
"""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)
UpperCAmelCase__				:   int   							=			1E-10
UpperCAmelCase__				:   str   							=			'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase__				:   Tuple   							=			1_0_0



class      lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__="gpt2-medium"       ,	SCREAMING_SNAKE_CASE__=False       ,	SCREAMING_SNAKE_CASE__="cpu"   ) ->		List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       GPTaTokenizer.from_pretrained(lowerCAmelCase__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       GPTaLMHeadModel.from_pretrained(lowerCAmelCase__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       self.encoder.transformer.config.hidden_size
											SCREAMING_SNAKE_CASE__						:	Any									=       ClassificationHead(class_size=lowerCAmelCase__       ,	embed_size=self.embed_size   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       cached_mode
											SCREAMING_SNAKE_CASE__						:	int									=       device



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											return self.classifier_head



				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											if self.cached_mode:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       x.to(self.device   )
											else:
																		SCREAMING_SNAKE_CASE__						:	int									=       self.avg_representation(x.to(self.device   )   )

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

											return probs






class      lowerCAmelCase_						(data.Dataset  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       X
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       y



				def __len__(self   ) ->		Tuple:



											"""simple docstring"""




											return len(self.X   )



				def __getitem__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       {}
											SCREAMING_SNAKE_CASE__						:	Dict									=       self.X[index]
											SCREAMING_SNAKE_CASE__						:	int									=       self.y[index]
											return data






def lowercase_  (       _snake_case						):

							def pad_sequences(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [len(lowercase_						) for seq in sequences]

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

														for i, seq in enumerate(lowercase_						):
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       lengths[i]
																					SCREAMING_SNAKE_CASE__						:	Any									=       seq[:end]

														return padded_sequences, lengths

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

							SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	str									=       pad_sequences(item_info["""X"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(item_info["""y"""]       ,dtype=torch.long						)

							return x_batch, y_batch

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Dict									=       {}
							for key in data[0].keys():
														SCREAMING_SNAKE_CASE__						:	Dict									=       [d[key] for d in data]

							SCREAMING_SNAKE_CASE__						:	str									=       torch.cat(item_info["""X"""]       ,0						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(item_info["""y"""]       ,dtype=torch.long						)

							return x_batch, y_batch

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case=0       ,_snake_case=10       ,_snake_case="cpu"						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       0
							discriminator.train_custom()
							for batch_idx, (input_t, target_t) in enumerate(lowercase_						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       input_t.to(lowercase_						), target_t.to(lowercase_						)

														optimizer.zero_grad()

														SCREAMING_SNAKE_CASE__						:	Any									=       discriminator(lowercase_						)
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       nn.functional.nll_loss(lowercase_       ,lowercase_						)
														loss.backward(retain_graph=lowercase_						)
														optimizer.step()

														samples_so_far += len(lowercase_						)

														if batch_idx % log_interval == 0:
																					print(
																					    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
																					        epoch + 1       ,lowercase_       ,len(data_loader.dataset						)       ,100 * samples_so_far / len(data_loader.dataset						)       ,loss.item()       ,)						)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case="cpu"						):
							discriminator.eval()
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
							SCREAMING_SNAKE_CASE__						:	List[str]									=       0
							with torch.no_grad():
														for input_t, target_t in data_loader:
																					SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	int									=       input_t.to(lowercase_						), target_t.to(lowercase_						)
																					SCREAMING_SNAKE_CASE__						:	Tuple									=       discriminator(lowercase_						)
																					# sum up batch loss
																					test_loss += nn.functional.nll_loss(lowercase_       ,lowercase_       ,reduction="""sum"""						).item()
																					# get the index of the max log-probability
																					SCREAMING_SNAKE_CASE__						:	Tuple									=       output_t.argmax(dim=1       ,keepdim=lowercase_						)
																					correct += pred_t.eq(target_t.view_as(lowercase_						)						).sum().item()

							test_loss /= len(data_loader.dataset						)

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

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case=False       ,_snake_case="cpu"						):
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       model.tokenizer.encode(lowercase_						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor([input_t]       ,dtype=torch.long       ,device=lowercase_						)
							if cached:
														SCREAMING_SNAKE_CASE__						:	str									=       model.avg_representation(lowercase_						)

							SCREAMING_SNAKE_CASE__						:	Any									=       model(lowercase_						).data.cpu().numpy().flatten().tolist()
							print("""Input sentence:"""       ,lowercase_						)
							print(
							    """Predictions:"""       ,""", """.join("""{}: {:.4f}""".format(lowercase_       ,math.exp(lowercase_						)						) for c, log_prob in zip(lowercase_       ,lowercase_						)						)       ,)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case=False       ,_snake_case="cpu"						):
							SCREAMING_SNAKE_CASE__						:	str									=       torch.utils.data.DataLoader(dataset=lowercase_       ,batch_size=lowercase_       ,collate_fn=lowercase_						)

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

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

							return data_loader

def lowercase_  (       _snake_case       ,_snake_case=None       ,_snake_case="gpt2-medium"       ,_snake_case=10       ,_snake_case=64       ,_snake_case=10       ,_snake_case=False       ,_snake_case=False       ,_snake_case=False       ,):
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       """cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

							print("""Preprocessing {} dataset...""".format(lowercase_						)						)
							SCREAMING_SNAKE_CASE__						:	Tuple									=       time.time()

							if dataset == "SST":
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       ["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
														SCREAMING_SNAKE_CASE__						:	Dict									=       {c: i for i, c in enumerate(lowercase_						)}

														SCREAMING_SNAKE_CASE__						:	int									=       Discriminator(
														    class_size=len(lowercase_						)       ,pretrained_model=lowercase_       ,cached_mode=lowercase_       ,device=lowercase_						).to(lowercase_						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torchtext_data.Field()
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torchtext_data.Field(sequential=lowercase_						)
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[str]									=       datasets.SST.splits(
														    lowercase_       ,lowercase_       ,fine_grained=lowercase_       ,train_subtrees=lowercase_       ,)

														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       []
														SCREAMING_SNAKE_CASE__						:	Tuple									=       []
														for i in trange(len(lowercase_						)       ,ascii=lowercase_						):
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       TreebankWordDetokenizer().detokenize(vars(train_data[i]						)["""text"""]						)
																					SCREAMING_SNAKE_CASE__						:	str									=       discriminator.tokenizer.encode(lowercase_						)
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor([50_256] + seq       ,device=lowercase_       ,dtype=torch.long						)
																					x.append(lowercase_						)
																					y.append(classaidx[vars(train_data[i]						)["""label"""]]						)
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       Dataset(lowercase_       ,lowercase_						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
														for i in trange(len(lowercase_						)       ,ascii=lowercase_						):
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       TreebankWordDetokenizer().detokenize(vars(test_data[i]						)["""text"""]						)
																					SCREAMING_SNAKE_CASE__						:	str									=       discriminator.tokenizer.encode(lowercase_						)
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor([50_256] + seq       ,device=lowercase_       ,dtype=torch.long						)
																					test_x.append(lowercase_						)
																					test_y.append(classaidx[vars(test_data[i]						)["""label"""]]						)
														SCREAMING_SNAKE_CASE__						:	Any									=       Dataset(lowercase_       ,lowercase_						)

														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {
														    """class_size""": len(lowercase_						),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 2,
														}

							elif dataset == "clickbait":
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       ["""non_clickbait""", """clickbait"""]
														SCREAMING_SNAKE_CASE__						:	int									=       {c: i for i, c in enumerate(lowercase_						)}

														SCREAMING_SNAKE_CASE__						:	str									=       Discriminator(
														    class_size=len(lowercase_						)       ,pretrained_model=lowercase_       ,cached_mode=lowercase_       ,device=lowercase_						).to(lowercase_						)

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

																																			if len(lowercase_						) < max_length_seq:
																																										SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor([50_256] + seq       ,device=lowercase_       ,dtype=torch.long						)
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(lowercase_       ,lowercase_						)						)
																																										continue
																																			x.append(lowercase_						)
																																			y.append(d["""label"""]						)
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(lowercase_						)						)
																																			pass

														SCREAMING_SNAKE_CASE__						:	Tuple									=       Dataset(lowercase_       ,lowercase_						)
														SCREAMING_SNAKE_CASE__						:	Dict									=       int(0.9 * len(lowercase_						)						)
														SCREAMING_SNAKE_CASE__						:	str									=       len(lowercase_						) - train_size
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.utils.data.random_split(lowercase_       ,[train_size, test_size]						)

														SCREAMING_SNAKE_CASE__						:	List[str]									=       {
														    """class_size""": len(lowercase_						),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 1,
														}

							elif dataset == "toxic":
														SCREAMING_SNAKE_CASE__						:	int									=       ["""non_toxic""", """toxic"""]
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {c: i for i, c in enumerate(lowercase_						)}

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

														SCREAMING_SNAKE_CASE__						:	int									=       []
														SCREAMING_SNAKE_CASE__						:	Tuple									=       []
														with open("""datasets/toxic/toxic_train.txt"""						) as f:
																					for i, line in enumerate(tqdm(lowercase_       ,ascii=lowercase_						)						):
																												try:
																																			SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       eval(lowercase_						)
																																			SCREAMING_SNAKE_CASE__						:	List[str]									=       discriminator.tokenizer.encode(d["""text"""]						)

																																			if len(lowercase_						) < max_length_seq:
																																										SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor([50_256] + seq       ,device=lowercase_       ,dtype=torch.long						)
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(lowercase_       ,lowercase_						)						)
																																										continue
																																			x.append(lowercase_						)
																																			y.append(int(np.sum(d["""label"""]						) > 0						)						)
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(lowercase_						)						)
																																			pass

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       Dataset(lowercase_       ,lowercase_						)
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       int(0.9 * len(lowercase_						)						)
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       len(lowercase_						) - train_size
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       torch.utils.data.random_split(lowercase_       ,[train_size, test_size]						)

														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
														    """class_size""": len(lowercase_						),
														    """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."""						)

														SCREAMING_SNAKE_CASE__						:	int									=       set()
														with open(lowercase_						) as f:
																					SCREAMING_SNAKE_CASE__						:	str									=       csv.reader(lowercase_       ,delimiter="""\t"""						)
																					for row in tqdm(lowercase_       ,ascii=lowercase_						):
																												if row:
																																			classes.add(row[0]						)

														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       sorted(lowercase_						)
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {c: i for i, c in enumerate(lowercase_						)}

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

														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       []
														SCREAMING_SNAKE_CASE__						:	Dict									=       []
														with open(lowercase_						) as f:
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       csv.reader(lowercase_       ,delimiter="""\t"""						)
																					for i, row in enumerate(tqdm(lowercase_       ,ascii=lowercase_						)						):
																												if row:
																																			SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       row[0]
																																			SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       row[1]

																																			try:
																																										SCREAMING_SNAKE_CASE__						:	int									=       discriminator.tokenizer.encode(lowercase_						)
																																										if len(lowercase_						) < max_length_seq:
																																																	SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor([50_256] + seq       ,device=lowercase_       ,dtype=torch.long						)

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

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

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

														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       Dataset(lowercase_       ,lowercase_						)
														SCREAMING_SNAKE_CASE__						:	Dict									=       int(0.9 * len(lowercase_						)						)
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       len(lowercase_						) - train_size
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.utils.data.random_split(lowercase_       ,[train_size, test_size]						)

														SCREAMING_SNAKE_CASE__						:	str									=       {
														    """class_size""": len(lowercase_						),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 0,
														}

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       time.time()
							print("""Preprocessed {} data points""".format(len(lowercase_						) + len(lowercase_						)						)						)
							print("""Data preprocessing took: {:.3f}s""".format(end - start						)						)

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

														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       time.time()

														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       get_cached_data_loader(lowercase_       ,lowercase_       ,lowercase_       ,shuffle=lowercase_       ,device=lowercase_						)

														SCREAMING_SNAKE_CASE__						:	str									=       get_cached_data_loader(lowercase_       ,lowercase_       ,lowercase_       ,device=lowercase_						)

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

							else:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.utils.data.DataLoader(
														    dataset=lowercase_       ,batch_size=lowercase_       ,shuffle=lowercase_       ,collate_fn=lowercase_						)
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.utils.data.DataLoader(dataset=lowercase_       ,batch_size=lowercase_       ,collate_fn=lowercase_						)

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

							SCREAMING_SNAKE_CASE__						:	Dict									=       optim.Adam(discriminator.parameters()       ,lr=0.0001						)

							for epoch in range(lowercase_						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       time.time()
														print("""\nEpoch"""       ,epoch + 1						)

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

														SCREAMING_SNAKE_CASE__						:	List[str]									=       time.time()
														print("""Epoch took: {:.3f}s""".format(end - start						)						)

														print("""\nExample prediction"""						)
														predict(lowercase_       ,lowercase_       ,lowercase_       ,cached=lowercase_       ,device=lowercase_						)

														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(lowercase_       ,epoch + 1						)       ,)


if __name__ == "__main__":
						UpperCAmelCase__				:   Optional[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')
						UpperCAmelCase__				:   Any   							=			parser.parse_args()

						train_discriminator(**(vars(args)))






"""simple docstring"""




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy


UpperCAmelCase__				:   Union[str, Any]   							=			datasets.utils.logging.get_logger(__name__)



@dataclass
class      lowerCAmelCase_						(datasets.BuilderConfig  ):


				"""simple docstring"""



				__UpperCamelCase				:						Union[str, "sqlalchemy.sql.Selectable"] 	=       None
				__UpperCamelCase				:						Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] 	=       None
				__UpperCamelCase				:						Optional[Union[str, List[str]]] 	=       None
				__UpperCamelCase				:						bool 	=       True
				__UpperCamelCase				:						Optional[Union[List, Tuple, Dict]] 	=       None
				__UpperCamelCase				:						Optional[Union[List, Dict]] 	=       None
				__UpperCamelCase				:						Optional[List[str]] 	=       None
				__UpperCamelCase				:						Optional[int] 	=       10000
				__UpperCamelCase				:						Optional[datasets.Features] 	=       None



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											if self.sql is None:
																		raise ValueError("""sql must be specified"""   )
											if self.con is None:
																		raise ValueError("""con must be specified"""   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       config_kwargs.copy()
											# We need to stringify the Selectable object to make its hash deterministic

											# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
											SCREAMING_SNAKE_CASE__						:	Dict									=       config_kwargs["""sql"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																									import sqlalchemy

																									if isinstance(SCREAMING_SNAKE_CASE__       ,	sqlalchemy.sql.Selectable   ):
																																SCREAMING_SNAKE_CASE__						:	str									=       sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""   )[0] + """://"""   )
																																SCREAMING_SNAKE_CASE__						:	Tuple									=       str(sql.compile(dialect=engine.dialect   )   )
																																SCREAMING_SNAKE_CASE__						:	Any									=       sql_str
																									else:
																																raise TypeError(
																																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
																		else:
																									raise TypeError(
																									    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       config_kwargs["""con"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       id(SCREAMING_SNAKE_CASE__   )
																		logger.info(
																		    F'''SQL connection \'con\' of type {type(SCREAMING_SNAKE_CASE__   )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''   )

											return super().create_config_id(SCREAMING_SNAKE_CASE__       ,	custom_features=SCREAMING_SNAKE_CASE__   )



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
											    """index_col""": self.index_col,
											    """columns""": self.columns,
											    """params""": self.params,
											    """coerce_float""": self.coerce_float,
											    """parse_dates""": self.parse_dates,
											}
											return pd_read_sql_kwargs






class      lowerCAmelCase_						(datasets.ArrowBasedBuilder  ):


				"""simple docstring"""



				__UpperCamelCase				:						List[str] 	=       SqlConfig



				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




											return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={}   )]



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.config.features.arrow_schema
																		if all(not require_storage_cast(SCREAMING_SNAKE_CASE__   ) for feature in self.config.features.values()   ):
																									# cheaper cast
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       pa.Table.from_arrays([pa_table[field.name] for field in schema]       ,	schema=SCREAMING_SNAKE_CASE__   )
																		else:
																									# more expensive cast; allows str <-> int/float or str to Audio for example
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       table_cast(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											return pa_table



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.config.chunksize
											SCREAMING_SNAKE_CASE__						:	Any									=       pd.read_sql(
											    self.config.sql       ,	self.config.con       ,	chunksize=SCREAMING_SNAKE_CASE__       ,	**self.config.pd_read_sql_kwargs   )
											SCREAMING_SNAKE_CASE__						:	str									=       [sql_reader] if chunksize is None else sql_reader
											for chunk_idx, df in enumerate(SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	int									=       pa.Table.from_pandas(SCREAMING_SNAKE_CASE__   )
																		yield chunk_idx, self._cast_table(SCREAMING_SNAKE_CASE__   )




