
import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
				import torch_xla.core.xla_model as xm  # noqa: F401

				lowercase__							:							Union[str, Any]													=     True
except ImportError:
				lowercase__							:							str													=     False


# Cache this result has it's a C FFI call which can be pretty time-consuming
lowercase__							:							Optional[Any]													=     torch.distributed.is_available()




def 		A_	(							snake_case							:				Union[str, Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     importlib.util.find_spec(snake_case	) is not None
						if package_exists:
												try:
																		__UpperCamelCase       					=     importlib.metadata.metadata(snake_case	)
																		return True
												except importlib.metadata.PackageNotFoundError:
																		return False




def 		A_	(							) ->      bool:



						'''simple docstring'''

						return _torch_distributed_available




def 		A_	(							) ->      str:



						'''simple docstring'''

						try:
												pass
						except ImportError:
												print(
												    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
												    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
												    ''' Bindings for PyTorch*.'''	)
						return (
						    importlib.util.find_spec('''torch_ccl'''	) is not None
						    or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''	) is not None
						)




def 		A_	(							) ->      Tuple:



						'''simple docstring'''

						return importlib.metadata.version('''oneccl_bind_pt'''	)




def 		A_	(							) ->      Optional[Any]:



						'''simple docstring'''

						return _is_package_available('''transformer_engine'''	)




@lru_cache
def 		A_	(							snake_case							:				List[str]=True	) ->      int:



						'''simple docstring'''

						if torch.cuda.is_available():
												return False
						if _tpu_available and check_device:
												try:
																		# Will raise a RuntimeError if no XLA configuration is found
																		__UpperCamelCase       					=     xm.xla_device()
																		return True
												except RuntimeError:
																		return False
						return _tpu_available




def 		A_	(							) ->      int:



						'''simple docstring'''

						return _is_package_available('''deepspeed'''	)




def 		A_	(							snake_case							:				Any=False	) ->      int:



						'''simple docstring'''

						if is_tpu_available():
												return not ignore_tpu
						if torch.cuda.is_available():
												return torch.cuda.is_bfaa_supported()
						if is_npu_available():
												return False
						return True




def 		A_	(							) ->      str:



						'''simple docstring'''

						__UpperCamelCase       					=     _is_package_available('''bitsandbytes'''	)
						if package_exists:
												__UpperCamelCase       					=     version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
												return compare_versions(snake_case	, '''>='''	, '''0.39.0'''	)
						return False




def 		A_	(							) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     _is_package_available('''bitsandbytes'''	)
						if package_exists:
												__UpperCamelCase       					=     version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
												return compare_versions(snake_case	, '''>='''	, '''0.37.2'''	)
						return False




def 		A_	(							) ->      Optional[Any]:



						'''simple docstring'''

						return _is_package_available('''bitsandbytes'''	)




def 		A_	(							) ->      Any:



						'''simple docstring'''

						if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''	, '''False'''	)	) == 1:
												__UpperCamelCase       					=     importlib.util.find_spec('''megatron'''	) is not None
												if package_exists:
																		try:
																								__UpperCamelCase       					=     parse(importlib.metadata.version('''megatron-lm'''	)	)
																								return compare_versions(snake_case	, '''>='''	, '''2.2.0'''	)
																		except Exception as e:
																								warnings.warn(f"Parse Megatron version failed. Exception:{e}"	)
																								return False




def 		A_	(							) ->      Union[str, Any]:



						'''simple docstring'''

						return _is_package_available('''safetensors'''	)




def 		A_	(							) ->      Union[str, Any]:



						'''simple docstring'''

						return _is_package_available('''transformers'''	)




def 		A_	(							) ->      List[Any]:



						'''simple docstring'''

						return _is_package_available('''datasets'''	)




def 		A_	(							) ->      Any:



						'''simple docstring'''

						return _is_package_available('''aim'''	)




def 		A_	(							) ->      List[Any]:



						'''simple docstring'''

						return _is_package_available('''tensorboard'''	) or _is_package_available('''tensorboardX'''	)




def 		A_	(							) ->      str:



						'''simple docstring'''

						return _is_package_available('''wandb'''	)




def 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

						return _is_package_available('''comet_ml'''	)




def 		A_	(							) ->      Dict:



						'''simple docstring'''

						return _is_package_available('''boto3'''	)




def 		A_	(							) ->      List[Any]:



						'''simple docstring'''

						if _is_package_available('''rich'''	):
												if "ACCELERATE_DISABLE_RICH" in os.environ:
																		warnings.warn(
																		    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''	)
																		return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''	, snake_case	)
												return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''	, snake_case	)
						return False




def 		A_	(							) ->      Any:



						'''simple docstring'''

						return _is_package_available('''sagemaker'''	)




def 		A_	(							) ->      int:



						'''simple docstring'''

						return _is_package_available('''tqdm'''	)




def 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

						return _is_package_available('''mlflow'''	)




def 		A_	(							) ->      Dict:



						'''simple docstring'''

						return is_torch_version('''>='''	, '''1.12'''	) and torch.backends.mps.is_available() and torch.backends.mps.is_built()




def 		A_	(							) ->      Tuple:



						'''simple docstring'''

						def get_major_and_minor_from_version(snake_case							:				str	):
												return str(version.parse(snake_case	).major	) + "." + str(version.parse(snake_case	).minor	)

						__UpperCamelCase       					=     importlib.metadata.version('''torch'''	)
						if importlib.util.find_spec('''intel_extension_for_pytorch'''	) is None:
												return False
						__UpperCamelCase       					=     '''N/A'''
						try:
												__UpperCamelCase       					=     importlib.metadata.version('''intel_extension_for_pytorch'''	)
						except importlib.metadata.PackageNotFoundError:
												return False
						__UpperCamelCase       					=     get_major_and_minor_from_version(_torch_version	)
						__UpperCamelCase       					=     get_major_and_minor_from_version(_ipex_version	)
						if torch_major_and_minor != ipex_major_and_minor:
												warnings.warn(
												    f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
												    f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."	)
												return False
						return True




@lru_cache
def 		A_	(							snake_case							:				Any=False	) ->      List[str]:



						'''simple docstring'''

						if importlib.util.find_spec('''torch'''	) is None or importlib.util.find_spec('''torch_npu'''	) is None:
												return False

						import torch
						import torch_npu  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no NPU is found
																		__UpperCamelCase       					=     torch.npu.device_count()
																		return torch.npu.is_available()
												except RuntimeError:
																		return False
						return hasattr(snake_case	, '''npu'''	) and torch.npu.is_available()




@lru_cache
def 		A_	(							snake_case							:				Optional[Any]=False	) ->      int:



						'''simple docstring'''

						if not parse_flag_from_env('''ACCELERATE_USE_XPU'''	, default=snake_case	):
												return False
						if is_ipex_available():
												import torch

												if is_torch_version('''<='''	, '''1.12'''	):
																		return False
						else:
												return False

						import intel_extension_for_pytorch  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no XPU  is found
																		__UpperCamelCase       					=     torch.xpu.device_count()
																		return torch.xpu.is_available()
												except RuntimeError:
																		return False
						return hasattr(snake_case	, '''xpu'''	) and torch.xpu.is_available()






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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=3.6       ,		SCREAMING_SNAKE_CASE_=False       ,		)->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     tokenizer
													__UpperCamelCase       					=     tokenizer.bos_token_id
													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     seq_length
													__UpperCamelCase       					=     0
													__UpperCamelCase       					=     infinite
													__UpperCamelCase       					=     0
													__UpperCamelCase       					=     tokenized

													if self.tokenized:
																			__UpperCamelCase       					=     seq_length * num_of_sequences
																			__UpperCamelCase       					=     '''input_ids'''
													else:
																			__UpperCamelCase       					=     seq_length * chars_per_token * num_of_sequences
																			__UpperCamelCase       					=     '''content'''



							def __iter__(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     iter(self.dataset     )
													__UpperCamelCase       					=     True
													while more_examples:
																			__UpperCamelCase					,							__UpperCamelCase       					=     [], 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:
																																					__UpperCamelCase       					=     iter(self.dataset     )
																																					self.epoch += 1
																																					logger.info(F"Dataset epoch: {self.epoch}"     )
																															else:
																																					__UpperCamelCase       					=     False
																																					break
																			if self.tokenized:
																									__UpperCamelCase       					=     buffer
																			else:
																									__UpperCamelCase       					=     self.tokenizer(SCREAMING_SNAKE_CASE_       ,		truncation=SCREAMING_SNAKE_CASE_     )['''input_ids''']
																			__UpperCamelCase       					=     []
																			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     ):
																									__UpperCamelCase       					=     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       A__						(					self       ,		SCREAMING_SNAKE_CASE_=1000     )->				int:





													'''simple docstring'''






													return ShufflerIterDataPipe(self       ,		buffer_size=SCREAMING_SNAKE_CASE_     )





def 		A_	(							snake_case							:				Any	) ->      Dict:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	) ->      Tuple:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				List[str]	, snake_case							:				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	) ->      Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase					,							__UpperCamelCase       					=     [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay	):
																		params_without_wd.append(snake_case	)
												else:
																		params_with_wd.append(snake_case	)
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]




def 		A_	(							snake_case							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	, snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	) ->      Union[str, Any]:



						'''simple docstring'''

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


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

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

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

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

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

# Load dataset and dataloader
lowercase__						,					lowercase__							:							Union[str, Any]													=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase__							:							int													=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase__							:							int													=     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_	(							) ->      Optional[int]:



						'''simple docstring'''

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


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

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




