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





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__="gpt2-medium" ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__="cpu"    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Dict				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Dict				      =			GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			self.encoder.transformer.config.hidden_size
								lowerCAmelCase__ :    int				      =			ClassificationHead(class_size=SCREAMING_SNAKE_CASE__ ,	embed_size=self.embed_size    )
								lowerCAmelCase__ :    str				      =			cached_mode
								lowerCAmelCase__ :    List[str]				      =			device
					def lowercase_			(		self    ):
								"""simple docstring"""







								return self.classifier_head
					def lowercase_			(		self    ):
								"""simple docstring"""







								for param in self.encoder.parameters():
											lowerCAmelCase__ :    Union[str, Any]				      =			False
								self.classifier_head.train()
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			x.ne(0    ).unsqueeze(2    ).repeat(1 ,	1 ,	self.embed_size    ).float().to(self.device    ).detach()
								lowerCAmelCase__ :    int				      =			self.encoder.transformer(SCREAMING_SNAKE_CASE__    )['''last_hidden_state''']
								lowerCAmelCase__ :    Optional[Any]				      =			hidden * mask
								lowerCAmelCase__ :    int				      =			torch.sum(SCREAMING_SNAKE_CASE__ ,	dim=1    ) / (torch.sum(SCREAMING_SNAKE_CASE__ ,	dim=1    ).detach() + EPSILON)
								return avg_hidden
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								if self.cached_mode:
											lowerCAmelCase__ :    Optional[Any]				      =			x.to(self.device    )
								else:
											lowerCAmelCase__ :    List[Any]				      =			self.avg_representation(x.to(self.device    )    )

								lowerCAmelCase__ :    int				      =			self.classifier_head(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[str]				      =			nn.functional.log_softmax(SCREAMING_SNAKE_CASE__ ,	dim=-1    )

								return probs





class 						lowercase       (  data.Dataset					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			X
								lowerCAmelCase__ :    List[str]				      =			y
					def __len__(		self    ):
								"""simple docstring"""







								return len(self.X    )
					def __getitem__(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{}
								lowerCAmelCase__ :    str				      =			self.X[index]
								lowerCAmelCase__ :    List[str]				      =			self.y[index]
								return data





def      _a     (   __UpperCamelCase       :  int    ):
			def pad_sequences(__UpperCamelCase       :  str    ):
						lowerCAmelCase__ :    Optional[Any]				      =			[len(__UpperCamelCase    ) for seq in sequences]

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

						for i, seq in enumerate(__UpperCamelCase    ):
									lowerCAmelCase__ :    List[Any]				      =			lengths[i]
									lowerCAmelCase__ :    Union[str, Any]				      =			seq[:end]

						return padded_sequences, lengths

			lowerCAmelCase__ :    List[str]				      =			{}
			for key in data[0].keys():
						lowerCAmelCase__ :    Optional[int]				      =			[d[key] for d in data]

			lowerCAmelCase__      ,				lowerCAmelCase__ :    int				      =			pad_sequences(item_info['''X''']    )
			lowerCAmelCase__ :    Dict				      =			torch.tensor(item_info['''y''']	,dtype=torch.long    )

			return x_batch, y_batch



def      _a     (   __UpperCamelCase       :  str    ):
			lowerCAmelCase__ :    Optional[int]				      =			{}
			for key in data[0].keys():
						lowerCAmelCase__ :    Tuple				      =			[d[key] for d in data]

			lowerCAmelCase__ :    Optional[int]				      =			torch.cat(item_info['''X''']	,0    )
			lowerCAmelCase__ :    Any				      =			torch.tensor(item_info['''y''']	,dtype=torch.long    )

			return x_batch, y_batch



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

						optimizer.zero_grad()

						lowerCAmelCase__ :    int				      =			discriminator(__UpperCamelCase    )
						lowerCAmelCase__ :    str				      =			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      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[str]	,__UpperCamelCase       :  List[str]="cpu"    ):
			discriminator.eval()
			lowerCAmelCase__ :    int				      =			0
			lowerCAmelCase__ :    List[str]				      =			0
			with torch.no_grad():
						for input_t, target_t in data_loader:
									lowerCAmelCase__      ,				lowerCAmelCase__ :    Union[str, Any]				      =			input_t.to(__UpperCamelCase    ), target_t.to(__UpperCamelCase    )
									lowerCAmelCase__ :    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__ :    Dict				      =			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      _a     (   __UpperCamelCase       :  List[str]	,__UpperCamelCase       :  Dict	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  int=False	,__UpperCamelCase       :  str="cpu"    ):
			lowerCAmelCase__ :    Dict				      =			model.tokenizer.encode(__UpperCamelCase    )
			lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor([input_t]	,dtype=torch.long	,device=__UpperCamelCase    )
			if cached:
						lowerCAmelCase__ :    Optional[int]				      =			model.avg_representation(__UpperCamelCase    )

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

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

			lowerCAmelCase__ :    List[str]				      =			torch.utils.data.DataLoader(
			    dataset=Dataset(__UpperCamelCase	,__UpperCamelCase    )	,batch_size=__UpperCamelCase	,shuffle=__UpperCamelCase	,collate_fn=__UpperCamelCase    )

			return data_loader



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

			print('''Preprocessing {} dataset...'''.format(__UpperCamelCase    )    )
			lowerCAmelCase__ :    Union[str, Any]				      =			time.time()

			if dataset == "SST":
						lowerCAmelCase__ :    Optional[Any]				      =			['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
						lowerCAmelCase__ :    List[Any]				      =			{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[Any]				      =			torchtext_data.Field()
						lowerCAmelCase__ :    Optional[int]				      =			torchtext_data.Field(sequential=__UpperCamelCase    )
						lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    Dict				      =			datasets.SST.splits(
						    __UpperCamelCase	,__UpperCamelCase	,fine_grained=__UpperCamelCase	,train_subtrees=__UpperCamelCase	,)

						lowerCAmelCase__ :    int				      =			[]
						lowerCAmelCase__ :    Any				      =			[]
						for i in trange(len(__UpperCamelCase    )	,ascii=__UpperCamelCase    ):
									lowerCAmelCase__ :    Union[str, Any]				      =			TreebankWordDetokenizer().detokenize(vars(train_data[i]    )['''text''']    )
									lowerCAmelCase__ :    Tuple				      =			discriminator.tokenizer.encode(__UpperCamelCase    )
									lowerCAmelCase__ :    List[Any]				      =			torch.tensor([5_0256] + seq	,device=__UpperCamelCase	,dtype=torch.long    )
									x.append(__UpperCamelCase    )
									y.append(classaidx[vars(train_data[i]    )['''label''']]    )
						lowerCAmelCase__ :    int				      =			Dataset(__UpperCamelCase	,__UpperCamelCase    )

						lowerCAmelCase__ :    Optional[int]				      =			[]
						lowerCAmelCase__ :    List[Any]				      =			[]
						for i in trange(len(__UpperCamelCase    )	,ascii=__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[Any]				      =			TreebankWordDetokenizer().detokenize(vars(test_data[i]    )['''text''']    )
									lowerCAmelCase__ :    int				      =			discriminator.tokenizer.encode(__UpperCamelCase    )
									lowerCAmelCase__ :    Optional[int]				      =			torch.tensor([5_0256] + 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__ :    Tuple				      =			{
						    '''class_size''': len(__UpperCamelCase    ),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 2,
						}

			elif dataset == "clickbait":
						lowerCAmelCase__ :    List[Any]				      =			['''non_clickbait''', '''clickbait''']
						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    )

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

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

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

						lowerCAmelCase__ :    Optional[Any]				      =			{
						    '''class_size''': len(__UpperCamelCase    ),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 1,
						}

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

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

						lowerCAmelCase__ :    List[str]				      =			Dataset(__UpperCamelCase	,__UpperCamelCase    )
						lowerCAmelCase__ :    List[str]				      =			int(0.9 * len(__UpperCamelCase    )    )
						lowerCAmelCase__ :    Any				      =			len(__UpperCamelCase    ) - train_size
						lowerCAmelCase__      ,				lowerCAmelCase__ :    Tuple				      =			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''': 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__ :    List[str]				      =			set()
						with open(__UpperCamelCase    ) as f:
									lowerCAmelCase__ :    Optional[Any]				      =			csv.reader(__UpperCamelCase	,delimiter='''\t'''    )
									for row in tqdm(__UpperCamelCase	,ascii=__UpperCamelCase    ):
												if row:
															classes.add(row[0]    )

						lowerCAmelCase__ :    List[str]				      =			sorted(__UpperCamelCase    )
						lowerCAmelCase__ :    Any				      =			{c: i for i, c in enumerate(__UpperCamelCase    )}

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

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

															try:
																		lowerCAmelCase__ :    Optional[Any]				      =			discriminator.tokenizer.encode(__UpperCamelCase    )
																		if len(__UpperCamelCase    ) < max_length_seq:
																					lowerCAmelCase__ :    Tuple				      =			torch.tensor([5_0256] + seq	,device=__UpperCamelCase	,dtype=torch.long    )

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

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

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

						lowerCAmelCase__ :    Any				      =			Dataset(__UpperCamelCase	,__UpperCamelCase    )
						lowerCAmelCase__ :    Union[str, Any]				      =			int(0.9 * len(__UpperCamelCase    )    )
						lowerCAmelCase__ :    Optional[int]				      =			len(__UpperCamelCase    ) - train_size
						lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			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''': 0,
						}

			lowerCAmelCase__ :    Any				      =			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__ :    str				      =			time.time()

						lowerCAmelCase__ :    Optional[Any]				      =			get_cached_data_loader(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,shuffle=__UpperCamelCase	,device=__UpperCamelCase    )

						lowerCAmelCase__ :    int				      =			get_cached_data_loader(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,device=__UpperCamelCase    )

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

			else:
						lowerCAmelCase__ :    int				      =			torch.utils.data.DataLoader(
						    dataset=__UpperCamelCase	,batch_size=__UpperCamelCase	,shuffle=__UpperCamelCase	,collate_fn=__UpperCamelCase    )
						lowerCAmelCase__ :    Union[str, Any]				      =			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__ :    str				      =			optim.Adam(discriminator.parameters()	,lr=0.0_001    )

			for epoch in range(__UpperCamelCase    ):
						lowerCAmelCase__ :    Any				      =			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__ :    Optional[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__       :   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[str]   						=					parser.parse_args()

							train_discriminator(**(vars(args)))





# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__       :   Optional[Any]   						=					abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def      _a     (   __UpperCamelCase       :  Tuple    ):
			from transformers.testing_utils import pytest_addoption_shared

			pytest_addoption_shared(__UpperCamelCase    )



def      _a     (   __UpperCamelCase       :  int    ):
			from transformers.testing_utils import pytest_terminal_summary_main

			lowerCAmelCase__ :    int				      =			terminalreporter.config.getoption('''--make-reports'''    )
			if make_reports:
						pytest_terminal_summary_main(__UpperCamelCase	,id=__UpperCamelCase    )



