

'''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()








'''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




