





"""simple docstring"""


from typing import Any

import numpy as np



class    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if np.any(tableau[:, -1]      ,    where=tableau[:, -1] < 0   ):
         raise ValueError("RHS must be > 0"   )

        snake_case__										= tableau
        snake_case__							,      snake_case__										= tableau.shape

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

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

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

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

        # Objectives for each stage
        snake_case__										= ["max"]

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

        snake_case__										= [""]

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

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

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	*__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if len(__SCREAMING_SNAKE_CASE   ) != 3:
         raise ValueError("Must provide n_vars, n_slack, and n_art_vars"   )

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

        # decision | slack | artificial
        snake_case__										= ["x", "s", "a"]
        snake_case__										= []
        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      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.objectives[-1]

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

        # RHS
        snake_case__										= tableau[s, -1]

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

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= tableau[row_idx].copy()

        snake_case__										= 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
        snake_case__										= piv_row
        return tableau

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        self.objectives.pop()

        if not self.objectives:
         return tableau

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

        # Delete the artificial variable columns
        snake_case__										= np.delete(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    axis=1   )

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

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        for _ in range(100   ):
         # Completion of each stage removes an objective. If both stages
         # are complete, then no objectives are left
         if not self.objectives:
          snake_case__										= 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   )

         snake_case__							,      snake_case__										= 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
          snake_case__										= self.change_stage(self.tableau   )
         else:
          snake_case__										= self.pivot(self.tableau      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        return {}

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= {"P": abs(tableau[0, -1]   )}

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

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()








"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

