


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
_SCREAMING_SNAKE_CASE    :				List[Any]       =					False
_SCREAMING_SNAKE_CASE    :				Any       =					Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_SCREAMING_SNAKE_CASE    :				Tuple       =					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 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case=sys.argv[1:]					):
         snake_case					=       None
         snake_case					=       None
         snake_case					=       None
         snake_case					=       1
         snake_case				,						snake_case					=       getopt.getopt(__snake_case    ,      '''i:o:b:s'''    ,      ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']					)
         for opt, arg in opts:
              if opt in ("-i", "--inputdir"):
                   snake_case					=       arg
              elif opt in ("-o", "--outfile"):
                   snake_case					=       arg
              elif opt in ("-b", "--batch_size"):
                   snake_case					=       int(__snake_case					)
              elif opt in ("-s", "--subset_list"):
                   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(__snake_case					), F'''{outputfile}'''
         if subset_list is not None:
              with open(os.path.realpath(__snake_case					)					) as f:
                   snake_case					=       {self._vqa_file_split()[0] for x in tryload(__snake_case					)}
         else:
              snake_case					=       None

         snake_case					=       CONFIG
         if torch.cuda.is_available():
              snake_case					=       '''cuda'''
         snake_case					=       os.path.realpath(__snake_case					)
         snake_case					=       os.path.realpath(__snake_case					)
         snake_case					=       Preprocess(self.config					)
         snake_case					=       GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''    ,      config=self.config					)
         snake_case					=       batch_size if batch_size != 0 else 1
         snake_case					=       DEFAULT_SCHEMA
    def 	a_    ( self    ,      __snake_case					):
         snake_case					=       int(file.split('''.'''					)[0].split('''_'''					)[-1]					)
         snake_case					=       os.path.join(self.inputdir    ,      __snake_case					)
         return (img_id, filepath)
    @property
    def 	a_    ( self					):
         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(__snake_case					)					)
              if len(__snake_case					) == self.batch:
                   snake_case					=       batch
                   snake_case					=       []
                   yield list(map(__snake_case    ,      zip(*__snake_case					)					)					)

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




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






def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     try:
          snake_case					=       json.load(UpperCamelCase_       )
          try:
               snake_case					=       list(data.keys()       )
          except Exception:
               snake_case					=       [d['''img_id'''] for d in data]
     except Exception:
          try:
               snake_case					=       eval(stream.read()       )
          except Exception:
               snake_case					=       stream.read().split('''\n'''       )
     return data


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










from manim import *

class 						A__      (					snake_case__			):







    """simple docstring"""

    def 	a_    ( self					):
         snake_case					=       Rectangle(height=0.5    ,      width=0.5					)
         snake_case					=       Rectangle(height=0.46    ,      width=0.46					).set_stroke(width=0					)
         snake_case					=       Rectangle(height=0.25    ,      width=0.25					)

         snake_case					=       [mem.copy() for i in range(6					)]
         snake_case					=       [mem.copy() for i in range(6					)]
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       VGroup(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       Text('''CPU'''    ,      font_size=2_4					)
         snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
         cpu.move_to([-2.5, -0.5, 0]					)
         self.add(__snake_case					)

         snake_case					=       [mem.copy() for i in range(4					)]
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       Text('''GPU'''    ,      font_size=2_4					)
         snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
         gpu.move_to([-1, -1, 0]					)
         self.add(__snake_case					)

         snake_case					=       [mem.copy() for i in range(6					)]
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)

         snake_case					=       Text('''Model'''    ,      font_size=2_4					)
         snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
         model.move_to([3, -1.0, 0]					)
         self.add(__snake_case					)

         snake_case					=       []
         snake_case					=       []

         for i, rect in enumerate(__snake_case					):
              rect.set_stroke(__snake_case					)

              snake_case					=       Rectangle(height=0.46 / 4    ,      width=0.46 / 3					).set_stroke(width=0.0					).set_fill(__snake_case    ,      opacity=0.7					)

              if i == 0:
                   cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)    ,      buff=0.02    ,      direction=__snake_case					)
                   cpu_target.set_x(cpu_target.get_x() + 0.1					)
              elif i == 3:
                   cpu_target.next_to(model_cpu_arr[0]    ,      direction=__snake_case    ,      buff=0.0					)
              else:
                   cpu_target.next_to(model_cpu_arr[i - 1]    ,      direction=__snake_case    ,      buff=0.0					)
              self.add(__snake_case					)
              model_cpu_arr.append(__snake_case					)

         self.add(*__snake_case    ,      *__snake_case					)

         snake_case					=       [meta_mem.copy() for i in range(6					)]
         snake_case					=       [meta_mem.copy() for i in range(6					)]
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       VGroup(*__snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       VGroup(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0					)
         snake_case					=       Text('''Disk'''    ,      font_size=2_4					)
         snake_case					=       Group(__snake_case    ,      __snake_case					).arrange(__snake_case    ,      buff=0.5    ,      aligned_edge=__snake_case					)
         disk.move_to([-4.0, -1.25, 0]					)
         self.add(__snake_case    ,      __snake_case					)

         snake_case					=       []

         for i in range(6					):
              snake_case					=       fill.copy().set_fill(__snake_case    ,      opacity=0.8					)
              target.move_to(disk_left_col_base[i]					).scale(0.5					)
              cpu_disk_arr.append(__snake_case					)

         self.add(*__snake_case					)

         snake_case					=       Square(side_length=2.2					)
         key.move_to([-5, 2, 0]					)

         snake_case					=       MarkupText(
             F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''    ,      font_size=1_8    ,      )

         key_text.move_to([-5, 2.4, 0]					)

         self.add(__snake_case    ,      __snake_case					)

         snake_case					=       MarkupText(
             F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''    ,      font_size=1_8    ,      )

         blue_text.next_to(__snake_case    ,      DOWN * 2.4    ,      aligned_edge=key_text.get_left()					)
         self.add(__snake_case					)

         snake_case					=       MarkupText(F'''The offloaded weights are all sent to the CPU.'''    ,      font_size=2_4					)
         step_a.move_to([2, 2, 0]					)

         self.play(Write(__snake_case    ,      run_time=3					)					)

         for i in range(6					):
              snake_case					=       cpu_disk_arr[i]
              snake_case					=       rect.copy().set_fill(__snake_case    ,      opacity=0.8					).scale(2.0					)
              cpa.generate_target()
              cpa.target.move_to(model_base[i]					)

              if i == 0:
                   rect.set_fill(__snake_case    ,      opacity=0.8					)
                   rect.generate_target()
                   rect.target.move_to(cpu_left_col_base[0]					).scale(2.0					)

                   self.remove(
                       *__snake_case    ,      *__snake_case    ,      )

              else:
                   rect.generate_target()
                   rect.target.move_to(cpu_left_col_base[i]					).scale(2.0					)
              self.play(MoveToTarget(__snake_case					)    ,      MoveToTarget(__snake_case					)    ,      model_base[i].animate.set_stroke(__snake_case					)					)
         self.play(FadeOut(__snake_case					)					)

         snake_case					=       MarkupText(
             F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.'''    ,      font_size=2_4    ,      )
         step_a.move_to([2, 2, 0]					)

         self.play(Write(__snake_case    ,      run_time=3					)					)

         snake_case					=       []
         snake_case					=       []
         for i in range(6					):
              snake_case					=       Arrow(start=__snake_case    ,      end=__snake_case    ,      color=__snake_case    ,      buff=0.5					)
              a.next_to(model_base[i].get_left()    ,      __snake_case    ,      buff=0.2					)
              arrows.append(__snake_case					)
              animations.append(Write(__snake_case					)					)
         self.play(*__snake_case					)
         self.wait()




