



from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											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 _a   (				*a_		):
											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 _a   (				self	,	a_		):
											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 _a   (				self	,	a_	,	a_	,	a_		):
											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 _a   (				self	,	a_		):
											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 _a   (				self		):
											# Stop simplex algorithm from cycling.
											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:
																			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 _a   (				self	,	a_	,	a_		):
											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()



"""simple docstring"""




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__="[PAD]"						,      lowercase__="[BOS]"						,      lowercase__="[EOS]"						,      lowercase__="[MASK]"						,      lowercase__="[CLS]"						,      lowercase__="[SEP]"						,      lowercase__=2048						,      **lowercase__						,      )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else pad_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else bos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else eos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else mask_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else cls_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else sep_token

									super().__init__(
									    pad_token=lowercase__						,      bos_token=lowercase__						,      eos_token=lowercase__						,      mask_token=lowercase__						,      cls_token=lowercase__						,      sep_token=lowercase__						,      model_max_length=lowercase__						,      **lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      2**8  # utf is 8 bits

									# define special tokens dict
									SCREAMING_SNAKE_CASE           =      {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									SCREAMING_SNAKE_CASE           =      len(self.special_tokens_encoder     )
									SCREAMING_SNAKE_CASE           =      {v: k for k, v in self.special_tokens_encoder.items()}


				def 				A   (							self     )       ->       Dict[str, int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder     )
									for i in range(self._utf_vocab_size     ):
														SCREAMING_SNAKE_CASE           =      chr(lowercase__     )
														SCREAMING_SNAKE_CASE           =      i + len(self.special_tokens_encoder     )
									return vocab


				@property
				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									return self._utf_vocab_size + self._num_special_tokens


				def 				A   (							self						,      lowercase__						,      lowercase__ = None						,      lowercase__ = False     )       ->       List[int]:
									"""simple docstring"""







									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase__						,      token_ids_a=lowercase__						,      already_has_special_tokens=lowercase__     )

									# normal case: some special tokens
									if token_ids_a is None:
														return [1] + [0] * len(lowercase__     ) + [1]
									return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


				def 				A   (							self						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [chr(lowercase__     ) for i in text.encode('utf-8'     )]
									return tokens


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if token in self.special_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_encoder[token]
									elif len(lowercase__     ) != 1:
														SCREAMING_SNAKE_CASE           =      self.unk_token_id
									else:
														SCREAMING_SNAKE_CASE           =      ord(lowercase__     ) + self._num_special_tokens
									return token_id


				def 				A   (							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if index in self.special_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_decoder[index]
									else:
														SCREAMING_SNAKE_CASE           =      chr(index - self._num_special_tokens     )
									return token


				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      b''
									for token in tokens:
														if token in self.special_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.added_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.special_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														elif token in self.added_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														else:
																			SCREAMING_SNAKE_CASE           =      bytes([ord(lowercase__     )]     )
														bstring += tok_string
									SCREAMING_SNAKE_CASE           =      bstring.decode('utf-8'						,      errors='replace'     )
									return string




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									return ()
