





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       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			int=False       , _SCREAMING_SNAKE_CASE:			Tuple=1024       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1024       , _SCREAMING_SNAKE_CASE:			str=3.6       , _SCREAMING_SNAKE_CASE:			Any=False       , )							->			Optional[int]:




              """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:			Dict		)							->			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 lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Dict=1000		)							->			List[Any]:




              """simple docstring"""


              return ShufflerIterDataPipe(self       , buffer_size=_SCREAMING_SNAKE_CASE		)




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	args.model_ckpt.split("/"	)[-1]
       UpperCamelCase_ 							=	logging.getLogger(__name__	)
       UpperCamelCase_ 							=	Path(args.save_dir	) / "log/"
       log_dir.mkdir(exist_ok=UpperCamelCase_	)
       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(UpperCamelCase_       ,		vars(UpperCamelCase_	)	)
              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 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	{"streaming": True}
       UpperCamelCase_ 							=	load_dataset(args.dataset_name_train       ,		split="train"       ,		**UpperCamelCase_	)
       UpperCamelCase_ 							=	train_data.shuffle(buffer_size=args.shuffle_buffer       ,		seed=args.seed	)
       UpperCamelCase_ 							=	load_dataset(args.dataset_name_valid       ,		split="train"       ,		**UpperCamelCase_	)
       UpperCamelCase_ 							=	ConstantLengthDataset(
           UpperCamelCase_       ,		UpperCamelCase_       ,		infinite=UpperCamelCase_       ,		seq_length=args.seq_length       ,		tokenized=args.tokenized	)
       UpperCamelCase_ 							=	ConstantLengthDataset(
           UpperCamelCase_       ,		UpperCamelCase_       ,		infinite=UpperCamelCase_       ,		seq_length=args.seq_length       ,		tokenized=args.tokenized	)
       UpperCamelCase_ 							=	train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
       UpperCamelCase_ 							=	DataLoader(UpperCamelCase_       ,		batch_size=args.train_batch_size       ,		shuffle=UpperCamelCase_	)
       UpperCamelCase_ 							=	DataLoader(UpperCamelCase_       ,		batch_size=args.valid_batch_size	)
       return train_dataloader, eval_dataloader




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)  ->		str:
       UpperCamelCase_							,			UpperCamelCase_ 							=	[], []
       for n, p in model.named_parameters():
              if any(nd in n for nd in no_decay	):
                     params_without_wd.append(UpperCamelCase_	)
              else:
                     params_with_wd.append(UpperCamelCase_	)
       return [
           {"params": params_with_wd, "weight_decay": args.weight_decay},
           {"params": params_without_wd, "weight_decay": 0.0},
       ]




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       logger.info(F'''Step {step}: {metrics}'''	)
       if accelerator.is_main_process:
              accelerator.log(UpperCamelCase_       ,		UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
       UpperCamelCase_ 							=	accelerator.unwrap_model(UpperCamelCase_	).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 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[Any]:
       model.eval()
       UpperCamelCase_ 							=	[]
       for step, batch in enumerate(UpperCamelCase_	):
              with torch.no_grad():
                     UpperCamelCase_ 							=	model(UpperCamelCase_       ,		labels=UpperCamelCase_	)
              UpperCamelCase_ 							=	outputs.loss.repeat(args.valid_batch_size	)
              losses.append(accelerator.gather(UpperCamelCase_	)	)
              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       UpperCamelCase_ 							=	torch.cat(UpperCamelCase_	)
       UpperCamelCase_ 							=	losses[: eval_dataloader.dataset.current_size].mean()
       try:
              UpperCamelCase_ 							=	torch.exp(UpperCamelCase_	)
       except OverflowError:
              UpperCamelCase_ 							=	float("inf"	)
       return loss.item(), perplexity.item()


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

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

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

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

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

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

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


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

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





from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


