






"""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       (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_=False			,				UpperCAmelCase_=1_024			,				UpperCAmelCase_=1_024			,				UpperCAmelCase_=3.6			,				UpperCAmelCase_=False			,				)	-> Any:




          '''simple docstring'''




          lowercase__:    Optional[int]        =      tokenizer
          lowercase__:    Tuple        =      tokenizer.bos_token_id
          lowercase__:    str        =      dataset
          lowercase__:    Optional[int]        =      seq_length
          lowercase__:    List[str]        =      0
          lowercase__:    Union[str, Any]        =      infinite
          lowercase__:    Any        =      0
          lowercase__:    Optional[int]        =      tokenized

          if self.tokenized:
                 lowercase__:    Optional[int]        =      seq_length * num_of_sequences
                 lowercase__:    str        =      "input_ids"
          else:
                 lowercase__:    Union[str, Any]        =      seq_length * chars_per_token * num_of_sequences
                 lowercase__:    Union[str, Any]        =      "content"





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




          '''simple docstring'''




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





   def __lowercase    (   self			,				UpperCAmelCase_=1_000)	-> Dict:




          '''simple docstring'''




          return ShufflerIterDataPipe(self			,				buffer_size=UpperCAmelCase_)







def     A(  snake_case_		):
       """simple docstring"""





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






def     A(  snake_case_		):
       """simple docstring"""





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






def     A(  snake_case_						,       snake_case_						,       snake_case_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):
       """simple docstring"""





       lowercase__  ,     lowercase__:    Union[str, Any]        =      [], []
       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(  snake_case_						,       snake_case_		):
       """simple docstring"""





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






def     A(  snake_case_						,       snake_case_						,       snake_case_		):
       """simple docstring"""





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






def     A(  snake_case_		):
       """simple docstring"""





       model.eval()
       lowercase__:    int        =      []
       for step, batch in enumerate(UpperCamelCase_		):
              with torch.no_grad():
                     lowercase__:    List[Any]        =      model(UpperCamelCase_						,       labels=UpperCamelCase_		)
              lowercase__:    Tuple        =      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
       lowercase__:    List[str]        =      torch.cat(UpperCamelCase_		)
       lowercase__:    Union[str, Any]        =      losses[: eval_dataloader.dataset.current_size].mean()
       try:
              lowercase__:    str        =      torch.exp(UpperCamelCase_		)
       except OverflowError:
              lowercase__:    Union[str, Any]        =      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			=      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			=      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     A(  ):
       """simple docstring"""





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


# Prepare everything with our `accelerator`.
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			=      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			=      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""")














"""simple docstring"""





from __future__ import annotations






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__  ,     lowercase__:    Optional[int]        =      0, len(snake_case_		) - 1
       while start_ind <= end_ind:
              if array[start_ind] == search_item:
                     return start_ind
              elif array[end_ind] == search_item:
                     return end_ind
              else:
                     start_ind += 1
                     end_ind -= 1
    # returns -1 if search_item is not found in array
       return -1


if __name__ == "__main__":
  print(double_linear_search(list(range(1_00)), 40))






