
'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def     snake_case							(						UpperCAmelCase		)->     Dict:







      """simple docstring"""
      return 1 / (1 + np.exp(-1 * x		))


class 	UpperCamelCase__					:






 def __init__(     self					:Any		,			_A					:List[str]		,			_A					:List[Any]=None		,			_A					:str=None		,			_A					:Optional[int]=False     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     units
       __A    		=     None
       __A    		=     None
       __A    		=     activation
       if learning_rate is None:
             __A    		=     0.3
       __A    		=     learning_rate
       __A    		=     is_input_layer






 def 		lowercase_  (     self					:Optional[Any]		,			_A					:str     )			->     Any:







       '''simple docstring'''



       __A    		=     np.asmatrix(np.random.normal(0		,			0.5		,			(self.units, back_units)     )     )
       __A    		=     np.asmatrix(np.random.normal(0		,			0.5		,			self.units     )     ).T
       if self.activation is None:
             __A    		=     sigmoid






 def 		lowercase_  (     self					:Optional[Any]     )			->     Tuple:







       '''simple docstring'''



       if self.activation == sigmoid:
             __A    		=     np.dot(self.output		,			(1 - self.output).T     )
             __A    		=     np.diag(np.diag(_A     )     )
       else:
             __A    		=     1
       return gradient_activation






 def 		lowercase_  (     self					:Any		,			_A					:Dict     )			->     Optional[Any]:







       '''simple docstring'''



       __A    		=     xdata
       if self.is_input_layer:
             # input layer
             __A    		=     xdata
             __A    		=     xdata
             return xdata
       else:
             __A    		=     np.dot(self.weight		,			self.xdata     ) - self.bias
             __A    		=     self.activation(self.wx_plus_b     )
             return self.output






 def 		lowercase_  (     self					:str		,			_A					:Dict     )			->     Union[str, Any]:







       '''simple docstring'''



       __A    		=     self.cal_gradient()  # i * i 维
       __A    		=     np.asmatrix(np.dot(gradient.T		,			_A     )     )

       __A    		=     np.asmatrix(self.xdata     )
       __A    		=     -1
       __A    		=     self.weight

       __A    		=     np.dot(gradient.T		,			self._gradient_weight.T     )
       __A    		=     gradient * self._gradient_bias
       __A    		=     np.dot(_A		,			self._gradient_x     ).T
       # upgrade: the Negative gradient direction
       __A    		=     self.weight - self.learn_rate * self.gradient_weight
       __A    		=     self.bias - self.learn_rate * self.gradient_bias.T
       # updates the weights and bias according to learning rate (0.3 if undefined)
       return self.gradient


class 	UpperCamelCase__					:






 def __init__(     self					:Dict     )			->     Tuple:







       '''simple docstring'''



       __A    		=     []
       __A    		=     []
       __A    		=     plt.figure()
       __A    		=     self.fig_loss.add_subplot(1		,			1		,			1     )






 def 		lowercase_  (     self					:Tuple		,			_A					:List[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       self.layers.append(_A     )






 def 		lowercase_  (     self					:Any     )			->     Dict:







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]     ):
             if i < 1:
                   __A    		=     True
             else:
                   layer.initializer(self.layers[i - 1].units     )






 def 		lowercase_  (     self					:int     )			->     Optional[int]:







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]     ):
             print(F'------- layer {i} -------'     )
             print('weight.shape '		,			np.shape(layer.weight     )     )
             print('bias.shape '		,			np.shape(layer.bias     )     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:Tuple		,			_A					:Tuple		,			_A					:List[Any]		,			_A					:Optional[int]     )			->     Optional[Any]:







       '''simple docstring'''



       __A    		=     train_round
       __A    		=     accuracy

       self.ax_loss.hlines(self.accuracy		,			0		,			self.train_round * 1.1     )

       __A    		=     np.shape(_A     )
       for _ in range(_A     ):
             __A    		=     0
             for row in range(x_shape[0]     ):
                   __A    		=     np.asmatrix(xdata[row, :]     ).T
                   __A    		=     np.asmatrix(ydata[row, :]     ).T

                   # forward propagation
                   for layer in self.layers:
                         __A    		=     layer.forward_propagation(_xdata     )

                   __A		, __A    		=     self.cal_loss(_ydata		,			_xdata     )
                   __A    		=     all_loss + loss

                   # back propagation: the input_layer does not upgrade
                   for layer in self.layers[:0:-1]:
                         __A    		=     layer.back_propagation(_A     )

             __A    		=     all_loss / x_shape[0]
             self.train_mse.append(_A     )

             self.plot_loss()

             if mse < self.accuracy:
                   print('----达到精度----'     )
                   return mse
       return None






 def 		lowercase_  (     self					:str		,			_A					:str		,			_A					:Tuple     )			->     int:







       '''simple docstring'''



       __A    		=     np.sum(np.power((ydata - ydata_)		,			2     )     )
       __A    		=     2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient






 def 		lowercase_  (     self					:Tuple     )			->     int:







       '''simple docstring'''



       if self.ax_loss.lines:
             self.ax_loss.lines.remove(self.ax_loss.lines[0]     )
       self.ax_loss.plot(self.train_mse		,			'r-'     )
       plt.ion()
       plt.xlabel('step'     )
       plt.ylabel('loss'     )
       plt.show()
       plt.pause(0.1     )





def     snake_case							(						)->     Optional[int]:







      """simple docstring"""
      __A    		=     np.random.randn(1_0 ,					1_0		)
      __A    		=     np.asarray(
          [
              [0.8, 0.4],
              [0.4, 0.3],
              [0.34, 0.45],
              [0.67, 0.32],
              [0.88, 0.67],
              [0.78, 0.77],
              [0.55, 0.66],
              [0.55, 0.43],
              [0.54, 0.1],
              [0.1, 0.5],
          ]		)
      __A    		=     BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(UpperCAmelCase		)		)
      model.build()
      model.summary()
      model.train(xdata=UpperCAmelCase ,					ydata=UpperCAmelCase ,					train_round=1_0_0 ,					accuracy=0.01		)


if __name__ == "__main__":
 example()








'''simple docstring'''





# 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__						:				Optional[int]           =    1E-10
a__						:				str           =    "This is incredible! I love it, this is the best chicken I have ever had."
a__						:				List[Any]           =    1_0_0


class 	UpperCamelCase__					(      nn.Module):






 def __init__(     self					:List[Any]		,			_A					:List[str]		,			_A					:Optional[int]="gpt2-medium"		,			_A					:Tuple=False		,			_A					:List[str]="cpu"     )			->     int:







       '''simple docstring'''



       super().__init__()
       __A    		=     GPTaTokenizer.from_pretrained(_A     )
       __A    		=     GPTaLMHeadModel.from_pretrained(_A     )
       __A    		=     self.encoder.transformer.config.hidden_size
       __A    		=     ClassificationHead(class_size=_A		,			embed_size=self.embed_size     )
       __A    		=     cached_mode
       __A    		=     device






 def 		lowercase_  (     self					:Tuple     )			->     List[str]:







       '''simple docstring'''



       return self.classifier_head






 def 		lowercase_  (     self					:Optional[int]     )			->     Any:







       '''simple docstring'''



       for param in self.encoder.parameters():
             __A    		=     False
       self.classifier_head.train()






 def 		lowercase_  (     self					:List[Any]		,			_A					:Any     )			->     Tuple:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:Optional[int]		,			_A					:Union[str, Any]     )			->     Dict:







       '''simple docstring'''



       if self.cached_mode:
             __A    		=     x.to(self.device     )
       else:
             __A    		=     self.avg_representation(x.to(self.device     )     )

       __A    		=     self.classifier_head(_A     )
       __A    		=     nn.functional.log_softmax(_A		,			dim=-1     )

       return probs


class 	UpperCamelCase__					(      data.Dataset):






 def __init__(     self					:Optional[Any]		,			_A					:str		,			_A					:Dict     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     X
       __A    		=     y






 def __len__(     self					:int     )			->     int:







       '''simple docstring'''



       return len(self.X     )






 def __getitem__(     self					:Union[str, Any]		,			_A					:str     )			->     List[str]:







       '''simple docstring'''



       __A    		=     {}
       __A    		=     self.X[index]
       __A    		=     self.y[index]
       return data





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      def pad_sequences(UpperCAmelCase		):
            __A    		=     [len(UpperCAmelCase		) for seq in sequences]

            __A    		=     torch.zeros(len(UpperCAmelCase		) ,					max(UpperCAmelCase		)		).long()  # padding value = 0

            for i, seq in enumerate(UpperCAmelCase		):
                  __A    		=     lengths[i]
                  __A    		=     seq[:end]

            return padded_sequences, lengths

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

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

      return x_batch, y_batch





def     snake_case							(						UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     {}
      for key in data[0].keys():
            __A    		=     [d[key] for d in data]

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

      return x_batch, y_batch





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=0 ,					UpperCAmelCase=1_0 ,					UpperCAmelCase="cpu"		)->     Optional[Any]:







      """simple docstring"""
      __A    		=     0
      discriminator.train_custom()
      for batch_idx, (input_t, target_t) in enumerate(UpperCAmelCase		):
            __A		, __A    		=     input_t.to(UpperCAmelCase		), target_t.to(UpperCAmelCase		)

            optimizer.zero_grad()

            __A    		=     discriminator(UpperCAmelCase		)
            __A    		=     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		) ,					1_0_0 * samples_so_far / len(data_loader.dataset		) ,					loss.item() ,					)		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase="cpu"		)->     Tuple:







      """simple docstring"""
      discriminator.eval()
      __A    		=     0
      __A    		=     0
      with torch.no_grad():
            for input_t, target_t in data_loader:
                  __A		, __A    		=     input_t.to(UpperCAmelCase		), target_t.to(UpperCAmelCase		)
                  __A    		=     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
                  __A    		=     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     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase="cpu"		)->     Tuple:







      """simple docstring"""
      __A    		=     model.tokenizer.encode(UpperCAmelCase		)
      __A    		=     torch.tensor([input_t] ,					dtype=torch.long ,					device=UpperCAmelCase		)
      if cached:
            __A    		=     model.avg_representation(UpperCAmelCase		)

      __A    		=     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     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase="cpu"		)->     Dict:







      """simple docstring"""
      __A    		=     torch.utils.data.DataLoader(dataset=UpperCAmelCase ,					batch_size=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)

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

      __A    		=     torch.utils.data.DataLoader(
          dataset=Dataset(UpperCAmelCase ,					UpperCAmelCase		) ,					batch_size=UpperCAmelCase ,					shuffle=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)

      return data_loader





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase="gpt2-medium" ,					UpperCAmelCase=1_0 ,					UpperCAmelCase=6_4 ,					UpperCAmelCase=1_0 ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					)->     Optional[int]:







      """simple docstring"""
      __A    		=     'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

      print('Preprocessing {} dataset...'.format(UpperCAmelCase		)		)
      __A    		=     time.time()

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

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

            __A    		=     torchtext_data.Field()
            __A    		=     torchtext_data.Field(sequential=UpperCAmelCase		)
            __A		, __A		, __A    		=     datasets.SST.splits(
                UpperCAmelCase ,					UpperCAmelCase ,					fine_grained=UpperCAmelCase ,					train_subtrees=UpperCAmelCase ,					)

            __A    		=     []
            __A    		=     []
            for i in trange(len(UpperCAmelCase		) ,					ascii=UpperCAmelCase		):
                  __A    		=     TreebankWordDetokenizer().detokenize(vars(train_data[i]		)['text']		)
                  __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                  __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                  x.append(UpperCAmelCase		)
                  y.append(classaidx[vars(train_data[i]		)['label']]		)
            __A    		=     Dataset(UpperCAmelCase ,					UpperCAmelCase		)

            __A    		=     []
            __A    		=     []
            for i in trange(len(UpperCAmelCase		) ,					ascii=UpperCAmelCase		):
                  __A    		=     TreebankWordDetokenizer().detokenize(vars(test_data[i]		)['text']		)
                  __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                  __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                  test_x.append(UpperCAmelCase		)
                  test_y.append(classaidx[vars(test_data[i]		)['label']]		)
            __A    		=     Dataset(UpperCAmelCase ,					UpperCAmelCase		)

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

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

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

                              if len(UpperCAmelCase		) < max_length_seq:
                                    __A    		=     torch.tensor([5_0_2_5_6] + 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

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

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

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

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

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

                              if len(UpperCAmelCase		) < max_length_seq:
                                    __A    		=     torch.tensor([5_0_2_5_6] + 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

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

            __A    		=     {
                '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.'		)

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

            __A    		=     sorted(UpperCAmelCase		)
            __A    		=     {c: i for i, c in enumerate(UpperCAmelCase		)}

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

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

                              try:
                                    __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                                    if len(UpperCAmelCase		) < max_length_seq:
                                          __A    		=     torch.tensor([5_0_2_5_6] + 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

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

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

      __A    		=     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...'		)

            __A    		=     time.time()

            __A    		=     get_cached_data_loader(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					shuffle=UpperCAmelCase ,					device=UpperCAmelCase		)

            __A    		=     get_cached_data_loader(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					device=UpperCAmelCase		)

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

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

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

      for epoch in range(UpperCAmelCase		):
            __A    		=     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		)

            __A    		=     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__						:				Union[str, 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=1_0, metavar="N", help="Number of training epochs")
 parser.add_argument(
     "--batch_size", type=int, default=6_4, metavar="N", help="input batch size for training (default: 64)"
 )
 parser.add_argument(
     "--log_interval",
     type=int,
     default=1_0,
     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__						:				List[Any]           =    parser.parse_args()

 train_discriminator(**(vars(args)))






