
'''simple docstring'''
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os






class      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		"""block_{0}.dat"""



		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       str	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		filename
				snake_case   :    Any			       =		[]



		def 	lowerCAmelCase							(	self							:       Any   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       str	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.BLOCK_FILENAME_FORMAT.format(UpperCamelCase__	)
				with open(UpperCamelCase__   ,     '''w'''	) as file:
						file.write(UpperCamelCase__	)
				self.block_filenames.append(UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				return self.block_filenames



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]=None	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		0
				with open(self.filename	) as file:
						while True:
								snake_case   :    List[str]			       =		file.readlines(UpperCamelCase__	)

								if lines == []:
										break

								if sort_key is None:
										lines.sort()
								else:
										lines.sort(key=UpperCamelCase__	)

								self.write_block(''''''.join(UpperCamelCase__	)   ,     UpperCamelCase__	)
								i += 1



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Optional[Any]:






				"""simple docstring"""







				map(os.remove   ,     self.block_filenames	)







class      snake_case__    :







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		-1
				snake_case   :    Optional[Any]			       =		None

				for i in range(len(UpperCamelCase__	)	):
						if min_str is None or choices[i] < min_str:
								snake_case   :    Union[str, Any]			       =		i

				return min_index







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Dict			       =		files
				snake_case   :    Optional[Any]			       =		set()
				snake_case   :    Tuple			       =		len(UpperCamelCase__	)
				snake_case   :    List[Any]			       =		{i: None for i in range(self.num_buffers	)}



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       int:






				"""simple docstring"""







				return {
				    i: self.buffers[i] for i in range(self.num_buffers	) if i not in self.empty
				}



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







				for i in range(self.num_buffers	):
						if self.buffers[i] is None and i not in self.empty:
								snake_case   :    Optional[Any]			       =		self.files[i].readline()

								if self.buffers[i] == "":
										self.empty.add(UpperCamelCase__	)
										self.files[i].close()

				if len(self.empty	) == self.num_buffers:
						return False

				return True



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.buffers[index]
				snake_case   :    Optional[int]			       =		None

				return value







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[Any]   ,     UpperCamelCase__							:       Optional[Any]	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		merge_strategy



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Dict	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		FilesArray(self.get_file_handles(UpperCamelCase__   ,     UpperCamelCase__	)	)
				with open(UpperCamelCase__   ,     '''w'''   ,     UpperCamelCase__	) as outfile:
						while buffers.refresh():
								snake_case   :    List[Any]			       =		self.merge_strategy.select(buffers.get_dict()	)
								outfile.write(buffers.unshift(UpperCamelCase__	)	)



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       Tuple   ,     UpperCamelCase__							:       str	)		->       int:






				"""simple docstring"""







				snake_case   :    str			       =		{}

				for i in range(len(UpperCamelCase__	)	):
						snake_case   :    Any			       =		open(filenames[i]   ,     '''r'''   ,     UpperCamelCase__	)  # noqa: UP015

				return files







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       Any   ,     UpperCamelCase__							:       Any	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		block_size



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Tuple=None	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_number_blocks(UpperCamelCase__   ,     self.block_size	)
				snake_case   :    Optional[Any]			       =		FileSplitter(UpperCamelCase__	)
				splitter.split(self.block_size   ,     UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		FileMerger(NWayMerge()	)
				snake_case   :    Any			       =		self.block_size / (num_blocks + 1)
				merger.merge(splitter.get_block_filenames()   ,     filename + '''.out'''   ,     UpperCamelCase__	)

				splitter.cleanup()



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







				return (os.stat(UpperCamelCase__	).st_size / block_size) + 1







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		if string[-1].lower() == "k":
				return int(string[:-1]       ) * 1024
		elif string[-1].lower() == "m":
				return int(string[:-1]       ) * 1024 * 1024
		elif string[-1].lower() == "g":
				return int(string[:-1]       ) * 1024 * 1024 * 1024
		else:
				return int(SCREAMING_SNAKE_CASE__       )




def 					_UpperCamelCase		(    )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		argparse.ArgumentParser()
		parser.add_argument(
		    '''-m'''      ,    '''--mem'''      ,    help='''amount of memory to use for sorting'''      ,    default='''100M'''       )
		parser.add_argument(
		    '''filename'''      ,    metavar='''<filename>'''      ,    nargs=1      ,    help='''name of file to sort'''       )
		snake_case   :    Any			       =		parser.parse_args()

		snake_case   :    int			       =		ExternalSort(parse_memory(args.mem       )       )
		sorter.sort(args.filename[0]       )


if __name__ == "__main__":
						main()




'''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
lowercase__		    =	False
lowercase__		    =	Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowercase__		    =	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      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       List[str]=sys.argv[1:]	)		->       str:






				"""simple docstring"""







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

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



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[str]	)		->       List[Any]:






				"""simple docstring"""







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



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, 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:
								snake_case   :    Optional[int]			       =		batch
								snake_case   :    int			       =		[]
								yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)

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



		def __call__(	self							:       Dict	)		->       str:






				"""simple docstring"""







				if not TEST:
						snake_case   :    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   :    Dict			       =		self.preprocess(UpperCamelCase__	)
						snake_case   :    List[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'''   ,     )
						snake_case   :    Dict			       =		output_dict.pop('''normalized_boxes'''	)
						if not TEST:
								snake_case   :    str			       =		np.array(UpperCamelCase__	)
								snake_case   :    Tuple			       =		self.schema.encode_batch(UpperCamelCase__	)
								writer.write_batch(UpperCamelCase__	)
						if TEST:
								break
						# finalizer the writer
				if not TEST:
						snake_case       ,snake_case   :    int			       =		writer.finalize()
						print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


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


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


