



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 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    np.argmax(_UpperCamelCase ,axis=1							)
   return np.sum(outputs == labels							)





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   with open(_UpperCamelCase ,encoding='''utf_8'''							) as f:
      __lowerCamelCase													=    csv.reader(_UpperCamelCase							)
      __lowerCamelCase													=    []
      next(_UpperCamelCase							)  # skip the first line
      for line in tqdm(_UpperCamelCase							):
         output.append((''' '''.join(line[1:5]							), line[5], line[6], int(line[-1]							) - 1)							)
   return output





def 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: Dict ,_UpperCamelCase			: str ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any ,_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    []
   for dataset in encoded_datasets:
      __lowerCamelCase													=    len(_UpperCamelCase							)
      __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(_UpperCamelCase							):
         __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(_UpperCamelCase							) - 1
         __lowerCamelCase													=    len(_UpperCamelCase							) - 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(_UpperCamelCase							) for t in all_inputs							)							)
   return tensor_datasets





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

   parser.add_argument('''--server_ip''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.'''							)
   parser.add_argument('''--server_port''' ,type=_UpperCamelCase ,default='''''' ,help='''Can be used for distant debugging.'''							)
   __lowerCamelCase													=    parser.parse_args()
   print(_UpperCamelCase							)

   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=_UpperCamelCase							)
      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(_UpperCamelCase ,_UpperCamelCase							)							)

   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(_UpperCamelCase							)
   __lowerCamelCase													=    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)
   __lowerCamelCase													=    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name							)
   model.resize_token_embeddings(len(_UpperCamelCase							)							)
   model.to(_UpperCamelCase							)

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

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

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

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

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

   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(_UpperCamelCase ,desc='''Training'''							)
         for step, batch in enumerate(_UpperCamelCase							):
            __lowerCamelCase													=    tuple(t.to(_UpperCamelCase							) for t in batch							)
            __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    batch
            __lowerCamelCase													=    model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase							)
            __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(_UpperCamelCase ,scheduler.get_lr()[0]							)

    # Save a trained model
   if args.do_train:
      # Save a trained model, configuration and tokenizer
      __lowerCamelCase													=    model.module if hasattr(_UpperCamelCase ,'''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 ,_UpperCamelCase							)
      __lowerCamelCase													=    os.path.join(args.output_dir ,_UpperCamelCase							)

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

   if args.do_eval:
      model.eval()
      __lowerCamelCase							,__lowerCamelCase													=    0, 0
      __lowerCamelCase							,__lowerCamelCase													=    0, 0
      for batch in tqdm(_UpperCamelCase ,desc='''Evaluating'''							):
         __lowerCamelCase													=    tuple(t.to(_UpperCamelCase							) for t in batch							)
         __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    batch
         with torch.no_grad():
            __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    model(
                _UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase							)

         __lowerCamelCase													=    mc_logits.detach().cpu().numpy()
         __lowerCamelCase													=    mc_labels.to('''cpu'''							).numpy()
         __lowerCamelCase													=    accuracy(_UpperCamelCase ,_UpperCamelCase							)

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


if __name__ == "__main__":
  main()











import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
       for i in range(0	,				len(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   __lowerCamelCase													=    torch.mean(torch.cat(_UpperCamelCase							)							)
   try:
      __lowerCamelCase													=    torch.exp(_UpperCamelCase							)
   except OverflowError:
      __lowerCamelCase													=    float('''inf'''							)
   return loss.item(), perplexity.item()


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")






