code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)
  
     print(model.load_state_dict(a_				)				)
     model.eval()
     model.save_pretrained(a_				)
  
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  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     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					tokenizer
            _UpperCAmelCase   	=					tokenizer.bos_token_id
            _UpperCAmelCase   	=					dataset
            _UpperCAmelCase   	=					seq_length
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					infinite
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					tokenized
  
            if self.tokenized:
               _UpperCAmelCase   	=					seq_length * num_of_sequences
               _UpperCAmelCase   	=					"input_ids"
            else:
               _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
               _UpperCAmelCase   	=					"content"
  
  
  
  
  
  
  
         def __iter__(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					iter(self.dataset		)
            _UpperCAmelCase   	=					True
            while more_examples:
               _UpperCAmelCase	,							_UpperCAmelCase   	=					[], 0
               while True:
                  if buffer_len >= self.max_buffer_size:
                     break
                  try:
                     buffer.append(next(lowerCamelCase_		)[self.content_field]		)
                     buffer_len += len(buffer[-1]		)
                  except StopIteration:
                     if self.infinite:
                        _UpperCAmelCase   	=					iter(self.dataset		)
                        self.epoch += 1
                        logger.info(f'''Dataset epoch: {self.epoch}'''		)
                     else:
                        _UpperCAmelCase   	=					False
                        break
               if self.tokenized:
                  _UpperCAmelCase   	=					buffer
               else:
                  _UpperCAmelCase   	=					self.tokenizer(lowerCamelCase_							,truncation=lowerCamelCase_		)["input_ids"]
               _UpperCAmelCase   	=					[]
               for tokenized_input in tokenized_inputs:
                  all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
               for i in range(0							,len(lowerCamelCase_		)							,self.seq_length		):
                  _UpperCAmelCase   	=					all_token_ids[i : i + self.seq_length]
                  if len(lowerCamelCase_		) == self.seq_length:
                     self.current_size += 1
                     yield torch.tensor(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=1000		):
  
  
  
            '''simple docstring'''
  
            return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)
  
  
  
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
     _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
     _UpperCAmelCase   	=					logging.getLogger(__name__				)
     _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
     log_dir.mkdir(exist_ok=a_				)
     _UpperCAmelCase   	=					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(a_    ,  vars(a_				)				)
        _UpperCAmelCase   	=					accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO				)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
     else:
        _UpperCAmelCase   	=					""
        logger.setLevel(logging.ERROR				)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
     return logger, run_name
  def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
     _UpperCAmelCase   	=					{"streaming": True}
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
     return train_dataloader, eval_dataloader
  def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
     _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
     for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay				):
           params_without_wd.append(a_				)
        else:
           params_with_wd.append(a_				)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
     logger.info(F'''Step {step}: {metrics}'''				)
     if accelerator.is_main_process:
        accelerator.log(a_    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
     _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
     _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _UpperCAmelCase   	=					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))
     )
     _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     model.eval()
     _UpperCAmelCase   	=					[]
     for step, batch in enumerate(a_				):
        with torch.no_grad():
           _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
        _UpperCAmelCase   	=					outputs.loss.repeat(args.valid_batch_size				)
        losses.append(accelerator.gather(a_				)				)
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
           break
     _UpperCAmelCase   	=					torch.cat(a_				)
     _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
     try:
        _UpperCAmelCase   	=					torch.exp(a_				)
     except OverflowError:
        _UpperCAmelCase   	=					float("inf"				)
     return loss.item(), perplexity.item()
  
  
  # Settings
  SCREAMING_SNAKE_CASE_     				=   HfArgumentParser(TrainingArguments)
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
  # Accelerator
  SCREAMING_SNAKE_CASE_     				=   ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
  SCREAMING_SNAKE_CASE_     				=   Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
  SCREAMING_SNAKE_CASE_     				=   {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  SCREAMING_SNAKE_CASE_     				=   Namespace(**vars(args), **acc_state)
  SCREAMING_SNAKE_CASE_     				=   accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    SCREAMING_SNAKE_CASE_     				=   Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   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
  SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  SCREAMING_SNAKE_CASE_     				=   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 			__UpperCAmelCase		(		)     ->       int:
     return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   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)
      SCREAMING_SNAKE_CASE_     				=   os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      SCREAMING_SNAKE_CASE_     				=   [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)
      SCREAMING_SNAKE_CASE_     				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    SCREAMING_SNAKE_CASE_     				=   os.path.splitext(path)[0]
    SCREAMING_SNAKE_CASE_     				=   int(training_difference.replace('step_', ''))
  
  # Train model
  model.train()
  SCREAMING_SNAKE_CASE_     				=   0
  SCREAMING_SNAKE_CASE_     				=   time.time()
  SCREAMING_SNAKE_CASE_     				=   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
    SCREAMING_SNAKE_CASE_     				=   model(batch, labels=batch, use_cache=False).loss
    SCREAMING_SNAKE_CASE_     				=   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()})
    SCREAMING_SNAKE_CASE_     				=   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:
      SCREAMING_SNAKE_CASE_     				=   get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      SCREAMING_SNAKE_CASE_     				=   time.time() - t_start
      SCREAMING_SNAKE_CASE_     				=   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,
          },
      )
      SCREAMING_SNAKE_CASE_     				=   time.time()
      SCREAMING_SNAKE_CASE_     				=   0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      SCREAMING_SNAKE_CASE_     				=   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')
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE_     				=   accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  SCREAMING_SNAKE_CASE_     				=   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')
  
  