
"""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  (						lowerCAmelCase_       ):






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






												"""simple docstring"""



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

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






							def __iter__(					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					iter(self.dataset    )
												__a:						List[Any]		    =					True
												while more_examples:
																	__a:						List[str]		    =					[], 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:
																																__a:						Dict		    =					iter(self.dataset    )
																																self.epoch += 1
																																logger.info(f"""Dataset epoch: {self.epoch}"""    )
																											else:
																																__a:						List[str]		    =					False
																																break
																	if self.tokenized:
																						__a:						List[Any]		    =					buffer
																	else:
																						__a:						Optional[int]		    =					self.tokenizer(__a						, truncation=__a    )['input_ids']
																	__a:						Optional[Any]		    =					[]
																	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    ):
																						__a:						Optional[int]		    =					all_token_ids[i : i + self.seq_length]
																						if len(__a    ) == self.seq_length:
																											self.current_size += 1
																											yield torch.tensor(__a    )






							def 							__lowercase  (					self						:			int						, snake_case__						:			str=1_0_0_0    ):






												"""simple docstring"""



												return ShufflerIterDataPipe(self						, buffer_size=__a    )






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


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


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


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


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] )     ->			Dict:


					'''simple docstring'''
					__a:						Any		    =					[], []
					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     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[Any]:


					'''simple docstring'''
					logger.info(f"""Step {step}: {metrics}""" )
					if accelerator.is_main_process:
										accelerator.log(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
					__a:						Dict		    =					accelerator.unwrap_model(_SCREAMING_SNAKE_CASE ).config
					__a:						List[str]		    =					4 if args.gradient_checkpointing else 3
					__a:						Optional[Any]		    =					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
					__a:						List[str]		    =					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
					__a:						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))
					)
					__a:						Optional[Any]		    =					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
					return tflops


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


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


# Settings
lowercase		:						str =					HfArgumentParser(TrainingArguments)
lowercase		:						List[Any] =					parser.parse_args()

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

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

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

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

# Load dataset and dataloader
lowercase		:						Dict =					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase		:						str =					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase		:						Any =					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     lowerCamelCase_    (	)     ->			Optional[Any]:


					'''simple docstring'''
					return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowercase		:						Dict =					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		:						Optional[Any] =					os.path.basename(args.resume_from_checkpoint)
							else:
														# Get the most recent checkpoint
														lowercase		:						Union[str, 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		:						List[Any] =					os.path.splitext(path)[0]
							lowercase		:						Optional[int] =					int(training_difference.replace('step_', ''))

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


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
