

"""simple docstring"""





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : bool = False)			->		Tuple:
   '''simple docstring'''


   if device_specific:
      seed += AcceleratorState().process_index
   random.seed(UpperCamelCase_)
   np.random.seed(UpperCamelCase_)
   torch.manual_seed(UpperCamelCase_)
   if is_xpu_available():
      torch.xpu.manual_seed_all(UpperCamelCase_)
   elif is_npu_available():
      torch.npu.manual_seed_all(UpperCamelCase_)
   else:
      torch.cuda.manual_seed_all(UpperCamelCase_)
   # ^^ safe to call this function even if cuda is not available
   if is_tpu_available():
      xm.set_rng_state(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : Optional[RNGType] = None,    UpperCamelCase_  : Optional[torch.Generator] = None)			->		str:
   '''simple docstring'''


   if rng_type == RNGType.TORCH:
      __lowercase			=		torch.get_rng_state()
   elif rng_type == RNGType.CUDA:
      __lowercase			=		torch.cuda.get_rng_state()
   elif rng_type == RNGType.XLA:
      assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
      __lowercase			=		torch.tensor(xm.get_rng_state())
   elif rng_type == RNGType.NPU:
      assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
      __lowercase			=		torch.npu.get_rng_state()
   elif rng_type == RNGType.XPU:
      assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
      __lowercase			=		torch.xpu.get_rng_state()
   elif rng_type == RNGType.GENERATOR:
      assert generator is not None, "Need a generator to synchronize its seed."
      __lowercase			=		generator.get_state()

   # Broadcast the rng state from device 0 to other devices
   __lowercase			=		AcceleratorState()
   if state.distributed_type == DistributedType.TPU:
      __lowercase			=		rng_state.to(xm.xla_device())
      xm.collective_broadcast([rng_state])
      xm.mark_step()
      __lowercase			=		rng_state.cpu()
   elif (
       state.distributed_type in CUDA_DISTRIBUTED_TYPES
       or state.distributed_type == DistributedType.MULTI_NPU
       or state.distributed_type == DistributedType.MULTI_XPU
   ):
      __lowercase			=		rng_state.to(state.device)
      torch.distributed.broadcast(UpperCamelCase_,    0)
      __lowercase			=		rng_state.cpu()
   elif state.distributed_type == DistributedType.MULTI_CPU:
      torch.distributed.broadcast(UpperCamelCase_,    0)

   # Set the broadcast rng state
   if rng_type == RNGType.TORCH:
      torch.set_rng_state(UpperCamelCase_)
   elif rng_type == RNGType.CUDA:
      torch.cuda.set_rng_state(UpperCamelCase_)
   elif rng_type == RNGType.NPU:
      torch.npu.set_rng_state(UpperCamelCase_)
   elif rng_type == RNGType.XPU:
      torch.xpu.set_rng_state(UpperCamelCase_)
   elif rng_type == RNGType.XLA:
      xm.set_rng_state(rng_state.item())
   elif rng_type == RNGType.GENERATOR:
      generator.set_state(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : List[Union[str, RNGType]],    UpperCamelCase_  : Optional[torch.Generator] = None)			->		List[str]:
   '''simple docstring'''


   for rng_type in rng_types:
      synchronize_rng_state(RNGType(UpperCamelCase_),    generator=UpperCamelCase_)







"""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)




