text: # coding=utf-8
  # Copyright 2020 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 causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
  
  Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
  https://huggingface.co/models?filter=causal-lm
  """
  # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
  
  import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  from datasets import load_dataset
  
  import transformers
  from transformers import (
      CONFIG_MAPPING,
      MODEL_FOR_CAUSAL_LM_MAPPING,
      AutoConfig,
      AutoModelForCausalLM,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  
  
  logger = logging.getLogger(__name__)
  
  
  MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
  MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
  
  
  @dataclass
  class ModelArguments:
      """
      Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
      """
  
      model_name_or_path: Optional[str] = field(
          default=None,
          metadata={
              "help": "The model checkpoint for weights initialization."
              "Don't set if you want to train a model from scratch."
          },
      )
      model_type: Optional[str] = field(
          default=None,
          metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
      )
      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 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.
      """
  
      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 text file)."})
      validation_file: Optional[str] = field(
          default=None,
          metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
      )
      block_size: Optional[int] = field(
          default=None,
          metadata={
              "help": "Optional input sequence length after tokenization."
              "The training dataset will be truncated in block of this size for training."
              "Default to the model max input length for single sentence inputs (take into account special tokens)."
          },
      )
      overwrite_cache: bool = field(
          default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
      )
      validation_split_percentage: Optional[int] = field(
          default=5,
          metadata={
              "help": "The percentage of the train set used as validation set in case there's no validation split"
          },
      )
      preprocessing_num_workers: Optional[int] = field(
          default=None,
          metadata={"help": "The number of processes to use for the preprocessing."},
      )
  
      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.")
          else:
              if self.train_file is not None:
                  extension = self.train_file.split(".")[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
              if self.validation_file is not None:
                  extension = self.validation_file.split(".")[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
  
  
  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()
  
      # 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:
              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",
          level=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()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # Get the datasets: you can either provide your own CSV/JSON/TXT 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 CSV/JSON files, this script will use the column called 'text' or the first column if no column called
      # 'text' is found. You can easily tweak this behavior (see below).
      #
      # 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)
          if "validation" not in datasets.keys():
              datasets["validation"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[:{data_args.validation_split_percentage}%]",
              )
              datasets["train"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[{data_args.validation_split_percentage}%:]",
              )
      else:
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
          extension = data_args.train_file.split(".")[-1]
          if extension == "txt":
              extension = "text"
          datasets = load_dataset(extension, data_files=data_files)
      # 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_kwargs = {
          "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_args.config_name:
          config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
      else:
          config = CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch.")
  
      tokenizer_kwargs = {
          "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,
      }
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
      else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."
          )
  
      if model_args.model_name_or_path:
          model = AutoModelForCausalLM.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,
          )
      else:
          logger.info("Training new model from scratch")
          model = AutoModelForCausalLM.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
          column_names = datasets["train"].column_names
      else:
          column_names = datasets["validation"].column_names
      text_column_name = "text" if "text" in column_names else column_names[0]
  
      def tokenize_function(examples):
          return tokenizer(examples[text_column_name])
  
      tokenized_datasets = datasets.map(
          tokenize_function,
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
          remove_columns=column_names,
          load_from_cache_file=not data_args.overwrite_cache,
      )
  
      if data_args.block_size is None:
          block_size = tokenizer.model_max_length
          if block_size > 1024:
              logger.warn(
                  f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                  "Picking 1024 instead. You can change that default value by passing --block_size xxx."
              )
          block_size = 1024
      else:
          if data_args.block_size > tokenizer.model_max_length:
              logger.warn(
                  f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
                  f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
              )
          block_size = min(data_args.block_size, tokenizer.model_max_length)
  
      # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
      def group_texts(examples):
          # Concatenate all texts.
          concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
          total_length = len(concatenated_examples[list(examples.keys())[0]])
          # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
          # customize this part to your needs.
          total_length = (total_length // block_size) * block_size
          # Split by chunks of max_len.
          result = {
              k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
              for k, t in concatenated_examples.items()
          }
          result["labels"] = result["input_ids"].copy()
          return result
  
      # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
      # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
      # to preprocess.
      #
      # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
      # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
      lm_datasets = tokenized_datasets.map(
          group_texts,
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
          load_from_cache_file=not data_args.overwrite_cache,
      )
  
      # Initialize our Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=lm_datasets["train"] if training_args.do_train else None,
          eval_dataset=lm_datasets["validation"] if training_args.do_eval else None,
          tokenizer=tokenizer,
          # Data collator will default to DataCollatorWithPadding, so we change it.
          data_collator=default_data_collator,
      )
  
      # Training
      if training_args.do_train:
          if last_checkpoint is not None:
              model_path = last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
              model_path = model_args.model_name_or_path
          else:
              model_path = None
          train_result = trainer.train(model_path=model_path)
          trainer.save_model()  # Saves the tokenizer too for easy upload
  
          output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
          if trainer.is_world_process_zero():
              with open(output_train_file, "w") as writer:
                  logger.info("***** Train results *****")
                  for key, value in sorted(train_result.metrics.items()):
                      logger.info(f"  {key} = {value}")
                      writer.write(f"{key} = {value}\n")
  
              # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
              trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
  
      # Evaluation
      results = {}
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          eval_output = trainer.evaluate()
  
          perplexity = math.exp(eval_output["eval_loss"])
          results["perplexity"] = perplexity
  
          output_eval_file = os.path.join(training_args.output_dir, "eval_results_clm.txt")
          if trainer.is_world_process_zero():
              with open(output_eval_file, "w") as writer:
                  logger.info("***** Eval results *****")
                  for key, value in sorted(results.items()):
                      logger.info(f"  {key} = {value}")
                      writer.write(f"{key} = {value}\n")
  
      return results
  
  
  def _mp_fn(index):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
      main()
  