code: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import Any
  
  import numpy as np
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  int							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             if np.any(tableau[:, -1]							,						where=tableau[:, -1] < 0   ):
                    raise ValueError('RHS must be > 0'   )
  
             __magic_name__										=						tableau
             __magic_name__							,     __magic_name__										=						tableau.shape
  
             # Number of decision variables x1, x2, x3...
             __magic_name__										=						n_vars
  
             # Number of artificial variables to be minimised
             __magic_name__										=						len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )
  
             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             __magic_name__										=						(self.n_art_vars > 0) + 1
  
             # Number of slack variables added to make inequalities into equalities
             __magic_name__										=						self.n_rows - self.n_stages
  
             # Objectives for each stage
             __magic_name__										=						['max']
  
             # In two stage simplex, first minimise then maximise
             if self.n_art_vars:
                    self.objectives.append('min'   )
  
             __magic_name__										=						['']
  
             # Index of current pivot row and column
             __magic_name__										=						None
             __magic_name__										=						None
  
             # Does objective row only contain (non)-negative values?
             __magic_name__										=						False
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		*UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if len(__snake_case   ) != 3:
                    raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )
  
             if not all(x >= 0 and isinstance(__snake_case							,						__snake_case   ) for x in args   ):
                    raise ValueError('All arguments must be non-negative integers'   )
  
             # decision | slack | artificial
             __magic_name__										=						['x', 's', 'a']
             __magic_name__										=						[]
             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 							a__ (		self   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.objectives[-1]
  
             # Find entries of highest magnitude in objective rows
             __magic_name__										=						(objective == 'min') - (objective == 'max')
             __magic_name__										=						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:
                    __magic_name__										=						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
             __magic_name__										=						slice(self.n_stages							,						self.n_rows   )
  
             # RHS
             __magic_name__										=						tableau[s, -1]
  
             # Elements of pivot column within slice
             __magic_name__										=						tableau[s, col_idx]
  
             # Array filled with nans
             __magic_name__										=						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
             __magic_name__										=						np.divide(__snake_case							,						__snake_case							,						out=__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
             __magic_name__										=						np.nanargmin(__snake_case   ) + self.n_stages
             return row_idx, col_idx
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						tableau[row_idx].copy()
  
             __magic_name__										=						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
             __magic_name__										=						piv_row
             return tableau
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.objectives.pop()
  
             if not self.objectives:
                    return tableau
  
             # Slice containing ids for artificial columns
             __magic_name__										=						slice(-self.n_art_vars - 1							,						-1   )
  
             # Delete the artificial variable columns
             __magic_name__										=						np.delete(__snake_case							,						__snake_case							,						axis=1   )
  
             # Delete the objective row of the first stage
             __magic_name__										=						np.delete(__snake_case							,						0							,						axis=0   )
  
             __magic_name__										=						1
             self.n_rows -= 1
             __magic_name__										=						0
             __magic_name__										=						False
             return tableau
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             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:
                           __magic_name__										=						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   )
  
                    __magic_name__							,     __magic_name__										=						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
                           __magic_name__										=						self.change_stage(self.tableau   )
                    else:
                           __magic_name__										=						self.pivot(self.tableau							,						__snake_case							,						__snake_case   )
             return {}
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						{'P': abs(tableau[0, -1]   )}
  
             for i in range(self.n_vars   ):
                    # Gives ids of nonzero entries in the ith column
                    __magic_name__										=						np.nonzero(tableau[:, i]   )
                    __magic_name__										=						len(nonzero[0]   )
  
                    # First entry in the nonzero ids
                    __magic_name__										=						nonzero[0][0]
                    __magic_name__										=						tableau[nonzero_rowidx, i]
  
                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           __magic_name__										=						tableau[nonzero_rowidx, -1]
                           __magic_name__										=						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(__snake_case							,						0   )
             return output_dict
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						np.where(remainder == value     )
                       # if we need_to_add less than what's in inds
                       # we draw randomly from them.
                       # if we need to add more, we add them all and
                       # go to the next value
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 2:
                raise ValueError('Minimum class count error'     )
         if n_train < n_classes:
                raise ValueError(
                    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
         if n_test < n_classes:
                raise ValueError(
                    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                       train.extend(perm_indices_class_i[: n_i[i]]     )
                       test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test