





'''simple docstring'''







import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


snake_case_  :							List[str]            =	logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

snake_case_  :							List[str]            =	{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
snake_case_  :							int            =	{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
snake_case_  :							Any            =	sorted(arg_to_scheduler.keys())
snake_case_  :							Union[str, Any]            =	"{" + ", ".join(arg_to_scheduler_choices) + "}"


class        __a   (pl.LightningModule  ):

							def __init__(				self				:  str							,  __magic_name__				:  argparse.Namespace							,  __magic_name__				:  Tuple=None							,  __magic_name__				:  Any="base"							,  __magic_name__				:  str=None							,  __magic_name__				:  Optional[int]=None							,  __magic_name__				:  Any=None							,  **__magic_name__				:  str							,  )       ->							Optional[int]:







													"""simple docstring"""
													super().__init__()
													# TODO: move to self.save_hyperparameters()
													# self.save_hyperparameters()
													# can also expand arguments into trainer signature for easier reading

													self.save_hyperparameters(__magic_name__ )
													UpperCAmelCase_      :   Any      =	0
													UpperCAmelCase_      :   Union[str, Any]      =	Path(self.hparams.output_dir )
													UpperCAmelCase_      :   List[Any]      =	self.hparams.cache_dir if self.hparams.cache_dir else None
													if config is None:
																			UpperCAmelCase_      :   Optional[Any]      =	AutoConfig.from_pretrained(
																			    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,  **({'''num_labels''': num_labels} if num_labels is not None else {})							,  cache_dir=__magic_name__							,  **__magic_name__							,  )
													else:
																			UpperCAmelCase_      :   PretrainedConfig      =	config

													UpperCAmelCase_      :   Tuple      =	('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
													for p in extra_model_params:
																			if getattr(self.hparams							,  __magic_name__							,  __magic_name__ ):
																									assert hasattr(self.config							,  __magic_name__ ), F"""model config doesn't have a `{p}` attribute"""
																									setattr(self.config							,  __magic_name__							,  getattr(self.hparams							,  __magic_name__ ) )

													if tokenizer is None:
																			UpperCAmelCase_      :   Any      =	AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,  cache_dir=__magic_name__							,  )
													else:
																			UpperCAmelCase_      :   PreTrainedTokenizer      =	tokenizer
													UpperCAmelCase_      :   str      =	MODEL_MODES[mode]
													if model is None:
																			UpperCAmelCase_      :   Dict      =	self.model_type.from_pretrained(
																			    self.hparams.model_name_or_path							,  from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path )							,  config=self.config							,  cache_dir=__magic_name__							,  )
													else:
																			UpperCAmelCase_      :   Optional[Any]      =	model

							def      UpperCAmelCase__						(				self				:  int							,  *__magic_name__				:  Any							,  **__magic_name__				:  Dict )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self.model_type.from_pretrained(*__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Any )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	arg_to_scheduler[self.hparams.lr_scheduler]
													UpperCAmelCase_      :   Union[str, Any]      =	get_schedule_func(
													    self.opt							,  num_warmup_steps=self.hparams.warmup_steps							,  num_training_steps=self.total_steps() )
													UpperCAmelCase_      :   Optional[int]      =	{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
													return scheduler

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







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	self.model
													UpperCAmelCase_      :   int      =	['''bias''', '''LayerNorm.weight''']
													UpperCAmelCase_      :   Optional[Any]      =	[
													    {
													        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
													        '''weight_decay''': self.hparams.weight_decay,
													    },
													    {
													        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
													        '''weight_decay''': 0.0,
													    },
													]
													if self.hparams.adafactor:
																			UpperCAmelCase_      :   Dict      =	Adafactor(
																			    __magic_name__							,  lr=self.hparams.learning_rate							,  scale_parameter=__magic_name__							,  relative_step=__magic_name__ )

													else:
																			UpperCAmelCase_      :   Tuple      =	AdamW(
																			    __magic_name__							,  lr=self.hparams.learning_rate							,  eps=self.hparams.adam_epsilon )
													UpperCAmelCase_      :   Union[str, Any]      =	optimizer

													UpperCAmelCase_      :   str      =	self.get_lr_scheduler()

													return [optimizer], [scheduler]

							def      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Tuple							,  __magic_name__				:  Union[str, Any] )       ->							str:







													"""simple docstring"""
													return self.validation_step(__magic_name__							,  __magic_name__ )

							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  str )       ->							Optional[Any]:







													"""simple docstring"""
													return self.validation_end(__magic_name__ )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	max(1							,  self.hparams.gpus )  # TODO: consider num_tpu_cores
													UpperCAmelCase_      :   Tuple      =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
													return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  Union[str, Any] )       ->							Dict:







													"""simple docstring"""
													if stage == "test":
																			UpperCAmelCase_      :   Optional[int]      =	len(self.test_dataloader().dataset )
													else:
																			UpperCAmelCase_      :   Dict      =	self.get_dataloader('''train'''							,  self.hparams.train_batch_size							,  shuffle=__magic_name__ )
																			UpperCAmelCase_      :   List[str]      =	len(self.train_dataloader().dataset )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  bool = False )       ->							int:







													"""simple docstring"""
													raise NotImplementedError('''You must implement this for your task''' )

							def      UpperCAmelCase__						(				self				:  Dict )       ->							Union[str, Any]:







													"""simple docstring"""
													return self.train_loader

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







													"""simple docstring"""
													return self.get_dataloader('''dev'''							,  self.hparams.eval_batch_size							,  shuffle=__magic_name__ )

							def      UpperCAmelCase__						(				self				:  int )       ->							int:







													"""simple docstring"""
													return self.get_dataloader('''test'''							,  self.hparams.eval_batch_size							,  shuffle=__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  List[str] )       ->							Optional[Any]:







													"""simple docstring"""
													return os.path.join(
													    self.hparams.data_dir							,  '''cached_{}_{}_{}'''.format(
													        __magic_name__							,  list(filter(__magic_name__							,  self.hparams.model_name_or_path.split('''/''' ) ) ).pop()							,  str(self.hparams.max_seq_length )							,  )							,  )

							@pl.utilities.rank_zero_only
							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  Dict[str, Any] )       ->							None:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.output_dir.joinpath('''best_tfmr''' )
													UpperCAmelCase_      :   int      =	self.step_count
													self.model.save_pretrained(__magic_name__ )
													self.tokenizer.save_pretrained(__magic_name__ )



							@staticmethod
							def      UpperCAmelCase__						(				__magic_name__				:  int							,  __magic_name__				:  str )       ->							str:







													"""simple docstring"""
													parser.add_argument(
													    '''--model_name_or_path'''							,  default=__magic_name__							,  type=__magic_name__							,  required=__magic_name__							,  help='''Path to pretrained model or model identifier from huggingface.co/models'''							,  )
													parser.add_argument(
													    '''--config_name'''							,  default=''''''							,  type=__magic_name__							,  help='''Pretrained config name or path if not the same as model_name''' )
													parser.add_argument(
													    '''--tokenizer_name'''							,  default=__magic_name__							,  type=__magic_name__							,  help='''Pretrained tokenizer name or path if not the same as model_name'''							,  )
													parser.add_argument(
													    '''--cache_dir'''							,  default=''''''							,  type=__magic_name__							,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''							,  )
													parser.add_argument(
													    '''--encoder_layerdrop'''							,  type=__magic_name__							,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''							,  )
													parser.add_argument(
													    '''--decoder_layerdrop'''							,  type=__magic_name__							,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''							,  )
													parser.add_argument(
													    '''--dropout'''							,  type=__magic_name__							,  help='''Dropout probability (Optional). Goes into model.config'''							,  )
													parser.add_argument(
													    '''--attention_dropout'''							,  type=__magic_name__							,  help='''Attention dropout probability (Optional). Goes into model.config'''							,  )
													parser.add_argument('''--learning_rate'''							,  default=5E-5							,  type=__magic_name__							,  help='''The initial learning rate for Adam.''' )
													parser.add_argument(
													    '''--lr_scheduler'''							,  default='''linear'''							,  choices=__magic_name__							,  metavar=__magic_name__							,  type=__magic_name__							,  help='''Learning rate scheduler'''							,  )
													parser.add_argument('''--weight_decay'''							,  default=0.0							,  type=__magic_name__							,  help='''Weight decay if we apply some.''' )
													parser.add_argument('''--adam_epsilon'''							,  default=1E-8							,  type=__magic_name__							,  help='''Epsilon for Adam optimizer.''' )
													parser.add_argument('''--warmup_steps'''							,  default=0							,  type=__magic_name__							,  help='''Linear warmup over warmup_steps.''' )
													parser.add_argument('''--num_workers'''							,  default=4							,  type=__magic_name__							,  help='''kwarg passed to DataLoader''' )
													parser.add_argument('''--num_train_epochs'''							,  dest='''max_epochs'''							,  default=3							,  type=__magic_name__ )
													parser.add_argument('''--train_batch_size'''							,  default=32							,  type=__magic_name__ )
													parser.add_argument('''--eval_batch_size'''							,  default=32							,  type=__magic_name__ )
													parser.add_argument('''--adafactor'''							,  action='''store_true''' )





class        __a   (pl.Callback  ):

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  List[Any] )       ->							Any:







													"""simple docstring"""
													if (
													    trainer.is_global_zero and trainer.global_rank == 0
													):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																			pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class        __a   (pl.Callback  ):

							def      UpperCAmelCase__						(				self				:  Tuple							,  __magic_name__				:  int							,  __magic_name__				:  Any )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	trainer.lr_schedulers[0]['''scheduler''']
													UpperCAmelCase_      :   int      =	{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
													pl_module.logger.log_metrics(__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  pl.Trainer							,  __magic_name__				:  pl.LightningModule )       ->							Any:







													"""simple docstring"""
													rank_zero_info('''***** Validation results *****''' )
													UpperCAmelCase_      :   List[str]      =	trainer.callback_metrics
													# Log results
													for key in sorted(__magic_name__ ):
																			if key not in ["log", "progress_bar"]:
																									rank_zero_info('''{} = {}\n'''.format(__magic_name__							,  str(metrics[key] ) ) )



							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  pl.Trainer							,  __magic_name__				:  pl.LightningModule )       ->							List[Any]:







													"""simple docstring"""
													rank_zero_info('''***** Test results *****''' )
													UpperCAmelCase_      :   int      =	trainer.callback_metrics
													# Log and save results to file
													UpperCAmelCase_      :   Any      =	os.path.join(pl_module.hparams.output_dir							,  '''test_results.txt''' )
													with open(__magic_name__							,  '''w''' ) as writer:
																			for key in sorted(__magic_name__ ):
																									if key not in ["log", "progress_bar"]:
																															rank_zero_info('''{} = {}\n'''.format(__magic_name__							,  str(metrics[key] ) ) )
																															writer.write('''{} = {}\n'''.format(__magic_name__							,  str(metrics[key] ) ) )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Any )			->						None:
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir''',      default=SCREAMING_SNAKE_CASE__,      type=SCREAMING_SNAKE_CASE__,      required=SCREAMING_SNAKE_CASE__,      help='''The output directory where the model predictions and checkpoints will be written.''',      )
						parser.add_argument(
						    '''--fp16''',      action='''store_true''',      help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',      )

						parser.add_argument(
						    '''--fp16_opt_level''',      type=SCREAMING_SNAKE_CASE__,      default='''O2''',      help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    ),      )
						parser.add_argument('''--n_tpu_cores''',      dest='''tpu_cores''',      type=SCREAMING_SNAKE_CASE__ )
						parser.add_argument('''--max_grad_norm''',      dest='''gradient_clip_val''',      default=1.0,      type=SCREAMING_SNAKE_CASE__,      help='''Max gradient norm''' )
						parser.add_argument('''--do_train''',      action='''store_true''',      help='''Whether to run training.''' )
						parser.add_argument('''--do_predict''',      action='''store_true''',      help='''Whether to run predictions on the test set.''' )
						parser.add_argument(
						    '''--gradient_accumulation_steps''',      dest='''accumulate_grad_batches''',      type=SCREAMING_SNAKE_CASE__,      default=1,      help='''Number of updates steps to accumulate before performing a backward/update pass.''',      )
						parser.add_argument('''--seed''',      type=SCREAMING_SNAKE_CASE__,      default=42,      help='''random seed for initialization''' )
						parser.add_argument(
						    '''--data_dir''',      default=SCREAMING_SNAKE_CASE__,      type=SCREAMING_SNAKE_CASE__,      required=SCREAMING_SNAKE_CASE__,      help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''',      )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      BaseTransformer,      SCREAMING_SNAKE_CASE__   :      argparse.Namespace,      SCREAMING_SNAKE_CASE__   :      Union[str, Any]=None,      SCREAMING_SNAKE_CASE__   :      Union[str, Any]=True,      SCREAMING_SNAKE_CASE__   :      List[Any]=None,      SCREAMING_SNAKE_CASE__   :      str=[],      SCREAMING_SNAKE_CASE__   :      List[str]=None,      SCREAMING_SNAKE_CASE__   :      List[str]=None,      **SCREAMING_SNAKE_CASE__   :      Tuple,      )			->						Optional[int]:
						pl.seed_everything(args.seed )

						# init model
						UpperCAmelCase_      :   int      =	Path(model.hparams.output_dir )
						odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )

						# add custom checkpoints
						if checkpoint_callback is None:
												UpperCAmelCase_      :   Union[str, Any]      =	pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir,      prefix='''checkpoint''',      monitor='''val_loss''',      mode='''min''',      save_top_k=1 )
						if early_stopping_callback:
												extra_callbacks.append(SCREAMING_SNAKE_CASE__ )
						if logging_callback is None:
												UpperCAmelCase_      :   Any      =	LoggingCallback()

						UpperCAmelCase_      :   Dict      =	{}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												UpperCAmelCase_      :   int      =	16
												# train_params["amp_level"] = args.fp16_opt_level

						if args.gpus > 1:
												UpperCAmelCase_      :   str      =	'''auto'''  # "ddp"
												UpperCAmelCase_      :   List[str]      =	'''ddp'''

						UpperCAmelCase_      :   Any      =	args.accumulate_grad_batches
						UpperCAmelCase_      :   Tuple      =	None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
						UpperCAmelCase_      :   Union[str, Any]      =	'''auto'''

						UpperCAmelCase_      :   int      =	pl.Trainer.from_argparse_args(
						    SCREAMING_SNAKE_CASE__,      weights_summary=SCREAMING_SNAKE_CASE__,      callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],      logger=SCREAMING_SNAKE_CASE__,      **SCREAMING_SNAKE_CASE__,      )

						if args.do_train:
												trainer.fit(SCREAMING_SNAKE_CASE__ )

						return trainer













'''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 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             super().__init__()

             self.register_modules(vae=_UpperCAmelCase  ,		text_encoder=_UpperCAmelCase  ,		tokenizer=_UpperCAmelCase  ,		unet=_UpperCAmelCase  ,		scheduler=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with torch.no_grad():
                    UpperCAmelCase__	:   Dict												=	self.vae.encode(tfms.ToTensor()(_UpperCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                    UpperCAmelCase__	:   int												=	0.1_82_15 * latent.latent_dist.sample()
             return latent




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	(1 / 0.1_82_15) * latent
             with torch.no_grad():
                    UpperCAmelCase__	:   Optional[Any]												=	self.vae.decode(_UpperCAmelCase						).sample
             UpperCAmelCase__	:   List[str]												=	(img / 2 + 0.5).clamp(0  ,		1						)
             UpperCAmelCase__	:   List[Any]												=	img.detach().cpu().permute(0  ,		2  ,		3  ,		1						).numpy()
             UpperCAmelCase__	:   Union[str, Any]												=	(img * 255).round().astype('''uint8'''						)
             return Image.fromarray(img[0]						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 _UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   Tuple												=	self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 ''''''  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   List[Any]												=	self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 0.3  ,		_UpperCAmelCase = 0.6  ,		_UpperCAmelCase = 0.5  ,		_UpperCAmelCase = 42  ,		_UpperCAmelCase = 50  ,		_UpperCAmelCase = 7.5  ,		):
             UpperCAmelCase__	:   Any												=	steps - int(kmin * steps						)
             UpperCAmelCase__	:   Any												=	steps - int(kmax * steps						)

             UpperCAmelCase__	:   List[str]												=	self.prep_text(_UpperCAmelCase						)

             self.scheduler.set_timesteps(_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[str]												=	img.size
             UpperCAmelCase__	:   str												=	self.encode(_UpperCAmelCase						)

             torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)  ,		).to(self.device						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler.add_noise(
                 _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=self.scheduler.timesteps[tmax]  ,		)

             UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

             UpperCAmelCase__	:   Dict												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]						)

             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	self.unet(
                        _UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	pred.chunk(2						)
             UpperCAmelCase__	:   List[str]												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase__	:   Union[str, Any]												=	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
                                  UpperCAmelCase__	:   Dict												=	self.scheduler.add_noise(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=_UpperCAmelCase  ,		)

                                  UpperCAmelCase__	:   Union[str, Any]												=	(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  UpperCAmelCase__	:   Tuple												=	torch.cat([input] * 2						)

                           else:  # content generation phase
                                  UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

                           UpperCAmelCase__	:   str												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		_UpperCAmelCase						)

                           with torch.no_grad():
                                  UpperCAmelCase__	:   Any												=	self.unet(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

                           UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	pred.chunk(2						)
                           UpperCAmelCase__	:   str												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return self.decode(_UpperCAmelCase						)