




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 ConvNextImageProcessor




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Optional[int]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	Tuple=3   , __lowerCamelCase							:	Optional[int]=18   , __lowerCamelCase							:	Union[str, Any]=30   , __lowerCamelCase							:	Any=4_00   , __lowerCamelCase							:	Optional[Any]=True   , __lowerCamelCase							:	List[str]=None   , __lowerCamelCase							:	Any=0.875   , __lowerCamelCase							:	List[str]=True   , __lowerCamelCase							:	int=[0.5, 0.5, 0.5]   , __lowerCamelCase							:	Dict=[0.5, 0.5, 0.5]   , )   ->	Union[str, Any]:
							a       =     size if size is not None else {"""shortest_edge""": 20}
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_resize
							a       =     size
							a       =     crop_pct
							a       =     do_normalize
							a       =     image_mean
							a       =     image_std





				def 				__UpperCAmelCase				( self							:	int				)   ->	List[Any]:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "crop_pct": self.crop_pct,
							}




@require_torch
@require_vision
class        snake_case__				(lowercase__       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   ConvNextImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							a       =     ConvNextImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	str				)   ->	Any:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "do_resize"				)				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "size"				)				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "crop_pct"				)				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "do_normalize"				)				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "image_mean"				)				)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   , "image_std"				)				)





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							a       =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size   , {"shortest_edge": 20}				)

							a       =     self.image_processing_class.from_dict(self.image_processor_dict   , size=42				)
							self.assertEqual(image_processor.size   , {"shortest_edge": 42}				)





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							pass





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Optional[int]:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=SCREAMING_SNAKE_CASE__				)
							for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__   , Image.Image				)

							# Test not batched input
							a       =     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.size["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )

							# Test batched
							a       =     image_processing(SCREAMING_SNAKE_CASE__   , 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["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Optional[int]:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=SCREAMING_SNAKE_CASE__   , numpify=SCREAMING_SNAKE_CASE__				)
							for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__   , np.ndarray				)

							# Test not batched input
							a       =     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.size["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )

							# Test batched
							a       =     image_processing(SCREAMING_SNAKE_CASE__   , 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["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	List[Any]:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     prepare_image_inputs(self.image_processor_tester   , equal_resolution=SCREAMING_SNAKE_CASE__   , torchify=SCREAMING_SNAKE_CASE__				)
							for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__   , torch.Tensor				)

							# Test not batched input
							a       =     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.size["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )

							# Test batched
							a       =     image_processing(SCREAMING_SNAKE_CASE__   , 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["shortest_edge"],
							        self.image_processor_tester.size["shortest_edge"],
							    )   , )











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






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=sys.argv[1:]      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None
					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							Union[str, Any]					     =   1
					_snake_case     , _snake_case					:							Dict					     =   getopt.getopt(SCREAMING_SNAKE_CASE__						,	"""i:o:b:s"""						,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]      )
					for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
							_snake_case					:							List[Any]					     =   arg
						elif opt in ("-o", "--outfile"):
							_snake_case					:							Dict					     =   arg
						elif opt in ("-b", "--batch_size"):
							_snake_case					:							Union[str, Any]					     =   int(SCREAMING_SNAKE_CASE__      )
						elif opt in ("-s", "--subset_list"):
							_snake_case					:							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(SCREAMING_SNAKE_CASE__      ), f'''{outputfile}'''
					if subset_list is not None:
						with open(os.path.realpath(SCREAMING_SNAKE_CASE__      )      ) as f:
							_snake_case					:							int					     =   {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__      )}
					else:
						_snake_case					:							List[str]					     =   None

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   int(file.split("""."""      )[0].split("""_"""      )[-1]      )
					_snake_case					:							Optional[int]					     =   os.path.join(self.inputdir						,	SCREAMING_SNAKE_CASE__      )
					return (img_id, filepath)







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   []
					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(SCREAMING_SNAKE_CASE__      )      )
						if len(SCREAMING_SNAKE_CASE__      ) == self.batch:
							_snake_case					:							Union[str, Any]					     =   batch
							_snake_case					:							Optional[Any]					     =   []
							yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )

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







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[Any]					     =   datasets.ArrowWriter(features=self.schema						,	path=self.outputfile      )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
						_snake_case     , _snake_case     , _snake_case					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
						_snake_case					:							Dict					     =   output_dict.pop("""normalized_boxes"""      )
						if not TEST:
							_snake_case					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_snake_case     , _snake_case					:							Optional[int]					     =   writer.finalize()
						print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''      )
def   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   stream.read().split("""\n"""     )
	return data


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





