


'''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__  =     False
lowerCAmelCase__  =     Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
lowerCAmelCase__  =     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	:					Any					, lowerCamelCase	:					List[Any]=sys.argv[1:]						)->	str:
          snake_case__					:	int          =					None
          snake_case__					:	Dict          =					None
          snake_case__					:	Any          =					None
          snake_case__					:	Union[str, Any]          =					1
          snake_case__       ,   snake_case__					:	Optional[int]          =					getopt.getopt(lowerCamelCase					, """i:o:b:s"""					, ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]						)
          for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                        snake_case__					:	int          =					arg
                 elif opt in ("-o", "--outfile"):
                        snake_case__					:	List[Any]          =					arg
                 elif opt in ("-b", "--batch_size"):
                        snake_case__					:	Union[str, Any]          =					int(lowerCamelCase						)
                 elif opt in ("-s", "--subset_list"):
                        snake_case__					:	Optional[int]          =					arg

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

          snake_case__					:	Tuple          =					CONFIG
          if torch.cuda.is_available():
                 snake_case__					:	Optional[int]          =					"""cuda"""
          snake_case__					:	int          =					os.path.realpath(lowerCamelCase						)
          snake_case__					:	Any          =					os.path.realpath(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					Preprocess(self.config						)
          snake_case__					:	str          =					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""					, config=self.config						)
          snake_case__					:	str          =					batch_size if batch_size != 0 else 1
          snake_case__					:	str          =					DEFAULT_SCHEMA




   def   __lowerCAmelCase						(	self	:					List[str]					, lowerCamelCase	:					Optional[int]						)->	Dict:
          snake_case__					:	Dict          =					int(file.split("""."""						)[0].split("""_"""						)[-1]						)
          snake_case__					:	Optional[Any]          =					os.path.join(self.inputdir					, lowerCamelCase						)
          return (img_id, filepath)




   @property
   def   __lowerCAmelCase						(	self	:					str						)->	Union[str, Any]:
          snake_case__					:	Union[str, Any]          =					[]
          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(lowerCamelCase						)						)
                 if len(lowerCamelCase						) == self.batch:
                        snake_case__					:	str          =					batch
                        snake_case__					:	Optional[int]          =					[]
                        yield list(map(lowerCamelCase					, zip(*lowerCamelCase						)						)						)

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




   def __call__(	self	:					Optional[Any]						)->	str:
          # make writer
          if not TEST:
                 snake_case__					:	List[str]          =					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__					:	Tuple          =					self.preprocess(lowerCamelCase						)
                 snake_case__					:	Optional[Any]          =					self.model(
                     lowerCamelCase					, lowerCamelCase					, scales_yx=lowerCamelCase					, padding="""max_detections"""					, max_detections=self.config.MAX_DETECTIONS					, pad_value=0					, return_tensors="""np"""					, location="""cpu"""					, )
                 snake_case__					:	Tuple          =					output_dict.pop("""normalized_boxes"""						)
                 if not TEST:
                        snake_case__					:	int          =					np.array(lowerCamelCase						)
                        snake_case__					:	int          =					self.schema.encode_batch(lowerCamelCase						)
                        writer.write_batch(lowerCamelCase						)
                 if TEST:
                        break
                 # finalizer the writer
          if not TEST:
                 snake_case__       ,   snake_case__					:	Optional[Any]          =					writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""						)







def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       try:
              snake_case__					:	Any          =					json.load(UpperCAmelCase     )
              try:
                     snake_case__					:	Any          =					list(data.keys()     )
              except Exception:
                     snake_case__					:	Union[str, Any]          =					[d["""img_id"""] for d in data]
       except Exception:
              try:
                     snake_case__					:	Union[str, Any]          =					eval(stream.read()     )
              except Exception:
                     snake_case__					:	Any          =					stream.read().split("""\n"""     )
       return data


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








'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					boundaries[1] - boundaries[0]
       snake_case__					:	Optional[Any]          =					boundaries + step / 2
       snake_case__					:	str          =					torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1     )]    ,       dim=0     )
       return bin_centers



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	str          =					_calculate_bin_centers(UpperCAmelCase     )
       return (
           torch.sum(aligned_distance_error_probs * bin_centers    ,       dim=-1     ),
           bin_centers[-1],
       )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Dict          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	str          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )
       snake_case__       ,   snake_case__					:	List[str]          =					_calculate_expected_aligned_error(
           alignment_confidence_breaks=UpperCAmelCase    ,       aligned_distance_error_probs=UpperCAmelCase    ,       )

       return {
           "aligned_confidence_probs": aligned_confidence_probs,
           "predicted_aligned_error": predicted_aligned_error,
           "max_predicted_aligned_error": max_predicted_aligned_error,
       }






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       UpperCAmelCase = 1E-8    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       if residue_weights is None:
              snake_case__					:	List[Any]          =					logits.new_ones(logits.shape[-2]     )

       snake_case__					:	Optional[Any]          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	int          =					_calculate_bin_centers(UpperCAmelCase     )
       torch.sum(UpperCAmelCase     )
       snake_case__					:	Dict          =					logits.shape[-2]
       snake_case__					:	Optional[Any]          =					max(UpperCAmelCase    ,       19     )

       snake_case__					:	Optional[int]          =					1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       snake_case__					:	Tuple          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )

       snake_case__					:	int          =					1.0 / (1 + (bin_centers**2) / (da**2))
       snake_case__					:	Optional[int]          =					torch.sum(probs * tm_per_bin    ,       dim=-1     )

       snake_case__					:	Optional[Any]          =					residue_weights / (eps + residue_weights.sum())
       snake_case__					:	Any          =					torch.sum(predicted_tm_term * normed_residue_mask    ,       dim=-1     )

       snake_case__					:	Dict          =					per_alignment * residue_weights

       snake_case__					:	List[str]          =					(weighted == torch.max(UpperCAmelCase     )).nonzero()[0]
       return per_alignment[tuple(UpperCAmelCase     )]




