code: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # 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.
  """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
  
  
  import dataclasses
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Dict, Optional
  
  import numpy as np
  
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction, GlueDataset
  from transformers import GlueDataTrainingArguments as DataTrainingArguments
  from transformers import (
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      glue_compute_metrics,
      glue_output_modes,
      glue_tasks_num_labels,
      set_seed,
  )
  
  
  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"}
      )
      cache_dir: Optional[str] = field(
          default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
      )
  
  
  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, TrainingArguments))
  
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
      else:
          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 if training_args.local_rank in [-1, 0] else logging.WARN,
      )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
          training_args.local_rank,
          training_args.device,
          training_args.n_gpu,
          bool(training_args.local_rank != -1),
          training_args.fp16,
      )
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Set seed
      set_seed(training_args.seed)
  
      try:
          num_labels = glue_tasks_num_labels[data_args.task_name]
          output_mode = glue_output_modes[data_args.task_name]
      except KeyError:
          raise ValueError("Task not found: %s" % (data_args.task_name))
  
      # 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,
          finetuning_task=data_args.task_name,
          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,
      )
      model = AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
          cache_dir=model_args.cache_dir,
      )
  
      # Get datasets
      train_dataset = GlueDataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
      eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") if training_args.do_eval else None
      test_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="test") if training_args.do_predict else None
  
      def compute_metrics(p: EvalPrediction) -> Dict:
          if output_mode == "classification":
              preds = np.argmax(p.predictions, axis=1)
          elif output_mode == "regression":
              preds = np.squeeze(p.predictions)
          return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
  
      # Initialize our Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          compute_metrics=compute_metrics,
      )
  
      # Training
      if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
          )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
              tokenizer.save_pretrained(training_args.output_dir)
  
      # Evaluation
      eval_results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          # Loop to handle MNLI double evaluation (matched, mis-matched)
          eval_datasets = [eval_dataset]
          if data_args.task_name == "mnli":
              mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
              eval_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev"))
  
          for eval_dataset in eval_datasets:
              eval_result = trainer.evaluate(eval_dataset=eval_dataset)
  
              output_eval_file = os.path.join(
                  training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
              )
              if trainer.is_world_master():
                  with open(output_eval_file, "w") as writer:
                      logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
                      for key, value in eval_result.items():
                          logger.info("  %s = %s", key, value)
                          writer.write("%s = %s\n" % (key, value))
  
              eval_results.update(eval_result)
  
      if training_args.do_predict:
          logging.info("*** Test ***")
          test_datasets = [test_dataset]
          if data_args.task_name == "mnli":
              mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
              test_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test"))
  
          for test_dataset in test_datasets:
              predictions = trainer.predict(test_dataset=test_dataset).predictions
              if output_mode == "classification":
                  predictions = np.argmax(predictions, axis=1)
  
              output_test_file = os.path.join(
                  training_args.output_dir, f"test_results_{test_dataset.args.task_name}.txt"
              )
              if trainer.is_world_master():
                  with open(output_test_file, "w") as writer:
                      logger.info("***** Test results {} *****".format(test_dataset.args.task_name))
                      writer.write("index\tprediction\n")
                      for index, item in enumerate(predictions):
                          if output_mode == "regression":
                              writer.write("%d\t%3.3f\n" % (index, item))
                          else:
                              item = test_dataset.get_labels()[item]
                              writer.write("%d\t%s\n" % (index, item))
      return eval_results
  
  
  def _mp_fn(index):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
      main()
  
apis: numpy.squeeze numpy.argmax