






"""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
)
lowercase    			=       logging.getLogger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     np.argmax(__lowerCamelCase ,						axis=1   )
						return np.sum(outputs == labels   )






def _lowerCAmelCase	(  __lowerCamelCase:List[Any]   ):

						'''simple docstring'''






						with open(__lowerCamelCase ,						encoding="utf_8"   ) as f:
												__magic_name__            =     csv.reader(__lowerCamelCase   )
												__magic_name__            =     []
												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 _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     []
						for dataset in encoded_datasets:
												__magic_name__            =     len(__lowerCamelCase   )
												__magic_name__            =     np.zeros((n_batch, 2, input_len) ,						dtype=np.intaa   )
												__magic_name__            =     np.zeros((n_batch, 2) ,						dtype=np.intaa   )
												__magic_name__            =     np.full((n_batch, 2, input_len) ,						fill_value=-1_0_0 ,						dtype=np.intaa   )
												__magic_name__            =     np.zeros((n_batch,) ,						dtype=np.intaa   )
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(__lowerCamelCase   ):
																		__magic_name__            =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		__magic_name__            =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		__magic_name__            =     with_conta
																		__magic_name__            =     with_conta
																		__magic_name__            =     len(__lowerCamelCase   ) - 1
																		__magic_name__            =     len(__lowerCamelCase   ) - 1
																		__magic_name__            =     with_conta
																		__magic_name__            =     with_conta
																		__magic_name__            =     mc_label
												__magic_name__            =     (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 _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     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=4_2   )
						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=1_6   )
						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=6.25E-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=3_7_4   )

						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."   )
						__magic_name__            =     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   )

						__magic_name__            =     torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
						__magic_name__            =     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
						__magic_name__            =     ["_start_", "_delimiter_", "_classify_"]
						__magic_name__            =     OpenAIGPTTokenizer.from_pretrained(args.model_name   )
						tokenizer.add_tokens(__lowerCamelCase   )
						__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase   )
						__magic_name__            =     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:Union[str, Any]   ):
												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..."   )
						__magic_name__            =     load_rocstories_dataset(args.train_dataset   )
						__magic_name__            =     load_rocstories_dataset(args.eval_dataset   )
						__magic_name__            =     (train_dataset, eval_dataset)
						__magic_name__            =     tokenize_and_encode(__lowerCamelCase   )

						# Compute the max input length for the Transformer
						__magic_name__            =     model.config.n_positions // 2 - 2
						__magic_name__            =     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   )
						__magic_name__            =     min(__lowerCamelCase ,						model.config.n_positions   )  # Max size of input for the pre-trained model

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

						__magic_name__            =     TensorDataset(*__lowerCamelCase   )
						__magic_name__            =     RandomSampler(__lowerCamelCase   )
						__magic_name__            =     DataLoader(__lowerCamelCase ,						sampler=__lowerCamelCase ,						batch_size=args.train_batch_size   )

						__magic_name__            =     TensorDataset(*__lowerCamelCase   )
						__magic_name__            =     SequentialSampler(__lowerCamelCase   )
						__magic_name__            =     DataLoader(__lowerCamelCase ,						sampler=__lowerCamelCase ,						batch_size=args.eval_batch_size   )

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

												__magic_name__            =     list(model.named_parameters()   )
												__magic_name__            =     ["bias", "LayerNorm.bias", "LayerNorm.weight"]
												__magic_name__            =     [
												    {
												        "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},
												]
												__magic_name__            =     AdamW(__lowerCamelCase ,						lr=args.learning_rate ,						eps=args.adam_epsilon   )
												__magic_name__            =     get_linear_schedule_with_warmup(
												    __lowerCamelCase ,						num_warmup_steps=args.warmup_steps ,						num_training_steps=__lowerCamelCase   )

						if args.do_train:
												__magic_name__      ,    __magic_name__      ,    __magic_name__            =     0, 0, None
												model.train()
												for _ in trange(int(args.num_train_epochs   ) ,						desc="Epoch"   ):
																		__magic_name__            =     0
																		__magic_name__            =     0
																		__magic_name__            =     tqdm(__lowerCamelCase ,						desc="Training"   )
																		for step, batch in enumerate(__lowerCamelCase   ):
																								__magic_name__            =     tuple(t.to(__lowerCamelCase   ) for t in batch   )
																								__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     batch
																								__magic_name__            =     model(__lowerCamelCase ,						mc_token_ids=__lowerCamelCase ,						lm_labels=__lowerCamelCase ,						mc_labels=__lowerCamelCase   )
																								__magic_name__            =     args.lm_coef * losses[0] + losses[1]
																								loss.backward()
																								optimizer.step()
																								scheduler.step()
																								optimizer.zero_grad()
																								tr_loss += loss.item()
																								__magic_name__            =     (
																								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																								)
																								nb_tr_steps += 1
																								__magic_name__            =     "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
												__magic_name__            =     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`
												__magic_name__            =     os.path.join(args.output_dir ,						__lowerCamelCase   )
												__magic_name__            =     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
												__magic_name__            =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
												__magic_name__            =     OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
												model.to(__lowerCamelCase   )

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

																		__magic_name__            =     mc_logits.detach().cpu().numpy()
																		__magic_name__            =     mc_labels.to("cpu"   ).numpy()
																		__magic_name__            =     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

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

												__magic_name__            =     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()











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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



