"""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        __UpperCAmelCase						(     __lowerCAmelCase     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=False     ,			_lowerCamelCase=1024     ,			_lowerCamelCase=1024     ,			_lowerCamelCase=3.6     ,			_lowerCamelCase=False     ,			):
										lowerCamelCase__ =tokenizer
										lowerCamelCase__ =tokenizer.bos_token_id
										lowerCamelCase__ =dataset
										lowerCamelCase__ =seq_length
										lowerCamelCase__ =0
										lowerCamelCase__ =infinite
										lowerCamelCase__ =0
										lowerCamelCase__ =tokenized

										if self.tokenized:
																lowerCamelCase__ =seq_length * num_of_sequences
																lowerCamelCase__ ="input_ids"
										else:
																lowerCamelCase__ =seq_length * chars_per_token * num_of_sequences
																lowerCamelCase__ ="content"

				def __iter__(				self				):
										lowerCamelCase__ =iter(self.dataset				)
										lowerCamelCase__ =True
										while more_examples:
																lowerCamelCase__ ,				lowerCamelCase__ =[], 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:
																																		lowerCamelCase__ =iter(self.dataset				)
																																		self.epoch += 1
																																		logger.info(F'''Dataset epoch: {self.epoch}'''				)
																												else:
																																		lowerCamelCase__ =False
																																		break
																if self.tokenized:
																						lowerCamelCase__ =buffer
																else:
																						lowerCamelCase__ =self.tokenizer(_lowerCamelCase     ,			truncation=_lowerCamelCase				)["input_ids"]
																lowerCamelCase__ =[]
																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				):
																						lowerCamelCase__ =all_token_ids[i : i + self.seq_length]
																						if len(_lowerCamelCase				) == self.seq_length:
																												self.current_size += 1
																												yield torch.tensor(_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase=1000				):
										return ShufflerIterDataPipe(self     ,			buffer_size=_lowerCamelCase				)





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Dict:




						'''simple docstring'''



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





def       lowerCamelCase_			( __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =[], []
						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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> str:




						'''simple docstring'''



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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ =accelerator.unwrap_model(__lowerCAmelCase    ).config
						lowerCamelCase__ =4 if args.gradient_checkpointing else 3
						lowerCamelCase__ =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						lowerCamelCase__ =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						lowerCamelCase__ =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))
						)
						lowerCamelCase__ =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						model.eval()
						lowerCamelCase__ =[]
						for step, batch in enumerate(__lowerCAmelCase    ):
												with torch.no_grad():
																		lowerCamelCase__ =model(__lowerCAmelCase       ,   labels=__lowerCAmelCase    )
												lowerCamelCase__ =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
						lowerCamelCase__ =torch.cat(__lowerCAmelCase    )
						lowerCamelCase__ =losses[: eval_dataloader.dataset.current_size].mean()
						try:
												lowerCamelCase__ =torch.exp(__lowerCAmelCase    )
						except OverflowError:
												lowerCamelCase__ =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       lowerCamelCase_			( )							-> str:




						'''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')



"""simple docstring"""


from __future__ import annotations





def       lowerCamelCase_			( __lowerCAmelCase    )							-> int | float:




						'''simple docstring'''



						lowerCamelCase__ =sorted(__lowerCAmelCase    )
						lowerCamelCase__ =len(__lowerCAmelCase    )
						lowerCamelCase__ =length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


