



'''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
)
__a          =		logging.getLogger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Dict    						=      np.argmax(SCREAMING_SNAKE_CASE_     ,   axis=1		)
      return np.sum(outputs == labels		)





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





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





def 						__snake_case(				)		-> int:
      snake_case__  :    Tuple    						=      argparse.ArgumentParser()
      parser.add_argument("""--model_name"""     ,   type=SCREAMING_SNAKE_CASE_     ,   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=SCREAMING_SNAKE_CASE_     ,   type=SCREAMING_SNAKE_CASE_     ,   required=SCREAMING_SNAKE_CASE_     ,   help="""The output directory where the model predictions and checkpoints will be written."""     ,   )
      parser.add_argument("""--train_dataset"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=""""""		)
      parser.add_argument("""--eval_dataset"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=""""""		)
      parser.add_argument("""--seed"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=42		)
      parser.add_argument("""--num_train_epochs"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=3		)
      parser.add_argument("""--train_batch_size"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=8		)
      parser.add_argument("""--eval_batch_size"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=16		)
      parser.add_argument("""--adam_epsilon"""     ,   default=1e-8     ,   type=SCREAMING_SNAKE_CASE_     ,   help="""Epsilon for Adam optimizer."""		)
      parser.add_argument("""--max_grad_norm"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=1		)
      parser.add_argument(
          """--max_steps"""     ,   default=-1     ,   type=SCREAMING_SNAKE_CASE_     ,   help=(
              """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
          )     ,   )
      parser.add_argument(
          """--gradient_accumulation_steps"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=1     ,   help="""Number of updates steps to accumulate before                        performing a backward/update pass."""     ,   )
      parser.add_argument("""--learning_rate"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=6.25e-5		)
      parser.add_argument("""--warmup_steps"""     ,   default=0     ,   type=SCREAMING_SNAKE_CASE_     ,   help="""Linear warmup over warmup_steps."""		)
      parser.add_argument("""--lr_schedule"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default="""warmup_linear"""		)
      parser.add_argument("""--weight_decay"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=0.01		)
      parser.add_argument("""--lm_coef"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=0.9		)
      parser.add_argument("""--n_valid"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=374		)

      parser.add_argument("""--server_ip"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=""""""     ,   help="""Can be used for distant debugging."""		)
      parser.add_argument("""--server_port"""     ,   type=SCREAMING_SNAKE_CASE_     ,   default=""""""     ,   help="""Can be used for distant debugging."""		)
      snake_case__  :    List[str]    						=      parser.parse_args()
      print(SCREAMING_SNAKE_CASE_		)

      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=SCREAMING_SNAKE_CASE_		)
            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		)

      snake_case__  :    int    						=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
      snake_case__  :    Tuple    						=      torch.cuda.device_count()
      logger.info("""device: {}, n_gpu {}""".format(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_		)		)

      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
      snake_case__  :    Tuple    						=      ["""_start_""", """_delimiter_""", """_classify_"""]
      snake_case__  :    Tuple    						=      OpenAIGPTTokenizer.from_pretrained(args.model_name		)
      tokenizer.add_tokens(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    Union[str, Any]    						=      tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    Optional[Any]    						=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name		)
      model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_		)		)
      model.to(SCREAMING_SNAKE_CASE_		)

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

      logger.info("""Encoding dataset..."""		)
      snake_case__  :    Optional[int]    						=      load_rocstories_dataset(args.train_dataset		)
      snake_case__  :    List[str]    						=      load_rocstories_dataset(args.eval_dataset		)
      snake_case__  :    Union[str, Any]    						=      (train_dataset, eval_dataset)
      snake_case__  :    Dict    						=      tokenize_and_encode(SCREAMING_SNAKE_CASE_		)

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

      # Prepare inputs tensors and dataloaders
      snake_case__  :    Optional[int]    						=      pre_process_datasets(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   *SCREAMING_SNAKE_CASE_		)
      snake_case__						,				snake_case__  :    Optional[Any]    						=      tensor_datasets[0], tensor_datasets[1]

      snake_case__  :    Optional[Any]    						=      TensorDataset(*SCREAMING_SNAKE_CASE_		)
      snake_case__  :    str    						=      RandomSampler(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    Union[str, Any]    						=      DataLoader(SCREAMING_SNAKE_CASE_     ,   sampler=SCREAMING_SNAKE_CASE_     ,   batch_size=args.train_batch_size		)

      snake_case__  :    Optional[int]    						=      TensorDataset(*SCREAMING_SNAKE_CASE_		)
      snake_case__  :    List[str]    						=      SequentialSampler(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    Optional[Any]    						=      DataLoader(SCREAMING_SNAKE_CASE_     ,   sampler=SCREAMING_SNAKE_CASE_     ,   batch_size=args.eval_batch_size		)

      # Prepare optimizer
      if args.do_train:
            if args.max_steps > 0:
                  snake_case__  :    Tuple    						=      args.max_steps
                  snake_case__  :    Any    						=      args.max_steps // (len(SCREAMING_SNAKE_CASE_		) // args.gradient_accumulation_steps) + 1
            else:
                  snake_case__  :    Dict    						=      len(SCREAMING_SNAKE_CASE_		) // args.gradient_accumulation_steps * args.num_train_epochs

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

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

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

            # If we save using the predefined names, we can load using `from_pretrained`
            snake_case__  :    Optional[Any]    						=      os.path.join(args.output_dir     ,   SCREAMING_SNAKE_CASE_		)
            snake_case__  :    int    						=      os.path.join(args.output_dir     ,   SCREAMING_SNAKE_CASE_		)

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

            # Load a trained model and vocabulary that you have fine-tuned
            snake_case__  :    int    						=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir		)
            snake_case__  :    Union[str, Any]    						=      OpenAIGPTTokenizer.from_pretrained(args.output_dir		)
            model.to(SCREAMING_SNAKE_CASE_		)

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

                  snake_case__  :    Optional[int]    						=      mc_logits.detach().cpu().numpy()
                  snake_case__  :    Union[str, Any]    						=      mc_labels.to("""cpu"""		).numpy()
                  snake_case__  :    Union[str, Any]    						=      accuracy(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_		)

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

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

            snake_case__  :    Optional[int]    						=      eval_loss / nb_eval_steps
            snake_case__  :    Any    						=      eval_accuracy / nb_eval_examples
            snake_case__  :    Optional[int]    						=      tr_loss / nb_tr_steps if args.do_train else None
            snake_case__  :    Optional[int]    						=      {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

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


if __name__ == "__main__":
       main()






class 			a__    (					snake_case__      ):
						pass






class 			a__    (					snake_case__      ):
						pass






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [
											    [],
											    [],
											    [],
											]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																if len(self.queues[priority]			) >= 1_0_0:
																					raise OverflowError("Maximum queue size is 100"			)
																self.queues[priority].append(_A			)
											except IndexError:
																raise ValueError("Valid priorities are 0, 1, and 2"			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for queue in self.queues:
																if queue:
																					return queue.pop(0			)
											raise UnderFlowError("All queues are empty"			)






						def __str__(   self			):




											"""simple docstring"""



											return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues			)			)






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if len(self.queue			) == 1_0_0:
																raise OverFlowError("Maximum queue size is 100"			)
											self.queue.append(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.queue:
																raise UnderFlowError("The queue is empty"			)
											else:
																__lowerCAmelCase				   =       min(self.queue			)
																self.queue.remove(_A			)
																return data






						def __str__(   self			):




											"""simple docstring"""



											return str(self.queue			)







def 	_a      (						):
					__lowerCAmelCase				   =       FixedPriorityQueue()
					fpq.enqueue(0      ,      10     )
					fpq.enqueue(1      ,      70     )
					fpq.enqueue(0      ,      1_00     )
					fpq.enqueue(2      ,      1     )
					fpq.enqueue(2      ,      5     )
					fpq.enqueue(1      ,      7     )
					fpq.enqueue(2      ,      4     )
					fpq.enqueue(1      ,      64     )
					fpq.enqueue(0      ,      1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )
					print(fpq.dequeue()     )







def 	_a      (						):
					__lowerCAmelCase				   =       ElementPriorityQueue()
					epq.enqueue(10     )
					epq.enqueue(70     )
					epq.enqueue(1_00     )
					epq.enqueue(1     )
					epq.enqueue(5     )
					epq.enqueue(7     )
					epq.enqueue(4     )
					epq.enqueue(64     )
					epq.enqueue(1_28     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(SCREAMING_SNAKE_CASE_     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )
					print(epq.dequeue()     )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()




