optional[str] = field(
          metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}
      )
      max_seq_length: int = 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."
          },
      )
      overwrite_cache: bool = field(
          default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
      )
  
  
  def main():
      # 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.
      parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
      model_args, data_args, training_args = 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."
          )
  
      # 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_gpu: %s, distributed training: %s, 16-bits training: %s",
          training_args.n_gpu,
          bool(training_args.n_gpu > 1),
          training_args.fp16,
      )
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Prepare Token Classification task
      labels = get_labels(data_args.labels)
      label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
      num_labels = len(labels)
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      config = AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path,
          num_labels=num_labels,
          id2label=label_map,
          label2id={label: i for i, label in enumerate(labels)},
          cache_dir=model_args.cache_dir,
      )
      tokenizer = 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():
          model = TFAutoModelForTokenClassification.from_pretrained(
              model_args.model_name_or_path,
              from_pt=bool(".bin" in model_args.model_name_or_path),
              config=config,
              cache_dir=model_args.cache_dir,
          )
  
      # Get datasets
      train_dataset = (
          TFNerDataset(
              data_dir=data_args.data_dir,
              tokenizer=tokenizer,
              labels=labels,
              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
      )
      eval_dataset = (
          TFNerDataset(
              data_dir=data_args.data_dir,
              tokenizer=tokenizer,
              labels=labels,
              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(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
          preds = np.argmax(predictions, axis=2)
          batch_size, seq_len = preds.shape
          out_label_list = [[] for _ in range(batch_size)]
          preds_list = [[] for _ in range(batch_size)]
  
          for i in range(batch_size):
              for j in range(seq_len):
                  if label_ids[i, j] != -1:
                      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(p: EvalPrediction) -> Dict:
          preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
  
          return {
              "precision": precision_score(out_label_list, preds_list),
              "recall": recall_score(out_label_list, preds_list),
              "f1": f1_score(out_label_list, preds_list),
          }
  
      # Initialize our Trainer
      trainer = TFTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset.get_dataset() if train_dataset else None,
          eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
          compute_metrics=compute_metrics,
      )
  
      # Training
      if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir)
  
      # Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          result = trainer.evaluate()
          output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
  
          with open(output_eval_file, "w") as writer:
              logger.info("***** Eval results *****")
  
              for key, value in result.items():
                  logger.info("  %s = %s", key, value)
                  writer.write("%s = %s\n" % (key, value))
  
              results.update(result)
  
      # Predict
      if training_args.do_predict:
          test_dataset = TFNerDataset(
              data_dir=data_args.data_dir,
              tokenizer=tokenizer,
              labels=labels,
              model_type=config.model_type,
              max_seq_length=data_args.max_seq_length,
              overwrite_cache=data_args.overwrite_cache,
              mode=Split.test,
          )
  
          predictions, label_ids, metrics = trainer.predict(test_dataset.get_dataset())
          preds_list, labels_list = align_predictions(predictions, label_ids)
          report = classification_report(labels_list, preds_list)
  
          logger.info("\n%s", report)
  
          output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
  
          with open(output_test_results_file, "w") as writer:
              writer.write("%s\n" % report)
  
          # Save predictions
          output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
  
          with open(output_test_predictions_file, "w") as writer:
              with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
                  example_id = 0
  
                  for line in f:
                      if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                          writer.write(line)
  
                          if not preds_list[example_id]:
                              example_id += 1
                      elif preds_list[example_id]:
                          output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
  
                          writer.write(output_line)
                      else:
                          logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
  
      return results
  
  
  if __name__ == "__main__":
      main()
  