

"""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 					snake_case			(       __snake_case						):

				def __init__(			self							:       Union[str, Any]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Tuple=False  ,						UpperCamelCase__							:       Optional[Any]=1_0_2_4  ,						UpperCamelCase__							:       Any=1_0_2_4  ,						UpperCamelCase__							:       Dict=3.6  ,						UpperCamelCase__							:       str=False  ,						)->     List[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    tokenizer
						__lowerCAmelCase: Union[str, Any]					   =    tokenizer.bos_token_id
						__lowerCAmelCase: Tuple					   =    dataset
						__lowerCAmelCase: Tuple					   =    seq_length
						__lowerCAmelCase: Union[str, Any]					   =    0
						__lowerCAmelCase: Dict					   =    infinite
						__lowerCAmelCase: List[Any]					   =    0
						__lowerCAmelCase: int					   =    tokenized

						if self.tokenized:
								__lowerCAmelCase: Dict					   =    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							:       str)->     Any:



						'''simple docstring'''


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




				def 							lowercase_  (			self							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]=1_0_0_0)->     int:



						'''simple docstring'''


						return ShufflerIterDataPipe(self  ,						buffer_size=UpperCamelCase__)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			List[Any]:
		__lowerCAmelCase: Tuple					   =    args.model_ckpt.split("/"				)[-1]
		__lowerCAmelCase: Any					   =    logging.getLogger(__name__				)
		__lowerCAmelCase: str					   =    Path(args.save_dir				) / "log/"
		log_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
		__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(__SCREAMING_SNAKE_CASE      ,				vars(__SCREAMING_SNAKE_CASE				)				)
				__lowerCAmelCase: List[str]					   =    accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO				)
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				__lowerCAmelCase: str					   =    ""
				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				)			->			List[str]:
		__lowerCAmelCase: str					   =    {"streaming": True}
		__lowerCAmelCase: Optional[int]					   =    load_dataset(args.dataset_name_train      ,				split="train"      ,				**__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[int]					   =    train_data.shuffle(buffer_size=args.shuffle_buffer      ,				seed=args.seed				)
		__lowerCAmelCase: Dict					   =    load_dataset(args.dataset_name_valid      ,				split="train"      ,				**__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: str					   =    ConstantLengthDataset(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				infinite=__SCREAMING_SNAKE_CASE      ,				seq_length=args.seq_length      ,				tokenized=args.tokenized				)
		__lowerCAmelCase: List[Any]					   =    ConstantLengthDataset(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				infinite=__SCREAMING_SNAKE_CASE      ,				seq_length=args.seq_length      ,				tokenized=args.tokenized				)
		__lowerCAmelCase: str					   =    train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
		__lowerCAmelCase: Optional[Any]					   =    DataLoader(__SCREAMING_SNAKE_CASE      ,				batch_size=args.train_batch_size      ,				shuffle=__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: str					   =    DataLoader(__SCREAMING_SNAKE_CASE      ,				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"]				)			->			List[Any]:
		__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    [], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay				):
						params_without_wd.append(__SCREAMING_SNAKE_CASE				)
				else:
						params_with_wd.append(__SCREAMING_SNAKE_CASE				)
		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				)			->			Any:
		logger.info(F"Step {step}: {metrics}"				)
		if accelerator.is_main_process:
				accelerator.log(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Dict:
		# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
		__lowerCAmelCase: List[Any]					   =    accelerator.unwrap_model(__SCREAMING_SNAKE_CASE				).config
		__lowerCAmelCase: int					   =    4 if args.gradient_checkpointing else 3
		__lowerCAmelCase: Union[str, Any]					   =    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		__lowerCAmelCase: Optional[int]					   =    2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		__lowerCAmelCase: 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))
		)
		__lowerCAmelCase: List[Any]					   =    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: Any					   =    []
		for step, batch in enumerate(__SCREAMING_SNAKE_CASE				):
				with torch.no_grad():
						__lowerCAmelCase: Tuple					   =    model(__SCREAMING_SNAKE_CASE      ,				labels=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: str					   =    outputs.loss.repeat(args.valid_batch_size				)
				losses.append(accelerator.gather(__SCREAMING_SNAKE_CASE				)				)
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		__lowerCAmelCase: Dict					   =    torch.cat(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Tuple					   =    losses[: eval_dataloader.dataset.current_size].mean()
		try:
				__lowerCAmelCase: Optional[int]					   =    torch.exp(__SCREAMING_SNAKE_CASE				)
		except OverflowError:
				__lowerCAmelCase: int					   =    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 					a__					(					)			->			Any:
		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"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    "", "", ""
		__lowerCAmelCase: Any					   =    []

		for character in message_part:
				tmp.append(character_to_number[character]				)

		for each in tmp:
				one += each[0]
				two += each[1]
				three += each[2]

		return one + two + three






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, str]:
		__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    "", ""
		__lowerCAmelCase: int					   =    []

		for character in message_part:
				this_part += character_to_number[character]

		for digit in this_part:
				tmp += digit
				if len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				):
						result.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: Union[str, Any]					   =    ""

		return result[0], result[1], result[2]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, dict[str, str], dict[str, str]]:
		# Validate message and alphabet, set to upper and remove spaces
		__lowerCAmelCase: int					   =    alphabet.replace(" "      ,				""				).upper()
		__lowerCAmelCase: int					   =    message.replace(" "      ,				""				).upper()

		# Check length and characters
		if len(__SCREAMING_SNAKE_CASE				) != 2_7:
				raise KeyError("Length of alphabet has to be 27."				)
		for each in message:
				if each not in alphabet:
						raise ValueError("Each message character has to be included in alphabet!"				)

    # Generate dictionares
		__lowerCAmelCase: Optional[int]					   =    (
		    "111",
		    "112",
		    "113",
		    "121",
		    "122",
		    "123",
		    "131",
		    "132",
		    "133",
		    "211",
		    "212",
		    "213",
		    "221",
		    "222",
		    "223",
		    "231",
		    "232",
		    "233",
		    "311",
		    "312",
		    "313",
		    "321",
		    "322",
		    "323",
		    "331",
		    "332",
		    "333",
		)
		__lowerCAmelCase: Tuple					   =    {}
		__lowerCAmelCase: Optional[int]					   =    {}
		for letter, number in zip(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: List[Any]					   =    number
				__lowerCAmelCase: Any					   =    letter

		return message, alphabet, character_to_number, number_to_character






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase   ,				__lowerCAmelCase: str					   =    "", ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				encrypted_numeric += __encrypt_part(
				    message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				)      ,				3				):
				encrypted += number_to_character[encrypted_numeric[i : i + 3]]

		return encrypted






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Dict					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[str]					   =    []
		__lowerCAmelCase: Optional[int]					   =    ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    __decrypt_part(message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

				for j in range(0      ,				len(__SCREAMING_SNAKE_CASE				)				):
						decrypted_numeric.append(a[j] + b[j] + c[j]				)

		for each in decrypted_numeric:
				decrypted += number_to_character[each]

		return decrypted


if __name__ == "__main__":
			__A		     =    "DEFEND THE EAST WALL OF THE CASTLE."
			__A		     =    encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			__A		     =    decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')


