




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
)
SCREAMING_SNAKE_CASE__       :      str  =   logging.getLogger(__name__)

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> List[str]:
       lowerCamelCase   :      List[Any]			 =					np.argmax(_SCREAMING_SNAKE_CASE ,axis=1    )
       return np.sum(outputs == labels    )

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Dict:
       with open(_SCREAMING_SNAKE_CASE ,encoding="utf_8"    ) as f:
              lowerCamelCase   :      str			 =					csv.reader(_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      int			 =					[]
              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      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Dict			 =					[]
       for dataset in encoded_datasets:
              lowerCamelCase   :      int			 =					len(_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      Optional[Any]			 =					np.zeros((n_batch, 2, input_len) ,dtype=np.intaa    )
              lowerCamelCase   :      str			 =					np.zeros((n_batch, 2) ,dtype=np.intaa    )
              lowerCamelCase   :      Optional[Any]			 =					np.full((n_batch, 2, input_len) ,fill_value=-100 ,dtype=np.intaa    )
              lowerCamelCase   :      Dict			 =					np.zeros((n_batch,) ,dtype=np.intaa    )
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(_SCREAMING_SNAKE_CASE    ):
                     lowerCamelCase   :      str			 =					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowerCamelCase   :      List[str]			 =					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowerCamelCase   :      Any			 =					with_conta
                     lowerCamelCase   :      Tuple			 =					with_conta
                     lowerCamelCase   :      Tuple			 =					len(_SCREAMING_SNAKE_CASE    ) - 1
                     lowerCamelCase   :      int			 =					len(_SCREAMING_SNAKE_CASE    ) - 1
                     lowerCamelCase   :      Tuple			 =					with_conta
                     lowerCamelCase   :      Dict			 =					with_conta
                     lowerCamelCase   :      Union[str, Any]			 =					mc_label
              lowerCamelCase   :      List[Any]			 =					(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      A      (      )	-> int:
       lowerCamelCase   :      Dict			 =					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=374    )

       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."    )
       lowerCamelCase   :      Union[str, Any]			 =					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    )

       lowerCamelCase   :      List[Any]			 =					torch.device("cuda" if torch.cuda.is_available() else "cpu"    )
       lowerCamelCase   :      Tuple			 =					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
       lowerCamelCase   :      Union[str, Any]			 =					["_start_", "_delimiter_", "_classify_"]
       lowerCamelCase   :      Dict			 =					OpenAIGPTTokenizer.from_pretrained(args.model_name    )
       tokenizer.add_tokens(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					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    ):
              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..."    )
       lowerCamelCase   :      Optional[int]			 =					load_rocstories_dataset(args.train_dataset    )
       lowerCamelCase   :      Optional[Any]			 =					load_rocstories_dataset(args.eval_dataset    )
       lowerCamelCase   :      List[str]			 =					(train_dataset, eval_dataset)
       lowerCamelCase   :      Dict			 =					tokenize_and_encode(_SCREAMING_SNAKE_CASE    )

       # Compute the max input length for the Transformer
       lowerCamelCase   :      List[str]			 =					model.config.n_positions // 2 - 2
       lowerCamelCase   :      List[Any]			 =					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   :      List[str]			 =					min(_SCREAMING_SNAKE_CASE ,model.config.n_positions    )  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       lowerCamelCase   :      Any			 =					pre_process_datasets(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,*_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					tensor_datasets[0], tensor_datasets[1]

       lowerCamelCase   :      Dict			 =					TensorDataset(*_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					RandomSampler(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					DataLoader(_SCREAMING_SNAKE_CASE ,sampler=_SCREAMING_SNAKE_CASE ,batch_size=args.train_batch_size    )

       lowerCamelCase   :      Any			 =					TensorDataset(*_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					SequentialSampler(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					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:
                     lowerCamelCase   :      List[str]			 =					args.max_steps
                     lowerCamelCase   :      Optional[int]			 =					args.max_steps // (len(_SCREAMING_SNAKE_CASE    ) // args.gradient_accumulation_steps) + 1
              else:
                     lowerCamelCase   :      List[Any]			 =					len(_SCREAMING_SNAKE_CASE    ) // args.gradient_accumulation_steps * args.num_train_epochs

              lowerCamelCase   :      Optional[Any]			 =					list(model.named_parameters()    )
              lowerCamelCase   :      Union[str, Any]			 =					["bias", "LayerNorm.bias", "LayerNorm.weight"]
              lowerCamelCase   :      List[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},
              ]
              lowerCamelCase   :      Optional[Any]			 =					AdamW(_SCREAMING_SNAKE_CASE ,lr=args.learning_rate ,eps=args.adam_epsilon    )
              lowerCamelCase   :      List[Any]			 =					get_linear_schedule_with_warmup(
                  _SCREAMING_SNAKE_CASE ,num_warmup_steps=args.warmup_steps ,num_training_steps=_SCREAMING_SNAKE_CASE    )

       if args.do_train:
              lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs    ) ,desc="Epoch"    ):
                     lowerCamelCase   :      Any			 =					0
                     lowerCamelCase   :      str			 =					0
                     lowerCamelCase   :      str			 =					tqdm(_SCREAMING_SNAKE_CASE ,desc="Training"    )
                     for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
                            lowerCamelCase   :      Optional[Any]			 =					tuple(t.to(_SCREAMING_SNAKE_CASE    ) for t in batch    )
                            lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Dict			 =					batch
                            lowerCamelCase   :      Optional[int]			 =					model(_SCREAMING_SNAKE_CASE ,mc_token_ids=_SCREAMING_SNAKE_CASE ,lm_labels=_SCREAMING_SNAKE_CASE ,mc_labels=_SCREAMING_SNAKE_CASE    )
                            lowerCamelCase   :      str			 =					args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowerCamelCase   :      Tuple			 =					(
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowerCamelCase   :      int			 =					"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
              lowerCamelCase   :      Any			 =					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`
              lowerCamelCase   :      Dict			 =					os.path.join(args.output_dir ,_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      List[str]			 =					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
              lowerCamelCase   :      Optional[Any]			 =					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
              lowerCamelCase   :      Tuple			 =					OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
              model.to(_SCREAMING_SNAKE_CASE    )

       if args.do_eval:
              model.eval()
              lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					0, 0
              lowerCamelCase ,      lowerCamelCase   :      List[Any]			 =					0, 0
              for batch in tqdm(_SCREAMING_SNAKE_CASE ,desc="Evaluating"    ):
                     lowerCamelCase   :      Union[str, Any]			 =					tuple(t.to(_SCREAMING_SNAKE_CASE    ) for t in batch    )
                     lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					batch
                     with torch.no_grad():
                            lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					model(
                                _SCREAMING_SNAKE_CASE ,mc_token_ids=_SCREAMING_SNAKE_CASE ,lm_labels=_SCREAMING_SNAKE_CASE ,mc_labels=_SCREAMING_SNAKE_CASE    )

                     lowerCamelCase   :      List[str]			 =					mc_logits.detach().cpu().numpy()
                     lowerCamelCase   :      Any			 =					mc_labels.to("cpu"    ).numpy()
                     lowerCamelCase   :      str			 =					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

              lowerCamelCase   :      Any			 =					eval_loss / nb_eval_steps
              lowerCamelCase   :      Optional[Any]			 =					eval_accuracy / nb_eval_examples
              lowerCamelCase   :      Any			 =					tr_loss / nb_tr_steps if args.do_train else None
              lowerCamelCase   :      List[Any]			 =					{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

              lowerCamelCase   :      Optional[Any]			 =					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
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






