import json
import os
import tempfile

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


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image


def SCREAMING_SNAKE_CASE					(  lowercase_:						int		,    lowercase_:						Optional[int]=False		,    lowercase_:						List[str]=False		,    lowercase_:						Optional[Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Dict	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							Union[str, Any]	    =	image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     _UpperCamelCase  :							Optional[int]	    =	image_processor_tester.min_resolution
                     if getattr(lowercase_		,    'size_divisor'		,    lowercase_					):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            _UpperCamelCase  :							Dict	    =	max(image_processor_tester.size_divisor		,    lowercase_					)
                     _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	np.random.choice(np.arange(lowercase_		,    image_processor_tester.max_resolution					)		,    2					)
              image_inputs.append(
                  np.random.randint(255		,    size=(image_processor_tester.num_channels, width, height)		,    dtype=np.uinta					)					)

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

       if torchify:
              _UpperCamelCase  :							Union[str, Any]	    =	[torch.from_numpy(lowercase_					) for image in image_inputs]

       return image_inputs


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[Any]=10		,    lowercase_:						int=10		,    lowercase_:						Optional[int]=False		,    lowercase_:						str=False					):
       _UpperCamelCase  :							Dict	    =	[]
       for i in range(image_processor_tester.num_frames					):
              video.append(np.random.randint(255		,    size=(image_processor_tester.num_channels, width, height)		,    dtype=np.uinta					)					)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase  :							Any	    =	[Image.fromarray(np.moveaxis(lowercase_		,    0		,    -1					)					) for frame in video]

       if torchify:
              _UpperCamelCase  :							Any	    =	[torch.from_numpy(lowercase_					) for frame in video]

       return video


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						int=False		,    lowercase_:						List[Any]=False		,    lowercase_:						Union[str, Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Union[str, Any]	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							Any	    =	image_processor_tester.max_resolution
              else:
                     _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,    image_processor_tester.max_resolution					)		,    2					)
                     _UpperCamelCase  :							Optional[int]	    =	prepare_video(
                         image_processor_tester=lowercase_		,    width=lowercase_		,    height=lowercase_		,    numpify=lowercase_		,    torchify=lowercase_		,    )
              video_inputs.append(lowercase_					)

       return video_inputs





class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			None

 def        __snake_case       (      self							:		Dict	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	self.image_processing_class(**self.image_processor_dict	)
        _UpperCamelCase  :							str	    =	json.loads(image_processor.to_json_string()	)
        for key, value in self.image_processor_dict.items():
               self.assertEqual(obj[key]							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCamelCase  :							Tuple	    =	os.path.join(lowerCamelCase							,      'image_processor.json'	)
               image_processor_first.to_json_file(lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	self.image_processing_class.from_json_file(lowerCamelCase	)

        self.assertEqual(image_processor_second.to_dict()							,      image_processor_first.to_dict()	)

 def        __snake_case       (      self							:		str	)			->				Optional[int]:
        _UpperCamelCase  :							Dict	    =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCamelCase  :							List[Any]	    =	image_processor_first.save_pretrained(lowerCamelCase	)[0]
               check_json_file_has_correct_format(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.image_processing_class.from_pretrained(lowerCamelCase	)

        self.assertEqual(image_processor_second.to_dict()							,      image_processor_first.to_dict()	)

 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class()
        self.assertIsNotNone(lowerCamelCase	)

 @require_torch
 @require_vision
 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        if self.test_cast_dtype is not None:
               # Initialize image_processor
               _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)

               # create random PyTorch tensors
               _UpperCamelCase  :							Any	    =	prepare_image_inputs(self.image_processor_tester							,      equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)

               _UpperCamelCase  :							Any	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							str	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.floataa	)
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							Tuple	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to('cpu'							,      torch.bfloataa	)
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.bfloataa	)

               with self.assertRaises(lowerCamelCase	):
                      _UpperCamelCase  :							int	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.bfloataa							,      'cpu'	)

               # Try with text + image feature
               _UpperCamelCase  :							Union[str, Any]	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]	)}	)
               _UpperCamelCase  :							Any	    =	encoding.to(torch.floataa	)

               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)
               self.assertEqual(encoding.input_ids.dtype							,      torch.long	)




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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class  __lowerCAmelCase	:



 '''simple docstring'''

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

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

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

 def __call__(      self							:		Optional[Any]	)			->				Any:
        # make writer
        if not TEST:
               _UpperCamelCase  :							int	    =	datasets.ArrowWriter(features=self.schema							,      path=self.outputfile	)
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	self.preprocess(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.model(
                   lowerCamelCase							,      lowerCamelCase							,      scales_yx=lowerCamelCase							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
               _UpperCamelCase  :							Tuple	    =	output_dict.pop('normalized_boxes'	)
               if not TEST:
                      _UpperCamelCase  :							Dict	    =	np.array(lowerCamelCase	)
                      _UpperCamelCase  :							Optional[int]	    =	self.schema.encode_batch(lowerCamelCase	)
                      writer.write_batch(lowerCamelCase	)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	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)



