

# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
A__							=				1e-10
A__							=				"This is incredible! I love it, this is the best chicken I have ever had."
A__							=				100

class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			str	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			Tuple="gpt2-medium"	,			__UpperCamelCase:			Union[str, Any]=False	,			__UpperCamelCase:			str="cpu"   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     GPTaTokenizer.from_pretrained(__UpperCamelCase   )
						__magic_name__				=     GPTaLMHeadModel.from_pretrained(__UpperCamelCase   )
						__magic_name__				=     self.encoder.transformer.config.hidden_size
						__magic_name__				=     ClassificationHead(class_size=__UpperCamelCase	,			embed_size=self.embed_size   )
						__magic_name__				=     cached_mode
						__magic_name__				=     device







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						return self.classifier_head







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						for param in self.encoder.parameters():
											__magic_name__				=     False
						self.classifier_head.train()







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     x.ne(0   ).unsqueeze(2   ).repeat(1	,			1	,			self.embed_size   ).float().to(self.device   ).detach()
						__magic_name__				=     self.encoder.transformer(__UpperCamelCase   )['last_hidden_state']
						__magic_name__				=     hidden * mask
						__magic_name__				=     torch.sum(__UpperCamelCase	,			dim=1   ) / (torch.sum(__UpperCamelCase	,			dim=1   ).detach() + EPSILON)
						return avg_hidden







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						if self.cached_mode:
											__magic_name__				=     x.to(self.device   )
						else:
											__magic_name__				=     self.avg_representation(x.to(self.device   )   )

						__magic_name__				=     self.classifier_head(__UpperCamelCase   )
						__magic_name__				=     nn.functional.log_softmax(__UpperCamelCase	,			dim=-1   )

						return probs




class 					__UpperCamelCase					(       data.Dataset      ):







	def __init__( self:			Dict	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     X
						__magic_name__				=     y







	def __len__( self:			Any   ):

						'''simple docstring'''





						return len(self.X   )







	def __getitem__( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     {}
						__magic_name__				=     self.X[index]
						__magic_name__				=     self.y[index]
						return data




def        _lowercase  (					a_					:       int					)					->						str:




					'''simple docstring'''







					def pad_sequences(a_					:       List[str]					):
										__magic_name__				=     [len(a_					) for seq in sequences]

										__magic_name__				=     torch.zeros(len(a_					)       ,max(a_					)					).long()  # padding value = 0

										for i, seq in enumerate(a_					):
															__magic_name__				=     lengths[i]
															__magic_name__				=     seq[:end]

										return padded_sequences, lengths

					__magic_name__				=     {}
					for key in data[0].keys():
										__magic_name__				=     [d[key] for d in data]

					__magic_name__,						__magic_name__				=     pad_sequences(item_info['X']					)
					__magic_name__				=     torch.tensor(item_info['y']       ,dtype=torch.long					)

					return x_batch, y_batch




def        _lowercase  (					a_					:       Optional[int]					)					->						Optional[Any]:




					'''simple docstring'''







					__magic_name__				=     {}
					for key in data[0].keys():
										__magic_name__				=     [d[key] for d in data]

					__magic_name__				=     torch.cat(item_info['X']       ,0					)
					__magic_name__				=     torch.tensor(item_info['y']       ,dtype=torch.long					)

					return x_batch, y_batch




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       int       ,a_					:       List[Any]       ,a_					:       str=0       ,a_					:       Union[str, Any]=1_0       ,a_					:       Optional[int]="cpu"					)					->						str:




					'''simple docstring'''







					__magic_name__				=     0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(a_					):
										__magic_name__,						__magic_name__				=     input_t.to(a_					), target_t.to(a_					)

										optimizer.zero_grad()

										__magic_name__				=     discriminator(a_					)
										__magic_name__				=     nn.functional.nll_loss(a_       ,a_					)
										loss.backward(retain_graph=a_					)
										optimizer.step()

										samples_so_far += len(a_					)

										if batch_idx % log_interval == 0:
															print(
															    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
															        epoch + 1       ,a_       ,len(data_loader.dataset					)       ,1_0_0 * samples_so_far / len(data_loader.dataset					)       ,loss.item()       ,)					)




def        _lowercase  (					a_					:       str       ,a_					:       str       ,a_					:       Optional[Any]="cpu"					)					->						Dict:




					'''simple docstring'''







					discriminator.eval()
					__magic_name__				=     0
					__magic_name__				=     0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															__magic_name__,						__magic_name__				=     input_t.to(a_					), target_t.to(a_					)
															__magic_name__				=     discriminator(a_					)
															# sum up batch loss
															test_loss += nn.functional.nll_loss(a_       ,a_       ,reduction='sum'					).item()
															# get the index of the max log-probability
															__magic_name__				=     output_t.argmax(dim=1       ,keepdim=a_					)
															correct += pred_t.eq(target_t.view_as(a_					)					).sum().item()

					test_loss /= len(data_loader.dataset					)

					print(
					    'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
					        a_       ,a_       ,len(data_loader.dataset					)       ,100.0 * correct / len(data_loader.dataset					)					)					)




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       Optional[int]       ,a_					:       Optional[Any]       ,a_					:       Optional[int]=False       ,a_					:       List[Any]="cpu"					)					->						int:




					'''simple docstring'''







					__magic_name__				=     model.tokenizer.encode(a_					)
					__magic_name__				=     torch.tensor([input_t]       ,dtype=torch.long       ,device=a_					)
					if cached:
										__magic_name__				=     model.avg_representation(a_					)

					__magic_name__				=     model(a_					).data.cpu().numpy().flatten().tolist()
					print('Input sentence:'       ,a_					)
					print(
					    'Predictions:'       ,', '.join('{}: {:.4f}'.format(a_       ,math.exp(a_					)					) for c, log_prob in zip(a_       ,a_					)					)       ,)




def        _lowercase  (					a_					:       List[Any]       ,a_					:       Optional[int]       ,a_					:       int       ,a_					:       Optional[int]=False       ,a_					:       Any="cpu"					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     torch.utils.data.DataLoader(dataset=a_       ,batch_size=a_       ,collate_fn=a_					)

					__magic_name__				=     []
					__magic_name__				=     []
					for batch_idx, (x, y) in enumerate(tqdm(a_       ,ascii=a_					)					):
										with torch.no_grad():
															__magic_name__				=     x.to(a_					)
															__magic_name__				=     discriminator.avg_representation(a_					).cpu().detach()
															__magic_name__				=     torch.unbind(avg_rep.unsqueeze(1					)					)
															xs += avg_rep_list
															ys += y.cpu().numpy().tolist()

					__magic_name__				=     torch.utils.data.DataLoader(
					    dataset=Dataset(a_       ,a_					)       ,batch_size=a_       ,shuffle=a_       ,collate_fn=a_					)

					return data_loader




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       Dict=None       ,a_					:       List[Any]="gpt2-medium"       ,a_					:       Any=1_0       ,a_					:       List[str]=6_4       ,a_					:       Dict=1_0       ,a_					:       Tuple=False       ,a_					:       Union[str, Any]=False       ,a_					:       List[str]=False       ,)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

					print('Preprocessing {} dataset...'.format(a_					)					)
					__magic_name__				=     time.time()

					if dataset == "SST":
										__magic_name__				=     ['positive', 'negative', 'very positive', 'very negative', 'neutral']
										__magic_name__				=     {c: i for i, c in enumerate(a_					)}

										__magic_name__				=     Discriminator(
										    class_size=len(a_					)       ,pretrained_model=a_       ,cached_mode=a_       ,device=a_					).to(a_					)

										__magic_name__				=     torchtext_data.Field()
										__magic_name__				=     torchtext_data.Field(sequential=a_					)
										__magic_name__,						__magic_name__,						__magic_name__				=     datasets.SST.splits(
										    a_       ,a_       ,fine_grained=a_       ,train_subtrees=a_       ,)

										__magic_name__				=     []
										__magic_name__				=     []
										for i in trange(len(a_					)       ,ascii=a_					):
															__magic_name__				=     TreebankWordDetokenizer().detokenize(vars(train_data[i]					)['text']					)
															__magic_name__				=     discriminator.tokenizer.encode(a_					)
															__magic_name__				=     torch.tensor([5_0_2_5_6] + seq       ,device=a_       ,dtype=torch.long					)
															x.append(a_					)
															y.append(classaidx[vars(train_data[i]					)['label']]					)
										__magic_name__				=     Dataset(a_       ,a_					)

										__magic_name__				=     []
										__magic_name__				=     []
										for i in trange(len(a_					)       ,ascii=a_					):
															__magic_name__				=     TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['text']					)
															__magic_name__				=     discriminator.tokenizer.encode(a_					)
															__magic_name__				=     torch.tensor([5_0_2_5_6] + seq       ,device=a_       ,dtype=torch.long					)
															test_x.append(a_					)
															test_y.append(classaidx[vars(test_data[i]					)['label']]					)
										__magic_name__				=     Dataset(a_       ,a_					)

										__magic_name__				=     {
										    'class_size': len(a_					),
										    'embed_size': discriminator.embed_size,
										    'pretrained_model': pretrained_model,
										    'class_vocab': classaidx,
										    'default_class': 2,
										}

					elif dataset == "clickbait":
										__magic_name__				=     ['non_clickbait', 'clickbait']
										__magic_name__				=     {c: i for i, c in enumerate(a_					)}

										__magic_name__				=     Discriminator(
										    class_size=len(a_					)       ,pretrained_model=a_       ,cached_mode=a_       ,device=a_					).to(a_					)

										with open('datasets/clickbait/clickbait_train_prefix.txt'					) as f:
															__magic_name__				=     []
															for i, line in enumerate(a_					):
																				try:
																									data.append(eval(a_					)					)
																				except Exception:
																									print('Error evaluating line {}: {}'.format(a_       ,a_					)					)
																									continue
										__magic_name__				=     []
										__magic_name__				=     []
										with open('datasets/clickbait/clickbait_train_prefix.txt'					) as f:
															for i, line in enumerate(tqdm(a_       ,ascii=a_					)					):
																				try:
																									__magic_name__				=     eval(a_					)
																									__magic_name__				=     discriminator.tokenizer.encode(d['text']					)

																									if len(a_					) < max_length_seq:
																														__magic_name__				=     torch.tensor([5_0_2_5_6] + seq       ,device=a_       ,dtype=torch.long					)
																									else:
																														print('Line {} is longer than maximum length {}'.format(a_       ,a_					)					)
																														continue
																									x.append(a_					)
																									y.append(d['label']					)
																				except Exception:
																									print('Error evaluating / tokenizing line {}, skipping it'.format(a_					)					)
																									pass

										__magic_name__				=     Dataset(a_       ,a_					)
										__magic_name__				=     int(0.9 * len(a_					)					)
										__magic_name__				=     len(a_					) - train_size
										__magic_name__,						__magic_name__				=     torch.utils.data.random_split(a_       ,[train_size, test_size]					)

										__magic_name__				=     {
										    'class_size': len(a_					),
										    'embed_size': discriminator.embed_size,
										    'pretrained_model': pretrained_model,
										    'class_vocab': classaidx,
										    'default_class': 1,
										}

					elif dataset == "toxic":
										__magic_name__				=     ['non_toxic', 'toxic']
										__magic_name__				=     {c: i for i, c in enumerate(a_					)}

										__magic_name__				=     Discriminator(
										    class_size=len(a_					)       ,pretrained_model=a_       ,cached_mode=a_       ,device=a_					).to(a_					)

										__magic_name__				=     []
										__magic_name__				=     []
										with open('datasets/toxic/toxic_train.txt'					) as f:
															for i, line in enumerate(tqdm(a_       ,ascii=a_					)					):
																				try:
																									__magic_name__				=     eval(a_					)
																									__magic_name__				=     discriminator.tokenizer.encode(d['text']					)

																									if len(a_					) < max_length_seq:
																														__magic_name__				=     torch.tensor([5_0_2_5_6] + seq       ,device=a_       ,dtype=torch.long					)
																									else:
																														print('Line {} is longer than maximum length {}'.format(a_       ,a_					)					)
																														continue
																									x.append(a_					)
																									y.append(int(np.sum(d['label']					) > 0					)					)
																				except Exception:
																									print('Error evaluating / tokenizing line {}, skipping it'.format(a_					)					)
																									pass

										__magic_name__				=     Dataset(a_       ,a_					)
										__magic_name__				=     int(0.9 * len(a_					)					)
										__magic_name__				=     len(a_					) - train_size
										__magic_name__,						__magic_name__				=     torch.utils.data.random_split(a_       ,[train_size, test_size]					)

										__magic_name__				=     {
										    'class_size': len(a_					),
										    'embed_size': discriminator.embed_size,
										    'pretrained_model': pretrained_model,
										    'class_vocab': classaidx,
										    'default_class': 0,
										}

					else:  # if dataset == "generic":
										# This assumes the input dataset is a TSV with the following structure:
										# class \t text

										if dataset_fp is None:
															raise ValueError('When generic dataset is selected, dataset_fp needs to be specified aswell.'					)

										__magic_name__				=     set()
										with open(a_					) as f:
															__magic_name__				=     csv.reader(a_       ,delimiter='\t'					)
															for row in tqdm(a_       ,ascii=a_					):
																				if row:
																									classes.add(row[0]					)

										__magic_name__				=     sorted(a_					)
										__magic_name__				=     {c: i for i, c in enumerate(a_					)}

										__magic_name__				=     Discriminator(
										    class_size=len(a_					)       ,pretrained_model=a_       ,cached_mode=a_       ,device=a_					).to(a_					)

										__magic_name__				=     []
										__magic_name__				=     []
										with open(a_					) as f:
															__magic_name__				=     csv.reader(a_       ,delimiter='\t'					)
															for i, row in enumerate(tqdm(a_       ,ascii=a_					)					):
																				if row:
																									__magic_name__				=     row[0]
																									__magic_name__				=     row[1]

																									try:
																														__magic_name__				=     discriminator.tokenizer.encode(a_					)
																														if len(a_					) < max_length_seq:
																																			__magic_name__				=     torch.tensor([5_0_2_5_6] + seq       ,device=a_       ,dtype=torch.long					)

																														else:
																																			print('Line {} is longer than maximum length {}'.format(a_       ,a_					)					)
																																			continue

																														x.append(a_					)
																														y.append(classaidx[label]					)

																									except Exception:
																														print('Error tokenizing line {}, skipping it'.format(a_					)					)
																														pass

										__magic_name__				=     Dataset(a_       ,a_					)
										__magic_name__				=     int(0.9 * len(a_					)					)
										__magic_name__				=     len(a_					) - train_size
										__magic_name__,						__magic_name__				=     torch.utils.data.random_split(a_       ,[train_size, test_size]					)

										__magic_name__				=     {
										    'class_size': len(a_					),
										    'embed_size': discriminator.embed_size,
										    'pretrained_model': pretrained_model,
										    'class_vocab': classaidx,
										    'default_class': 0,
										}

					__magic_name__				=     time.time()
					print('Preprocessed {} data points'.format(len(a_					) + len(a_					)					)					)
					print('Data preprocessing took: {:.3f}s'.format(end - start					)					)

					if cached:
										print('Building representation cache...'					)

										__magic_name__				=     time.time()

										__magic_name__				=     get_cached_data_loader(a_       ,a_       ,a_       ,shuffle=a_       ,device=a_					)

										__magic_name__				=     get_cached_data_loader(a_       ,a_       ,a_       ,device=a_					)

										__magic_name__				=     time.time()
										print('Building representation cache took: {:.3f}s'.format(end - start					)					)

					else:
										__magic_name__				=     torch.utils.data.DataLoader(
										    dataset=a_       ,batch_size=a_       ,shuffle=a_       ,collate_fn=a_					)
										__magic_name__				=     torch.utils.data.DataLoader(dataset=a_       ,batch_size=a_       ,collate_fn=a_					)

					if save_model:
										with open('{}_classifier_head_meta.json'.format(a_					)       ,'w'					) as meta_file:
															json.dump(a_       ,a_					)

					__magic_name__				=     optim.Adam(discriminator.parameters()       ,lr=0.0001					)

					for epoch in range(a_					):
										__magic_name__				=     time.time()
										print('\nEpoch'       ,epoch + 1					)

										train_epoch(
										    discriminator=a_       ,data_loader=a_       ,optimizer=a_       ,epoch=a_       ,log_interval=a_       ,device=a_       ,)
										evaluate_performance(data_loader=a_       ,discriminator=a_       ,device=a_					)

										__magic_name__				=     time.time()
										print('Epoch took: {:.3f}s'.format(end - start					)					)

										print('\nExample prediction'					)
										predict(a_       ,a_       ,a_       ,cached=a_       ,device=a_					)

										if save_model:
															# torch.save(discriminator.state_dict(),
															#           "{}_discriminator_{}.pt".format(
															#               args.dataset, epoch + 1
															#               ))
															torch.save(
															    discriminator.get_classifier().state_dict()       ,'{}_classifier_head_epoch_{}.pt'.format(a_       ,epoch + 1					)       ,)


if __name__ == "__main__":
						A__							=				argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
						parser.add_argument(
						    "--dataset",
						    type=str,
						    default="SST",
						    choices=("SST", "clickbait", "toxic", "generic"),
						    help=(
						        "dataset to train the discriminator on."
						        "In case of generic, the dataset is expected"
						        "to be a TSBV file with structure: class \\t text"
						    ),
						)
						parser.add_argument(
						    "--dataset_fp",
						    type=str,
						    default="",
						    help="File path of the dataset to use. Needed only in case of generic datadset",
						)
						parser.add_argument(
						    "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
						)
						parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
						parser.add_argument(
						    "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
						)
						parser.add_argument(
						    "--log_interval",
						    type=int,
						    default=10,
						    metavar="N",
						    help="how many batches to wait before logging training status",
						)
						parser.add_argument("--save_model", action="store_true", help="whether to save the model")
						parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
						parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
						A__							=				parser.parse_args()

						train_discriminator(**(vars(args)))







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()




