


'''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 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def __init__(     self       :			List[str]   ,     lowerCAmelCase       :			List[Any]   ,     lowerCAmelCase       :			List[str]   ,     lowerCAmelCase       :			List[str]=False   ,     lowerCAmelCase       :			Any=10_24   ,     lowerCAmelCase       :			List[str]=10_24   ,     lowerCAmelCase       :			List[str]=3.6   ,     lowerCAmelCase       :			Tuple=False   ,     ):
													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       :			Tuple  ):
													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(lowerCAmelCase  )[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(lowerCAmelCase   ,     truncation=lowerCAmelCase  )["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(lowerCAmelCase  )   ,     self.seq_length  ):
																											A_ 						=		all_token_ids[i : i + self.seq_length]
																											if len(lowerCAmelCase  ) == self.seq_length:
																																		self.current_size += 1
																																		yield torch.tensor(lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			Any=10_00  ):
													return ShufflerIterDataPipe(self   ,     buffer_size=lowerCAmelCase  )







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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   ):
							A_						,				A_ 						=		[], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay   ):
																					params_without_wd.append(UpperCamelCase_   )
														else:
																					params_with_wd.append(UpperCamelCase_   )
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]







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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							A_ 						=		accelerator.unwrap_model(UpperCamelCase_   ).config
							A_ 						=		4 if args.gradient_checkpointing else 3
							A_ 						=		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							A_ 						=		2_4 * 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 / (16.0 * config_model.n_layer * config_model.n_embd))
							)
							A_ 						=		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
							return tflops







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


# Settings
__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		HfArgumentParser(TrainingArguments)
__SCREAMING_SNAKE_CASE				:							int		       =		parser.parse_args()

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

__SCREAMING_SNAKE_CASE				:							Any		       =		Namespace(**vars(args), **acc_state)
__SCREAMING_SNAKE_CASE				:							int		       =		accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

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

# Load dataset and dataloader
__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE				:							List[str]		       =		create_dataloaders(args)

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


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

# Train model
model.train()
__SCREAMING_SNAKE_CASE				:							int		       =		0
__SCREAMING_SNAKE_CASE				:							Tuple		       =		time.time()
__SCREAMING_SNAKE_CASE				:							Dict		       =		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
							__SCREAMING_SNAKE_CASE				:							Tuple		       =		model(batch, labels=batch, use_cache=False).loss
							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		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()})
							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		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:
														__SCREAMING_SNAKE_CASE				:							str		       =		get_lr()
														accelerator.backward(loss)
														accelerator.clip_grad_norm_(model.parameters(), 1.0)
														optimizer.step()
														lr_scheduler.step()
														optimizer.zero_grad()
														__SCREAMING_SNAKE_CASE				:							int		       =		time.time() - t_start
														__SCREAMING_SNAKE_CASE				:							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,
														    },
														)
														__SCREAMING_SNAKE_CASE				:							List[Any]		       =		time.time()
														__SCREAMING_SNAKE_CASE				:							List[Any]		       =		0
														completed_steps += 1
							if step % args.save_checkpoint_steps == 0:
														logger.info('''Evaluating and saving model checkpoint''')
														__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE				:							Any		       =		evaluate(args)
														log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
														accelerator.wait_for_everyone()
														__SCREAMING_SNAKE_CASE				:							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''')
__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE				:							Union[str, Any]		       =		evaluate(args)
log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
accelerator.wait_for_everyone()
__SCREAMING_SNAKE_CASE				:							List[str]		       =		accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__SCREAMING_SNAKE_CASE				:							Optional[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'''
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = 1_0_0_0   ):
							if (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (digit_position <= 0):
														raise ValueError("Digit position must be a positive integer"   )
							elif (not isinstance(UpperCamelCase_	,     UpperCamelCase_   )) or (precision < 0):
														raise ValueError("Precision must be a nonnegative integer"   )

							# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
							# accurate
							A_ 						=		(
							    4 * _subsum(UpperCamelCase_	,     1	,     UpperCamelCase_   )
							    - 2 * _subsum(UpperCamelCase_	,     4	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     5	,     UpperCamelCase_   )
							    - _subsum(UpperCamelCase_	,     6	,     UpperCamelCase_   )
							)

							# return the first hex digit of the fractional part of the result
							return hex(int((sum_result % 1) * 1_6   )   )[2:]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		0.0
							for sum_index in range(digit_pos_to_extract + precision   ):
														A_ 						=		8 * sum_index + denominator_addend
														if sum_index < digit_pos_to_extract:
																					# if the exponential term is an integer and we mod it by the denominator
																					# before dividing, only the integer part of the sum will change;
																					# the fractional part will not
																					A_ 						=		pow(
																					    1_6	,     digit_pos_to_extract - 1 - sum_index	,     UpperCamelCase_   )
														else:
																					A_ 						=		pow(1_6	,     digit_pos_to_extract - 1 - sum_index   )
														total += exponential_term / denominator
							return total


if __name__ == "__main__":
							import doctest

							doctest.testmod()





