



"""simple docstring"""






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

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					_a       =				'block_{0}.dat'



					def __init__(			self				:    Optional[int],    lowerCamelCase				:    str	)->   int:
								lowerCamelCase__						:						str						      =filename
								lowerCamelCase__						:						Dict						      =[]



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Any,    lowerCamelCase				:    str	)->   int:
								lowerCamelCase__						:						Any						      =self.BLOCK_FILENAME_FORMAT.format(lowerCamelCase	)
								with open(lowerCamelCase,    '''w'''	) as file:
											file.write(lowerCamelCase	)
								self.block_filenames.append(lowerCamelCase	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Dict:
								return self.block_filenames



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    str=None	)->   List[str]:
								lowerCamelCase__						:						str						      =0
								with open(self.filename	) as file:
											while True:
														lowerCamelCase__						:						Tuple						      =file.readlines(lowerCamelCase	)

														if lines == []:
																	break

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

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




					def 						snake_case			(			self				:    Tuple	)->   Union[str, Any]:
								map(os.remove,    self.block_filenames	)





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Union[str, Any]	)->   int:
								lowerCamelCase__						:						List[Any]						      =-1
								lowerCamelCase__						:						Any						      =None

								for i in range(len(lowerCamelCase	)	):
											if min_str is None or choices[i] < min_str:
														lowerCamelCase__						:						List[Any]						      =i

								return min_index





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[Any]	)->   List[Any]:
								lowerCamelCase__						:						Any						      =files
								lowerCamelCase__						:						Optional[Any]						      =set()
								lowerCamelCase__						:						Any						      =len(lowerCamelCase	)
								lowerCamelCase__						:						int						      ={i: None for i in range(self.num_buffers	)}



					def 						snake_case			(			self				:    Optional[int]	)->   Optional[Any]:
								return {
								    i: self.buffers[i] for i in range(self.num_buffers	) if i not in self.empty
								}



					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								for i in range(self.num_buffers	):
											if self.buffers[i] is None and i not in self.empty:
														lowerCamelCase__						:						Tuple						      =self.files[i].readline()

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

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

								return True




					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Union[str, Any]	)->   List[str]:
								lowerCamelCase__						:						Tuple						      =self.buffers[index]
								lowerCamelCase__						:						List[str]						      =None

								return value





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Union[str, Any],    lowerCamelCase				:    str	)->   List[str]:
								lowerCamelCase__						:						Tuple						      =merge_strategy



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    List[str],    lowerCamelCase				:    List[Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						str						      =FilesArray(self.get_file_handles(lowerCamelCase,    lowerCamelCase	)	)
								with open(lowerCamelCase,    '''w''',    lowerCamelCase	) as outfile:
											while buffers.refresh():
														lowerCamelCase__						:						List[Any]						      =self.merge_strategy.select(buffers.get_dict()	)
														outfile.write(buffers.unshift(lowerCamelCase	)	)




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    List[Any]	)->   str:
								lowerCamelCase__						:						Optional[int]						      ={}

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

								return files





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    str,    lowerCamelCase				:    int	)->   Union[str, Any]:
								lowerCamelCase__						:						List[Any]						      =block_size



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Dict,    lowerCamelCase				:    Union[str, Any]=None	)->   str:
								lowerCamelCase__						:						Tuple						      =self.get_number_blocks(lowerCamelCase,    self.block_size	)
								lowerCamelCase__						:						Union[str, Any]						      =FileSplitter(lowerCamelCase	)
								splitter.split(self.block_size,    lowerCamelCase	)

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

								splitter.cleanup()




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   List[Any]:
								return (os.stat(lowerCamelCase	).st_size / block_size) + 1






def 	snake_case__  (						__lowerCamelCase							:       str      ):
			"""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(__lowerCamelCase      )






def 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =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'''      )
			lowerCamelCase__						:						str						      =parser.parse_args()

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


if __name__ == "__main__":
		main()









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, 2048), 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:     Dict  ,lowerCamelCase_:     Optional[Any]=sys.argv[1:]							)	->      Optional[int]:
       UpperCAmelCase_     :       Union[str, Any]  		=					None
       UpperCAmelCase_     :       Any  		=					None
       UpperCAmelCase_     :       List[str]  		=					None
       UpperCAmelCase_     :       List[str]  		=					1
       UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					getopt.getopt(lowerCamelCase_  ,"""i:o:b:s"""  ,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]							)
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               UpperCAmelCase_     :       List[Any]  		=					arg
           elif opt in ("-o", "--outfile"):
               UpperCAmelCase_     :       Optional[int]  		=					arg
           elif opt in ("-b", "--batch_size"):
               UpperCAmelCase_     :       Tuple  		=					int(lowerCamelCase_							)
           elif opt in ("-s", "--subset_list"):
               UpperCAmelCase_     :       Dict  		=					arg

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

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


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       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(lowerCamelCase_							)							)
           if len(lowerCamelCase_							) == self.batch:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

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






   def __call__(    self:     Any							)	->      Optional[Any]:
       # make writer
       if not TEST:
           UpperCAmelCase_     :       Tuple  		=					datasets.ArrowWriter(features=self.schema  ,path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.preprocess(lowerCamelCase_							)
           UpperCAmelCase_     :       Any  		=					self.model(
               lowerCamelCase_  ,lowerCamelCase_  ,scales_yx=lowerCamelCase_  ,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(lowerCamelCase_							)
               UpperCAmelCase_     :       int  		=					self.schema.encode_batch(lowerCamelCase_							)
               writer.write_batch(lowerCamelCase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					writer.finalize()
           print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''							)







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					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)
