




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







class __snake_case     :







   def __init__( self						:		List[str]   ,		_snake_case						:		Optional[int]=sys.argv[1:]):

        """simple docstring"""


        UpperCAmelCase_		=				None
        UpperCAmelCase_		=				None
        UpperCAmelCase_		=				None
        UpperCAmelCase_		=				1
        UpperCAmelCase_	,			UpperCAmelCase_		=				getopt.getopt(_snake_case   ,		'''i:o:b:s'''   ,		['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list='''])
        for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                  UpperCAmelCase_		=				arg
             elif opt in ("-o", "--outfile"):
                  UpperCAmelCase_		=				arg
             elif opt in ("-b", "--batch_size"):
                  UpperCAmelCase_		=				int(_snake_case)
             elif opt in ("-s", "--subset_list"):
                  UpperCAmelCase_		=				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:
                  UpperCAmelCase_		=				{self._vqa_file_split()[0] for x in tryload(_snake_case)}
        else:
             UpperCAmelCase_		=				None

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







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				int(file.split('''.''')[0].split('''_''')[-1])
        UpperCAmelCase_		=				os.path.join(self.inputdir   ,		_snake_case)
        return (img_id, filepath)







   @property
   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				[]
        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:
                  UpperCAmelCase_		=				batch
                  UpperCAmelCase_		=				[]
                  yield list(map(_snake_case   ,		zip(*_snake_case)))

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







   def __call__( self						:		Any):

        """simple docstring"""


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







def   A		(__A  :       Any			)  -> str:







     """simple docstring"""


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


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












from .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






