
'''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     a (					__magic_name__     ):




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

										if self.tokenized:
																snake_case							:  Optional[Any]			=     seq_length * num_of_sequences
																snake_case							:  Dict			=     '''input_ids'''
										else:
																snake_case							:  List[str]			=     seq_length * chars_per_token * num_of_sequences
																snake_case							:  int			=     '''content'''




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




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




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

def   A			(							A_				:       Optional[Any]							):
						snake_case							:  Dict			=     {'''streaming''': True}
						snake_case							:  Any			=     load_dataset(args.dataset_name_train     ,				split='''train'''     ,				**A_							)
						snake_case							:  Optional[int]			=     train_data.shuffle(buffer_size=args.shuffle_buffer     ,				seed=args.seed							)
						snake_case							:  Tuple			=     load_dataset(args.dataset_name_valid     ,				split='''train'''     ,				**A_							)
						snake_case							:  Optional[int]			=     ConstantLengthDataset(
						    A_     ,				A_     ,				infinite=A_     ,				seq_length=args.seq_length     ,				tokenized=args.tokenized							)
						snake_case							:  Optional[int]			=     ConstantLengthDataset(
						    A_     ,				A_     ,				infinite=A_     ,				seq_length=args.seq_length     ,				tokenized=args.tokenized							)
						snake_case							:  Optional[int]			=     train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
						snake_case							:  Any			=     DataLoader(A_     ,				batch_size=args.train_batch_size     ,				shuffle=A_							)
						snake_case							:  Optional[int]			=     DataLoader(A_     ,				batch_size=args.valid_batch_size							)
						return train_dataloader, eval_dataloader

def   A			(							A_				:       List[Any]     ,				A_				:       Dict     ,				A_				:       Optional[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):
						snake_case,       snake_case							:  Optional[Any]			=     [], []
						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   A			(							A_				:       Union[str, Any]     ,				A_				:       Optional[Any]							):
						logger.info(F"""Step {step}: {metrics}"""							)
						if accelerator.is_main_process:
												accelerator.log(A_     ,				A_							)

def   A			(							A_				:       Optional[int]     ,				A_				:       Optional[int]     ,				A_				:       Dict							):
						# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
						snake_case							:  Dict			=     accelerator.unwrap_model(A_							).config
						snake_case							:  Any			=     4 if args.gradient_checkpointing else 3
						snake_case							:  List[Any]			=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						snake_case							:  str			=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						snake_case							:  Dict			=     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))
						)
						snake_case							:  Dict			=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops

def   A			(							A_				:       Optional[Any]							):
						model.eval()
						snake_case							:  Optional[int]			=     []
						for step, batch in enumerate(A_							):
												with torch.no_grad():
																		snake_case							:  str			=     model(A_     ,				labels=A_							)
												snake_case							:  Optional[Any]			=     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
						snake_case							:  int			=     torch.cat(A_							)
						snake_case							:  Optional[int]			=     losses[: eval_dataloader.dataset.current_size].mean()
						try:
												snake_case							:  List[str]			=     torch.exp(A_							)
						except OverflowError:
												snake_case							:  Tuple			=     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       ,						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       ,						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   A			(							):
						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCAmelCase       ,						UpperCAmelCase       ,						UpperCAmelCase       ,						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       ,						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       ,						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"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


