


'''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 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




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

										if self.tokenized:
														__magic_name__	  = seq_length * num_of_sequences
														__magic_name__	  = "input_ids"
										else:
														__magic_name__	  = seq_length * chars_per_token * num_of_sequences
														__magic_name__	  = "content"
						def __iter__( self				:					Optional[Any]  )							->					int:
										__magic_name__	  = iter(self.dataset  )
										__magic_name__	  = True
										while more_examples:
														__magic_name__  ,      __magic_name__	  = [], 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:
																										__magic_name__	  = iter(self.dataset  )
																										self.epoch += 1
																										logger.info(f'Dataset epoch: {self.epoch}'  )
																						else:
																										__magic_name__	  = False
																										break
														if self.tokenized:
																		__magic_name__	  = buffer
														else:
																		__magic_name__	  = self.tokenizer(_lowerCamelCase	,				truncation=_lowerCamelCase  )["input_ids"]
														__magic_name__	  = []
														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  ):
																		__magic_name__	  = all_token_ids[i : i + self.seq_length]
																		if len(_lowerCamelCase  ) == self.seq_length:
																						self.current_size += 1
																						yield torch.tensor(_lowerCamelCase  )

						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str=10_00  )							->					Union[str, Any]:
										return ShufflerIterDataPipe(self	,				buffer_size=_lowerCamelCase  )





def      __snake_case       (       lowerCamelCase_ :				Optional[Any]						):
				'''simple docstring'''




				__magic_name__	  = args.model_ckpt.split("/"						)[-1]
				__magic_name__	  = logging.getLogger(__name__						)
				__magic_name__	  = Path(args.save_dir						) / "log/"
				log_dir.mkdir(exist_ok=lowerCamelCase_						)
				__magic_name__	  = 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_						)						)
								__magic_name__	  = accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO						)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__magic_name__	  = ""
								logger.setLevel(logging.ERROR						)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name




def      __snake_case       (       lowerCamelCase_ :				Optional[int]						):
				'''simple docstring'''




				__magic_name__	  = {"streaming": True}
				__magic_name__	  = load_dataset(args.dataset_name_train	,  split="train"	,  **lowerCamelCase_						)
				__magic_name__	  = train_data.shuffle(buffer_size=args.shuffle_buffer	,  seed=args.seed						)
				__magic_name__	  = load_dataset(args.dataset_name_valid	,  split="train"	,  **lowerCamelCase_						)
				__magic_name__	  = ConstantLengthDataset(
				    lowerCamelCase_	,  lowerCamelCase_	,  infinite=lowerCamelCase_	,  seq_length=args.seq_length	,  tokenized=args.tokenized						)
				__magic_name__	  = ConstantLengthDataset(
				    lowerCamelCase_	,  lowerCamelCase_	,  infinite=lowerCamelCase_	,  seq_length=args.seq_length	,  tokenized=args.tokenized						)
				__magic_name__	  = train_dataset.shuffle(buffer_size=args.shuffle_buffer						)
				__magic_name__	  = DataLoader(lowerCamelCase_	,  batch_size=args.train_batch_size	,  shuffle=lowerCamelCase_						)
				__magic_name__	  = DataLoader(lowerCamelCase_	,  batch_size=args.valid_batch_size						)
				return train_dataloader, eval_dataloader




def      __snake_case       (       lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):
				'''simple docstring'''




				__magic_name__  ,      __magic_name__	  = [], []
				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      __snake_case       (       lowerCamelCase_ :				Tuple	,  lowerCamelCase_ :				int						):
				'''simple docstring'''




				logger.info(F'Step {step}: {metrics}'						)
				if accelerator.is_main_process:
								accelerator.log(lowerCamelCase_	,  lowerCamelCase_						)




def      __snake_case       (       lowerCamelCase_ :				Any	,  lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				__magic_name__	  = accelerator.unwrap_model(lowerCamelCase_						).config
				__magic_name__	  = 4 if args.gradient_checkpointing else 3
				__magic_name__	  = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__magic_name__	  = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__magic_name__	  = 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))
				)
				__magic_name__	  = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops




def      __snake_case       (       lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




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


# Settings
__magic_name__   :   Dict  							=HfArgumentParser(TrainingArguments)
__magic_name__   :   Any  							=parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
						__magic_name__   :   List[Any]  							=Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__magic_name__							,    __magic_name__   :   Dict  							=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
__magic_name__   :   Tuple  							=AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
__magic_name__   :   List[str]  							=AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
__magic_name__   :   List[str]  							=AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__magic_name__   :   List[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      __snake_case       (       ):
				'''simple docstring'''




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


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

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







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



