




"""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								=							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, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)






class  SCREAMING_SNAKE_CASE__   :






						"""simple docstring"""




						def __init__(							self			:     Optional[int]  ,  lowerCAmelCase			:     Dict=sys.argv[1:]   ):
										lowerCAmelCase		=			None
										lowerCAmelCase		=			None
										lowerCAmelCase		=			None
										lowerCAmelCase		=			1
										lowerCAmelCase	,  lowerCAmelCase		=			getopt.getopt(__UpperCamelCase  ,  """i:o:b:s"""  ,  ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
										for opt, arg in opts:
														if opt in ("-i", "--inputdir"):
																		lowerCAmelCase		=			arg
														elif opt in ("-o", "--outfile"):
																		lowerCAmelCase		=			arg
														elif opt in ("-b", "--batch_size"):
																		lowerCAmelCase		=			int(__UpperCamelCase   )
														elif opt in ("-s", "--subset_list"):
																		lowerCAmelCase		=			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:
																		lowerCAmelCase		=			{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase   )}
										else:
														lowerCAmelCase		=			None

										lowerCAmelCase		=			CONFIG
										if torch.cuda.is_available():
														lowerCAmelCase		=			"""cuda"""
										lowerCAmelCase		=			os.path.realpath(__UpperCamelCase   )
										lowerCAmelCase		=			os.path.realpath(__UpperCamelCase   )
										lowerCAmelCase		=			Preprocess(self.config   )
										lowerCAmelCase		=			GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,  config=self.config   )
										lowerCAmelCase		=			batch_size if batch_size != 0 else 1
										lowerCAmelCase		=			DEFAULT_SCHEMA


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[Any]   ):
										lowerCAmelCase		=			int(file.split("""."""   )[0].split("""_"""   )[-1]   )
										lowerCAmelCase		=			os.path.join(self.inputdir  ,  __UpperCamelCase   )
										return (img_id, filepath)


						@property
						def 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			[]
										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:
																		lowerCAmelCase		=			batch
																		lowerCAmelCase		=			[]
																		yield list(map(__UpperCamelCase  ,  zip(*__UpperCamelCase   )   )   )

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




						def __call__(							self			:     Dict   ):
										# make writer
										if not TEST:
														lowerCAmelCase		=			datasets.ArrowWriter(features=self.schema  ,  path=self.outputfile   )
										# do file generator
										for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
														lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			self.preprocess(__UpperCamelCase   )
														lowerCAmelCase		=			self.model(
														    __UpperCamelCase  ,  __UpperCamelCase  ,  scales_yx=__UpperCamelCase  ,  padding="""max_detections"""  ,  max_detections=self.config.MAX_DETECTIONS  ,  pad_value=0  ,  return_tensors="""np"""  ,  location="""cpu"""  ,  )
														lowerCAmelCase		=			output_dict.pop("""normalized_boxes"""   )
														if not TEST:
																		lowerCAmelCase		=			np.array(__UpperCamelCase   )
																		lowerCAmelCase		=			self.schema.encode_batch(__UpperCamelCase   )
																		writer.write_batch(__UpperCamelCase   )
														if TEST:
																		break
														# finalizer the writer
										if not TEST:
														lowerCAmelCase	,  lowerCAmelCase		=			writer.finalize()
														print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''   )





def 							lowercase   (snake_case__				:	Any	)       ->					Optional[int]:


				'''simple docstring'''






				try:
								lowerCAmelCase		=			json.load(lowercase_	)
								try:
												lowerCAmelCase		=			list(data.keys()	)
								except Exception:
												lowerCAmelCase		=			[d["""img_id"""] for d in data]
				except Exception:
								try:
												lowerCAmelCase		=			eval(stream.read()	)
								except Exception:
												lowerCAmelCase		=			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)











"""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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84   )
										self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}   )


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )


						def 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )




						def 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )





