code: 
  
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=1024							,						SCREAMING_SNAKE_CASE__=1024							,						SCREAMING_SNAKE_CASE__=3.6							,						SCREAMING_SNAKE_CASE__=False							,						):
              lowercase			:   List[Any]					   =		tokenizer
              lowercase			:   Union[str, Any]					   =		tokenizer.bos_token_id
              lowercase			:   List[str]					   =		dataset
              lowercase			:   List[str]					   =		seq_length
              lowercase			:   List[str]					   =		0
              lowercase			:   Optional[int]					   =		infinite
              lowercase			:   Dict					   =		0
              lowercase			:   Dict					   =		tokenized
  
              if self.tokenized:
                    lowercase			:   Union[str, Any]					   =		seq_length * num_of_sequences
                    lowercase			:   str					   =		'''input_ids'''
              else:
                    lowercase			:   List[str]					   =		seq_length * chars_per_token * num_of_sequences
                    lowercase			:   Any					   =		'''content'''
  
  
        def __iter__(							self		):
              lowercase			:   Optional[Any]					   =		iter(self.dataset		)
              lowercase			:   Dict					   =		True
              while more_examples:
                    lowercase			:   Dict					   =		[], 0
                    while True:
                          if buffer_len >= self.max_buffer_size:
                                break
                          try:
                                buffer.append(next(SCREAMING_SNAKE_CASE__		)[self.content_field]		)
                                buffer_len += len(buffer[-1]		)
                          except StopIteration:
                                if self.infinite:
                                      lowercase			:   str					   =		iter(self.dataset		)
                                      self.epoch += 1
                                      logger.info(f"""Dataset epoch: {self.epoch}"""		)
                                else:
                                      lowercase			:   Union[str, Any]					   =		False
                                      break
                    if self.tokenized:
                          lowercase			:   Optional[Any]					   =		buffer
                    else:
                          lowercase			:   Optional[int]					   =		self.tokenizer(SCREAMING_SNAKE_CASE__							,						truncation=SCREAMING_SNAKE_CASE__		)['''input_ids''']
                    lowercase			:   str					   =		[]
                    for tokenized_input in tokenized_inputs:
                          all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
                    for i in range(0							,						len(SCREAMING_SNAKE_CASE__		)							,						self.seq_length		):
                          lowercase			:   Optional[Any]					   =		all_token_ids[i : i + self.seq_length]
                          if len(SCREAMING_SNAKE_CASE__		) == self.seq_length:
                                self.current_size += 1
                                yield torch.tensor(SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=1000		):
              return ShufflerIterDataPipe(self							,						buffer_size=SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		args.model_ckpt.split('''/'''     )[-1]
        lowercase			:   Optional[Any]					   =		logging.getLogger(__name__     )
        lowercase			:   Tuple					   =		Path(args.save_dir     ) / '''log/'''
        log_dir.mkdir(exist_ok=_UpperCamelCase     )
        lowercase			:   Dict					   =		f"""debug_{accelerator.process_index}.log"""
        logging.basicConfig(
            format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',    datefmt='''%m/%d/%Y %H:%M:%S''',    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename     ), logging.StreamHandler()],    )
        if accelerator.is_main_process:  # we only want to setup logging once
              accelerator.init_trackers(_UpperCamelCase,    vars(_UpperCamelCase     )     )
              lowercase			:   Tuple					   =		accelerator.trackers[0].run.name
              logger.setLevel(logging.INFO     )
              datasets.utils.logging.set_verbosity_info()
              transformers.utils.logging.set_verbosity_info()
        else:
              lowercase			:   Optional[Any]					   =		''''''
              logger.setLevel(logging.ERROR     )
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
        return logger, run_name
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		{'''streaming''': True}
        lowercase			:   Any					   =		load_dataset(args.dataset_name_train,    split='''train''',    **_UpperCamelCase     )
        lowercase			:   Tuple					   =		train_data.shuffle(buffer_size=args.shuffle_buffer,    seed=args.seed     )
        lowercase			:   Optional[Any]					   =		load_dataset(args.dataset_name_valid,    split='''train''',    **_UpperCamelCase     )
        lowercase			:   int					   =		ConstantLengthDataset(
            _UpperCamelCase,    _UpperCamelCase,    infinite=_UpperCamelCase,    seq_length=args.seq_length,    tokenized=args.tokenized     )
        lowercase			:   List[Any]					   =		ConstantLengthDataset(
            _UpperCamelCase,    _UpperCamelCase,    infinite=_UpperCamelCase,    seq_length=args.seq_length,    tokenized=args.tokenized     )
        lowercase			:   int					   =		train_dataset.shuffle(buffer_size=args.shuffle_buffer     )
        lowercase			:   str					   =		DataLoader(_UpperCamelCase,    batch_size=args.train_batch_size,    shuffle=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		DataLoader(_UpperCamelCase,    batch_size=args.valid_batch_size     )
        return train_dataloader, eval_dataloader
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		[], []
        for n, p in model.named_parameters():
              if any(nd in n for nd in no_decay     ):
                    params_without_wd.append(_UpperCamelCase     )
              else:
                    params_with_wd.append(_UpperCamelCase     )
        return [
            {"params": params_with_wd, "weight_decay": args.weight_decay},
            {"params": params_without_wd, "weight_decay": 0.0},
        ]
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        logger.info(f"""Step {step}: {metrics}"""     )
        if accelerator.is_main_process:
              accelerator.log(_UpperCamelCase,    _UpperCamelCase     )
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		accelerator.unwrap_model(_UpperCamelCase     ).config
        lowercase			:   Optional[int]					   =		4 if args.gradient_checkpointing else 3
        lowercase			:   Any					   =		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
        lowercase			:   Any					   =		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
        lowercase			:   Dict					   =		factor * (
            1.0
            + (args.seq_length / (6.0 * config_model.n_embd))
            + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
        )
        lowercase			:   Optional[Any]					   =		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
        return tflops
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        model.eval()
        lowercase			:   Dict					   =		[]
        for step, batch in enumerate(_UpperCamelCase     ):
              with torch.no_grad():
                    lowercase			:   int					   =		model(_UpperCamelCase,    labels=_UpperCamelCase     )
              lowercase			:   Union[str, Any]					   =		outputs.loss.repeat(args.valid_batch_size     )
              losses.append(accelerator.gather(_UpperCamelCase     )     )
              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                    break
        lowercase			:   Any					   =		torch.cat(_UpperCamelCase     )
        lowercase			:   Optional[Any]					   =		losses[: eval_dataloader.dataset.current_size].mean()
        try:
              lowercase			:   Tuple					   =		torch.exp(_UpperCamelCase     )
        except OverflowError:
              lowercase			:   Union[str, Any]					   =		float('''inf'''     )
        return loss.item(), perplexity.item()
  
  
  # Settings
  __a   				=      HfArgumentParser(TrainingArguments)
  __a   				=      parser.parse_args()
  
  # Accelerator
  __a   				=      ProjectConfiguration(project_dir=args.save_dir, logging_dir='''log''')
  __a   				=      Accelerator(log_with=['''wandb''', '''tensorboard'''], project_config=config)
  __a   				=      {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  __a   				=      Namespace(**vars(args), **acc_state)
  __a   				=      accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    __a   				=      Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  __a				,      __a   				=      setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
    hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  __a   				=      AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  __a   				=      AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  __a				,      __a   				=      create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  __a   				=      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  __a   				=      get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  
  
  
  
  
  def 			__lowercase	(						)    ->Optional[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  __a				,      __a				,      __a				,      __a   				=      accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
      accelerator.load_state(args.resume_from_checkpoint)
      __a   				=      os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      __a   				=      [f.name for f in os.scandir(args.save_dir) if f.is_dir() and '''step''' in str(f)]
      dirs.sort(key=os.path.getctime)
      __a   				=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    __a   				=      os.path.splitext(path)[0]
    __a   				=      int(training_difference.replace('''step_''', ''''''))
  
  # Train model
  model.train()
  __a   				=      0
  __a   				=      time.time()
  __a   				=      0
  for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
      continue  # we need to skip steps until we reach the resumed step
    __a   				=      model(batch, labels=batch, use_cache=False).loss
    __a   				=      accelerator.gather(loss.repeat(args.train_batch_size)).mean()
    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
    log_metrics(step, {'''samples''': step * samples_per_step, '''loss_per_step/train''': loss.item()})
    __a   				=      loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
      # Prevent backward from doing gradient all_reduce in every step
      if accelerator.distributed_type == DistributedType.MULTI_GPU:
        with model.no_sync():
          accelerator.backward(loss)
      else:
        accelerator.backward(loss)
    else:
      __a   				=      get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      __a   				=      time.time() - t_start
      __a   				=      compute_tflops(elapsed_time, accelerator, args)
      log_metrics(
          step,
          {
              '''steps''': completed_steps,
              '''loss/train''': loss_tracking,
              '''lr''': lr,
              '''tflops''': tflops,
              '''time_per_iteration''': elapsed_time,
          },
      )
      __a   				=      time.time()
      __a   				=      0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info('''Evaluating and saving model checkpoint''')
      __a				,      __a   				=      evaluate(args)
      log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
      accelerator.wait_for_everyone()
      __a   				=      os.path.join(args.save_dir, F'''step_{step}''')
      accelerator.save_state(save_dir)
      if accelerator.is_main_process:
        hf_repo.push_to_hub(commit_message=F'''step {step}''')
      model.train()
    if completed_steps >= args.max_train_steps:
      break
  
  # Evaluate and save the last checkpoint
  logger.info('''Evaluating and saving model after training''')
  __a				,      __a   				=      evaluate(args)
  log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
  accelerator.wait_for_everyone()
  __a   				=      accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  __a   				=      os.path.join(args.save_dir, F'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
    hf_repo.push_to_hub(commit_message='''final model''')
  
  
style_context: 
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
      datefmt='''%m/%d/%Y %H:%M:%S''',
      level=logging.INFO,
  )
  __a   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        np.random.seed(args.seed     )
        torch.manual_seed(args.seed     )
        if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
        parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
        parser.add_argument(
            '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()
  
        logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
        set_seed(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )
  
        # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
        # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
        model.to(args.device     )
  
        if args.fpaa:
              model.half()
  
        logger.info(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]
  
              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  