

'''simple docstring'''




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_a			:	int								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
  parser.add_argument(
      '--dataset_name'    ,				type=SCREAMING_SNAKE_CASE    ,				default='wikitext'    ,				help='Name of the training. Explore datasets at: hf.co/datasets.'    ,				)
  parser.add_argument(
      '--dataset_config'    ,				type=SCREAMING_SNAKE_CASE    ,				default='wikitext-103-raw-v1'    ,				help='Configuration name of the dataset.'  )
  parser.add_argument(
      '--batch_size'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1000    ,				help='Batch size during training.'    ,				)
  parser.add_argument(
      '--vocab_size'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1_0048    ,				help='Size of the desired vocabulary.'    ,				)
  parser.add_argument(
      '--limit'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				help='Limit the number of shards (used for debugging).'    ,				)
  parser.add_argument(
      '--export_to_hub'    ,				action='store_true'    ,				)

  UpperCAmelCase  							=		parser.parse_args()
  return args


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		datasets.load_dataset(args.dataset_name    ,				args.dataset_config    ,				split='train'  )

  if args.limit is not None:
    UpperCAmelCase  							=		min(len(SCREAMING_SNAKE_CASE  )    ,				args.limit  )
    UpperCAmelCase  							=		dataset.select(range(SCREAMING_SNAKE_CASE  )  )
    logger.info(f'''Limiting the dataset to {args.limit} entries.'''  )

  def batch_iterator():
    for i in range(0    ,				len(SCREAMING_SNAKE_CASE  )    ,				args.batch_size  ):
      yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
  UpperCAmelCase  							=		Tokenizer(Unigram()  )
  UpperCAmelCase  							=		normalizers.Sequence([normalizers.Replace('``'    ,				'"'  ), normalizers.Replace('\'\''    ,				'"'  )]  )
  UpperCAmelCase  							=		pre_tokenizers.Metaspace()

  # Prepare the trainer.
  UpperCAmelCase  							=		UnigramTrainer(
      unk_token='<unk>'    ,				special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']    ,				vocab_size=args.vocab_size    ,				)

  logger.info('Training the tokenizer.'  )
  tokenizer.train_from_iterator(batch_iterator()    ,				trainer=SCREAMING_SNAKE_CASE  )
  logger.info('Tokenizer training complete!'  )

  UpperCAmelCase  							=		tokenizer.token_to_id('[CLS]'  )
  UpperCAmelCase  							=		tokenizer.token_to_id('[SEP]'  )
  UpperCAmelCase  							=		processors.TemplateProcessing(
      single='[CLS]:0 $A:0 [SEP]:0'    ,				pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'    ,				special_tokens=[
          ('[CLS]', cls_token_id),
          ('[SEP]', sep_token_id),
      ]    ,				)
  UpperCAmelCase  							=		decoders.Metaspace()

  if args.export_to_hub:
    logger.info('Exporting the trained tokenzier to Hub.'  )
    UpperCAmelCase  							=		AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE  )
    new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
  _a			:	Any								=  parse_args()
  main(args)





'''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
_a			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





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


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		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  							=		{self._vqa_file_split()[0] for x in tryload(a_       )}
    else:
      UpperCAmelCase  							=		None

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    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  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

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





  def __call__(       self       )							->			str:


    """simple docstring"""
    # make writer
    if not TEST:
      UpperCAmelCase  							=		datasets.ArrowWriter(features=self.schema				,	path=self.outputfile       )
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
      UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		self.preprocess(a_       )
      UpperCAmelCase  							=		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  							=		output_dict.pop('normalized_boxes'       )
      if not TEST:
        UpperCAmelCase  							=		np.array(a_       )
        UpperCAmelCase  							=		self.schema.encode_batch(a_       )
        writer.write_batch(a_       )
      if TEST:
        break
      # finalizer the writer
    if not TEST:
      UpperCAmelCase							, UpperCAmelCase  							=		writer.finalize()
      print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


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


