



# 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__						:						int          =      1E-10
A__						:						List[Any]          =      '''This is incredible! I love it, this is the best chicken I have ever had.'''
A__						:						str          =      100



class        __snake_case		(  nn.Module		):




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




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




							def      UpperCAmelCase__    ( self  : Optional[int]):
									for param in self.encoder.parameters():
											lowerCAmelCase_    :  List[str]             =				False
									self.classifier_head.train()




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







							def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : str):
									if self.cached_mode:
											lowerCAmelCase_    :  Union[str, Any]             =				x.to(self.device)
									else:
											lowerCAmelCase_    :  Tuple             =				self.avg_representation(x.to(self.device))

									lowerCAmelCase_    :  Optional[int]             =				self.classifier_head(A_)
									lowerCAmelCase_    :  Dict             =				nn.functional.log_softmax(A_       ,       dim=-1)

									return probs





class        __snake_case		(  data.Dataset		):




							def __init__( self  : List[Any]       ,       A_  : Any       ,       A_  : Optional[int]):
									lowerCAmelCase_    :  List[str]             =				X
									lowerCAmelCase_    :  Dict             =				y




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







							def __getitem__( self  : Union[str, Any]       ,       A_  : List[Any]):
									lowerCAmelCase_    :  Any             =				{}
									lowerCAmelCase_    :  List[str]             =				self.X[index]
									lowerCAmelCase_    :  List[Any]             =				self.y[index]
									return data





def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]				):
		def pad_sequences(__UpperCamelCase							:	int				):
				lowerCAmelCase_    :  List[str]             =				[len(__UpperCamelCase				) for seq in sequences]

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

				for i, seq in enumerate(__UpperCamelCase				):
						lowerCAmelCase_    :  List[Any]             =				lengths[i]
						lowerCAmelCase_    :  Dict             =				seq[:end]

				return padded_sequences, lengths

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

		lowerCAmelCase_      ,						lowerCAmelCase_    :  Tuple             =				pad_sequences(item_info['''X''']				)
		lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor(item_info['''y''']  ,dtype=torch.long				)

		return x_batch, y_batch


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]				):
		lowerCAmelCase_    :  List[str]             =				{}
		for key in data[0].keys():
				lowerCAmelCase_    :  Dict             =				[d[key] for d in data]

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

		return x_batch, y_batch


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

				optimizer.zero_grad()

				lowerCAmelCase_    :  Optional[int]             =				discriminator(__UpperCamelCase				)
				lowerCAmelCase_    :  Dict             =				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     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Optional[int]="cpu"				):
		discriminator.eval()
		lowerCAmelCase_    :  List[str]             =				0
		lowerCAmelCase_    :  Dict             =				0
		with torch.no_grad():
				for input_t, target_t in data_loader:
						lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				input_t.to(__UpperCamelCase				), target_t.to(__UpperCamelCase				)
						lowerCAmelCase_    :  Union[str, 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_    :  Union[str, 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				)  ,1_0_0.0 * correct / len(data_loader.dataset				)				)				)


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Any  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Dict=False  ,__UpperCamelCase							:	Any="cpu"				):
		lowerCAmelCase_    :  Union[str, Any]             =				model.tokenizer.encode(__UpperCamelCase				)
		lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([input_t]  ,dtype=torch.long  ,device=__UpperCamelCase				)
		if cached:
				lowerCAmelCase_    :  Tuple             =				model.avg_representation(__UpperCamelCase				)

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

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

		lowerCAmelCase_    :  str             =				torch.utils.data.DataLoader(
		    dataset=Dataset(__UpperCamelCase  ,__UpperCamelCase				)  ,batch_size=__UpperCamelCase  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase				)

		return data_loader


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

		print('''Preprocessing {} dataset...'''.format(__UpperCamelCase				)				)
		lowerCAmelCase_    :  Tuple             =				time.time()

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

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

				lowerCAmelCase_    :  Dict             =				[]
				lowerCAmelCase_    :  int             =				[]
				for i in trange(len(__UpperCamelCase				)  ,ascii=__UpperCamelCase				):
						lowerCAmelCase_    :  List[Any]             =				TreebankWordDetokenizer().detokenize(vars(test_data[i]				)['''text''']				)
						lowerCAmelCase_    :  Dict             =				discriminator.tokenizer.encode(__UpperCamelCase				)
						lowerCAmelCase_    :  int             =				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_    :  str             =				{
				    '''class_size''': len(__UpperCamelCase				),
				    '''embed_size''': discriminator.embed_size,
				    '''pretrained_model''': pretrained_model,
				    '''class_vocab''': classaidx,
				    '''default_class''': 2,
				}

		elif dataset == "clickbait":
				lowerCAmelCase_    :  Dict             =				['''non_clickbait''', '''clickbait''']
				lowerCAmelCase_    :  Tuple             =				{c: i for i, c in enumerate(__UpperCamelCase				)}

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

										if len(__UpperCamelCase				) < max_length_seq:
												lowerCAmelCase_    :  Optional[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_    :  Union[str, Any]             =				Dataset(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  int             =				int(0.9 * len(__UpperCamelCase				)				)
				lowerCAmelCase_    :  List[Any]             =				len(__UpperCamelCase				) - train_size
				lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				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''': 1,
				}

		elif dataset == "toxic":
				lowerCAmelCase_    :  Union[str, Any]             =				['''non_toxic''', '''toxic''']
				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				)

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

										if len(__UpperCamelCase				) < max_length_seq:
												lowerCAmelCase_    :  Optional[int]             =				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_    :  Optional[int]             =				Dataset(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  int             =				int(0.9 * len(__UpperCamelCase				)				)
				lowerCAmelCase_    :  str             =				len(__UpperCamelCase				) - train_size
				lowerCAmelCase_      ,						lowerCAmelCase_    :  int             =				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_    :  Tuple             =				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_    :  Union[str, Any]             =				sorted(__UpperCamelCase				)
				lowerCAmelCase_    :  Dict             =				{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_    :  Optional[Any]             =				[]
				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_    :  str             =				row[0]
										lowerCAmelCase_    :  List[str]             =				row[1]

										try:
												lowerCAmelCase_    :  Any             =				discriminator.tokenizer.encode(__UpperCamelCase				)
												if len(__UpperCamelCase				) < max_length_seq:
														lowerCAmelCase_    :  Tuple             =				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_    :  List[str]             =				int(0.9 * len(__UpperCamelCase				)				)
				lowerCAmelCase_    :  Union[str, Any]             =				len(__UpperCamelCase				) - train_size
				lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				torch.utils.data.random_split(__UpperCamelCase  ,[train_size, test_size]				)

				lowerCAmelCase_    :  Optional[int]             =				{
				    '''class_size''': len(__UpperCamelCase				),
				    '''embed_size''': discriminator.embed_size,
				    '''pretrained_model''': pretrained_model,
				    '''class_vocab''': classaidx,
				    '''default_class''': 0,
				}

		lowerCAmelCase_    :  str             =				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_    :  Tuple             =				time.time()

				lowerCAmelCase_    :  List[Any]             =				get_cached_data_loader(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,shuffle=__UpperCamelCase  ,device=__UpperCamelCase				)

				lowerCAmelCase_    :  List[Any]             =				get_cached_data_loader(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,device=__UpperCamelCase				)

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

		else:
				lowerCAmelCase_    :  Optional[Any]             =				torch.utils.data.DataLoader(
				    dataset=__UpperCamelCase  ,batch_size=__UpperCamelCase  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[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_    :  Optional[int]             =				optim.Adam(discriminator.parameters()  ,lr=0.0_0_0_1				)

		for epoch in range(__UpperCamelCase				):
				lowerCAmelCase_    :  str             =				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_    :  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__						:						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=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__						:						int          =      parser.parse_args()

						train_discriminator(**(vars(args)))










from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A__						:						int          =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str				):

		try:
				# Has user provided both inputs?
				if not drug_name or not zip_code:
						return None

				lowerCAmelCase_    :  Union[str, Any]             =				BASE_URL.format(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[int]             =				get(__UpperCamelCase				)

				# Is the response ok?
				response.raise_for_status()

				# Scrape the data using bs4
				lowerCAmelCase_    :  Optional[int]             =				BeautifulSoup(response.text  ,'''html.parser'''				)

				# This list will store the name and price.
				lowerCAmelCase_    :  List[Any]             =				[]

				# Fetch all the grids that contains the items.
				lowerCAmelCase_    :  Union[str, Any]             =				soup.find_all('''div'''  ,{'''class''': '''grid-x pharmCard'''}				)
				if grid_list and len(__UpperCamelCase				) > 0:
						for grid in grid_list:
								# Get the pharmacy price.
								lowerCAmelCase_    :  int             =				grid.find('''p'''  ,{'''class''': '''list-title'''}				).text

								# Get price of the drug.
								lowerCAmelCase_    :  List[Any]             =				grid.find('''span'''  ,{'''p''', '''price price-large'''}				).text

								pharmacy_price_list.append(
								    {
								        '''pharmacy_name''': pharmacy_name,
								        '''price''': price,
								    }				)

				return pharmacy_price_list

		except (HTTPError, exceptions.RequestException, ValueError):
				return None


if __name__ == "__main__":
						# Enter a drug name and a zip code
						A__						:						Dict          =      input('''Enter drug name: ''').strip()
						A__						:						int          =      input('''Enter zip code: ''').strip()

						A__						:						list | None          =      fetch_pharmacy_and_price_list(
						    drug_name, zip_code
						)

						if pharmacy_price_list:
												print(F'''\nSearch results for {drug_name} at location {zip_code}:''')
												for pharmacy_price in pharmacy_price_list:
																		A__						:						Union[str, Any]          =      pharmacy_price['''pharmacy_name''']
																		A__						:						Tuple          =      pharmacy_price['''price''']

																		print(F'''Pharmacy: {name} Price: {price}''')
						else:
												print(F'''No results found for {drug_name}''')





