
"""simple docstring"""







import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
SCREAMING_SNAKE_CASE_     				=   False
SCREAMING_SNAKE_CASE_     				=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
SCREAMING_SNAKE_CASE_     				=   datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)






class     __a			:







       def __init__(					self							,lowerCamelCase_=sys.argv[1:]		):



          '''simple docstring'''

          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					1
          _UpperCAmelCase	,							_UpperCAmelCase   	=					getopt.getopt(lowerCamelCase_							,"i:o:b:s"							,["inputdir=", "outfile=", "batch_size=", "subset_list="]		)
          for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                _UpperCAmelCase   	=					arg
             elif opt in ("-o", "--outfile"):
                _UpperCAmelCase   	=					arg
             elif opt in ("-b", "--batch_size"):
                _UpperCAmelCase   	=					int(lowerCamelCase_		)
             elif opt in ("-s", "--subset_list"):
                _UpperCAmelCase   	=					arg

          assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
          assert outputfile is not None and not os.path.isfile(lowerCamelCase_		), f'''{outputfile}'''
          if subset_list is not None:
             with open(os.path.realpath(lowerCamelCase_		)		) as f:
                _UpperCAmelCase   	=					{self._vqa_file_split()[0] for x in tryload(lowerCamelCase_		)}
          else:
             _UpperCAmelCase   	=					None

          _UpperCAmelCase   	=					CONFIG
          if torch.cuda.is_available():
             _UpperCAmelCase   	=					"cuda"
          _UpperCAmelCase   	=					os.path.realpath(lowerCamelCase_		)
          _UpperCAmelCase   	=					os.path.realpath(lowerCamelCase_		)
          _UpperCAmelCase   	=					Preprocess(self.config		)
          _UpperCAmelCase   	=					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"							,config=self.config		)
          _UpperCAmelCase   	=					batch_size if batch_size != 0 else 1
          _UpperCAmelCase   	=					DEFAULT_SCHEMA







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					int(file.split("."		)[0].split("_"		)[-1]		)
          _UpperCAmelCase   	=					os.path.join(self.inputdir							,lowerCamelCase_		)
          return (img_id, filepath)







       @property
       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					[]
          for i, file in enumerate(os.listdir(self.inputdir		)		):
             if self.subset_list is not None and i not in self.subset_list:
                continue
             batch.append(self._vqa_file_split(lowerCamelCase_		)		)
             if len(lowerCamelCase_		) == self.batch:
                _UpperCAmelCase   	=					batch
                _UpperCAmelCase   	=					[]
                yield list(map(lowerCamelCase_							,zip(*lowerCamelCase_		)		)		)

          for i in range(1		):
             yield list(map(lowerCamelCase_							,zip(*lowerCamelCase_		)		)		)







       def __call__(					self		):



          '''simple docstring'''

          if not TEST:
             _UpperCAmelCase   	=					datasets.ArrowWriter(features=self.schema							,path=self.outputfile		)
          # do file generator
          for i, (img_ids, filepaths) in enumerate(self.file_generator		):
             _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					self.preprocess(lowerCamelCase_		)
             _UpperCAmelCase   	=					self.model(
                 lowerCamelCase_							,lowerCamelCase_							,scales_yx=lowerCamelCase_							,padding="max_detections"							,max_detections=self.config.MAX_DETECTIONS							,pad_value=0							,return_tensors="np"							,location="cpu"							,)
             _UpperCAmelCase   	=					output_dict.pop("normalized_boxes"		)
             if not TEST:
                _UpperCAmelCase   	=					np.array(lowerCamelCase_		)
                _UpperCAmelCase   	=					self.schema.encode_batch(lowerCamelCase_		)
                writer.write_batch(lowerCamelCase_		)
             if TEST:
                break
             # finalizer the writer
          if not TEST:
             _UpperCAmelCase	,							_UpperCAmelCase   	=					writer.finalize()
             print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''		)



def 			__UpperCAmelCase		(		a_			:		Optional[Any]				)     ->       Union[str, Any]:
   try:
      _UpperCAmelCase   	=					json.load(a_				)
      try:
         _UpperCAmelCase   	=					list(data.keys()				)
      except Exception:
         _UpperCAmelCase   	=					[d["img_id"] for d in data]
   except Exception:
      try:
         _UpperCAmelCase   	=					eval(stream.read()				)
      except Exception:
         _UpperCAmelCase   	=					stream.read().split("\n"				)
   return data


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   Extract(sys.argv[1:])
  extract()
  if not TEST:
    SCREAMING_SNAKE_CASE_     				=   datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)



"""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     __a			(					lowerCAmelCase_			):







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



          '''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		):



          '''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(lowerCamelCase_		)[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(lowerCamelCase_							,truncation=lowerCamelCase_		)["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(lowerCamelCase_		)							,self.seq_length		):
                _UpperCAmelCase   	=					all_token_ids[i : i + self.seq_length]
                if len(lowerCamelCase_		) == self.seq_length:
                   self.current_size += 1
                   yield torch.tensor(lowerCamelCase_		)







       def    _snake_case			(					self							,lowerCamelCase_=1000		):



          '''simple docstring'''

          return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)



def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
   _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 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
   _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 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
   _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 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
   logger.info(F'''Step {step}: {metrics}'''				)
   if accelerator.is_main_process:
      accelerator.log(a_    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       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 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   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
SCREAMING_SNAKE_CASE_     				=   HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE_     				=   parser.parse_args()

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

SCREAMING_SNAKE_CASE_     				=   Namespace(**vars(args), **acc_state)
SCREAMING_SNAKE_CASE_     				=   accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   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
SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
  model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE_     				=   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 			__UpperCAmelCase		(		)     ->       int:
   return optimizer.param_groups[0]["lr"]


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

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

