



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 		UpperCAmelCase__      (      snake_case_       ):




			'''simple docstring'''






			def __init__(       self							:			Dict	,	a_							:			Any	,	a_							:			Union[str, Any]	,	a_							:			List[str]=False	,	a_							:			List[Any]=10_24	,	a_							:			str=10_24	,	a_							:			int=3.6	,	a_							:			Any=False	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     tokenizer
					__UpperCAmelCase   :					int													=     tokenizer.bos_token_id
					__UpperCAmelCase   :					Tuple													=     dataset
					__UpperCAmelCase   :					Any													=     seq_length
					__UpperCAmelCase   :					List[str]													=     0
					__UpperCAmelCase   :					List[str]													=     infinite
					__UpperCAmelCase   :					str													=     0
					__UpperCAmelCase   :					List[Any]													=     tokenized

					if self.tokenized:
							__UpperCAmelCase   :					List[str]													=     seq_length * num_of_sequences
							__UpperCAmelCase   :					Any													=     "input_ids"
					else:
							__UpperCAmelCase   :					int													=     seq_length * chars_per_token * num_of_sequences
							__UpperCAmelCase   :					Optional[Any]													=     "content"




			def __iter__(       self							:			Optional[Any]			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			Optional[Any]=10_00			):
					'''simple docstring'''





					return ShufflerIterDataPipe(self	,	buffer_size=a_			)

def 	a						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     args.model_ckpt.split('''/'''						)[-1]
		__UpperCAmelCase   :					List[Any]													=     logging.getLogger(__name__						)
		__UpperCAmelCase   :					Tuple													=     Path(args.save_dir						) / "log/"
		log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
		__UpperCAmelCase   :					Optional[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_						)						)
				__UpperCAmelCase   :					Any													=     accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO						)
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				__UpperCAmelCase   :					Dict													=     ""
				logger.setLevel(logging.ERROR						)
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name

def 	a						(				_UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):







		'''simple docstring'''





		__UpperCAmelCase   :					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 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: int			,       _UpperCAmelCase							: int			,       _UpperCAmelCase							: Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     accelerator.unwrap_model(SCREAMING_SNAKE_CASE_						).config
		__UpperCAmelCase   :					Optional[int]													=     4 if args.gradient_checkpointing else 3
		__UpperCAmelCase   :					int													=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		__UpperCAmelCase   :					Optional[int]													=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		__UpperCAmelCase   :					List[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))
		)
		__UpperCAmelCase   :					List[Any]													=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops

def 	a						(				_UpperCAmelCase							: int						):







		'''simple docstring'''





		model.eval()
		__UpperCAmelCase   :					Any													=     []
		for step, batch in enumerate(SCREAMING_SNAKE_CASE_						):
				with torch.no_grad():
						__UpperCAmelCase   :					str													=     model(SCREAMING_SNAKE_CASE_			,       labels=SCREAMING_SNAKE_CASE_						)
				__UpperCAmelCase   :					Union[str, Any]													=     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
		__UpperCAmelCase   :					Any													=     torch.cat(SCREAMING_SNAKE_CASE_						)
		__UpperCAmelCase   :					Union[str, Any]													=     losses[: eval_dataloader.dataset.current_size].mean()
		try:
				__UpperCAmelCase   :					Tuple													=     torch.exp(SCREAMING_SNAKE_CASE_						)
		except OverflowError:
				__UpperCAmelCase   :					Any													=     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             =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             =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						(				):







		'''simple docstring'''





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


# Prepare everything with our `accelerator`.
__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             =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             =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")










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





