






'''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      :     List[Any]       	=1e-10
_A      :     Optional[Any]       	='''This is incredible! I love it, this is the best chicken I have ever had.'''
_A      :     Optional[Any]       	=100

class _lowercase      (							nn.Module						):




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




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




							def 						lowerCamelCase_							(				self:							List[Any]	):
														for param in self.encoder.parameters():
																					lowerCamelCase__   :      Optional[Any]			    =			False
														self.classifier_head.train()




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




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							Optional[int]	):
														if self.cached_mode:
																					lowerCamelCase__   :      Optional[int]			    =			x.to(self.device	)
														else:
																					lowerCamelCase__   :      Tuple			    =			self.avg_representation(x.to(self.device	)	)

														lowerCamelCase__   :      Union[str, Any]			    =			self.classifier_head(UpperCamelCase__	)
														lowerCamelCase__   :      str			    =			nn.functional.log_softmax(UpperCamelCase__     ,				dim=-1	)

														return probs



class _lowercase      (							data.Dataset						):




							def __init__(				self:							str     ,				UpperCamelCase__:							Union[str, Any]     ,				UpperCamelCase__:							Dict	):
														lowerCamelCase__   :      str			    =			X
														lowerCamelCase__   :      List[str]			    =			y




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




							def __getitem__(				self:							str     ,				UpperCamelCase__:							Optional[Any]	):
														lowerCamelCase__   :      Union[str, Any]			    =			{}
														lowerCamelCase__   :      Optional[int]			    =			self.X[index]
														lowerCamelCase__   :      int			    =			self.y[index]
														return data





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Any:
							def pad_sequences(UpperCamelCase ):
														lowerCamelCase__   :      Optional[Any]			    =			[len(UpperCamelCase ) for seq in sequences]

														lowerCamelCase__   :      Optional[int]			    =			torch.zeros(len(UpperCamelCase )							,     max(UpperCamelCase ) ).long()  # padding value = 0

														for i, seq in enumerate(UpperCamelCase ):
																					lowerCamelCase__   :      Dict			    =			lengths[i]
																					lowerCamelCase__   :      int			    =			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__   :      str			    =			torch.tensor(item_info["""y"""]							,     dtype=torch.long )

							return x_batch, y_batch





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Dict:
							lowerCamelCase__   :      List[str]			    =			{}
							for key in data[0].keys():
														lowerCamelCase__   :      int			    =			[d[key] for d in data]

							lowerCamelCase__   :      Dict			    =			torch.cat(item_info["""X"""]							,     0 )
							lowerCamelCase__   :      Optional[int]			    =			torch.tensor(item_info["""y"""]							,     dtype=torch.long )

							return x_batch, y_batch





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=0							,     UpperCamelCase=10							,     UpperCamelCase="cpu" ) ->			List[str]:
							lowerCamelCase__   :      Any			    =			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__   :      str			    =			discriminator(UpperCamelCase )
														lowerCamelCase__   :      List[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 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase="cpu" ) ->			Dict:
							discriminator.eval()
							lowerCamelCase__   :      int			    =			0
							lowerCamelCase__   :      List[Any]			    =			0
							with torch.no_grad():
														for input_t, target_t in data_loader:
																					lowerCamelCase__    ,						lowerCamelCase__   :      Optional[Any]			    =			input_t.to(UpperCamelCase ), target_t.to(UpperCamelCase )
																					lowerCamelCase__   :      Optional[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__   :      Optional[Any]			    =			output_t.argmax(dim=1							,     keepdim=UpperCamelCase )
																					correct += pred_t.eq(target_t.view_as(UpperCamelCase ) ).sum().item()

							test_loss /= len(data_loader.dataset )

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





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=False							,     UpperCamelCase="cpu" ) ->			List[Any]:
							lowerCamelCase__   :      Optional[Any]			    =			model.tokenizer.encode(UpperCamelCase )
							lowerCamelCase__   :      int			    =			torch.tensor([input_t]							,     dtype=torch.long							,     device=UpperCamelCase )
							if cached:
														lowerCamelCase__   :      Dict			    =			model.avg_representation(UpperCamelCase )

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





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=False							,     UpperCamelCase="cpu" ) ->			List[str]:
							lowerCamelCase__   :      Tuple			    =			torch.utils.data.DataLoader(dataset=UpperCamelCase							,     batch_size=UpperCamelCase							,     collate_fn=UpperCamelCase )

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

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

							return data_loader





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase=None							,     UpperCamelCase="gpt2-medium"							,     UpperCamelCase=10							,     UpperCamelCase=64							,     UpperCamelCase=10							,     UpperCamelCase=False							,     UpperCamelCase=False							,     UpperCamelCase=False							,     ) ->			Any:
							lowerCamelCase__   :      Dict			    =			"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

							print("""Preprocessing {} dataset...""".format(UpperCamelCase ) )
							lowerCamelCase__   :      str			    =			time.time()

							if dataset == "SST":
														lowerCamelCase__   :      int			    =			["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
														lowerCamelCase__   :      int			    =			{c: i for i, c in enumerate(UpperCamelCase )}

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

														lowerCamelCase__   :      Any			    =			torchtext_data.Field()
														lowerCamelCase__   :      Optional[int]			    =			torchtext_data.Field(sequential=UpperCamelCase )
														lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__   :      Optional[Any]			    =			datasets.SST.splits(
														    UpperCamelCase							,     UpperCamelCase							,     fine_grained=UpperCamelCase							,     train_subtrees=UpperCamelCase							,     )

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

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

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

							elif dataset == "clickbait":
														lowerCamelCase__   :      str			    =			["""non_clickbait""", """clickbait"""]
														lowerCamelCase__   :      Union[str, Any]			    =			{c: i for i, c in enumerate(UpperCamelCase )}

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

																																			if len(UpperCamelCase ) < max_length_seq:
																																										lowerCamelCase__   :      Union[str, Any]			    =			torch.tensor([50256] + seq							,     device=UpperCamelCase							,     dtype=torch.long )
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(UpperCamelCase							,     UpperCamelCase ) )
																																										continue
																																			x.append(UpperCamelCase )
																																			y.append(d["""label"""] )
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(UpperCamelCase ) )
																																			pass

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

														lowerCamelCase__   :      List[Any]			    =			{
														    """class_size""": len(UpperCamelCase ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 1,
														}

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

														lowerCamelCase__   :      Tuple			    =			Discriminator(
														    class_size=len(UpperCamelCase )							,     pretrained_model=UpperCamelCase							,     cached_mode=UpperCamelCase							,     device=UpperCamelCase ).to(UpperCamelCase )

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

																																			if len(UpperCamelCase ) < max_length_seq:
																																										lowerCamelCase__   :      Any			    =			torch.tensor([50256] + seq							,     device=UpperCamelCase							,     dtype=torch.long )
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(UpperCamelCase							,     UpperCamelCase ) )
																																										continue
																																			x.append(UpperCamelCase )
																																			y.append(int(np.sum(d["""label"""] ) > 0 ) )
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(UpperCamelCase ) )
																																			pass

														lowerCamelCase__   :      int			    =			Dataset(UpperCamelCase							,     UpperCamelCase )
														lowerCamelCase__   :      List[str]			    =			int(0.9 * len(UpperCamelCase ) )
														lowerCamelCase__   :      int			    =			len(UpperCamelCase ) - train_size
														lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			torch.utils.data.random_split(UpperCamelCase							,     [train_size, test_size] )

														lowerCamelCase__   :      Union[str, Any]			    =			{
														    """class_size""": len(UpperCamelCase ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 0,
														}

							else:  # if dataset == "generic":
														# This assumes the input dataset is a TSV with the following structure:
														# class \t text

														if dataset_fp is None:
																					raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell.""" )

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

														lowerCamelCase__   :      Dict			    =			sorted(UpperCamelCase )
														lowerCamelCase__   :      Tuple			    =			{c: i for i, c in enumerate(UpperCamelCase )}

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

														lowerCamelCase__   :      str			    =			[]
														lowerCamelCase__   :      List[str]			    =			[]
														with open(UpperCamelCase ) as f:
																					lowerCamelCase__   :      List[str]			    =			csv.reader(UpperCamelCase							,     delimiter="""\t""" )
																					for i, row in enumerate(tqdm(UpperCamelCase							,     ascii=UpperCamelCase ) ):
																												if row:
																																			lowerCamelCase__   :      Tuple			    =			row[0]
																																			lowerCamelCase__   :      List[str]			    =			row[1]

																																			try:
																																										lowerCamelCase__   :      Union[str, Any]			    =			discriminator.tokenizer.encode(UpperCamelCase )
																																										if len(UpperCamelCase ) < max_length_seq:
																																																	lowerCamelCase__   :      str			    =			torch.tensor([50256] + seq							,     device=UpperCamelCase							,     dtype=torch.long )

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

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

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

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

														lowerCamelCase__   :      List[str]			    =			{
														    """class_size""": len(UpperCamelCase ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 0,
														}

							lowerCamelCase__   :      Optional[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__   :      Optional[int]			    =			time.time()

														lowerCamelCase__   :      Optional[int]			    =			get_cached_data_loader(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     shuffle=UpperCamelCase							,     device=UpperCamelCase )

														lowerCamelCase__   :      List[Any]			    =			get_cached_data_loader(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     device=UpperCamelCase )

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

							else:
														lowerCamelCase__   :      str			    =			torch.utils.data.DataLoader(
														    dataset=UpperCamelCase							,     batch_size=UpperCamelCase							,     shuffle=UpperCamelCase							,     collate_fn=UpperCamelCase )
														lowerCamelCase__   :      int			    =			torch.utils.data.DataLoader(dataset=UpperCamelCase							,     batch_size=UpperCamelCase							,     collate_fn=UpperCamelCase )

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

							lowerCamelCase__   :      Union[str, Any]			    =			optim.Adam(discriminator.parameters()							,     lr=0.0001 )

							for epoch in range(UpperCamelCase ):
														lowerCamelCase__   :      Tuple			    =			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__   :      Tuple			    =			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      :     str       	=argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
			parser.add_argument(
			    '''--dataset''',
			    type=str,
			    default='''SST''',
			    choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
			    help=(
			        '''dataset to train the discriminator on.'''
			        '''In case of generic, the dataset is expected'''
			        '''to be a TSBV file with structure: class \\t text'''
			    ),
			)
			parser.add_argument(
			    '''--dataset_fp''',
			    type=str,
			    default='''''',
			    help='''File path of the dataset to use. Needed only in case of generic datadset''',
			)
			parser.add_argument(
			    '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
			)
			parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
			parser.add_argument(
			    '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
			)
			parser.add_argument(
			    '''--log_interval''',
			    type=int,
			    default=10,
			    metavar='''N''',
			    help='''how many batches to wait before logging training status''',
			)
			parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
			parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
			parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
			_A      :     Optional[Any]       	=parser.parse_args()

			train_discriminator(**(vars(args)))









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :     Union[str, Any]       	=logging.get_logger(__name__)

_A      :     Tuple       	={
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class _lowercase      (							_lowercase						):
							a							 =			"""splinter"""




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							List[Any]=30_522     ,				UpperCamelCase__:							Union[str, Any]=768     ,				UpperCamelCase__:							str=12     ,				UpperCamelCase__:							Dict=12     ,				UpperCamelCase__:							Tuple=3_072     ,				UpperCamelCase__:							Optional[int]="gelu"     ,				UpperCamelCase__:							Optional[int]=0.1     ,				UpperCamelCase__:							str=0.1     ,				UpperCamelCase__:							List[Any]=512     ,				UpperCamelCase__:							List[str]=2     ,				UpperCamelCase__:							Any=0.02     ,				UpperCamelCase__:							Optional[Any]=1e-12     ,				UpperCamelCase__:							Dict=True     ,				UpperCamelCase__:							str=0     ,				UpperCamelCase__:							int=104     ,				**UpperCamelCase__:							Any     ,				):
														super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			vocab_size
														lowerCamelCase__   :      str			    =			max_position_embeddings
														lowerCamelCase__   :      Tuple			    =			hidden_size
														lowerCamelCase__   :      Dict			    =			num_hidden_layers
														lowerCamelCase__   :      Tuple			    =			num_attention_heads
														lowerCamelCase__   :      Tuple			    =			intermediate_size
														lowerCamelCase__   :      Any			    =			hidden_act
														lowerCamelCase__   :      int			    =			hidden_dropout_prob
														lowerCamelCase__   :      List[str]			    =			attention_probs_dropout_prob
														lowerCamelCase__   :      Dict			    =			initializer_range
														lowerCamelCase__   :      List[Any]			    =			type_vocab_size
														lowerCamelCase__   :      Tuple			    =			layer_norm_eps
														lowerCamelCase__   :      Optional[Any]			    =			use_cache
														lowerCamelCase__   :      Optional[Any]			    =			question_token_id

