





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				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=False				,       lowerCAmelCase__=10_24				,       lowerCAmelCase__=10_24				,       lowerCAmelCase__=3.6				,       lowerCAmelCase__=False				,       )					-> int:

							'''simple docstring'''


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

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





			def __iter__(       self						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							iter(self.dataset						)
							__lowercase			      =							True
							while more_examples:
											__lowercase      ,					__lowercase			      =							[], 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:
																							__lowercase			      =							iter(self.dataset						)
																							self.epoch += 1
																							logger.info(F"Dataset epoch: {self.epoch}"						)
																			else:
																							__lowercase			      =							False
																							break
											if self.tokenized:
															__lowercase			      =							buffer
											else:
															__lowercase			      =							self.tokenizer(lowerCAmelCase__				,       truncation=lowerCAmelCase__						)['''input_ids''']
											__lowercase			      =							[]
											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						):
															__lowercase			      =							all_token_ids[i : i + self.seq_length]
															if len(lowerCAmelCase__						) == self.seq_length:
																			self.current_size += 1
																			yield torch.tensor(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=10_00						)					-> int:

							'''simple docstring'''


							return ShufflerIterDataPipe(self				,       buffer_size=lowerCAmelCase__						)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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


# Settings
__a  :					Union[str, Any]          =  HfArgumentParser(TrainingArguments)
__a  :					Tuple          =  parser.parse_args()

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

__a  :					str          =  Namespace(**vars(args), **acc_state)
__a  :					Dict          =  accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

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

# Load dataset and dataloader
__a  :					Any          =  create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__a  :					str          =  AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__a  :					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     UpperCAmelCase						(   ):







				"""simple docstring"""



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


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

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

import datasets
from utils import generate_example_dataset, get_duration


__a  :					int          =  5_0_0_0_0_0

__a   ,						__a  :					Any          =  os.path.split(__file__)
__a  :					Dict          =  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.select(range(0							,      len(lowercase							)							,      2							)							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.sort('''numbers'''							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.shuffle()





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.train_test_split(0.1							)





@get_duration
def     UpperCAmelCase						(   lowercase							,      lowercase=10							):







				"""simple docstring"""



				for shard_id in range(lowercase							):
								__lowercase			      =							dataset.shard(lowercase							,      lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							{'''num examples''': SPEED_TEST_N_EXAMPLES}
				__lowercase			      =							(select, sort, shuffle, train_test_split, shard)
				with tempfile.TemporaryDirectory() as tmp_dir:
								print('''generating dataset'''							)
								__lowercase			      =							datasets.Features({'''text''': datasets.Value('''string'''							), '''numbers''': datasets.Value('''float32'''							)}							)
								__lowercase			      =							generate_example_dataset(
								    os.path.join(lowercase							,      '''dataset.arrow'''							)							,      lowercase							,      num_examples=lowercase							)
								print('''Functions'''							)
								for func in functions:
												print(func.__name__							)
												__lowercase			      =							func(lowercase							)

				with open(lowercase							,      '''wb'''							) as f:
								f.write(json.dumps(lowercase							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_indices_mapping()