code: 
  
  
  """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)
  __SCREAMING_SNAKE_CASE					=						1E-1_0
  __SCREAMING_SNAKE_CASE					=						"""This is incredible! I love it, this is the best chicken I have ever had."""
  __SCREAMING_SNAKE_CASE					=						1_00
  
  
  
  
  
  class __snake_case		(					nn.Module      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:Dict				,  UpperCamelCase__							:Union[str, Any]="gpt2-medium"				,  UpperCamelCase__							:Dict=False				,  UpperCamelCase__							:Any="cpu"       ):
            super().__init__()
            _a          =				GPTaTokenizer.from_pretrained(UpperCamelCase__       )
            _a          =				GPTaLMHeadModel.from_pretrained(UpperCamelCase__       )
            _a          =				self.encoder.transformer.config.hidden_size
            _a          =				ClassificationHead(class_size=UpperCamelCase__				,  embed_size=self.embed_size       )
            _a          =				cached_mode
            _a          =				device
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            return self.classifier_head
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            for param in self.encoder.parameters():
                   _a          =				False
            self.classifier_head.train()
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:List[str]       ):
            _a          =				x.ne(0       ).unsqueeze(2       ).repeat(1				,  1				,  self.embed_size       ).float().to(self.device       ).detach()
            _a          =				self.encoder.transformer(UpperCamelCase__       )["last_hidden_state"]
            _a          =				hidden * mask
            _a          =				torch.sum(UpperCamelCase__				,  dim=1       ) / (torch.sum(UpperCamelCase__				,  dim=1       ).detach() + EPSILON)
            return avg_hidden
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:str       ):
            if self.cached_mode:
                   _a          =				x.to(self.device       )
            else:
                   _a          =				self.avg_representation(x.to(self.device       )       )
  
            _a          =				self.classifier_head(UpperCamelCase__       )
            _a          =				nn.functional.log_softmax(UpperCamelCase__				,  dim=-1       )
  
            return probs
  
  
  
  
  
  
  class __snake_case		(					data.Dataset      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:str				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:Union[str, Any]       ):
            _a          =				X
            _a          =				y
  
  
  
  
     def __len__(		self							:Tuple       ):
            return len(self.X       )
  
  
  
  
     def __getitem__(		self							:List[str]				,  UpperCamelCase__							:List[str]       ):
            _a          =				{}
            _a          =				self.X[index]
            _a          =				self.y[index]
            return data
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         def pad_sequences(a					):
                _a          =				[len(a					) for seq in sequences]
  
                _a          =				torch.zeros(len(a					),		max(a					)					).long()  # padding value = 0
  
                for i, seq in enumerate(a					):
                       _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      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _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      (  a,		a,		a,		a=0,		a=1_0,		a="cpu"					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				0
         discriminator.train_custom()
         for batch_idx, (input_t, target_t) in enumerate(a					):
                _a   , _a          =				input_t.to(a					), target_t.to(a					)
  
                optimizer.zero_grad()
  
                _a          =				discriminator(a					)
                _a          =				nn.functional.nll_loss(a,		a					)
                loss.backward(retain_graph=a					)
                optimizer.step()
  
                samples_so_far += len(a					)
  
                if batch_idx % log_interval == 0:
                       print(
                           "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                               epoch + 1,		a,		len(data_loader.dataset					),		1_0_0 * samples_so_far / len(data_loader.dataset					),		loss.item(),		)					)
  
  
  
  def 						__a      (  a,		a,		a="cpu"					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         discriminator.eval()
         _a          =				0
         _a          =				0
         with torch.no_grad():
                for input_t, target_t in data_loader:
                       _a   , _a          =				input_t.to(a					), target_t.to(a					)
                       _a          =				discriminator(a					)
                       # sum up batch loss
                       test_loss += nn.functional.nll_loss(a,		a,		reduction="sum"					).item()
                       # get the index of the max log-probability
                       _a          =				output_t.argmax(dim=1,		keepdim=a					)
                       correct += pred_t.eq(target_t.view_as(a					)					).sum().item()
  
         test_loss /= len(data_loader.dataset					)
  
         print(
             "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
                 a,		a,		len(data_loader.dataset					),		100.0 * correct / len(data_loader.dataset					)					)					)
  
  
  
  def 						__a      (  a,		a,		a,		a=False,		a="cpu"					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				model.tokenizer.encode(a					)
         _a          =				torch.tensor([input_t],		dtype=torch.long,		device=a					)
         if cached:
                _a          =				model.avg_representation(a					)
  
         _a          =				model(a					).data.cpu().numpy().flatten().tolist()
         print("Input sentence:",		a					)
         print(
             "Predictions:",		", ".join("{}: {:.4f}".format(a,		math.exp(a					)					) for c, log_prob in zip(a,		a					)					),		)
  
  
  
  def 						__a      (  a,		a,		a,		a=False,		a="cpu"					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				torch.utils.data.DataLoader(dataset=a,		batch_size=a,		collate_fn=a					)
  
         _a          =				[]
         _a          =				[]
         for batch_idx, (x, y) in enumerate(tqdm(a,		ascii=a					)					):
                with torch.no_grad():
                       _a          =				x.to(a					)
                       _a          =				discriminator.avg_representation(a					).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(a,		a					),		batch_size=a,		shuffle=a,		collate_fn=a					)
  
         return data_loader
  
  
  
  def 						__a      (  a,		a=None,		a="gpt2-medium",		a=1_0,		a=6_4,		a=1_0,		a=False,		a=False,		a=False,		):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
  
         print("Preprocessing {} dataset...".format(a					)					)
         _a          =				time.time()
  
         if dataset == "SST":
                _a          =				["positive", "negative", "very positive", "very negative", "neutral"]
                _a          =				{c: i for i, c in enumerate(a					)}
  
                _a          =				Discriminator(
                    class_size=len(a					),		pretrained_model=a,		cached_mode=a,		device=a					).to(a					)
  
                _a          =				torchtext_data.Field()
                _a          =				torchtext_data.Field(sequential=a					)
                _a   , _a   , _a          =				datasets.SST.splits(
                    a,		a,		fine_grained=a,		train_subtrees=a,		)
  
                _a          =				[]
                _a          =				[]
                for i in trange(len(a					),		ascii=a					):
                       _a          =				TreebankWordDetokenizer().detokenize(vars(train_data[i]					)["text"]					)
                       _a          =				discriminator.tokenizer.encode(a					)
                       _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                       x.append(a					)
                       y.append(classaidx[vars(train_data[i]					)["label"]]					)
                _a          =				Dataset(a,		a					)
  
                _a          =				[]
                _a          =				[]
                for i in trange(len(a					),		ascii=a					):
                       _a          =				TreebankWordDetokenizer().detokenize(vars(test_data[i]					)["text"]					)
                       _a          =				discriminator.tokenizer.encode(a					)
                       _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                       test_x.append(a					)
                       test_y.append(classaidx[vars(test_data[i]					)["label"]]					)
                _a          =				Dataset(a,		a					)
  
                _a          =				{
                    "class_size": len(a					),
                    "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(a					)}
  
                _a          =				Discriminator(
                    class_size=len(a					),		pretrained_model=a,		cached_mode=a,		device=a					).to(a					)
  
                with open("datasets/clickbait/clickbait_train_prefix.txt"					) as f:
                       _a          =				[]
                       for i, line in enumerate(a					):
                              try:
                                     data.append(eval(a					)					)
                              except Exception:
                                     print("Error evaluating line {}: {}".format(a,		a					)					)
                                     continue
                _a          =				[]
                _a          =				[]
                with open("datasets/clickbait/clickbait_train_prefix.txt"					) as f:
                       for i, line in enumerate(tqdm(a,		ascii=a					)					):
                              try:
                                     _a          =				eval(a					)
                                     _a          =				discriminator.tokenizer.encode(d["text"]					)
  
                                     if len(a					) < max_length_seq:
                                            _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                                     else:
                                            print("Line {} is longer than maximum length {}".format(a,		a					)					)
                                            continue
                                     x.append(a					)
                                     y.append(d["label"]					)
                              except Exception:
                                     print("Error evaluating / tokenizing line {}, skipping it".format(a					)					)
                                     pass
  
                _a          =				Dataset(a,		a					)
                _a          =				int(0.9 * len(a					)					)
                _a          =				len(a					) - train_size
                _a   , _a          =				torch.utils.data.random_split(a,		[train_size, test_size]					)
  
                _a          =				{
                    "class_size": len(a					),
                    "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(a					)}
  
                _a          =				Discriminator(
                    class_size=len(a					),		pretrained_model=a,		cached_mode=a,		device=a					).to(a					)
  
                _a          =				[]
                _a          =				[]
                with open("datasets/toxic/toxic_train.txt"					) as f:
                       for i, line in enumerate(tqdm(a,		ascii=a					)					):
                              try:
                                     _a          =				eval(a					)
                                     _a          =				discriminator.tokenizer.encode(d["text"]					)
  
                                     if len(a					) < max_length_seq:
                                            _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                                     else:
                                            print("Line {} is longer than maximum length {}".format(a,		a					)					)
                                            continue
                                     x.append(a					)
                                     y.append(int(np.sum(d["label"]					) > 0					)					)
                              except Exception:
                                     print("Error evaluating / tokenizing line {}, skipping it".format(a					)					)
                                     pass
  
                _a          =				Dataset(a,		a					)
                _a          =				int(0.9 * len(a					)					)
                _a          =				len(a					) - train_size
                _a   , _a          =				torch.utils.data.random_split(a,		[train_size, test_size]					)
  
                _a          =				{
                    "class_size": len(a					),
                    "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(a					) as f:
                       _a          =				csv.reader(a,		delimiter="\t"					)
                       for row in tqdm(a,		ascii=a					):
                              if row:
                                     classes.add(row[0]					)
  
                _a          =				sorted(a					)
                _a          =				{c: i for i, c in enumerate(a					)}
  
                _a          =				Discriminator(
                    class_size=len(a					),		pretrained_model=a,		cached_mode=a,		device=a					).to(a					)
  
                _a          =				[]
                _a          =				[]
                with open(a					) as f:
                       _a          =				csv.reader(a,		delimiter="\t"					)
                       for i, row in enumerate(tqdm(a,		ascii=a					)					):
                              if row:
                                     _a          =				row[0]
                                     _a          =				row[1]
  
                                     try:
                                            _a          =				discriminator.tokenizer.encode(a					)
                                            if len(a					) < max_length_seq:
                                                   _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
  
                                            else:
                                                   print("Line {} is longer than maximum length {}".format(a,		a					)					)
                                                   continue
  
                                            x.append(a					)
                                            y.append(classaidx[label]					)
  
                                     except Exception:
                                            print("Error tokenizing line {}, skipping it".format(a					)					)
                                            pass
  
                _a          =				Dataset(a,		a					)
                _a          =				int(0.9 * len(a					)					)
                _a          =				len(a					) - train_size
                _a   , _a          =				torch.utils.data.random_split(a,		[train_size, test_size]					)
  
                _a          =				{
                    "class_size": len(a					),
                    "embed_size": discriminator.embed_size,
                    "pretrained_model": pretrained_model,
                    "class_vocab": classaidx,
                    "default_class": 0,
                }
  
         _a          =				time.time()
         print("Preprocessed {} data points".format(len(a					) + len(a					)					)					)
         print("Data preprocessing took: {:.3f}s".format(end - start					)					)
  
         if cached:
                print("Building representation cache..."					)
  
                _a          =				time.time()
  
                _a          =				get_cached_data_loader(a,		a,		a,		shuffle=a,		device=a					)
  
                _a          =				get_cached_data_loader(a,		a,		a,		device=a					)
  
                _a          =				time.time()
                print("Building representation cache took: {:.3f}s".format(end - start					)					)
  
         else:
                _a          =				torch.utils.data.DataLoader(
                    dataset=a,		batch_size=a,		shuffle=a,		collate_fn=a					)
                _a          =				torch.utils.data.DataLoader(dataset=a,		batch_size=a,		collate_fn=a					)
  
         if save_model:
                with open("{}_classifier_head_meta.json".format(a					),		"w"					) as meta_file:
                       json.dump(a,		a					)
  
         _a          =				optim.Adam(discriminator.parameters(),		lr=0.0001					)
  
         for epoch in range(a					):
                _a          =				time.time()
                print("\nEpoch",		epoch + 1					)
  
                train_epoch(
                    discriminator=a,		data_loader=a,		optimizer=a,		epoch=a,		log_interval=a,		device=a,		)
                evaluate_performance(data_loader=a,		discriminator=a,		device=a					)
  
                _a          =				time.time()
                print("Epoch took: {:.3f}s".format(end - start					)					)
  
                print("\nExample prediction"					)
                predict(a,		a,		a,		cached=a,		device=a					)
  
                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(a,		epoch + 1					),		)
  
  
  if __name__ == "__main__":
     __SCREAMING_SNAKE_CASE					=						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""")
     __SCREAMING_SNAKE_CASE					=						parser.parse_args()
  
     train_discriminator(**(vars(args)))
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
            super().__init__()
  
            self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
            with torch.no_grad():
                   _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                   _a          =				0.18215 * latent.latent_dist.sample()
            return latent
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				(1 / 0.18215) * latent
            with torch.no_grad():
                   _a          =				self.vae.decode(UpperCamelCase__       ).sample
            _a          =				(img / 2 + 0.5).clamp(0				,  1       )
            _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
            _a          =				(img * 255).round().astype("uint8"       )
            return Image.fromarray(img[0]       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
            _a          =				self.tokenizer(
                UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
  
            _a          =				self.tokenizer(
                ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
  
            return torch.cat([uncond_embedding, text_embedding]       )
  
  
  
  
     def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
            _a          =				steps - int(kmin * steps       )
            _a          =				steps - int(kmax * steps       )
  
            _a          =				self.prep_text(UpperCamelCase__       )
  
            self.scheduler.set_timesteps(UpperCamelCase__       )
  
            _a   , _a          =				img.size
            _a          =				self.encode(UpperCamelCase__       )
  
            torch.manual_seed(UpperCamelCase__       )
            _a          =				torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )
  
            _a          =				self.scheduler.add_noise(
                UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )
  
            _a          =				torch.cat([latents] * 2       )
  
            _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )
  
            with torch.no_grad():
                   _a          =				self.unet(
                       UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
            _a   , _a          =				pred.chunk(2       )
            _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
            _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample
  
            for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                   if i > tmax:
                          if i < tmin:  # layout generation phase
                                 _a          =				self.scheduler.add_noise(
                                     UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
                                 _a          =				(mix_factor * latents) + (
                                     1 - mix_factor
                                 ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                 _a          =				torch.cat([input] * 2       )
  
                          else:  # content generation phase
                                 _a          =				torch.cat([latents] * 2       )
  
                          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )
  
                          with torch.no_grad():
                                 _a          =				self.unet(
                                     UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
                          _a   , _a          =				pred.chunk(2       )
                          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                          _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample
  
            return self.decode(UpperCamelCase__       )
  