




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_A							    =	logging.getLogger(__name__)
def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:





       """simple docstring"""





       SCREAMING_SNAKE_CASE			: List[Any]	     =p * torch.log(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =0
       return -plogp.sum(dim=-1 )
def 						lowerCAmelCase_       (	__a )       ->			Optional[Any]:





       """simple docstring"""





       logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(__lowerCamelCase ) ) ) )
       for row in range(len(__lowerCamelCase ) ):
              if tensor.dtype != torch.long:
                     logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
              else:
                     logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data ) )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a=True	,	__a=True	,	__a=None	,	__a=False )       ->			List[str]:





       """simple docstring"""





       SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: int	     =model.config.num_hidden_layers, model.config.num_attention_heads
       SCREAMING_SNAKE_CASE			: Tuple	     =torch.zeros(__lowerCamelCase	,	__lowerCamelCase ).to(args.device )
       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.zeros(__lowerCamelCase	,	__lowerCamelCase ).to(args.device )

       if head_mask is None:
              SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.ones(__lowerCamelCase	,	__lowerCamelCase ).to(args.device )

       head_mask.requires_grad_(requires_grad=__lowerCamelCase )
       # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
       if actually_pruned:
              SCREAMING_SNAKE_CASE			: Tuple	     =None

       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =None
       SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
       SCREAMING_SNAKE_CASE			: int	     =0.0

       for step, inputs in enumerate(tqdm(__lowerCamelCase	,	desc='''Iteration'''	,	disable=args.local_rank not in [-1, 0] ) ):
              for k, v in inputs.items():
                     SCREAMING_SNAKE_CASE			: Union[str, Any]	     =v.to(args.device )

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              SCREAMING_SNAKE_CASE			: Any	     =model(**__lowerCamelCase	,	head_mask=__lowerCamelCase )
              SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Dict	     =(
                  outputs[0],
                  outputs[1],
                  outputs[-1],
              )  # Loss and logits are the first, attention the last
              loss.backward()  # Backpropagate to populate the gradients in the head mask

              if compute_entropy:
                     for layer, attn in enumerate(__lowerCamelCase ):
                            SCREAMING_SNAKE_CASE			: Any	     =entropy(attn.detach() ) * inputs['''attention_mask'''].float().unsqueeze(1 )
                            attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()

              # Also store our logits/labels if we want to compute metrics afterwards
              if preds is None:
                     SCREAMING_SNAKE_CASE			: Optional[int]	     =logits.detach().cpu().numpy()
                     SCREAMING_SNAKE_CASE			: Dict	     =inputs['''labels'''].detach().cpu().numpy()
              else:
                     SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.append(__lowerCamelCase	,	logits.detach().cpu().numpy()	,	axis=0 )
                     SCREAMING_SNAKE_CASE			: List[str]	     =np.append(__lowerCamelCase	,	inputs['''labels'''].detach().cpu().numpy()	,	axis=0 )

              tot_tokens += inputs["attention_mask"].float().detach().sum().data

       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
              SCREAMING_SNAKE_CASE			: Union[str, Any]	     =2
              SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.pow(torch.pow(__lowerCamelCase	,	__lowerCamelCase ).sum(-1 )	,	1 / exponent )
              head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20

       if not args.dont_normalize_global_importance:
              SCREAMING_SNAKE_CASE			: Tuple	     =(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

       # Print/save matrices
       np.save(os.path.join(args.output_dir	,	'''attn_entropy.npy''' )	,	attn_entropy.detach().cpu().numpy() )
       np.save(os.path.join(args.output_dir	,	'''head_importance.npy''' )	,	head_importance.detach().cpu().numpy() )

       logger.info('''Attention entropies''' )
       print_ad_tensor(__lowerCamelCase )
       logger.info('''Head importance scores''' )
       print_ad_tensor(__lowerCamelCase )
       logger.info('''Head ranked by importance scores''' )
       SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.zeros(head_importance.numel()	,	dtype=torch.long	,	device=args.device )
       SCREAMING_SNAKE_CASE			: List[Any]	     =torch.arange(
           head_importance.numel()	,	device=args.device )
       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =head_ranks.view_as(__lowerCamelCase )
       print_ad_tensor(__lowerCamelCase )

       return attn_entropy, head_importance, preds, labels
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			List[Any]:





       """simple docstring"""





       SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =compute_heads_importance(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	compute_entropy=__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Optional[int]	     =np.argmax(__lowerCamelCase	,	axis=1 ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Optional[Any]	     =glue_compute_metrics(args.task_name	,	__lowerCamelCase	,	__lowerCamelCase )[args.metric_name]
       logger.info('''Pruning: original score: %f, threshold: %f'''	,	__lowerCamelCase	,	original_score * args.masking_threshold )

       SCREAMING_SNAKE_CASE			: List[str]	     =torch.ones_like(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Tuple	     =max(1	,	int(new_head_mask.numel() * args.masking_amount ) )

       SCREAMING_SNAKE_CASE			: List[str]	     =original_score
       while current_score >= original_score * args.masking_threshold:
              SCREAMING_SNAKE_CASE			: Any	     =new_head_mask.clone()  # save current head mask
              # heads from least important to most - keep only not-masked heads
              SCREAMING_SNAKE_CASE			: List[str]	     =float('''Inf''' )
              SCREAMING_SNAKE_CASE			: Optional[Any]	     =head_importance.view(-1 ).sort()[1]

              if len(__lowerCamelCase ) <= num_to_mask:
                     break

              # mask heads
              SCREAMING_SNAKE_CASE			: Union[str, Any]	     =current_heads_to_mask[:num_to_mask]
              logger.info('''Heads to mask: %s'''	,	str(current_heads_to_mask.tolist() ) )
              SCREAMING_SNAKE_CASE			: Union[str, Any]	     =new_head_mask.view(-1 )
              SCREAMING_SNAKE_CASE			: List[Any]	     =0.0
              SCREAMING_SNAKE_CASE			: Dict	     =new_head_mask.view_as(__lowerCamelCase )
              SCREAMING_SNAKE_CASE			: Dict	     =new_head_mask.clone().detach()
              print_ad_tensor(__lowerCamelCase )

              # Compute metric and head importance again
              SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: int	     =compute_heads_importance(
                  __lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	compute_entropy=__lowerCamelCase	,	head_mask=__lowerCamelCase )
              SCREAMING_SNAKE_CASE			: List[str]	     =np.argmax(__lowerCamelCase	,	axis=1 ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase )
              SCREAMING_SNAKE_CASE			: Any	     =glue_compute_metrics(args.task_name	,	__lowerCamelCase	,	__lowerCamelCase )[args.metric_name]
              logger.info(
                  '''Masking: current score: %f, remaining heads %d (%.1f percents)'''	,	__lowerCamelCase	,	new_head_mask.sum()	,	new_head_mask.sum() / new_head_mask.numel() * 100	,	)

       logger.info('''Final head mask''' )
       print_ad_tensor(__lowerCamelCase )
       np.save(os.path.join(args.output_dir	,	'''head_mask.npy''' )	,	head_mask.detach().cpu().numpy() )

       return head_mask
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			str:





       """simple docstring"""





       SCREAMING_SNAKE_CASE			: List[Any]	     =datetime.now()
       SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =compute_heads_importance(
           __lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	compute_entropy=__lowerCamelCase	,	compute_importance=__lowerCamelCase	,	head_mask=__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: int	     =np.argmax(__lowerCamelCase	,	axis=1 ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Tuple	     =glue_compute_metrics(args.task_name	,	__lowerCamelCase	,	__lowerCamelCase )[args.metric_name]
       SCREAMING_SNAKE_CASE			: str	     =datetime.now() - before_time

       SCREAMING_SNAKE_CASE			: Tuple	     =sum(p.numel() for p in model.parameters() )
       SCREAMING_SNAKE_CASE			: str	     ={
           layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase ) )
       }

       assert sum(len(__lowerCamelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
       model.prune_heads(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: str	     =sum(p.numel() for p in model.parameters() )

       SCREAMING_SNAKE_CASE			: Union[str, Any]	     =datetime.now()
       SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =compute_heads_importance(
           __lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	compute_entropy=__lowerCamelCase	,	compute_importance=__lowerCamelCase	,	head_mask=__lowerCamelCase	,	actually_pruned=__lowerCamelCase	,	)
       SCREAMING_SNAKE_CASE			: Tuple	     =np.argmax(__lowerCamelCase	,	axis=1 ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: List[Any]	     =glue_compute_metrics(args.task_name	,	__lowerCamelCase	,	__lowerCamelCase )[args.metric_name]
       SCREAMING_SNAKE_CASE			: Dict	     =datetime.now() - before_time

       logger.info(
           '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''	,	__lowerCamelCase	,	__lowerCamelCase	,	pruned_num_params / original_num_params * 100	,	)
       logger.info('''Pruning: score with masking: %f score with pruning: %f'''	,	__lowerCamelCase	,	__lowerCamelCase )
       logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''	,	original_time / new_time * 100 )
def 						lowerCAmelCase_       (	)       ->			Any:





       """simple docstring"""





       SCREAMING_SNAKE_CASE			: Optional[int]	     =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--data_dir'''	,	default=__lowerCamelCase	,	type=__lowerCamelCase	,	required=__lowerCamelCase	,	help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''	,	)
       parser.add_argument(
           '''--model_name_or_path'''	,	default=__lowerCamelCase	,	type=__lowerCamelCase	,	required=__lowerCamelCase	,	help='''Path to pretrained model or model identifier from huggingface.co/models'''	,	)
       parser.add_argument(
           '''--task_name'''	,	default=__lowerCamelCase	,	type=__lowerCamelCase	,	required=__lowerCamelCase	,	help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys() )	,	)
       parser.add_argument(
           '''--output_dir'''	,	default=__lowerCamelCase	,	type=__lowerCamelCase	,	required=__lowerCamelCase	,	help='''The output directory where the model predictions and checkpoints will be written.'''	,	)

       # Other parameters
       parser.add_argument(
           '''--config_name'''	,	default=''''''	,	type=__lowerCamelCase	,	help='''Pretrained config name or path if not the same as model_name_or_path'''	,	)
       parser.add_argument(
           '''--tokenizer_name'''	,	default=''''''	,	type=__lowerCamelCase	,	help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''	,	)
       parser.add_argument(
           '''--cache_dir'''	,	default=__lowerCamelCase	,	type=__lowerCamelCase	,	help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''	,	)
       parser.add_argument(
           '''--data_subset'''	,	type=__lowerCamelCase	,	default=-1	,	help='''If > 0: limit the data to a subset of data_subset instances.''' )
       parser.add_argument(
           '''--overwrite_output_dir'''	,	action='''store_true'''	,	help='''Whether to overwrite data in output directory''' )
       parser.add_argument(
           '''--overwrite_cache'''	,	action='''store_true'''	,	help='''Overwrite the cached training and evaluation sets''' )

       parser.add_argument(
           '''--dont_normalize_importance_by_layer'''	,	action='''store_true'''	,	help='''Don\'t normalize importance score by layers''' )
       parser.add_argument(
           '''--dont_normalize_global_importance'''	,	action='''store_true'''	,	help='''Don\'t normalize all importance scores between 0 and 1'''	,	)

       parser.add_argument(
           '''--try_masking'''	,	action='''store_true'''	,	help='''Whether to try to mask head until a threshold of accuracy.''' )
       parser.add_argument(
           '''--masking_threshold'''	,	default=0.9	,	type=__lowerCamelCase	,	help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''	,	)
       parser.add_argument(
           '''--masking_amount'''	,	default=0.1	,	type=__lowerCamelCase	,	help='''Amount to heads to masking at each masking step.''' )
       parser.add_argument('''--metric_name'''	,	default='''acc'''	,	type=__lowerCamelCase	,	help='''Metric to use for head masking.''' )

       parser.add_argument(
           '''--max_seq_length'''	,	default=128	,	type=__lowerCamelCase	,	help=(
               '''The maximum total input sequence length after WordPiece tokenization. \n'''
               '''Sequences longer than this will be truncated, sequences shorter padded.'''
           )	,	)
       parser.add_argument('''--batch_size'''	,	default=1	,	type=__lowerCamelCase	,	help='''Batch size.''' )

       parser.add_argument('''--seed'''	,	type=__lowerCamelCase	,	default=42 )
       parser.add_argument('''--local_rank'''	,	type=__lowerCamelCase	,	default=-1	,	help='''local_rank for distributed training on gpus''' )
       parser.add_argument('''--no_cuda'''	,	action='''store_true'''	,	help='''Whether not to use CUDA when available''' )
       parser.add_argument('''--server_ip'''	,	type=__lowerCamelCase	,	default=''''''	,	help='''Can be used for distant debugging.''' )
       parser.add_argument('''--server_port'''	,	type=__lowerCamelCase	,	default=''''''	,	help='''Can be used for distant debugging.''' )
       SCREAMING_SNAKE_CASE			: List[Any]	     =parser.parse_args()

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print('''Waiting for debugger attach''' )
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,	redirect_output=__lowerCamelCase )
              ptvsd.wait_for_attach()

       # Setup devices and distributed training
       if args.local_rank == -1 or args.no_cuda:
              SCREAMING_SNAKE_CASE			: Any	     =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
              SCREAMING_SNAKE_CASE			: List[str]	     =0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank )
              SCREAMING_SNAKE_CASE			: str	     =torch.device('''cuda'''	,	args.local_rank )
              SCREAMING_SNAKE_CASE			: List[Any]	     =1
              torch.distributed.init_process_group(backend='''nccl''' )  # Initializes the distributed backend

       # Setup logging
       logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
       logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device	,	args.n_gpu	,	bool(args.local_rank != -1 ) ) )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(args.local_rank ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()

       # Set seeds
       set_seed(args.seed )

       # Prepare GLUE task
       SCREAMING_SNAKE_CASE			: int	     =args.task_name.lower()
       if args.task_name not in glue_processors:
              raise ValueError('''Task not found: %s''' % (args.task_name) )
       SCREAMING_SNAKE_CASE			: List[Any]	     =glue_processors[args.task_name]()
       SCREAMING_SNAKE_CASE			: Optional[int]	     =glue_output_modes[args.task_name]
       SCREAMING_SNAKE_CASE			: Optional[int]	     =processor.get_labels()
       SCREAMING_SNAKE_CASE			: Dict	     =len(__lowerCamelCase )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       SCREAMING_SNAKE_CASE			: Optional[int]	     =AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path	,	num_labels=__lowerCamelCase	,	finetuning_task=args.task_name	,	output_attentions=__lowerCamelCase	,	cache_dir=args.cache_dir	,	)
       SCREAMING_SNAKE_CASE			: Any	     =AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path	,	cache_dir=args.cache_dir	,	)
       SCREAMING_SNAKE_CASE			: Dict	     =AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path	,	from_tf=bool('''.ckpt''' in args.model_name_or_path )	,	config=__lowerCamelCase	,	cache_dir=args.cache_dir	,	)

       # Distributed and parallel training
       model.to(args.device )
       if args.local_rank != -1:
              SCREAMING_SNAKE_CASE			: Dict	     =nn.parallel.DistributedDataParallel(
                  __lowerCamelCase	,	device_ids=[args.local_rank]	,	output_device=args.local_rank	,	find_unused_parameters=__lowerCamelCase )
       elif args.n_gpu > 1:
              SCREAMING_SNAKE_CASE			: Optional[Any]	     =nn.DataParallel(__lowerCamelCase )

       # Print/save training arguments
       os.makedirs(args.output_dir	,	exist_ok=__lowerCamelCase )
       torch.save(__lowerCamelCase	,	os.path.join(args.output_dir	,	'''run_args.bin''' ) )
       logger.info('''Training/evaluation parameters %s'''	,	__lowerCamelCase )

       # Prepare dataset for the GLUE task
       SCREAMING_SNAKE_CASE			: Tuple	     =GlueDataset(__lowerCamelCase	,	tokenizer=__lowerCamelCase	,	mode='''dev''' )
       if args.data_subset > 0:
              SCREAMING_SNAKE_CASE			: Any	     =Subset(__lowerCamelCase	,	list(range(min(args.data_subset	,	len(__lowerCamelCase ) ) ) ) )
       SCREAMING_SNAKE_CASE			: Any	     =SequentialSampler(__lowerCamelCase ) if args.local_rank == -1 else DistributedSampler(__lowerCamelCase )
       SCREAMING_SNAKE_CASE			: Optional[Any]	     =DataLoader(
           __lowerCamelCase	,	sampler=__lowerCamelCase	,	batch_size=args.batch_size	,	collate_fn=__lowerCamelCase )

       # Compute head entropy and importance score
       compute_heads_importance(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

       # Try head masking (set heads to zero until the score goes under a threshole)
       # and head pruning (remove masked heads and see the effect on the network)
       if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
              SCREAMING_SNAKE_CASE			: int	     =mask_heads(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )
              prune_heads(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )


if __name__ == "__main__":
       main()












from typing import Any

import numpy as np


class 							_lowerCAmelCase		:




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

											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape

											# Number of decision variables x1, x2, x3...
											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars

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

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages

											# Objectives for each stage
											SCREAMING_SNAKE_CASE			: str	     =['''max''']

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

											SCREAMING_SNAKE_CASE			: Dict	     =['''''']

											# Index of current pivot row and column
											SCREAMING_SNAKE_CASE			: str	     =None
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None

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




				@staticmethod
				def     __a		( *snake_case_							)    ->      list[str]:
											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
											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
											SCREAMING_SNAKE_CASE			: int	     =[]
											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     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]

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

											# RHS
											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]

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

											# Array filled with nans
											SCREAMING_SNAKE_CASE			: Union[str, 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
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
											return row_idx, col_idx




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

											SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
											return tableau




				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
											self.objectives.pop()

											if not self.objectives:
																		return tableau

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

											# Delete the artificial variable columns
											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)

											# Delete the objective row of the first stage
											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)

											SCREAMING_SNAKE_CASE			: Any	     =1
											self.n_rows -= 1
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
											SCREAMING_SNAKE_CASE			: List[str]	     =False
											return tableau




				def     __a		( self							)    ->      dict[Any, Any]:
											# Stop simplex algorithm from cycling.
											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:
																									SCREAMING_SNAKE_CASE			: int	     =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							)

																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: 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
																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
																		else:
																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
											return {}







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

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

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()






