





"""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__       :  Dict       		=					False
a__       :  List[str]       		=					Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
a__       :  str       		=					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   lowercase_					:

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=sys.argv[1:]  ):






        '''simple docstring'''

        snake_case__     				=					None
        snake_case__     				=					None
        snake_case__     				=					None
        snake_case__     				=					1
        snake_case__				, snake_case__     				=					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"):
          snake_case__     				=					arg
         elif opt in ("-o", "--outfile"):
          snake_case__     				=					arg
         elif opt in ("-b", "--batch_size"):
          snake_case__     				=					int(__SCREAMING_SNAKE_CASE  )
         elif opt in ("-s", "--subset_list"):
          snake_case__     				=					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:
          snake_case__     				=					{self._vqa_file_split()[0] for x in tryload(__SCREAMING_SNAKE_CASE  )}
        else:
         snake_case__     				=					None

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

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					[]
        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:
          snake_case__     				=					batch
          snake_case__     				=					[]
          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  ):






        '''simple docstring'''

        if not TEST:
         snake_case__     				=					datasets.ArrowWriter(features=self.schema ,       path=self.outputfile  )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
         snake_case__				, snake_case__				, snake_case__     				=					self.preprocess(__SCREAMING_SNAKE_CASE  )
         snake_case__     				=					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" ,       )
         snake_case__     				=					output_dict.pop("normalized_boxes"  )
         if not TEST:
          snake_case__     				=					np.array(__SCREAMING_SNAKE_CASE  )
          snake_case__     				=					self.schema.encode_batch(__SCREAMING_SNAKE_CASE  )
          writer.write_batch(__SCREAMING_SNAKE_CASE  )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         snake_case__				, snake_case__     				=					writer.finalize()
         print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"  )





def 				a__      (	_UpperCAmelCase						)     ->	Dict:

 '''simple docstring'''






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


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








"""simple docstring"""


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


a__       :  List[Any]       		=					logging.get_logger(__name__)

a__       :  int       		=					{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """mpnet"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=3_0527 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=3072 ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=512 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=1e-12 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=2 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,       bos_token_id=__SCREAMING_SNAKE_CASE ,       eos_token_id=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					vocab_size
        snake_case__     				=					hidden_size
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					hidden_act
        snake_case__     				=					intermediate_size
        snake_case__     				=					hidden_dropout_prob
        snake_case__     				=					attention_probs_dropout_prob
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					initializer_range
        snake_case__     				=					layer_norm_eps
        snake_case__     				=					relative_attention_num_buckets

