





'''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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      str=10		, _lowerCamelCase      :      Optional[Any]=10		, _lowerCamelCase      :      List[Any]=False		, _lowerCamelCase      :      Tuple=False ):
    A__       =    []
    for i in range(image_processor_tester.num_frames ):
        video.append(np.random.randint(2_55		, 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
        A__       =    [Image.fromarray(np.moveaxis(_lowerCamelCase		, 0		, -1 ) ) for frame in video]

    if torchify:
        A__       =    [torch.from_numpy(_lowerCamelCase ) for frame in video]

    return video





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

    A__       =    []
    for i in range(image_processor_tester.batch_size ):
        if equal_resolution:
            A__       =    A__       =    image_processor_tester.max_resolution
        else:
            A__,			A__       =    np.random.choice(
                np.arange(image_processor_tester.min_resolution		, image_processor_tester.max_resolution )		, 2 )
            A__       =    prepare_video(
                image_processor_tester=_lowerCamelCase		, width=_lowerCamelCase		, height=_lowerCamelCase		, numpify=_lowerCamelCase		, torchify=_lowerCamelCase		, )
        video_inputs.append(_lowerCamelCase )

    return video_inputs






class      UpperCAmelCase			( unittest.TestCase       ):
 def __init__(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Tuple=7		, lowercase_   :Optional[Any]=3		, lowercase_   :Optional[Any]=4		, lowercase_   :Any=18		, lowercase_   :Optional[Any]=30		, lowercase_   :Tuple=4_00		, lowercase_   :Tuple=True		, lowercase_   :str=None		, lowercase_   :Dict=True		, lowercase_   :int=[0.5, 0.5, 0.5]		, lowercase_   :Any=[0.5, 0.5, 0.5]		, lowercase_   :int=True		, lowercase_   :Dict=None		, )->							List[Any]:
     A__       =    size if size is not None else {"shortest_edge": 18}
     A__       =    crop_size if crop_size is not None else {"height": 18, "width": 18}

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



 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Dict:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
     }






@require_torch
@require_vision
class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
 __lowercase     	=       TvltImageProcessor if is_vision_available() else None
 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     A__       =    TvltImageProcessorTester(self							)
 @property
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Any:
     return self.image_processor_tester.prepare_image_processor_dict()
 def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(lowercase_		, "image_mean"							)							)
     self.assertTrue(hasattr(lowercase_		, "image_std"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_normalize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_resize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_center_crop"							)							)
     self.assertTrue(hasattr(lowercase_		, "size"							)							)
 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PIL videos
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, Image.Image							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, numpify=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, np.ndarray							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )



 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Union[str, Any]:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, torchify=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, torch.Tensor							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )










'''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
__lowerCAmelCase							:			str				=False
__lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__lowerCAmelCase							:			int				=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, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class      UpperCAmelCase			:
 def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
     A__       =    None
     A__       =    None
     A__       =    None
     A__       =    1
     A__,			A__       =    getopt.getopt(lowercase_		, "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(lowercase_							)
         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(lowercase_							), F"{outputfile}"
     if subset_list is not None:
         with open(os.path.realpath(lowercase_							)							) as f:
             A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
     else:
         A__       =    None

     A__       =    CONFIG
     if torch.cuda.is_available():
         A__       =    "cuda"
     A__       =    os.path.realpath(lowercase_							)
     A__       =    os.path.realpath(lowercase_							)
     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   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
     A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
     A__       =    os.path.join(self.inputdir		, lowercase_							)
     return (img_id, filepath)
 @property
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
     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(lowercase_							)							)
         if len(lowercase_							) == self.batch:
             A__       =    batch
             A__       =    []
             yield list(map(lowercase_		, zip(*lowercase_							)							)							)

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



 def __call__(       self   :Optional[int]							)->							List[str]:
     # make writer
     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(lowercase_							)
         A__       =    self.model(
             lowercase_		, lowercase_		, scales_yx=lowercase_		, 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(lowercase_							)
             A__       =    self.schema.encode_batch(lowercase_							)
             writer.write_batch(lowercase_							)
         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 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
    try:
        A__       =    json.load(_lowerCamelCase )
        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__":
  __lowerCAmelCase							:			int				=Extract(sys.argv[1:])
  extract()
  if not TEST:
    __lowerCAmelCase							:			Optional[Any]				=datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)



