


'''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)
lowerCamelCase__     						=  1e-10
lowerCamelCase__     						=  'This is incredible! I love it, this is the best chicken I have ever had.'
lowerCamelCase__     						=  100






class 						lowerCAmelCase__      (      nn.Module						):




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



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					return self.classifier_head




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



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Any:



					'''simple docstring'''







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






			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->int:



					'''simple docstring'''







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

					_UpperCAmelCase		:							int             =   self.classifier_head(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   nn.functional.log_softmax(lowerCamelCase__						,	dim=-1			)

					return probs






class 						lowerCAmelCase__      (      data.Dataset						):




			def __init__(		self	:  Union[str, Any]						,	lowerCamelCase__	:  Any						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   X
					_UpperCAmelCase		:							Tuple             =   y




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



					'''simple docstring'''







					return len(self.X			)






			def __getitem__(		self	:  int						,	lowerCamelCase__	:  Optional[int]			) ->Any:



					'''simple docstring'''







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






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		def pad_sequences(__lowerCAmelCase ):
				_UpperCAmelCase		:							str             =   [len(__lowerCAmelCase ) for seq in sequences]

				_UpperCAmelCase		:							str             =   torch.zeros(len(__lowerCAmelCase )   ,			max(__lowerCAmelCase ) ).long()  # padding value = 0

				for i, seq in enumerate(__lowerCAmelCase ):
						_UpperCAmelCase		:							Union[str, Any]             =   lengths[i]
						_UpperCAmelCase		:							Any             =   seq[:end]

				return padded_sequences, lengths

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

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

		return x_batch, y_batch






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   {}
		for key in data[0].keys():
				_UpperCAmelCase		:							List[Any]             =   [d[key] for d in data]

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

		return x_batch, y_batch






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=0   ,			__lowerCAmelCase=10   ,			__lowerCAmelCase="cpu" ):
		_UpperCAmelCase		:							Union[str, Any]             =   0
		discriminator.train_custom()
		for batch_idx, (input_t, target_t) in enumerate(__lowerCAmelCase ):
				_UpperCAmelCase       ,     _UpperCAmelCase		:							Dict             =   input_t.to(__lowerCAmelCase ), target_t.to(__lowerCAmelCase )

				optimizer.zero_grad()

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

				samples_so_far += len(__lowerCAmelCase )

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






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

		test_loss /= len(data_loader.dataset )

		print(
		    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
		        __lowerCAmelCase   ,			__lowerCAmelCase   ,			len(data_loader.dataset )   ,			1_0_0.0 * correct / len(data_loader.dataset ) ) )






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

		_UpperCAmelCase		:							List[str]             =   model(__lowerCAmelCase ).data.cpu().numpy().flatten().tolist()
		print("Input sentence:"   ,			__lowerCAmelCase )
		print(
		    "Predictions:"   ,			", ".join("{}: {:.4f}".format(__lowerCAmelCase   ,			math.exp(__lowerCAmelCase ) ) for c, log_prob in zip(__lowerCAmelCase   ,			__lowerCAmelCase ) )   ,			)






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=False   ,			__lowerCAmelCase="cpu" ):
		_UpperCAmelCase		:							List[str]             =   torch.utils.data.DataLoader(dataset=__lowerCAmelCase   ,			batch_size=__lowerCAmelCase   ,			collate_fn=__lowerCAmelCase )

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

		_UpperCAmelCase		:							Dict             =   torch.utils.data.DataLoader(
		    dataset=Dataset(__lowerCAmelCase   ,			__lowerCAmelCase )   ,			batch_size=__lowerCAmelCase   ,			shuffle=__lowerCAmelCase   ,			collate_fn=__lowerCAmelCase )

		return data_loader






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase="gpt2-medium"   ,			__lowerCAmelCase=10   ,			__lowerCAmelCase=64   ,			__lowerCAmelCase=10   ,			__lowerCAmelCase=False   ,			__lowerCAmelCase=False   ,			__lowerCAmelCase=False   ,			):
		_UpperCAmelCase		:							Union[str, Any]             =   "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

		print("Preprocessing {} dataset...".format(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Tuple             =   time.time()

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

				_UpperCAmelCase		:							Tuple             =   Discriminator(
				    class_size=len(__lowerCAmelCase )   ,			pretrained_model=__lowerCAmelCase   ,			cached_mode=__lowerCAmelCase   ,			device=__lowerCAmelCase ).to(__lowerCAmelCase )

				_UpperCAmelCase		:							List[str]             =   torchtext_data.Field()
				_UpperCAmelCase		:							Any             =   torchtext_data.Field(sequential=__lowerCAmelCase )
				_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[int]             =   datasets.SST.splits(
				    __lowerCAmelCase   ,			__lowerCAmelCase   ,			fine_grained=__lowerCAmelCase   ,			train_subtrees=__lowerCAmelCase   ,			)

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

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

				_UpperCAmelCase		:							List[Any]             =   {
				    "class_size": len(__lowerCAmelCase ),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 2,
				}

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

				_UpperCAmelCase		:							int             =   Discriminator(
				    class_size=len(__lowerCAmelCase )   ,			pretrained_model=__lowerCAmelCase   ,			cached_mode=__lowerCAmelCase   ,			device=__lowerCAmelCase ).to(__lowerCAmelCase )

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

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

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

				_UpperCAmelCase		:							Any             =   {
				    "class_size": len(__lowerCAmelCase ),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 1,
				}

		elif dataset == "toxic":
				_UpperCAmelCase		:							Tuple             =   ["non_toxic", "toxic"]
				_UpperCAmelCase		:							str             =   {c: i for i, c in enumerate(__lowerCAmelCase )}

				_UpperCAmelCase		:							Tuple             =   Discriminator(
				    class_size=len(__lowerCAmelCase )   ,			pretrained_model=__lowerCAmelCase   ,			cached_mode=__lowerCAmelCase   ,			device=__lowerCAmelCase ).to(__lowerCAmelCase )

				_UpperCAmelCase		:							Optional[Any]             =   []
				_UpperCAmelCase		:							Tuple             =   []
				with open("datasets/toxic/toxic_train.txt" ) as f:
						for i, line in enumerate(tqdm(__lowerCAmelCase   ,			ascii=__lowerCAmelCase ) ):
								try:
										_UpperCAmelCase		:							Optional[Any]             =   eval(__lowerCAmelCase )
										_UpperCAmelCase		:							Tuple             =   discriminator.tokenizer.encode(d["text"] )

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

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

				_UpperCAmelCase		:							List[str]             =   {
				    "class_size": len(__lowerCAmelCase ),
				    "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." )

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

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

				_UpperCAmelCase		:							str             =   Discriminator(
				    class_size=len(__lowerCAmelCase )   ,			pretrained_model=__lowerCAmelCase   ,			cached_mode=__lowerCAmelCase   ,			device=__lowerCAmelCase ).to(__lowerCAmelCase )

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

										try:
												_UpperCAmelCase		:							Optional[Any]             =   discriminator.tokenizer.encode(__lowerCAmelCase )
												if len(__lowerCAmelCase ) < max_length_seq:
														_UpperCAmelCase		:							Dict             =   torch.tensor([50_256] + seq   ,			device=__lowerCAmelCase   ,			dtype=torch.long )

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

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

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

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

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

		_UpperCAmelCase		:							Union[str, Any]             =   time.time()
		print("Preprocessed {} data points".format(len(__lowerCAmelCase ) + len(__lowerCAmelCase ) ) )
		print("Data preprocessing took: {:.3f}s".format(end - start ) )

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

				_UpperCAmelCase		:							str             =   time.time()

				_UpperCAmelCase		:							str             =   get_cached_data_loader(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			shuffle=__lowerCAmelCase   ,			device=__lowerCAmelCase )

				_UpperCAmelCase		:							Dict             =   get_cached_data_loader(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			device=__lowerCAmelCase )

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

		else:
				_UpperCAmelCase		:							Optional[int]             =   torch.utils.data.DataLoader(
				    dataset=__lowerCAmelCase   ,			batch_size=__lowerCAmelCase   ,			shuffle=__lowerCAmelCase   ,			collate_fn=__lowerCAmelCase )
				_UpperCAmelCase		:							int             =   torch.utils.data.DataLoader(dataset=__lowerCAmelCase   ,			batch_size=__lowerCAmelCase   ,			collate_fn=__lowerCAmelCase )

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

		_UpperCAmelCase		:							List[str]             =   optim.Adam(discriminator.parameters()   ,			lr=0.0_0_0_1 )

		for epoch in range(__lowerCAmelCase ):
				_UpperCAmelCase		:							Any             =   time.time()
				print("\nEpoch"   ,			epoch + 1 )

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

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

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

				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(__lowerCAmelCase   ,			epoch + 1 )   ,			)


if __name__ == "__main__":
						lowerCamelCase__     						=  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')
						lowerCamelCase__     						=  parser.parse_args()

						train_discriminator(**(vars(args)))







'''simple docstring'''


import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Optional[int]             =   Dataset.from_dict({"i": range(17 )} )
		_UpperCAmelCase		:							int             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   3
		_UpperCAmelCase		:							str             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert sum(len(__lowerCAmelCase ) for ds in datasets_per_rank ) == full_size
		assert len({tuple(x.values() ) for ds in datasets_per_rank for x in ds} ) == full_size






def 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							Any             =   3
		_UpperCAmelCase		:							Any             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							str             =   len(list(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Tuple             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert sum(len(list(__lowerCAmelCase ) ) for ds in datasets_per_rank ) == full_size
		assert len({tuple(x.values() ) for ds in datasets_per_rank for x in ds} ) == full_size






@pytest.mark.parametrize("shards_per_node"   ,			[1, 2, 3] )
def 			__lowerCAmelCase			(__lowerCAmelCase ):
		def gen(__lowerCAmelCase ):
				for shard in shards:
						yield from ({"i": i, "shard": shard} for i in range(17 ))

		_UpperCAmelCase		:							Tuple             =   3
		_UpperCAmelCase		:							Tuple             =   shards_per_node * world_size
		_UpperCAmelCase		:							Optional[int]             =   {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowerCAmelCase )]}
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase   ,			gen_kwargs=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   len(list(__lowerCAmelCase ) )
		assert full_ds.n_shards == world_size * shards_per_node
		_UpperCAmelCase		:							Optional[Any]             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
		assert sum(len(list(__lowerCAmelCase ) ) for ds in datasets_per_rank ) == full_size
		assert len({tuple(x.values() ) for ds in datasets_per_rank for x in ds} ) == full_size






def 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							List[str]             =   2
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   len(list(__lowerCAmelCase ) )

		_UpperCAmelCase		:							int             =   split_dataset_by_node(__lowerCAmelCase   ,			rank=0   ,			world_size=__lowerCAmelCase ).shuffle(seed=42 )
		assert len(list(__lowerCAmelCase ) ) == 1 + full_size // world_size
		with pytest.raises(__lowerCAmelCase ):
				split_dataset_by_node(__lowerCAmelCase   ,			rank=0   ,			world_size=__lowerCAmelCase ).shuffle()

		_UpperCAmelCase		:							Dict             =   split_dataset_by_node(full_ds.shuffle(seed=42 )   ,			rank=0   ,			world_size=__lowerCAmelCase )
		assert len(list(__lowerCAmelCase ) ) == 1 + full_size // world_size
		with pytest.raises(__lowerCAmelCase ):
				split_dataset_by_node(full_ds.shuffle()   ,			rank=0   ,			world_size=__lowerCAmelCase )






@pytest.mark.parametrize("streaming"   ,			[False, True] )
@require_torch
@pytest.mark.skipif(os.name == "nt"   ,			reason="execute_subprocess_async doesn't support windows" )
@pytest.mark.integration
def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Union[str, Any]             =   2
		_UpperCAmelCase		:							Dict             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							int             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[Any]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							List[str]             =   F"""
        --streaming={streaming}
    """.split()
		_UpperCAmelCase		:							Tuple             =   [sys.executable] + distributed_args + args
		execute_subprocess_async(__lowerCAmelCase   ,			env=os.environ.copy() )






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"   ,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]   ,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"   ,			reason="execute_subprocess_async doesn't support windows" )
@pytest.mark.integration
def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   True
		_UpperCAmelCase		:							int             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							Optional[int]             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[str]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							int             =   F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
		_UpperCAmelCase		:							Optional[int]             =   [sys.executable] + distributed_args + args
		execute_subprocess_async(__lowerCAmelCase   ,			env=os.environ.copy() )



