




from scipy.constants import g




def      __magic_name__							(					A						:   float,    A						:   float  ):






			'''simple docstring'''







			if mass < 0:
						# handling of negative values of mass
						raise ValueError("The mass of a body cannot be negative"  )
			if height < 0:
						# handling of negative values of height
						raise ValueError("The height above the ground cannot be negative"  )
			return mass * g * height


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

		testmod(name='potential_energy')












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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Optional[Any]=10_24   , __lowerCamelCase							:	str=10_24   , __lowerCamelCase							:	Any=3.6   , __lowerCamelCase							:	List[Any]=False   , )   ->	Optional[Any]:
							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							:	Union[str, Any]				)   ->	List[str]:
							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							:	Optional[int]=10_00				)   ->	Union[str, Any]:
							return ShufflerIterDataPipe(self   , buffer_size=__lowerCamelCase				)




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     args.model_ckpt.split("/"  )[-1]
			a       =     logging.getLogger(__name__  )
			a       =     Path(args.save_dir  ) / "log/"
			log_dir.mkdir(exist_ok=A  )
			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(A,    vars(A  )  )
						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      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any],    A						:   Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):






			'''simple docstring'''







			a       ,						a       =     [], []
			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      __magic_name__							(					A						:   List[str],    A						:   List[str]  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[int],    A						:   List[str]  ):






			'''simple docstring'''







			a       =     accelerator.unwrap_model(A  ).config
			a       =     4 if args.gradient_checkpointing else 3
			a       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			a       =     24 * 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 * (10**12))
			return tflops




def      __magic_name__							(					A						:   List[Any]  ):






			'''simple docstring'''







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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase						:							Any			     =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase						:							Optional[Any]			     =						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      __magic_name__							(					):






			'''simple docstring'''







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


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

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






