



'''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
lowercase__     :  Dict    					=			False
lowercase__     :  Optional[int]    					=			Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
lowercase__     :  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, 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     ,						__UpperCamelCase=sys.argv[1:] ):
       UpperCamelCase__      =			None
       UpperCamelCase__      =			None
       UpperCamelCase__      =			None
       UpperCamelCase__      =			1
       UpperCamelCase__    ,  UpperCamelCase__      =			getopt.getopt(__UpperCamelCase     ,						"i:o:b:s"     ,						["inputdir=", "outfile=", "batch_size=", "subset_list="] )
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               UpperCamelCase__      =			arg
           elif opt in ("-o", "--outfile"):
               UpperCamelCase__      =			arg
           elif opt in ("-b", "--batch_size"):
               UpperCamelCase__      =			int(__UpperCamelCase )
           elif opt in ("-s", "--subset_list"):
               UpperCamelCase__      =			arg

       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(__UpperCamelCase ), f'{outputfile}'
       if subset_list is not None:
           with open(os.path.realpath(__UpperCamelCase ) ) as f:
               UpperCamelCase__      =			{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase )}
       else:
           UpperCamelCase__      =			None

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			int(file.split("." )[0].split("_" )[-1] )
       UpperCamelCase__      =			os.path.join(self.inputdir     ,						__UpperCamelCase )
       return (img_id, filepath)


   @property
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			[]
       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(__UpperCamelCase ) )
           if len(__UpperCamelCase ) == self.batch:
               UpperCamelCase__      =			batch
               UpperCamelCase__      =			[]
               yield list(map(__UpperCamelCase     ,						zip(*__UpperCamelCase ) ) )

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







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





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


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









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

       self.register_modules(unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

       UpperCamelCase__      =			(image / 2 + 0.5).clamp(0     ,						1 )
       UpperCamelCase__      =			image.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       if output_type == "pil":
           UpperCamelCase__      =			self.numpy_to_pil(__UpperCamelCase )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




