





"""simple docstring"""
import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase     :						str						       =  logging.getLogger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= np.argmax(_UpperCAmelCase  ,     axis=1			)
 return np.sum(outputs == labels			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 with open(_UpperCAmelCase  ,     encoding='utf_8'			) as f:
  lowerCAmelCase    				= csv.reader(_UpperCAmelCase			)
  lowerCAmelCase    				= []
  next(_UpperCAmelCase			)  # skip the first line
  for line in tqdm(_UpperCAmelCase			):
   output.append((' '.join(line[1:5]			), line[5], line[6], int(line[-1]			) - 1)			)
 return output

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Dict			):
 lowerCAmelCase    				= []
 for dataset in encoded_datasets:
  lowerCAmelCase    				= len(_UpperCAmelCase			)
  lowerCAmelCase    				= np.zeros((n_batch, 2, input_len)  ,     dtype=np.intaa			)
  lowerCAmelCase    				= np.zeros((n_batch, 2)  ,     dtype=np.intaa			)
  lowerCAmelCase    				= np.full((n_batch, 2, input_len)  ,     fill_value=-100  ,     dtype=np.intaa			)
  lowerCAmelCase    				= np.zeros((n_batch,)  ,     dtype=np.intaa			)
  for (
      i,
      (story, conta, conta, mc_label),
  ) in enumerate(_UpperCAmelCase			):
   lowerCAmelCase    				= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
   lowerCAmelCase    				= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
   lowerCAmelCase    				= with_conta
   lowerCAmelCase    				= with_conta
   lowerCAmelCase    				= len(_UpperCAmelCase			) - 1
   lowerCAmelCase    				= len(_UpperCAmelCase			) - 1
   lowerCAmelCase    				= with_conta
   lowerCAmelCase    				= with_conta
   lowerCAmelCase    				= mc_label
  lowerCAmelCase    				= (input_ids, mc_token_ids, lm_labels, mc_labels)
  tensor_datasets.append(tuple(torch.tensor(_UpperCAmelCase			) for t in all_inputs			)			)
 return tensor_datasets

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser()
 parser.add_argument('--model_name'  ,     type=_UpperCAmelCase  ,     default='openai-gpt'  ,     help='pretrained model name'			)
 parser.add_argument('--do_train'  ,     action='store_true'  ,     help='Whether to run training.'			)
 parser.add_argument('--do_eval'  ,     action='store_true'  ,     help='Whether to run eval on the dev set.'			)
 parser.add_argument(
     '--output_dir'  ,     default=_UpperCAmelCase  ,     type=_UpperCAmelCase  ,     required=_UpperCAmelCase  ,     help='The output directory where the model predictions and checkpoints will be written.'  ,     )
 parser.add_argument('--train_dataset'  ,     type=_UpperCAmelCase  ,     default=''			)
 parser.add_argument('--eval_dataset'  ,     type=_UpperCAmelCase  ,     default=''			)
 parser.add_argument('--seed'  ,     type=_UpperCAmelCase  ,     default=42			)
 parser.add_argument('--num_train_epochs'  ,     type=_UpperCAmelCase  ,     default=3			)
 parser.add_argument('--train_batch_size'  ,     type=_UpperCAmelCase  ,     default=8			)
 parser.add_argument('--eval_batch_size'  ,     type=_UpperCAmelCase  ,     default=16			)
 parser.add_argument('--adam_epsilon'  ,     default=1e-8  ,     type=_UpperCAmelCase  ,     help='Epsilon for Adam optimizer.'			)
 parser.add_argument('--max_grad_norm'  ,     type=_UpperCAmelCase  ,     default=1			)
 parser.add_argument(
     '--max_steps'  ,     default=-1  ,     type=_UpperCAmelCase  ,     help=(
         'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
     )  ,     )
 parser.add_argument(
     '--gradient_accumulation_steps'  ,     type=_UpperCAmelCase  ,     default=1  ,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'  ,     )
 parser.add_argument('--learning_rate'  ,     type=_UpperCAmelCase  ,     default=6.25e-5			)
 parser.add_argument('--warmup_steps'  ,     default=0  ,     type=_UpperCAmelCase  ,     help='Linear warmup over warmup_steps.'			)
 parser.add_argument('--lr_schedule'  ,     type=_UpperCAmelCase  ,     default='warmup_linear'			)
 parser.add_argument('--weight_decay'  ,     type=_UpperCAmelCase  ,     default=0.01			)
 parser.add_argument('--lm_coef'  ,     type=_UpperCAmelCase  ,     default=0.9			)
 parser.add_argument('--n_valid'  ,     type=_UpperCAmelCase  ,     default=374			)

 parser.add_argument('--server_ip'  ,     type=_UpperCAmelCase  ,     default=''  ,     help='Can be used for distant debugging.'			)
 parser.add_argument('--server_port'  ,     type=_UpperCAmelCase  ,     default=''  ,     help='Can be used for distant debugging.'			)
 lowerCAmelCase    				= parser.parse_args()
 print(_UpperCAmelCase			)

 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=_UpperCAmelCase			)
  ptvsd.wait_for_attach()

 random.seed(args.seed			)
 np.random.seed(args.seed			)
 torch.manual_seed(args.seed			)
 torch.cuda.manual_seed_all(args.seed			)

 lowerCAmelCase    				= torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
 lowerCAmelCase    				= torch.cuda.device_count()
 logger.info('device: {}, n_gpu {}'.format(_UpperCAmelCase  ,     _UpperCAmelCase			)			)

 if not args.do_train and not args.do_eval:
  raise ValueError('At least one of `do_train` or `do_eval` must be True.'			)

 if not os.path.exists(args.output_dir			):
  os.makedirs(args.output_dir			)

 # Load tokenizer and model
 # This loading functions also add new tokens and embeddings called `special tokens`
 # These new embeddings will be fine-tuned on the RocStories dataset
 lowerCAmelCase    				= ['_start_', '_delimiter_', '_classify_']
 lowerCAmelCase    				= OpenAIGPTTokenizer.from_pretrained(args.model_name			)
 tokenizer.add_tokens(_UpperCAmelCase			)
 lowerCAmelCase    				= tokenizer.convert_tokens_to_ids(_UpperCAmelCase			)
 lowerCAmelCase    				= OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
 model.resize_token_embeddings(len(_UpperCAmelCase			)			)
 model.to(_UpperCAmelCase			)

 # Load and encode the datasets
 def tokenize_and_encode(_UpperCAmelCase					:    Optional[int]			):
  if isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			):
   return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCAmelCase			)			)
  elif isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			):
   return obj
  return [tokenize_and_encode(_UpperCAmelCase			) for o in obj]

 logger.info('Encoding dataset...'			)
 lowerCAmelCase    				= load_rocstories_dataset(args.train_dataset			)
 lowerCAmelCase    				= load_rocstories_dataset(args.eval_dataset			)
 lowerCAmelCase    				= (train_dataset, eval_dataset)
 lowerCAmelCase    				= tokenize_and_encode(_UpperCAmelCase			)

 # Compute the max input length for the Transformer
 lowerCAmelCase    				= model.config.n_positions // 2 - 2
 lowerCAmelCase    				= max(
     len(story[:max_length]			) + max(len(conta[:max_length]			)  ,     len(conta[:max_length]			)			) + 3
     for dataset in encoded_datasets
     for story, conta, conta, _ in dataset			)
 lowerCAmelCase    				= min(_UpperCAmelCase  ,     model.config.n_positions			)  # Max size of input for the pre-trained model

 # Prepare inputs tensors and dataloaders
 lowerCAmelCase    				= pre_process_datasets(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     *_UpperCAmelCase			)
 lowerCAmelCase				,lowerCAmelCase    				= tensor_datasets[0], tensor_datasets[1]

 lowerCAmelCase    				= TensorDataset(*_UpperCAmelCase			)
 lowerCAmelCase    				= RandomSampler(_UpperCAmelCase			)
 lowerCAmelCase    				= DataLoader(_UpperCAmelCase  ,     sampler=_UpperCAmelCase  ,     batch_size=args.train_batch_size			)

 lowerCAmelCase    				= TensorDataset(*_UpperCAmelCase			)
 lowerCAmelCase    				= SequentialSampler(_UpperCAmelCase			)
 lowerCAmelCase    				= DataLoader(_UpperCAmelCase  ,     sampler=_UpperCAmelCase  ,     batch_size=args.eval_batch_size			)

 # Prepare optimizer
 if args.do_train:
  if args.max_steps > 0:
   lowerCAmelCase    				= args.max_steps
   lowerCAmelCase    				= args.max_steps // (len(_UpperCAmelCase			) // args.gradient_accumulation_steps) + 1
  else:
   lowerCAmelCase    				= len(_UpperCAmelCase			) // args.gradient_accumulation_steps * args.num_train_epochs

  lowerCAmelCase    				= list(model.named_parameters()			)
  lowerCAmelCase    				= ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
  lowerCAmelCase    				= [
      {
          'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay			)],
          'weight_decay': args.weight_decay,
      },
      {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay			)], 'weight_decay': 0.0},
  ]
  lowerCAmelCase    				= AdamW(_UpperCAmelCase  ,     lr=args.learning_rate  ,     eps=args.adam_epsilon			)
  lowerCAmelCase    				= get_linear_schedule_with_warmup(
      _UpperCAmelCase  ,     num_warmup_steps=args.warmup_steps  ,     num_training_steps=_UpperCAmelCase			)

 if args.do_train:
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= 0, 0, None
  model.train()
  for _ in trange(int(args.num_train_epochs			)  ,     desc='Epoch'			):
   lowerCAmelCase    				= 0
   lowerCAmelCase    				= 0
   lowerCAmelCase    				= tqdm(_UpperCAmelCase  ,     desc='Training'			)
   for step, batch in enumerate(_UpperCAmelCase			):
    lowerCAmelCase    				= tuple(t.to(_UpperCAmelCase			) for t in batch			)
    lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= batch
    lowerCAmelCase    				= model(_UpperCAmelCase  ,     mc_token_ids=_UpperCAmelCase  ,     lm_labels=_UpperCAmelCase  ,     mc_labels=_UpperCAmelCase			)
    lowerCAmelCase    				= args.lm_coef * losses[0] + losses[1]
    loss.backward()
    optimizer.step()
    scheduler.step()
    optimizer.zero_grad()
    tr_loss += loss.item()
    lowerCAmelCase    				= (
        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
    )
    nb_tr_steps += 1
    lowerCAmelCase    				= 'Training loss: {:.2e} lr: {:.2e}'.format(_UpperCAmelCase  ,     scheduler.get_lr()[0]			)

    # Save a trained model
 if args.do_train:
  # Save a trained model, configuration and tokenizer
  lowerCAmelCase    				= model.module if hasattr(_UpperCAmelCase  ,     'module'			) else model  # Only save the model itself

  # If we save using the predefined names, we can load using `from_pretrained`
  lowerCAmelCase    				= os.path.join(args.output_dir  ,     _UpperCAmelCase			)
  lowerCAmelCase    				= os.path.join(args.output_dir  ,     _UpperCAmelCase			)

  torch.save(model_to_save.state_dict()  ,     _UpperCAmelCase			)
  model_to_save.config.to_json_file(_UpperCAmelCase			)
  tokenizer.save_vocabulary(args.output_dir			)

  # Load a trained model and vocabulary that you have fine-tuned
  lowerCAmelCase    				= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
  lowerCAmelCase    				= OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
  model.to(_UpperCAmelCase			)

 if args.do_eval:
  model.eval()
  lowerCAmelCase				,lowerCAmelCase    				= 0, 0
  lowerCAmelCase				,lowerCAmelCase    				= 0, 0
  for batch in tqdm(_UpperCAmelCase  ,     desc='Evaluating'			):
   lowerCAmelCase    				= tuple(t.to(_UpperCAmelCase			) for t in batch			)
   lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= batch
   with torch.no_grad():
    lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= model(
        _UpperCAmelCase  ,     mc_token_ids=_UpperCAmelCase  ,     lm_labels=_UpperCAmelCase  ,     mc_labels=_UpperCAmelCase			)

   lowerCAmelCase    				= mc_logits.detach().cpu().numpy()
   lowerCAmelCase    				= mc_labels.to('cpu'			).numpy()
   lowerCAmelCase    				= accuracy(_UpperCAmelCase  ,     _UpperCAmelCase			)

   eval_loss += mc_loss.mean().item()
   eval_accuracy += tmp_eval_accuracy

   nb_eval_examples += input_ids.size(0			)
   nb_eval_steps += 1

  lowerCAmelCase    				= eval_loss / nb_eval_steps
  lowerCAmelCase    				= eval_accuracy / nb_eval_examples
  lowerCAmelCase    				= tr_loss / nb_tr_steps if args.do_train else None
  lowerCAmelCase    				= {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

  lowerCAmelCase    				= os.path.join(args.output_dir  ,     'eval_results.txt'			)
  with open(_UpperCAmelCase  ,     'w'			) as writer:
   logger.info('***** Eval results *****'			)
   for key in sorted(result.keys()			):
    logger.info('  %s = %s'  ,     _UpperCAmelCase  ,     str(result[key]			)			)
    writer.write('%s = %s\n' % (key, str(result[key]			))			)


if __name__ == "__main__":
       main()












"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vertices
  lowerCAmelCase    				= {
      (min(_snake_case     ), max(_snake_case     )): weight for edge, weight in edges.items()
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.vertices.add(edge[0]     )
  self.vertices.add(edge[1]     )
  lowerCAmelCase    				= weight



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Graph({min(self.vertices     )}				,       {}     )
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42

  while len(subgraph.vertices     ) < len(self.vertices     ):
   lowerCAmelCase    				= max(self.edges.values()     ) + 1
   for edge, weight in self.edges.items():
    if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
     if weight < min_weight:
      lowerCAmelCase    				= edge
      lowerCAmelCase    				= weight

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

 for edgea in range(1  ,     len(_UpperCAmelCase			)			):
  for edgea in range(_UpperCAmelCase			):
   if adjaceny_matrix[edgea][edgea] != "-":
    lowerCAmelCase    				= int(adjaceny_matrix[edgea][edgea]			)

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

 lowerCAmelCase    				= sum(graph.edges.values()			)
 lowerCAmelCase    				= sum(subgraph.edges.values()			)

 return initial_total - optimal_total


if __name__ == "__main__":
       print(f'''{solution() = }''')





