






"""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 :				List[str]      					=		False
a :				Tuple      					=		Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
a :				int      					=		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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)


class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__=sys.argv[1:]			):
												'''simple docstring'''
												lowercase__				:      int=       None
												lowercase__				:      Dict=       None
												lowercase__				:      Tuple=       None
												lowercase__				:      Any=       1
												lowercase__,	lowercase__				:      List[Any]=       getopt.getopt(snake_case__       ,  "i:o:b:s"       ,  ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
												for opt, arg in opts:
																		if opt in ("-i", "--inputdir"):
																								lowercase__				:      Tuple=       arg
																		elif opt in ("-o", "--outfile"):
																								lowercase__				:      Union[str, Any]=       arg
																		elif opt in ("-b", "--batch_size"):
																								lowercase__				:      List[Any]=       int(snake_case__			)
																		elif opt in ("-s", "--subset_list"):
																								lowercase__				:      Dict=       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:
																								lowercase__				:      Tuple=       {self._vqa_file_split()[0] for x in tryload(snake_case__			)}
												else:
																		lowercase__				:      Optional[Any]=       None

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Tuple=       int(file.split("."			)[0].split("_"			)[-1]			)
												lowercase__				:      Union[str, Any]=       os.path.join(self.inputdir       ,  snake_case__			)
												return (img_id, filepath)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       []
												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:
																								lowercase__				:      List[str]=       batch
																								lowercase__				:      List[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			):
												'''simple docstring'''
												# make writer
												if not TEST:
																		lowercase__				:      Optional[Any]=       datasets.ArrowWriter(features=self.schema       ,  path=self.outputfile			)
												# do file generator
												for i, (img_ids, filepaths) in enumerate(self.file_generator			):
																		lowercase__,	lowercase__,	lowercase__				:      Tuple=       self.preprocess(snake_case__			)
																		lowercase__				:      Optional[Any]=       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"       ,  )
																		lowercase__				:      Optional[Any]=       output_dict.pop("normalized_boxes"			)
																		if not TEST:
																								lowercase__				:      List[Any]=       np.array(snake_case__			)
																								lowercase__				:      str=       self.schema.encode_batch(snake_case__			)
																								writer.write_batch(snake_case__			)
																		if TEST:
																								break
																		# finalizer the writer
												if not TEST:
																		lowercase__,	lowercase__				:      Optional[Any]=       writer.finalize()
																		print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''			)





def 						lowercase__(A						)				->int:




						"""simple docstring"""



						try:
												lowercase__				:      List[Any]=       json.load(A						)
												try:
																		lowercase__				:      List[Any]=       list(data.keys()						)
												except Exception:
																		lowercase__				:      Tuple=       [d["img_id"] for d in data]
						except Exception:
												try:
																		lowercase__				:      Tuple=       eval(stream.read()						)
												except Exception:
																		lowercase__				:      Dict=       stream.read().split("\n"						)
						return data


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













from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = ['MPNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase				       = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

