






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





       """simple docstring"""







       def __init__(					self  ,			_lowerCamelCase=sys.argv[1:]							):
         UpperCamelCase_:					List[str]          =				None
         UpperCamelCase_:					Tuple          =				None
         UpperCamelCase_:					List[Any]          =				None
         UpperCamelCase_:					Tuple          =				1
         UpperCamelCase_:					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"):
             UpperCamelCase_:					Any          =				arg
           elif opt in ("-o", "--outfile"):
             UpperCamelCase_:					str          =				arg
           elif opt in ("-b", "--batch_size"):
             UpperCamelCase_:					Optional[Any]          =				int(UpperCamelCase__							)
           elif opt in ("-s", "--subset_list"):
             UpperCamelCase_:					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:
             UpperCamelCase_:					Optional[Any]          =				{self._vqa_file_split()[0] for x in tryload(UpperCamelCase__							)}
         else:
           UpperCamelCase_:					Union[str, Any]          =				None

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





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





       @property
       def 			_a    (					self							):
         UpperCamelCase_:					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:
             UpperCamelCase_:					Optional[int]          =				batch
             UpperCamelCase_:					int          =				[]
             yield list(map(UpperCamelCase__  ,			zip(*UpperCamelCase__							)							)							)

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





       def __call__(					self							):







         if not TEST:
           UpperCamelCase_:					Any          =				datasets.ArrowWriter(features=self.schema  ,			path=self.outputfile							)
         # do file generator
         for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           UpperCamelCase_:					Dict          =				self.preprocess(UpperCamelCase__							)
           UpperCamelCase_:					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'  ,			)
           UpperCamelCase_:					Dict          =				output_dict.pop('normalized_boxes'							)
           if not TEST:
             UpperCamelCase_:					str          =				np.array(UpperCamelCase__							)
             UpperCamelCase_:					Tuple          =				self.schema.encode_batch(UpperCamelCase__							)
             writer.write_batch(UpperCamelCase__							)
           if TEST:
             break
           # finalizer the writer
         if not TEST:
           UpperCamelCase_:					int          =				writer.finalize()
           print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''							)







def   snake_case					(UpperCAmelCase__  )       ->				Dict:


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


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

'''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()


