




"""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
UpperCamelCase            =   False
UpperCamelCase            =   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
UpperCamelCase            =   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        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_=sys.argv[1:]   )		->   Any:
					__lowercase	:       Tuple				=   None
					__lowercase	:       List[str]				=   None
					__lowercase	:       List[Any]				=   None
					__lowercase	:       Tuple				=   1
					__lowercase	,__lowercase	:       List[str]				=   getopt.getopt(__UpperCamelCase							,		'''i:o:b:s'''							,		['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']   )
					for opt, arg in opts:
								if opt in ("-i", "--inputdir"):
											__lowercase	:       List[Any]				=   arg
								elif opt in ("-o", "--outfile"):
											__lowercase	:       str				=   arg
								elif opt in ("-b", "--batch_size"):
											__lowercase	:       Optional[Any]				=   int(__UpperCamelCase   )
								elif opt in ("-s", "--subset_list"):
											__lowercase	:       Tuple				=   arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(__UpperCamelCase   ), F"""{outputfile}"""
					if subset_list is not None:
								with open(os.path.realpath(__UpperCamelCase   )   ) as f:
											__lowercase	:       Optional[int]				=   {self._vqa_file_split()[0] for x in tryload(__UpperCamelCase   )}
					else:
								__lowercase	:       List[Any]				=   None

					__lowercase	:       List[str]				=   CONFIG
					if torch.cuda.is_available():
								__lowercase	:       Tuple				=   '''cuda'''
					__lowercase	:       str				=   os.path.realpath(__UpperCamelCase   )
					__lowercase	:       Union[str, Any]				=   os.path.realpath(__UpperCamelCase   )
					__lowercase	:       List[str]				=   Preprocess(self.config   )
					__lowercase	:       Tuple				=   GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''							,		config=self.config   )
					__lowercase	:       str				=   batch_size if batch_size != 0 else 1
					__lowercase	:       Optional[int]				=   DEFAULT_SCHEMA
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Union[str, Any]:
					__lowercase	:       str				=   int(file.split('''.'''   )[0].split('''_'''   )[-1]   )
					__lowercase	:       int				=   os.path.join(self.inputdir							,		__UpperCamelCase   )
					return (img_id, filepath)
		@property
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       Optional[Any]				=   []
					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(__UpperCamelCase   )   )
								if len(__UpperCamelCase   ) == self.batch:
											__lowercase	:       int				=   batch
											__lowercase	:       List[Any]				=   []
											yield list(map(__UpperCamelCase							,		zip(*__UpperCamelCase   )   )   )

					for i in range(1   ):
								yield list(map(__UpperCamelCase							,		zip(*__UpperCamelCase   )   )   )
		def __call__(		self   )		->   List[Any]:
					# make writer
					if not TEST:
								__lowercase	:       Dict				=   datasets.ArrowWriter(features=self.schema							,		path=self.outputfile   )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
								__lowercase	,__lowercase	,__lowercase	:       Tuple				=   self.preprocess(__UpperCamelCase   )
								__lowercase	:       Union[str, Any]				=   self.model(
								    __UpperCamelCase							,		__UpperCamelCase							,		scales_yx=__UpperCamelCase							,		padding='''max_detections'''							,		max_detections=self.config.MAX_DETECTIONS							,		pad_value=0							,		return_tensors='''np'''							,		location='''cpu'''							,		)
								__lowercase	:       Union[str, Any]				=   output_dict.pop('''normalized_boxes'''   )
								if not TEST:
											__lowercase	:       str				=   np.array(__UpperCamelCase   )
											__lowercase	:       Any				=   self.schema.encode_batch(__UpperCamelCase   )
											writer.write_batch(__UpperCamelCase   )
								if TEST:
											break
								# finalizer the writer
					if not TEST:
								__lowercase	,__lowercase	:       int				=   writer.finalize()
								print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""   )







def       __UpperCAmelCase      (				__UpperCamelCase					):







			try:
						__lowercase	:       Any				=   json.load(lowercase_					)
						try:
									__lowercase	:       Dict				=   list(data.keys()					)
						except Exception:
									__lowercase	:       List[str]				=   [d['''img_id'''] for d in data]
			except Exception:
						try:
									__lowercase	:       Tuple				=   eval(stream.read()					)
						except Exception:
									__lowercase	:       str				=   stream.read().split('''\n'''					)
			return data


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









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



