

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__										=      False
A__										=      Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A__										=      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_      :


  """simple docstring"""



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

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



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



  @property
  def   _UpperCamelCase     (						self     :					int						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    []
      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:
              SCREAMING_SNAKE_CASE__         =    batch
              SCREAMING_SNAKE_CASE__         =    []
              yield list(map(A_  ,	zip(*A_						)						)						)

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







  def __call__(						self     :					Any						)       ->			Union[str, Any]:
      # make writer
      if not TEST:
          SCREAMING_SNAKE_CASE__         =    datasets.ArrowWriter(features=self.schema  ,	path=self.outputfile						)
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator						):
          SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.preprocess(A_						)
          SCREAMING_SNAKE_CASE__         =    self.model(
              A_  ,	A_  ,	scales_yx=A_  ,	padding="max_detections"  ,	max_detections=self.config.MAX_DETECTIONS  ,	pad_value=0  ,	return_tensors="np"  ,	location="cpu"  ,	)
          SCREAMING_SNAKE_CASE__         =    output_dict.pop("normalized_boxes"						)
          if not TEST:
              SCREAMING_SNAKE_CASE__         =    np.array(A_						)
              SCREAMING_SNAKE_CASE__         =    self.schema.encode_batch(A_						)
              writer.write_batch(A_						)
          if TEST:
              break
          # finalizer the writer
      if not TEST:
          SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    writer.finalize()
          print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"						)





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


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





from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    np.stack((horizontal_flow, vertical_flow)			,							2						)

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    SCREAMING_SNAKE_CASE__         =    np.stack(
        np.meshgrid(np.arange(0			,							image.shape[1]						)			,							np.arange(0			,							image.shape[0]						)						)			,							2						)
    SCREAMING_SNAKE_CASE__         =    np.round(grid - flow						).astype(np.intaa						)

    # Find the locations outside of the original image
    SCREAMING_SNAKE_CASE__         =    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
    SCREAMING_SNAKE_CASE__         =    0

    SCREAMING_SNAKE_CASE__         =    image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    SCREAMING_SNAKE_CASE__         =    0

    return warped





def 				__a							(       __A			,							__A			,							__A			,							__A = None			,							):
    if alpha is None:
        SCREAMING_SNAKE_CASE__         =    0.1

    # Initialize flow
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)

    # Prepare kernels for the calculation of the derivatives and the average velocity
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, 1], [-1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, -1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[1, 1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

    # Iteratively refine the flow
    for _ in range(__A						):
        SCREAMING_SNAKE_CASE__         =    warp(__A			,							__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							-kernel_t						)

        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)

        # This updates the flow as proposed in the paper (Step 12)
        SCREAMING_SNAKE_CASE__         =    (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        SCREAMING_SNAKE_CASE__         =    update / (alpha**2 + derivative_x**2 + derivative_y**2)

        SCREAMING_SNAKE_CASE__         =    avg_horizontal_velocity - derivative_x * update
        SCREAMING_SNAKE_CASE__         =    avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()


