



"""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      ( lowerCAmelCase_  ):


					'''simple docstring'''





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

								if self.tokenized:
											lowerCamelCase__						:						int						      =seq_length * num_of_sequences
											lowerCamelCase__						:						List[Any]						      ='''input_ids'''
								else:
											lowerCamelCase__						:						List[Any]						      =seq_length * chars_per_token * num_of_sequences
											lowerCamelCase__						:						Tuple						      ='''content'''



					def __iter__(			self				:    List[str]	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
								lowerCamelCase__						:						Tuple						      =True
								while more_examples:
											lowerCamelCase__      ,  lowerCamelCase__						:						str						      =[], 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:
																				lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
																				self.epoch += 1
																				logger.info(F'''Dataset epoch: {self.epoch}'''	)
																	else:
																				lowerCamelCase__						:						List[str]						      =False
																				break
											if self.tokenized:
														lowerCamelCase__						:						Union[str, Any]						      =buffer
											else:
														lowerCamelCase__						:						Any						      =self.tokenizer(lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
											lowerCamelCase__						:						Optional[int]						      =[]
											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	):
														lowerCamelCase__						:						Tuple						      =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				:    Tuple,    lowerCamelCase				:    Optional[int]=1000	)->   Optional[int]:
								return ShufflerIterDataPipe(self,    buffer_size=lowerCamelCase	)






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






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






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
			"""simple docstring"""
			lowerCamelCase__      ,  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 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			logger.info(f'''Step {step}: {metrics}'''      )
			if accelerator.is_main_process:
						accelerator.log(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowerCamelCase__						:						Tuple						      =accelerator.unwrap_model(__lowerCamelCase      ).config
			lowerCamelCase__						:						Any						      =4 if args.gradient_checkpointing else 3
			lowerCamelCase__						:						Any						      =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCamelCase__						:						str						      =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCamelCase__						:						Dict						      =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))
			)
			lowerCamelCase__						:						Dict						      =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						str						      =[]
			for step, batch in enumerate(__lowerCamelCase      ):
						with torch.no_grad():
									lowerCamelCase__						:						Optional[Any]						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						int						      =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__						:						str						      =torch.cat(__lowerCamelCase      )
			lowerCamelCase__						:						str						      =losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCamelCase__						:						Optional[Any]						      =torch.exp(__lowerCamelCase      )
			except OverflowError:
						lowerCamelCase__						:						Dict						      =float('''inf'''      )
			return loss.item(), perplexity.item()


# Settings
_lowercase					:					Dict 		=		HfArgumentParser(TrainingArguments)
_lowercase					:					Optional[Any] 		=		parser.parse_args()

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

_lowercase					:					Union[str, Any] 		=		Namespace(**vars(args), **acc_state)
_lowercase					:					List[str] 		=		accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		_lowercase					:					List[Any] 		=		Repository(args.save_dir, clone_from=args.model_ckpt)

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

# Load dataset and dataloader
_lowercase		,      _lowercase					:					Union[str, Any] 		=		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowercase					:					Tuple 		=		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowercase					:					List[str] 		=		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 	snake_case__  (						):
			"""simple docstring"""
			return optimizer.param_groups[0]["lr"]


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

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










import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase__    :							str       		=     128
UpperCAmelCase__    :							int       		=     256


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			List[str]      =     message.encode('ascii'				)

  _lowercase:			str      =     []
  for block_start in range(0       ,  len(UpperCamelCase				)       ,  UpperCamelCase				):
    _lowercase:			Optional[int]      =     0
    for i in range(UpperCamelCase       ,  min(block_start + block_size       ,  len(UpperCamelCase				)				)				):
      block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
    block_ints.append(UpperCamelCase				)
  return block_ints


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		str:





  """simple docstring"""





  _lowercase:			list[str]      =     []
  for block_int in block_ints:
    _lowercase:			list[str]      =     []
    for i in range(block_size - 1       ,  -1       ,  -1				):
      if len(UpperCamelCase				) + i < message_length:
        _lowercase:			Optional[Any]      =     block_int // (BYTE_SIZE**i)
        _lowercase:			Dict      =     block_int % (BYTE_SIZE**i)
        block_message.insert(0       ,  chr(UpperCamelCase				)				)
    message.extend(UpperCamelCase				)
  return "".join(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key

  for block in get_blocks_from_text(UpperCamelCase       ,  UpperCamelCase				):
    encrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return encrypted_blocks


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     []
  _lowercase  ,       _lowercase:			str      =     key
  for block in encrypted_blocks:
    decrypted_blocks.append(pow(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)				)
  return get_text_from_blocks(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		tuple[int, int, int]:





  """simple docstring"""





  with open(UpperCamelCase				) as fo:
    _lowercase:			Any      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Optional[Any]      =     content.split(','				)
  return (int(UpperCamelCase				), int(UpperCamelCase				), int(UpperCamelCase				))


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase = DEFAULT_BLOCK_SIZE       ,  )      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			int      =     read_key_file(UpperCamelCase				)
  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Either decrease the block size or use different keys.'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			int      =     [str(UpperCamelCase				) for i in encrypt_message(UpperCamelCase       ,  (n, e)       ,  UpperCamelCase				)]

  _lowercase:			List[Any]      =     ','.join(UpperCamelCase				)
  _lowercase:			Tuple      =     F'{len(UpperCamelCase				)}_{block_size}_{encrypted_content}'
  with open(UpperCamelCase       ,  'w'				) as fo:
    fo.write(UpperCamelCase				)
  return encrypted_content


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			Tuple      =     read_key_file(UpperCamelCase				)
  with open(UpperCamelCase				) as fo:
    _lowercase:			Optional[Any]      =     fo.read()
  _lowercase  ,       _lowercase  ,       _lowercase:			Union[str, Any]      =     content.split('_'				)
  _lowercase:			List[Any]      =     int(UpperCamelCase				)
  _lowercase:			Dict      =     int(UpperCamelCase				)

  if key_size < block_size * 8:
    sys.exit(
        'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
        'requires the block size to be equal to or greater than the key size. '
        'Did you specify the correct key file and encrypted file?'.format(
            block_size * 8       ,  UpperCamelCase				)				)

  _lowercase:			Union[str, Any]      =     []
  for block in encrypted_message.split(','				):
    encrypted_blocks.append(int(UpperCamelCase				)				)

  return decrypt_message(UpperCamelCase       ,  UpperCamelCase       ,  (n, d)       ,  UpperCamelCase				)


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Tuple      =     'encrypted_file.txt'
  _lowercase:			Optional[Any]      =     input(r'Encrypt\Decrypt [e\d]: '				)

  if response.lower().startswith('e'				):
    _lowercase:			Optional[Any]      =     'encrypt'
  elif response.lower().startswith('d'				):
    _lowercase:			Union[str, Any]      =     'decrypt'

  if mode == "encrypt":
    if not os.path.exists('rsa_pubkey.txt'				):
      rkg.make_key_files('rsa'       ,  1_024				)

    _lowercase:			Union[str, Any]      =     input('\nEnter message: '				)
    _lowercase:			Tuple      =     'rsa_pubkey.txt'
    print(F'Encrypting and writing to {filename}...'				)
    _lowercase:			Optional[Any]      =     encrypt_and_write_to_file(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

    print('\nEncrypted text:'				)
    print(UpperCamelCase				)

  elif mode == "decrypt":
    _lowercase:			Optional[Any]      =     'rsa_privkey.txt'
    print(F'Reading from {filename} and decrypting...'				)
    _lowercase:			int      =     read_from_file_and_decrypt(UpperCamelCase       ,  UpperCamelCase				)
    print('writing decryption to rsa_decryption.txt...'				)
    with open('rsa_decryption.txt'       ,  'w'				) as dec:
      dec.write(UpperCamelCase				)

    print('\nDecryption:'				)
    print(UpperCamelCase				)


if __name__ == "__main__":
   main()
