




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






 def __init__(     self    ,					lowerCamelCase=sys.argv[1:]						)				->       Union[str, Any]:




      """simple docstring"""






      __magic_name__ :							List[str]		       =							None
      __magic_name__ :							Union[str, Any]		       =							None
      __magic_name__ :							Optional[Any]		       =							None
      __magic_name__ :							Any		       =							1
      __magic_name__ :							str		       =							getopt.getopt(lowerCamelCase    ,					'''i:o:b:s'''    ,					['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']						)
      for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
                __magic_name__ :							Union[str, Any]		       =							arg
           elif opt in ("-o", "--outfile"):
                __magic_name__ :							str		       =							arg
           elif opt in ("-b", "--batch_size"):
                __magic_name__ :							Optional[int]		       =							int(lowerCamelCase						)
           elif opt in ("-s", "--subset_list"):
                __magic_name__ :							List[str]		       =							arg

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

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






 def 			lowercase				(     self    ,					lowerCamelCase						)				->       str:




      """simple docstring"""






      __magic_name__ :							List[str]		       =							int(file.split('''.'''						)[0].split('''_'''						)[-1]						)
      __magic_name__ :							Dict		       =							os.path.join(self.inputdir    ,					lowerCamelCase						)
      return (img_id, filepath)






 @property
 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






      __magic_name__ :							Union[str, Any]		       =							[]
      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(lowerCamelCase						)						)
           if len(lowerCamelCase						) == self.batch:
                __magic_name__ :							Dict		       =							batch
                __magic_name__ :							List[Any]		       =							[]
                yield list(map(lowerCamelCase    ,					zip(*lowerCamelCase						)						)						)

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






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




      """simple docstring"""






      if not TEST:
           __magic_name__ :							Tuple		       =							datasets.ArrowWriter(features=self.schema    ,					path=self.outputfile						)
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator						):
           __magic_name__ :							Optional[Any]		       =							self.preprocess(lowerCamelCase						)
           __magic_name__ :							int		       =							self.model(
               lowerCamelCase    ,					lowerCamelCase    ,					scales_yx=lowerCamelCase    ,					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__ :							Dict		       =							np.array(lowerCamelCase						)
                __magic_name__ :							Optional[Any]		       =							self.schema.encode_batch(lowerCamelCase						)
                writer.write_batch(lowerCamelCase						)
           if TEST:
                break
           # finalizer the writer
      if not TEST:
           __magic_name__ :							Dict		       =							writer.finalize()
           print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''						)







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->int:






     """simple docstring"""






     try:
          __magic_name__ :							Any		       =							json.load(_UpperCAmelCase	)
          try:
               __magic_name__ :							int		       =							list(data.keys()	)
          except Exception:
               __magic_name__ :							Optional[Any]		       =							[d["img_id"] for d in data]
     except Exception:
          try:
               __magic_name__ :							List[Any]		       =							eval(stream.read()	)
          except Exception:
               __magic_name__ :							int		       =							stream.read().split('''\n'''	)
     return data


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









import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



