

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
__UpperCamelCase						:   Tuple							   =	False
__UpperCamelCase						:   List[Any]							   =	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
__UpperCamelCase						:   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, 2_0_4_8), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)







class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



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

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







					def  __lowerCAmelCase				( self    :       str     ,lowercase_    :       Union[str, Any]	):
						lowerCAmelCase__			:		str						       =			int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
						lowerCAmelCase__			:		Any						       =			os.path.join(self.inputdir     ,lowercase_	)
						return (img_id, filepath)







					@property
					def  __lowerCAmelCase				( self    :       List[Any]	):
						lowerCAmelCase__			:		Dict						       =			[]
						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:
								lowerCAmelCase__			:		int						       =			batch
								lowerCAmelCase__			:		Dict						       =			[]
								yield list(map(lowercase_     ,zip(*lowercase_	)	)	)

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







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

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	try:
		lowerCAmelCase__			:		Optional[Any]						       =			json.load(A_				)
		try:
			lowerCAmelCase__			:		int						       =			list(data.keys()				)
		except Exception:
			lowerCAmelCase__			:		Any						       =			[d['''img_id'''] for d in data]
	except Exception:
		try:
			lowerCAmelCase__			:		Dict						       =			eval(stream.read()				)
		except Exception:
			lowerCAmelCase__			:		int						       =			stream.read().split('''\n'''				)
	return data


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







"""simple docstring"""





class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					def __init__( self    :       List[Any]	):
						lowerCAmelCase__			:		List[str]						       =			[0, 1]







					def  __lowerCAmelCase				( self    :       str     ,lowercase_    :       int	):
						if (difference := index - (len(self.sequence	) - 2)) >= 1:
							for _ in range(lowercase_	):
								self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
						return self.sequence[:index]

def 						__SCREAMING_SNAKE_CASE						(   ):
	print(
	    '''Fibonacci Series Using Dynamic Programming\n'''		,  '''Enter the index of the Fibonacci number you want to calculate '''		,  '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''		,  sep=''''''		,  )
	lowerCAmelCase__			:		Optional[int]						       =			Fibonacci()

	while True:
		lowerCAmelCase__			:		str						       =			input('''>> '''				)
		if prompt in {"exit", "quit"}:
			break

		try:
			lowerCAmelCase__			:		int						       =			int(A_				)
		except ValueError:
			print('''Enter a number or \'exit\''''				)
			continue

		print(fibonacci.get(A_				)				)


if __name__ == "__main__":
							main()




