





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_												=       False
lowerCAmelCase_												=       Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCAmelCase_												=       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, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)



class       __lowerCAmelCase :






			def __init__(self      ,							__magic_name__=sys.argv[1:]					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					str			    =						None
							snake_case_		:					List[Any]			    =						None
							snake_case_		:					int			    =						None
							snake_case_		:					List[str]			    =						1
							snake_case_    ,							snake_case_		:					Union[str, Any]			    =						getopt.getopt(__magic_name__      ,							'''i:o:b:s'''      ,							['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']					)
							for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
															snake_case_		:					str			    =						arg
											elif opt in ("-o", "--outfile"):
															snake_case_		:					Dict			    =						arg
											elif opt in ("-b", "--batch_size"):
															snake_case_		:					Optional[Any]			    =						int(__magic_name__					)
											elif opt in ("-s", "--subset_list"):
															snake_case_		:					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(__magic_name__					), F'''{outputfile}'''
							if subset_list is not None:
											with open(os.path.realpath(__magic_name__					)					) as f:
															snake_case_		:					Optional[Any]			    =						{self._vqa_file_split()[0] for x in tryload(__magic_name__					)}
							else:
											snake_case_		:					Tuple			    =						None

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






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Union[str, Any]:


							'''simple docstring'''



							snake_case_		:					Optional[int]			    =						int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
							snake_case_		:					str			    =						os.path.join(self.inputdir      ,							__magic_name__					)
							return (img_id, filepath)






			@property
			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					int			    =						[]
							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(__magic_name__					)					)
											if len(__magic_name__					) == self.batch:
															snake_case_		:					str			    =						batch
															snake_case_		:					List[Any]			    =						[]
															yield list(map(__magic_name__      ,							zip(*__magic_name__					)					)					)

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






			def __call__(self					)  ->     Any:


							'''simple docstring'''



							if not TEST:
											snake_case_		:					Optional[Any]			    =						datasets.ArrowWriter(features=self.schema      ,							path=self.outputfile					)
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator					):
											snake_case_    ,							snake_case_    ,							snake_case_		:					Any			    =						self.preprocess(__magic_name__					)
											snake_case_		:					Optional[Any]			    =						self.model(
											    __magic_name__      ,							__magic_name__      ,							scales_yx=__magic_name__      ,							padding='''max_detections'''      ,							max_detections=self.config.MAX_DETECTIONS      ,							pad_value=0      ,							return_tensors='''np'''      ,							location='''cpu'''      ,							)
											snake_case_		:					Any			    =						output_dict.pop('''normalized_boxes'''					)
											if not TEST:
															snake_case_		:					List[str]			    =						np.array(__magic_name__					)
															snake_case_		:					Optional[int]			    =						self.schema.encode_batch(__magic_name__					)
															writer.write_batch(__magic_name__					)
											if TEST:
															break
											# finalizer the writer
							if not TEST:
											snake_case_    ,							snake_case_		:					List[str]			    =						writer.finalize()
											print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


				try:
								snake_case_		:					Union[str, Any]			    =						json.load(_UpperCamelCase				)
								try:
												snake_case_		:					Optional[Any]			    =						list(data.keys()				)
								except Exception:
												snake_case_		:					Optional[Any]			    =						[d['''img_id'''] for d in data]
				except Exception:
								try:
												snake_case_		:					int			    =						eval(stream.read()				)
								except Exception:
												snake_case_		:					Dict			    =						stream.read().split('''\n'''				)
				return data


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







class       __lowerCAmelCase :






			def __init__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						[]
							snake_case_		:					Any			    =						0
							snake_case_		:					Optional[int]			    =						0






			def __str__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					int			    =						'''<''' + str(self.entries					)[1:-1] + '''>'''
							return printed






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							self.entries.append(__magic_name__					)
							snake_case_		:					List[str]			    =						self.length + 1






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.length - 1
							snake_case_		:					Dict			    =						self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							snake_case_		:					List[str]			    =						self.entries[1:]
							return dequeued






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							for _ in range(__magic_name__					):
											self.put(self.get()					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							return self.entries[0]






			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							return self.length
