




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_	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_=False							,			UpperCAmelCase_=10_24							,			UpperCAmelCase_=10_24							,			UpperCAmelCase_=3.6							,			UpperCAmelCase_=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 =		[], 0
																		while True:
																									if buffer_len >= self.max_buffer_size:
																																break
																									try:
																																buffer.append(next(UpperCAmelCase_    )[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(UpperCAmelCase_							,			truncation=UpperCAmelCase_    )['''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(UpperCAmelCase_    )							,			self.seq_length    ):
																									lowerCAmelCase =		all_token_ids[i : i + self.seq_length]
																									if len(UpperCAmelCase_    ) == self.seq_length:
																																self.current_size += 1
																																yield torch.tensor(UpperCAmelCase_    )






				def    __snake_case		(			self							,			UpperCAmelCase_=10_00    ):
											return ShufflerIterDataPipe(self							,			buffer_size=UpperCAmelCase_    )




def    UpperCAmelCase							(  _snake_case				):
							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    UpperCAmelCase							(  _snake_case				):
							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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				):
							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    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							logger.info(F"""Step {step}: {metrics}"""				)
							if accelerator.is_main_process:
														accelerator.log(__lowerCamelCase    ,			__lowerCamelCase				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case				):
							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    UpperCAmelCase							(  _snake_case				):
							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
UpperCAmelCase_    =HfArgumentParser(TrainingArguments)
UpperCAmelCase_    =parser.parse_args()

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

UpperCAmelCase_    =Namespace(**vars(args), **acc_state)
UpperCAmelCase_    =accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		UpperCAmelCase_    =Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCAmelCase_    =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
UpperCAmelCase_    =AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
UpperCAmelCase_    =AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCAmelCase_    =create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase_    =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase_    =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							(  ):
							return optimizer.param_groups[0]["lr"]


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

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






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




