






"""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        __lowercase(       _a  ):






		def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=False    ,						_SCREAMING_SNAKE_CASE=10_24    ,						_SCREAMING_SNAKE_CASE=10_24    ,						_SCREAMING_SNAKE_CASE=3.6    ,						_SCREAMING_SNAKE_CASE=False    ,						)			->  Tuple:




							"""simple docstring"""





							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)			->  Tuple:




							"""simple docstring"""





							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(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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE=10_00)			->  int:




							"""simple docstring"""





							return ShufflerIterDataPipe(self    ,						buffer_size=lowerCamelCase__)




def 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=args.model_ckpt.split('/'			)[-1]
					lowerCamelCase__				=logging.getLogger(__name__			)
					lowerCamelCase__				=Path(args.save_dir			) / "log/"
					log_dir.mkdir(exist_ok=__A			)
					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(__A			,	vars(__A			)			)
										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_				(  __A  :		str			):
					lowerCamelCase__				={"streaming": True}
					lowerCamelCase__				=load_dataset(args.dataset_name_train			,	split='train'			,	**__A			)
					lowerCamelCase__				=train_data.shuffle(buffer_size=args.shuffle_buffer			,	seed=args.seed			)
					lowerCamelCase__				=load_dataset(args.dataset_name_valid			,	split='train'			,	**__A			)
					lowerCamelCase__				=ConstantLengthDataset(
					    __A			,	__A			,	infinite=__A			,	seq_length=args.seq_length			,	tokenized=args.tokenized			)
					lowerCamelCase__				=ConstantLengthDataset(
					    __A			,	__A			,	infinite=__A			,	seq_length=args.seq_length			,	tokenized=args.tokenized			)
					lowerCamelCase__				=train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
					lowerCamelCase__				=DataLoader(__A			,	batch_size=args.train_batch_size			,	shuffle=__A			)
					lowerCamelCase__				=DataLoader(__A			,	batch_size=args.valid_batch_size			)
					return train_dataloader, eval_dataloader




def 						lowerCamelCase_				(  __A  :		List[str]			,	__A  :		int			,	__A  :		int=["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(__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 						lowerCamelCase_				(  __A  :		str			,	__A  :		Dict			):
					logger.info(f"Step {step}: {metrics}"			)
					if accelerator.is_main_process:
										accelerator.log(__A			,	__A			)




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		str			,	__A  :		Dict			):
					# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
					lowerCamelCase__				=accelerator.unwrap_model(__A			).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 / (1_6.0 * config_model.n_layer * config_model.n_embd))
					)
					lowerCamelCase__				=flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
					return tflops




def 						lowerCamelCase_				(  __A  :		int			):
					model.eval()
					lowerCamelCase__				=[]
					for step, batch in enumerate(__A			):
										with torch.no_grad():
															lowerCamelCase__				=model(__A			,	labels=__A			)
										lowerCamelCase__				=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
					lowerCamelCase__				=torch.cat(__A			)
					lowerCamelCase__				=losses[: eval_dataloader.dataset.current_size].mean()
					try:
										lowerCamelCase__				=torch.exp(__A			)
					except OverflowError:
										lowerCamelCase__				=float('inf'			)
					return loss.item(), perplexity.item()


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

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

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

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

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

# Load dataset and dataloader
_snake_case       :      str       =					create_dataloaders(args)

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


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

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







"""simple docstring"""



from __future__ import annotations




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					assert n > 1 and a > 0 and greatest_common_divisor(__A			,	__A			) == 1
					((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	__A			)  # Implemented below
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	__A			)  # Implemented below
					if b < 0:
										lowerCamelCase__				=(b % n + n) % n
					return b




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					lowerCamelCase__				=invert_modulo(__A			,	__A			)
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					assert a >= 0 and b >= 0

					if b == 0:
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=a, 1, 0
					else:
										((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	a % b			)
										lowerCamelCase__				=q
										lowerCamelCase__				=p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if b == 0:
										return (1, 0)
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	a % b			)
					lowerCamelCase__				=a // b
					return (y, x - k * y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if a < b:
										lowerCamelCase__				,     lowerCamelCase__				=b, a

					while a % b != 0:
										lowerCamelCase__				,     lowerCamelCase__				=b, a % b

					return b


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="modular_division", verbose=True)
						testmod(name="modular_division2", verbose=True)
						testmod(name="invert_modulo", verbose=True)
						testmod(name="extended_gcd", verbose=True)
						testmod(name="extended_euclid", verbose=True)
						testmod(name="greatest_common_divisor", verbose=True)