
'''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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)


class   a_  :




       def __init__(     self   ,   snake_case_=sys.argv[1:]  ):
         _lowerCAmelCase       :  Dict   				=					None
         _lowerCAmelCase       :  Optional[Any]   				=					None
         _lowerCAmelCase       :  Optional[int]   				=					None
         _lowerCAmelCase       :  Optional[int]   				=					1
         _lowerCAmelCase				,					_lowerCAmelCase       :  str   				=					getopt.getopt(snake_case_   ,   """i:o:b:s"""   ,   ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]  )
         for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
             _lowerCAmelCase       :  int   				=					arg
           elif opt in ("-o", "--outfile"):
             _lowerCAmelCase       :  Tuple   				=					arg
           elif opt in ("-b", "--batch_size"):
             _lowerCAmelCase       :  Any   				=					int(snake_case_  )
           elif opt in ("-s", "--subset_list"):
             _lowerCAmelCase       :  List[str]   				=					arg

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

         _lowerCAmelCase       :  Optional[int]   				=					CONFIG
         if torch.cuda.is_available():
           _lowerCAmelCase       :  List[str]   				=					"""cuda"""
         _lowerCAmelCase       :  Tuple   				=					os.path.realpath(snake_case_  )
         _lowerCAmelCase       :  Optional[int]   				=					os.path.realpath(snake_case_  )
         _lowerCAmelCase       :  Optional[Any]   				=					Preprocess(self.config  )
         _lowerCAmelCase       :  List[str]   				=					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""   ,   config=self.config  )
         _lowerCAmelCase       :  Optional[Any]   				=					batch_size if batch_size != 0 else 1
         _lowerCAmelCase       :  int   				=					DEFAULT_SCHEMA




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         _lowerCAmelCase       :  Any   				=					int(file.split("""."""  )[0].split("""_"""  )[-1]  )
         _lowerCAmelCase       :  Dict   				=					os.path.join(self.inputdir   ,   snake_case_  )
         return (img_id, filepath)




       @property
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  Any   				=					[]
         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(snake_case_  )  )
           if len(snake_case_  ) == self.batch:
             _lowerCAmelCase       :  Union[str, Any]   				=					batch
             _lowerCAmelCase       :  str   				=					[]
             yield list(map(snake_case_   ,   zip(*snake_case_  )  )  )

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







       def __call__(     self  ):
         # make writer
         if not TEST:
           _lowerCAmelCase       :  Optional[Any]   				=					datasets.ArrowWriter(features=self.schema   ,   path=self.outputfile  )
         # do file generator
         for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
           _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase       :  Any   				=					self.preprocess(snake_case_  )
           _lowerCAmelCase       :  Dict   				=					self.model(
               snake_case_   ,   snake_case_   ,   scales_yx=snake_case_   ,   padding="""max_detections"""   ,   max_detections=self.config.MAX_DETECTIONS   ,   pad_value=0   ,   return_tensors="""np"""   ,   location="""cpu"""   ,   )
           _lowerCAmelCase       :  Any   				=					output_dict.pop("""normalized_boxes"""  )
           if not TEST:
             _lowerCAmelCase       :  Tuple   				=					np.array(snake_case_  )
             _lowerCAmelCase       :  Tuple   				=					self.schema.encode_batch(snake_case_  )
             writer.write_batch(snake_case_  )
           if TEST:
             break
           # finalizer the writer
         if not TEST:
           _lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					writer.finalize()
           print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'  )



def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[int]		)    ->						List[str]:
  try:
    _lowerCAmelCase       :  str   				=					json.load(_lowerCamelCase		)
    try:
      _lowerCAmelCase       :  List[Any]   				=					list(data.keys()		)
    except Exception:
      _lowerCAmelCase       :  List[Any]   				=					[d["""img_id"""] for d in data]
  except Exception:
    try:
      _lowerCAmelCase       :  Tuple   				=					eval(stream.read()		)
    except Exception:
      _lowerCAmelCase       :  Optional[Any]   				=					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'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase_     			=       {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       [
         """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTSanJapaneseForConditionalGeneration""",
         """GPTSanJapaneseModel""",
         """GPTSanJapanesePreTrainedModel""",
     ]
     UpperCamelCase_     			=       [
         """GPTSanJapaneseTokenizer""",
     ]


if TYPE_CHECKING:
     from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
     from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptsan_japanese import (
              GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTSanJapaneseForConditionalGeneration,
              GPTSanJapaneseModel,
              GPTSanJapanesePreTrainedModel,
          )
          from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
     import sys

     UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


