

'''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
_lowerCAmelCase     :			Union[str, Any]    =False
_lowerCAmelCase     :			Optional[int]    =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_lowerCAmelCase     :			Optional[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, 20_48), 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							,			lowerCamelCase__=sys.argv[1:]	):
						UpperCAmelCase__:  Optional[int]		       =   None
						UpperCAmelCase__:  List[Any]		       =   None
						UpperCAmelCase__:  Any		       =   None
						UpperCAmelCase__:  int		       =   1
						UpperCAmelCase__:  Optional[Any]		       =   getopt.getopt(lowerCamelCase__							,			"i:o:b:s"							,			["inputdir=", "outfile=", "batch_size=", "subset_list="]	)
						for opt, arg in opts:
							if opt in ("-i", "--inputdir"):
								UpperCAmelCase__:  Union[str, Any]		       =   arg
							elif opt in ("-o", "--outfile"):
								UpperCAmelCase__:  Tuple		       =   arg
							elif opt in ("-b", "--batch_size"):
								UpperCAmelCase__:  Any		       =   int(lowerCamelCase__	)
							elif opt in ("-s", "--subset_list"):
								UpperCAmelCase__:  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(lowerCamelCase__	), F"{outputfile}"
						if subset_list is not None:
							with open(os.path.realpath(lowerCamelCase__	)	) as f:
								UpperCAmelCase__:  Optional[int]		       =   {self._vqa_file_split()[0] for x in tryload(lowerCamelCase__	)}
						else:
							UpperCAmelCase__:  Optional[Any]		       =   None

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





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





					@property
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Tuple		       =   []
						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(lowerCamelCase__	)	)
							if len(lowerCamelCase__	) == self.batch:
								UpperCAmelCase__:  str		       =   batch
								UpperCAmelCase__:  Tuple		       =   []
								yield list(map(lowerCamelCase__							,			zip(*lowerCamelCase__	)	)	)

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





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



def  _A  (			SCREAMING_SNAKE_CASE			):
	try:
		UpperCAmelCase__:  Dict		       =   json.load(SCREAMING_SNAKE_CASE			)
		try:
			UpperCAmelCase__:  List[Any]		       =   list(data.keys()			)
		except Exception:
			UpperCAmelCase__:  List[Any]		       =   [d["img_id"] for d in data]
	except Exception:
		try:
			UpperCAmelCase__:  Optional[Any]		       =   eval(stream.read()			)
		except Exception:
			UpperCAmelCase__:  Any		       =   stream.read().split("\n"			)
	return data


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


import numpy as np



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)

	# Add up the all the exponentials
	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)

	# Divide every exponent by the sum of all exponents
	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents

	return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))