




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, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class 	lowercase_      :






      '''simple docstring'''




      def __init__(   self   :		str				,      __UpperCAmelCase   :		List[Any]=sys.argv[1:]						)					->Optional[Any]:




       """simple docstring"""


       a							=   None
       a							=   None
       a							=   None
       a							=   1
       a						,  a							=   getopt.getopt(__UpperCAmelCase				,      '''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(__UpperCAmelCase						)
        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(__UpperCAmelCase						), F"""{outputfile}"""
       if subset_list is not None:
        with open(os.path.realpath(__UpperCAmelCase						)						) as f:
         a							=   {self._vqa_file_split()[0] for x in tryload(__UpperCAmelCase						)}
       else:
        a							=   None

       a							=   CONFIG
       if torch.cuda.is_available():
        a							=   '''cuda'''
       a							=   os.path.realpath(__UpperCAmelCase						)
       a							=   os.path.realpath(__UpperCAmelCase						)
       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    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		List[str]						)					->List[Any]:




       """simple docstring"""


       a							=   int(file.split('''.'''						)[0].split('''_'''						)[-1]						)
       a							=   os.path.join(self.inputdir				,      __UpperCAmelCase						)
       return (img_id, filepath)







      @property
      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->List[Any]:




       """simple docstring"""


       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(__UpperCAmelCase						)						)
        if len(__UpperCAmelCase						) == self.batch:
         a							=   batch
         a							=   []
         yield list(map(__UpperCAmelCase				,      zip(*__UpperCAmelCase						)						)						)

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







      def __call__(   self   :		Optional[int]						)					->Union[str, Any]:




       """simple docstring"""


       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(__UpperCAmelCase						)
        a							=   self.model(
            __UpperCAmelCase				,      __UpperCAmelCase				,      scales_yx=__UpperCAmelCase				,      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(__UpperCAmelCase						)
         a							=   self.schema.encode_batch(__UpperCAmelCase						)
         writer.write_batch(__UpperCAmelCase						)
        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 		_a					(   a				:Any				)							->    List[Any]:
 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__":
     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)










from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


