
"""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   _UpperCAmelCase					(       _SCREAMING_SNAKE_CASE):
    def __init__(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_=False    ,     snake_case_=10_24    ,     snake_case_=10_24    ,     snake_case_=3.6    ,     snake_case_=False    ,     ):
      _snake_case  :    Any            = tokenizer
      _snake_case  :    Optional[int]            = tokenizer.bos_token_id
      _snake_case  :    Optional[Any]            = dataset
      _snake_case  :    Dict            = seq_length
      _snake_case  :    int            = 0
      _snake_case  :    int            = infinite
      _snake_case  :    List[str]            = 0
      _snake_case  :    int            = tokenized

      if self.tokenized:
        _snake_case  :    Union[str, Any]            = seq_length * num_of_sequences
        _snake_case  :    Any            = "input_ids"
      else:
        _snake_case  :    Union[str, Any]            = seq_length * chars_per_token * num_of_sequences
        _snake_case  :    int            = "content"
    def __iter__(     self  ):
      _snake_case  :    Tuple            = iter(self.dataset  )
      _snake_case  :    Tuple            = True
      while more_examples:
        _snake_case      ,       _snake_case  :    int            = [], 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:
              _snake_case  :    List[str]            = iter(self.dataset  )
              self.epoch += 1
              logger.info(F'Dataset epoch: {self.epoch}'  )
            else:
              _snake_case  :    str            = False
              break
        if self.tokenized:
          _snake_case  :    Dict            = buffer
        else:
          _snake_case  :    Dict            = self.tokenizer(_SCREAMING_SNAKE_CASE    ,     truncation=_SCREAMING_SNAKE_CASE  )["input_ids"]
        _snake_case  :    List[str]            = []
        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  ):
          _snake_case  :    Dict            = 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        lowerCamelCase__						(     self    ,     snake_case_=10_00  ):
      return ShufflerIterDataPipe(self    ,     buffer_size=_SCREAMING_SNAKE_CASE  )


def 					a__ ( a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = args.model_ckpt.split("/"		)[-1]
  _snake_case  :    List[str]            = logging.getLogger(__name__		)
  _snake_case  :    List[str]            = Path(args.save_dir		) / "log/"
  log_dir.mkdir(exist_ok=a		)
  _snake_case  :    Union[str, 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(a							,  vars(a		)		)
    _snake_case  :    Tuple            = accelerator.trackers[0].run.name
    logger.setLevel(logging.INFO		)
    datasets.utils.logging.set_verbosity_info()
    transformers.utils.logging.set_verbosity_info()
  else:
    _snake_case  :    Optional[Any]            = ""
    logger.setLevel(logging.ERROR		)
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  return logger, run_name
def 					a__ ( a :					Any		):

  """simple docstring"""







  _snake_case  :    Optional[Any]            = {"streaming": True}
  _snake_case  :    Dict            = load_dataset(args.dataset_name_train							,  split="train"							,  **a		)
  _snake_case  :    Union[str, Any]            = train_data.shuffle(buffer_size=args.shuffle_buffer							,  seed=args.seed		)
  _snake_case  :    Union[str, Any]            = load_dataset(args.dataset_name_valid							,  split="train"							,  **a		)
  _snake_case  :    Optional[Any]            = ConstantLengthDataset(
      a							,  a							,  infinite=a							,  seq_length=args.seq_length							,  tokenized=args.tokenized		)
  _snake_case  :    Tuple            = ConstantLengthDataset(
      a							,  a							,  infinite=a							,  seq_length=args.seq_length							,  tokenized=args.tokenized		)
  _snake_case  :    Union[str, Any]            = train_dataset.shuffle(buffer_size=args.shuffle_buffer		)
  _snake_case  :    Dict            = DataLoader(a							,  batch_size=args.train_batch_size							,  shuffle=a		)
  _snake_case  :    List[Any]            = DataLoader(a							,  batch_size=args.valid_batch_size		)
  return train_dataloader, eval_dataloader
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):

  """simple docstring"""







  _snake_case      ,       _snake_case  :    int            = [], []
  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 					a__ ( a :					Any							,  a :					List[Any]		):

  """simple docstring"""







  logger.info(f'Step {step}: {metrics}'		)
  if accelerator.is_main_process:
    accelerator.log(a							,  a		)
def 					a__ ( a :					Dict							,  a :					Dict							,  a :					str		):

  """simple docstring"""







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

  """simple docstring"""







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


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

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

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

# Clone model repository
if accelerator.is_main_process:
    _a   :      int       =				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   :      Optional[int]       =				AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
_a   :      int       =				AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_a,      _a   :      List[str]       =				create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_a   :      Optional[int]       =				AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_a   :      Union[str, 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 					a__ ( ):

  """simple docstring"""







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


# Prepare everything with our `accelerator`.
_a,      _a,      _a,      _a   :      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   :      Optional[int]       =				os.path.basename(args.resume_from_checkpoint)
    else:
        # Get the most recent checkpoint
        _a   :      Optional[int]       =				[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[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   :      str       =				os.path.splitext(path)[0]
    _a   :      Optional[Any]       =				int(training_difference.replace("""step_""", """"""))

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





class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(_SCREAMING_SNAKE_CASE    ):
										self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
								return self.sequence[:index]







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	print(
	    """Fibonacci Series Using Dynamic Programming\n"""     ,					"""Enter the index of the Fibonacci number you want to calculate """     ,					"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""     ,					sep=""""""     ,					)
	a_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()