





from typing import Any

import numpy as np







class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   None:
     # Check if RHS is negative
     if np.any(tableau[:, -1]    ,							where=tableau[:, -1] < 0      ):
        raise ValueError('''RHS must be > 0'''      )

     __A    =  tableau
     __A   ,		__A    =  tableau.shape

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

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

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

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

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

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

     __A    =  ['''''']

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

     # Does objective row only contain (non)-negative values?
     __A    =  False

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   *UpperCAmelCase      )->   list[str]:
     if len(UpperCAmelCase      ) != 3:
        raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''      )

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

     # decision | slack | artificial
     __A    =  ['''x''', '''s''', '''a''']
     __A    =  []
     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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   tuple[Any, Any]:
     __A    =  self.objectives[-1]

     # Find entries of highest magnitude in objective rows
     __A    =  (objective == '''min''') - (objective == '''max''')
     __A    =  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:
        __A    =  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
     __A    =  slice(self.n_stages    ,							self.n_rows      )

     # RHS
     __A    =  tableau[s, -1]

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

     # Array filled with nans
     __A    =  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
     __A    =  np.divide(UpperCAmelCase    ,							UpperCAmelCase    ,							out=UpperCAmelCase    ,							where=divisor > 0      )

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

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   np.ndarray:
     __A    =  tableau[row_idx].copy()

     __A    =  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
     __A    =  piv_row
     return tableau

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   np.ndarray:
     self.objectives.pop()

     if not self.objectives:
        return tableau

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

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

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

     __A    =  1
     self.n_rows -= 1
     __A    =  0
     __A    =  False
     return tableau

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   dict[Any, Any]:
     # Stop simplex algorithm from cycling.
     for _ in range(1_00      ):
        # Completion of each stage removes an objective. If both stages
        # are complete, then no objectives are left
        if not self.objectives:
           __A    =  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      )

        __A   ,		__A    =  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
           __A    =  self.change_stage(self.tableau      )
        else:
           __A    =  self.pivot(self.tableau    ,							UpperCAmelCase    ,							UpperCAmelCase      )
     return {}





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   dict[str, float]:
     __A    =  {'''P''': abs(tableau[0, -1]      )}

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

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

        # If there is only one nonzero value in column, which is one
        if n_nonzero == nonzero_val == 1:
           __A    =  tableau[nonzero_rowidx, -1]
           __A    =  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(UpperCAmelCase    ,							0      )
     return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()








import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_UpperCamelCase       :				Tuple           =			logging.get_logger(__name__)







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def __init__(   self    ,							*UpperCAmelCase    ,							**UpperCAmelCase      )->   None:
     warnings.warn(
         '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use DetrImageProcessor instead.'''    ,							UpperCAmelCase    ,							)
     super().__init__(*UpperCAmelCase    ,							**UpperCAmelCase      )

