




'''simple docstring'''




from typing import Any

import numpy as np

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Any						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						*_UpperCAmelCase    :	int ):






   """simple docstring"""






   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
   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 							SCREAMING_SNAKE_CASE__			(						self    :	str						,		_UpperCAmelCase    :	np.ndarray ):






   """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(_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
   UpperCAmelCase__										=    np.nanargmin(_UpperCAmelCase ) + self.n_stages
   return row_idx, col_idx


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	int ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any]						,		_UpperCAmelCase    :	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(_UpperCAmelCase						,		_UpperCAmelCase						,		axis=1 )

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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   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:
     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						,		_UpperCAmelCase						,		_UpperCAmelCase )
   return {}



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	list[str] ):






   """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(_UpperCAmelCase						,		0 )
   return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''simple docstring'''




import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase_       				=      2 << 2_0  # 2mb
UpperCAmelCase_       				=      get_logger(__name__)

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self._options.get_all_options().get("""runner""" )
   return runner in [None, "DirectRunner", "PortableRunner"]

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if fs.exists(SCREAMING_SNAKE_CASE__       ):
  if force_upload:
   logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.create(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""rb"""       ) as local_file:
   UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    remote_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Dict=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if os.path.exists(SCREAMING_SNAKE_CASE__       ):
  if force_download:
   logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.open(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""wb"""       ) as local_file:
   UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    local_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
