




'''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  A		(			nn.Module		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase="gpt2-medium"			,  _UpperCAmelCase=False			,  _UpperCAmelCase="cpu"      )     ->	int:
							super().__init__()
							__UpperCamelCase				:      Dict									=       GPTaTokenizer.from_pretrained(_UpperCAmelCase      )
							__UpperCamelCase				:      int									=       GPTaLMHeadModel.from_pretrained(_UpperCAmelCase      )
							__UpperCamelCase				:      Union[str, Any]									=       self.encoder.transformer.config.hidden_size
							__UpperCamelCase				:      Dict									=       ClassificationHead(class_size=_UpperCAmelCase			,  embed_size=self.embed_size      )
							__UpperCamelCase				:      List[Any]									=       cached_mode
							__UpperCamelCase				:      int									=       device
				def 	a_	(self      )     ->	str:
							return self.classifier_head
				def 	a_	(self      )     ->	Tuple:
							for param in self.encoder.parameters():
										__UpperCamelCase				:      Optional[Any]									=       False
							self.classifier_head.train()
				def 	a_	(self			,  _UpperCAmelCase      )     ->	Tuple:
							__UpperCamelCase				:      str									=       x.ne(0      ).unsqueeze(2      ).repeat(1			,  1			,  self.embed_size      ).float().to(self.device      ).detach()
							__UpperCamelCase				:      int									=       self.encoder.transformer(_UpperCAmelCase      )["last_hidden_state"]
							__UpperCamelCase				:      List[Any]									=       hidden * mask
							__UpperCamelCase				:      Optional[Any]									=       torch.sum(_UpperCAmelCase			,  dim=1      ) / (torch.sum(_UpperCAmelCase			,  dim=1      ).detach() + EPSILON)
							return avg_hidden





				def 	a_	(self			,  _UpperCAmelCase      )     ->	List[str]:
							if self.cached_mode:
										__UpperCamelCase				:      str									=       x.to(self.device      )
							else:
										__UpperCamelCase				:      List[Any]									=       self.avg_representation(x.to(self.device      )      )

							__UpperCamelCase				:      Any									=       self.classifier_head(_UpperCAmelCase      )
							__UpperCamelCase				:      List[Any]									=       nn.functional.log_softmax(_UpperCAmelCase			,  dim=-1      )

							return probs







class  A		(			data.Dataset		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	int:
							__UpperCamelCase				:      Dict									=       X
							__UpperCamelCase				:      Union[str, Any]									=       y
				def __len__(self      )     ->	Union[str, Any]:
							return len(self.X      )





				def __getitem__(self			,  _UpperCAmelCase      )     ->	Optional[int]:
							__UpperCamelCase				:      int									=       {}
							__UpperCamelCase				:      Dict									=       self.X[index]
							__UpperCamelCase				:      List[str]									=       self.y[index]
							return data







def       __lowerCAmelCase (   snake_case__						):
			def pad_sequences(snake_case__						):
						__UpperCamelCase				:      Optional[Any]									=       [len(snake_case__						) for seq in sequences]

						__UpperCamelCase				:      Optional[Any]									=       torch.zeros(len(snake_case__						) ,				max(snake_case__						)						).long()  # padding value = 0

						for i, seq in enumerate(snake_case__						):
									__UpperCamelCase				:      List[str]									=       lengths[i]
									__UpperCamelCase				:      str									=       seq[:end]

						return padded_sequences, lengths

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

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

			return x_batch, y_batch

def       __lowerCAmelCase (   snake_case__						):
			__UpperCamelCase				:      Dict									=       {}
			for key in data[0].keys():
						__UpperCamelCase				:      Optional[Any]									=       [d[key] for d in data]

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

			return x_batch, y_batch

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

						optimizer.zero_grad()

						__UpperCamelCase				:      Any									=       discriminator(snake_case__						)
						__UpperCamelCase				:      Union[str, Any]									=       nn.functional.nll_loss(snake_case__ ,				snake_case__						)
						loss.backward(retain_graph=snake_case__						)
						optimizer.step()

						samples_so_far += len(snake_case__						)

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

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

			test_loss /= len(data_loader.dataset						)

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

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

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

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__=False ,				snake_case__="cpu"						):
			__UpperCamelCase				:      Dict									=       torch.utils.data.DataLoader(dataset=snake_case__ ,				batch_size=snake_case__ ,				collate_fn=snake_case__						)

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

			__UpperCamelCase				:      List[Any]									=       torch.utils.data.DataLoader(
			    dataset=Dataset(snake_case__ ,				snake_case__						) ,				batch_size=snake_case__ ,				shuffle=snake_case__ ,				collate_fn=snake_case__						)

			return data_loader

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

			print("Preprocessing {} dataset...".format(snake_case__						)						)
			__UpperCamelCase				:      List[str]									=       time.time()

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

						__UpperCamelCase				:      Optional[Any]									=       Discriminator(
						    class_size=len(snake_case__						) ,				pretrained_model=snake_case__ ,				cached_mode=snake_case__ ,				device=snake_case__						).to(snake_case__						)

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

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

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

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

			elif dataset == "clickbait":
						__UpperCamelCase				:      Optional[Any]									=       ["non_clickbait", "clickbait"]
						__UpperCamelCase				:      List[str]									=       {c: i for i, c in enumerate(snake_case__						)}

						__UpperCamelCase				:      List[str]									=       Discriminator(
						    class_size=len(snake_case__						) ,				pretrained_model=snake_case__ ,				cached_mode=snake_case__ ,				device=snake_case__						).to(snake_case__						)

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

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

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

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

			elif dataset == "toxic":
						__UpperCamelCase				:      List[Any]									=       ["non_toxic", "toxic"]
						__UpperCamelCase				:      Dict									=       {c: i for i, c in enumerate(snake_case__						)}

						__UpperCamelCase				:      Optional[Any]									=       Discriminator(
						    class_size=len(snake_case__						) ,				pretrained_model=snake_case__ ,				cached_mode=snake_case__ ,				device=snake_case__						).to(snake_case__						)

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

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

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

						__UpperCamelCase				:      Union[str, Any]									=       {
						    "class_size": len(snake_case__						),
						    "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				:      List[Any]									=       set()
						with open(snake_case__						) as f:
									__UpperCamelCase				:      Union[str, Any]									=       csv.reader(snake_case__ ,				delimiter="\t"						)
									for row in tqdm(snake_case__ ,				ascii=snake_case__						):
												if row:
															classes.add(row[0]						)

						__UpperCamelCase				:      Optional[int]									=       sorted(snake_case__						)
						__UpperCamelCase				:      Any									=       {c: i for i, c in enumerate(snake_case__						)}

						__UpperCamelCase				:      str									=       Discriminator(
						    class_size=len(snake_case__						) ,				pretrained_model=snake_case__ ,				cached_mode=snake_case__ ,				device=snake_case__						).to(snake_case__						)

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

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

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

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

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

						__UpperCamelCase				:      int									=       Dataset(snake_case__ ,				snake_case__						)
						__UpperCamelCase				:      Tuple									=       int(0.9 * len(snake_case__						)						)
						__UpperCamelCase				:      Optional[Any]									=       len(snake_case__						) - train_size
						__UpperCamelCase		, __UpperCamelCase				:      int									=       torch.utils.data.random_split(snake_case__ ,				[train_size, test_size]						)

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

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

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

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

						__UpperCamelCase				:      Tuple									=       get_cached_data_loader(snake_case__ ,				snake_case__ ,				snake_case__ ,				shuffle=snake_case__ ,				device=snake_case__						)

						__UpperCamelCase				:      Optional[Any]									=       get_cached_data_loader(snake_case__ ,				snake_case__ ,				snake_case__ ,				device=snake_case__						)

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

			else:
						__UpperCamelCase				:      Optional[int]									=       torch.utils.data.DataLoader(
						    dataset=snake_case__ ,				batch_size=snake_case__ ,				shuffle=snake_case__ ,				collate_fn=snake_case__						)
						__UpperCamelCase				:      List[Any]									=       torch.utils.data.DataLoader(dataset=snake_case__ ,				batch_size=snake_case__ ,				collate_fn=snake_case__						)

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

			__UpperCamelCase				:      Optional[int]									=       optim.Adam(discriminator.parameters() ,				lr=0.0001						)

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

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

						__UpperCamelCase				:      Union[str, Any]									=       time.time()
						print("Epoch took: {:.3f}s".format(end - start						)						)

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

						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(snake_case__ ,				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'''






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_lowerCAmelCase				     = logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["safety_checker", "feature_extractor"]
				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = True			,  )     ->	List[str]:
							super().__init__()
							if hasattr(scheduler.config			,  "steps_offset"      ) and scheduler.config.steps_offset != 1:
										__UpperCamelCase				:      List[str]									=       (
										    f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
										    f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
										    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
										    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
										    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
										    " file"
										)
										deprecate("steps_offset!=1"			,  "1.0.0"			,  _UpperCAmelCase			,  standard_warn=_UpperCAmelCase      )
										__UpperCamelCase				:      str									=       dict(scheduler.config      )
										__UpperCamelCase				:      Optional[Any]									=       1
										__UpperCamelCase				:      Dict									=       FrozenDict(_UpperCAmelCase      )

							self.register_modules(
							    vae=_UpperCAmelCase			,  text_encoder=_UpperCAmelCase			,  tokenizer=_UpperCAmelCase			,  unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase			,  safety_checker=_UpperCAmelCase			,  feature_extractor=_UpperCAmelCase			,  )
							self.register_to_config(requires_safety_checker=_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict[str, Any]:
							return {k: getattr(self			,  _UpperCAmelCase      ) for k in self.config.keys() if not k.startswith("_"      )}
				def 	a_	(self			,  _UpperCAmelCase = "auto"      )     ->	List[str]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										__UpperCamelCase				:      Dict									=       self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase      )
				def 	a_	(self      )     ->	Dict:
							self.enable_attention_slicing(_UpperCAmelCase      )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	List[str]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionInpaintPipelineLegacy(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  mask_image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  )
				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.8			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  **_UpperCAmelCase			,  )     ->	Optional[Any]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionImgaImgPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  image=_UpperCAmelCase			,  strength=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )





				@torch.no_grad()
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_1_2			,  _UpperCAmelCase = 5_0			,  _UpperCAmelCase = 7.5			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  _UpperCAmelCase = 0.0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "pil"			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 1			,  )     ->	Union[str, Any]:
							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
							return StableDiffusionPipeline(**self.components      )(
							    prompt=_UpperCAmelCase			,  height=_UpperCAmelCase			,  width=_UpperCAmelCase			,  num_inference_steps=_UpperCAmelCase			,  guidance_scale=_UpperCAmelCase			,  negative_prompt=_UpperCAmelCase			,  num_images_per_prompt=_UpperCAmelCase			,  eta=_UpperCAmelCase			,  generator=_UpperCAmelCase			,  latents=_UpperCAmelCase			,  output_type=_UpperCAmelCase			,  return_dict=_UpperCAmelCase			,  callback=_UpperCAmelCase			,  callback_steps=_UpperCAmelCase			,  )



