
'''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
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, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class 		snake_case_       :



   """simple docstring"""





   def __init__( self			,   UpperCamelCase=sys.argv[1:]):
          lowerCamelCase__				   =      None
          lowerCamelCase__				   =      None
          lowerCamelCase__				   =      None
          lowerCamelCase__				   =      1
          lowerCamelCase__   ,		lowerCamelCase__				   =      getopt.getopt(UpperCamelCase			,   "i:o:b:s"			,   ["inputdir=", "outfile=", "batch_size=", "subset_list="])
          for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                        lowerCamelCase__				   =      arg
                 elif opt in ("-o", "--outfile"):
                        lowerCamelCase__				   =      arg
                 elif opt in ("-b", "--batch_size"):
                        lowerCamelCase__				   =      int(UpperCamelCase)
                 elif opt in ("-s", "--subset_list"):
                        lowerCamelCase__				   =      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:
                        lowerCamelCase__				   =      {self._vqa_file_split()[0] for x in tryload(UpperCamelCase)}
          else:
                 lowerCamelCase__				   =      None

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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      int(file.split(".")[0].split("_")[-1])
          lowerCamelCase__				   =      os.path.join(self.inputdir			,   UpperCamelCase)
          return (img_id, filepath)







   @property
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      []
          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:
                        lowerCamelCase__				   =      batch
                        lowerCamelCase__				   =      []
                        yield list(map(UpperCamelCase			,   zip(*UpperCamelCase)))

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







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







def   lowerCAmelCase(  a__    :  List[Any]      ):







       '''simple docstring'''






       try:
              lowerCamelCase__				   =      json.load(a__      )
              try:
                     lowerCamelCase__				   =      list(data.keys()      )
              except Exception:
                     lowerCamelCase__				   =      [d["img_id"] for d in data]
       except Exception:
              try:
                     lowerCamelCase__				   =      eval(stream.read()      )
              except Exception:
                     lowerCamelCase__				   =      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)



'''simple docstring'''







from ..utils import DummyObject, requires_backends







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[str]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							Union[str, Any]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])

