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
)
lowerCamelCase     :							Any    =logging.getLogger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  np.argmax(__lowerCAmelCase ,   axis=1      )
     return np.sum(outputs == labels      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     List[str]:
     with open(__lowerCAmelCase ,   encoding="utf_8"      ) as f:
          UpperCamelCase__      :       Tuple	    =  csv.reader(__lowerCAmelCase      )
          UpperCamelCase__      :       Optional[Any]	    =  []
          next(__lowerCAmelCase      )  # skip the first line
          for line in tqdm(__lowerCAmelCase      ):
               output.append((" ".join(line[1:5]      ), line[5], line[6], int(line[-1]      ) - 1)      )
     return output







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







def 		SCREAMING_SNAKE_CASE							( )			->     Tuple:
     UpperCamelCase__      :       int	    =  argparse.ArgumentParser()
     parser.add_argument("--model_name" ,   type=__lowerCAmelCase ,   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=__lowerCAmelCase ,   type=__lowerCAmelCase ,   required=__lowerCAmelCase ,   help="The output directory where the model predictions and checkpoints will be written." ,   )
     parser.add_argument("--train_dataset" ,   type=__lowerCAmelCase ,   default=""      )
     parser.add_argument("--eval_dataset" ,   type=__lowerCAmelCase ,   default=""      )
     parser.add_argument("--seed" ,   type=__lowerCAmelCase ,   default=42      )
     parser.add_argument("--num_train_epochs" ,   type=__lowerCAmelCase ,   default=3      )
     parser.add_argument("--train_batch_size" ,   type=__lowerCAmelCase ,   default=8      )
     parser.add_argument("--eval_batch_size" ,   type=__lowerCAmelCase ,   default=16      )
     parser.add_argument("--adam_epsilon" ,   default=1E-8 ,   type=__lowerCAmelCase ,   help="Epsilon for Adam optimizer."      )
     parser.add_argument("--max_grad_norm" ,   type=__lowerCAmelCase ,   default=1      )
     parser.add_argument(
         "--max_steps" ,   default=-1 ,   type=__lowerCAmelCase ,   help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         ) ,   )
     parser.add_argument(
         "--gradient_accumulation_steps" ,   type=__lowerCAmelCase ,   default=1 ,   help="Number of updates steps to accumulate before                        performing a backward/update pass." ,   )
     parser.add_argument("--learning_rate" ,   type=__lowerCAmelCase ,   default=6.2_5E-5      )
     parser.add_argument("--warmup_steps" ,   default=0 ,   type=__lowerCAmelCase ,   help="Linear warmup over warmup_steps."      )
     parser.add_argument("--lr_schedule" ,   type=__lowerCAmelCase ,   default="warmup_linear"      )
     parser.add_argument("--weight_decay" ,   type=__lowerCAmelCase ,   default=0.0_1      )
     parser.add_argument("--lm_coef" ,   type=__lowerCAmelCase ,   default=0.9      )
     parser.add_argument("--n_valid" ,   type=__lowerCAmelCase ,   default=374      )

     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."      )
     UpperCamelCase__      :       List[Any]	    =  parser.parse_args()
     print(__lowerCAmelCase      )

     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()

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

     UpperCamelCase__      :       Any	    =  torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
     UpperCamelCase__      :       Tuple	    =  torch.cuda.device_count()
     logger.info("device: {}, n_gpu {}".format(__lowerCAmelCase ,   __lowerCAmelCase      )      )

     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
     UpperCamelCase__      :       Optional[int]	    =  ["_start_", "_delimiter_", "_classify_"]
     UpperCamelCase__      :       Tuple	    =  OpenAIGPTTokenizer.from_pretrained(args.model_name      )
     tokenizer.add_tokens(__lowerCAmelCase      )
     UpperCamelCase__      :       str	    =  tokenizer.convert_tokens_to_ids(__lowerCAmelCase      )
     UpperCamelCase__      :       List[str]	    =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name      )
     model.resize_token_embeddings(len(__lowerCAmelCase      )      )
     model.to(__lowerCAmelCase      )

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

     logger.info("Encoding dataset..."      )
     UpperCamelCase__      :       List[Any]	    =  load_rocstories_dataset(args.train_dataset      )
     UpperCamelCase__      :       Optional[int]	    =  load_rocstories_dataset(args.eval_dataset      )
     UpperCamelCase__      :       str	    =  (train_dataset, eval_dataset)
     UpperCamelCase__      :       List[str]	    =  tokenize_and_encode(__lowerCAmelCase      )

     # Compute the max input length for the Transformer
     UpperCamelCase__      :       Optional[Any]	    =  model.config.n_positions // 2 - 2
     UpperCamelCase__      :       Optional[Any]	    =  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      )
     UpperCamelCase__      :       Union[str, Any]	    =  min(__lowerCAmelCase ,   model.config.n_positions      )  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     UpperCamelCase__      :       Dict	    =  pre_process_datasets(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   *__lowerCAmelCase      )
     UpperCamelCase__      :       Tuple	    =  tensor_datasets[0], tensor_datasets[1]

     UpperCamelCase__      :       Any	    =  TensorDataset(*__lowerCAmelCase      )
     UpperCamelCase__      :       List[Any]	    =  RandomSampler(__lowerCAmelCase      )
     UpperCamelCase__      :       Dict	    =  DataLoader(__lowerCAmelCase ,   sampler=__lowerCAmelCase ,   batch_size=args.train_batch_size      )

     UpperCamelCase__      :       Optional[Any]	    =  TensorDataset(*__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  SequentialSampler(__lowerCAmelCase      )
     UpperCamelCase__      :       int	    =  DataLoader(__lowerCAmelCase ,   sampler=__lowerCAmelCase ,   batch_size=args.eval_batch_size      )

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               UpperCamelCase__      :       str	    =  args.max_steps
               UpperCamelCase__      :       int	    =  args.max_steps // (len(__lowerCAmelCase      ) // args.gradient_accumulation_steps) + 1
          else:
               UpperCamelCase__      :       Optional[Any]	    =  len(__lowerCAmelCase      ) // args.gradient_accumulation_steps * args.num_train_epochs

          UpperCamelCase__      :       Optional[int]	    =  list(model.named_parameters()      )
          UpperCamelCase__      :       List[str]	    =  ["bias", "LayerNorm.bias", "LayerNorm.weight"]
          UpperCamelCase__      :       Tuple	    =  [
              {
                  "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},
          ]
          UpperCamelCase__      :       Union[str, Any]	    =  AdamW(__lowerCAmelCase ,   lr=args.learning_rate ,   eps=args.adam_epsilon      )
          UpperCamelCase__      :       Any	    =  get_linear_schedule_with_warmup(
              __lowerCAmelCase ,   num_warmup_steps=args.warmup_steps ,   num_training_steps=__lowerCAmelCase      )

     if args.do_train:
          UpperCamelCase__      :       Union[str, Any]	    =  0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs      ) ,   desc="Epoch"      ):
               UpperCamelCase__      :       List[Any]	    =  0
               UpperCamelCase__      :       str	    =  0
               UpperCamelCase__      :       Tuple	    =  tqdm(__lowerCAmelCase ,   desc="Training"      )
               for step, batch in enumerate(__lowerCAmelCase      ):
                    UpperCamelCase__      :       Any	    =  tuple(t.to(__lowerCAmelCase      ) for t in batch      )
                    UpperCamelCase__      :       int	    =  batch
                    UpperCamelCase__      :       Optional[int]	    =  model(__lowerCAmelCase ,   mc_token_ids=__lowerCAmelCase ,   lm_labels=__lowerCAmelCase ,   mc_labels=__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    UpperCamelCase__      :       str	    =  (
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    UpperCamelCase__      :       Union[str, Any]	    =  "Training loss: {:.2e} lr: {:.2e}".format(__lowerCAmelCase ,   scheduler.get_lr()[0]      )

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          UpperCamelCase__      :       Any	    =  model.module if hasattr(__lowerCAmelCase ,   "module"      ) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          UpperCamelCase__      :       Any	    =  os.path.join(args.output_dir ,   __lowerCAmelCase      )
          UpperCamelCase__      :       List[str]	    =  os.path.join(args.output_dir ,   __lowerCAmelCase      )

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

          # Load a trained model and vocabulary that you have fine-tuned
          UpperCamelCase__      :       Tuple	    =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir      )
          UpperCamelCase__      :       Optional[Any]	    =  OpenAIGPTTokenizer.from_pretrained(args.output_dir      )
          model.to(__lowerCAmelCase      )

     if args.do_eval:
          model.eval()
          UpperCamelCase__      :       Union[str, Any]	    =  0, 0
          UpperCamelCase__      :       Dict	    =  0, 0
          for batch in tqdm(__lowerCAmelCase ,   desc="Evaluating"      ):
               UpperCamelCase__      :       Tuple	    =  tuple(t.to(__lowerCAmelCase      ) for t in batch      )
               UpperCamelCase__      :       Any	    =  batch
               with torch.no_grad():
                    UpperCamelCase__      :       int	    =  model(
                        __lowerCAmelCase ,   mc_token_ids=__lowerCAmelCase ,   lm_labels=__lowerCAmelCase ,   mc_labels=__lowerCAmelCase      )

               UpperCamelCase__      :       int	    =  mc_logits.detach().cpu().numpy()
               UpperCamelCase__      :       Optional[int]	    =  mc_labels.to("cpu"      ).numpy()
               UpperCamelCase__      :       Optional[int]	    =  accuracy(__lowerCAmelCase ,   __lowerCAmelCase      )

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

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

          UpperCamelCase__      :       Any	    =  eval_loss / nb_eval_steps
          UpperCamelCase__      :       Any	    =  eval_accuracy / nb_eval_examples
          UpperCamelCase__      :       List[str]	    =  tr_loss / nb_tr_steps if args.do_train else None
          UpperCamelCase__      :       Dict	    =  {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

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


if __name__ == "__main__":
 main()







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     bool:
     if len(__lowerCAmelCase      ) == 0:
          return False
     UpperCamelCase__      :       Any	    =  len(__lowerCAmelCase      ) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint] ,   __lowerCAmelCase      )
     else:
          return binary_search(a_list[midpoint + 1 :] ,   __lowerCAmelCase      )


if __name__ == "__main__":
 lowerCamelCase     :							Any    =input('''Enter numbers separated by comma:\n''').strip()
 lowerCamelCase     :							Dict    =[int(item.strip()) for item in user_input.split(''',''')]
 lowerCamelCase     :							List[str]    =int(input('''Enter the number to be found in the list:\n''').strip())
 lowerCamelCase     :							Union[str, Any]    ='''''' if binary_search(sequence, target) else '''not '''
 print(F"""{target} was {not_str}found in {sequence}""")