




'''simple docstring'''



from typing import Any

import numpy as np





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''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  lowerCAmelCase (				*__SCREAMING_SNAKE_CASE					) ->					list[str]:



            '''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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					tuple[Any, Any]:



            '''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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					np.ndarray:



            '''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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					np.ndarray:



            '''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  lowerCAmelCase (				self					) ->					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:
                      __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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					dict[str, float]:



            '''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 os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCAmelCase_    :					Optional[Any]					      =						get_logger(__name__)







def      _UpperCamelCase	(_lowerCamelCase							:       Dict		)->				Tuple:




     '''simple docstring'''





     return TestCommand(
         args.dataset				,		args.name				,		args.cache_dir				,		args.data_dir				,		args.all_configs				,		args.save_info or args.save_infos				,		args.ignore_verifications				,		args.force_redownload				,		args.clear_cache				,		)







class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: int	    =     False  # to tell pytest it's not a test class


       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					parser.add_parser('''test'''      ,					help='''Test dataset implementation.'''					)
            test_parser.add_argument('''--name'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Dataset processing name'''					)
            test_parser.add_argument(
                '''--cache_dir'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Cache directory where the datasets are stored.'''      ,					)
            test_parser.add_argument(
                '''--data_dir'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Can be used to specify a manual directory to get the files from.'''      ,					)
            test_parser.add_argument('''--all_configs'''      ,					action='''store_true'''      ,					help='''Test all dataset configurations'''					)
            test_parser.add_argument(
                '''--save_info'''      ,					action='''store_true'''      ,					help='''Save the dataset infos in the dataset card (README.md)'''					)
            test_parser.add_argument(
                '''--ignore_verifications'''      ,					action='''store_true'''      ,					help='''Run the test without checksums and splits checks.'''      ,					)
            test_parser.add_argument('''--force_redownload'''      ,					action='''store_true'''      ,					help='''Force dataset redownload'''					)
            test_parser.add_argument(
                '''--clear_cache'''      ,					action='''store_true'''      ,					help='''Remove downloaded files and cached datasets after each config test'''      ,					)
            # aliases
            test_parser.add_argument('''--save_infos'''      ,					action='''store_true'''      ,					help='''alias to save_info'''					)
            test_parser.add_argument('''dataset'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Name of the dataset to download'''					)
            test_parser.set_defaults(func=_test_command_factory					)


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					Any:



            '''simple docstring'''




            __snake_case         =					dataset
            __snake_case         =					name
            __snake_case         =					cache_dir
            __snake_case         =					data_dir
            __snake_case         =					all_configs
            __snake_case         =					save_infos
            __snake_case         =					ignore_verifications
            __snake_case         =					force_redownload
            __snake_case         =					clear_cache
            if clear_cache and not cache_dir:
                 print(
                     '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                     '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                     '''Please provide a --cache_dir that will be used to test the dataset script.'''					)
                 exit(1					)
            if save_infos:
                 __snake_case         =					True




       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            fl_logger().setLevel(__SCREAMING_SNAKE_CASE					)
            if self._name is not None and self._all_configs:
                 print('''Both parameters `config` and `all_configs` can\'t be used at once.'''					)
                 exit(1					)
            __snake_case   ,      __snake_case         =					self._dataset, self._name
            __snake_case         =					dataset_module_factory(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					import_main_class(module.module_path					)
            __snake_case         =					len(builder_cls.BUILDER_CONFIGS					) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

            def get_builders() -> Generator[DatasetBuilder, None, None]:
                 if self._all_configs and builder_cls.BUILDER_CONFIGS:
                      for i, config in enumerate(builder_cls.BUILDER_CONFIGS					):
                           if "config_name" in module.builder_kwargs:
                                yield builder_cls(
                                    cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)
                           else:
                                yield builder_cls(
                                    config_name=config.name      ,					cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)
                 else:
                      if "config_name" in module.builder_kwargs:
                           yield builder_cls(cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs					)
                      else:
                           yield builder_cls(
                               config_name=__SCREAMING_SNAKE_CASE      ,					cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)

            for j, builder in enumerate(get_builders()					):
                 print(F'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''					)
                 __snake_case         =					os.path.exists(
                     os.path.join(builder.get_imported_module_dir()      ,					datasets.config.DATASETDICT_INFOS_FILENAME					)					)  # record checksums only if we need to update a (deprecated) dataset_infos.json
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD      ,					verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS      ,					try_from_hf_gcs=__SCREAMING_SNAKE_CASE      ,					)
                 builder.as_dataset()
                 if self._save_infos:
                      builder._save_infos()

                 # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
                 # The dataset_infos are saved in the YAML part of the README.md

                 # Let's move it to the original directory of the dataset script, to allow the user to
                 # upload them on S3 at the same time afterwards.
                 if self._save_infos:
                      __snake_case         =					os.path.join(builder_cls.get_imported_module_dir()      ,					'''README.md'''					)
                      __snake_case         =					Path(__SCREAMING_SNAKE_CASE					).name + '''.py'''
                      __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                      if os.path.isfile(__SCREAMING_SNAKE_CASE					):
                           __snake_case         =					os.path.dirname(__SCREAMING_SNAKE_CASE					)
                      elif os.path.isfile(__SCREAMING_SNAKE_CASE					):
                           __snake_case         =					path
                      elif os.path.isdir(__SCREAMING_SNAKE_CASE					):  # for local directories containing only data files
                           __snake_case         =					path
                      else:  # in case of a remote dataset
                           __snake_case         =					None
                           print(F'''Dataset card saved at {dataset_readme_path}'''					)

                      # Move dataset_info back to the user
                      if dataset_dir is not None:
                           __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					'''README.md'''					)
                           copyfile(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                           print(F'''Dataset card saved at {user_dataset_readme_path}'''					)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
                 if self._clear_cache:
                      if os.path.isdir(builder._cache_dir					):
                           logger.warning(F'''Clearing cache at {builder._cache_dir}'''					)
                           rmtree(builder._cache_dir					)
                      __snake_case         =					os.path.join(self._cache_dir      ,					datasets.config.DOWNLOADED_DATASETS_DIR					)
                      if os.path.isdir(__SCREAMING_SNAKE_CASE					):
                           logger.warning(F'''Clearing cache at {download_dir}'''					)
                           rmtree(__SCREAMING_SNAKE_CASE					)

            print('''Test successful.'''					)






