

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_						:     Tuple			  =				False
A_						:     Any			  =				Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
A_						:     Optional[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    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__=sys.argv[1:]					)       -> List[str]:
        __UpperCAmelCase			   =     None
        __UpperCAmelCase			   =     None
        __UpperCAmelCase			   =     None
        __UpperCAmelCase			   =     1
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     getopt.getopt(lowercase__       , '''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(lowercase__					)
            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(lowercase__					), F'''{outputfile}'''
        if subset_list is not None:
            with open(os.path.realpath(lowercase__					)					) as f:
                __UpperCAmelCase			   =     {self._vqa_file_split()[0] for x in tryload(lowercase__					)}
        else:
            __UpperCAmelCase			   =     None

        __UpperCAmelCase			   =     CONFIG
        if torch.cuda.is_available():
            __UpperCAmelCase			   =     '''cuda'''
        __UpperCAmelCase			   =     os.path.realpath(lowercase__					)
        __UpperCAmelCase			   =     os.path.realpath(lowercase__					)
        __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       , lowercase__					)       -> List[str]:
        __UpperCAmelCase			   =     int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
        __UpperCAmelCase			   =     os.path.join(self.inputdir       , lowercase__					)
        return (img_id, filepath)


    @property
    def        lowerCAmelCase_  (self					)       -> str:
        __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(lowercase__					)					)
            if len(lowercase__					) == self.batch:
                __UpperCAmelCase			   =     batch
                __UpperCAmelCase			   =     []
                yield list(map(lowercase__       , zip(*lowercase__					)					)					)

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






    def __call__(self					)       -> Union[str, Any]:
        # make writer
        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(lowercase__					)
            __UpperCAmelCase			   =     self.model(
                lowercase__       , lowercase__       , scales_yx=lowercase__       , 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(lowercase__					)
                __UpperCAmelCase			   =     self.schema.encode_batch(lowercase__					)
                writer.write_batch(lowercase__					)
            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    (				SCREAMING_SNAKE_CASE ) ->       Any:






    '''simple docstring'''
    try:
        __UpperCAmelCase			   =     json.load(SCREAMING_SNAKE_CASE )
        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__":
       A_						:     List[str]			  =				Extract(sys.argv[1:])
       extract()
       if not TEST:
              A_						:     Optional[Any]			  =				datasets.Dataset.from_file(extract.outputfile)
              # wala!
              # print(np.array(dataset[0:2]["roi_features"]).shape)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




