





'''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_         =			False
lowercase_         =			Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase_         =			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, 2_048), 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			,		A=sys.argv[1:]	)		->       Optional[int]:
          _SCREAMING_SNAKE_CASE     =      None
          _SCREAMING_SNAKE_CASE     =      None
          _SCREAMING_SNAKE_CASE     =      None
          _SCREAMING_SNAKE_CASE     =      1
          _SCREAMING_SNAKE_CASE     =      getopt.getopt(A			,		"""i:o:b:s"""			,		["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]	)
          for opt, arg in opts:
              if opt in ("-i", "--inputdir"):
                  _SCREAMING_SNAKE_CASE     =      arg
              elif opt in ("-o", "--outfile"):
                  _SCREAMING_SNAKE_CASE     =      arg
              elif opt in ("-b", "--batch_size"):
                  _SCREAMING_SNAKE_CASE     =      int(A	)
              elif opt in ("-s", "--subset_list"):
                  _SCREAMING_SNAKE_CASE     =      arg

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

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


      def       snake_case_(		self			,		A	)		->       Dict:
          _SCREAMING_SNAKE_CASE     =      int(file.split("""."""	)[0].split("""_"""	)[-1]	)
          _SCREAMING_SNAKE_CASE     =      os.path.join(self.inputdir			,		A	)
          return (img_id, filepath)


      @property
      def       snake_case_(		self	)		->       Dict:
          _SCREAMING_SNAKE_CASE     =      []
          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(A	)	)
              if len(A	) == self.batch:
                  _SCREAMING_SNAKE_CASE     =      batch
                  _SCREAMING_SNAKE_CASE     =      []
                  yield list(map(A			,		zip(*A	)	)	)

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





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





def lowerCamelCase						(					__lowerCamelCase			:   Optional[int]   )						->Any:






    try:
        _SCREAMING_SNAKE_CASE     =      json.load(__lowerCamelCase   )
        try:
            _SCREAMING_SNAKE_CASE     =      list(data.keys()   )
        except Exception:
            _SCREAMING_SNAKE_CASE     =      [d['img_id'] for d in data]
    except Exception:
        try:
            _SCREAMING_SNAKE_CASE     =      eval(stream.read()   )
        except Exception:
            _SCREAMING_SNAKE_CASE     =      stream.read().split("""\n"""   )
    return data


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










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A	)		->       Optional[int]:
        if isinstance(A			,		A	):  # Initializes a matrix identical to the one provided.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      len(A	)
        else:  # Initializes a square matrix of the given size and set values to zero.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      [[0 for _ in range(self.n	)] for _ in range(self.n	)]





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      Matrix(self.n	)
        for i in range(self.n	):
            for j in range(self.n	):
                for k in range(self.n	):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

    while n > 0:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      fn_a + fn_a, fn_a
        n -= 1

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



