





from __future__ import annotations

__lowercase							:int							=	[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]



class  _a  :






							"""simple docstring"""

							def __init__(   self					:  Any				,						a					:  int     )						->None:
													SCREAMING_SNAKE_CASE__  :					int =       -1
													SCREAMING_SNAKE_CASE__  :					int =       -1
													SCREAMING_SNAKE_CASE__  :					list[int] =       [-1] * length
													SCREAMING_SNAKE_CASE__  :					Node | None =       None
													SCREAMING_SNAKE_CASE__  :					Node | None =       None






							def __repr__(   self					:  str     )						->str:
													return f"""Node(min_value={self.minn} max_value={self.maxx})"""







def    UpperCAmelCase				(  _lowerCamelCase   :	list[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       Node(len(_lowerCamelCase  )  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       min(_lowerCamelCase  ), max(_lowerCamelCase  )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (root.minn + root.maxx) // 2

						SCREAMING_SNAKE_CASE__  :					list[int] =       []
						SCREAMING_SNAKE_CASE__  :					list[int] =       []

						for index, num in enumerate(_lowerCamelCase  ):
												if num <= pivot:
																		left_arr.append(_lowerCamelCase  )
												else:
																		right_arr.append(_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       len(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       build_tree(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       build_tree(_lowerCamelCase  )
						return root







def    UpperCAmelCase				(  _lowerCamelCase   :	Node | None      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left      , _lowerCamelCase      , node.map_left[index] - 1  )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right      , _lowerCamelCase      , index - node.map_left[index]  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Node | None      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						if start > end:
												return 0
						SCREAMING_SNAKE_CASE__  :					Dict =       rank_till_index(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       rank_till_index(_lowerCamelCase      , _lowerCamelCase      , start - 1  )
						return rank_till_end - rank_before_start







def    UpperCAmelCase				(  _lowerCamelCase   :	Node | None      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						SCREAMING_SNAKE_CASE__  :					int =       node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left      , _lowerCamelCase      , (node.map_left[start - 1] if start else 0)      , node.map_left[end] - 1      , )
						else:
												return quantile(
												    node.right      , index - num_elements_in_left_tree      , start - (node.map_left[start - 1] if start else 0)      , end - node.map_left[end]      , )







def    UpperCAmelCase				(  _lowerCamelCase   :	Node | None      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						SCREAMING_SNAKE_CASE__  :					List[Any] =       range_counting(
						    node.left      , (node.map_left[start - 1] if start else 0)      , node.map_left[end] - 1      , _lowerCamelCase      , _lowerCamelCase      , )
						SCREAMING_SNAKE_CASE__  :					Dict =       range_counting(
						    node.right      , start - (node.map_left[start - 1] if start else 0)      , end - node.map_left[end]      , _lowerCamelCase      , _lowerCamelCase      , )
						return left + right


if __name__ == "__main__":
				import doctest

				doctest.testmod()






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  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  List[Any]				,						a					:  Tuple				,						a					:  Dict=False				,						a					:  Union[str, Any]=10_24				,						a					:  List[str]=10_24				,						a					:  Optional[Any]=3.6				,						a					:  List[Any]=False				,						)						->List[str]:
													SCREAMING_SNAKE_CASE__  :					str =       tokenizer
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tokenizer.bos_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataset
													SCREAMING_SNAKE_CASE__  :					List[Any] =       seq_length
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       infinite
													SCREAMING_SNAKE_CASE__  :					Tuple =       0
													SCREAMING_SNAKE_CASE__  :					Dict =       tokenized

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

							def __iter__(   self					:  Union[str, Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       iter(self.dataset     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
													while more_examples:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [], 0
																			while True:
																									if buffer_len >= self.max_buffer_size:
																															break
																									try:
																															buffer.append(next(a     )[self.content_field]     )
																															buffer_len += len(buffer[-1]     )
																									except StopIteration:
																															if self.infinite:
																																					SCREAMING_SNAKE_CASE__  :					Dict =       iter(self.dataset     )
																																					self.epoch += 1
																																					logger.info(f"""Dataset epoch: {self.epoch}"""     )
																															else:
																																					SCREAMING_SNAKE_CASE__  :					int =       False
																																					break
																			if self.tokenized:
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       buffer
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer(a				,						truncation=a     )["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Tuple =       []
																			for tokenized_input in tokenized_inputs:
																									all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																			for i in range(0				,						len(a     )				,						self.seq_length     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       all_token_ids[i : i + self.seq_length]
																									if len(a     ) == self.seq_length:
																															self.current_size += 1
																															yield torch.tensor(a     )






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						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				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       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))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						model.eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for step, batch in enumerate(_lowerCamelCase  ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE__  :					List[str] =       model(_lowerCamelCase      , labels=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       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
						SCREAMING_SNAKE_CASE__  :					str =       torch.cat(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       losses[: eval_dataloader.dataset.current_size].mean()
						try:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.exp(_lowerCamelCase  )
						except OverflowError:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       float("inf"  )
						return loss.item(), perplexity.item()


# Settings
__lowercase							:List[Any]							=	HfArgumentParser(TrainingArguments)
__lowercase							:Any							=	parser.parse_args()

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

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	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
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	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				(  ):





						'''simple docstring'''


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


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

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