




'''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   (lowerCAmelCase_  ):





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a       ,__a=False       ,__a=1_024       ,__a=1_024       ,__a=3.6       ,__a=False       ,)			->							str:
          snake_case :			Optional[Any]					   = tokenizer
          snake_case :			Optional[Any]					   = tokenizer.bos_token_id
          snake_case :			Dict					   = dataset
          snake_case :			Dict					   = seq_length
          snake_case :			Any					   = 0
          snake_case :			List[str]					   = infinite
          snake_case :			List[str]					   = 0
          snake_case :			Union[str, Any]					   = tokenized

          if self.tokenized:
               snake_case :			str					   = seq_length * num_of_sequences
               snake_case :			str					   = """input_ids"""
          else:
               snake_case :			str					   = seq_length * chars_per_token * num_of_sequences
               snake_case :			Any					   = """content"""







     def __iter__(							self       )			->							Any:
          snake_case :			List[str]					   = iter(self.dataset       )
          snake_case :			List[str]					   = True
          while more_examples:
               snake_case   ,		snake_case :			Any					   = [], 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:
                              snake_case :			Optional[int]					   = iter(self.dataset       )
                              self.epoch += 1
                              logger.info(F'''Dataset epoch: {self.epoch}'''       )
                         else:
                              snake_case :			Tuple					   = False
                              break
               if self.tokenized:
                    snake_case :			Any					   = buffer
               else:
                    snake_case :			List[str]					   = self.tokenizer(lowerCamelCase_       ,truncation=lowerCamelCase_       )["""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(lowerCamelCase_       )       ,self.seq_length       ):
                    snake_case :			List[Any]					   = all_token_ids[i : i + self.seq_length]
                    if len(lowerCamelCase_       ) == self.seq_length:
                         self.current_size += 1
                         yield torch.tensor(lowerCamelCase_       )







     def  snake_case_			(							self       ,__a=1_000       )			->							Optional[Any]:
          return ShufflerIterDataPipe(self       ,buffer_size=lowerCamelCase_       )




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



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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):
     snake_case   ,		snake_case :			List[str]					   = [], []
     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 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     logger.info(F'''Step {step}: {metrics}'''							)
     if accelerator.is_main_process:
          accelerator.log(a_    ,      a_							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase							):
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     snake_case :			Optional[int]					   = accelerator.unwrap_model(a_							).config
     snake_case :			List[str]					   = 4 if args.gradient_checkpointing else 3
     snake_case :			Dict					   = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     snake_case :			str					   = 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 :			Dict					   = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops



def 					lowerCamelCase__							(			__lowercase							):
     model.eval()
     snake_case :			Optional[int]					   = []
     for step, batch in enumerate(a_							):
          with torch.no_grad():
               snake_case :			int					   = model(a_    ,      labels=a_							)
          snake_case :			str					   = 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 :			Union[str, Any]					   = torch.cat(a_							)
     snake_case :			Optional[int]					   = 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
lowercase			:      Optional[int]			=					HfArgumentParser(TrainingArguments)
lowercase			:      str			=					parser.parse_args()

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

lowercase			:      Any			=					Namespace(**vars(args), **acc_state)
lowercase			:      int			=					accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
lowercase,  lowercase			:      Dict			=					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
lowercase			:      Optional[int]			=					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
      model.gradient_checkpointing_enable()
lowercase			:      Optional[Any]			=					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowercase,  lowercase			:      Dict			=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase			:      Dict			=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase			:      Dict			=					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__							(			):
     return optimizer.param_groups[0]["lr"]


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

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



def 					lowerCamelCase__							(			__lowercase							):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__lowercase    ,      -1							)



def 					lowerCamelCase__							(			__lowercase							):
     if not balanced_parentheses(__lowercase							):
          raise ValueError("""Mismatched parentheses"""							)
     snake_case :			Stack[str]					   = Stack()
     snake_case :			List[Any]					   = []
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__lowercase							)
          elif char == "(":
               stack.push(__lowercase							)
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()							)
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__lowercase							) <= precedence(stack.peek()							):
                    postfix.append(stack.pop()							)
               stack.push(__lowercase							)
     while not stack.is_empty():
          postfix.append(stack.pop()							)
     return " ".join(__lowercase							)


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

      testmod()
      lowercase			:      Tuple			=					"""a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))



