





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   :							Optional[Any]     =	False
_UpperCamelCase   :							Dict     =	Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase   :							List[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 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase=sys.argv[1:]     )-> Dict:
     __A				 =       None
     __A				 =       None
     __A				 =       None
     __A				 =       1
     __A ,						__A				 =       getopt.getopt(UpperCAmelCase  ,    '''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(UpperCAmelCase     )
        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(UpperCAmelCase     ), f"{outputfile}"
     if subset_list is not None:
        with open(os.path.realpath(UpperCAmelCase     )     ) as f:
           __A				 =       {self._vqa_file_split()[0] for x in tryload(UpperCAmelCase     )}
     else:
        __A				 =       None

     __A				 =       CONFIG
     if torch.cuda.is_available():
        __A				 =       '''cuda'''
     __A				 =       os.path.realpath(UpperCAmelCase     )
     __A				 =       os.path.realpath(UpperCAmelCase     )
     __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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> str:
     __A				 =       int(file.split('''.'''     )[0].split('''_'''     )[-1]     )
     __A				 =       os.path.join(self.inputdir  ,    UpperCAmelCase     )
     return (img_id, filepath)

  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __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(UpperCAmelCase     )     )
        if len(UpperCAmelCase     ) == self.batch:
           __A				 =       batch
           __A				 =       []
           yield list(map(UpperCAmelCase  ,    zip(*UpperCAmelCase     )     )     )

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





  def __call__( self     )-> Optional[Any]:
     # make writer
     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(UpperCAmelCase     )
        __A				 =       self.model(
            UpperCAmelCase  ,    UpperCAmelCase  ,    scales_yx=UpperCAmelCase  ,    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(UpperCAmelCase     )
           __A				 =       self.schema.encode_batch(UpperCAmelCase     )
           writer.write_batch(UpperCAmelCase     )
        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 				__UpperCamelCase  (		snake_case     )  ->						str:


   '''simple docstring'''







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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase   :							Dict     =	logging.get_logger(__name__)

_UpperCamelCase   :							Optional[int]     =	{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''videomae'''

  def __init__( self  ,    UpperCAmelCase=2_24  ,    UpperCAmelCase=16  ,    UpperCAmelCase=3  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=7_68  ,    UpperCAmelCase=12  ,    UpperCAmelCase=12  ,    UpperCAmelCase=30_72  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=1e-12  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase=6  ,    UpperCAmelCase=3_84  ,    UpperCAmelCase=4  ,    UpperCAmelCase=15_36  ,    UpperCAmelCase=True  ,    **UpperCAmelCase  ,    )-> Any:
     super().__init__(**UpperCAmelCase     )

     __A				 =       image_size
     __A				 =       patch_size
     __A				 =       num_channels
     __A				 =       num_frames
     __A				 =       tubelet_size

     __A				 =       hidden_size
     __A				 =       num_hidden_layers
     __A				 =       num_attention_heads
     __A				 =       intermediate_size
     __A				 =       hidden_act
     __A				 =       hidden_dropout_prob
     __A				 =       attention_probs_dropout_prob
     __A				 =       initializer_range
     __A				 =       layer_norm_eps
     __A				 =       qkv_bias
     __A				 =       use_mean_pooling

     __A				 =       decoder_num_attention_heads
     __A				 =       decoder_hidden_size
     __A				 =       decoder_num_hidden_layers
     __A				 =       decoder_intermediate_size
     __A				 =       norm_pix_loss

