




'''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
UpperCAmelCase_       				=      False
UpperCAmelCase_       				=      Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCAmelCase_       				=      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 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Any						,		_UpperCAmelCase    :	List[Any]=sys.argv[1:] ):






   """simple docstring"""






   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    1
   UpperCAmelCase__							,			UpperCAmelCase__										=    getopt.getopt(_UpperCAmelCase						,		"""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(_UpperCAmelCase )
    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(_UpperCAmelCase ), f'''{outputfile}'''
   if subset_list is not None:
    with open(os.path.realpath(_UpperCAmelCase ) ) as f:
     UpperCAmelCase__										=    {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase )}
   else:
    UpperCAmelCase__										=    None

   UpperCAmelCase__										=    CONFIG
   if torch.cuda.is_available():
    UpperCAmelCase__										=    """cuda"""
   UpperCAmelCase__										=    os.path.realpath(_UpperCAmelCase )
   UpperCAmelCase__										=    os.path.realpath(_UpperCAmelCase )
   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 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






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


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """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(_UpperCAmelCase ) )
    if len(_UpperCAmelCase ) == self.batch:
     UpperCAmelCase__										=    batch
     UpperCAmelCase__										=    []
     yield list(map(_UpperCAmelCase						,		zip(*_UpperCAmelCase ) ) )

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



  def __call__(						self    :	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(_UpperCAmelCase )
    UpperCAmelCase__										=    self.model(
        _UpperCAmelCase						,		_UpperCAmelCase						,		scales_yx=_UpperCAmelCase						,		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(_UpperCAmelCase )
     UpperCAmelCase__										=    self.schema.encode_batch(_UpperCAmelCase )
     writer.write_batch(_UpperCAmelCase )
    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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 try:
  UpperCAmelCase__										=    json.load(SCREAMING_SNAKE_CASE__       )
  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__":
    UpperCAmelCase_       				=      Extract(sys.argv[1:])
    extract()
    if not TEST:
        UpperCAmelCase_       				=      datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)






'''simple docstring'''




import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase_       				=      2 << 2_0  # 2mb
UpperCAmelCase_       				=      get_logger(__name__)

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self._options.get_all_options().get("""runner""" )
   return runner in [None, "DirectRunner", "PortableRunner"]

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if fs.exists(SCREAMING_SNAKE_CASE__       ):
  if force_upload:
   logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.create(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""rb"""       ) as local_file:
   UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    remote_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Dict=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if os.path.exists(SCREAMING_SNAKE_CASE__       ):
  if force_download:
   logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.open(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""wb"""       ) as local_file:
   UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    local_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
