


from typing import Any

import numpy as np




class A  :






      def __init__( self						:					Union[str, Any]      ,       lowercase_						:					np.ndarray      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




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

             _lowerCamelCase	:		List[str]          =tableau
             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =tableau.shape

             # Number of decision variables x1, x2, x3...
             _lowerCamelCase	:		Dict          =n_vars

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

             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             _lowerCamelCase	:		Optional[Any]          =(self.n_art_vars > 0) + 1

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

             # Objectives for each stage
             _lowerCamelCase	:		Tuple          =['max']

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

             _lowerCamelCase	:		Optional[Any]          =['']

             # Index of current pivot row and column
             _lowerCamelCase	:		Optional[Any]          =None
             _lowerCamelCase	:		Any          =None

             # Does objective row only contain (non)-negative values?
             _lowerCamelCase	:		Optional[Any]          =False






      @staticmethod
      def 	lowerCamelCase	( *lowercase_						:					int			) ->						list[str]:
             """simple docstring"""




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

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

             # decision | slack | artificial
             _lowerCamelCase	:		Union[str, Any]          =['x', 's', 'a']
             _lowerCamelCase	:		Optional[Any]          =[]
             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 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					np.ndarray			) ->						tuple[Any, Any]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _lowerCamelCase	:		Optional[int]          =(objective == 'min') - (objective == 'max')
             _lowerCamelCase	:		Dict          =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:
                    _lowerCamelCase	:		Optional[int]          =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
             _lowerCamelCase	:		Dict          =slice(self.n_stages      ,       self.n_rows			)

             # RHS
             _lowerCamelCase	:		Any          =tableau[s, -1]

             # Elements of pivot column within slice
             _lowerCamelCase	:		Optional[int]          =tableau[s, col_idx]

             # Array filled with nans
             _lowerCamelCase	:		int          =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
             _lowerCamelCase	:		List[Any]          =np.divide(lowercase_      ,       lowercase_      ,       out=lowercase_      ,       where=divisor > 0			)

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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					np.ndarray      ,       lowercase_						:					int      ,       lowercase_						:					int			) ->						np.ndarray:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =tableau[row_idx].copy()

             _lowerCamelCase	:		List[str]          =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
             _lowerCamelCase	:		Union[str, Any]          =piv_row
             return tableau






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					np.ndarray			) ->						np.ndarray:
             """simple docstring"""




             self.objectives.pop()

             if not self.objectives:
                    return tableau

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

             # Delete the artificial variable columns
             _lowerCamelCase	:		List[str]          =np.delete(lowercase_      ,       lowercase_      ,       axis=1			)

             # Delete the objective row of the first stage
             _lowerCamelCase	:		Any          =np.delete(lowercase_      ,       0      ,       axis=0			)

             _lowerCamelCase	:		str          =1
             self.n_rows -= 1
             _lowerCamelCase	:		int          =0
             _lowerCamelCase	:		str          =False
             return tableau






      def 	lowerCamelCase	( self						:					Dict			) ->						dict[Any, Any]:
             """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:
                           _lowerCamelCase	:		Optional[Any]          =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			)

                    _lowerCamelCase     ,					_lowerCamelCase	:		List[str]          =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
                           _lowerCamelCase	:		Optional[int]          =self.change_stage(self.tableau			)
                    else:
                           _lowerCamelCase	:		Optional[int]          =self.pivot(self.tableau      ,       lowercase_      ,       lowercase_			)
             return {}






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					np.ndarray      ,       lowercase_						:					list[str]			) ->						dict[str, float]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          ={'P': abs(tableau[0, -1]			)}

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

                    # First entry in the nonzero ids
                    _lowerCamelCase	:		Optional[int]          =nonzero[0][0]
                    _lowerCamelCase	:		Any          =tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _lowerCamelCase	:		List[Any]          =tableau[nonzero_rowidx, -1]
                           _lowerCamelCase	:		Optional[int]          =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(lowercase_      ,       0			)
             return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase						 =	logging.getLogger()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =argparse.ArgumentParser()
       parser.add_argument('-f'					)
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()
       return args.f




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Any          =logging.StreamHandler(sys.stdout			)
             logger.addHandler(lowercase_			)

             _lowerCamelCase	:		Dict          =self.get_auto_remove_tmp_dir()
             _lowerCamelCase	:		int          =F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

             with patch.object(lowercase_      ,       'argv'      ,       lowercase_			):
                    _lowerCamelCase	:		Dict          =run_glue_with_pabee.main()
                    for value in result.values():
                           self.assertGreaterEqual(lowercase_      ,       0.75			)


