



# 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)
A__						:						int          =      1E-10
A__						:						List[Any]          =      '''This is incredible! I love it, this is the best chicken I have ever had.'''
A__						:						str          =      100



class        __snake_case		(  nn.Module		):




       def __init__( self  : str       ,       A_  : Optional[Any]       ,       A_  : Union[str, Any]="gpt2-medium"       ,       A_  : Optional[Any]=False       ,       A_  : Tuple="cpu"):
         super().__init__()
         lowerCAmelCase_    :  List[str]             =				GPTaTokenizer.from_pretrained(A_)
         lowerCAmelCase_    :  List[Any]             =				GPTaLMHeadModel.from_pretrained(A_)
         lowerCAmelCase_    :  List[Any]             =				self.encoder.transformer.config.hidden_size
         lowerCAmelCase_    :  Optional[int]             =				ClassificationHead(class_size=A_       ,       embed_size=self.embed_size)
         lowerCAmelCase_    :  Dict             =				cached_mode
         lowerCAmelCase_    :  Optional[Any]             =				device




       def      UpperCAmelCase__    ( self  : List[Any]):
         return self.classifier_head




       def      UpperCAmelCase__    ( self  : Optional[int]):
         for param in self.encoder.parameters():
           lowerCAmelCase_    :  List[str]             =				False
         self.classifier_head.train()




       def      UpperCAmelCase__    ( self  : Dict       ,       A_  : Tuple):
         lowerCAmelCase_    :  Optional[int]             =				x.ne(0).unsqueeze(2).repeat(1       ,       1       ,       self.embed_size).float().to(self.device).detach()
         lowerCAmelCase_    :  str             =				self.encoder.transformer(A_)['''last_hidden_state''']
         lowerCAmelCase_    :  str             =				hidden * mask
         lowerCAmelCase_    :  Optional[Any]             =				torch.sum(A_       ,       dim=1) / (torch.sum(A_       ,       dim=1).detach() + EPSILON)
         return avg_hidden







       def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : str):
         if self.cached_mode:
           lowerCAmelCase_    :  Union[str, Any]             =				x.to(self.device)
         else:
           lowerCAmelCase_    :  Tuple             =				self.avg_representation(x.to(self.device))

         lowerCAmelCase_    :  Optional[int]             =				self.classifier_head(A_)
         lowerCAmelCase_    :  Dict             =				nn.functional.log_softmax(A_       ,       dim=-1)

         return probs





class        __snake_case		(  data.Dataset		):




       def __init__( self  : List[Any]       ,       A_  : Any       ,       A_  : Optional[int]):
         lowerCAmelCase_    :  List[str]             =				X
         lowerCAmelCase_    :  Dict             =				y




       def __len__( self  : Any):
         return len(self.X)







       def __getitem__( self  : Union[str, Any]       ,       A_  : List[Any]):
         lowerCAmelCase_    :  Any             =				{}
         lowerCAmelCase_    :  List[str]             =				self.X[index]
         lowerCAmelCase_    :  List[Any]             =				self.y[index]
         return data





def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]				):
  def pad_sequences(__UpperCamelCase							:	int				):
    lowerCAmelCase_    :  List[str]             =				[len(__UpperCamelCase				) for seq in sequences]

    lowerCAmelCase_    :  Any             =				torch.zeros(len(__UpperCamelCase				)  ,max(__UpperCamelCase				)				).long()  # padding value = 0

    for i, seq in enumerate(__UpperCamelCase				):
      lowerCAmelCase_    :  List[Any]             =				lengths[i]
      lowerCAmelCase_    :  Dict             =				seq[:end]

    return padded_sequences, lengths

  lowerCAmelCase_    :  Optional[Any]             =				{}
  for key in data[0].keys():
    lowerCAmelCase_    :  Optional[int]             =				[d[key] for d in data]

  lowerCAmelCase_    :  Tuple             =				pad_sequences(item_info['''X''']				)
  lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor(item_info['''y''']  ,dtype=torch.long				)

  return x_batch, y_batch


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]				):
  lowerCAmelCase_    :  List[str]             =				{}
  for key in data[0].keys():
    lowerCAmelCase_    :  Dict             =				[d[key] for d in data]

  lowerCAmelCase_    :  List[str]             =				torch.cat(item_info['''X''']  ,0				)
  lowerCAmelCase_    :  Tuple             =				torch.tensor(item_info['''y''']  ,dtype=torch.long				)

  return x_batch, y_batch


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	List[str]=0  ,__UpperCamelCase							:	Tuple=10  ,__UpperCamelCase							:	Tuple="cpu"				):
  lowerCAmelCase_    :  Optional[int]             =				0
  discriminator.train_custom()
  for batch_idx, (input_t, target_t) in enumerate(__UpperCamelCase				):
    lowerCAmelCase_    :  Optional[int]             =				input_t.to(__UpperCamelCase				), target_t.to(__UpperCamelCase				)

    optimizer.zero_grad()

    lowerCAmelCase_    :  Optional[int]             =				discriminator(__UpperCamelCase				)
    lowerCAmelCase_    :  Dict             =				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     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Optional[int]="cpu"				):
  discriminator.eval()
  lowerCAmelCase_    :  List[str]             =				0
  lowerCAmelCase_    :  Dict             =				0
  with torch.no_grad():
    for input_t, target_t in data_loader:
      lowerCAmelCase_    :  Any             =				input_t.to(__UpperCamelCase				), target_t.to(__UpperCamelCase				)
      lowerCAmelCase_    :  Union[str, Any]             =				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_    :  Union[str, Any]             =				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				)  ,100.0 * correct / len(data_loader.dataset				)				)				)


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Any  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Dict=False  ,__UpperCamelCase							:	Any="cpu"				):
  lowerCAmelCase_    :  Union[str, Any]             =				model.tokenizer.encode(__UpperCamelCase				)
  lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([input_t]  ,dtype=torch.long  ,device=__UpperCamelCase				)
  if cached:
    lowerCAmelCase_    :  Tuple             =				model.avg_representation(__UpperCamelCase				)

  lowerCAmelCase_    :  Optional[Any]             =				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     UpperCamelCase(						__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	str  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int=False  ,__UpperCamelCase							:	Dict="cpu"				):
  lowerCAmelCase_    :  str             =				torch.utils.data.DataLoader(dataset=__UpperCamelCase  ,batch_size=__UpperCamelCase  ,collate_fn=__UpperCamelCase				)

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

  lowerCAmelCase_    :  str             =				torch.utils.data.DataLoader(
      dataset=Dataset(__UpperCamelCase  ,__UpperCamelCase				)  ,batch_size=__UpperCamelCase  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase				)

  return data_loader


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str=None  ,__UpperCamelCase							:	Any="gpt2-medium"  ,__UpperCamelCase							:	str=10  ,__UpperCamelCase							:	List[Any]=64  ,__UpperCamelCase							:	int=10  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	Any=False  ,__UpperCamelCase							:	Dict=False  ,):
  lowerCAmelCase_    :  Dict             =				'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

  print('''Preprocessing {} dataset...'''.format(__UpperCamelCase				)				)
  lowerCAmelCase_    :  Tuple             =				time.time()

  if dataset == "SST":
    lowerCAmelCase_    :  Union[str, Any]             =				['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
    lowerCAmelCase_    :  Optional[int]             =				{c: i for i, c in enumerate(__UpperCamelCase				)}

    lowerCAmelCase_    :  Any             =				Discriminator(
        class_size=len(__UpperCamelCase				)  ,pretrained_model=__UpperCamelCase  ,cached_mode=__UpperCamelCase  ,device=__UpperCamelCase				).to(__UpperCamelCase				)

    lowerCAmelCase_    :  Optional[int]             =				torchtext_data.Field()
    lowerCAmelCase_    :  Optional[Any]             =				torchtext_data.Field(sequential=__UpperCamelCase				)
    lowerCAmelCase_    :  List[Any]             =				datasets.SST.splits(
        __UpperCamelCase  ,__UpperCamelCase  ,fine_grained=__UpperCamelCase  ,train_subtrees=__UpperCamelCase  ,)

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

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

    lowerCAmelCase_    :  str             =				{
        '''class_size''': len(__UpperCamelCase				),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 2,
    }

  elif dataset == "clickbait":
    lowerCAmelCase_    :  Dict             =				['''non_clickbait''', '''clickbait''']
    lowerCAmelCase_    :  Tuple             =				{c: i for i, c in enumerate(__UpperCamelCase				)}

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

          if len(__UpperCamelCase				) < max_length_seq:
            lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([50256] + 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_    :  Union[str, Any]             =				Dataset(__UpperCamelCase  ,__UpperCamelCase				)
    lowerCAmelCase_    :  int             =				int(0.9 * len(__UpperCamelCase				)				)
    lowerCAmelCase_    :  List[Any]             =				len(__UpperCamelCase				) - train_size
    lowerCAmelCase_    :  Optional[Any]             =				torch.utils.data.random_split(__UpperCamelCase  ,[train_size, test_size]				)

    lowerCAmelCase_    :  Tuple             =				{
        '''class_size''': len(__UpperCamelCase				),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 1,
    }

  elif dataset == "toxic":
    lowerCAmelCase_    :  Union[str, Any]             =				['''non_toxic''', '''toxic''']
    lowerCAmelCase_    :  Dict             =				{c: i for i, c in enumerate(__UpperCamelCase				)}

    lowerCAmelCase_    :  Dict             =				Discriminator(
        class_size=len(__UpperCamelCase				)  ,pretrained_model=__UpperCamelCase  ,cached_mode=__UpperCamelCase  ,device=__UpperCamelCase				).to(__UpperCamelCase				)

    lowerCAmelCase_    :  Optional[int]             =				[]
    lowerCAmelCase_    :  Union[str, Any]             =				[]
    with open('''datasets/toxic/toxic_train.txt'''				) as f:
      for i, line in enumerate(tqdm(__UpperCamelCase  ,ascii=__UpperCamelCase				)				):
        try:
          lowerCAmelCase_    :  int             =				eval(__UpperCamelCase				)
          lowerCAmelCase_    :  Dict             =				discriminator.tokenizer.encode(d['''text''']				)

          if len(__UpperCamelCase				) < max_length_seq:
            lowerCAmelCase_    :  Optional[int]             =				torch.tensor([50256] + 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_    :  Optional[int]             =				Dataset(__UpperCamelCase  ,__UpperCamelCase				)
    lowerCAmelCase_    :  int             =				int(0.9 * len(__UpperCamelCase				)				)
    lowerCAmelCase_    :  str             =				len(__UpperCamelCase				) - train_size
    lowerCAmelCase_    :  int             =				torch.utils.data.random_split(__UpperCamelCase  ,[train_size, test_size]				)

    lowerCAmelCase_    :  Union[str, Any]             =				{
        '''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_    :  Tuple             =				set()
    with open(__UpperCamelCase				) as f:
      lowerCAmelCase_    :  Union[str, Any]             =				csv.reader(__UpperCamelCase  ,delimiter='''\t'''				)
      for row in tqdm(__UpperCamelCase  ,ascii=__UpperCamelCase				):
        if row:
          classes.add(row[0]				)

    lowerCAmelCase_    :  Union[str, Any]             =				sorted(__UpperCamelCase				)
    lowerCAmelCase_    :  Dict             =				{c: i for i, c in enumerate(__UpperCamelCase				)}

    lowerCAmelCase_    :  int             =				Discriminator(
        class_size=len(__UpperCamelCase				)  ,pretrained_model=__UpperCamelCase  ,cached_mode=__UpperCamelCase  ,device=__UpperCamelCase				).to(__UpperCamelCase				)

    lowerCAmelCase_    :  Optional[Any]             =				[]
    lowerCAmelCase_    :  List[str]             =				[]
    with open(__UpperCamelCase				) as f:
      lowerCAmelCase_    :  List[str]             =				csv.reader(__UpperCamelCase  ,delimiter='''\t'''				)
      for i, row in enumerate(tqdm(__UpperCamelCase  ,ascii=__UpperCamelCase				)				):
        if row:
          lowerCAmelCase_    :  str             =				row[0]
          lowerCAmelCase_    :  List[str]             =				row[1]

          try:
            lowerCAmelCase_    :  Any             =				discriminator.tokenizer.encode(__UpperCamelCase				)
            if len(__UpperCamelCase				) < max_length_seq:
              lowerCAmelCase_    :  Tuple             =				torch.tensor([50256] + 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_    :  Tuple             =				Dataset(__UpperCamelCase  ,__UpperCamelCase				)
    lowerCAmelCase_    :  List[str]             =				int(0.9 * len(__UpperCamelCase				)				)
    lowerCAmelCase_    :  Union[str, Any]             =				len(__UpperCamelCase				) - train_size
    lowerCAmelCase_    :  Optional[Any]             =				torch.utils.data.random_split(__UpperCamelCase  ,[train_size, test_size]				)

    lowerCAmelCase_    :  Optional[int]             =				{
        '''class_size''': len(__UpperCamelCase				),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 0,
    }

  lowerCAmelCase_    :  str             =				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_    :  Tuple             =				time.time()

    lowerCAmelCase_    :  List[Any]             =				get_cached_data_loader(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,shuffle=__UpperCamelCase  ,device=__UpperCamelCase				)

    lowerCAmelCase_    :  List[Any]             =				get_cached_data_loader(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,device=__UpperCamelCase				)

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

  else:
    lowerCAmelCase_    :  Optional[Any]             =				torch.utils.data.DataLoader(
        dataset=__UpperCamelCase  ,batch_size=__UpperCamelCase  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase				)
    lowerCAmelCase_    :  Optional[int]             =				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_    :  Optional[int]             =				optim.Adam(discriminator.parameters()  ,lr=0.0_0_0_1				)

  for epoch in range(__UpperCamelCase				):
    lowerCAmelCase_    :  str             =				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_    :  int             =				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__":
      A__						:						Any          =      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''')
      A__						:						int          =      parser.parse_args()

      train_discriminator(**(vars(args)))










from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class        __snake_case		:
							_a       =				MarianConfig
							_a       =				{}
							_a       =				'''gelu'''




							def __init__( self  : Dict       ,       A_  : Optional[int]       ,       A_  : Any=1_3       ,       A_  : Any=7       ,       A_  : int=True       ,       A_  : int=False       ,       A_  : str=9_9       ,       A_  : Tuple=3_2       ,       A_  : Dict=2       ,       A_  : Union[str, Any]=4       ,       A_  : Optional[int]=3_7       ,       A_  : Any=0.1       ,       A_  : str=0.1       ,       A_  : Optional[int]=2_0       ,       A_  : Any=2       ,       A_  : Any=1       ,       A_  : Any=0       ,       ):
									lowerCAmelCase_    :  Any             =				parent
									lowerCAmelCase_    :  Dict             =				batch_size
									lowerCAmelCase_    :  Any             =				seq_length
									lowerCAmelCase_    :  Union[str, Any]             =				is_training
									lowerCAmelCase_    :  Any             =				use_labels
									lowerCAmelCase_    :  Tuple             =				vocab_size
									lowerCAmelCase_    :  Optional[int]             =				hidden_size
									lowerCAmelCase_    :  Optional[int]             =				num_hidden_layers
									lowerCAmelCase_    :  Union[str, Any]             =				num_attention_heads
									lowerCAmelCase_    :  List[str]             =				intermediate_size

									lowerCAmelCase_    :  Union[str, Any]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Union[str, Any]             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				max_position_embeddings
									lowerCAmelCase_    :  List[str]             =				eos_token_id
									lowerCAmelCase_    :  Optional[Any]             =				pad_token_id
									lowerCAmelCase_    :  Dict             =				bos_token_id




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  str             =				ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size)
									lowerCAmelCase_    :  Optional[int]             =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)       ,       1)
									lowerCAmelCase_    :  List[str]             =				tf.concat([input_ids, eos_tensor]       ,       axis=1)

									lowerCAmelCase_    :  Optional[Any]             =				ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)

									lowerCAmelCase_    :  Optional[int]             =				self.config_cls(
									    vocab_size=self.vocab_size       ,       d_model=self.hidden_size       ,       encoder_layers=self.num_hidden_layers       ,       decoder_layers=self.num_hidden_layers       ,       encoder_attention_heads=self.num_attention_heads       ,       decoder_attention_heads=self.num_attention_heads       ,       encoder_ffn_dim=self.intermediate_size       ,       decoder_ffn_dim=self.intermediate_size       ,       dropout=self.hidden_dropout_prob       ,       attention_dropout=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       eos_token_ids=[2]       ,       bos_token_id=self.bos_token_id       ,       pad_token_id=self.pad_token_id       ,       decoder_start_token_id=self.pad_token_id       ,       **self.config_updates       ,       )
									lowerCAmelCase_    :  int             =				prepare_marian_inputs_dict(A_       ,       A_       ,       A_)
									return config, inputs_dict







							def      UpperCAmelCase__    ( self  : int       ,       A_  : int       ,       A_  : str):
									lowerCAmelCase_    :  Any             =				TFMarianModel(config=A_).get_decoder()
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''input_ids''']

									lowerCAmelCase_    :  str             =				input_ids[:1, :]
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''attention_mask'''][:1, :]
									lowerCAmelCase_    :  Dict             =				inputs_dict['''head_mask''']
									lowerCAmelCase_    :  Optional[int]             =				1

									# first forward pass
									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_       ,       head_mask=A_       ,       use_cache=A_)

									lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowerCAmelCase_    :  int             =				ids_tensor((self.batch_size, 3)       ,       config.vocab_size)
									lowerCAmelCase_    :  str             =				tf.cast(ids_tensor((self.batch_size, 3)       ,       2)       ,       tf.inta)

									# append to next input_ids and
									lowerCAmelCase_    :  Optional[int]             =				tf.concat([input_ids, next_tokens]       ,       axis=-1)
									lowerCAmelCase_    :  int             =				tf.concat([attention_mask, next_attn_mask]       ,       axis=-1)

									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_)[0]
									lowerCAmelCase_    :  List[str]             =				model(A_       ,       attention_mask=A_       ,       past_key_values=A_)[0]

									self.parent.assertEqual(next_tokens.shape[1]       ,       output_from_past.shape[1])

									# select random slice
									lowerCAmelCase_    :  Optional[Any]             =				int(ids_tensor((1,)       ,       output_from_past.shape[-1]))
									lowerCAmelCase_    :  Tuple             =				output_from_no_past[:, -3:, random_slice_idx]
									lowerCAmelCase_    :  Optional[Any]             =				output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(A_       ,       A_       ,       rtol=1e-3)





def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]=None  ,__UpperCamelCase							:	Union[str, Any]=None  ,__UpperCamelCase							:	List[Any]=None  ,__UpperCamelCase							:	str=None  ,__UpperCamelCase							:	Optional[int]=None  ,):
		if attention_mask is None:
				lowerCAmelCase_    :  Optional[int]             =				tf.cast(tf.math.not_equal(__UpperCamelCase  ,config.pad_token_id				)  ,tf.inta				)
		if decoder_attention_mask is None:
				lowerCAmelCase_    :  List[str]             =				tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape  ,dtype=tf.inta				),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,config.pad_token_id				)  ,tf.inta				),
				    ]  ,axis=-1  ,)
		if head_mask is None:
				lowerCAmelCase_    :  Union[str, Any]             =				tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
		if decoder_head_mask is None:
				lowerCAmelCase_    :  List[str]             =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		if cross_attn_head_mask is None:
				lowerCAmelCase_    :  Union[str, Any]             =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}



@require_tf
class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
							_a       =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							_a       =				(TFMarianMTModel,) if is_tf_available() else ()
							_a       =				(
							    {
							        '''conversational''': TFMarianMTModel,
							        '''feature-extraction''': TFMarianModel,
							        '''summarization''': TFMarianMTModel,
							        '''text2text-generation''': TFMarianMTModel,
							        '''translation''': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							_a       =				True
							_a       =				False
							_a       =				False




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Optional[Any]             =				TFMarianModelTester(self)
									lowerCAmelCase_    :  List[str]             =				ConfigTester(self       ,       config_class=A_)




							def      UpperCAmelCase__    ( self  : List[Any]):
									self.config_tester.run_common_tests()







							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  List[Any]             =				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*A_)





@require_tf
class        __snake_case		(  unittest.TestCase		):
							_a       =				1_000  # show more chars for failing integration tests




							@classmethod
							def      UpperCAmelCase__    ( cls  : Optional[Any]):
									lowerCAmelCase_    :  Union[str, Any]             =				F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls




							@cached_property
							def      UpperCAmelCase__    ( self  : List[str]):
									return AutoTokenizer.from_pretrained(self.model_name)




							@property
							def      UpperCAmelCase__    ( self  : Dict):
									return self.tokenizer.eos_token_id




							@cached_property
							def      UpperCAmelCase__    ( self  : int):
									warnings.simplefilter('''error''')
									lowerCAmelCase_    :  TFMarianMTModel             =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
									assert isinstance(A_       ,       A_)
									lowerCAmelCase_    :  int             =				model.config
									self.assertListEqual(c.bad_words_ids       ,       [[c.pad_token_id]])
									self.assertEqual(c.max_length       ,       5_1_2)
									self.assertEqual(c.decoder_start_token_id       ,       c.pad_token_id)
									return model




							def      UpperCAmelCase__    ( self  : Dict       ,       **A_  : Tuple):
									lowerCAmelCase_    :  Optional[int]             =				self.translate_src_text(**A_)
									self.assertListEqual(self.expected_text       ,       A_)







							def      UpperCAmelCase__    ( self  : Any       ,       **A_  : List[str]):
									lowerCAmelCase_    :  List[Any]             =				self.tokenizer(self.src_text       ,       **A_       ,       padding=A_       ,       return_tensors='''tf''')
									lowerCAmelCase_    :  List[str]             =				self.model.generate(
									    model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       max_length=1_2_8)
									lowerCAmelCase_    :  Any             =				self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=A_)
									return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''mt'''
							_a       =				'''en'''
							_a       =				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							_a       =				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Any):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''zh'''
							_a       =				['''My name is Wolfgang and I live in Berlin''']
							_a       =				['''我叫沃尔夫冈 我住在柏林''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : str):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''ROMANCE'''
							_a       =				[
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							_a       =				[
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									self._assert_generated_batch_equal_expected()







							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  Dict             =				TranslationPipeline(self.model       ,       self.tokenizer       ,       framework='''tf''')
									lowerCAmelCase_    :  Union[str, Any]             =				pipeline(self.src_text)
									self.assertEqual(self.expected_text       ,       [x['''translation_text'''] for x in output])





