





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 							snake_case_		(      lowerCamelCase_):







		def __init__(		self      ,  __lowercase      ,  __lowercase      ,  __lowercase=False      ,  __lowercase=1_0_2_4      ,  __lowercase=1_0_2_4      ,  __lowercase=3.6      ,  __lowercase=False      ,  )					-> Any:
				lowerCamelCase :			List[str]    		=tokenizer
				lowerCamelCase :			Any    		=tokenizer.bos_token_id
				lowerCamelCase :			int    		=dataset
				lowerCamelCase :			Optional[Any]    		=seq_length
				lowerCamelCase :			Any    		=0
				lowerCamelCase :			Any    		=infinite
				lowerCamelCase :			Optional[Any]    		=0
				lowerCamelCase :			List[Any]    		=tokenized

				if self.tokenized:
						lowerCamelCase :			Optional[Any]    		=seq_length * num_of_sequences
						lowerCamelCase :			List[Any]    		='input_ids'
				else:
						lowerCamelCase :			Optional[Any]    		=seq_length * chars_per_token * num_of_sequences
						lowerCamelCase :			Optional[Any]    		='content'







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







		def       __lowercase (		self      ,  __lowercase=1_0_0_0 )					-> Tuple:
				return ShufflerIterDataPipe(self      ,  buffer_size=snake_case_ )


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[int]    		=args.model_ckpt.split('''/'''		)[-1]
		lowerCamelCase :			Dict    		=logging.getLogger(__name__		)
		lowerCamelCase :			List[Any]    		=Path(args.save_dir		) / 'log/'
		log_dir.mkdir(exist_ok=lowerCamelCase_		)
		lowerCamelCase :			List[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(lowerCamelCase_				,							vars(lowerCamelCase_		)		)
				lowerCamelCase :			int    		=accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO		)
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				lowerCamelCase :			int    		=''
				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_		) ->  Optional[Any]:
		lowerCamelCase :			Optional[int]    		={'streaming': True}
		lowerCamelCase :			Optional[int]    		=load_dataset(args.dataset_name_train				,							split='''train'''				,							**lowerCamelCase_		)
		lowerCamelCase :			int    		=train_data.shuffle(buffer_size=args.shuffle_buffer				,							seed=args.seed		)
		lowerCamelCase :			Tuple    		=load_dataset(args.dataset_name_valid				,							split='''train'''				,							**lowerCamelCase_		)
		lowerCamelCase :			Tuple    		=ConstantLengthDataset(
		    lowerCamelCase_				,							lowerCamelCase_				,							infinite=lowerCamelCase_				,							seq_length=args.seq_length				,							tokenized=args.tokenized		)
		lowerCamelCase :			Any    		=ConstantLengthDataset(
		    lowerCamelCase_				,							lowerCamelCase_				,							infinite=lowerCamelCase_				,							seq_length=args.seq_length				,							tokenized=args.tokenized		)
		lowerCamelCase :			Optional[int]    		=train_dataset.shuffle(buffer_size=args.shuffle_buffer		)
		lowerCamelCase :			Optional[int]    		=DataLoader(lowerCamelCase_				,							batch_size=args.train_batch_size				,							shuffle=lowerCamelCase_		)
		lowerCamelCase :			Dict    		=DataLoader(lowerCamelCase_				,							batch_size=args.valid_batch_size		)
		return train_dataloader, eval_dataloader


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		) ->  int:
		lowerCamelCase :			Dict    		=[], []
		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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		logger.info(F"Step {step}: {metrics}"		)
		if accelerator.is_main_process:
				accelerator.log(lowerCamelCase_				,							lowerCamelCase_		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			Optional[int]    		=accelerator.unwrap_model(lowerCamelCase_		).config
		lowerCamelCase :			Union[str, Any]    		=4 if args.gradient_checkpointing else 3
		lowerCamelCase :			Dict    		=args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		lowerCamelCase :			List[str]    		=2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		lowerCamelCase :			int    		=factor * (
		    1.0
		    + (args.seq_length / (6.0 * config_model.n_embd))
		    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
		)
		lowerCamelCase :			Tuple    		=flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
		return tflops


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		model.eval()
		lowerCamelCase :			Dict    		=[]
		for step, batch in enumerate(lowerCamelCase_		):
				with torch.no_grad():
						lowerCamelCase :			str    		=model(lowerCamelCase_				,							labels=lowerCamelCase_		)
				lowerCamelCase :			Any    		=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
		lowerCamelCase :			Union[str, Any]    		=torch.cat(lowerCamelCase_		)
		lowerCamelCase :			Optional[int]    		=losses[: eval_dataloader.dataset.current_size].mean()
		try:
				lowerCamelCase :			int    		=torch.exp(lowerCamelCase_		)
		except OverflowError:
				lowerCamelCase :			Any    		=float('''inf'''		)
		return loss.item(), perplexity.item()


# Settings
snake_case_				=      HfArgumentParser(TrainingArguments)
snake_case_				=      parser.parse_args()

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

snake_case_				=      Namespace(**vars(args), **acc_state)
snake_case_				=      accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

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

# Load dataset and dataloader
snake_case_				=      create_dataloaders(args)

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


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

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

snake_case__	:     str      =    [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  list[int]  = [-1] * length
       UpperCAmelCase_				:  Node | None  = None
       UpperCAmelCase_				:  Node | None  = None






  def __repr__(					self		):




       '''simple docstring'''

       return F'''Node(min_value={self.minn} max_value={self.maxx})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				list[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = Node(len(lowerCamelCase_  )  )
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = min(lowerCamelCase_  ), max(lowerCamelCase_  )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     UpperCAmelCase_				:  str  = (root.minn + root.maxx) // 2

     UpperCAmelCase_				:  list[int]  = []
     UpperCAmelCase_				:  list[int]  = []

     for index, num in enumerate(lowerCamelCase_  ):
          if num <= pivot:
               left_arr.append(lowerCamelCase_  )
          else:
               right_arr.append(lowerCamelCase_  )
          UpperCAmelCase_				:  Tuple  = len(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     return root


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     UpperCAmelCase_				:  Any  = (node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left      ,	lowerCamelCase_      ,	node.map_left[index] - 1  )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right      ,	lowerCamelCase_      ,	index - node.map_left[index]  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if start > end:
          return 0
     UpperCAmelCase_				:  int  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     UpperCAmelCase_				:  List[Any]  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	start - 1  )
     return rank_till_end - rank_before_start


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     UpperCAmelCase_				:  Tuple  = node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left      ,	lowerCamelCase_      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	)
     else:
          return quantile(
              node.right      ,	index - num_elements_in_left_tree      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	)


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     UpperCAmelCase_				:  Any  = range_counting(
         node.left      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     UpperCAmelCase_				:  List[str]  = range_counting(
         node.right      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     return left + right


if __name__ == "__main__":
 import doctest

 doctest.testmod()






