text: #!/usr/bin/env python
  # coding=utf-8
  # Copyright {{cookiecutter.authors}} 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 a 🤗 Transformers model on {{cookiecutter.example_name}}.
  """
  # You can also adapt this script on your own {{cookiecutter.example_name}} task. Pointers for this are left as comments.
  
  {%- if cookiecutter.with_trainer == "True" %}
  
  import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import datasets
  from datasets import load_dataset
  
  import transformers
  from transformers import (
      CONFIG_MAPPING,
      MODEL_MAPPING,
      AutoConfig,
      {{cookiecutter.model_class}},
      AutoTokenizer,
      DataCollatorWithPadding,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import get_last_checkpoint
  
  
  logger = logging.getLogger(__name__)
  
  
  {%- if cookiecutter.can_train_from_scratch == "True" %}
  # You should update this to your particular problem to have better documentation of `model_type`
  MODEL_CONFIG_CLASSES = list(MODEL_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."},
      )
  {%- elif cookiecutter.can_train_from_scratch == "False" %}
  @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 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)."
          },
      )
  {% endif %}
  
  
  @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)."},
      )
      test_file: Optional[str] = field(
          default=None,
          metadata={"help": "An optional input test data file to predict the label on (a text 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_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."
          },
      )
  
      def __post_init__(self):
          if (
              self.dataset_name is None
              and self.train_file is None
              and self.validation_file is None
              and self.test_file is None
          ):
              raise ValueError("Need either a dataset name or a training/validation/test 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."
              if self.test_file is not None:
                  extension = self.test_file.split(".")[-1]
                  assert extension in ["csv", "json", "txt"], "`test_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",
          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()
  
      # 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 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.
          raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
      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]
          if extension == "txt":
              extension = "text"
          raw_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.
  {%- if cookiecutter.can_train_from_scratch == "True" %}
      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 = {{cookiecutter.model_class}}.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 = {{cookiecutter.model_class}}.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  {%- elif cookiecutter.can_train_from_scratch == "False" %}
      config = AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path,
          # num_labels=num_labels, Uncomment if you have a certain number of labels
          finetuning_task=data_args.task_name,
          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 = 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,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  {% endif %}
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
          column_names = raw_datasets["train"].column_names
      elif training_args.do_eval:
          column_names = raw_datasets["validation"].column_names
      elif training_args.do_predict:
          column_names = raw_datasets["test"].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], padding="max_length", truncation=True)
  
      if training_args.do_train:
          if "train" not in raw_datasets:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = raw_datasets["train"]
          if data_args.max_train_samples is not None:
              # Select Sample from Dataset
              train_dataset = train_dataset.select(range(data_args.max_train_samples))
          # tokenize train dataset in batch
          with training_args.main_process_first(desc="train dataset map tokenization"):
              train_dataset = train_dataset.map(
                  tokenize_function,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  remove_columns=[text_column_name],
                  load_from_cache_file=not data_args.overwrite_cache,
              )
  
      if training_args.do_eval:
          if "validation" not in raw_datasets:
              raise ValueError("--do_eval requires a validation dataset")
          eval_dataset = raw_datasets["validation"]
          # Selecting samples from dataset
          if data_args.max_eval_samples is not None:
              eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
          # tokenize validation dataset
          with training_args.main_process_first(desc="validation dataset map tokenization"):
              eval_dataset = eval_dataset.map(
                  tokenize_function,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  remove_columns=[text_column_name],
                  load_from_cache_file=not data_args.overwrite_cache,
              )
  
      if training_args.do_predict:
          if "test" not in raw_datasets:
              raise ValueError("--do_predict requires a test dataset")
          predict_dataset = raw_datasets["test"]
          # Selecting samples from dataset
          if data_args.max_predict_samples is not None:
              predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
          # tokenize predict dataset
          with training_args.main_process_first(desc="prediction dataset map tokenization"):
              predict_dataset = predict_dataset.map(
                  tokenize_function,
                  batched=True,
                  num_proc=data_args.preprocessing_num_workers,
                  remove_columns=[text_column_name],
                  load_from_cache_file=not data_args.overwrite_cache,
              )
  
      # Data collator
      data_collator=default_data_collator if not training_args.fp16 else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
  
      # Initialize our Trainer
      trainer = Trainer(
          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,
      )
  
      # Training
      if training_args.do_train:
  {%- if cookiecutter.can_train_from_scratch == "False" %}
          if last_checkpoint is not None:
              checkpoint = last_checkpoint
          elif os.path.isdir(model_args.model_name_or_path):
              checkpoint = model_args.model_name_or_path
          else:
              checkpoint = None
  {%- elif cookiecutter.can_train_from_scratch == "True" %}
          if last_checkpoint is not None:
              checkpoint = last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
              checkpoint = model_args.model_name_or_path
          else:
              checkpoint = None
  {% endif %}
          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
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          metrics = trainer.evaluate()
  
          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)
  
      # Prediction
      if training_args.do_predict:
          logger.info("*** Predict ***")
          predictions, labels, metrics = trainer.predict(predict_dataset)
  
          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)
          
          # write custom code for saving predictions according to task
  
  def _mp_fn(index):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
      main()
  
  {%- elif cookiecutter.with_trainer == "False" %}
  
  import argparse
  import logging
  import math
  import os
  import random
  
  import datasets
  from datasets import load_dataset, load_metric
  from torch.utils.data.dataloader import DataLoader
  from tqdm.auto import tqdm
  
  import transformers
  from accelerate import Accelerator
  from transformers import (
      CONFIG_MAPPING,
      MODEL_MAPPING,
      AdamW,
      AutoConfig,
      {{cookiecutter.model_class}},
      AutoTokenizer,
      DataCollatorWithPadding,
      PretrainedConfig,
      SchedulerType,
      default_data_collator,
      get_scheduler,
      set_seed,
  )
  
  
  logger = logging.getLogger(__name__)
  
  
  {%- if cookiecutter.can_train_from_scratch == "True" %}
  # You should update this to your particular problem to have better documentation of `model_type`
  MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
  MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
  {% endif %}
  
  def parse_args():
      parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help="The name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help= "The configuration name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
      )
      parser.add_argument(
          "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
      )
      parser.add_argument(
          "--max_length",
          type=int,
          default=128,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
              " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
          ),
      )
      parser.add_argument(
          "--pad_to_max_length",
          action="store_true",
          help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=True,
      )
      parser.add_argument(
          "--config_name",
          type=str,
          default=None,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          type=str,
          default=None,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--use_slow_tokenizer",
          action="store_true",
          help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
      parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
      )
      parser.add_argument(
          "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
      )
      parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
      parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
  {%- if cookiecutter.can_train_from_scratch == "True" %}
      parser.add_argument(
          "--model_type",
          type=str,
          default=None,
          help="Model type to use if training from scratch.",
          choices=MODEL_TYPES,
      )
  {% endif %}
      args = parser.parse_args()
  
      # Sanity checks
      if args.task_name is None and args.train_file is None and args.validation_file is None:
          raise ValueError("Need either a task name or a training/validation file.")
      else:
          if args.train_file is not None:
              extension = args.train_file.split(".")[-1]
              assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
          if args.validation_file is not None:
              extension = args.validation_file.split(".")[-1]
              assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
  
      if args.output_dir is not None:
          os.makedirs(args.output_dir, exist_ok=True)
  
      return args
  
  
  def main():
      args = parse_args()
  
      # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
      accelerator = Accelerator()
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state)
  
      # Setup logging, we only want one process per machine to log things on the screen.
      # accelerator.is_local_main_process is only True for one process per machine.
      logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(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 args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
      else:
          data_files = {}
          if args.train_file is not None:
              data_files["train"] = args.train_file
          if args.validation_file is not None:
              data_files["validation"] = args.validation_file
          extension = args.train_file.split(".")[-1]
          raw_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
      #
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  {%- if cookiecutter.can_train_from_scratch == "True" %}
      if model_args.config_name:
          config = AutoConfig.from_pretrained(args.model_name_or_path)
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(args.model_name_or_path)
      else:
          config = CONFIG_MAPPING[args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch.")
  
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
      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 = {{cookiecutter.model_class}}.from_pretrained(
              model_args.model_name_or_path,
              from_tf=bool(".ckpt" in model_args.model_name_or_path),
              config=config,
          )
      else:
          logger.info("Training new model from scratch")
          model = {{cookiecutter.model_class}}.from_config(config)
  
      model.resize_token_embeddings(len(tokenizer))
  {%- elif cookiecutter.can_train_from_scratch == "False" %}
      config = AutoConfig.from_pretrained(
          args.config_name if model_args.config_name else args.model_name_or_path,
          # num_labels=num_labels, Uncomment if you have a certain number of labels
          finetuning_task=data_args.task_name,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          args.tokenizer_name if model_args.tokenizer_name else args.model_name_or_path,
          use_fast=not args.use_slow_tokenizer,
      )
      model = AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
      )
  {% endif %}
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      column_names = raw_datasets["train"].column_names
      text_column_name = "text" if "text" in column_names else column_names[0]
  
      padding = "max_length" if args.pad_to_max_length else False
      def tokenize_function(examples):
          result = tokenizer(examples[text_column_name], padding=padding, max_length=args.max_length, truncation=True)
          if "label" in examples:
              result["labels"] = examples["label"]
          return result
  
      processed_datasets = raw_datasets.map(
          preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
      )
  
      train_dataset = processed_datasets["train"]
      eval_dataset = processed_datasets["validation"]
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
  
      # DataLoaders creation:
      if args.pad_to_max_length:
          # If padding was already done ot max length, we use the default data collator that will just convert everything
          # to tensors.
          data_collator = default_data_collator
      else:
          # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
          # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
          # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
          data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
  
      train_dataloader = DataLoader(
          train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
      )
      eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
  
      # Optimizer
      # Split weights in two groups, one with weight decay and the other not.
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
              "weight_decay": args.weight_decay,
          },
          {
              "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
              "weight_decay": 0.0,
          },
      ]
      optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
  
      # Prepare everything with our `accelerator`.
      model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
          model, optimizer, train_dataloader, eval_dataloader
      )
  
      # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
      # shorter in multiprocess)
  
      # Scheduler and math around the number of training steps.
      num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
      if args.max_train_steps is None:
          args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
      else:
          args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
  
      lr_scheduler = get_scheduler(
          name=args.lr_scheduler_type,
          optimizer=optimizer,
          num_warmup_steps=args.num_warmup_steps,
          num_training_steps=args.max_train_steps,
      )
  
      # TODO Get the proper metric function
      # metric = load_metric(xxx)
  
      # Train!
      total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
      logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
      logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
      logger.info(f"  Total optimization steps = {args.max_train_steps}")
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
      completed_steps = 0
  
      for epoch in range(args.num_train_epochs):
          model.train()
          for step, batch in enumerate(train_dataloader):
              outputs = model(**batch)
              loss = outputs.loss
              loss = loss / args.gradient_accumulation_steps
              accelerator.backward(loss)
              if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  progress_bar.update(1)
                  completed_steps += 1
  
              if completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          for step, batch in enumerate(eval_dataloader):
              outputs = model(**batch)
              predictions = outputs.logits.argmax(dim=-1)
              metric.add_batch(
                  predictions=accelerator.gather(predictions),
                  references=accelerator.gather(batch["labels"]),
              )
  
          eval_metric = metric.compute()
          logger.info(f"epoch {epoch}: {eval_metric}")
  
      if args.output_dir is not None:
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
  
  
  if __name__ == "__main__":
      main()
  
  {% endif %}
  