

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





class A__     :



 """simple docstring"""







 def __init__(		self			:						Optional[int]  ,    A_			:						Optional[Any]=sys.argv[1:]							):






      '''simple docstring'''




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

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

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     int(file.split("."							)[0].split("_"							)[-1]							)
      _lowerCAmelCase :       List[str]	   =     os.path.join(self.inputdir  ,    A_							)
      return (img_id, filepath)

 @property
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     []
      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:
                _lowerCAmelCase :       List[Any]	   =     batch
                _lowerCAmelCase :       Union[str, Any]	   =     []
                yield list(map(A_  ,    zip(*A_							)							)							)

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


 def __call__(		self			:						Any							):






      '''simple docstring'''




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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







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









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 CLIPImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Union[str, Any]  ,    A_			:						Optional[Any]  ,    A_			:						List[str]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						int=1_8  ,    A_			:						List[Any]=3_0  ,    A_			:						str=4_0_0  ,    A_			:						Union[str, Any]=True  ,    A_			:						Union[str, Any]=None  ,    A_			:						Union[str, Any]=True  ,    A_			:						Optional[int]=None  ,    A_			:						List[str]=True  ,    A_			:						Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]  ,    A_			:						int=[0.26862954, 0.26130258, 0.27577711]  ,    A_			:						Optional[int]=True  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      _lowerCAmelCase :       Tuple	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       str	   =     num_channels
      _lowerCAmelCase :       int	   =     image_size
      _lowerCAmelCase :       Optional[int]	   =     min_resolution
      _lowerCAmelCase :       Any	   =     max_resolution
      _lowerCAmelCase :       str	   =     do_resize
      _lowerCAmelCase :       Union[str, Any]	   =     size
      _lowerCAmelCase :       Optional[int]	   =     do_center_crop
      _lowerCAmelCase :       Union[str, Any]	   =     crop_size
      _lowerCAmelCase :       Optional[int]	   =     do_normalize
      _lowerCAmelCase :       Optional[int]	   =     image_mean
      _lowerCAmelCase :       int	   =     image_std
      _lowerCAmelCase :       Optional[Any]	   =     do_convert_rgb

 def  __magic_name__							(		self			:						Any							):






      '''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,
          "do_convert_rgb": self.do_convert_rgb,
      }


 def  __magic_name__							(		self			:						List[Any]  ,    A_			:						List[Any]=False  ,    A_			:						Optional[Any]=False  ,    A_			:						Tuple=False							):






      '''simple docstring'''




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

      if equal_resolution:
           _lowerCAmelCase :       Tuple	   =     []
           for i in range(self.batch_size							):
                image_inputs.append(
                    np.random.randint(
                        2_5_5  ,    size=(self.num_channels, self.max_resolution, self.max_resolution)  ,    dtype=np.uinta							)							)
      else:
           _lowerCAmelCase :       Optional[int]	   =     []
           for i in range(self.batch_size							):
                _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     np.random.choice(np.arange(self.min_resolution  ,    self.max_resolution							)  ,    2							)
                image_inputs.append(np.random.randint(2_5_5  ,    size=(self.num_channels, width, height)  ,    dtype=np.uinta							)							)

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

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

      return image_inputs





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Any  			=    CLIPImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     CLIPImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     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"							)							)
      self.assertTrue(hasattr(A_  ,    "do_convert_rgb"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)
      self.assertEqual(image_processor.crop_size  ,    {"height": 1_8, "width": 1_8}							)

      _lowerCAmelCase :       List[Any]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2  ,    crop_size=8_4							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)
      self.assertEqual(image_processor.crop_size  ,    {"height": 8_4, "width": 8_4}							)

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




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

      # Test not batched input
      _lowerCAmelCase :       Optional[Any]	   =     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 :       Optional[Any]	   =     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  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




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

      # Test not batched input
      _lowerCAmelCase :       Optional[Any]	   =     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 :       Union[str, Any]	   =     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  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       Any	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_  ,    torchify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    torch.Tensor							)

      # Test not batched input
      _lowerCAmelCase :       Tuple	   =     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 :       List[Any]	   =     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"],
          )  ,    )





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       List[str]  			=    CLIPImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     CLIPImageProcessingTester(self  ,    num_channels=4							)
      _lowerCAmelCase :       Tuple	   =     3

 @property
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     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"							)							)
      self.assertTrue(hasattr(A_  ,    "do_convert_rgb"							)							)

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      pass


 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Optional[int]	   =     self.image_processor_tester.prepare_inputs(equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

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

      # Test batched
      _lowerCAmelCase :       int	   =     image_processing(A_  ,    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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,    )






