# 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         =     1_00







class       __snake_case (  nn.Module ):
 def __init__(	self		, snake_case__		, snake_case__="gpt2-medium"		, snake_case__=False		, snake_case__="cpu"    )						->				Tuple:
       '''simple docstring'''





       super().__init__()
       UpperCAmelCase     :			str     	=GPTaTokenizer.from_pretrained(snake_case__    )
       UpperCAmelCase     :			List[str]     	=GPTaLMHeadModel.from_pretrained(snake_case__    )
       UpperCAmelCase     :			int     	=self.encoder.transformer.config.hidden_size
       UpperCAmelCase     :			Any     	=ClassificationHead(class_size=snake_case__		, embed_size=self.embed_size    )
       UpperCAmelCase     :			List[str]     	=cached_mode
       UpperCAmelCase     :			int     	=device
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return self.classifier_head
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       for param in self.encoder.parameters():
             UpperCAmelCase     :			List[str]     	=False
       self.classifier_head.train()
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=x.ne(0    ).unsqueeze(2    ).repeat(1		, 1		, self.embed_size    ).float().to(self.device    ).detach()
       UpperCAmelCase     :			str     	=self.encoder.transformer(snake_case__    )['''last_hidden_state''']
       UpperCAmelCase     :			List[Any]     	=hidden * mask
       UpperCAmelCase     :			Union[str, Any]     	=torch.sum(snake_case__		, dim=1    ) / (torch.sum(snake_case__		, dim=1    ).detach() + EPSILON)
       return avg_hidden






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       if self.cached_mode:
             UpperCAmelCase     :			str     	=x.to(self.device    )
       else:
             UpperCAmelCase     :			Tuple     	=self.avg_representation(x.to(self.device    )    )

       UpperCAmelCase     :			Optional[int]     	=self.classifier_head(snake_case__    )
       UpperCAmelCase     :			Tuple     	=nn.functional.log_softmax(snake_case__		, dim=-1    )

       return probs







class       __snake_case (  data.Dataset ):
 def __init__(	self		, snake_case__		, snake_case__    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=X
       UpperCAmelCase     :			str     	=y
 def __len__(	self    )						->				List[Any]:
       '''simple docstring'''





       return len(self.X    )






 def __getitem__(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	={}
       UpperCAmelCase     :			List[str]     	=self.X[index]
       UpperCAmelCase     :			Tuple     	=self.y[index]
       return data





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	str:






      '''simple docstring'''





      def pad_sequences(__lowerCAmelCase    ):
            UpperCAmelCase     :			List[Any]     	=[len(__lowerCAmelCase    ) for seq in sequences]

            UpperCAmelCase     :			Union[str, Any]     	=torch.zeros(len(__lowerCAmelCase    )		,       max(__lowerCAmelCase    )    ).long()  # padding value = 0

            for i, seq in enumerate(__lowerCAmelCase    ):
                  UpperCAmelCase     :			Dict     	=lengths[i]
                  UpperCAmelCase     :			List[str]     	=seq[:end]

            return padded_sequences, lengths

      UpperCAmelCase     :			Tuple     	={}
      for key in data[0].keys():
            UpperCAmelCase     :			List[Any]     	=[d[key] for d in data]

      UpperCAmelCase	,    UpperCAmelCase     :			List[Any]     	=pad_sequences(item_info['''X''']    )
      UpperCAmelCase     :			Dict     	=torch.tensor(item_info['''y''']		,       dtype=torch.long    )

      return x_batch, y_batch





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	str:






      '''simple docstring'''





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

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

      return x_batch, y_batch





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=0		,       __lowerCAmelCase=10		,       __lowerCAmelCase="cpu"    )->	List[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=0
      discriminator.train_custom()
      for batch_idx, (input_t, target_t) in enumerate(__lowerCAmelCase    ):
            UpperCAmelCase	,    UpperCAmelCase     :			Tuple     	=input_t.to(__lowerCAmelCase    ), target_t.to(__lowerCAmelCase    )

            optimizer.zero_grad()

            UpperCAmelCase     :			str     	=discriminator(__lowerCAmelCase    )
            UpperCAmelCase     :			Dict     	=nn.functional.nll_loss(__lowerCAmelCase		,       __lowerCAmelCase    )
            loss.backward(retain_graph=__lowerCAmelCase    )
            optimizer.step()

            samples_so_far += len(__lowerCAmelCase    )

            if batch_idx % log_interval == 0:
                  print(
                      '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
                          epoch + 1		,       __lowerCAmelCase		,       len(data_loader.dataset    )		,       1_00 * samples_so_far / len(data_loader.dataset    )		,       loss.item()		,       )    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase="cpu"    )->	Any:






      '''simple docstring'''





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

      test_loss /= len(data_loader.dataset    )

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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase="cpu"    )->	Tuple:






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=model.tokenizer.encode(__lowerCAmelCase    )
      UpperCAmelCase     :			Tuple     	=torch.tensor([input_t]		,       dtype=torch.long		,       device=__lowerCAmelCase    )
      if cached:
            UpperCAmelCase     :			int     	=model.avg_representation(__lowerCAmelCase    )

      UpperCAmelCase     :			List[str]     	=model(__lowerCAmelCase    ).data.cpu().numpy().flatten().tolist()
      print('''Input sentence:'''		,       __lowerCAmelCase    )
      print(
          '''Predictions:'''		,       ''', '''.join('''{}: {:.4f}'''.format(__lowerCAmelCase		,       math.exp(__lowerCAmelCase    )    ) for c, log_prob in zip(__lowerCAmelCase		,       __lowerCAmelCase    )    )		,       )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase="cpu"    )->	str:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	=torch.utils.data.DataLoader(dataset=__lowerCAmelCase		,       batch_size=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase    )

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

      UpperCAmelCase     :			Optional[int]     	=torch.utils.data.DataLoader(
          dataset=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )		,       batch_size=__lowerCAmelCase		,       shuffle=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase    )

      return data_loader





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=None		,       __lowerCAmelCase="gpt2-medium"		,       __lowerCAmelCase=10		,       __lowerCAmelCase=64		,       __lowerCAmelCase=10		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       )->	Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	='''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

      print('''Preprocessing {} dataset...'''.format(__lowerCAmelCase    )    )
      UpperCAmelCase     :			List[str]     	=time.time()

      if dataset == "SST":
            UpperCAmelCase     :			List[Any]     	=['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
            UpperCAmelCase     :			int     	={c: i for i, c in enumerate(__lowerCAmelCase    )}

            UpperCAmelCase     :			List[Any]     	=Discriminator(
                class_size=len(__lowerCAmelCase    )		,       pretrained_model=__lowerCAmelCase		,       cached_mode=__lowerCAmelCase		,       device=__lowerCAmelCase    ).to(__lowerCAmelCase    )

            UpperCAmelCase     :			Union[str, Any]     	=torchtext_data.Field()
            UpperCAmelCase     :			Dict     	=torchtext_data.Field(sequential=__lowerCAmelCase    )
            UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=datasets.SST.splits(
                __lowerCAmelCase		,       __lowerCAmelCase		,       fine_grained=__lowerCAmelCase		,       train_subtrees=__lowerCAmelCase		,       )

            UpperCAmelCase     :			Union[str, Any]     	=[]
            UpperCAmelCase     :			Union[str, Any]     	=[]
            for i in trange(len(__lowerCAmelCase    )		,       ascii=__lowerCAmelCase    ):
                  UpperCAmelCase     :			List[str]     	=TreebankWordDetokenizer().detokenize(vars(train_data[i]    )['''text''']    )
                  UpperCAmelCase     :			str     	=discriminator.tokenizer.encode(__lowerCAmelCase    )
                  UpperCAmelCase     :			int     	=torch.tensor([5_02_56] + seq		,       device=__lowerCAmelCase		,       dtype=torch.long    )
                  x.append(__lowerCAmelCase    )
                  y.append(classaidx[vars(train_data[i]    )['''label''']]    )
            UpperCAmelCase     :			Any     	=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )

            UpperCAmelCase     :			Any     	=[]
            UpperCAmelCase     :			Union[str, Any]     	=[]
            for i in trange(len(__lowerCAmelCase    )		,       ascii=__lowerCAmelCase    ):
                  UpperCAmelCase     :			Any     	=TreebankWordDetokenizer().detokenize(vars(test_data[i]    )['''text''']    )
                  UpperCAmelCase     :			List[str]     	=discriminator.tokenizer.encode(__lowerCAmelCase    )
                  UpperCAmelCase     :			Optional[int]     	=torch.tensor([5_02_56] + seq		,       device=__lowerCAmelCase		,       dtype=torch.long    )
                  test_x.append(__lowerCAmelCase    )
                  test_y.append(classaidx[vars(test_data[i]    )['''label''']]    )
            UpperCAmelCase     :			List[str]     	=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )

            UpperCAmelCase     :			Any     	={
                '''class_size''': len(__lowerCAmelCase    ),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 2,
            }

      elif dataset == "clickbait":
            UpperCAmelCase     :			Union[str, Any]     	=['''non_clickbait''', '''clickbait''']
            UpperCAmelCase     :			List[Any]     	={c: i for i, c in enumerate(__lowerCAmelCase    )}

            UpperCAmelCase     :			Union[str, Any]     	=Discriminator(
                class_size=len(__lowerCAmelCase    )		,       pretrained_model=__lowerCAmelCase		,       cached_mode=__lowerCAmelCase		,       device=__lowerCAmelCase    ).to(__lowerCAmelCase    )

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

                              if len(__lowerCAmelCase    ) < max_length_seq:
                                    UpperCAmelCase     :			int     	=torch.tensor([5_02_56] + seq		,       device=__lowerCAmelCase		,       dtype=torch.long    )
                              else:
                                    print('''Line {} is longer than maximum length {}'''.format(__lowerCAmelCase		,       __lowerCAmelCase    )    )
                                    continue
                              x.append(__lowerCAmelCase    )
                              y.append(d['''label''']    )
                        except Exception:
                              print('''Error evaluating / tokenizing line {}, skipping it'''.format(__lowerCAmelCase    )    )
                              pass

            UpperCAmelCase     :			int     	=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )
            UpperCAmelCase     :			Optional[Any]     	=int(0.9 * len(__lowerCAmelCase    )    )
            UpperCAmelCase     :			Any     	=len(__lowerCAmelCase    ) - train_size
            UpperCAmelCase	,    UpperCAmelCase     :			List[str]     	=torch.utils.data.random_split(__lowerCAmelCase		,       [train_size, test_size]    )

            UpperCAmelCase     :			Dict     	={
                '''class_size''': len(__lowerCAmelCase    ),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 1,
            }

      elif dataset == "toxic":
            UpperCAmelCase     :			Dict     	=['''non_toxic''', '''toxic''']
            UpperCAmelCase     :			int     	={c: i for i, c in enumerate(__lowerCAmelCase    )}

            UpperCAmelCase     :			int     	=Discriminator(
                class_size=len(__lowerCAmelCase    )		,       pretrained_model=__lowerCAmelCase		,       cached_mode=__lowerCAmelCase		,       device=__lowerCAmelCase    ).to(__lowerCAmelCase    )

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

                              if len(__lowerCAmelCase    ) < max_length_seq:
                                    UpperCAmelCase     :			Optional[Any]     	=torch.tensor([5_02_56] + seq		,       device=__lowerCAmelCase		,       dtype=torch.long    )
                              else:
                                    print('''Line {} is longer than maximum length {}'''.format(__lowerCAmelCase		,       __lowerCAmelCase    )    )
                                    continue
                              x.append(__lowerCAmelCase    )
                              y.append(int(np.sum(d['''label''']    ) > 0    )    )
                        except Exception:
                              print('''Error evaluating / tokenizing line {}, skipping it'''.format(__lowerCAmelCase    )    )
                              pass

            UpperCAmelCase     :			str     	=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )
            UpperCAmelCase     :			Dict     	=int(0.9 * len(__lowerCAmelCase    )    )
            UpperCAmelCase     :			Union[str, Any]     	=len(__lowerCAmelCase    ) - train_size
            UpperCAmelCase	,    UpperCAmelCase     :			List[str]     	=torch.utils.data.random_split(__lowerCAmelCase		,       [train_size, test_size]    )

            UpperCAmelCase     :			Optional[Any]     	={
                '''class_size''': len(__lowerCAmelCase    ),
                '''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.'''    )

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

            UpperCAmelCase     :			Union[str, Any]     	=sorted(__lowerCAmelCase    )
            UpperCAmelCase     :			Any     	={c: i for i, c in enumerate(__lowerCAmelCase    )}

            UpperCAmelCase     :			Optional[Any]     	=Discriminator(
                class_size=len(__lowerCAmelCase    )		,       pretrained_model=__lowerCAmelCase		,       cached_mode=__lowerCAmelCase		,       device=__lowerCAmelCase    ).to(__lowerCAmelCase    )

            UpperCAmelCase     :			int     	=[]
            UpperCAmelCase     :			Optional[Any]     	=[]
            with open(__lowerCAmelCase    ) as f:
                  UpperCAmelCase     :			Optional[Any]     	=csv.reader(__lowerCAmelCase		,       delimiter='''\t'''    )
                  for i, row in enumerate(tqdm(__lowerCAmelCase		,       ascii=__lowerCAmelCase    )    ):
                        if row:
                              UpperCAmelCase     :			Dict     	=row[0]
                              UpperCAmelCase     :			Union[str, Any]     	=row[1]

                              try:
                                    UpperCAmelCase     :			Union[str, Any]     	=discriminator.tokenizer.encode(__lowerCAmelCase    )
                                    if len(__lowerCAmelCase    ) < max_length_seq:
                                          UpperCAmelCase     :			str     	=torch.tensor([5_02_56] + seq		,       device=__lowerCAmelCase		,       dtype=torch.long    )

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

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

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

            UpperCAmelCase     :			Union[str, Any]     	=Dataset(__lowerCAmelCase		,       __lowerCAmelCase    )
            UpperCAmelCase     :			Tuple     	=int(0.9 * len(__lowerCAmelCase    )    )
            UpperCAmelCase     :			str     	=len(__lowerCAmelCase    ) - train_size
            UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=torch.utils.data.random_split(__lowerCAmelCase		,       [train_size, test_size]    )

            UpperCAmelCase     :			int     	={
                '''class_size''': len(__lowerCAmelCase    ),
                '''embed_size''': discriminator.embed_size,
                '''pretrained_model''': pretrained_model,
                '''class_vocab''': classaidx,
                '''default_class''': 0,
            }

      UpperCAmelCase     :			Optional[int]     	=time.time()
      print('''Preprocessed {} data points'''.format(len(__lowerCAmelCase    ) + len(__lowerCAmelCase    )    )    )
      print('''Data preprocessing took: {:.3f}s'''.format(end - start    )    )

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

            UpperCAmelCase     :			List[Any]     	=time.time()

            UpperCAmelCase     :			Optional[int]     	=get_cached_data_loader(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       shuffle=__lowerCAmelCase		,       device=__lowerCAmelCase    )

            UpperCAmelCase     :			List[Any]     	=get_cached_data_loader(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       device=__lowerCAmelCase    )

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

      else:
            UpperCAmelCase     :			Any     	=torch.utils.data.DataLoader(
                dataset=__lowerCAmelCase		,       batch_size=__lowerCAmelCase		,       shuffle=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase    )
            UpperCAmelCase     :			int     	=torch.utils.data.DataLoader(dataset=__lowerCAmelCase		,       batch_size=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase    )

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

      UpperCAmelCase     :			Tuple     	=optim.Adam(discriminator.parameters()		,       lr=0.0001    )

      for epoch in range(__lowerCAmelCase    ):
            UpperCAmelCase     :			str     	=time.time()
            print('''\nEpoch'''		,       epoch + 1    )

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

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

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

            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(__lowerCAmelCase		,       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 __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__snake_case         =     20

# Time between time steps in seconds
__snake_case         =     INTERVAL / 10_00







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = 1.0		, snake_case__ = 1.0		, snake_case__ = "blue"		, )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=position_x
       UpperCAmelCase     :			Dict     	=position_y
       UpperCAmelCase     :			List[Any]     	=velocity_x
       UpperCAmelCase     :			Optional[int]     	=velocity_y
       UpperCAmelCase     :			List[str]     	=mass
       UpperCAmelCase     :			Any     	=size
       UpperCAmelCase     :			List[str]     	=color
 @property
 def 						UpperCAmelCase__							(	self    )						->				tuple[float, float]:
       '''simple docstring'''





       return self.position_x, self.position_y
 @property
 def 						UpperCAmelCase__							(	self    )						->				tuple[float, float]:
       '''simple docstring'''





       return self.velocity_x, self.velocity_y
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				None:
       '''simple docstring'''





       self.velocity_x += force_x * delta_time
       self.velocity_y += force_y * delta_time






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       self.position_x += self.velocity_x * delta_time
       self.position_y += self.velocity_y * delta_time







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__ = 1.0		, snake_case__ = 1.0		, snake_case__ = 0.0		, )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=bodies
       UpperCAmelCase     :			List[str]     	=gravitation_constant
       UpperCAmelCase     :			int     	=time_factor
       UpperCAmelCase     :			List[Any]     	=softening_factor
 def __len__(	self    )						->				int:
       '''simple docstring'''





       return len(self.bodies    )






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       for bodya in self.bodies:
             UpperCAmelCase     :			Tuple     	=0.0
             UpperCAmelCase     :			Union[str, Any]     	=0.0
             for bodya in self.bodies:
                   if bodya != bodya:
                         UpperCAmelCase     :			Optional[Any]     	=bodya.position_x - bodya.position_x
                         UpperCAmelCase     :			Any     	=bodya.position_y - bodya.position_y

                         # Calculation of the distance using Pythagoras's theorem
                         # Extra factor due to the softening technique
                         UpperCAmelCase     :			Optional[Any]     	=(dif_x**2 + dif_y**2 + self.softening_factor) ** (
                             1 / 2
                         )

                         # Newton's law of universal gravitation.
                         force_x += (
                             self.gravitation_constant * bodya.mass * dif_x / distance**3
                         )
                         force_y += (
                             self.gravitation_constant * bodya.mass * dif_y / distance**3
                         )

            # Update the body's velocity once all the force components have been added
             bodya.update_velocity(snake_case__		, snake_case__		, delta_time * self.time_factor    )

       # Update the positions only after all the velocities have been updated
       for body in self.bodies:
             body.update_position(delta_time * self.time_factor    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	None:






      '''simple docstring'''





      body_system.update_system(__lowerCAmelCase    )

      # Update the positions of the patches
      for patch, body in zip(__lowerCAmelCase		,       body_system.bodies    ):
            UpperCAmelCase     :			Dict     	=(body.position_x, body.position_y)





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = 1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = 1		,       )->	None:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[Any]     	=plt.figure()
      fig.canvas.set_window_title(__lowerCAmelCase    )
      UpperCAmelCase     :			Dict     	=plt.axes(
          xlim=(x_start, x_end)		,       ylim=(y_start, y_end)    )  # Set section to be plotted
      plt.gca().set_aspect('''equal'''    )  # Fix aspect ratio

      # Each body is drawn as a patch by the plt-function
      UpperCAmelCase     :			Tuple     	=[
          plt.Circle((body.position_x, body.position_y)		,       body.size		,       fc=body.color    )
          for body in body_system.bodies
      ]

      for patch in patches:
            ax.add_patch(__lowerCAmelCase    )

      # Function called at each step of the animation
      def update(__lowerCAmelCase    ) -> list[plt.Circle]:
            update_step(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )
            return patches

      UpperCAmelCase     :			List[Any]     	=animation.FuncAnimation(  # noqa: F841
          __lowerCAmelCase		,       __lowerCAmelCase		,       interval=__lowerCAmelCase		,       blit=__lowerCAmelCase    )

      plt.show()





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=0.9700436
      UpperCAmelCase     :			Dict     	=-0.24308753
      UpperCAmelCase     :			List[Any]     	=0.466203685
      UpperCAmelCase     :			Dict     	=0.43236573

      UpperCAmelCase     :			Optional[Any]     	=[
          Body(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       size=0.2		,       color='''red'''    ),
          Body(-position_x		,       -position_y		,       __lowerCAmelCase		,       __lowerCAmelCase		,       size=0.2		,       color='''green'''    ),
          Body(0		,       0		,       -2 * velocity_x		,       -2 * velocity_y		,       size=0.2		,       color='''blue'''    ),
      ]
      return BodySystem(__lowerCAmelCase		,       time_factor=3    )





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	=7.3_4_7_6e2_2
      UpperCAmelCase     :			Tuple     	=5.9_7_2e2_4
      UpperCAmelCase     :			str     	=10_22
      UpperCAmelCase     :			Any     	=3_84_39_90_00
      UpperCAmelCase     :			List[Any]     	=6.6_7_4e-1_1

      # Calculation of the respective velocities so that total impulse is zero,
      # i.e. the two bodies together don't move
      UpperCAmelCase     :			Optional[int]     	=earth_mass * velocity_dif / (earth_mass + moon_mass)
      UpperCAmelCase     :			Optional[Any]     	=moon_velocity - velocity_dif

      UpperCAmelCase     :			Optional[int]     	=Body(-earth_moon_distance		,       0		,       0		,       __lowerCAmelCase		,       __lowerCAmelCase		,       10_00_00_00		,       '''grey'''    )
      UpperCAmelCase     :			Dict     	=Body(0		,       0		,       0		,       __lowerCAmelCase		,       __lowerCAmelCase		,       50_00_00_00		,       '''blue'''    )
      return BodySystem([earth, moon]		,       __lowerCAmelCase		,       time_factor=1_00_00_00    )





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=[]
      for _ in range(10    ):
            UpperCAmelCase     :			Union[str, Any]     	=random.uniform(-0.5		,       0.5    )
            UpperCAmelCase     :			List[Any]     	=random.uniform(-0.5		,       0.5    )

            # Bodies are created pairwise with opposite velocities so that the
            # total impulse remains zero
            bodies.append(
                Body(
                    random.uniform(-0.5		,       0.5    )		,       random.uniform(-0.5		,       0.5    )		,       __lowerCAmelCase		,       __lowerCAmelCase		,       size=0.05		,       )    )
            bodies.append(
                Body(
                    random.uniform(-0.5		,       0.5    )		,       random.uniform(-0.5		,       0.5    )		,       -velocity_x		,       -velocity_y		,       size=0.05		,       )    )
      return BodySystem(__lowerCAmelCase		,       0.01		,       10		,       0.1    )


if __name__ == "__main__":
     plot('''Figure-8 solution to the 3-body-problem''', example_a(), -2, 2, -2, 2)
     plot(
         '''Moon\'s orbit around the earth''',
         example_a(),
         -4_30_00_00_00,
         4_30_00_00_00,
         -4_30_00_00_00,
         4_30_00_00_00,
     )
     plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)
