


'''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    						=logging.getLogger(__name__)





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			np.argmax(A_	, axis=1				)
   return np.sum(outputs == labels				)





def 	a_					( lowerCamelCase	:     Optional[int]				):





   with open(A_	, encoding='utf_8'				) as f:
      lowerCAmelCase     =			csv.reader(A_				)
      lowerCAmelCase     =			[]
      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 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     str	, lowerCamelCase	:     List[str]				):
   lowerCAmelCase     =			[]
   for dataset in encoded_datasets:
      lowerCAmelCase     =			len(A_				)
      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=-100	, dtype=np.intaa				)
      lowerCAmelCase     =			np.zeros((n_batch,)	, dtype=np.intaa				)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(A_				):
         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(A_				) - 1
         lowerCAmelCase     =			len(A_				) - 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(A_				) for t in all_inputs				)				)
   return tensor_datasets





def 	a_					( ):
   lowerCAmelCase     =			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=6.25e-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.'				)
   lowerCAmelCase     =			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				)

   lowerCAmelCase     =			torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
   lowerCAmelCase     =			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
   lowerCAmelCase     =			['_start_', '_delimiter_', '_classify_']
   lowerCAmelCase     =			OpenAIGPTTokenizer.from_pretrained(args.model_name				)
   tokenizer.add_tokens(A_				)
   lowerCAmelCase     =			tokenizer.convert_tokens_to_ids(A_				)
   lowerCAmelCase     =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
   model.resize_token_embeddings(len(A_				)				)
   model.to(A_				)

   # Load and encode the datasets
   def tokenize_and_encode(lowerCamelCase	:     Union[str, Any]				):
      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...'				)
   lowerCAmelCase     =			load_rocstories_dataset(args.train_dataset				)
   lowerCAmelCase     =			load_rocstories_dataset(args.eval_dataset				)
   lowerCAmelCase     =			(train_dataset, eval_dataset)
   lowerCAmelCase     =			tokenize_and_encode(A_				)

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

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

   lowerCAmelCase     =			TensorDataset(*A_				)
   lowerCAmelCase     =			RandomSampler(A_				)
   lowerCAmelCase     =			DataLoader(A_	, sampler=A_	, batch_size=args.train_batch_size				)

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

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

      # Save a trained model
   if args.do_train:
      # Save a trained model, configuration and tokenizer
      lowerCAmelCase     =			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`
      lowerCAmelCase     =			os.path.join(args.output_dir	, A_				)
      lowerCAmelCase     =			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
      lowerCAmelCase     =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
      lowerCAmelCase     =			OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
      model.to(A_				)

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

         lowerCAmelCase     =			mc_logits.detach().cpu().numpy()
         lowerCAmelCase     =			mc_labels.to('cpu'				).numpy()
         lowerCAmelCase     =			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

      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(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()







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
    import torch




@is_pipeline_test
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  a__           =		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  a__           =		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       TextaTextGenerationPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__    )
    return generator, ["Something to write", "Something else"]
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: List[str]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       generator("""Something there"""    )
    self.assertEqual(UpperCamelCase__			,							[{"""generated_text""": ANY(UpperCamelCase__    )}]    )
    # These are encoder decoder, they don't just append to incoming string
    self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""    )    )

    __magic_name__				     =       generator(["""This is great !""", """Something else"""]			,							num_return_sequences=2			,							do_sample=UpperCamelCase__    )
    self.assertEqual(
        UpperCamelCase__			,							[
            [{"""generated_text""": ANY(UpperCamelCase__    )}, {"""generated_text""": ANY(UpperCamelCase__    )}],
            [{"""generated_text""": ANY(UpperCamelCase__    )}, {"""generated_text""": ANY(UpperCamelCase__    )}],
        ]			,							)

    __magic_name__				     =       generator(
        ["""This is great !""", """Something else"""]			,							num_return_sequences=2			,							batch_size=2			,							do_sample=UpperCamelCase__    )
    self.assertEqual(
        UpperCamelCase__			,							[
            [{"""generated_text""": ANY(UpperCamelCase__    )}, {"""generated_text""": ANY(UpperCamelCase__    )}],
            [{"""generated_text""": ANY(UpperCamelCase__    )}, {"""generated_text""": ANY(UpperCamelCase__    )}],
        ]			,							)

    with self.assertRaises(UpperCamelCase__    ):
      generator(4    )
  @require_torch
  def 							_lowercase							( self			: Optional[int]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       pipeline("""text2text-generation"""			,							model="""patrickvonplaten/t5-tiny-random"""			,							framework="""pt"""    )
    # do_sample=False necessary for reproducibility
    __magic_name__				     =       generator("""Something there"""			,							do_sample=UpperCamelCase__    )
    self.assertEqual(UpperCamelCase__			,							[{"""generated_text""": """"""}]    )

    __magic_name__				     =       3
    __magic_name__				     =       generator(
        """Something there"""			,							num_return_sequences=UpperCamelCase__			,							num_beams=UpperCamelCase__			,							)
    __magic_name__				     =       [
        {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
        {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
        {"""generated_text""": """"""},
    ]
    self.assertEqual(UpperCamelCase__			,							UpperCamelCase__    )

    __magic_name__				     =       generator("""This is a test"""			,							do_sample=UpperCamelCase__			,							num_return_sequences=2			,							return_tensors=UpperCamelCase__    )
    self.assertEqual(
        UpperCamelCase__			,							[
            {"""generated_token_ids""": ANY(torch.Tensor    )},
            {"""generated_token_ids""": ANY(torch.Tensor    )},
        ]			,							)
    __magic_name__				     =       generator.model.config.eos_token_id
    __magic_name__				     =       """<pad>"""
    __magic_name__				     =       generator(
        ["""This is a test""", """This is a second test"""]			,							do_sample=UpperCamelCase__			,							num_return_sequences=2			,							batch_size=2			,							return_tensors=UpperCamelCase__			,							)
    self.assertEqual(
        UpperCamelCase__			,							[
            [
                {"""generated_token_ids""": ANY(torch.Tensor    )},
                {"""generated_token_ids""": ANY(torch.Tensor    )},
            ],
            [
                {"""generated_token_ids""": ANY(torch.Tensor    )},
                {"""generated_token_ids""": ANY(torch.Tensor    )},
            ],
        ]			,							)





  @require_tf
  def 							_lowercase							( self			: int    )			->	str:







    """simple docstring"""






    __magic_name__				     =       pipeline("""text2text-generation"""			,							model="""patrickvonplaten/t5-tiny-random"""			,							framework="""tf"""    )
    # do_sample=False necessary for reproducibility
    __magic_name__				     =       generator("""Something there"""			,							do_sample=UpperCamelCase__    )
    self.assertEqual(UpperCamelCase__			,							[{"""generated_text""": """"""}]    )

