



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       _snake_case				:






   '''simple docstring'''






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

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


   def       A__    (    self:     str  ,lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					int(file.split("""."""							)[0].split("""_"""							)[-1]							)
       UpperCAmelCase_     :       int  		=					os.path.join(self.inputdir  ,lowerCamelCase_							)
       return (img_id, filepath)


   @property
   def       A__    (    self:     Tuple							)	->      List[str]:
       UpperCAmelCase_     :       int  		=					[]
       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:
               UpperCAmelCase_     :       Tuple  		=					batch
               UpperCAmelCase_     :       Union[str, Any]  		=					[]
               yield list(map(lowerCamelCase_  ,zip(*lowerCamelCase_							)							)							)

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






   def __call__(    self:     Any							)	->      Optional[Any]:
       # make writer
       if not TEST:
           UpperCAmelCase_     :       Tuple  		=					datasets.ArrowWriter(features=self.schema  ,path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.preprocess(lowerCamelCase_							)
           UpperCAmelCase_     :       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"""  ,)
           UpperCAmelCase_     :       Dict  		=					output_dict.pop("""normalized_boxes"""							)
           if not TEST:
               UpperCAmelCase_     :       str  		=					np.array(lowerCamelCase_							)
               UpperCAmelCase_     :       int  		=					self.schema.encode_batch(lowerCamelCase_							)
               writer.write_batch(lowerCamelCase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					writer.finalize()
           print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''							)







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       Optional[int]  		=					json.load(_a    )
        try:
            UpperCAmelCase_     :       List[Any]  		=					list(data.keys()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					[d["""img_id"""] for d in data]
    except Exception:
        try:
            UpperCAmelCase_     :       str  		=					eval(stream.read()    )
        except Exception:
            UpperCAmelCase_     :       List[Any]  		=					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 math import cos, sin







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					float(_a    )
    return [[scaling_factor * int(x == y    ) for x in range(2    )] for y in range(2    )]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cos(_a    ), sin(_a    )
    return [[c, -s], [s, c]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Dict  		=					c * s
    return [[c * c, cs], [cs, s * s]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					c * s
    return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"    {scaling(5) = }")
print(F"  {rotation(45) = }")
print(F"{projection(45) = }")






print(F"{reflection(45) = }")
