

'''simple docstring'''




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class     lowercase_		(						lowercase_	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = False				,	a_ = False				,	a_ = None				,	**a_				,	)							->			List[str]:


    """simple docstring"""
    super().__init__(
        a_				,	split=a_				,	features=a_				,	cache_dir=a_				,	keep_in_memory=a_				,	streaming=a_				,	num_proc=a_				,	**a_				,	)
    UpperCAmelCase  							=		path_or_paths if isinstance(a_				,	a_       ) else {self.split: path_or_paths}
    UpperCAmelCase  							=		Csv(
        cache_dir=a_				,	data_files=a_				,	features=a_				,	**a_				,	)





  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""







    if self.streaming:
      UpperCAmelCase  							=		self.builder.as_streaming_dataset(split=self.split       )
    # Build regular (map-style) dataset
    else:
      UpperCAmelCase  							=		None
      UpperCAmelCase  							=		None
      UpperCAmelCase  							=		None
      UpperCAmelCase  							=		None

      self.builder.download_and_prepare(
          download_config=a_				,	download_mode=a_				,	verification_mode=a_				,	base_path=a_				,	num_proc=self.num_proc				,	)
      UpperCAmelCase  							=		self.builder.as_dataset(
          split=self.split				,	verification_mode=a_				,	in_memory=self.keep_in_memory       )
    return dataset




class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_				,	a_ = None				,	a_ = None				,	**a_				,	)							->			str:


    """simple docstring"""







    if num_proc is not None and num_proc <= 0:
      raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''       )

    UpperCAmelCase  							=		dataset
    UpperCAmelCase  							=		path_or_buf
    UpperCAmelCase  							=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    UpperCAmelCase  							=		num_proc
    UpperCAmelCase  							=		'utf-8'
    UpperCAmelCase  							=		to_csv_kwargs
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		self.to_csv_kwargs.pop('path_or_buf'				,	a_       )
    UpperCAmelCase  							=		self.to_csv_kwargs.pop('header'				,	a_       )
    UpperCAmelCase  							=		self.to_csv_kwargs.pop('index'				,	a_       )

    if isinstance(self.path_or_buf				,	(str, bytes, os.PathLike)       ):
      with open(self.path_or_buf				,	'wb+'       ) as buffer:
        UpperCAmelCase  							=		self._write(file_obj=a_				,	header=a_				,	index=a_				,	**self.to_csv_kwargs       )
    else:
      UpperCAmelCase  							=		self._write(file_obj=self.path_or_buf				,	header=a_				,	index=a_				,	**self.to_csv_kwargs       )
    return written
  def   snake_case_     (       self				,	a_       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		args

    UpperCAmelCase  							=		query_table(
        table=self.dataset.data				,	key=slice(a_				,	offset + self.batch_size       )				,	indices=self.dataset._indices				,	)
    UpperCAmelCase  							=		batch.to_pandas().to_csv(
        path_or_buf=a_				,	header=header if (offset == 0) else False				,	index=a_				,	**a_       )
    return csv_str.encode(self.encoding       )





  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	**a_       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		0

    if self.num_proc is None or self.num_proc == 1:
      for offset in logging.tqdm(
          range(0				,	len(self.dataset       )				,	self.batch_size       )				,	unit='ba'				,	disable=not logging.is_progress_bar_enabled()				,	desc='Creating CSV from Arrow format'				,	):
        UpperCAmelCase  							=		self._batch_csv((offset, header, index, to_csv_kwargs)       )
        written += file_obj.write(a_       )

    else:
      UpperCAmelCase							, UpperCAmelCase  							=		len(self.dataset       ), self.batch_size
      with multiprocessing.Pool(self.num_proc       ) as pool:
        for csv_str in logging.tqdm(
            pool.imap(
                self._batch_csv				,	[(offset, header, index, to_csv_kwargs) for offset in range(0				,	a_				,	a_       )]				,	)				,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,	unit='ba'				,	disable=not logging.is_progress_bar_enabled()				,	desc='Creating CSV from Arrow format'				,	):
          written += file_obj.write(a_       )

    return written





'''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)


