
'''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 			__A					(			_snake_case      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Any					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[int]=False					, lowerCamelCase      :     List[Any]=10_24					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     str=3.6					, lowerCamelCase      :     Optional[int]=False					, ):



   """simple docstring"""





   __A		:  Optional[Any]       				=		tokenizer
   __A		:  Optional[int]       				=		tokenizer.bos_token_id
   __A		:  Tuple       				=		dataset
   __A		:  Optional[Any]       				=		seq_length
   __A		:  List[Any]       				=		0
   __A		:  Optional[Any]       				=		infinite
   __A		:  Dict       				=		0
   __A		:  Optional[Any]       				=		tokenized

   if self.tokenized:
     __A		:  List[Any]       				=		seq_length * num_of_sequences
     __A		:  Any       				=		"""input_ids"""
   else:
     __A		:  Optional[int]       				=		seq_length * chars_per_token * num_of_sequences
     __A		:  Dict       				=		"""content"""
 def __iter__( self      :     Tuple							):



   """simple docstring"""





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






 def 		lowercase_( self      :     int					, lowerCamelCase      :     Any=10_00							):



   """simple docstring"""





   return ShufflerIterDataPipe(self					, buffer_size=lowercase__							)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							List[str]:





  """simple docstring"""

  __A		:  List[str]       				=		args.model_ckpt.split("""/"""			)[-1]
  __A		:  Any       				=		logging.getLogger(__name__			)
  __A		:  Any       				=		Path(args.save_dir			) / """log/"""
  log_dir.mkdir(exist_ok=_UpperCAmelCase			)
  __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(_UpperCAmelCase      ,  vars(_UpperCAmelCase			)			)
    __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 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  Dict       				=		{"""streaming""": True}
  __A		:  str       				=		load_dataset(args.dataset_name_train      ,  split="""train"""      ,  **_UpperCAmelCase			)
  __A		:  int       				=		train_data.shuffle(buffer_size=args.shuffle_buffer      ,  seed=args.seed			)
  __A		:  Dict       				=		load_dataset(args.dataset_name_valid      ,  split="""train"""      ,  **_UpperCAmelCase			)
  __A		:  Tuple       				=		ConstantLengthDataset(
      _UpperCAmelCase      ,  _UpperCAmelCase      ,  infinite=_UpperCAmelCase      ,  seq_length=args.seq_length      ,  tokenized=args.tokenized			)
  __A		:  List[Any]       				=		ConstantLengthDataset(
      _UpperCAmelCase      ,  _UpperCAmelCase      ,  infinite=_UpperCAmelCase      ,  seq_length=args.seq_length      ,  tokenized=args.tokenized			)
  __A		:  List[Any]       				=		train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
  __A		:  List[str]       				=		DataLoader(_UpperCAmelCase      ,  batch_size=args.train_batch_size      ,  shuffle=_UpperCAmelCase			)
  __A		:  Optional[Any]       				=		DataLoader(_UpperCAmelCase      ,  batch_size=args.valid_batch_size			)
  return train_dataloader, eval_dataloader







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





  """simple docstring"""

  __A				,       __A		:  Dict       				=		[], []
  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 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Tuple			)					->							int:





  """simple docstring"""

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







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





  """simple docstring"""

  __A		:  Tuple       				=		accelerator.unwrap_model(_UpperCAmelCase			).config
  __A		:  Tuple       				=		4 if args.gradient_checkpointing else 3
  __A		:  List[str]       				=		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
  __A		:  str       				=		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
  __A		:  List[Any]       				=		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		:  Optional[int]       				=		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
  return tflops







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





  """simple docstring"""

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


# Settings
A__ :		Optional[Any]					=HfArgumentParser(TrainingArguments)
A__ :		str					=parser.parse_args()

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

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

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

# Logging
A__		,   A__ :		List[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__ :		Any					=AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
     model.gradient_checkpointing_enable()
A__ :		Optional[Any]					=AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
A__		,   A__ :		str					=create_dataloaders(args)

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





  """simple docstring"""

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


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

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








from __future__ import annotations





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert (
					    c % greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				) == 0
					)  # greatest_common_divisor(a,b) function implemented below
					((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			_UpperCAmelCase				)  # extended_gcd(a,b) function implemented below
					lowercase						   =       c / d
					return (r * x, r * y)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = 2				):
					"""simple docstring"""






					((lowercase)   ,      (lowercase))						   =       diophantine(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)  # Initial value
					lowercase						   =       greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				)
					lowercase						   =       a // d
					lowercase						   =       b // d

					for i in range(_UpperCAmelCase				):
										lowercase						   =       xa + i * q
										lowercase						   =       ya - i * p
										print(_UpperCAmelCase       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if a < b:
										lowercase   ,      lowercase						   =       b, a

					while a % b != 0:
										lowercase   ,      lowercase						   =       b, a % b

					return b





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert a >= 0 and b >= 0

					if b == 0:
										lowercase   ,      lowercase   ,      lowercase						   =       a, 1, 0
					else:
										((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			a % b				)
										lowercase						   =       q
										lowercase						   =       p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)


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

		testmod(name='''diophantine''', verbose=True)
		testmod(name='''diophantine_all_soln''', verbose=True)
		testmod(name='''extended_gcd''', verbose=True)
		testmod(name='''greatest_common_divisor''', verbose=True)






