'''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 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_							, snake_case_=False							, snake_case_=1_024							, snake_case_=1_024							, snake_case_=3.6							, snake_case_=False							, ):
									snake_case   :					Optional[Any]							=						tokenizer
									snake_case   :					Optional[int]							=						tokenizer.bos_token_id
									snake_case   :					Optional[int]							=						dataset
									snake_case   :					Optional[int]							=						seq_length
									snake_case   :					List[Any]							=						0
									snake_case   :					Any							=						infinite
									snake_case   :					Optional[int]							=						0
									snake_case   :					Any							=						tokenized

									if self.tokenized:
															snake_case   :					Dict							=						seq_length * num_of_sequences
															snake_case   :					Dict							=						"input_ids"
									else:
															snake_case   :					Union[str, Any]							=						seq_length * chars_per_token * num_of_sequences
															snake_case   :					List[Any]							=						"content"



			def __iter__(       self				):
									snake_case   :					str							=						iter(self.dataset				)
									snake_case   :					int							=						True
									while more_examples:
															snake_case							,				snake_case   :					Optional[int]							=						[], 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:
																																	snake_case   :					Any							=						iter(self.dataset				)
																																	self.epoch += 1
																																	logger.info(F"""Dataset epoch: {self.epoch}"""				)
																											else:
																																	snake_case   :					List[Any]							=						False
																																	break
															if self.tokenized:
																					snake_case   :					Union[str, Any]							=						buffer
															else:
																					snake_case   :					Union[str, Any]							=						self.tokenizer(snake_case_							, truncation=snake_case_				)["input_ids"]
															snake_case   :					List[Any]							=						[]
															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				):
																					snake_case   :					List[Any]							=						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      lowerCamelCase   (       self							, snake_case_=1_000				):
									return ShufflerIterDataPipe(self							, buffer_size=snake_case_				)







def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any])						->		Any:



						"""simple docstring"""

						snake_case   :					List[str]							=						args.model_ckpt.split("/")[-1]
						snake_case   :					Optional[Any]							=						logging.getLogger(__name__)
						snake_case   :					int							=						Path(args.save_dir) / "log/"
						log_dir.mkdir(exist_ok=_lowerCAmelCase)
						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(_lowerCAmelCase,			vars(_lowerCAmelCase))
												snake_case   :					int							=						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[str]							=						""
												logger.setLevel(logging.ERROR)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any])						->		Any:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Dict,			_lowerCAmelCase						: Any,			_lowerCAmelCase						: str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"])						->		int:



						"""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(_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 					UpperCAmelCase__      (							_lowerCAmelCase						: Any,			_lowerCAmelCase						: Union[str, Any])						->		Optional[Any]:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: Any,			_lowerCAmelCase						: Any)						->		str:



						"""simple docstring"""

						snake_case   :					Dict							=						accelerator.unwrap_model(_lowerCAmelCase).config
						snake_case   :					Dict							=						4 if args.gradient_checkpointing else 3
						snake_case   :					Optional[int]							=						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   :					str							=						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   :					List[Any]							=						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[int])						->		Union[str, Any]:



						"""simple docstring"""

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


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

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

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

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

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

# Load dataset and dataloader
A_      ,   A_        =   create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A_        =   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A_        =   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 					UpperCAmelCase__      (							)						->		Tuple:



						"""simple docstring"""

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


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

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

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5,			int(math.sqrt(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


if __name__ == "__main__":
						print(f"{solution() = }")





