

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__							=				logging.get_logger(__name__)

A__							=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A__							=				{
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

A__							=				{
    "microsoft/mpnet-base": 512,
}

A__							=				{
    "microsoft/mpnet-base": {"do_lower_case": True},
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: str												=	VOCAB_FILES_NAMES
	_lowercase	: Tuple												=	PRETRAINED_VOCAB_FILES_MAP
	_lowercase	: str												=	PRETRAINED_INIT_CONFIGURATION
	_lowercase	: List[Any]												=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase	: Tuple												=	MPNetTokenizer
	_lowercase	: Optional[int]												=	["input_ids", "attention_mask"]







	def __init__( self:			int	,			__UpperCamelCase:			Any=None	,			__UpperCamelCase:			Any=None	,			__UpperCamelCase:			Tuple=True	,			__UpperCamelCase:			List[str]="<s>"	,			__UpperCamelCase:			Dict="</s>"	,			__UpperCamelCase:			List[Any]="</s>"	,			__UpperCamelCase:			List[str]="<s>"	,			__UpperCamelCase:			List[Any]="[UNK]"	,			__UpperCamelCase:			Optional[int]="<pad>"	,			__UpperCamelCase:			str="<mask>"	,			__UpperCamelCase:			Dict=True	,			__UpperCamelCase:			Union[str, Any]=None	,			**__UpperCamelCase:			Dict	,			):

						'''simple docstring'''





						super().__init__(
						    __UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			do_lower_case=__UpperCamelCase	,			bos_token=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			tokenize_chinese_chars=__UpperCamelCase	,			strip_accents=__UpperCamelCase	,			**__UpperCamelCase	,			)

						__magic_name__				=     json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
						if (
						    pre_tok_state.get('lowercase'	,			__UpperCamelCase   ) != do_lower_case
						    or pre_tok_state.get('strip_accents'	,			__UpperCamelCase   ) != strip_accents
						):
											__magic_name__				=     getattr(__UpperCamelCase	,			pre_tok_state.pop('type'   )   )
											__magic_name__				=     do_lower_case
											__magic_name__				=     strip_accents
											__magic_name__				=     pre_tok_class(**__UpperCamelCase   )

						__magic_name__				=     do_lower_case







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						if self._mask_token is None:
											if self.verbose:
																logger.error('Using mask_token, but it is not set yet.'   )
											return None
						return str(self._mask_token   )







	@mask_token.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase	,			__UpperCamelCase   ) else value
						__magic_name__				=     value







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			Optional[Any]=None   ):

						'''simple docstring'''





						__magic_name__				=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
											return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			List[int]	,			__UpperCamelCase:			Optional[List[int]] = None   ):

						'''simple docstring'''





						__magic_name__				=     [self.sep_token_id]
						__magic_name__				=     [self.cls_token_id]

						if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			Optional[str] = None   ):

						'''simple docstring'''





						__magic_name__				=     self._tokenizer.model.save(__UpperCamelCase	,			name=__UpperCamelCase   )
						return tuple(__UpperCamelCase   )







from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





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

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

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

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

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

						# Objectives for each stage
						__magic_name__				=     ['max']

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

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





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

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

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						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 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     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:
											__magic_name__				=     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
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     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
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     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
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

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

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''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:
																__magic_name__				=     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   )

											__magic_name__,						__magic_name__				=     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
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

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

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     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(__UpperCamelCase	,			0   )
						return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




