






'''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_     (		__lowerCamelCase		):




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=False  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=3.6  ,	_lowerCAmelCase=False  ,	):
          UpperCAmelCase__			:     Union[str, Any]										= tokenizer
          UpperCAmelCase__			:     Tuple										= tokenizer.bos_token_id
          UpperCAmelCase__			:     List[str]										= dataset
          UpperCAmelCase__			:     Optional[int]										= seq_length
          UpperCAmelCase__			:     List[Any]										= 0
          UpperCAmelCase__			:     int										= infinite
          UpperCAmelCase__			:     int										= 0
          UpperCAmelCase__			:     str										= tokenized

          if self.tokenized:
               UpperCAmelCase__			:     Optional[int]										= seq_length * num_of_sequences
               UpperCAmelCase__			:     Optional[Any]										= """input_ids"""
          else:
               UpperCAmelCase__			:     List[Any]										= seq_length * chars_per_token * num_of_sequences
               UpperCAmelCase__			:     Dict										= """content"""




     def __iter__(							self		):
          UpperCAmelCase__			:     Union[str, Any]										= iter(self.dataset		)
          UpperCAmelCase__			:     Optional[int]										= True
          while more_examples:
               UpperCAmelCase__			:     Optional[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:
                              UpperCAmelCase__			:     Dict										= iter(self.dataset		)
                              self.epoch += 1
                              logger.info(f"Dataset epoch: {self.epoch}"		)
                         else:
                              UpperCAmelCase__			:     Tuple										= False
                              break
               if self.tokenized:
                    UpperCAmelCase__			:     Union[str, Any]										= buffer
               else:
                    UpperCAmelCase__			:     List[str]										= self.tokenizer(_lowerCAmelCase  ,	truncation=_lowerCAmelCase		)["""input_ids"""]
               UpperCAmelCase__			:     List[Any]										= []
               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		):
                    UpperCAmelCase__			:     Tuple										= all_token_ids[i : i + self.seq_length]
                    if len(_lowerCAmelCase		) == self.seq_length:
                         self.current_size += 1
                         yield torch.tensor(_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=1000		):
          return ShufflerIterDataPipe(self  ,	buffer_size=_lowerCAmelCase		)





def    _lowerCamelCase	(	__lowerCamelCase					)   -> List[str]:

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





def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:

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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]					)   -> Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__			:     List[Any]										= [], []
     for n, p in model.named_parameters():
          if any(nd in n for nd in no_decay					):
               params_without_wd.append(__lowerCamelCase					)
          else:
               params_with_wd.append(__lowerCamelCase					)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

     '''simple docstring'''
     logger.info(F"Step {step}: {metrics}"					)
     if accelerator.is_main_process:
          accelerator.log(__lowerCamelCase   ,       __lowerCamelCase					)





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

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





def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:

     '''simple docstring'''
     model.eval()
     UpperCAmelCase__			:     Tuple										= []
     for step, batch in enumerate(__lowerCamelCase					):
          with torch.no_grad():
               UpperCAmelCase__			:     int										= model(__lowerCamelCase   ,       labels=__lowerCamelCase					)
          UpperCAmelCase__			:     Tuple										= outputs.loss.repeat(args.valid_batch_size					)
          losses.append(accelerator.gather(__lowerCamelCase					)					)
          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     UpperCAmelCase__			:     Union[str, Any]										= torch.cat(__lowerCamelCase					)
     UpperCAmelCase__			:     List[Any]										= losses[: eval_dataloader.dataset.current_size].mean()
     try:
          UpperCAmelCase__			:     Any										= torch.exp(__lowerCamelCase					)
     except OverflowError:
          UpperCAmelCase__			:     List[str]										= float("""inf"""					)
     return loss.item(), perplexity.item()


# Settings
SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE__		:       int   							=							parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
      SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							Repository(args.save_dir, clone_from=args.model_ckpt)

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

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__		:       Tuple   							=							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__		:       str   							=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__		:       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    _lowerCamelCase	(	)   -> str:

     '''simple docstring'''
     return optimizer.param_groups[0]["lr"]


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

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














import random







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Union[str, Any]										= x
          UpperCAmelCase__			:     Union[str, Any]										= y




     def      __UpperCAmelCase     (							self		):
          return (self.x**2 + self.y**2) <= 1




     @classmethod
     def      __UpperCAmelCase     (							cls		):
          return cls(x=random.random()  ,	y=random.random()		)





def    _lowerCamelCase	(	__lowerCamelCase					)   -> float:

     '''simple docstring'''
     if number_of_simulations < 1:
          raise ValueError("""At least one simulation is necessary to estimate PI."""					)

     UpperCAmelCase__			:     Dict										= 0
     for _ in range(__lowerCamelCase					):
          UpperCAmelCase__			:     Union[str, Any]										= Point.random_unit_square()

          if random_point.is_in_unit_circle():
               number_in_unit_circle += 1

     return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
      # import doctest

      # doctest.testmod()
      from math import pi

      SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""Please enter the desired number of Monte Carlo simulations: """
      SCREAMING_SNAKE_CASE__		:       int   							=							estimate_pi(int(input(prompt).strip()))
      print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






