






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				     =  False
_a				     =  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a				     =  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 __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase=sys.argv[1:]   ):
							"""simple docstring"""







							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      1
							_UpperCAmelCase ,			_UpperCAmelCase     =      getopt.getopt(UpperCAmelCase	,   '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(UpperCAmelCase   )
													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(UpperCAmelCase   ), F"""{outputfile}"""
							if subset_list is not None:
													with open(os.path.realpath(UpperCAmelCase   )   ) as f:
																			_UpperCAmelCase     =      {self._vqa_file_split()[0] for x in tryload(UpperCAmelCase   )}
							else:
													_UpperCAmelCase     =      None

							_UpperCAmelCase     =      CONFIG
							if torch.cuda.is_available():
													_UpperCAmelCase     =      'cuda'
							_UpperCAmelCase     =      os.path.realpath(UpperCAmelCase   )
							_UpperCAmelCase     =      os.path.realpath(UpperCAmelCase   )
							_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 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      int(file.split('.'   )[0].split('_'   )[-1]   )
							_UpperCAmelCase     =      os.path.join(self.inputdir	,   UpperCAmelCase   )
							return (img_id, filepath)

	@property
	def 	UpperCamelCase   (							self   ):
							"""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(UpperCAmelCase   )   )
													if len(UpperCAmelCase   ) == self.batch:
																			_UpperCAmelCase     =      batch
																			_UpperCAmelCase     =      []
																			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:
													_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(UpperCAmelCase   )
													_UpperCAmelCase     =      self.model(
													    UpperCAmelCase	,   UpperCAmelCase	,   scales_yx=UpperCAmelCase	,   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(UpperCAmelCase   )
																			_UpperCAmelCase     =      self.schema.encode_batch(UpperCAmelCase   )
																			writer.write_batch(UpperCAmelCase   )
													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 >> 20} mb"""   )



def __A   (				__lowerCAmelCase     )->  Union[str, Any]:





						"""simple docstring"""



						try:
												_UpperCAmelCase     =      json.load(__lowerCAmelCase     )
												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				     =  Extract(sys.argv[1:])
		extract()
		if not TEST:
				_a				     =  datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
