




"""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
)
lowerCAmelCase							:    List[Any]  							=     logging.getLogger(__name__)



def 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			lowerCamelCase       =      np.argmax(snake_case__ ,				axis=1       )
			return np.sum(outputs == labels       )



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



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Union[str, Any]:
			lowerCamelCase       =      []
			for dataset in encoded_datasets:
						lowerCamelCase       =      len(snake_case__       )
						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=-1_00 ,				dtype=np.intaa       )
						lowerCamelCase       =      np.zeros((n_batch,) ,				dtype=np.intaa       )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(snake_case__       ):
									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(snake_case__       ) - 1
									lowerCamelCase       =      len(snake_case__       ) - 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(snake_case__       ) for t in all_inputs       )       )
			return tensor_datasets



def 					a__				(       )		->      Optional[int]:
			lowerCamelCase       =      argparse.ArgumentParser()
			parser.add_argument("""--model_name""" ,				type=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=snake_case__ ,				type=snake_case__ ,				required=snake_case__ ,				help="""The output directory where the model predictions and checkpoints will be written.""" ,				)
			parser.add_argument("""--train_dataset""" ,				type=snake_case__ ,				default=""""""       )
			parser.add_argument("""--eval_dataset""" ,				type=snake_case__ ,				default=""""""       )
			parser.add_argument("""--seed""" ,				type=snake_case__ ,				default=42       )
			parser.add_argument("""--num_train_epochs""" ,				type=snake_case__ ,				default=3       )
			parser.add_argument("""--train_batch_size""" ,				type=snake_case__ ,				default=8       )
			parser.add_argument("""--eval_batch_size""" ,				type=snake_case__ ,				default=16       )
			parser.add_argument("""--adam_epsilon""" ,				default=1E-8 ,				type=snake_case__ ,				help="""Epsilon for Adam optimizer."""       )
			parser.add_argument("""--max_grad_norm""" ,				type=snake_case__ ,				default=1       )
			parser.add_argument(
			    """--max_steps""" ,				default=-1 ,				type=snake_case__ ,				help=(
			        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
			    ) ,				)
			parser.add_argument(
			    """--gradient_accumulation_steps""" ,				type=snake_case__ ,				default=1 ,				help="""Number of updates steps to accumulate before                        performing a backward/update pass.""" ,				)
			parser.add_argument("""--learning_rate""" ,				type=snake_case__ ,				default=6.25E-5       )
			parser.add_argument("""--warmup_steps""" ,				default=0 ,				type=snake_case__ ,				help="""Linear warmup over warmup_steps."""       )
			parser.add_argument("""--lr_schedule""" ,				type=snake_case__ ,				default="""warmup_linear"""       )
			parser.add_argument("""--weight_decay""" ,				type=snake_case__ ,				default=0.01       )
			parser.add_argument("""--lm_coef""" ,				type=snake_case__ ,				default=0.9       )
			parser.add_argument("""--n_valid""" ,				type=snake_case__ ,				default=3_74       )

			parser.add_argument("""--server_ip""" ,				type=snake_case__ ,				default="""""" ,				help="""Can be used for distant debugging."""       )
			parser.add_argument("""--server_port""" ,				type=snake_case__ ,				default="""""" ,				help="""Can be used for distant debugging."""       )
			lowerCamelCase       =      parser.parse_args()
			print(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=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       )

			lowerCamelCase       =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )
			lowerCamelCase       =      torch.cuda.device_count()
			logger.info("""device: {}, n_gpu {}""".format(snake_case__ ,				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
			lowerCamelCase       =      ["""_start_""", """_delimiter_""", """_classify_"""]
			lowerCamelCase       =      OpenAIGPTTokenizer.from_pretrained(args.model_name       )
			tokenizer.add_tokens(snake_case__       )
			lowerCamelCase       =      tokenizer.convert_tokens_to_ids(snake_case__       )
			lowerCamelCase       =      OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name       )
			model.resize_token_embeddings(len(snake_case__       )       )
			model.to(snake_case__       )

			# Load and encode the datasets
			def tokenize_and_encode(snake_case__       ):
						if isinstance(snake_case__ ,				snake_case__       ):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case__       )       )
						elif isinstance(snake_case__ ,				snake_case__       ):
									return obj
						return [tokenize_and_encode(snake_case__       ) 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(snake_case__       )

			# 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(snake_case__ ,				model.config.n_positions       )  # Max size of input for the pre-trained model

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

			lowerCamelCase       =      TensorDataset(*snake_case__       )
			lowerCamelCase       =      RandomSampler(snake_case__       )
			lowerCamelCase       =      DataLoader(snake_case__ ,				sampler=snake_case__ ,				batch_size=args.train_batch_size       )

			lowerCamelCase       =      TensorDataset(*snake_case__       )
			lowerCamelCase       =      SequentialSampler(snake_case__       )
			lowerCamelCase       =      DataLoader(snake_case__ ,				sampler=snake_case__ ,				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(snake_case__       ) // args.gradient_accumulation_steps) + 1
						else:
									lowerCamelCase       =      len(snake_case__       ) // 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(snake_case__ ,				lr=args.learning_rate ,				eps=args.adam_epsilon       )
						lowerCamelCase       =      get_linear_schedule_with_warmup(
						    snake_case__ ,				num_warmup_steps=args.warmup_steps ,				num_training_steps=snake_case__       )

			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(snake_case__ ,				desc="""Training"""       )
									for step, batch in enumerate(snake_case__       ):
												lowerCamelCase       =      tuple(t.to(snake_case__       ) for t in batch       )
												lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      batch
												lowerCamelCase       =      model(snake_case__ ,				mc_token_ids=snake_case__ ,				lm_labels=snake_case__ ,				mc_labels=snake_case__       )
												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(snake_case__ ,				scheduler.get_lr()[0]       )

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						lowerCamelCase       =      model.module if hasattr(snake_case__ ,				"""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 ,				snake_case__       )
						lowerCamelCase       =      os.path.join(args.output_dir ,				snake_case__       )

						torch.save(model_to_save.state_dict() ,				snake_case__       )
						model_to_save.config.to_json_file(snake_case__       )
						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(snake_case__       )

			if args.do_eval:
						model.eval()
						lowerCamelCase   ,     lowerCamelCase       =      0, 0
						lowerCamelCase   ,     lowerCamelCase       =      0, 0
						for batch in tqdm(snake_case__ ,				desc="""Evaluating"""       ):
									lowerCamelCase       =      tuple(t.to(snake_case__       ) for t in batch       )
									lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      batch
									with torch.no_grad():
												lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      model(
												    snake_case__ ,				mc_token_ids=snake_case__ ,				lm_labels=snake_case__ ,				mc_labels=snake_case__       )

									lowerCamelCase       =      mc_logits.detach().cpu().numpy()
									lowerCamelCase       =      mc_labels.to("""cpu"""       ).numpy()
									lowerCamelCase       =      accuracy(snake_case__ ,				snake_case__       )

									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(snake_case__ ,				"""w"""       ) as writer:
									logger.info("""***** Eval results *****"""       )
									for key in sorted(result.keys()       ):
												logger.info("""  %s = %s""" ,				snake_case__ ,				str(result[key]       )       )
												writer.write("""%s = %s\n""" % (key, str(result[key]       ))       )


if __name__ == "__main__":
					main()









"""simple docstring"""




from __future__ import annotations



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None       )		->      None:
			if start is None:
						lowerCamelCase       =      0

			if end is None:
						lowerCamelCase       =      len(snake_case__       ) - 1

			if start >= end:
						return

			lowerCamelCase       =      (start + end) // 2

			slowsort(snake_case__ ,				snake_case__ ,				snake_case__       )
			slowsort(snake_case__ ,				mid + 1 ,				snake_case__       )

			if sequence[end] < sequence[mid]:
						lowerCamelCase   ,     lowerCamelCase       =      sequence[mid], sequence[end]

			slowsort(snake_case__ ,				snake_case__ ,				end - 1       )


if __name__ == "__main__":
					from doctest import testmod

					testmod()



