'''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
)
snake_case__					:      Optional[int]				      =  logging.getLogger(__name__)


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							List[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				np.argmax(lowerCamelCase_     ,					axis=1       )
     return np.sum(outputs == labels       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     with open(lowerCamelCase_     ,					encoding='utf_8'       ) as f:
          UpperCAmelCase_							:       List[Any]       =				csv.reader(lowerCamelCase_       )
          UpperCAmelCase_							:       List[str]       =				[]
          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_ :							Tuple     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							Any     ,					lowerCamelCase_ :							List[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       str       =				[]
     for dataset in encoded_datasets:
          UpperCAmelCase_							:       Any       =				len(lowerCamelCase_       )
          UpperCAmelCase_							:       Tuple       =				np.zeros((n_batch, 2, input_len)     ,					dtype=np.intaa       )
          UpperCAmelCase_							:       Union[str, Any]       =				np.zeros((n_batch, 2)     ,					dtype=np.intaa       )
          UpperCAmelCase_							:       Any       =				np.full((n_batch, 2, input_len)     ,					fill_value=-100     ,					dtype=np.intaa       )
          UpperCAmelCase_							:       Any       =				np.zeros((n_batch,)     ,					dtype=np.intaa       )
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(lowerCamelCase_       ):
               UpperCAmelCase_							:       Tuple       =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               UpperCAmelCase_							:       Dict       =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               UpperCAmelCase_							:       Dict       =				with_conta
               UpperCAmelCase_							:       Tuple       =				with_conta
               UpperCAmelCase_							:       Dict       =				len(lowerCamelCase_       ) - 1
               UpperCAmelCase_							:       List[str]       =				len(lowerCamelCase_       ) - 1
               UpperCAmelCase_							:       int       =				with_conta
               UpperCAmelCase_							:       Optional[Any]       =				with_conta
               UpperCAmelCase_							:       Optional[Any]       =				mc_label
          UpperCAmelCase_							:       List[Any]       =				(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"""





     UpperCAmelCase_							:       Any       =				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=6.25e-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=0.01       )
     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_							:       Optional[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_							:       Optional[Any]       =				torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
     UpperCAmelCase_							:       Dict       =				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[Any]       =				['_start_', '_delimiter_', '_classify_']
     UpperCAmelCase_							:       Any       =				OpenAIGPTTokenizer.from_pretrained(args.model_name       )
     tokenizer.add_tokens(lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				tokenizer.convert_tokens_to_ids(lowerCamelCase_       )
     UpperCAmelCase_							:       Dict       =				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_ :							str       ):
          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_							:       Tuple       =				load_rocstories_dataset(args.train_dataset       )
     UpperCAmelCase_							:       Dict       =				load_rocstories_dataset(args.eval_dataset       )
     UpperCAmelCase_							:       Tuple       =				(train_dataset, eval_dataset)
     UpperCAmelCase_							:       Optional[int]       =				tokenize_and_encode(lowerCamelCase_       )

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

     # Prepare inputs tensors and dataloaders
     UpperCAmelCase_							:       List[str]       =				pre_process_datasets(lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					*lowerCamelCase_       )
     UpperCAmelCase_       ,      UpperCAmelCase_							:       str       =				tensor_datasets[0], tensor_datasets[1]

     UpperCAmelCase_							:       Any       =				TensorDataset(*lowerCamelCase_       )
     UpperCAmelCase_							:       Optional[Any]       =				RandomSampler(lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				DataLoader(lowerCamelCase_     ,					sampler=lowerCamelCase_     ,					batch_size=args.train_batch_size       )

     UpperCAmelCase_							:       str       =				TensorDataset(*lowerCamelCase_       )
     UpperCAmelCase_							:       str       =				SequentialSampler(lowerCamelCase_       )
     UpperCAmelCase_							:       Any       =				DataLoader(lowerCamelCase_     ,					sampler=lowerCamelCase_     ,					batch_size=args.eval_batch_size       )

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               UpperCAmelCase_							:       Tuple       =				args.max_steps
               UpperCAmelCase_							:       Union[str, Any]       =				args.max_steps // (len(lowerCamelCase_       ) // args.gradient_accumulation_steps) + 1
          else:
               UpperCAmelCase_							:       Dict       =				len(lowerCamelCase_       ) // args.gradient_accumulation_steps * args.num_train_epochs

          UpperCAmelCase_							:       Optional[Any]       =				list(model.named_parameters()       )
          UpperCAmelCase_							:       int       =				['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          UpperCAmelCase_							:       Any       =				[
              {
                  '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_							:       Dict       =				AdamW(lowerCamelCase_     ,					lr=args.learning_rate     ,					eps=args.adam_epsilon       )
          UpperCAmelCase_							:       Optional[Any]       =				get_linear_schedule_with_warmup(
              lowerCamelCase_     ,					num_warmup_steps=args.warmup_steps     ,					num_training_steps=lowerCamelCase_       )

     if args.do_train:
          UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs       )     ,					desc='Epoch'       ):
               UpperCAmelCase_							:       Dict       =				0
               UpperCAmelCase_							:       List[Any]       =				0
               UpperCAmelCase_							:       List[str]       =				tqdm(lowerCamelCase_     ,					desc='Training'       )
               for step, batch in enumerate(lowerCamelCase_       ):
                    UpperCAmelCase_							:       Dict       =				tuple(t.to(lowerCamelCase_       ) for t in batch       )
                    UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				batch
                    UpperCAmelCase_							:       List[str]       =				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_							:       int       =				(
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    UpperCAmelCase_							:       List[str]       =				'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_							:       List[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_							:       str       =				os.path.join(args.output_dir     ,					lowerCamelCase_       )
          UpperCAmelCase_							:       Optional[int]       =				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_							:       Optional[int]       =				OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir       )
          UpperCAmelCase_							:       List[Any]       =				OpenAIGPTTokenizer.from_pretrained(args.output_dir       )
          model.to(lowerCamelCase_       )

     if args.do_eval:
          model.eval()
          UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				0, 0
          UpperCAmelCase_       ,      UpperCAmelCase_							:       Tuple       =				0, 0
          for batch in tqdm(lowerCamelCase_     ,					desc='Evaluating'       ):
               UpperCAmelCase_							:       int       =				tuple(t.to(lowerCamelCase_       ) for t in batch       )
               UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				batch
               with torch.no_grad():
                    UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				model(
                        lowerCamelCase_     ,					mc_token_ids=lowerCamelCase_     ,					lm_labels=lowerCamelCase_     ,					mc_labels=lowerCamelCase_       )

               UpperCAmelCase_							:       Optional[Any]       =				mc_logits.detach().cpu().numpy()
               UpperCAmelCase_							:       int       =				mc_labels.to('cpu'       ).numpy()
               UpperCAmelCase_							:       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_							:       List[str]       =				eval_loss / nb_eval_steps
          UpperCAmelCase_							:       List[str]       =				eval_accuracy / nb_eval_examples
          UpperCAmelCase_							:       List[Any]       =				tr_loss / nb_tr_steps if args.do_train else None
          UpperCAmelCase_							:       Optional[int]       =				{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

          UpperCAmelCase_							:       Optional[int]       =				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'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1


  @register_to_config
  def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




       '''simple docstring'''

       self.set_timesteps(snake_case_						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase_							:       Union[str, Any]       =				1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       UpperCAmelCase_							:       int       =				4

       # running values
       UpperCAmelCase_							:       str       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
       UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
       UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

       if self.config.trained_betas is not None:
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

       UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

       UpperCAmelCase_							:       str       =				(torch.atana(self.betas				,		self.alphas						) / math.pi * 2)[:-1]
       UpperCAmelCase_							:       str       =				timesteps.to(snake_case_						)

       UpperCAmelCase_							:       Any       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = True				,		):




       '''simple docstring'''

       if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

       UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

       UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(snake_case_						)

       if len(self.ets						) == 1:
            UpperCAmelCase_							:       Tuple       =				self.ets[-1]
       elif len(self.ets						) == 2:
            UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets						) == 3:
            UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       UpperCAmelCase_							:       Union[str, Any]       =				self._get_prev_sample(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return sample


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
       UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

       UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
       UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

       UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
       UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

       return prev_sample






  def __len__(			self						):




       '''simple docstring'''

       return self.config.num_train_timesteps






