

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_lowerCamelCase								=		1e-10
_lowerCamelCase								=		"This is incredible! I love it, this is the best chicken I have ever had."
_lowerCamelCase								=		1_00



class 		a_	(							nn.Module					):



      """simple docstring"""







      def __init__(				self					:							Optional[int]      ,snake_case					:							Any      ,snake_case					:							Optional[int]="gpt2-medium"      ,snake_case					:							List[str]=False      ,snake_case					:							Tuple="cpu"    ):
           super().__init__()
           SCREAMING_SNAKE_CASE										=GPTaTokenizer.from_pretrained(_UpperCAmelCase    )
           SCREAMING_SNAKE_CASE										=GPTaLMHeadModel.from_pretrained(_UpperCAmelCase    )
           SCREAMING_SNAKE_CASE										=self.encoder.transformer.config.hidden_size
           SCREAMING_SNAKE_CASE										=ClassificationHead(class_size=_UpperCAmelCase      ,embed_size=self.embed_size    )
           SCREAMING_SNAKE_CASE										=cached_mode
           SCREAMING_SNAKE_CASE										=device
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           return self.classifier_head
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           for param in self.encoder.parameters():
                SCREAMING_SNAKE_CASE										=False
           self.classifier_head.train()
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=x.ne(0    ).unsqueeze(2    ).repeat(1      ,1      ,self.embed_size    ).float().to(self.device    ).detach()
           SCREAMING_SNAKE_CASE										=self.encoder.transformer(_UpperCAmelCase    )["last_hidden_state"]
           SCREAMING_SNAKE_CASE										=hidden * mask
           SCREAMING_SNAKE_CASE										=torch.sum(_UpperCAmelCase      ,dim=1    ) / (torch.sum(_UpperCAmelCase      ,dim=1    ).detach() + EPSILON)
           return avg_hidden


      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							int    ):
           if self.cached_mode:
                SCREAMING_SNAKE_CASE										=x.to(self.device    )
           else:
                SCREAMING_SNAKE_CASE										=self.avg_representation(x.to(self.device    )    )

           SCREAMING_SNAKE_CASE										=self.classifier_head(_UpperCAmelCase    )
           SCREAMING_SNAKE_CASE										=nn.functional.log_softmax(_UpperCAmelCase      ,dim=-1    )

           return probs







class 		a_	(							data.Dataset					):



      """simple docstring"""







      def __init__(				self					:							Union[str, Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str    ):
           SCREAMING_SNAKE_CASE										=X
           SCREAMING_SNAKE_CASE										=y
      def __len__(				self					:							str    ):
           return len(self.X    )


      def __getitem__(				self					:							str      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										={}
           SCREAMING_SNAKE_CASE										=self.X[index]
           SCREAMING_SNAKE_CASE										=self.y[index]
           return data







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     def pad_sequences(lowerCAmelCase_      ):
          SCREAMING_SNAKE_CASE										=[len(snake_case__      ) for seq in sequences]

          SCREAMING_SNAKE_CASE										=torch.zeros(len(snake_case__      ),   max(snake_case__      )      ).long()  # padding value = 0

          for i, seq in enumerate(snake_case__      ):
               SCREAMING_SNAKE_CASE										=lengths[i]
               SCREAMING_SNAKE_CASE										=seq[:end]

          return padded_sequences, lengths

     SCREAMING_SNAKE_CASE										={}
     for key in data[0].keys():
          SCREAMING_SNAKE_CASE										=[d[key] for d in data]

     SCREAMING_SNAKE_CASE										=pad_sequences(item_info['X']      )
     SCREAMING_SNAKE_CASE										=torch.tensor(item_info['y'],   dtype=torch.long      )

     return x_batch, y_batch







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										={}
     for key in data[0].keys():
          SCREAMING_SNAKE_CASE										=[d[key] for d in data]

     SCREAMING_SNAKE_CASE										=torch.cat(item_info['X'],   0      )
     SCREAMING_SNAKE_CASE										=torch.tensor(item_info['y'],   dtype=torch.long      )

     return x_batch, y_batch







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=0,   lowerCAmelCase_=10,   lowerCAmelCase_="cpu"      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=0
     discriminator.train_custom()
     for batch_idx, (input_t, target_t) in enumerate(snake_case__      ):
          SCREAMING_SNAKE_CASE										=input_t.to(snake_case__      ), target_t.to(snake_case__      )

          optimizer.zero_grad()

          SCREAMING_SNAKE_CASE										=discriminator(snake_case__      )
          SCREAMING_SNAKE_CASE										=nn.functional.nll_loss(snake_case__,   snake_case__      )
          loss.backward(retain_graph=snake_case__      )
          optimizer.step()

          samples_so_far += len(snake_case__      )

          if batch_idx % log_interval == 0:
               print(
                   'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                       epoch + 1,   snake_case__,   len(data_loader.dataset      ),   100 * samples_so_far / len(data_loader.dataset      ),   loss.item(),   )      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_="cpu"      ):





     """simple docstring"""







     discriminator.eval()
     SCREAMING_SNAKE_CASE										=0
     SCREAMING_SNAKE_CASE										=0
     with torch.no_grad():
          for input_t, target_t in data_loader:
               SCREAMING_SNAKE_CASE										=input_t.to(snake_case__      ), target_t.to(snake_case__      )
               SCREAMING_SNAKE_CASE										=discriminator(snake_case__      )
               # sum up batch loss
               test_loss += nn.functional.nll_loss(snake_case__,   snake_case__,   reduction='sum'      ).item()
               # get the index of the max log-probability
               SCREAMING_SNAKE_CASE										=output_t.argmax(dim=1,   keepdim=snake_case__      )
               correct += pred_t.eq(target_t.view_as(snake_case__      )      ).sum().item()

     test_loss /= len(data_loader.dataset      )

     print(
         'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
             snake_case__,   snake_case__,   len(data_loader.dataset      ),   100.0 * correct / len(data_loader.dataset      )      )      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=False,   lowerCAmelCase_="cpu"      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=model.tokenizer.encode(snake_case__      )
     SCREAMING_SNAKE_CASE										=torch.tensor([input_t],   dtype=torch.long,   device=snake_case__      )
     if cached:
          SCREAMING_SNAKE_CASE										=model.avg_representation(snake_case__      )

     SCREAMING_SNAKE_CASE										=model(snake_case__      ).data.cpu().numpy().flatten().tolist()
     print('Input sentence:',   snake_case__      )
     print(
         'Predictions:',   ', '.join('{}: {:.4f}'.format(snake_case__,   math.exp(snake_case__      )      ) for c, log_prob in zip(snake_case__,   snake_case__      )      ),   )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=False,   lowerCAmelCase_="cpu"      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=torch.utils.data.DataLoader(dataset=snake_case__,   batch_size=snake_case__,   collate_fn=snake_case__      )

     SCREAMING_SNAKE_CASE										=[]
     SCREAMING_SNAKE_CASE										=[]
     for batch_idx, (x, y) in enumerate(tqdm(snake_case__,   ascii=snake_case__      )      ):
          with torch.no_grad():
               SCREAMING_SNAKE_CASE										=x.to(snake_case__      )
               SCREAMING_SNAKE_CASE										=discriminator.avg_representation(snake_case__      ).cpu().detach()
               SCREAMING_SNAKE_CASE										=torch.unbind(avg_rep.unsqueeze(1      )      )
               xs += avg_rep_list
               ys += y.cpu().numpy().tolist()

     SCREAMING_SNAKE_CASE										=torch.utils.data.DataLoader(
         dataset=Dataset(snake_case__,   snake_case__      ),   batch_size=snake_case__,   shuffle=snake_case__,   collate_fn=snake_case__      )

     return data_loader







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=None,   lowerCAmelCase_="gpt2-medium",   lowerCAmelCase_=10,   lowerCAmelCase_=64,   lowerCAmelCase_=10,   lowerCAmelCase_=False,   lowerCAmelCase_=False,   lowerCAmelCase_=False,   ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										="cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print('Preprocessing {} dataset...'.format(snake_case__      )      )
     SCREAMING_SNAKE_CASE										=time.time()

     if dataset == "SST":
          SCREAMING_SNAKE_CASE										=["positive", "negative", "very positive", "very negative", "neutral"]
          SCREAMING_SNAKE_CASE										={c: i for i, c in enumerate(snake_case__      )}

          SCREAMING_SNAKE_CASE										=Discriminator(
              class_size=len(snake_case__      ),   pretrained_model=snake_case__,   cached_mode=snake_case__,   device=snake_case__      ).to(snake_case__      )

          SCREAMING_SNAKE_CASE										=torchtext_data.Field()
          SCREAMING_SNAKE_CASE										=torchtext_data.Field(sequential=snake_case__      )
          SCREAMING_SNAKE_CASE										=datasets.SST.splits(
              snake_case__,   snake_case__,   fine_grained=snake_case__,   train_subtrees=snake_case__,   )

          SCREAMING_SNAKE_CASE										=[]
          SCREAMING_SNAKE_CASE										=[]
          for i in trange(len(snake_case__      ),   ascii=snake_case__      ):
               SCREAMING_SNAKE_CASE										=TreebankWordDetokenizer().detokenize(vars(train_data[i]      )['text']      )
               SCREAMING_SNAKE_CASE										=discriminator.tokenizer.encode(snake_case__      )
               SCREAMING_SNAKE_CASE										=torch.tensor([50256] + seq,   device=snake_case__,   dtype=torch.long      )
               x.append(snake_case__      )
               y.append(classaidx[vars(train_data[i]      )['label']]      )
          SCREAMING_SNAKE_CASE										=Dataset(snake_case__,   snake_case__      )

          SCREAMING_SNAKE_CASE										=[]
          SCREAMING_SNAKE_CASE										=[]
          for i in trange(len(snake_case__      ),   ascii=snake_case__      ):
               SCREAMING_SNAKE_CASE										=TreebankWordDetokenizer().detokenize(vars(test_data[i]      )['text']      )
               SCREAMING_SNAKE_CASE										=discriminator.tokenizer.encode(snake_case__      )
               SCREAMING_SNAKE_CASE										=torch.tensor([50256] + seq,   device=snake_case__,   dtype=torch.long      )
               test_x.append(snake_case__      )
               test_y.append(classaidx[vars(test_data[i]      )['label']]      )
          SCREAMING_SNAKE_CASE										=Dataset(snake_case__,   snake_case__      )

          SCREAMING_SNAKE_CASE										={
              "class_size": len(snake_case__      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }

     elif dataset == "clickbait":
          SCREAMING_SNAKE_CASE										=["non_clickbait", "clickbait"]
          SCREAMING_SNAKE_CASE										={c: i for i, c in enumerate(snake_case__      )}

          SCREAMING_SNAKE_CASE										=Discriminator(
              class_size=len(snake_case__      ),   pretrained_model=snake_case__,   cached_mode=snake_case__,   device=snake_case__      ).to(snake_case__      )

          with open('datasets/clickbait/clickbait_train_prefix.txt'      ) as f:
               SCREAMING_SNAKE_CASE										=[]
               for i, line in enumerate(snake_case__      ):
                    try:
                         data.append(eval(snake_case__      )      )
                    except Exception:
                         print('Error evaluating line {}: {}'.format(snake_case__,   snake_case__      )      )
                         continue
          SCREAMING_SNAKE_CASE										=[]
          SCREAMING_SNAKE_CASE										=[]
          with open('datasets/clickbait/clickbait_train_prefix.txt'      ) as f:
               for i, line in enumerate(tqdm(snake_case__,   ascii=snake_case__      )      ):
                    try:
                         SCREAMING_SNAKE_CASE										=eval(snake_case__      )
                         SCREAMING_SNAKE_CASE										=discriminator.tokenizer.encode(d['text']      )

                         if len(snake_case__      ) < max_length_seq:
                              SCREAMING_SNAKE_CASE										=torch.tensor([50256] + seq,   device=snake_case__,   dtype=torch.long      )
                         else:
                              print('Line {} is longer than maximum length {}'.format(snake_case__,   snake_case__      )      )
                              continue
                         x.append(snake_case__      )
                         y.append(d['label']      )
                    except Exception:
                         print('Error evaluating / tokenizing line {}, skipping it'.format(snake_case__      )      )
                         pass

          SCREAMING_SNAKE_CASE										=Dataset(snake_case__,   snake_case__      )
          SCREAMING_SNAKE_CASE										=int(0.9 * len(snake_case__      )      )
          SCREAMING_SNAKE_CASE										=len(snake_case__      ) - train_size
          SCREAMING_SNAKE_CASE										=torch.utils.data.random_split(snake_case__,   [train_size, test_size]      )

          SCREAMING_SNAKE_CASE										={
              "class_size": len(snake_case__      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }

     elif dataset == "toxic":
          SCREAMING_SNAKE_CASE										=["non_toxic", "toxic"]
          SCREAMING_SNAKE_CASE										={c: i for i, c in enumerate(snake_case__      )}

          SCREAMING_SNAKE_CASE										=Discriminator(
              class_size=len(snake_case__      ),   pretrained_model=snake_case__,   cached_mode=snake_case__,   device=snake_case__      ).to(snake_case__      )

          SCREAMING_SNAKE_CASE										=[]
          SCREAMING_SNAKE_CASE										=[]
          with open('datasets/toxic/toxic_train.txt'      ) as f:
               for i, line in enumerate(tqdm(snake_case__,   ascii=snake_case__      )      ):
                    try:
                         SCREAMING_SNAKE_CASE										=eval(snake_case__      )
                         SCREAMING_SNAKE_CASE										=discriminator.tokenizer.encode(d['text']      )

                         if len(snake_case__      ) < max_length_seq:
                              SCREAMING_SNAKE_CASE										=torch.tensor([50256] + seq,   device=snake_case__,   dtype=torch.long      )
                         else:
                              print('Line {} is longer than maximum length {}'.format(snake_case__,   snake_case__      )      )
                              continue
                         x.append(snake_case__      )
                         y.append(int(np.sum(d['label']      ) > 0      )      )
                    except Exception:
                         print('Error evaluating / tokenizing line {}, skipping it'.format(snake_case__      )      )
                         pass

          SCREAMING_SNAKE_CASE										=Dataset(snake_case__,   snake_case__      )
          SCREAMING_SNAKE_CASE										=int(0.9 * len(snake_case__      )      )
          SCREAMING_SNAKE_CASE										=len(snake_case__      ) - train_size
          SCREAMING_SNAKE_CASE										=torch.utils.data.random_split(snake_case__,   [train_size, test_size]      )

          SCREAMING_SNAKE_CASE										={
              "class_size": len(snake_case__      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

     else:  # if dataset == "generic":
          # This assumes the input dataset is a TSV with the following structure:
          # class \t text

          if dataset_fp is None:
               raise ValueError('When generic dataset is selected, dataset_fp needs to be specified aswell.'      )

          SCREAMING_SNAKE_CASE										=set()
          with open(snake_case__      ) as f:
               SCREAMING_SNAKE_CASE										=csv.reader(snake_case__,   delimiter='\t'      )
               for row in tqdm(snake_case__,   ascii=snake_case__      ):
                    if row:
                         classes.add(row[0]      )

          SCREAMING_SNAKE_CASE										=sorted(snake_case__      )
          SCREAMING_SNAKE_CASE										={c: i for i, c in enumerate(snake_case__      )}

          SCREAMING_SNAKE_CASE										=Discriminator(
              class_size=len(snake_case__      ),   pretrained_model=snake_case__,   cached_mode=snake_case__,   device=snake_case__      ).to(snake_case__      )

          SCREAMING_SNAKE_CASE										=[]
          SCREAMING_SNAKE_CASE										=[]
          with open(snake_case__      ) as f:
               SCREAMING_SNAKE_CASE										=csv.reader(snake_case__,   delimiter='\t'      )
               for i, row in enumerate(tqdm(snake_case__,   ascii=snake_case__      )      ):
                    if row:
                         SCREAMING_SNAKE_CASE										=row[0]
                         SCREAMING_SNAKE_CASE										=row[1]

                         try:
                              SCREAMING_SNAKE_CASE										=discriminator.tokenizer.encode(snake_case__      )
                              if len(snake_case__      ) < max_length_seq:
                                   SCREAMING_SNAKE_CASE										=torch.tensor([50256] + seq,   device=snake_case__,   dtype=torch.long      )

                              else:
                                   print('Line {} is longer than maximum length {}'.format(snake_case__,   snake_case__      )      )
                                   continue

                              x.append(snake_case__      )
                              y.append(classaidx[label]      )

                         except Exception:
                              print('Error tokenizing line {}, skipping it'.format(snake_case__      )      )
                              pass

          SCREAMING_SNAKE_CASE										=Dataset(snake_case__,   snake_case__      )
          SCREAMING_SNAKE_CASE										=int(0.9 * len(snake_case__      )      )
          SCREAMING_SNAKE_CASE										=len(snake_case__      ) - train_size
          SCREAMING_SNAKE_CASE										=torch.utils.data.random_split(snake_case__,   [train_size, test_size]      )

          SCREAMING_SNAKE_CASE										={
              "class_size": len(snake_case__      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

     SCREAMING_SNAKE_CASE										=time.time()
     print('Preprocessed {} data points'.format(len(snake_case__      ) + len(snake_case__      )      )      )
     print('Data preprocessing took: {:.3f}s'.format(end - start      )      )

     if cached:
          print('Building representation cache...'      )

          SCREAMING_SNAKE_CASE										=time.time()

          SCREAMING_SNAKE_CASE										=get_cached_data_loader(snake_case__,   snake_case__,   snake_case__,   shuffle=snake_case__,   device=snake_case__      )

          SCREAMING_SNAKE_CASE										=get_cached_data_loader(snake_case__,   snake_case__,   snake_case__,   device=snake_case__      )

          SCREAMING_SNAKE_CASE										=time.time()
          print('Building representation cache took: {:.3f}s'.format(end - start      )      )

     else:
          SCREAMING_SNAKE_CASE										=torch.utils.data.DataLoader(
              dataset=snake_case__,   batch_size=snake_case__,   shuffle=snake_case__,   collate_fn=snake_case__      )
          SCREAMING_SNAKE_CASE										=torch.utils.data.DataLoader(dataset=snake_case__,   batch_size=snake_case__,   collate_fn=snake_case__      )

     if save_model:
          with open('{}_classifier_head_meta.json'.format(snake_case__      ),   'w'      ) as meta_file:
               json.dump(snake_case__,   snake_case__      )

     SCREAMING_SNAKE_CASE										=optim.Adam(discriminator.parameters(),   lr=0.0001      )

     for epoch in range(snake_case__      ):
          SCREAMING_SNAKE_CASE										=time.time()
          print('\nEpoch',   epoch + 1      )

          train_epoch(
              discriminator=snake_case__,   data_loader=snake_case__,   optimizer=snake_case__,   epoch=snake_case__,   log_interval=snake_case__,   device=snake_case__,   )
          evaluate_performance(data_loader=snake_case__,   discriminator=snake_case__,   device=snake_case__      )

          SCREAMING_SNAKE_CASE										=time.time()
          print('Epoch took: {:.3f}s'.format(end - start      )      )

          print('\nExample prediction'      )
          predict(snake_case__,   snake_case__,   snake_case__,   cached=snake_case__,   device=snake_case__      )

          if save_model:
               # torch.save(discriminator.state_dict(),
               #           "{}_discriminator_{}.pt".format(
               #               args.dataset, epoch + 1
               #               ))
               torch.save(
                   discriminator.get_classifier().state_dict(),   '{}_classifier_head_epoch_{}.pt'.format(snake_case__,   epoch + 1      ),   )


if __name__ == "__main__":
 _lowerCamelCase								=		argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
 parser.add_argument(
     "--dataset",
     type=str,
     default="SST",
     choices=("SST", "clickbait", "toxic", "generic"),
     help=(
         "dataset to train the discriminator on."
         "In case of generic, the dataset is expected"
         "to be a TSBV file with structure: class \\t text"
     ),
 )
 parser.add_argument(
     "--dataset_fp",
     type=str,
     default="",
     help="File path of the dataset to use. Needed only in case of generic datadset",
 )
 parser.add_argument(
     "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
 )
 parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
 parser.add_argument(
     "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
 )
 parser.add_argument(
     "--log_interval",
     type=int,
     default=10,
     metavar="N",
     help="how many batches to wait before logging training status",
 )
 parser.add_argument("--save_model", action="store_true", help="whether to save the model")
 parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
 parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
 _lowerCamelCase								=		parser.parse_args()

 train_discriminator(**(vars(args)))




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase								={
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=["XLMProphetNetTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=[
     "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
     "XLMProphetNetForCausalLM",
     "XLMProphetNetForConditionalGeneration",
     "XLMProphetNetModel",
     "XLMProphetNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_prophetnet import (
      XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMProphetNetDecoder,
      XLMProphetNetEncoder,
      XLMProphetNetForCausalLM,
      XLMProphetNetForConditionalGeneration,
      XLMProphetNetModel,
      XLMProphetNetPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase								=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

