

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

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

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

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







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





def 				A_					(	snake_case	):
  try:
    SCREAMING_SNAKE_CASE:List[str]   =   json.load(snake_case	)
    try:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   list(data.keys()	)
    except Exception:
      SCREAMING_SNAKE_CASE:Any   =   [d["img_id"] for d in data]
  except Exception:
    try:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   eval(stream.read()	)
    except Exception:
      SCREAMING_SNAKE_CASE:Any   =   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)









'''simple docstring'''

from typing import TYPE_CHECKING

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


A_      =							{"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_      =							[
        "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "YosoForMaskedLM",
        "YosoForMultipleChoice",
        "YosoForQuestionAnswering",
        "YosoForSequenceClassification",
        "YosoForTokenClassification",
        "YosoLayer",
        "YosoModel",
        "YosoPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yoso import (
            YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
            YosoForMaskedLM,
            YosoForMultipleChoice,
            YosoForQuestionAnswering,
            YosoForSequenceClassification,
            YosoForTokenClassification,
            YosoLayer,
            YosoModel,
            YosoPreTrainedModel,
        )


else:
    import sys

    A_      =							_LazyModule(__name__, globals()["__file__"], _import_structure)






