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

        a  :    List[str]				=						CONFIG
        if torch.cuda.is_available():
             a  :    List[Any]				=						'''cuda'''
        a  :    str				=						os.path.realpath(__UpperCamelCase    )
        a  :    Union[str, Any]				=						os.path.realpath(__UpperCamelCase    )
        a  :    Tuple				=						Preprocess(self.config    )
        a  :    List[str]				=						GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''							,     config=self.config    )
        a  :    Dict				=						batch_size if batch_size != 0 else 1
        a  :    int				=						DEFAULT_SCHEMA
   def _lowercase(    self							,     lowerCAmelCase_    )	->Dict:
        a  :    List[Any]				=						int(file.split('''.'''    )[0].split('''_'''    )[-1]    )
        a  :    Union[str, Any]				=						os.path.join(self.inputdir							,     __UpperCamelCase    )
        return (img_id, filepath)
   @property
   def _lowercase(    self    )	->int:
        a  :    Optional[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(__UpperCamelCase    )    )
             if len(__UpperCamelCase    ) == self.batch:
                  a  :    Tuple				=						batch
                  a  :    Optional[Any]				=						[]
                  yield list(map(__UpperCamelCase							,     zip(*__UpperCamelCase    )    )    )

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


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







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:







     try:
          a  :    Union[str, Any]				=						json.load(lowercase_       )
          try:
               a  :    Optional[int]				=						list(data.keys()       )
          except Exception:
               a  :    List[str]				=						[d['''img_id'''] for d in data]
     except Exception:
          try:
               a  :    List[str]				=						eval(stream.read()       )
          except Exception:
               a  :    Optional[int]				=						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 __future__ import annotations







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	list[float | int]:
     if not all((nth_term, start_term_a, common_ratio_r)       ):
          return []
     a  :    list[float | int]				=						[]
     a  :    str				=						1
     a  :    Dict				=						common_ratio_r
     for _ in range(int(lowerCAmelCase_       )       ):
          if not series:
               series.append(lowerCAmelCase_       )
          else:
               power += 1
               series.append(float(start_term_a * multiple       )       )
               a  :    Dict				=						pow(float(lowerCAmelCase_       )						,      lowerCAmelCase_       )
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase							   =      float(input("""Enter the last number (n term) of the Geometric Series"""))
  UpperCAmelCase							   =      float(input("""Enter the starting term (a) of the Geometric Series"""))
  UpperCAmelCase							   =      float(
      input("""Enter the common ratio between two terms (r) of the Geometric Series""")
  )
  print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
  print(geometric_series(nth_term, start_term_a, common_ratio_r))