


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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=np.argmax(SCREAMING_SNAKE_CASE__      ,					axis=1    )
     return np.sum(outputs == labels    )

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=[]
     for dataset in encoded_datasets:
          __UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=np.zeros((n_batch, 2, input_len)      ,					dtype=np.intaa    )
          __UpperCamelCase   							=np.zeros((n_batch, 2)      ,					dtype=np.intaa    )
          __UpperCamelCase   							=np.full((n_batch, 2, input_len)      ,					fill_value=-1_00      ,					dtype=np.intaa    )
          __UpperCamelCase   							=np.zeros((n_batch,)      ,					dtype=np.intaa    )
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase   							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase   							=with_conta
               __UpperCamelCase   							=with_conta
               __UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ) - 1
               __UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ) - 1
               __UpperCamelCase   							=with_conta
               __UpperCamelCase   							=with_conta
               __UpperCamelCase   							=mc_label
          __UpperCamelCase   							=(input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE__    ) for t in all_inputs    )    )
     return tensor_datasets

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

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

     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=SCREAMING_SNAKE_CASE__    )
          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   							=torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
     __UpperCamelCase   							=torch.cuda.device_count()
     logger.info('device: {}, n_gpu {}'.format(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    )

     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   							=['_start_', '_delimiter_', '_classify_']
     __UpperCamelCase   							=OpenAIGPTTokenizer.from_pretrained(args.model_name    )
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__    )    )
     model.to(SCREAMING_SNAKE_CASE__    )

     # Load and encode the datasets
     def tokenize_and_encode(SCREAMING_SNAKE_CASE__      : Optional[int]    ):
          if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               return obj
          return [tokenize_and_encode(SCREAMING_SNAKE_CASE__    ) for o in obj]

     logger.info('Encoding dataset...'    )
     __UpperCamelCase   							=load_rocstories_dataset(args.train_dataset    )
     __UpperCamelCase   							=load_rocstories_dataset(args.eval_dataset    )
     __UpperCamelCase   							=(train_dataset, eval_dataset)
     __UpperCamelCase   							=tokenize_and_encode(SCREAMING_SNAKE_CASE__    )

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

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

     __UpperCamelCase   							=TensorDataset(*SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=RandomSampler(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=DataLoader(SCREAMING_SNAKE_CASE__      ,					sampler=SCREAMING_SNAKE_CASE__      ,					batch_size=args.train_batch_size    )

     __UpperCamelCase   							=TensorDataset(*SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=SequentialSampler(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=DataLoader(SCREAMING_SNAKE_CASE__      ,					sampler=SCREAMING_SNAKE_CASE__      ,					batch_size=args.eval_batch_size    )

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

          __UpperCamelCase   							=list(model.named_parameters()    )
          __UpperCamelCase   							=['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          __UpperCamelCase   							=[
              {
                  '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   							=AdamW(SCREAMING_SNAKE_CASE__      ,					lr=args.learning_rate      ,					eps=args.adam_epsilon    )
          __UpperCamelCase   							=get_linear_schedule_with_warmup(
              SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=args.warmup_steps      ,					num_training_steps=SCREAMING_SNAKE_CASE__    )

     if args.do_train:
          __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs    )      ,					desc='Epoch'    ):
               __UpperCamelCase   							=0
               __UpperCamelCase   							=0
               __UpperCamelCase   							=tqdm(SCREAMING_SNAKE_CASE__      ,					desc='Training'    )
               for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                    __UpperCamelCase   							=tuple(t.to(SCREAMING_SNAKE_CASE__    ) for t in batch    )
                    __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=batch
                    __UpperCamelCase   							=model(SCREAMING_SNAKE_CASE__      ,					mc_token_ids=SCREAMING_SNAKE_CASE__      ,					lm_labels=SCREAMING_SNAKE_CASE__      ,					mc_labels=SCREAMING_SNAKE_CASE__    )
                    __UpperCamelCase   							=args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    __UpperCamelCase   							=(
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    __UpperCamelCase   							='Training loss: {:.2e} lr: {:.2e}'.format(SCREAMING_SNAKE_CASE__      ,					scheduler.get_lr()[0]    )

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          __UpperCamelCase   							=model.module if hasattr(SCREAMING_SNAKE_CASE__      ,					'module'    ) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          __UpperCamelCase   							=os.path.join(args.output_dir      ,					SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=os.path.join(args.output_dir      ,					SCREAMING_SNAKE_CASE__    )

          torch.save(model_to_save.state_dict()      ,					SCREAMING_SNAKE_CASE__    )
          model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE__    )
          tokenizer.save_vocabulary(args.output_dir    )

          # Load a trained model and vocabulary that you have fine-tuned
          __UpperCamelCase   							=OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
          __UpperCamelCase   							=OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
          model.to(SCREAMING_SNAKE_CASE__    )

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

               __UpperCamelCase   							=mc_logits.detach().cpu().numpy()
               __UpperCamelCase   							=mc_labels.to('cpu'    ).numpy()
               __UpperCamelCase   							=accuracy(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

               eval_loss += mc_loss.mean().item()
               eval_accuracy += tmp_eval_accuracy

               nb_eval_examples += input_ids.size(0    )
               nb_eval_steps += 1

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

          __UpperCamelCase   							=os.path.join(args.output_dir      ,					'eval_results.txt'    )
          with open(SCREAMING_SNAKE_CASE__      ,					'w'    ) as writer:
               logger.info('***** Eval results *****'    )
               for key in sorted(result.keys()    ):
                    logger.info('  %s = %s'      ,					SCREAMING_SNAKE_CASE__      ,					str(result[key]    )    )
                    writer.write('%s = %s\n' % (key, str(result[key]    ))    )


if __name__ == "__main__":
   main()









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='bert', choices=['bert'])
   parser.add_argument('--model_name', default='bert-base-uncased', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   _A			=						parser.parse_args()

   if args.model_type == "bert":
      _A			=						BertForMaskedLM.from_pretrained(args.model_name)
      _A			=						'bert'
   else:
      raise ValueError('args.model_type should be "bert".')

   _A			=						model.state_dict()
   _A			=						{}

   for w in ["word_embeddings", "position_embeddings"]:
      _A			=						state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _A			=						state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _A			=						0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      for w in ["weight", "bias"]:
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _A			=						state_dict['cls.predictions.decoder.weight']
   _A			=						state_dict['cls.predictions.bias']
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _A			=						state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _A			=						state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)





