
'''simple docstring'''

from typing import Any

import numpy as np







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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

							UpperCAmelCase		:							Tuple        =       tableau
							UpperCAmelCase		:							Union[str, Any]        =       tableau.shape

							# Number of decision variables x1, x2, x3...
							UpperCAmelCase		:							Optional[Any]        =       n_vars

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

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

							# Number of slack variables added to make inequalities into equalities
							UpperCAmelCase		:							Optional[Any]        =       self.n_rows - self.n_stages

							# Objectives for each stage
							UpperCAmelCase		:							Optional[int]        =       ["max"]

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

							UpperCAmelCase		:							Optional[int]        =       [""]

							# Index of current pivot row and column
							UpperCAmelCase		:							Optional[int]        =       None
							UpperCAmelCase		:							List[Any]        =       None

							# Does objective row only contain (non)-negative values?
							UpperCAmelCase		:							Any        =       False







		@staticmethod
		def 				A_	(			*snake_case      ):


							'''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
							UpperCAmelCase		:							List[Any]        =       ["x", "s", "a"]
							UpperCAmelCase		:							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 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       self.objectives[-1]

							# Find entries of highest magnitude in objective rows
							UpperCAmelCase		:							Union[str, Any]        =       (objective == "min") - (objective == "max")
							UpperCAmelCase		:							List[str]        =       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		:							Any        =       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		:							Tuple        =       slice(self.n_stages   ,      self.n_rows      )

							# RHS
							UpperCAmelCase		:							Union[str, Any]        =       tableau[s, -1]

							# Elements of pivot column within slice
							UpperCAmelCase		:							List[Any]        =       tableau[s, col_idx]

							# Array filled with nans
							UpperCAmelCase		:							Dict        =       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		:							Union[str, Any]        =       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
							UpperCAmelCase		:							Optional[int]        =       np.nanargmin(snake_case      ) + self.n_stages
							return row_idx, col_idx







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       tableau[row_idx].copy()

							UpperCAmelCase		:							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
							UpperCAmelCase		:							Any        =       piv_row
							return tableau







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.objectives.pop()

							if not self.objectives:
												return tableau

							# Slice containing ids for artificial columns
							UpperCAmelCase		:							List[Any]        =       slice(-self.n_art_vars - 1   ,      -1      )

							# Delete the artificial variable columns
							UpperCAmelCase		:							Union[str, Any]        =       np.delete(snake_case   ,      snake_case   ,      axis=1      )

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

							UpperCAmelCase		:							Optional[int]        =       1
							self.n_rows -= 1
							UpperCAmelCase		:							str        =       0
							UpperCAmelCase		:							List[Any]        =       False
							return tableau







		def 				A_	(			self      ):


							'''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:
																	UpperCAmelCase		:							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      )

												UpperCAmelCase		:							List[Any]        =       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		:							List[str]        =       self.change_stage(self.tableau      )
												else:
																	UpperCAmelCase		:							str        =       self.pivot(self.tableau   ,      snake_case   ,      snake_case      )
							return {}







		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       {"P": abs(tableau[0, -1]      )}

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

												# First entry in the nonzero ids
												UpperCAmelCase		:							str        =       nonzero[0][0]
												UpperCAmelCase		:							List[str]        =       tableau[nonzero_rowidx, i]

												# If there is only one nonzero value in column, which is one
												if n_nonzero == nonzero_val == 1:
																	UpperCAmelCase		:							Tuple        =       tableau[nonzero_rowidx, -1]
																	UpperCAmelCase		:							Optional[Any]        =       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()



'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(
							    snake_case   ,      split=snake_case   ,      features=snake_case   ,      cache_dir=snake_case   ,      keep_in_memory=snake_case   ,      streaming=snake_case   ,      num_proc=snake_case   ,      **snake_case   ,      )
							UpperCAmelCase		:							str        =       path_or_paths if isinstance(snake_case   ,      snake_case      ) else {self.split: path_or_paths}
							UpperCAmelCase		:							int        =       Csv(
							    cache_dir=snake_case   ,      data_files=snake_case   ,      features=snake_case   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							if self.streaming:
												UpperCAmelCase		:							Optional[Any]        =       self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							int        =       None

												self.builder.download_and_prepare(
												    download_config=snake_case   ,      download_mode=snake_case   ,      verification_mode=snake_case   ,      base_path=snake_case   ,      num_proc=self.num_proc   ,      )
												UpperCAmelCase		:							Tuple        =       self.builder.as_dataset(
												    split=self.split   ,      verification_mode=snake_case   ,      in_memory=self.keep_in_memory      )
							return dataset







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."      )

							UpperCAmelCase		:							Tuple        =       dataset
							UpperCAmelCase		:							Dict        =       path_or_buf
							UpperCAmelCase		:							List[Any]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							str        =       num_proc
							UpperCAmelCase		:							List[Any]        =       "utf-8"
							UpperCAmelCase		:							Tuple        =       to_csv_kwargs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.to_csv_kwargs.pop("path_or_buf"   ,      snake_case      )
							UpperCAmelCase		:							Any        =       self.to_csv_kwargs.pop("header"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       self.to_csv_kwargs.pop("index"   ,      snake_case      )

							if isinstance(self.path_or_buf   ,      (str, bytes, os.PathLike)      ):
												with open(self.path_or_buf   ,      "wb+"      ) as buffer:
																	UpperCAmelCase		:							Tuple        =       self._write(file_obj=snake_case   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							else:
												UpperCAmelCase		:							Any        =       self._write(file_obj=self.path_or_buf   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							return written







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							Optional[int]        =       args

							UpperCAmelCase		:							List[str]        =       query_table(
							    table=self.dataset.data   ,      key=slice(snake_case   ,      offset + self.batch_size      )   ,      indices=self.dataset._indices   ,      )
							UpperCAmelCase		:							int        =       batch.to_pandas().to_csv(
							    path_or_buf=snake_case   ,      header=header if (offset == 0) else False   ,      index=snake_case   ,      **snake_case      )
							return csv_str.encode(self.encoding      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0   ,      len(self.dataset      )   ,      self.batch_size      )   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																	UpperCAmelCase		:							Any        =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																	written += file_obj.write(snake_case      )

							else:
												UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       len(self.dataset      ), self.batch_size
												with multiprocessing.Pool(self.num_proc      ) as pool:
																	for csv_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_csv   ,      [(offset, header, index, to_csv_kwargs) for offset in range(0   ,      snake_case   ,      snake_case      )]   ,      )   ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																						written += file_obj.write(snake_case      )

							return written
