


"""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_											=			1E-10
a_											=			"This is incredible! I love it, this is the best chicken I have ever had."
a_											=			1_00
class snake_case					(     nn.Module):







		def __init__(    self					:				Optional[Any]			,      a__					:				List[str]			,      a__					:				Union[str, Any]="gpt2-medium"			,      a__					:				List[str]=False			,      a__					:				List[Any]="cpu"						)				->  Union[str, Any]:





						'''simple docstring'''







						super().__init__()
						_A         =	GPTaTokenizer.from_pretrained(a__						)
						_A         =	GPTaLMHeadModel.from_pretrained(a__						)
						_A         =	self.encoder.transformer.config.hidden_size
						_A         =	ClassificationHead(class_size=a__			,      embed_size=self.embed_size						)
						_A         =	cached_mode
						_A         =	device







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





						'''simple docstring'''







						return self.classifier_head







		def        a_       (    self					:				Tuple						)				->  Dict:





						'''simple docstring'''







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







		def        a_       (    self					:				Union[str, Any]			,      a__					:				str						)				->  str:





						'''simple docstring'''







						_A         =	x.ne(0						).unsqueeze(2						).repeat(1			,      1			,      self.embed_size						).float().to(self.device						).detach()
						_A         =	self.encoder.transformer(a__						)["last_hidden_state"]
						_A         =	hidden * mask
						_A         =	torch.sum(a__			,      dim=1						) / (torch.sum(a__			,      dim=1						).detach() + EPSILON)
						return avg_hidden







		def        a_       (    self					:				Optional[Any]			,      a__					:				List[str]						)				->  Optional[int]:





						'''simple docstring'''







						if self.cached_mode:
										_A         =	x.to(self.device						)
						else:
										_A         =	self.avg_representation(x.to(self.device						)						)

						_A         =	self.classifier_head(a__						)
						_A         =	nn.functional.log_softmax(a__			,      dim=-1						)

						return probs





class snake_case					(     data.Dataset):







		def __init__(    self					:				int			,      a__					:				Dict			,      a__					:				List[Any]						)				->  Tuple:





						'''simple docstring'''







						_A         =	X
						_A         =	y







		def __len__(    self					:				Tuple						)				->  Any:





						'''simple docstring'''







						return len(self.X						)







		def __getitem__(    self					:				Any			,      a__					:				List[str]						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	{}
						_A         =	self.X[index]
						_A         =	self.y[index]
						return data






def    a__       ( __lowercase      )		->		Optional[Any]:
				def pad_sequences(__lowercase      ):
								_A         =	[len(__lowercase      ) for seq in sequences]

								_A         =	torch.zeros(len(__lowercase      )   ,	max(__lowercase      )      ).long()  # padding value = 0

								for i, seq in enumerate(__lowercase      ):
												_A         =	lengths[i]
												_A         =	seq[:end]

								return padded_sequences, lengths

				_A         =	{}
				for key in data[0].keys():
								_A         =	[d[key] for d in data]

				_A     ,						_A         =	pad_sequences(item_info["X"]      )
				_A         =	torch.tensor(item_info["y"]   ,	dtype=torch.long      )

				return x_batch, y_batch






def    a__       ( __lowercase      )		->		Optional[int]:
				_A         =	{}
				for key in data[0].keys():
								_A         =	[d[key] for d in data]

				_A         =	torch.cat(item_info["X"]   ,	0      )
				_A         =	torch.tensor(item_info["y"]   ,	dtype=torch.long      )

				return x_batch, y_batch






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase=0   ,	__lowercase=10   ,	__lowercase="cpu"      )		->		Dict:
				_A         =	0
				discriminator.train_custom()
				for batch_idx, (input_t, target_t) in enumerate(__lowercase      ):
								_A     ,						_A         =	input_t.to(__lowercase      ), target_t.to(__lowercase      )

								optimizer.zero_grad()

								_A         =	discriminator(__lowercase      )
								_A         =	nn.functional.nll_loss(__lowercase   ,	__lowercase      )
								loss.backward(retain_graph=__lowercase      )
								optimizer.step()

								samples_so_far += len(__lowercase      )

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






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

				test_loss /= len(data_loader.dataset      )

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






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase=False   ,	__lowercase="cpu"      )		->		List[str]:
				_A         =	model.tokenizer.encode(__lowercase      )
				_A         =	torch.tensor([input_t]   ,	dtype=torch.long   ,	device=__lowercase      )
				if cached:
								_A         =	model.avg_representation(__lowercase      )

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






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase=False   ,	__lowercase="cpu"      )		->		Any:
				_A         =	torch.utils.data.DataLoader(dataset=__lowercase   ,	batch_size=__lowercase   ,	collate_fn=__lowercase      )

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

				_A         =	torch.utils.data.DataLoader(
				    dataset=Dataset(__lowercase   ,	__lowercase      )   ,	batch_size=__lowercase   ,	shuffle=__lowercase   ,	collate_fn=__lowercase      )

				return data_loader






def    a__       ( __lowercase   ,	__lowercase=None   ,	__lowercase="gpt2-medium"   ,	__lowercase=10   ,	__lowercase=64   ,	__lowercase=10   ,	__lowercase=False   ,	__lowercase=False   ,	__lowercase=False   ,	)		->		str:
				_A         =	"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

				print("Preprocessing {} dataset...".format(__lowercase      )      )
				_A         =	time.time()

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

								_A         =	Discriminator(
								    class_size=len(__lowercase      )   ,	pretrained_model=__lowercase   ,	cached_mode=__lowercase   ,	device=__lowercase      ).to(__lowercase      )

								_A         =	torchtext_data.Field()
								_A         =	torchtext_data.Field(sequential=__lowercase      )
								_A     ,						_A     ,						_A         =	datasets.SST.splits(
								    __lowercase   ,	__lowercase   ,	fine_grained=__lowercase   ,	train_subtrees=__lowercase   ,	)

								_A         =	[]
								_A         =	[]
								for i in trange(len(__lowercase      )   ,	ascii=__lowercase      ):
												_A         =	TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["text"]      )
												_A         =	discriminator.tokenizer.encode(__lowercase      )
												_A         =	torch.tensor([5_0256] + seq   ,	device=__lowercase   ,	dtype=torch.long      )
												x.append(__lowercase      )
												y.append(classaidx[vars(train_data[i]      )["label"]]      )
								_A         =	Dataset(__lowercase   ,	__lowercase      )

								_A         =	[]
								_A         =	[]
								for i in trange(len(__lowercase      )   ,	ascii=__lowercase      ):
												_A         =	TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["text"]      )
												_A         =	discriminator.tokenizer.encode(__lowercase      )
												_A         =	torch.tensor([5_0256] + seq   ,	device=__lowercase   ,	dtype=torch.long      )
												test_x.append(__lowercase      )
												test_y.append(classaidx[vars(test_data[i]      )["label"]]      )
								_A         =	Dataset(__lowercase   ,	__lowercase      )

								_A         =	{
								    "class_size": len(__lowercase      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 2,
								}

				elif dataset == "clickbait":
								_A         =	["non_clickbait", "clickbait"]
								_A         =	{c: i for i, c in enumerate(__lowercase      )}

								_A         =	Discriminator(
								    class_size=len(__lowercase      )   ,	pretrained_model=__lowercase   ,	cached_mode=__lowercase   ,	device=__lowercase      ).to(__lowercase      )

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

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

								_A         =	Dataset(__lowercase   ,	__lowercase      )
								_A         =	int(0.9 * len(__lowercase      )      )
								_A         =	len(__lowercase      ) - train_size
								_A     ,						_A         =	torch.utils.data.random_split(__lowercase   ,	[train_size, test_size]      )

								_A         =	{
								    "class_size": len(__lowercase      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 1,
								}

				elif dataset == "toxic":
								_A         =	["non_toxic", "toxic"]
								_A         =	{c: i for i, c in enumerate(__lowercase      )}

								_A         =	Discriminator(
								    class_size=len(__lowercase      )   ,	pretrained_model=__lowercase   ,	cached_mode=__lowercase   ,	device=__lowercase      ).to(__lowercase      )

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

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

								_A         =	Dataset(__lowercase   ,	__lowercase      )
								_A         =	int(0.9 * len(__lowercase      )      )
								_A         =	len(__lowercase      ) - train_size
								_A     ,						_A         =	torch.utils.data.random_split(__lowercase   ,	[train_size, test_size]      )

								_A         =	{
								    "class_size": len(__lowercase      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 0,
								}

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

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

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

								_A         =	sorted(__lowercase      )
								_A         =	{c: i for i, c in enumerate(__lowercase      )}

								_A         =	Discriminator(
								    class_size=len(__lowercase      )   ,	pretrained_model=__lowercase   ,	cached_mode=__lowercase   ,	device=__lowercase      ).to(__lowercase      )

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

																				try:
																								_A         =	discriminator.tokenizer.encode(__lowercase      )
																								if len(__lowercase      ) < max_length_seq:
																												_A         =	torch.tensor([5_0256] + seq   ,	device=__lowercase   ,	dtype=torch.long      )

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

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

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

								_A         =	Dataset(__lowercase   ,	__lowercase      )
								_A         =	int(0.9 * len(__lowercase      )      )
								_A         =	len(__lowercase      ) - train_size
								_A     ,						_A         =	torch.utils.data.random_split(__lowercase   ,	[train_size, test_size]      )

								_A         =	{
								    "class_size": len(__lowercase      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 0,
								}

				_A         =	time.time()
				print("Preprocessed {} data points".format(len(__lowercase      ) + len(__lowercase      )      )      )
				print("Data preprocessing took: {:.3f}s".format(end - start      )      )

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

								_A         =	time.time()

								_A         =	get_cached_data_loader(__lowercase   ,	__lowercase   ,	__lowercase   ,	shuffle=__lowercase   ,	device=__lowercase      )

								_A         =	get_cached_data_loader(__lowercase   ,	__lowercase   ,	__lowercase   ,	device=__lowercase      )

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

				else:
								_A         =	torch.utils.data.DataLoader(
								    dataset=__lowercase   ,	batch_size=__lowercase   ,	shuffle=__lowercase   ,	collate_fn=__lowercase      )
								_A         =	torch.utils.data.DataLoader(dataset=__lowercase   ,	batch_size=__lowercase   ,	collate_fn=__lowercase      )

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

				_A         =	optim.Adam(discriminator.parameters()   ,	lr=0.0_001      )

				for epoch in range(__lowercase      ):
								_A         =	time.time()
								print("\nEpoch"   ,	epoch + 1      )

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

								_A         =	time.time()
								print("Epoch took: {:.3f}s".format(end - start      )      )

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

								if save_model:
												# torch.save(discriminator.state_dict(),
												#           "{}_discriminator_{}.pt".format(
												#               args.dataset, epoch + 1
												#               ))
												torch.save(
												    discriminator.get_classifier().state_dict()   ,	"{}_classifier_head_epoch_{}.pt".format(__lowercase   ,	epoch + 1      )   ,	)


if __name__ == "__main__":
		a_											=			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_											=			parser.parse_args()

		train_discriminator(**(vars(args)))



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)