

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   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A = None ,							):
  # Model states
  for i, state in enumerate(__A      ):
    UpperCAmelCase          =	F"{MODEL_NAME}.bin" if i == 0 else F"{MODEL_NAME}_{i}.bin"
    UpperCAmelCase          =	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      ):
    UpperCAmelCase          =	opt.state_dict()
    UpperCAmelCase          =	F"{OPTIMIZER_NAME}.bin" if i == 0 else F"{OPTIMIZER_NAME}_{i}.bin"
    UpperCAmelCase          =	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      ):
    UpperCAmelCase          =	scheduler.state_dict()
    UpperCAmelCase          =	F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
    UpperCAmelCase          =	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:
    UpperCAmelCase          =	scaler.state_dict()
    UpperCAmelCase          =	os.path.join(__A ,							__A      )
    torch.save(__A ,							__A      )
    logger.info(F"Gradient scaler state saved in {output_scaler_file}"      )
  # Random number generator states
  UpperCAmelCase          =	{}
  UpperCAmelCase          =	F"{RNG_STATE_NAME}_{process_index}.pkl"
  UpperCAmelCase          =	random.getstate()
  UpperCAmelCase          =	np.random.get_state()
  UpperCAmelCase          =	torch.get_rng_state()
  if is_xpu_available():
    UpperCAmelCase          =	torch.xpu.get_rng_state_all()
  else:
    UpperCAmelCase          =	torch.cuda.get_rng_state_all()
  if is_tpu_available():
    UpperCAmelCase          =	xm.get_rng_state()
  UpperCAmelCase          =	os.path.join(__A ,							__A      )
  torch.save(__A ,							__A      )
  logger.info(F"Random states saved in {output_states_file}"      )
  return output_dir
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A=None ,							__A=None ,							**__A ,							):
  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:
    UpperCAmelCase          =	"cpu"
  elif map_location == "on_device":
    UpperCAmelCase          =	PartialState().device
  # Model states
  for i, model in enumerate(__A      ):
    UpperCAmelCase          =	F"{MODEL_NAME}.bin" if i == 0 else F"{MODEL_NAME}_{i}.bin"
    UpperCAmelCase          =	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      ):
    UpperCAmelCase          =	F"{OPTIMIZER_NAME}.bin" if i == 0 else F"{OPTIMIZER_NAME}_{i}.bin"
    UpperCAmelCase          =	os.path.join(__A ,							__A      )
    UpperCAmelCase          =	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      ):
    UpperCAmelCase          =	F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
    UpperCAmelCase          =	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:
    UpperCAmelCase          =	os.path.join(__A ,							__A      )
    scaler.load_state_dict(torch.load(__A      )      )
    logger.info("GradScaler state loaded successfully"      )

  # Random states
  try:
    UpperCAmelCase          =	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   _lowerCAmelCase(						__A ,							__A ,							__A = 0      ):
  UpperCAmelCase          =	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   _lowerCAmelCase(						__A ,							__A ,							__A = 0      ):
  UpperCAmelCase          =	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"      )      )








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


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

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

													a__:							List[Any]									=CONFIG
													if torch.cuda.is_available():
																				a__:							Dict									="cuda"
													a__:							Any									=os.path.realpath(_a      )
													a__:							Optional[int]									=os.path.realpath(_a      )
													a__:							Union[str, Any]									=Preprocess(self.config      )
													a__:							str									=GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"   ,							config=self.config      )
													a__:							List[Any]									=batch_size if batch_size != 0 else 1
													a__:							Union[str, Any]									=DEFAULT_SCHEMA


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Dict      ):
													a__:							Tuple									=int(file.split("."      )[0].split("_"      )[-1]      )
													a__:							int									=os.path.join(self.inputdir   ,							_a      )
													return (img_id, filepath)


						@property
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							List[Any]									=[]
													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(_a      )      )
																				if len(_a      ) == self.batch:
																											a__:							List[Any]									=batch
																											a__:							Union[str, Any]									=[]
																											yield list(map(_a   ,							zip(*_a      )      )      )

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







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





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							try:
														a__:							Any									=json.load(__magic_name__    )
														try:
																					a__:							List[str]									=list(data.keys()    )
														except Exception:
																					a__:							Union[str, Any]									=[d["img_id"] for d in data]
							except Exception:
														try:
																					a__:							Dict									=eval(stream.read()    )
														except Exception:
																					a__:							int									=stream.read().split("\n"    )
							return data


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





