





'''simple docstring'''


import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
    from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
    A_				     =							None

if is_ray_available():
    import ray  # noqa: E402 # isort:skip
    from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
    A_				     =							None
    A_				     =							None
    A_				     =							None




def       A							(      _UpperCAmelCase   :				Any					)  ->				List[str]:


  '''simple docstring'''




  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    __lowerCAmelCase		:    Optional[int]			     =  unittest.skip('test requires Datasets, Faiss, psutil'					)(_UpperCAmelCase					)
  return test_case


@require_distributed_retrieval
class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase		:    List[str]			     =  tempfile.mkdtemp()
        __lowerCAmelCase		:    int			     =  8

        # DPR tok
        __lowerCAmelCase		:    Tuple			     =  [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        __lowerCAmelCase		:    List[str]			     =  os.path.join(self.tmpdirname     , 'dpr_tokenizer' )
        os.makedirs(SCREAMING_SNAKE_CASE     , exist_ok=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[Any]			     =  os.path.join(SCREAMING_SNAKE_CASE     , DPR_VOCAB_FILES_NAMES['vocab_file'] )
        with open(self.vocab_file     , 'w'     , encoding='utf-8' ) as vocab_writer:
          vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

        # BART tok
        __lowerCAmelCase		:    Optional[Any]			     =  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        __lowerCAmelCase		:    int			     =  dict(zip(SCREAMING_SNAKE_CASE     , range(len(SCREAMING_SNAKE_CASE ) ) ) )
        __lowerCAmelCase		:    Tuple			     =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        __lowerCAmelCase		:    List[str]			     =  {'unk_token': '<unk>'}

        __lowerCAmelCase		:    Tuple			     =  os.path.join(self.tmpdirname     , 'bart_tokenizer' )
        os.makedirs(SCREAMING_SNAKE_CASE     , exist_ok=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  os.path.join(SCREAMING_SNAKE_CASE     , BART_VOCAB_FILES_NAMES['vocab_file'] )
        __lowerCAmelCase		:    List[str]			     =  os.path.join(SCREAMING_SNAKE_CASE     , BART_VOCAB_FILES_NAMES['merges_file'] )
        with open(self.vocab_file     , 'w'     , encoding='utf-8' ) as fp:
          fp.write(json.dumps(SCREAMING_SNAKE_CASE ) + '\n' )
        with open(self.merges_file     , 'w'     , encoding='utf-8' ) as fp:
          fp.write('\n'.join(SCREAMING_SNAKE_CASE ) )




      def 	snake_case  (     self )				->  DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     , 'dpr_tokenizer' ) )




      def 	snake_case  (     self )				->  BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     , 'bart_tokenizer' ) )




      def 	snake_case  (     self )				->  int:
        shutil.rmtree(self.tmpdirname )




      def 	snake_case  (     self )				->  Union[str, Any]:
        __lowerCAmelCase		:    Dict			     =  Dataset.from_dict(
            {
                'id': ['0', '1'],
                'text': ['foo', 'bar'],
                'title': ['Foo', 'Bar'],
                'embeddings': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
            } )
        dataset.add_faiss_index('embeddings'     , string_factory='Flat'     , metric_type=faiss.METRIC_INNER_PRODUCT )
        return dataset




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=1_23_45 )				->  RagPyTorchDistributedRetriever:
        __lowerCAmelCase		:    Any			     =  self.get_dummy_dataset()
        __lowerCAmelCase		:    Dict			     =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     , question_encoder=DPRConfig().to_dict()     , generator=BartConfig().to_dict()     , )
        with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
          __lowerCAmelCase		:    Tuple			     =  dataset
          __lowerCAmelCase		:    Optional[int]			     =  RagPyTorchDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , )
          if init_retrieval:
            retriever.init_retrieval(SCREAMING_SNAKE_CASE )
        return retriever




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  RagRayDistributedRetriever:
        # Have to run in local mode because sys.path modifications at top of
        # file are not propogated to remote workers.
        # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
        ray.init(local_mode=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     , question_encoder=DPRConfig().to_dict()     , generator=BartConfig().to_dict()     , )
        __lowerCAmelCase		:    List[Any]			     =  ray.remote(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  [remote_cls.remote() for _ in range(1 )]
        with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
          __lowerCAmelCase		:    List[str]			     =  self.get_dummy_dataset()
          __lowerCAmelCase		:    List[str]			     =  RagRayDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , retrieval_workers=SCREAMING_SNAKE_CASE     , )
          if init_retrieval:
            retriever.init_retrieval()
        return retriever




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=1_23_45 )				->  Optional[Any]:
        __lowerCAmelCase		:    Optional[Any]			     =  self.get_dummy_dataset()
        __lowerCAmelCase		:    Optional[int]			     =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     , question_encoder=DPRConfig().to_dict()     , generator=BartConfig().to_dict()     , index_name='custom'     , )
        if from_disk:
          __lowerCAmelCase		:    Dict			     =  os.path.join(self.tmpdirname     , 'dataset' )
          __lowerCAmelCase		:    Optional[Any]			     =  os.path.join(self.tmpdirname     , 'index.faiss' )
          dataset.get_index('embeddings' ).save(os.path.join(self.tmpdirname     , 'index.faiss' ) )
          dataset.drop_index('embeddings' )
          dataset.save_to_disk(os.path.join(self.tmpdirname     , 'dataset' ) )
          del dataset
          __lowerCAmelCase		:    Union[str, Any]			     =  RagPyTorchDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , )
        else:
          __lowerCAmelCase		:    Any			     =  RagPyTorchDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , index=CustomHFIndex(config.retrieval_vector_size     , SCREAMING_SNAKE_CASE )     , )
        if init_retrieval:
          retriever.init_retrieval(SCREAMING_SNAKE_CASE )
        return retriever




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Union[str, Any]:
        # Have to run in local mode because sys.path modifications at top of
        # file are not propogated to remote workers.
        # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
        ray.init(local_mode=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    str			     =  self.get_dummy_dataset()
        __lowerCAmelCase		:    Optional[int]			     =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     , question_encoder=DPRConfig().to_dict()     , generator=BartConfig().to_dict()     , index_name='custom'     , )
        __lowerCAmelCase		:    Optional[Any]			     =  ray.remote(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[Any]			     =  [remote_cls.remote() for _ in range(1 )]
        if from_disk:
          __lowerCAmelCase		:    Tuple			     =  os.path.join(self.tmpdirname     , 'dataset' )
          __lowerCAmelCase		:    List[str]			     =  os.path.join(self.tmpdirname     , 'index.faiss' )
          dataset.get_index('embeddings' ).save(os.path.join(self.tmpdirname     , 'index.faiss' ) )
          dataset.drop_index('embeddings' )
          dataset.save_to_disk(os.path.join(self.tmpdirname     , 'dataset' ) )
          del dataset
          __lowerCAmelCase		:    Tuple			     =  RagRayDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , retrieval_workers=SCREAMING_SNAKE_CASE     , index=CustomHFIndex.load_from_disk(
                  vector_size=config.retrieval_vector_size     , dataset_path=config.passages_path     , index_path=config.index_path     , )     , )
        else:
          __lowerCAmelCase		:    Any			     =  RagRayDistributedRetriever(
              SCREAMING_SNAKE_CASE     , question_encoder_tokenizer=self.get_dpr_tokenizer()     , generator_tokenizer=self.get_bart_tokenizer()     , retrieval_workers=SCREAMING_SNAKE_CASE     , index=CustomHFIndex(config.retrieval_vector_size     , SCREAMING_SNAKE_CASE )     , )
        if init_retrieval:
          retriever.init_retrieval()
        return retriever




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  None:
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Tuple			     =  retriever.retrieve(SCREAMING_SNAKE_CASE     , n_docs=SCREAMING_SNAKE_CASE )
        self.assertEqual(retrieved_doc_embeds.shape     , (2, n_docs, self.retrieval_vector_size) )
        self.assertEqual(len(SCREAMING_SNAKE_CASE )     , 2 )
        self.assertEqual(sorted(doc_dicts[0] )     , ['embeddings', 'id', 'text', 'title'] )
        self.assertEqual(len(doc_dicts[0]['id'] )     , SCREAMING_SNAKE_CASE )
        self.assertEqual(doc_dicts[0]['id'][0]     , '1' )  # max inner product is reached with second doc
        self.assertEqual(doc_dicts[1]['id'][0]     , '0' )  # max inner product is reached with first doc
        self.assertListEqual(doc_ids.tolist()     , [[1], [0]] )




      def 	snake_case  (     self )				->  str:
        __lowerCAmelCase		:    Dict			     =  1
        __lowerCAmelCase		:    List[str]			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )

        self.distributed_retriever_check(
            self.get_dummy_pytorch_distributed_retriever(init_retrieval=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Optional[int]:
        __lowerCAmelCase		:    List[Any]			     =  1
        __lowerCAmelCase		:    str			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=SCREAMING_SNAKE_CASE     , from_disk=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )




      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase		:    Optional[Any]			     =  1
        __lowerCAmelCase		:    List[Any]			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=SCREAMING_SNAKE_CASE     , from_disk=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )




      @require_ray
      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase		:    Dict			     =  1
        __lowerCAmelCase		:    str			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )

        self.distributed_retriever_check(
            self.get_dummy_ray_distributed_retriever(init_retrieval=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        ray.shutdown()




      @require_ray
      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    Optional[int]			     =  1
        __lowerCAmelCase		:    List[Any]			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )
        with self.assertRaises(SCREAMING_SNAKE_CASE ):
          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE     , from_disk=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )
        ray.shutdown()







      @require_ray
      def 	snake_case  (     self )				->  Union[str, Any]:
        __lowerCAmelCase		:    Dict			     =  1
        __lowerCAmelCase		:    str			     =  np.array(
            [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]     , dtype=np.floataa )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE     , from_disk=SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        ray.shutdown()












'''simple docstring'''


from typing import Any

import numpy as np


class 		UpperCamelCase__     :
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  None:
        # Check if RHS is negative
        if np.any(tableau[:, -1]     , where=tableau[:, -1] < 0 ):
          raise ValueError('RHS must be > 0' )

        __lowerCAmelCase		:    Any			     =  tableau
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[int]			     =  tableau.shape

        # Number of decision variables x1, x2, x3...
        __lowerCAmelCase		:    Any			     =  n_vars

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

        # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
        __lowerCAmelCase		:    Union[str, Any]			     =  (self.n_art_vars > 0) + 1

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

        # Objectives for each stage
        __lowerCAmelCase		:    List[Any]			     =  ['max']

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

        __lowerCAmelCase		:    Optional[Any]			     =  ['']

        # Index of current pivot row and column
        __lowerCAmelCase		:    List[Any]			     =  None
        __lowerCAmelCase		:    Dict			     =  None

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




      @staticmethod
      def 	snake_case  (     *SCREAMING_SNAKE_CASE )				->  list[str]:
        if len(SCREAMING_SNAKE_CASE ) != 3:
          raise ValueError('Must provide n_vars, n_slack, and n_art_vars' )

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

        # decision | slack | artificial
        __lowerCAmelCase		:    int			     =  ['x', 's', 'a']
        __lowerCAmelCase		:    Tuple			     =  []
        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 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  tuple[Any, Any]:
        __lowerCAmelCase		:    List[Any]			     =  self.objectives[-1]

        # Find entries of highest magnitude in objective rows
        __lowerCAmelCase		:    List[Any]			     =  (objective == 'min') - (objective == 'max')
        __lowerCAmelCase		:    Dict			     =  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:
          __lowerCAmelCase		:    Dict			     =  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
        __lowerCAmelCase		:    str			     =  slice(self.n_stages     , self.n_rows )

        # RHS
        __lowerCAmelCase		:    Union[str, Any]			     =  tableau[s, -1]

        # Elements of pivot column within slice
        __lowerCAmelCase		:    Optional[Any]			     =  tableau[s, col_idx]

        # Array filled with nans
        __lowerCAmelCase		:    Optional[Any]			     =  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
        __lowerCAmelCase		:    Tuple			     =  np.divide(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , out=SCREAMING_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
        __lowerCAmelCase		:    Any			     =  np.nanargmin(SCREAMING_SNAKE_CASE ) + self.n_stages
        return row_idx, col_idx




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        __lowerCAmelCase		:    Tuple			     =  tableau[row_idx].copy()

        __lowerCAmelCase		:    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
        __lowerCAmelCase		:    str			     =  piv_row
        return tableau




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        self.objectives.pop()

        if not self.objectives:
          return tableau

        # Slice containing ids for artificial columns
        __lowerCAmelCase		:    Optional[Any]			     =  slice(-self.n_art_vars - 1     , -1 )

        # Delete the artificial variable columns
        __lowerCAmelCase		:    str			     =  np.delete(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , axis=1 )

        # Delete the objective row of the first stage
        __lowerCAmelCase		:    Optional[int]			     =  np.delete(SCREAMING_SNAKE_CASE     , 0     , axis=0 )

        __lowerCAmelCase		:    Tuple			     =  1
        self.n_rows -= 1
        __lowerCAmelCase		:    str			     =  0
        __lowerCAmelCase		:    Any			     =  False
        return tableau




      def 	snake_case  (     self )				->  dict[Any, Any]:
        # Stop simplex algorithm from cycling.
        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:
            __lowerCAmelCase		:    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 )

          __lowerCAmelCase     ,					__lowerCAmelCase		:    Tuple			     =  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
            __lowerCAmelCase		:    Dict			     =  self.change_stage(self.tableau )
          else:
            __lowerCAmelCase		:    Optional[Any]			     =  self.pivot(self.tableau     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return {}







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  dict[str, float]:
        __lowerCAmelCase		:    List[Any]			     =  {'P': abs(tableau[0, -1] )}

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

          # First entry in the nonzero ids
          __lowerCAmelCase		:    Optional[Any]			     =  nonzero[0][0]
          __lowerCAmelCase		:    str			     =  tableau[nonzero_rowidx, i]

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()





