
'''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__	(			snake_case_):







				def __init__(    self		,							A_		,							A_		,							A_=False		,							A_=1024		,							A_=1024		,							A_=3.6		,							A_=False		,							)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			tokenizer
											UpperCamelCase					       =			tokenizer.bos_token_id
											UpperCamelCase					       =			dataset
											UpperCamelCase					       =			seq_length
											UpperCamelCase					       =			0
											UpperCamelCase					       =			infinite
											UpperCamelCase					       =			0
											UpperCamelCase					       =			tokenized

											if self.tokenized:
																		UpperCamelCase					       =			seq_length * num_of_sequences
																		UpperCamelCase					       =			'input_ids'
											else:
																		UpperCamelCase					       =			seq_length * chars_per_token * num_of_sequences
																		UpperCamelCase					       =			'content'







				def __iter__(    self			)->						List[Any]:







											'''simple docstring'''






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







				def 		UpperCAmelCase_  (    self		,							A_=1000			)->						Union[str, Any]:







											'''simple docstring'''






											return ShufflerIterDataPipe(self		,							buffer_size=A_			)







def 						A_(			A		:						str):
							UpperCamelCase					       =			args.model_ckpt.split('/')[-1]
							UpperCamelCase					       =			logging.getLogger(__name__)
							UpperCamelCase					       =			Path(args.save_dir) / 'log/'
							log_dir.mkdir(exist_ok=A)
							UpperCamelCase					       =			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(A						,			vars(A))
														UpperCamelCase					       =			accelerator.trackers[0].run.name
														logger.setLevel(logging.INFO)
														datasets.utils.logging.set_verbosity_info()
														transformers.utils.logging.set_verbosity_info()
							else:
														UpperCamelCase					       =			''
														logger.setLevel(logging.ERROR)
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name







def 						A_(			A		:						Union[str, Any]):
							UpperCamelCase					       =			{'streaming': True}
							UpperCamelCase					       =			load_dataset(args.dataset_name_train						,			split='train'						,			**A)
							UpperCamelCase					       =			train_data.shuffle(buffer_size=args.shuffle_buffer						,			seed=args.seed)
							UpperCamelCase					       =			load_dataset(args.dataset_name_valid						,			split='train'						,			**A)
							UpperCamelCase					       =			ConstantLengthDataset(
							    A						,			A						,			infinite=A						,			seq_length=args.seq_length						,			tokenized=args.tokenized)
							UpperCamelCase					       =			ConstantLengthDataset(
							    A						,			A						,			infinite=A						,			seq_length=args.seq_length						,			tokenized=args.tokenized)
							UpperCamelCase					       =			train_dataset.shuffle(buffer_size=args.shuffle_buffer)
							UpperCamelCase					       =			DataLoader(A						,			batch_size=args.train_batch_size						,			shuffle=A)
							UpperCamelCase					       =			DataLoader(A						,			batch_size=args.valid_batch_size)
							return train_dataloader, eval_dataloader







def 						A_(			A		:						str						,			A		:						List[Any]						,			A		:						str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]):
							UpperCamelCase       ,     UpperCamelCase					       =			[], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay):
																					params_without_wd.append(A)
														else:
																					params_with_wd.append(A)
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]







def 						A_(			A		:						Tuple						,			A		:						str):
							logger.info(f'''Step {step}: {metrics}''')
							if accelerator.is_main_process:
														accelerator.log(A						,			A)







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







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


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

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

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

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

# Logging
lowerCAmelCase  ,		lowerCAmelCase :  Dict      =      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
lowerCAmelCase :  str      =      AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
lowerCAmelCase :  Union[str, Any]      =      AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
lowerCAmelCase :  Union[str, Any]      =      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCAmelCase :  Dict      =      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_(			):
							return optimizer.param_groups[0]["lr"]


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

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






'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
lowerCAmelCase :  Union[str, Any]      =      [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 						A_(			A		:						int):
							return [int(A) for c in f'''{ruleset:08}'''[:8]]







def 						A_(			A		:						list[list[int]]						,			A		:						list[int]						,			A		:						int):
							UpperCamelCase					       =			len(cells[0])  # 31
							UpperCamelCase					       =			[]
							for i in range(A):
														# Get the neighbors of each cell
														# Handle neighbours outside bounds by using 0 as their value
														UpperCamelCase					       =			0 if i == 0 else cells[time][i - 1]
														UpperCamelCase					       =			0 if i == population - 1 else cells[time][i + 1]
														# Define a new cell and add it to the new generation
														UpperCamelCase					       =			7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''						,			2)
														next_generation.append(rule[situation])
							return next_generation







def 						A_(			A		:						list[list[int]]):
							UpperCamelCase					       =			Image.new('RGB'						,			(len(cells[0]), len(A)))
							UpperCamelCase					       =			img.load()
							# Generates image
							for w in range(img.width):
														for h in range(img.height):
																					UpperCamelCase					       =			255 - int(255 * cells[h][w])
																					UpperCamelCase					       =			(color, color, color)
							return img


if __name__ == "__main__":
						lowerCAmelCase :  Optional[int]      =      bin(int(input('Rule:\n').strip()))[2:]
						lowerCAmelCase :  Tuple      =      format_ruleset(int(rule_num))
						for time in range(16):
												CELLS.append(new_generation(CELLS, rule, time))
						lowerCAmelCase :  List[str]      =      generate_image(CELLS)
						# Uncomment to save the image
						# img.save(f"rule_{rule_num}.png")
						img.show()




