'''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_ : int						  =      logging.getLogger(__name__)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						List[str]				)	->							str:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						List[Any]				)	->							int:
      '''simple docstring'''


      with open(__lowerCamelCase ,		encoding="utf_8"				) as f:
            lowercase_     =      csv.reader(__lowerCamelCase				)
            lowercase_     =      []
            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 __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[Any]				)	->							Any:
      '''simple docstring'''


      lowercase_     =      []
      for dataset in encoded_datasets:
            lowercase_     =      len(__lowerCamelCase				)
            lowercase_     =      np.zeros((n_batch, 2, input_len) ,		dtype=np.intaa				)
            lowercase_     =      np.zeros((n_batch, 2) ,		dtype=np.intaa				)
            lowercase_     =      np.full((n_batch, 2, input_len) ,		fill_value=-100 ,		dtype=np.intaa				)
            lowercase_     =      np.zeros((n_batch,) ,		dtype=np.intaa				)
            for (
                i,
                (story, conta, conta, mc_label),
            ) in enumerate(__lowerCamelCase				):
                  lowercase_     =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowercase_     =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowercase_     =      with_conta
                  lowercase_     =      with_conta
                  lowercase_     =      len(__lowerCamelCase				) - 1
                  lowercase_     =      len(__lowerCamelCase				) - 1
                  lowercase_     =      with_conta
                  lowercase_     =      with_conta
                  lowercase_     =      mc_label
            lowercase_     =      (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 __a (	)	->							int:
      '''simple docstring'''


      lowercase_     =      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."				)
      lowercase_     =      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				)

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

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

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

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

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

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

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

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

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

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

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

            lowercase_     =      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'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






