
'''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__		: Union[str, Any]  							=							False
a__		: str  							=							Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a__		: Dict  							=							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 			UpperCamelCase__	:






 def __init__(      self	:int						,					_A	:List[Any]=sys.argv[1:]      )					->      str:







       '''simple docstring'''



       __A													=      None
       __A													=      None
       __A													=      None
       __A													=      1
       __A						,   __A													=      getopt.getopt(_A						,					'i:o:b:s'						,					['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   __A													=      arg
             elif opt in ("-o", "--outfile"):
                   __A													=      arg
             elif opt in ("-b", "--batch_size"):
                   __A													=      int(_A      )
             elif opt in ("-s", "--subset_list"):
                   __A													=      arg

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

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






 def 						lowercase_    (      self	:str						,					_A	:Tuple      )					->      List[Any]:







       '''simple docstring'''



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






 @property
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



       __A													=      []
       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(_A      )      )
             if len(_A      ) == self.batch:
                   __A													=      batch
                   __A													=      []
                   yield list(map(_A						,					zip(*_A      )      )      )

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






 def __call__(      self	:Dict      )					->      Any:







       '''simple docstring'''



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





def 							snake_case				(		UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      try:
            __A													=      json.load(UpperCAmelCase						)
            try:
                  __A													=      list(data.keys()						)
            except Exception:
                  __A													=      [d['img_id'] for d in data]
      except Exception:
            try:
                  __A													=      eval(stream.read()						)
            except Exception:
                  __A													=      stream.read().split('\n'						)
      return data


if __name__ == "__main__":
 a__		: Optional[int]  							=							Extract(sys.argv[1:])
 extract()
 if not TEST:
  a__		: Optional[int]  							=							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 DeiTImageProcessor

class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								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,
								}





@require_torch
@require_vision
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




	def      lowercase__							(  self		:  str					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A   ,		"""do_resize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""size"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_mean"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_std"""					)					)




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}					)
								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		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		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(__A   ,		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		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		numpify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		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(__A   ,		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		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		torchify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		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(__A   ,		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"""],
								    )   ,		)





