

from typing import Any

import numpy as np





class    __UpperCAmelCase	:



   def __init__(				self				:	List[str],						__A				:	Tuple,						__A				:	Tuple		):
     # Check if RHS is negative
     if np.any(tableau[:, -1],						where=tableau[:, -1] < 0		):
       raise ValueError('''RHS must be > 0'''		)

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

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

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

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

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

     # Objectives for each stage
     UpperCAmelCase			:				List[str]										=					['''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			:				Dict										=					None
     UpperCAmelCase			:				Optional[Any]										=					None

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



   @staticmethod
   def 			__magic_name__       (				*__A				:	Union[str, Any]		):
     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
     UpperCAmelCase			:				Optional[int]										=					['''x''', '''s''', '''a''']
     UpperCAmelCase			:				List[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 			__magic_name__       (				self				:	Union[str, Any],						__A				:	Dict		):
     UpperCAmelCase			:				Union[str, Any]										=					self.objectives[-1]

     # Find entries of highest magnitude in objective rows
     UpperCAmelCase			:				List[Any]										=					(objective == '''min''') - (objective == '''max''')
     UpperCAmelCase			:				Tuple										=					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			:				List[str]										=					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			:				Optional[int]										=					tableau[s, -1]

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

     # Array filled with nans
     UpperCAmelCase			:				str										=					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			:				str										=					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
     UpperCAmelCase			:				Any										=					np.nanargmin(UpperCAmelCase_		) + self.n_stages
     return row_idx, col_idx



   def 			__magic_name__       (				self				:	str,						__A				:	Optional[int],						__A				:	Optional[Any],						__A				:	List[str]		):
     UpperCAmelCase			:				Optional[int]										=					tableau[row_idx].copy()

     UpperCAmelCase			:				Optional[int]										=					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			:				int										=					piv_row
     return tableau



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Optional[int]		):
     self.objectives.pop()

     if not self.objectives:
       return tableau

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

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

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

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



   def 			__magic_name__       (				self				:	int		):
     # Stop simplex algorithm from cycling.
     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			:				Union[str, 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					,		UpperCAmelCase			:				Union[str, 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			:				str										=					self.change_stage(self.tableau		)
       else:
         UpperCAmelCase			:				str										=					self.pivot(self.tableau,						UpperCAmelCase_,						UpperCAmelCase_		)
     return {}





   def 			__magic_name__       (				self				:	List[str],						__A				:	Any,						__A				:	List[str]		):
     UpperCAmelCase			:				Any										=					{'''P''': abs(tableau[0, -1]		)}

     for i in range(self.n_vars		):
       # Gives ids of nonzero entries in the ith column
       UpperCAmelCase			:				Tuple										=					np.nonzero(tableau[:, i]		)
       UpperCAmelCase			:				Union[str, Any]										=					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			:				Union[str, Any]										=					tableau[nonzero_rowidx, -1]
         UpperCAmelCase			:				Dict										=					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()












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[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				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





