# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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)
snake_case__						      =			1E-10
snake_case__						      =			'This is incredible! I love it, this is the best chicken I have ever had.'
snake_case__						      =			100



class 			UpperCamelCase					(			nn.Module			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_="gpt2-medium"					,					A_=False					,					A_="cpu"    )			->					Optional[int]:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase           =					GPTaTokenizer.from_pretrained(A_    )
           _lowerCamelCase           =					GPTaLMHeadModel.from_pretrained(A_    )
           _lowerCamelCase           =					self.encoder.transformer.config.hidden_size
           _lowerCamelCase           =					ClassificationHead(class_size=A_					,					embed_size=self.embed_size    )
           _lowerCamelCase           =					cached_mode
           _lowerCamelCase           =					device



     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           return self.classifier_head



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           for param in self.encoder.parameters():
                 _lowerCamelCase           =					False
           self.classifier_head.train()



     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					x.ne(0    ).unsqueeze(2    ).repeat(1					,					1					,					self.embed_size    ).float().to(self.device    ).detach()
           _lowerCamelCase           =					self.encoder.transformer(A_    )['''last_hidden_state''']
           _lowerCamelCase           =					hidden * mask
           _lowerCamelCase           =					torch.sum(A_					,					dim=1    ) / (torch.sum(A_					,					dim=1    ).detach() + EPSILON)
           return avg_hidden





     def 		UpperCamelCase_    (       self					,					A_    )			->					Union[str, Any]:






           """simple docstring"""
           if self.cached_mode:
                 _lowerCamelCase           =					x.to(self.device    )
           else:
                 _lowerCamelCase           =					self.avg_representation(x.to(self.device    )    )

           _lowerCamelCase           =					self.classifier_head(A_    )
           _lowerCamelCase           =					nn.functional.log_softmax(A_					,					dim=-1    )

           return probs





class 			UpperCamelCase					(			data.Dataset			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					X
           _lowerCamelCase           =					y



     def __len__(       self    )			->					Any:






           """simple docstring"""
           return len(self.X    )





     def __getitem__(       self					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					{}
           _lowerCamelCase           =					self.X[index]
           _lowerCamelCase           =					self.y[index]
           return data





def    __magic_name__(	__UpperCAmelCase		)    ->     str:



      '''simple docstring'''

      def pad_sequences(__UpperCAmelCase		):
            _lowerCamelCase           =					[len(__UpperCAmelCase		) for seq in sequences]

            _lowerCamelCase           =					torch.zeros(len(__UpperCAmelCase		)			,    max(__UpperCAmelCase		)		).long()  # padding value = 0

            for i, seq in enumerate(__UpperCAmelCase		):
                  _lowerCamelCase           =					lengths[i]
                  _lowerCamelCase           =					seq[:end]

            return padded_sequences, lengths

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

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

      return x_batch, y_batch
def    __magic_name__(	__UpperCAmelCase		)    ->     Dict:



      '''simple docstring'''

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

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

      return x_batch, y_batch
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=0			,    __UpperCAmelCase=10			,    __UpperCAmelCase="cpu"		)    ->     str:



      '''simple docstring'''

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

            optimizer.zero_grad()

            _lowerCamelCase           =					discriminator(__UpperCAmelCase		)
            _lowerCamelCase           =					nn.functional.nll_loss(__UpperCAmelCase			,    __UpperCAmelCase		)
            loss.backward(retain_graph=__UpperCAmelCase		)
            optimizer.step()

            samples_so_far += len(__UpperCAmelCase		)

            if batch_idx % log_interval == 0:
                  print(
                      '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
                          epoch + 1			,    __UpperCAmelCase			,    len(data_loader.dataset		)			,    100 * samples_so_far / len(data_loader.dataset		)			,    loss.item()			,    )		)
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase="cpu"		)    ->     List[Any]:



      '''simple docstring'''

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

      test_loss /= len(data_loader.dataset		)

      print(
          '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
              __UpperCAmelCase			,    __UpperCAmelCase			,    len(data_loader.dataset		)			,    1_0_0.0 * correct / len(data_loader.dataset		)		)		)
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=False			,    __UpperCAmelCase="cpu"		)    ->     str:



      '''simple docstring'''

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

      _lowerCamelCase           =					model(__UpperCAmelCase		).data.cpu().numpy().flatten().tolist()
      print('''Input sentence:'''			,    __UpperCAmelCase		)
      print(
          '''Predictions:'''			,    ''', '''.join('''{}: {:.4f}'''.format(__UpperCAmelCase			,    math.exp(__UpperCAmelCase		)		) for c, log_prob in zip(__UpperCAmelCase			,    __UpperCAmelCase		)		)			,    )
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=False			,    __UpperCAmelCase="cpu"		)    ->     Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					torch.utils.data.DataLoader(dataset=__UpperCAmelCase			,    batch_size=__UpperCAmelCase			,    collate_fn=__UpperCAmelCase		)

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

      _lowerCamelCase           =					torch.utils.data.DataLoader(
          dataset=Dataset(__UpperCAmelCase			,    __UpperCAmelCase		)			,    batch_size=__UpperCAmelCase			,    shuffle=__UpperCAmelCase			,    collate_fn=__UpperCAmelCase		)

      return data_loader
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=None			,    __UpperCAmelCase="gpt2-medium"			,    __UpperCAmelCase=10			,    __UpperCAmelCase=64			,    __UpperCAmelCase=10			,    __UpperCAmelCase=False			,    __UpperCAmelCase=False			,    __UpperCAmelCase=False			,    )    ->     str:



      '''simple docstring'''

      _lowerCamelCase           =					'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

      print('''Preprocessing {} dataset...'''.format(__UpperCAmelCase		)		)
      _lowerCamelCase           =					time.time()

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

            _lowerCamelCase           =					Discriminator(
                class_size=len(__UpperCAmelCase		)			,    pretrained_model=__UpperCAmelCase			,    cached_mode=__UpperCAmelCase			,    device=__UpperCAmelCase		).to(__UpperCAmelCase		)

            _lowerCamelCase           =					torchtext_data.Field()
            _lowerCamelCase           =					torchtext_data.Field(sequential=__UpperCAmelCase		)
            _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					datasets.SST.splits(
                __UpperCAmelCase			,    __UpperCAmelCase			,    fine_grained=__UpperCAmelCase			,    train_subtrees=__UpperCAmelCase			,    )

            _lowerCamelCase           =					[]
            _lowerCamelCase           =					[]
            for i in trange(len(__UpperCAmelCase		)			,    ascii=__UpperCAmelCase		):
                  _lowerCamelCase           =					TreebankWordDetokenizer().detokenize(vars(train_data[i]		)['''text''']		)
                  _lowerCamelCase           =					discriminator.tokenizer.encode(__UpperCAmelCase		)
                  _lowerCamelCase           =					torch.tensor([5_0256] + seq			,    device=__UpperCAmelCase			,    dtype=torch.long		)
                  x.append(__UpperCAmelCase		)
                  y.append(classaidx[vars(train_data[i]		)['''label''']]		)
            _lowerCamelCase           =					Dataset(__UpperCAmelCase			,    __UpperCAmelCase		)

            _lowerCamelCase           =					[]
            _lowerCamelCase           =					[]
            for i in trange(len(__UpperCAmelCase		)			,    ascii=__UpperCAmelCase		):
                  _lowerCamelCase           =					TreebankWordDetokenizer().detokenize(vars(test_data[i]		)['''text''']		)
                  _lowerCamelCase           =					discriminator.tokenizer.encode(__UpperCAmelCase		)
                  _lowerCamelCase           =					torch.tensor([5_0256] + seq			,    device=__UpperCAmelCase			,    dtype=torch.long		)
                  test_x.append(__UpperCAmelCase		)
                  test_y.append(classaidx[vars(test_data[i]		)['''label''']]		)
            _lowerCamelCase           =					Dataset(__UpperCAmelCase			,    __UpperCAmelCase		)

            _lowerCamelCase           =					{
                '''class_size''': len(__UpperCAmelCase		),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 2,
            }

      elif dataset == "clickbait":
            _lowerCamelCase           =					['''non_clickbait''', '''clickbait''']
            _lowerCamelCase           =					{c: i for i, c in enumerate(__UpperCAmelCase		)}

            _lowerCamelCase           =					Discriminator(
                class_size=len(__UpperCAmelCase		)			,    pretrained_model=__UpperCAmelCase			,    cached_mode=__UpperCAmelCase			,    device=__UpperCAmelCase		).to(__UpperCAmelCase		)

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

                              if len(__UpperCAmelCase		) < max_length_seq:
                                    _lowerCamelCase           =					torch.tensor([5_0256] + seq			,    device=__UpperCAmelCase			,    dtype=torch.long		)
                              else:
                                    print('''Line {} is longer than maximum length {}'''.format(__UpperCAmelCase			,    __UpperCAmelCase		)		)
                                    continue
                              x.append(__UpperCAmelCase		)
                              y.append(d['''label''']		)
                        except Exception:
                              print('''Error evaluating / tokenizing line {}, skipping it'''.format(__UpperCAmelCase		)		)
                              pass

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

            _lowerCamelCase           =					{
                '''class_size''': len(__UpperCAmelCase		),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 1,
            }

      elif dataset == "toxic":
            _lowerCamelCase           =					['''non_toxic''', '''toxic''']
            _lowerCamelCase           =					{c: i for i, c in enumerate(__UpperCAmelCase		)}

            _lowerCamelCase           =					Discriminator(
                class_size=len(__UpperCAmelCase		)			,    pretrained_model=__UpperCAmelCase			,    cached_mode=__UpperCAmelCase			,    device=__UpperCAmelCase		).to(__UpperCAmelCase		)

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

                              if len(__UpperCAmelCase		) < max_length_seq:
                                    _lowerCamelCase           =					torch.tensor([5_0256] + seq			,    device=__UpperCAmelCase			,    dtype=torch.long		)
                              else:
                                    print('''Line {} is longer than maximum length {}'''.format(__UpperCAmelCase			,    __UpperCAmelCase		)		)
                                    continue
                              x.append(__UpperCAmelCase		)
                              y.append(int(np.sum(d['''label''']		) > 0		)		)
                        except Exception:
                              print('''Error evaluating / tokenizing line {}, skipping it'''.format(__UpperCAmelCase		)		)
                              pass

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

            _lowerCamelCase           =					{
                '''class_size''': len(__UpperCAmelCase		),
                '''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.'''		)

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

            _lowerCamelCase           =					sorted(__UpperCAmelCase		)
            _lowerCamelCase           =					{c: i for i, c in enumerate(__UpperCAmelCase		)}

            _lowerCamelCase           =					Discriminator(
                class_size=len(__UpperCAmelCase		)			,    pretrained_model=__UpperCAmelCase			,    cached_mode=__UpperCAmelCase			,    device=__UpperCAmelCase		).to(__UpperCAmelCase		)

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

                              try:
                                    _lowerCamelCase           =					discriminator.tokenizer.encode(__UpperCAmelCase		)
                                    if len(__UpperCAmelCase		) < max_length_seq:
                                          _lowerCamelCase           =					torch.tensor([5_0256] + seq			,    device=__UpperCAmelCase			,    dtype=torch.long		)

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

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

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

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

            _lowerCamelCase           =					{
                '''class_size''': len(__UpperCAmelCase		),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 0,
            }

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

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

            _lowerCamelCase           =					time.time()

            _lowerCamelCase           =					get_cached_data_loader(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    shuffle=__UpperCAmelCase			,    device=__UpperCAmelCase		)

            _lowerCamelCase           =					get_cached_data_loader(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    device=__UpperCAmelCase		)

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

      else:
            _lowerCamelCase           =					torch.utils.data.DataLoader(
                dataset=__UpperCAmelCase			,    batch_size=__UpperCAmelCase			,    shuffle=__UpperCAmelCase			,    collate_fn=__UpperCAmelCase		)
            _lowerCamelCase           =					torch.utils.data.DataLoader(dataset=__UpperCAmelCase			,    batch_size=__UpperCAmelCase			,    collate_fn=__UpperCAmelCase		)

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

      _lowerCamelCase           =					optim.Adam(discriminator.parameters()			,    lr=0.0_0_0_1		)

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

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

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

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

            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(__UpperCAmelCase			,    epoch + 1		)			,    )


if __name__ == "__main__":
     snake_case__						      =			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')
     snake_case__						      =			parser.parse_args()

     train_discriminator(**(vars(args)))
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__						      =			{
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPRContextEncoderTokenizerFast',
         'DPRQuestionEncoderTokenizerFast',
         'DPRReaderTokenizerFast',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPRContextEncoder',
         'DPRPretrainedContextEncoder',
         'DPRPreTrainedModel',
         'DPRPretrainedQuestionEncoder',
         'DPRPretrainedReader',
         'DPRQuestionEncoder',
         'DPRReader',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFDPRContextEncoder',
         'TFDPRPretrainedContextEncoder',
         'TFDPRPretrainedQuestionEncoder',
         'TFDPRPretrainedReader',
         'TFDPRQuestionEncoder',
         'TFDPRReader',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

     snake_case__						      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)