code: def main():
      # 1. Parse input arguments
      # 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,
              Seq2SeqTrainingArguments,
          )
      )
  
      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()
  
      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry(
          "run_speech_recognition_seq2seq", model_args, data_args
      )
  
      # 2. Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      log_level = training_args.get_process_log_level()
      logger.setLevel(log_level)
      datasets.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
  
      logger.setLevel(
          logging.INFO
          if is_main_process(training_args.local_rank)
          else logging.WARN
      )
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank):
          transformers.utils.logging.set_verbosity_info()
      logger.info("Training/evaluation parameters %s", training_args)
  
      # 3. Detecting last checkpoint and eventually continue from last checkpoint
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint(
              training_args.output_dir
          )
          if (
              last_checkpoint is None
              and len(os.listdir(training_args.output_dir)) > 0
          ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to overcome."
              )
          elif (
              last_checkpoint is not None
              and training_args.resume_from_checkpoint is None
          ):
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # 4. Load dataset
      raw_datasets = DatasetDict()
  
      if training_args.do_train:
          raw_datasets["train"] = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              split=data_args.train_split_name,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
  
      if training_args.do_eval:
          raw_datasets["eval"] = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              split=data_args.eval_split_name,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
  
      if (
          data_args.audio_column_name
          not in next(iter(raw_datasets.values())).column_names
      ):
          raise ValueError(
              f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
              "Make sure to set `--audio_column_name` to the correct audio column - one of "
              f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
          )
  
      if (
          data_args.text_column_name
          not in next(iter(raw_datasets.values())).column_names
      ):
          raise ValueError(
              f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
              "Make sure to set `--text_column_name` to the correct text column - one of "
              f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
          )
  
      # 5. Load pretrained model, tokenizer, and feature extractor
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      config = AutoConfig.from_pretrained(
          model_args.config_name
          if model_args.config_name
          else model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          model_args.feature_extractor_name
          if model_args.feature_extractor_name
          else model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      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_tokenizer,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      model = AutoModelForSpeechSeq2Seq.from_pretrained(
          model_args.model_name_or_path,
          config=config,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      if model.config.decoder_start_token_id is None:
          raise ValueError(
              "Make sure that `config.decoder_start_token_id` is correctly defined"
          )
  
      if model_args.freeze_feature_encoder:
          model.freeze_feature_encoder()
  
      # 6. Resample speech dataset if necessary
      dataset_sampling_rate = (
          next(iter(raw_datasets.values()))
          .features[data_args.audio_column_name]
          .sampling_rate
      )
      if dataset_sampling_rate != feature_extractor.sampling_rate:
          raw_datasets = raw_datasets.cast_column(
              data_args.audio_column_name,
              datasets.features.Audio(
                  sampling_rate=feature_extractor.sampling_rate
              ),
          )
  
      # 7. Preprocessing the datasets.
      # We need to read the audio files as arrays and tokenize the targets.
      max_input_length = (
          data_args.max_duration_in_seconds
          * feature_extractor.sampling_rate
      )
      min_input_length = (
          data_args.min_duration_in_seconds
          * feature_extractor.sampling_rate
      )
      audio_column_name = data_args.audio_column_name
      num_workers = data_args.preprocessing_num_workers
      text_column_name = data_args.text_column_name
      model_input_name = feature_extractor.model_input_names[0]
      do_lower_case = data_args.do_lower_case
  
      if data_args.max_train_samples is not None:
          raw_datasets["train"] = raw_datasets["train"].select(
              range(data_args.max_train_samples)
          )
  
      if data_args.max_eval_samples is not None:
          raw_datasets["eval"] = raw_datasets["eval"].select(
              range(data_args.max_eval_samples)
          )
  
      def prepare_dataset(batch):
          # process audio
          sample = batch[audio_column_name]
          inputs = feature_extractor(
              sample["array"], sampling_rate=sample["sampling_rate"]
          )
          # process audio length
          batch[model_input_name] = inputs.input_values[0]
          batch["input_length"] = len(batch["input_values"])
  
          # process targets
          input_str = (
              batch[text_column_name].lower()
              if do_lower_case
              else batch[text_column_name]
          )
          batch["labels"] = tokenizer(input_str).input_ids
          return batch
  
      with training_args.main_process_first(
          desc="dataset map pre-processing"
      ):
          vectorized_datasets = raw_datasets.map(
              prepare_dataset,
              remove_columns=next(
                  iter(raw_datasets.values())
              ).column_names,
              num_proc=data_args.preprocessing_num_workers,
              desc="preprocess train dataset",
          )
  
      # filter data that is shorter than min_input_length or longer than
      # max_input_length
      def is_audio_in_length_range(length):
          return length > min_input_length and length < max_input_length
  
      vectorized_datasets = vectorized_datasets.filter(
          is_audio_in_length_range,
          num_proc=num_workers,
          input_columns=["input_length"],
      )
  
      # for large datasets it is advised to run the preprocessing on a
      # single machine first with `args.preprocessing_only` since there will mostly likely
      # be a timeout when running the script in distributed mode.
      # In a second step `args.preprocessing_only` can then be set to `False` to load the
      # cached dataset
      if data_args.preprocessing_only:
          cache = {
              k: v.cache_files for k, v in vectorized_datasets.items()
          }
          logger.info(
              f"Data preprocessing finished. Files cached at {cache}."
          )
          return
  
      # 8. Load Metric
      metric = evaluate.load("wer")
  
      def compute_metrics(pred):
          pred_ids = pred.predictions
  
          pred.label_ids[
              pred.label_ids == -100
          ] = tokenizer.pad_token_id
  
          pred_str = tokenizer.batch_decode(
              pred_ids, skip_special_tokens=True
          )
          # we do not want to group tokens when computing the metrics
          label_str = tokenizer.batch_decode(
              pred.label_ids, skip_special_tokens=True
          )
  
          wer = metric.compute(
              predictions=pred_str, references=label_str
          )
  
          return {"wer": wer}
  
      # 9. Create a single speech processor
      if is_main_process(training_args.local_rank):
          # save feature extractor, tokenizer and config
          feature_extractor.save_pretrained(training_args.output_dir)
          tokenizer.save_pretrained(training_args.output_dir)
          config.save_pretrained(training_args.output_dir)
  
      processor = AutoProcessor.from_pretrained(
          training_args.output_dir
      )
  
      # 10. Define data collator
      data_collator = DataCollatorSpeechSeq2SeqWithPadding(
          processor=processor,
          decoder_start_token_id=model.config.decoder_start_token_id,
      )
  
      # 11. Initialize Trainer
      trainer = Seq2SeqTrainer(
          model=model,
          args=training_args,
          train_dataset=vectorized_datasets["train"]
          if training_args.do_train
          else None,
          eval_dataset=vectorized_datasets["eval"]
          if training_args.do_eval
          else None,
          tokenizer=feature_extractor,
          data_collator=data_collator,
          compute_metrics=compute_metrics
          if training_args.predict_with_generate
          else None,
      )
  
      # 12. Training
      if training_args.do_train:
          checkpoint = None
          if training_args.resume_from_checkpoint is not None:
              checkpoint = training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              checkpoint = last_checkpoint
          train_result = trainer.train(
              resume_from_checkpoint=checkpoint
          )
          trainer.save_model()  # Saves the feature extractor too for easy upload
  
          metrics = train_result.metrics
          max_train_samples = (
              data_args.max_train_samples
              if data_args.max_train_samples is not None
              else len(vectorized_datasets["train"])
          )
          metrics["train_samples"] = min(
              max_train_samples, len(vectorized_datasets["train"])
          )
          trainer.log_metrics("train", metrics)
          trainer.save_metrics("train", metrics)
          trainer.save_state()
  
      # 13. Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
          metrics = trainer.evaluate(
              metric_key_prefix="eval",
              max_length=model.config.max_length,
              num_beams=model.config.num_beams,
          )
          max_eval_samples = (
              data_args.max_eval_samples
              if data_args.max_eval_samples is not None
              else len(vectorized_datasets["eval"])
          )
          metrics["eval_samples"] = min(
              max_eval_samples, len(vectorized_datasets["eval"])
          )
  
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      # 14. Write Training Stats
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "automatic-speech-recognition",
      }
      if data_args.dataset_name is not None:
          kwargs["dataset_tags"] = data_args.dataset_name
          if data_args.dataset_config_name is not None:
              kwargs["dataset_args"] = data_args.dataset_config_name
              kwargs[
                  "dataset"
              ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
          else:
              kwargs["dataset"] = data_args.dataset_name
  
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
      return results
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Union
  import datasets
  import torch
  from datasets import DatasetDict, load_dataset
  import evaluate
  import transformers
  from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, set_seed
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: results, batch, unknown, dict