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
_SCREAMING_SNAKE_CASE   		=			False
_SCREAMING_SNAKE_CASE   		=			Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_SCREAMING_SNAKE_CASE   		=			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 SCREAMING_SNAKE_CASE_		:

   '''simple docstring'''





   def __init__(    self	:	int      ,      lowerCamelCase_	:	List[str]=sys.argv[1:]							):





    """simple docstring"""


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

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

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







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	Optional[Any]							):





    """simple docstring"""


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







   @property
   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


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

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







   def __call__(    self	:	List[Any]							):





    """simple docstring"""


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





def  lowercase(  UpperCamelCase_					)		->  Tuple:







 '''simple docstring'''




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


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

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			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      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 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      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				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      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
