text: #!/usr/bin/env python
  # coding=utf-8
  # Copyright The HuggingFace Team and The HuggingFace Inc. team. 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.
  """
  Fine-tuning the library models for sequence to sequence.
  """
  # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
  
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import numpy as np
  from datasets import load_dataset, load_metric
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSeq2SeqLM,
      AutoTokenizer,
      DataCollatorForSeq2Seq,
      HfArgumentParser,
      M2M100Tokenizer,
      MBart50Tokenizer,
      MBart50TokenizerFast,
      MBartTokenizer,
      MBartTokenizerFast,
      Seq2SeqTrainer,
      Seq2SeqTrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from transformers.utils import check_min_version
  
  
  # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
  check_min_version("4.6.0.dev0")
  
  logger = logging.getLogger(__name__)
  
  # A list of all multilingual tokenizer which require src_lang and tgt_lang attributes.
  MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]
  
  
  @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 to store the pretrained models downloaded from huggingface.co"},
      )
      use_fast_tokenizer: bool = field(
          default=True,
          metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
      )
      model_revision: str = field(
          default="main",
          metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
      )
      use_auth_token: bool = field(
          default=False,
          metadata={
              "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
              "with private models)."
          },
      )
  
  
  @dataclass
  class DataTrainingArguments:
      """
      Arguments pertaining to what data we are going to input our model for training and eval.
      """
  
      source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
      target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})
  
      dataset_name: Optional[str] = field(
          default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
      )
      dataset_config_name: Optional[str] = field(
          default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
      )
      train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."})
      validation_file: Optional[str] = field(
          default=None,
          metadata={
              "help": "An optional input evaluation data file to evaluate the metrics (sacreblue) on "
              "a jsonlines file."
          },
      )
      test_file: Optional[str] = field(
          default=None,
          metadata={
              "help": "An optional input test data file to evaluate the metrics (sacreblue) on " "a jsonlines file."
          },
      )
      overwrite_cache: bool = field(
          default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
      )
      preprocessing_num_workers: Optional[int] = field(
          default=None,
          metadata={"help": "The number of processes to use for the preprocessing."},
      )
      max_source_length: Optional[int] = field(
          default=1024,
          metadata={
              "help": "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          },
      )
      max_target_length: Optional[int] = field(
          default=128,
          metadata={
              "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          },
      )
      val_max_target_length: Optional[int] = field(
          default=None,
          metadata={
              "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
              "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
              "during ``evaluate`` and ``predict``."
          },
      )
      pad_to_max_length: bool = field(
          default=False,
          metadata={
              "help": "Whether to pad all samples to model maximum sentence length. "
              "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
              "efficient on GPU but very bad for TPU."
          },
      )
      max_train_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          },
      )
      max_eval_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          },
      )
      max_predict_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
              "value if set."
          },
      )
      num_beams: Optional[int] = field(
          default=None,
          metadata={
              "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
              "which is used during ``evaluate`` and ``predict``."
          },
      )
      ignore_pad_token_for_loss: bool = field(
          default=True,
          metadata={
              "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
          },
      )
      source_prefix: Optional[str] = field(
          default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
      )
      forced_bos_token: Optional[str] = field(
          default=None,
          metadata={
              "help": "The token to force as the first generated token after the :obj:`decoder_start_token_id`."
              "Useful for multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token "
              "needs to be the target language token.(Usually it is the target language token)"
          },
      )
  
      def __post_init__(self):
          if self.dataset_name is None and self.train_file is None and self.validation_file is None:
              raise ValueError("Need either a dataset name or a training/validation file.")
          elif self.source_lang is None or self.target_lang is None:
              raise ValueError("Need to specify the source language and the target language.")
  
          if self.train_file is not None:
              extension = self.train_file.split(".")[-1]
              assert extension == "json", "`train_file` should be a json file."
          if self.validation_file is not None:
              extension = self.validation_file.split(".")[-1]
              assert extension == "json", "`validation_file` should be a json file."
          if self.val_max_target_length is None:
              self.val_max_target_length = self.max_target_length
  
  
  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, 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()
  
      if data_args.source_prefix is None and model_args.model_name_or_path in [
          "t5-small",
          "t5-base",
          "t5-large",
          "t5-3b",
          "t5-11b",
      ]:
          logger.warning(
              "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
              "`--source_prefix 'translate English to German: ' `"
          )
  
      # Detecting 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."
              )
  
      # 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)],
      )
      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}"
      )
      # 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(f"Training/evaluation parameters {training_args}")
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # Get the datasets: you can either provide your own JSON training and evaluation files (see below)
      # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
      # (the dataset will be downloaded automatically from the datasets Hub).
      #
      # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
      # source and target languages (unless you adapt what follows).
      #
      # In distributed training, the load_dataset function guarantee that only one local process can concurrently
      # download the dataset.
      if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
      else:
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
              extension = data_args.train_file.split(".")[-1]
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
              extension = data_args.validation_file.split(".")[-1]
          if data_args.test_file is not None:
              data_files["test"] = data_args.test_file
              extension = data_args.test_file.split(".")[-1]
          datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
      # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # 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,
          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 = AutoModelForSeq2SeqLM.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,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      model.resize_token_embeddings(len(tokenizer))
  
      # Set decoder_start_token_id
      if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
          if isinstance(tokenizer, MBartTokenizer):
              model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
          else:
              model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)
  
      if model.config.decoder_start_token_id is None:
          raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
  
      prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
  
      # Preprocessing the datasets.
      # We need to tokenize inputs and targets.
      if training_args.do_train:
          column_names = datasets["train"].column_names
      elif training_args.do_eval:
          column_names = datasets["validation"].column_names
      elif training_args.do_predict:
          column_names = datasets["test"].column_names
      else:
          logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
          return
  
      # For translation we set the codes of our source and target languages (only useful for mBART, the others will
      # ignore those attributes).
      if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
          assert data_args.target_lang is not None and data_args.source_lang is not None, (
              f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
              "--target_lang arguments."
          )
  
          tokenizer.src_lang = data_args.source_lang
          tokenizer.tgt_lang = data_args.target_lang
  
          # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
          # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
          forced_bos_token_id = (
              tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
          )
          model.config.forced_bos_token_id = forced_bos_token_id
  
      # Get the language codes for input/target.
      source_lang = data_args.source_lang.split("_")[0]
      target_lang = data_args.target_lang.split("_")[0]
  
      # Temporarily set max_target_length for training.
      max_target_length = data_args.max_target_length
      padding = "max_length" if data_args.pad_to_max_length else False
  
      if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
          logger.warning(
              "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
              f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
          )
  
      def preprocess_function(examples):
          inputs = [ex[source_lang] for ex in examples["translation"]]
          targets = [ex[target_lang] for ex in examples["translation"]]
          inputs = [prefix + inp for inp in inputs]
          model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
  
          # Setup the tokenizer for targets
          with tokenizer.as_target_tokenizer():
              labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
  
          # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
          # padding in the loss.
          if padding == "max_length" and data_args.ignore_pad_token_for_loss:
              labels["input_ids"] = [
                  [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
              ]
  
          model_inputs["labels"] = labels["input_ids"]
          return model_inputs
  
      if training_args.do_train:
          train_dataset = datasets["train"]
          if "train" not in datasets:
              raise ValueError("--do_train requires a train dataset")
          if data_args.max_train_samples is not None:
              train_dataset = train_dataset.select(range(data_args.max_train_samples))
          train_dataset = train_dataset.map(
              preprocess_function,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=column_names,
              load_from_cache_file=not data_args.overwrite_cache,
          )
  
      if training_args.do_eval:
          max_target_length = data_args.val_max_target_length
          if "validation" not in datasets:
              raise ValueError("--do_eval requires a validation dataset")
          eval_dataset = datasets["validation"]
          if data_args.max_eval_samples is not None:
              eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
          eval_dataset = eval_dataset.map(
              preprocess_function,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=column_names,
              load_from_cache_file=not data_args.overwrite_cache,
          )
  
      if training_args.do_predict:
          max_target_length = data_args.val_max_target_length
          if "test" not in datasets:
              raise ValueError("--do_predict requires a test dataset")
          predict_dataset = datasets["test"]
          if data_args.max_predict_samples is not None:
              predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
          predict_dataset = predict_dataset.map(
              preprocess_function,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=column_names,
              load_from_cache_file=not data_args.overwrite_cache,
          )
  
      # Data collator
      label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
      if data_args.pad_to_max_length:
          data_collator = default_data_collator
      else:
          data_collator = DataCollatorForSeq2Seq(
              tokenizer,
              model=model,
              label_pad_token_id=label_pad_token_id,
              pad_to_multiple_of=8 if training_args.fp16 else None,
          )
  
      # Metric
      metric = load_metric("sacrebleu")
  
      def postprocess_text(preds, labels):
          preds = [pred.strip() for pred in preds]
          labels = [[label.strip()] for label in labels]
  
          return preds, labels
  
      def compute_metrics(eval_preds):
          preds, labels = eval_preds
          if isinstance(preds, tuple):
              preds = preds[0]
          decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
          if data_args.ignore_pad_token_for_loss:
              # Replace -100 in the labels as we can't decode them.
              labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
          decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
  
          # Some simple post-processing
          decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
  
          result = metric.compute(predictions=decoded_preds, references=decoded_labels)
          result = {"bleu": result["score"]}
  
          prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
          result["gen_len"] = np.mean(prediction_lens)
          result = {k: round(v, 4) for k, v in result.items()}
          return result
  
      # Initialize our Trainer
      trainer = Seq2SeqTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset if training_args.do_train else None,
          eval_dataset=eval_dataset if training_args.do_eval else None,
          tokenizer=tokenizer,
          data_collator=data_collator,
          compute_metrics=compute_metrics if training_args.predict_with_generate else None,
      )
  
      # 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 tokenizer 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(train_dataset)
          )
          metrics["train_samples"] = min(max_train_samples, len(train_dataset))
  
          trainer.log_metrics("train", metrics)
          trainer.save_metrics("train", metrics)
          trainer.save_state()
  
      # Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          metrics = trainer.evaluate(
              max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval"
          )
          max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
          metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
  
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      if training_args.do_predict:
          logger.info("*** Predict ***")
  
          predict_results = trainer.predict(
              predict_dataset,
              metric_key_prefix="predict",
              max_length=data_args.val_max_target_length,
              num_beams=data_args.num_beams,
          )
          metrics = predict_results.metrics
          max_predict_samples = (
              data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
          )
          metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
  
          trainer.log_metrics("predict", metrics)
          trainer.save_metrics("predict", metrics)
  
          if trainer.is_world_process_zero():
              if training_args.predict_with_generate:
                  predictions = tokenizer.batch_decode(
                      predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
                  )
                  predictions = [pred.strip() for pred in predictions]
                  output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
                  with open(output_prediction_file, "w") as writer:
                      writer.write("\n".join(predictions))
  
      if training_args.push_to_hub:
          trainer.push_to_hub()
  
      return results
  
  
  def _mp_fn(index):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
      main()
  