

'''simple docstring'''




from typing import Any

import numpy as np



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_       )							->			None:


    """simple docstring"""
    # Check if RHS is negative
    if np.any(tableau[:, -1]				,	where=tableau[:, -1] < 0       ):
      raise ValueError('RHS must be > 0'       )

    UpperCAmelCase  							=		tableau
    UpperCAmelCase							, UpperCAmelCase  							=		tableau.shape

    # Number of decision variables x1, x2, x3...
    UpperCAmelCase  							=		n_vars

    # Number of artificial variables to be minimised
    UpperCAmelCase  							=		len(np.where(tableau[self.n_vars : -1] == -1       )[0]       )

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    UpperCAmelCase  							=		(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    UpperCAmelCase  							=		self.n_rows - self.n_stages

    # Objectives for each stage
    UpperCAmelCase  							=		['max']

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
      self.objectives.append('min'       )

    UpperCAmelCase  							=		['']

    # Index of current pivot row and column
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None

    # Does objective row only contain (non)-negative values?
    UpperCAmelCase  							=		False
  @staticmethod
  def   snake_case_     (       *a_       )							->			list[str]:


    """simple docstring"""
    if len(a_       ) != 3:
      raise ValueError('Must provide n_vars, n_slack, and n_art_vars'       )

    if not all(x >= 0 and isinstance(a_				,	a_       ) for x in args       ):
      raise ValueError('All arguments must be non-negative integers'       )

    # decision | slack | artificial
    UpperCAmelCase  							=		['x', 's', 'a']
    UpperCAmelCase  							=		[]
    for i in range(3       ):
      for j in range(args[i]       ):
        titles.append(string_starts[i] + str(j + 1       )       )
    titles.append('RHS'       )
    return titles
  def   snake_case_     (       self				,	a_       )							->			tuple[Any, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    UpperCAmelCase  							=		(objective == 'min') - (objective == 'max')
    UpperCAmelCase  							=		np.argmax(sign * tableau[0, : self.n_vars]       )

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
      UpperCAmelCase  							=		True
      return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    UpperCAmelCase  							=		slice(self.n_stages				,	self.n_rows       )

    # RHS
    UpperCAmelCase  							=		tableau[s, -1]

    # Elements of pivot column within slice
    UpperCAmelCase  							=		tableau[s, col_idx]

    # Array filled with nans
    UpperCAmelCase  							=		np.full(self.n_rows - self.n_stages				,	np.nan       )

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    UpperCAmelCase  							=		np.divide(a_				,	a_				,	out=a_				,	where=divisor > 0       )

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    UpperCAmelCase  							=		np.nanargmin(a_       ) + self.n_stages
    return row_idx, col_idx
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase  							=		tableau[row_idx].copy()

    UpperCAmelCase  							=		piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]       ):
      tableau[idx] += -coeff * piv_row
    UpperCAmelCase  							=		piv_row
    return tableau
  def   snake_case_     (       self				,	a_       )							->			np.ndarray:


    """simple docstring"""
    self.objectives.pop()

    if not self.objectives:
      return tableau

    # Slice containing ids for artificial columns
    UpperCAmelCase  							=		slice(-self.n_art_vars - 1				,	-1       )

    # Delete the artificial variable columns
    UpperCAmelCase  							=		np.delete(a_				,	a_				,	axis=1       )

    # Delete the objective row of the first stage
    UpperCAmelCase  							=		np.delete(a_				,	0				,	axis=0       )

    UpperCAmelCase  							=		1
    self.n_rows -= 1
    UpperCAmelCase  							=		0
    UpperCAmelCase  							=		False
    return tableau
  def   snake_case_     (       self       )							->			dict[Any, Any]:


    """simple docstring"""
    # Stop simplex algorithm from cycling.
    for _ in range(1_0_0       ):
      # Completion of each stage removes an objective. If both stages
      # are complete, then no objectives are left
      if not self.objectives:
        UpperCAmelCase  							=		self.generate_col_titles(
            self.n_vars				,	self.n_slack				,	self.n_art_vars       )

        # Find the values of each variable at optimal solution
        return self.interpret_tableau(self.tableau				,	self.col_titles       )

      UpperCAmelCase							, UpperCAmelCase  							=		self.find_pivot(self.tableau       )

      # If there are no more negative values in objective row
      if self.stop_iter:
        # Delete artificial variable columns and rows. Update attributes
        UpperCAmelCase  							=		self.change_stage(self.tableau       )
      else:
        UpperCAmelCase  							=		self.pivot(self.tableau				,	a_				,	a_       )
    return {}





  def   snake_case_     (       self				,	a_				,	a_       )							->			dict[str, float]:


    """simple docstring"""
    UpperCAmelCase  							=		{'P': abs(tableau[0, -1]       )}

    for i in range(self.n_vars       ):
      # Gives ids of nonzero entries in the ith column
      UpperCAmelCase  							=		np.nonzero(tableau[:, i]       )
      UpperCAmelCase  							=		len(nonzero[0]       )

      # First entry in the nonzero ids
      UpperCAmelCase  							=		nonzero[0][0]
      UpperCAmelCase  							=		tableau[nonzero_rowidx, i]

      # If there is only one nonzero value in column, which is one
      if n_nonzero == nonzero_val == 1:
        UpperCAmelCase  							=		tableau[nonzero_rowidx, -1]
        UpperCAmelCase  							=		rhs_val

        # Check for basic variables
    for title in col_titles:
      # Don't add RHS or slack variables to output dict
      if title[0] not in "R-s-a":
        output_dict.setdefault(a_				,	0       )
    return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''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
_a			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  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     lowercase_		:

  '''simple docstring'''





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


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		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:
        UpperCAmelCase  							=		{self._vqa_file_split()[0] for x in tryload(a_       )}
    else:
      UpperCAmelCase  							=		None

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    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:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

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





  def __call__(       self       )							->			str:


    """simple docstring"""
    # make writer
    if not TEST:
      UpperCAmelCase  							=		datasets.ArrowWriter(features=self.schema				,	path=self.outputfile       )
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
      UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		self.preprocess(a_       )
      UpperCAmelCase  							=		self.model(
          a_				,	a_				,	scales_yx=a_				,	padding='max_detections'				,	max_detections=self.config.MAX_DETECTIONS				,	pad_value=0				,	return_tensors='np'				,	location='cpu'				,	)
      UpperCAmelCase  							=		output_dict.pop('normalized_boxes'       )
      if not TEST:
        UpperCAmelCase  							=		np.array(a_       )
        UpperCAmelCase  							=		self.schema.encode_batch(a_       )
        writer.write_batch(a_       )
      if TEST:
        break
      # finalizer the writer
    if not TEST:
      UpperCAmelCase							, UpperCAmelCase  							=		writer.finalize()
      print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


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


