


'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BlipImageProcessor



class     snake_case__  (      unittest.TestCase  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=7     ,      _UpperCAmelCase=3     ,      _UpperCAmelCase=18     ,      _UpperCAmelCase=30     ,      _UpperCAmelCase=400     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]     ,      _UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]     ,      _UpperCAmelCase=True     ,      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      size if size is not None else {'height': 20, 'width': 20}
										__magic_name__				:							Optional[Any]				=      parent
										__magic_name__				:							Union[str, Any]				=      batch_size
										__magic_name__				:							Optional[Any]				=      num_channels
										__magic_name__				:							Optional[Any]				=      image_size
										__magic_name__				:							List[str]				=      min_resolution
										__magic_name__				:							List[Any]				=      max_resolution
										__magic_name__				:							str				=      do_resize
										__magic_name__				:							Optional[Any]				=      size
										__magic_name__				:							Dict				=      do_normalize
										__magic_name__				:							Optional[int]				=      image_mean
										__magic_name__				:							List[Any]				=      image_std
										__magic_name__				:							int				=      do_pad
										__magic_name__				:							Any				=      do_convert_rgb
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										    "do_pad": self.do_pad,
										}





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False      ):




										"""simple docstring"""




										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																__magic_name__				:							Any				=      []
																for i in range(self.batch_size      ):
																						image_inputs.append(
																						    np.random.randint(
																						        255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta      )      )
										else:
																__magic_name__				:							Optional[int]				=      []
																for i in range(self.batch_size      ):
																						__magic_name__		, __magic_name__				:							Dict				=      np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution      )     ,      2      )
																						image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta      )      )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																__magic_name__				:							int				=      [Image.fromarray(np.moveaxis(_UpperCAmelCase     ,      0     ,      -1      )      ) for x in image_inputs]

										if torchify:
																__magic_name__				:							Tuple				=      [torch.from_numpy(_UpperCAmelCase      ) for x in image_inputs]

										return image_inputs






@require_torch
@require_vision
class     snake_case__  (      SCREAMING_SNAKE_CASE__						,       unittest.TestCase  ):
				__lowerCAmelCase		=      BlipImageProcessor if is_vision_available() else None
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      BlipImageProcessingTester(self      )
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_resize'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'size'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_normalize'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'image_mean'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'image_std'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_convert_rgb'      )      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										pass
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										__magic_name__				:							Dict				=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase      )
										for image in image_inputs:
																self.assertIsInstance(_UpperCAmelCase     ,      Image.Image      )

										# Test not batched input
										__magic_name__				:							Optional[int]				=      image_processor(image_inputs[0]     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )

										# Test batched
										__magic_name__				:							Dict				=      image_processor(_UpperCAmelCase     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										__magic_name__				:							Dict				=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase     ,      numpify=_UpperCAmelCase      )
										for image in image_inputs:
																self.assertIsInstance(_UpperCAmelCase     ,      np.ndarray      )

										# Test not batched input
										__magic_name__				:							List[str]				=      image_processor(image_inputs[0]     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )

										# Test batched
										__magic_name__				:							List[str]				=      image_processor(_UpperCAmelCase     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										__magic_name__				:							Optional[Any]				=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase     ,      torchify=_UpperCAmelCase      )
										for image in image_inputs:
																self.assertIsInstance(_UpperCAmelCase     ,      torch.Tensor      )

										# Test not batched input
										__magic_name__				:							int				=      image_processor(image_inputs[0]     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )

										# Test batched
										__magic_name__				:							Any				=      image_processor(_UpperCAmelCase     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )






@require_torch
@require_vision
class     snake_case__  (      SCREAMING_SNAKE_CASE__						,       unittest.TestCase  ):
				__lowerCAmelCase		=      BlipImageProcessor if is_vision_available() else None
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      BlipImageProcessingTester(self     ,      num_channels=4      )
										__magic_name__				:							Tuple				=      3
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_resize'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'size'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_normalize'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'image_mean'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'image_std'      )      )
										self.assertTrue(hasattr(_UpperCAmelCase     ,      'do_convert_rgb'      )      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										pass





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										__magic_name__				:							Union[str, Any]				=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase      )
										for image in image_inputs:
																self.assertIsInstance(_UpperCAmelCase     ,      Image.Image      )

										# Test not batched input
										__magic_name__				:							str				=      image_processor(image_inputs[0]     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )

										# Test batched
										__magic_name__				:							str				=      image_processor(_UpperCAmelCase     ,      return_tensors='pt'      ).pixel_values
										self.assertEqual(
										    encoded_images.shape     ,      (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,      )






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
snake_case__     							= 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     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      None
										__magic_name__				:							int				=      None
										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							Optional[int]				=      1
										__magic_name__		, __magic_name__				:							int				=      getopt.getopt(_UpperCAmelCase     ,      'i:o:b:s'     ,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
										for opt, arg in opts:
																if opt in ("-i", "--inputdir"):
																						__magic_name__				:							List[str]				=      arg
																elif opt in ("-o", "--outfile"):
																						__magic_name__				:							Union[str, Any]				=      arg
																elif opt in ("-b", "--batch_size"):
																						__magic_name__				:							Any				=      int(_UpperCAmelCase      )
																elif opt in ("-s", "--subset_list"):
																						__magic_name__				:							List[Any]				=      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:
																						__magic_name__				:							str				=      {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase      )}
										else:
																__magic_name__				:							Optional[int]				=      None

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




										"""simple docstring"""




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




										"""simple docstring"""




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






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


