


'''simple docstring'''







import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath('''project_euler''')
lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
    lowerCAmelCase_    	=							json.load(file_handle)







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    importlib.util.spec_from_file_location(file_path.name			,str(UpperCAmelCase	)	)
   A__         =    importlib.util.module_from_spec(UpperCAmelCase	)  # type: ignore
   spec.loader.exec_module(UpperCAmelCase	)  # type: ignore
   return module







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
      if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'	):
         continue
      for file_path in problem_dir_path.iterdir():
         if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')	):
            continue
         solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   with open(os.environ['GITHUB_EVENT_PATH']	) as file:
      A__         =    json.load(UpperCAmelCase	)
   return event["pull_request"]["url"] + "/files"







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   A__         =    {
       'Accept': 'application/vnd.github.v3+json',
       'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
   }
   A__         =    requests.get(get_files_url()			,headers=UpperCAmelCase	).json()
   for file in files:
      A__         =    pathlib.Path.cwd().joinpath(file['filename']	)
      if (
          filepath.suffix != ".py"
          or filepath.name.startswith(('_', 'test')	)
          or not filepath.name.startswith('sol'	)
      ):
         continue
      solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   if os.environ.get('CI'	) and os.environ.get('GITHUB_EVENT_NAME'	) == "pull_request":
      # Return only if there are any, otherwise default to all solutions
      if filepaths := added_solution_file_path():
         return filepaths
   return all_solution_file_paths()







@pytest.mark.parametrize(
    'solution_path'			,collect_solution_file_paths()			,ids=lambda UpperCAmelCase	: F"""{path.parent.name}/{path.name}"""			,)
def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    solution_path.parent.name[8:].zfill(3	)
   A__         =    PROBLEM_ANSWERS[problem_number]
   A__         =    convert_path_to_module(UpperCAmelCase	)
   A__         =    str(solution_module.solution()	)  # type: ignore
   A__         =    hashlib.shaaaa(answer.encode()	).hexdigest()
   assert (
       answer == expected
   ), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""








'''simple docstring'''







from typing import Any

import numpy as np

class   _snake_case:






 def __init__(self					:     int						,		a					:     np.ndarray						,		a					:     int						)					->      None:






    """simple docstring"""


    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 	_UpperCamelCase   (*a					:     int						)					->      list[str]:






    """simple docstring"""


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

    if not all(x >= 0 and isinstance(a						,		a						) 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 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						)					->      tuple[Any, Any]:






    """simple docstring"""


    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(a						,		a						,		out=a						,		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(a						) + self.n_stages
    return row_idx, col_idx






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     int						,		a					:     int						)					->      np.ndarray:






    """simple docstring"""


    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 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     np.ndarray						)					->      np.ndarray:






    """simple docstring"""


    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(a						,		a						,		axis=1						)

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

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






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      dict[Any, Any]:






    """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:
          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						,		a						,		a						)
    return {}






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     list[str]						)					->      dict[str, float]:






    """simple docstring"""


    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(a						,		0						)
    return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()


