
'''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__		: Tuple  							=							False
a__		: Dict  							=							Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a__		: Any  							=							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 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]						,					_A	:List[Any]=sys.argv[1:]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      None
       __A													=      None
       __A													=      None
       __A													=      1
       __A						,   __A													=      getopt.getopt(_A						,					'i:o:b:s'						,					['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   __A													=      arg
             elif opt in ("-o", "--outfile"):
                   __A													=      arg
             elif opt in ("-b", "--batch_size"):
                   __A													=      int(_A      )
             elif opt in ("-s", "--subset_list"):
                   __A													=      arg

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

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






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:int      )					->      Tuple:







       '''simple docstring'''



       __A													=      int(file.split('.'      )[0].split('_'      )[-1]      )
       __A													=      os.path.join(self.inputdir						,					_A      )
       return (img_id, filepath)






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      []
       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(_A      )      )
             if len(_A      ) == self.batch:
                   __A													=      batch
                   __A													=      []
                   yield list(map(_A						,					zip(*_A      )      )      )

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






 def __call__(      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



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





def 							snake_case				(		UpperCAmelCase						)->      List[Any]:







      """simple docstring"""
      try:
            __A													=      json.load(UpperCAmelCase						)
            try:
                  __A													=      list(data.keys()						)
            except Exception:
                  __A													=      [d['img_id'] for d in data]
      except Exception:
            try:
                  __A													=      eval(stream.read()						)
            except Exception:
                  __A													=      stream.read().split('\n'						)
      return data


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







'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



