



'''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 			__magic_name__		(					_UpperCamelCase     ):







							def __init__(							self      : Dict			,_UpperCAmelCase      : Optional[int]			,_UpperCAmelCase      : Any			,_UpperCAmelCase      : str=False			,_UpperCAmelCase      : List[str]=1024			,_UpperCAmelCase      : Optional[int]=1024			,_UpperCAmelCase      : Optional[int]=3.6			,_UpperCAmelCase      : Union[str, Any]=False			,):
									_a  :   Union[str, Any]				 =							tokenizer
									_a  :   List[str]				 =							tokenizer.bos_token_id
									_a  :   Union[str, Any]				 =							dataset
									_a  :   int				 =							seq_length
									_a  :   Tuple				 =							0
									_a  :   Optional[int]				 =							infinite
									_a  :   List[str]				 =							0
									_a  :   int				 =							tokenized

									if self.tokenized:
											_a  :   Union[str, Any]				 =							seq_length * num_of_sequences
											_a  :   Any				 =							'input_ids'
									else:
											_a  :   str				 =							seq_length * chars_per_token * num_of_sequences
											_a  :   Optional[Any]				 =							'content'







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







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Dict=1000	):
									return ShufflerIterDataPipe(self			,buffer_size=_UpperCAmelCase	)



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



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Union[str, Any]:
		_a  :   List[str]				 =							{'streaming': True}
		_a  :   Dict				 =							load_dataset(args.dataset_name_train		,    split='train'		,    **lowerCAmelCase_						)
		_a  :   List[str]				 =							train_data.shuffle(buffer_size=args.shuffle_buffer		,    seed=args.seed						)
		_a  :   Dict				 =							load_dataset(args.dataset_name_valid		,    split='train'		,    **lowerCAmelCase_						)
		_a  :   Dict				 =							ConstantLengthDataset(
		    lowerCAmelCase_		,    lowerCAmelCase_		,    infinite=lowerCAmelCase_		,    seq_length=args.seq_length		,    tokenized=args.tokenized						)
		_a  :   Dict				 =							ConstantLengthDataset(
		    lowerCAmelCase_		,    lowerCAmelCase_		,    infinite=lowerCAmelCase_		,    seq_length=args.seq_length		,    tokenized=args.tokenized						)
		_a  :   List[str]				 =							train_dataset.shuffle(buffer_size=args.shuffle_buffer						)
		_a  :   Tuple				 =							DataLoader(lowerCAmelCase_		,    batch_size=args.train_batch_size		,    shuffle=lowerCAmelCase_						)
		_a  :   List[str]				 =							DataLoader(lowerCAmelCase_		,    batch_size=args.valid_batch_size						)
		return train_dataloader, eval_dataloader



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						)    ->   List[str]:
		_a  :   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   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Dict:
		logger.info(f"""Step {step}: {metrics}"""						)
		if accelerator.is_main_process:
				accelerator.log(lowerCAmelCase_		,    lowerCAmelCase_						)



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



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   List[Any]:
		model.eval()
		_a  :   List[Any]				 =							[]
		for step, batch in enumerate(lowerCAmelCase_						):
				with torch.no_grad():
						_a  :   Any				 =							model(lowerCAmelCase_		,    labels=lowerCAmelCase_						)
				_a  :   List[str]				 =							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
		_a  :   Tuple				 =							torch.cat(lowerCAmelCase_						)
		_a  :   Optional[int]				 =							losses[: eval_dataloader.dataset.current_size].mean()
		try:
				_a  :   List[Any]				 =							torch.exp(lowerCAmelCase_						)
		except OverflowError:
				_a  :   int				 =							float('inf'						)
		return loss.item(), perplexity.item()


# Settings
__lowerCAmelCase		    =     HfArgumentParser(TrainingArguments)
__lowerCAmelCase		    =     parser.parse_args()

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

__lowerCAmelCase		    =     Namespace(**vars(args), **acc_state)
__lowerCAmelCase		    =     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
__lowerCAmelCase    ,      __lowerCAmelCase		    =     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
__lowerCAmelCase		    =     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
							model.gradient_checkpointing_enable()
__lowerCAmelCase		    =     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase    ,      __lowerCAmelCase		    =     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase		    =     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase		    =     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  (   )    ->   Tuple:
		return optimizer.param_groups[0]["lr"]


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

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

# Define the Gravitational Constant G and the function
__lowerCAmelCase		    =     6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   dict[str, float]:
		_a  :   str				 =							mass_a * mass_a

		if (force, mass_a, mass_a, distance).count(0						) != 1:
				raise ValueError('One and only one argument must be 0'						)
		if force < 0:
				raise ValueError('Gravitational force can not be negative'						)
		if distance < 0:
				raise ValueError('Distance can not be negative'						)
		if mass_a < 0 or mass_a < 0:
				raise ValueError('Mass can not be negative'						)
		if force == 0:
				_a  :   List[Any]				 =							GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
				return {"force": force}
		elif mass_a == 0:
				_a  :   Any				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_1": mass_a}
		elif mass_a == 0:
				_a  :   Tuple				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_2": mass_a}
		elif distance == 0:
				_a  :   str				 =							(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
				return {"distance": distance}
		raise ValueError('One and only one argument must be 0'						)


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()




