






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	: Dict      =				False
a	: int      =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a	: Optional[int]      =				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_048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class 					a					:



      """simple docstring"""


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

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

        __UpperCAmelCase							:			Union[str, Any]     =						CONFIG
        if torch.cuda.is_available():
          __UpperCAmelCase							:			Optional[int]     =						"""cuda"""
        __UpperCAmelCase							:			Optional[Any]     =						os.path.realpath(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						os.path.realpath(__lowercase      )
        __UpperCAmelCase							:			List[str]     =						Preprocess(self.config      )
        __UpperCAmelCase							:			Optional[int]     =						GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""	,   config=self.config      )
        __UpperCAmelCase							:			List[Any]     =						batch_size if batch_size != 0 else 1
        __UpperCAmelCase							:			List[str]     =						DEFAULT_SCHEMA
      def 			UpperCAmelCase   (   self      :    Optional[Any]	,   __lowercase      :    List[Any]      )							->   Dict:
        __UpperCAmelCase							:			int     =						int(file.split("""."""      )[0].split("""_"""      )[-1]      )
        __UpperCAmelCase							:			List[str]     =						os.path.join(self.inputdir	,   __lowercase      )
        return (img_id, filepath)
      @property
      def 			UpperCAmelCase   (   self      :    str      )							->   int:
        __UpperCAmelCase							:			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(__lowercase      )      )
          if len(__lowercase      ) == self.batch:
            __UpperCAmelCase							:			int     =						batch
            __UpperCAmelCase							:			int     =						[]
            yield list(map(__lowercase	,   zip(*__lowercase      )      )      )

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




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






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


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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a	: Union[str, Any]      =				{
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				["MobileNetV1FeatureExtractor"]
       a	: int      =				["MobileNetV1ImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				[
           "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileNetV1ForImageClassification",
           "MobileNetV1Model",
           "MobileNetV1PreTrainedModel",
           "load_tf_weights_in_mobilenet_v1",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       a	: str      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


