

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      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     int=100							,					UpperCAmelCase				:     Union[str, Any]=" "     )    ->		str:
  UpperCAmelCase			:				Dict										=					text.split(UpperCAmelCase     )
  return [character.join(text[i : i + n]     ).strip() for i in range(0							,					len(UpperCAmelCase     )							,					UpperCAmelCase     )]

def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase					,		UpperCAmelCase			:				List[Any]										=					[], []
  for title, text in zip(documents['''title''']							,					documents['''text''']     ):
    if text is not None:
      for passage in split_text(UpperCAmelCase     ):
        titles.append(title if title is not None else ''''''     )
        texts.append(UpperCAmelCase     )
  return {"title": titles, "text": texts}

def      a__	(						UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Optional[int]     )    ->		List[str]:
  UpperCAmelCase			:				Any										=					load_dataset(
      '''csv'''							,					data_files=[csv_path]							,					split='''train'''							,					delimiter='''\t'''							,					column_names=['''title''', '''text''']     )
  UpperCAmelCase			:				int										=					kb_dataset.map(
      UpperCAmelCase							,					batched=UpperCAmelCase							,					num_proc=1     )  # if you want you can load already splitted csv.
  UpperCAmelCase			:				Union[str, Any]										=					[kb_dataset.shard(UpperCAmelCase							,					UpperCAmelCase							,					contiguous=UpperCAmelCase     ) for i in range(UpperCAmelCase     )]
  UpperCAmelCase			:				int										=					kb_list[process_num]

  UpperCAmelCase			:				Tuple										=					'''data_''' + str(UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					os.path.join(UpperCAmelCase							,					UpperCAmelCase     )

  UpperCAmelCase			:				Any										=					DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''     )
  UpperCAmelCase			:				Optional[Any]										=					ctx_encoder.to(device=UpperCAmelCase     )

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

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

  UpperCAmelCase			:				int										=					data_shrad.map(
      partial(UpperCAmelCase							,					ctx_encoder=UpperCAmelCase							,					ctx_tokenizer=UpperCAmelCase							,					device=UpperCAmelCase     )							,					batched=UpperCAmelCase							,					batch_size=16							,					features=UpperCAmelCase							,					)
  dataset.save_to_disk(UpperCAmelCase     )






def      a__	(						UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     Optional[int]     )    ->		Union[str, Any]:
  UpperCAmelCase			:				int										=					[]

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

  UpperCAmelCase			:				List[str]										=					concatenate_datasets(UpperCAmelCase     )
  faiss.omp_set_num_threads(96     )

  UpperCAmelCase			:				Optional[Any]										=					faiss.IndexHNSWFlat(768							,					128							,					faiss.METRIC_INNER_PRODUCT     )
  concat.add_faiss_index('''embeddings'''							,					custom_index=UpperCAmelCase     )
  concat.get_index('''embeddings'''     ).save(
      UpperCAmelCase     )  # 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
_lowerCamelCase							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, 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, 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				:	List[str],						__A				:	int=sys.argv[1:]		):
     UpperCAmelCase			:				List[str]										=					None
     UpperCAmelCase			:				Optional[Any]										=					None
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Any										=					1
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					getopt.getopt(__A,						'''i:o:b:s''',						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         UpperCAmelCase			:				Dict										=					arg
       elif opt in ("-o", "--outfile"):
         UpperCAmelCase			:				Optional[int]										=					arg
       elif opt in ("-b", "--batch_size"):
         UpperCAmelCase			:				Tuple										=					int(__A		)
       elif opt in ("-s", "--subset_list"):
         UpperCAmelCase			:				Optional[Any]										=					arg

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

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



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



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     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(__A		)		)
       if len(__A		) == self.batch:
         UpperCAmelCase			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

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





   def __call__(				self				:	Optional[Any]		):
     # make writer
     if not TEST:
       UpperCAmelCase			:				int										=					datasets.ArrowWriter(features=self.schema,						path=self.outputfile		)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator		):
       UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.preprocess(__A		)
       UpperCAmelCase			:				Tuple										=					self.model(
           __A,						__A,						scales_yx=__A,						padding='''max_detections''',						max_detections=self.config.MAX_DETECTIONS,						pad_value=0,						return_tensors='''np''',						location='''cpu''',						)
       UpperCAmelCase			:				List[Any]										=					output_dict.pop('''normalized_boxes'''		)
       if not TEST:
         UpperCAmelCase			:				Tuple										=					np.array(__A		)
         UpperCAmelCase			:				Union[str, Any]										=					self.schema.encode_batch(__A		)
         writer.write_batch(__A		)
       if TEST:
         break
       # finalizer the writer
     if not TEST:
       UpperCAmelCase					,		UpperCAmelCase			:				Any										=					writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''		)






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


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





