
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Any							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DinatForImageClassification""",
      """DinatModel""",
      """DinatPreTrainedModel""",
      """DinatBackbone""",
  ]

if TYPE_CHECKING:
  from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dinat import (
        DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DinatBackbone,
        DinatForImageClassification,
        DinatModel,
        DinatPreTrainedModel,
    )

else:
  import sys

  lowercase						:      Dict							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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 __snake_case	:
    def __init__(						self	,snake_case=sys.argv[1:]   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   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:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         yield list(map(snake_case	,zip(*snake_case   )   )   )

           for i in range(1   ):
                  yield list(map(snake_case	,zip(*snake_case   )   )   )
    def __call__(						self   ):



           '''simple docstring'''







           if not TEST:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   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"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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