



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__   :					Union[str, Any] 						=      logging.getLogger(__name__)

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	np.argmax(A__    ,	axis=1						)
      return np.sum(outputs == labels						)

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	[]
      for dataset in encoded_datasets:
            lowercase__  =	len(A__						)
            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(A__						):
                  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(A__						) - 1
                  lowercase__  =	len(A__						) - 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(A__						) for t in all_inputs						)						)
      return tensor_datasets

def 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('--model_name'    ,	type=A__    ,	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=A__    ,	type=A__    ,	required=A__    ,	help='The output directory where the model predictions and checkpoints will be written.'    ,	)
      parser.add_argument('--train_dataset'    ,	type=A__    ,	default=''						)
      parser.add_argument('--eval_dataset'    ,	type=A__    ,	default=''						)
      parser.add_argument('--seed'    ,	type=A__    ,	default=42						)
      parser.add_argument('--num_train_epochs'    ,	type=A__    ,	default=3						)
      parser.add_argument('--train_batch_size'    ,	type=A__    ,	default=8						)
      parser.add_argument('--eval_batch_size'    ,	type=A__    ,	default=16						)
      parser.add_argument('--adam_epsilon'    ,	default=1E-8    ,	type=A__    ,	help='Epsilon for Adam optimizer.'						)
      parser.add_argument('--max_grad_norm'    ,	type=A__    ,	default=1						)
      parser.add_argument(
          '--max_steps'    ,	default=-1    ,	type=A__    ,	help=(
              'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
          )    ,	)
      parser.add_argument(
          '--gradient_accumulation_steps'    ,	type=A__    ,	default=1    ,	help='Number of updates steps to accumulate before                        performing a backward/update pass.'    ,	)
      parser.add_argument('--learning_rate'    ,	type=A__    ,	default=6.2_5E-5						)
      parser.add_argument('--warmup_steps'    ,	default=0    ,	type=A__    ,	help='Linear warmup over warmup_steps.'						)
      parser.add_argument('--lr_schedule'    ,	type=A__    ,	default='warmup_linear'						)
      parser.add_argument('--weight_decay'    ,	type=A__    ,	default=0.01						)
      parser.add_argument('--lm_coef'    ,	type=A__    ,	default=0.9						)
      parser.add_argument('--n_valid'    ,	type=A__    ,	default=374						)

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

      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=A__						)
            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(A__    ,	A__						)						)

      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(A__						)
      lowercase__  =	tokenizer.convert_tokens_to_ids(A__						)
      lowercase__  =	OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name						)
      model.resize_token_embeddings(len(A__						)						)
      model.to(A__						)

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

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

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

      lowercase__  =	TensorDataset(*A__						)
      lowercase__  =	RandomSampler(A__						)
      lowercase__  =	DataLoader(A__    ,	sampler=A__    ,	batch_size=args.train_batch_size						)

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

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

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

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

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

                  lowercase__  =	mc_logits.detach().cpu().numpy()
                  lowercase__  =	mc_labels.to('cpu'						).numpy()
                  lowercase__  =	accuracy(A__    ,	A__						)

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


if __name__ == "__main__":
       main()





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def 	_lowerCAmelCase (    ):
      lowercase__  =	ArgumentParser('Transformers CLI tool'    ,	usage='transformers-cli <command> [<args>]'						)
      lowercase__  =	parser.add_subparsers(help='transformers-cli command helpers'						)

      # Register commands
      ConvertCommand.register_subcommand(A__						)
      DownloadCommand.register_subcommand(A__						)
      EnvironmentCommand.register_subcommand(A__						)
      RunCommand.register_subcommand(A__						)
      ServeCommand.register_subcommand(A__						)
      UserCommands.register_subcommand(A__						)
      AddNewModelCommand.register_subcommand(A__						)
      AddNewModelLikeCommand.register_subcommand(A__						)
      LfsCommands.register_subcommand(A__						)
      PTtoTFCommand.register_subcommand(A__						)

      # Let's go
      lowercase__  =	parser.parse_args()

      if not hasattr(A__    ,	'func'						):
            parser.print_help()
            exit(1						)

      # Run
      lowercase__  =	args.func(A__						)
      service.run()


if __name__ == "__main__":
       main()
