


'''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  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__=False   ,					__magic_name__=10_24   ,					__magic_name__=10_24   ,					__magic_name__=3.6   ,					__magic_name__=False   ,					)       ->				List[str]:
      _a         =				tokenizer
      _a         =				tokenizer.bos_token_id
      _a         =				dataset
      _a         =				seq_length
      _a         =				0
      _a         =				infinite
      _a         =				0
      _a         =				tokenized

      if self.tokenized:
           _a         =				seq_length * num_of_sequences
           _a         =				'input_ids'
      else:
           _a         =				seq_length * chars_per_token * num_of_sequences
           _a         =				'content'
 def __iter__(			self    )       ->				List[str]:
      _a         =				iter(self.dataset    )
      _a         =				True
      while more_examples:
           _a   , _a         =				[], 0
           while True:
                if buffer_len >= self.max_buffer_size:
                     break
                try:
                     buffer.append(next(__magic_name__    )[self.content_field]    )
                     buffer_len += len(buffer[-1]    )
                except StopIteration:
                     if self.infinite:
                          _a         =				iter(self.dataset    )
                          self.epoch += 1
                          logger.info(f'Dataset epoch: {self.epoch}'    )
                     else:
                          _a         =				False
                          break
           if self.tokenized:
                _a         =				buffer
           else:
                _a         =				self.tokenizer(__magic_name__   ,					truncation=__magic_name__    )['input_ids']
           _a         =				[]
           for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id]    )
           for i in range(0   ,					len(__magic_name__    )   ,					self.seq_length    ):
                _a         =				all_token_ids[i : i + self.seq_length]
                if len(__magic_name__    ) == self.seq_length:
                     self.current_size += 1
                     yield torch.tensor(__magic_name__    )




 def 				__UpperCAmelCase		(			self   ,					__magic_name__=10_00    )       ->				Tuple:
      return ShufflerIterDataPipe(self   ,					buffer_size=__magic_name__    )






def 						_A					(lowerCAmelCase__	:Dict		) ->	Tuple:




     '''simple docstring'''



     _a         =				args.model_ckpt.split('/'		)[-1]
     _a         =				logging.getLogger(__name__		)
     _a         =				Path(args.save_dir		) / 'log/'
     log_dir.mkdir(exist_ok=lowerCAmelCase__		)
     _a         =				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(lowerCAmelCase__					,    vars(lowerCAmelCase__		)		)
          _a         =				accelerator.trackers[0].run.name
          logger.setLevel(logging.INFO		)
          datasets.utils.logging.set_verbosity_info()
          transformers.utils.logging.set_verbosity_info()
     else:
          _a         =				''
          logger.setLevel(logging.ERROR		)
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
     return logger, run_name






def 						_A					(lowerCAmelCase__	:Optional[int]		) ->	int:




     '''simple docstring'''



     _a         =				{'streaming': True}
     _a         =				load_dataset(args.dataset_name_train					,    split='train'					,    **lowerCAmelCase__		)
     _a         =				train_data.shuffle(buffer_size=args.shuffle_buffer					,    seed=args.seed		)
     _a         =				load_dataset(args.dataset_name_valid					,    split='train'					,    **lowerCAmelCase__		)
     _a         =				ConstantLengthDataset(
         lowerCAmelCase__					,    lowerCAmelCase__					,    infinite=lowerCAmelCase__					,    seq_length=args.seq_length					,    tokenized=args.tokenized		)
     _a         =				ConstantLengthDataset(
         lowerCAmelCase__					,    lowerCAmelCase__					,    infinite=lowerCAmelCase__					,    seq_length=args.seq_length					,    tokenized=args.tokenized		)
     _a         =				train_dataset.shuffle(buffer_size=args.shuffle_buffer		)
     _a         =				DataLoader(lowerCAmelCase__					,    batch_size=args.train_batch_size					,    shuffle=lowerCAmelCase__		)
     _a         =				DataLoader(lowerCAmelCase__					,    batch_size=args.valid_batch_size		)
     return train_dataloader, eval_dataloader






def 						_A					(lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:Any=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		) ->	Dict:




     '''simple docstring'''



     _a   , _a         =				[], []
     for n, p in model.named_parameters():
          if any(nd in n for nd in no_decay		):
               params_without_wd.append(lowerCAmelCase__		)
          else:
               params_with_wd.append(lowerCAmelCase__		)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]






def 						_A					(lowerCAmelCase__	:str					,    lowerCAmelCase__	:Union[str, Any]		) ->	int:




     '''simple docstring'''



     logger.info(f'Step {step}: {metrics}'		)
     if accelerator.is_main_process:
          accelerator.log(lowerCAmelCase__					,    lowerCAmelCase__		)






def 						_A					(lowerCAmelCase__	:List[Any]					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:Tuple		) ->	List[Any]:




     '''simple docstring'''



     _a         =				accelerator.unwrap_model(lowerCAmelCase__		).config
     _a         =				4 if args.gradient_checkpointing else 3
     _a         =				args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _a         =				24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _a         =				factor * (
         1.0
         + (args.seq_length / (6.0 * config_model.n_embd))
         + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
     )
     _a         =				flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops






def 						_A					(lowerCAmelCase__	:Any		) ->	Union[str, Any]:




     '''simple docstring'''



     model.eval()
     _a         =				[]
     for step, batch in enumerate(lowerCAmelCase__		):
          with torch.no_grad():
               _a         =				model(lowerCAmelCase__					,    labels=lowerCAmelCase__		)
          _a         =				outputs.loss.repeat(args.valid_batch_size		)
          losses.append(accelerator.gather(lowerCAmelCase__		)		)
          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     _a         =				torch.cat(lowerCAmelCase__		)
     _a         =				losses[: eval_dataloader.dataset.current_size].mean()
     try:
          _a         =				torch.exp(lowerCAmelCase__		)
     except OverflowError:
          _a         =				float('inf'		)
     return loss.item(), perplexity.item()


# Settings
a_       :     str 							=     HfArgumentParser(TrainingArguments)
a_       :     Any 							=     parser.parse_args()

# Accelerator
a_       :     Union[str, Any] 							=     ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
a_       :     Optional[Any] 							=     Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
a_       :     List[str] 							=     {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

a_       :     str 							=     Namespace(**vars(args), **acc_state)
a_       :     Union[str, Any] 							=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
 a_       :     Union[str, Any] 							=     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
a_							,				a_       :     List[str] 							=     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_       :     Optional[int] 							=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
 model.gradient_checkpointing_enable()
a_       :     Any 							=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
a_							,				a_       :     Any 							=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
a_       :     Dict 							=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
a_       :     Any 							=     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 						_A					() ->	Dict:




     '''simple docstring'''



     return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
a_							,				a_							,				a_							,				a_       :     Any 							=     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_       :     Any 							=     os.path.basename(args.resume_from_checkpoint)
 else:
  # Get the most recent checkpoint
  a_       :     Dict 							=     [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_       :     List[str] 							=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
 # Extract the step of the checkpoint to continue from there
 a_       :     List[Any] 							=     os.path.splitext(path)[0]
 a_       :     int 							=     int(training_difference.replace("step_", ""))

# Train model
model.train()
a_       :     Dict 							=     0
a_       :     str 							=     time.time()
a_       :     Optional[int] 							=     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_       :     int 							=     model(batch, labels=batch, use_cache=False).loss
 a_       :     str 							=     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_       :     List[str] 							=     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_       :     str 							=     get_lr()
  accelerator.backward(loss)
  accelerator.clip_grad_norm_(model.parameters(), 1.0)
  optimizer.step()
  lr_scheduler.step()
  optimizer.zero_grad()
  a_       :     Any 							=     time.time() - t_start
  a_       :     Optional[Any] 							=     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_       :     str 							=     time.time()
  a_       :     Tuple 							=     0
  completed_steps += 1
 if step % args.save_checkpoint_steps == 0:
  logger.info("Evaluating and saving model checkpoint")
  a_							,				a_       :     Union[str, Any] 							=     evaluate(args)
  log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
  accelerator.wait_for_everyone()
  a_       :     Union[str, Any] 							=     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_       :     Tuple 							=     evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
a_       :     Union[str, Any] 							=     accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
a_       :     int 							=     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")





'''simple docstring'''






import math
from decimal import Decimal, getcontext






def 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'		)
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


if __name__ == "__main__":
 print(f'''{solution() = }''')

