

"""simple docstring"""





import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _a				   =							None
   _a				   =							trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)







"""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
_a				   =							False
_a				   =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a				   =							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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]=sys.argv[1:]			):
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		1
    __lowercase	,__lowercase			=		getopt.getopt(UpperCAmelCase__,      "i:o:b:s",      ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
    for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
          __lowercase			=		arg
       elif opt in ("-o", "--outfile"):
          __lowercase			=		arg
       elif opt in ("-b", "--batch_size"):
          __lowercase			=		int(UpperCAmelCase__			)
       elif opt in ("-s", "--subset_list"):
          __lowercase			=		arg

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

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


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any]			):
    __lowercase			=		int(file.split("."			)[0].split("_"			)[-1]			)
    __lowercase			=		os.path.join(self.inputdir,      UpperCAmelCase__			)
    return (img_id, filepath)


 @property
 def       _lowercase				(    self							:    int			):
    __lowercase			=		[]
    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(UpperCAmelCase__			)			)
       if len(UpperCAmelCase__			) == self.batch:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



 def __call__(    self							:    Optional[Any]			):
    # make writer
    if not TEST:
       __lowercase			=		datasets.ArrowWriter(features=self.schema,      path=self.outputfile			)
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator			):
       __lowercase	,__lowercase	,__lowercase			=		self.preprocess(UpperCAmelCase__			)
       __lowercase			=		self.model(
           UpperCAmelCase__,      UpperCAmelCase__,      scales_yx=UpperCAmelCase__,      padding="max_detections",      max_detections=self.config.MAX_DETECTIONS,      pad_value=0,      return_tensors="np",      location="cpu",      )
       __lowercase			=		output_dict.pop("normalized_boxes"			)
       if not TEST:
          __lowercase			=		np.array(UpperCAmelCase__			)
          __lowercase			=		self.schema.encode_batch(UpperCAmelCase__			)
          writer.write_batch(UpperCAmelCase__			)
       if TEST:
          break
       # finalizer the writer
    if not TEST:
       __lowercase	,__lowercase			=		writer.finalize()
       print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      try:
         __lowercase			=		list(data.keys())
      except Exception:
         __lowercase			=		[d["img_id"] for d in data]
   except Exception:
      try:
         __lowercase			=		eval(stream.read())
      except Exception:
         __lowercase			=		stream.read().split("\n")
   return data


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




