code: 
  
  '''simple docstring'''
  
  from typing import Any
  
  import numpy as np
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					Dict     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if np.any(tableau[:, -1]     ,		where=tableau[:, -1] < 0				):
                      raise ValueError('''RHS must be > 0'''				)
  
               lowercase__       =				tableau
               lowercase__    ,lowercase__       =				tableau.shape
  
               # Number of decision variables x1, x2, x3...
               lowercase__       =				n_vars
  
               # Number of artificial variables to be minimised
               lowercase__       =				len(np.where(tableau[self.n_vars : -1] == -1				)[0]				)
  
               # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
               lowercase__       =				(self.n_art_vars > 0) + 1
  
               # Number of slack variables added to make inequalities into equalities
               lowercase__       =				self.n_rows - self.n_stages
  
               # Objectives for each stage
               lowercase__       =				['''max''']
  
               # In two stage simplex, first minimise then maximise
               if self.n_art_vars:
                      self.objectives.append('''min'''				)
  
               lowercase__       =				['''''']
  
               # Index of current pivot row and column
               lowercase__       =				None
               lowercase__       =				None
  
               # Does objective row only contain (non)-negative values?
               lowercase__       =				False
  
  
  
        @staticmethod
        def    UpperCamelCase__	(*UpperCamelCase					:					Optional[Any]				):
  
               '''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
               lowercase__       =				['''x''', '''s''', '''a''']
               lowercase__       =				[]
               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    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.objectives[-1]
  
               # Find entries of highest magnitude in objective rows
               lowercase__       =				(objective == '''min''') - (objective == '''max''')
               lowercase__       =				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:
                      lowercase__       =				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
               lowercase__       =				slice(self.n_stages     ,		self.n_rows				)
  
               # RHS
               lowercase__       =				tableau[s, -1]
  
               # Elements of pivot column within slice
               lowercase__       =				tableau[s, col_idx]
  
               # Array filled with nans
               lowercase__       =				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
               lowercase__       =				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
               lowercase__       =				np.nanargmin(__snake_case				) + self.n_stages
               return row_idx, col_idx
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				tableau[row_idx].copy()
  
               lowercase__       =				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
               lowercase__       =				piv_row
               return tableau
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.objectives.pop()
  
               if not self.objectives:
                      return tableau
  
               # Slice containing ids for artificial columns
               lowercase__       =				slice(-self.n_art_vars - 1     ,		-1				)
  
               # Delete the artificial variable columns
               lowercase__       =				np.delete(__snake_case     ,		__snake_case     ,		axis=1				)
  
               # Delete the objective row of the first stage
               lowercase__       =				np.delete(__snake_case     ,		0     ,		axis=0				)
  
               lowercase__       =				1
               self.n_rows -= 1
               lowercase__       =				0
               lowercase__       =				False
               return tableau
  
  
  
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''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:
                             lowercase__       =				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				)
  
                      lowercase__    ,lowercase__       =				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
                             lowercase__       =				self.change_stage(self.tableau				)
                      else:
                             lowercase__       =				self.pivot(self.tableau     ,		__snake_case     ,		__snake_case				)
               return {}
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				{'''P''': abs(tableau[0, -1]				)}
  
               for i in range(self.n_vars				):
                      # Gives ids of nonzero entries in the ith column
                      lowercase__       =				np.nonzero(tableau[:, i]				)
                      lowercase__       =				len(nonzero[0]				)
  
                      # First entry in the nonzero ids
                      lowercase__       =				nonzero[0][0]
                      lowercase__       =				tableau[nonzero_rowidx, i]
  
                      # If there is only one nonzero value in column, which is one
                      if n_nonzero == nonzero_val == 1:
                             lowercase__       =				tableau[nonzero_rowidx, -1]
                             lowercase__       =				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()
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import os
  from shutil import copyfile
  from typing import TYPE_CHECKING, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  from ...utils.versions import require_version
  
  
  if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation
  
  require_version('tokenizers>=0.13.3')
  
  if is_sentencepiece_available():
   from .tokenization_llama import LlamaTokenizer
  else:
   lowerCamelCase							:     Optional[int]								=			None
  
  lowerCamelCase							:     List[Any]								=			logging.get_logger(__name__)
  lowerCamelCase							:     Dict								=			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
  lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'
  
  # fmt: off
  lowerCamelCase							:     Optional[int]								=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
        lowerCAmelCase__      :  Tuple						      =							"""left"""
        lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Dict="<unk>"     ,		UpperCamelCase					:					int="<s>"     ,		UpperCamelCase					:					Dict="</s>"     ,		UpperCamelCase					:					Any=True     ,		UpperCamelCase					:					Tuple=False     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(
                   vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
               lowercase__       =				add_eos_token
               self.update_post_processor()
  
               lowercase__       =				vocab_file
               lowercase__       =				False if not self.vocab_file else True
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.bos_token
               lowercase__       =				self.bos_token_id
  
               lowercase__       =				self.eos_token
               lowercase__       =				self.eos_token_id
  
               lowercase__       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
               lowercase__       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"
  
               lowercase__       =				[]
               if self.add_bos_token:
                      special_tokens.append((bos, bos_token_id)				)
               if self.add_eos_token:
                      special_tokens.append((eos, eos_token_id)				)
               lowercase__       =				processors.TemplateProcessing(
                   single=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_eos_token
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_bos_token
  
  
  
        @add_eos_token.setter
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        @add_bos_token.setter
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if not self.can_save_slow_tokenizer:
                      raise ValueError(
                          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                          '''tokenizer.'''				)
  
               if not os.path.isdir(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
  
               if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase				):
                      copyfile(self.vocab_file     ,		UpperCamelCase				)
  
               return (out_vocab_file,)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					"Conversation"				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				list(conversation.iter_texts()				)
               if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
                   [not is_user for is_user, msg in dialogue[1::2]]				):
                      raise ValueError(
                          '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)
  
               lowercase__       =				[]
               if len(conversation.past_user_inputs				) > 0:
                      if not conversation.past_user_inputs[0].startswith(UpperCamelCase				) or E_SYS not in conversation.past_user_inputs[0]:
                             lowercase__       =				(
                                 B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                             )
               elif not dialogue[0][1].startswith(UpperCamelCase				) or E_SYS not in dialogue[0][1]:
                      lowercase__       =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])
  
               dialog_tokens += sum(
                   [
                       [self.bos_token_id]
                       + self.encode(
                           f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "     ,		add_special_tokens=UpperCamelCase				)
                       + [self.eos_token_id]
                       for prompt, answer in zip(dialogue[::2]     ,		dialogue[1::2]				)
                   ]     ,		[]     ,		)
               if not (dialogue[-1][0]):
                      raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"				)
               dialog_tokens += [self.bos_token_id] + self.encode(
                   f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"     ,		add_special_tokens=UpperCamelCase				)
               return dialog_tokens
  
  
  
  
  