

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
lowerCamelCase									= False
lowerCamelCase									= Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCamelCase									= 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 						__magic_name__      :



   def __init__(    self,   lowercase_=sys.argv[1:]	)							->   List[Any]:






     """simple docstring"""






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

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
     a__  =os.path.join(self.inputdir,   __snake_case	)
     return (img_id, filepath)



   @property
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     a__  =[]
     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(__snake_case	)	)
       if len(__snake_case	) == self.batch:
         a__  =batch
         a__  =[]
         yield list(map(__snake_case,   zip(*__snake_case	)	)	)

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



   def __call__(    self	)							->   Tuple:






     """simple docstring"""






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





def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







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


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



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase									= get_logger(__name__)




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[dict]     ,				_A				:			list     ,				_A				:			list     ,				_A				:			int     ,				_A				:			GradScaler = None     ,				):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =opt.state_dict()
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =scheduler.state_dict()
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__  =scaler.state_dict()
    a__  =os.path.join(_A     ,				_A							)
    torch.save(_A     ,				_A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__  ={}
  a__  =F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__  =random.getstate()
  a__  =np.random.get_state()
  a__  =torch.get_rng_state()
  if is_xpu_available():
    a__  =torch.xpu.get_rng_state_all()
  else:
    a__  =torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__  =xm.get_rng_state()
  a__  =os.path.join(_A     ,				_A							)
  torch.save(_A     ,				_A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			List[str]=None     ,				_A				:			Dict=None     ,				**_A				:			Tuple     ,				):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__  ='''cpu'''
  elif map_location == "on_device":
    a__  =PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    models[i].load_state_dict(torch.load(_A     ,				map_location=_A							)     ,				**_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    a__  =torch.load(_A     ,				map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__  =os.path.join(_A     ,				_A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__  =torch.load(os.path.join(_A     ,				F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()     ,				_A							)




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A     ,				map_location='''cpu'''							)							)
