


'''simple docstring'''





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict      ,						lowercase__  :str=1_0_0      ,						lowercase__  :Tuple=" "				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      text.split(lowercase__				)
						return [character.join(text[i : i + n]				).strip() for i in range(0      ,						len(lowercase__				)      ,						lowercase__				)]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]				)       ->  Dict:
						__magic_name__				:							Tuple				=      [], []
						for title, text in zip(documents['title']      ,						documents['text']				):
												if text is not None:
																		for passage in split_text(lowercase__				):
																								titles.append(title if title is not None else ''				)
																								texts.append(lowercase__				)
						return {"title": titles, "text": texts}
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]      ,						lowercase__  :int      ,						lowercase__  :Tuple      ,						lowercase__  :Optional[int]				)       ->  Dict:
						__magic_name__				:							Any				=      load_dataset(
						    'csv'      ,						data_files=[csv_path]      ,						split='train'      ,						delimiter='\t'      ,						column_names=['title', 'text']				)
						__magic_name__				:							int				=      kb_dataset.map(
						    lowercase__      ,						batched=lowercase__      ,						num_proc=1				)  # if you want you can load already splitted csv.
						__magic_name__				:							int				=      [kb_dataset.shard(lowercase__      ,						lowercase__      ,						contiguous=lowercase__				) for i in range(lowercase__				)]
						__magic_name__				:							List[Any]				=      kb_list[process_num]

						__magic_name__				:							Dict				=      'data_' + str(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      os.path.join(lowercase__      ,						lowercase__				)

						__magic_name__				:							Any				=      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'				)
						__magic_name__				:							Tuple				=      ctx_encoder.to(device=lowercase__				)

						def embed(
						    lowercase__  :dict      ,						lowercase__  :DPRContextEncoder      ,						lowercase__  :DPRContextEncoderTokenizerFast      ,						lowercase__  :Optional[Any]				) -> dict:
												__magic_name__				:							List[Any]				=      ctx_tokenizer(
												    documents['title']      ,						documents['text']      ,						truncation=lowercase__      ,						padding='longest'      ,						return_tensors='pt'				)['input_ids']
												__magic_name__				:							Optional[Any]				=      ctx_encoder(input_ids.to(device=lowercase__				)      ,						return_dict=lowercase__				).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						__magic_name__				:							Optional[Any]				=      Features(
						    {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space

						__magic_name__				:							List[str]				=      data_shrad.map(
						    partial(lowercase__      ,						ctx_encoder=lowercase__      ,						ctx_tokenizer=lowercase__      ,						device=lowercase__				)      ,						batched=lowercase__      ,						batch_size=1_6      ,						features=lowercase__      ,						)
						dataset.save_to_disk(lowercase__				)

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str				)       ->  Dict:
						__magic_name__				:							int				=      []

						for shard_address in glob(str(lowercase__				) + '/*/'				):
												data_shard_list.append(load_from_disk(lowercase__				)				)

						__magic_name__				:							int				=      concatenate_datasets(lowercase__				)
						faiss.omp_set_num_threads(9_6				)

						__magic_name__				:							Optional[Any]				=      faiss.IndexHNSWFlat(7_6_8      ,						1_2_8      ,						faiss.METRIC_INNER_PRODUCT				)
						concat.add_faiss_index('embeddings'      ,						custom_index=lowercase__				)
						concat.get_index('embeddings'				).save(
						    lowercase__				)  # since we load the index in to memory,we can directly update the index in the disk






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
snake_case__     							= 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__  :
				def __init__(  self     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




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

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




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      int(file.split('.'      )[0].split('_'      )[-1]      )
										__magic_name__				:							Union[str, Any]				=      os.path.join(self.inputdir     ,      _UpperCAmelCase      )
										return (img_id, filepath)
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      []
										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:
																						__magic_name__				:							int				=      batch
																						__magic_name__				:							Union[str, Any]				=      []
																						yield list(map(_UpperCAmelCase     ,      zip(*_UpperCAmelCase      )      )      )

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





				def __call__(  self      ):




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


