


'''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 							__UpperCamelCase       (	lowerCAmelCase_      ):




	def __init__(     self		,       __a		,       __a		,       __a=False		,       __a=1024		,       __a=1024		,       __a=3.6		,       __a=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							):

						'''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    __UpperCAmelCase							(     self		,       __a=1000							):

						'''simple docstring'''





						return ShufflerIterDataPipe(self		,       buffer_size=__a							)






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any  ):
					__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							(_SCREAMING_SNAKE_CASE   :     Dict  ):
					__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							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):
					__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							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     List[Any]  ):
					logger.info(F"""Step {step}: {metrics}"""  )
					if accelerator.is_main_process:
										accelerator.log(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE  )






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str  ):
					# 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							(_SCREAMING_SNAKE_CASE   :     Optional[int]  ):
					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							():
					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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Optional[int] =			[
	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VisualBertForMultipleChoice',
	    'VisualBertForPreTraining',
	    'VisualBertForQuestionAnswering',
	    'VisualBertForRegionToPhraseAlignment',
	    'VisualBertForVisualReasoning',
	    'VisualBertLayer',
	    'VisualBertModel',
	    'VisualBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_visual_bert import (
		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VisualBertForMultipleChoice,
		    VisualBertForPreTraining,
		    VisualBertForQuestionAnswering,
		    VisualBertForRegionToPhraseAlignment,
		    VisualBertForVisualReasoning,
		    VisualBertLayer,
		    VisualBertModel,
		    VisualBertPreTrainedModel,
		)


else:
	import sys

	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



