code: 
  
  
  
  
  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
  
  
  __A :		Union[str, Any]      							=    logging.getLogger(__name__)
  
  require_version('''pytorch_lightning>=1.0.4''')
  
  __A :		Optional[int]      							=    {
      '''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
  __A :		Union[str, Any]      							=    {
      '''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
  }
  __A :		List[Any]      							=    sorted(arg_to_scheduler.keys())
  __A :		Dict      							=    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
  class 							lowerCamelCase		( pl.LightningModule				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="base"    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading
  
         self.save_hyperparameters(lowerCamelCase__ )
         UpperCamelCase				:   Any          =							0
         UpperCamelCase				:   Any          =							Path(self.hparams.output_dir )
         UpperCamelCase				:   Union[str, 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=lowerCamelCase__    ,      **lowerCamelCase__    ,      )
         else:
             UpperCamelCase				:   int          =							config
  
         UpperCamelCase				:   str          =							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
         for p in extra_model_params:
             if getattr(self.hparams    ,      lowerCamelCase__    ,      lowerCamelCase__ ):
                 assert hasattr(self.config    ,      lowerCamelCase__ ), f'model config doesn\'t have a `{p}` attribute'
                 setattr(self.config    ,      lowerCamelCase__    ,      getattr(self.hparams    ,      lowerCamelCase__ ) )
  
         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=lowerCamelCase__    ,      )
         else:
             UpperCamelCase				:   List[str]          =							tokenizer
         UpperCamelCase				:   int          =							MODEL_MODES[mode]
         if model is None:
             UpperCamelCase				:   Any          =							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=lowerCamelCase__    ,      )
         else:
             UpperCamelCase				:   Any          =							model
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   int          =							self.model_type.from_pretrained(*lowerCamelCase__    ,      **lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							arg_to_scheduler[self.hparams.lr_scheduler]
         UpperCamelCase				:   Optional[int]          =							get_schedule_func(
             self.opt    ,      num_warmup_steps=self.hparams.warmup_steps    ,      num_training_steps=self.total_steps() )
         UpperCamelCase				:   int          =							{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
         return scheduler
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							self.model
         UpperCamelCase				:   Dict          =							["""bias""", """LayerNorm.weight"""]
         UpperCamelCase				:   int          =							[
             {
                 """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				:   Optional[Any]          =							Adafactor(
                 lowerCamelCase__    ,      lr=self.hparams.learning_rate    ,      scale_parameter=lowerCamelCase__    ,      relative_step=lowerCamelCase__ )
  
         else:
             UpperCamelCase				:   Any          =							AdamW(
                 lowerCamelCase__    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon )
         UpperCamelCase				:   List[str]          =							optimizer
  
         UpperCamelCase				:   List[str]          =							self.get_lr_scheduler()
  
         return [optimizer], [scheduler]
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         return self.validation_step(lowerCamelCase__    ,      lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return self.validation_end(lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Tuple          =							max(1    ,      self.hparams.gpus )  # TODO: consider num_tpu_cores
         UpperCamelCase				:   str          =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
  
  
  
         if stage == "test":
             UpperCamelCase				:   List[Any]          =							len(self.test_dataloader().dataset )
         else:
             UpperCamelCase				:   List[str]          =							self.get_dataloader("""train"""    ,      self.hparams.train_batch_size    ,      shuffle=lowerCamelCase__ )
             UpperCamelCase				:   Optional[int]          =							len(self.train_dataloader().dataset )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False ):
         raise NotImplementedError("""You must implement this for your task""" )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.train_loader
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.get_dataloader("""dev"""    ,      self.hparams.eval_batch_size    ,      shuffle=lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.get_dataloader("""test"""    ,      self.hparams.eval_batch_size    ,      shuffle=lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return os.path.join(
             self.hparams.data_dir    ,      """cached_{}_{}_{}""".format(
                 lowerCamelCase__    ,      list(filter(lowerCamelCase__    ,      self.hparams.model_name_or_path.split("""/""" ) ) ).pop()    ,      str(self.hparams.max_seq_length )    ,      )    ,      )
  
  
  
  
  
  
  
     @pl.utilities.rank_zero_only
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Tuple          =							self.output_dir.joinpath("""best_tfmr""" )
         UpperCamelCase				:   Tuple          =							self.step_count
         self.model.save_pretrained(lowerCamelCase__ )
         self.tokenizer.save_pretrained(lowerCamelCase__ )
  
  
  
  
  
  
  
     @staticmethod
     def       a_							(							SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         parser.add_argument(
             """--model_name_or_path"""    ,      default=lowerCamelCase__    ,      type=lowerCamelCase__    ,      required=lowerCamelCase__    ,      help="""Path to pretrained model or model identifier from huggingface.co/models"""    ,      )
         parser.add_argument(
             """--config_name"""    ,      default=""""""    ,      type=lowerCamelCase__    ,      help="""Pretrained config name or path if not the same as model_name""" )
         parser.add_argument(
             """--tokenizer_name"""    ,      default=lowerCamelCase__    ,      type=lowerCamelCase__    ,      help="""Pretrained tokenizer name or path if not the same as model_name"""    ,      )
         parser.add_argument(
             """--cache_dir"""    ,      default=""""""    ,      type=lowerCamelCase__    ,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""    ,      )
         parser.add_argument(
             """--encoder_layerdrop"""    ,      type=lowerCamelCase__    ,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""    ,      )
         parser.add_argument(
             """--decoder_layerdrop"""    ,      type=lowerCamelCase__    ,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""    ,      )
         parser.add_argument(
             """--dropout"""    ,      type=lowerCamelCase__    ,      help="""Dropout probability (Optional). Goes into model.config"""    ,      )
         parser.add_argument(
             """--attention_dropout"""    ,      type=lowerCamelCase__    ,      help="""Attention dropout probability (Optional). Goes into model.config"""    ,      )
         parser.add_argument("""--learning_rate"""    ,      default=5e-5    ,      type=lowerCamelCase__    ,      help="""The initial learning rate for Adam.""" )
         parser.add_argument(
             """--lr_scheduler"""    ,      default="""linear"""    ,      choices=lowerCamelCase__    ,      metavar=lowerCamelCase__    ,      type=lowerCamelCase__    ,      help="""Learning rate scheduler"""    ,      )
         parser.add_argument("""--weight_decay"""    ,      default=0.0    ,      type=lowerCamelCase__    ,      help="""Weight decay if we apply some.""" )
         parser.add_argument("""--adam_epsilon"""    ,      default=1e-8    ,      type=lowerCamelCase__    ,      help="""Epsilon for Adam optimizer.""" )
         parser.add_argument("""--warmup_steps"""    ,      default=0    ,      type=lowerCamelCase__    ,      help="""Linear warmup over warmup_steps.""" )
         parser.add_argument("""--num_workers"""    ,      default=4    ,      type=lowerCamelCase__    ,      help="""kwarg passed to DataLoader""" )
         parser.add_argument("""--num_train_epochs"""    ,      dest="""max_epochs"""    ,      default=3    ,      type=lowerCamelCase__ )
         parser.add_argument("""--train_batch_size"""    ,      default=32    ,      type=lowerCamelCase__ )
         parser.add_argument("""--eval_batch_size"""    ,      default=32    ,      type=lowerCamelCase__ )
         parser.add_argument("""--adafactor"""    ,      action="""store_true""" )
  
  
  
  
  
  
  
  class 							lowerCamelCase		( pl.Callback				):
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
  
  
  
         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 							lowerCamelCase		( pl.Callback				):
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							trainer.lr_schedulers[0]["""scheduler"""]
         UpperCamelCase				:   Optional[int]          =							{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
         pl_module.logger.log_metrics(lowerCamelCase__ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         rank_zero_info("""***** Validation results *****""" )
         UpperCamelCase				:   Tuple          =							trainer.callback_metrics
         # Log results
         for key in sorted(lowerCamelCase__ ):
             if key not in ["log", "progress_bar"]:
                 rank_zero_info("""{} = {}\n""".format(lowerCamelCase__    ,      str(metrics[key] ) ) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         rank_zero_info("""***** Test results *****""" )
         UpperCamelCase				:   Dict          =							trainer.callback_metrics
         # Log and save results to file
         UpperCamelCase				:   Dict          =							os.path.join(pl_module.hparams.output_dir    ,      """test_results.txt""" )
         with open(lowerCamelCase__    ,      """w""" ) as writer:
             for key in sorted(lowerCamelCase__ ):
                 if key not in ["log", "progress_bar"]:
                     rank_zero_info("""{} = {}\n""".format(lowerCamelCase__    ,      str(metrics[key] ) ) )
                     writer.write("""{} = {}\n""".format(lowerCamelCase__    ,      str(metrics[key] ) ) )
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       str						,snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          """--output_dir"""						,default=lowercase__						,type=lowercase__						,required=lowercase__						,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=lowercase__						,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=lowercase__    )
      parser.add_argument("""--max_grad_norm"""						,dest="""gradient_clip_val"""						,default=1.0						,type=lowercase__						,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=lowercase__						,default=1						,help="""Number of updates steps to accumulate before performing a backward/update pass."""						,)
      parser.add_argument("""--seed"""						,type=lowercase__						,default=4_2						,help="""random seed for initialization"""    )
      parser.add_argument(
          """--data_dir"""						,default=lowercase__						,type=lowercase__						,required=lowercase__						,help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""						,)
  
  
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       int						,snake_case_     :       Optional[Any]=None						,snake_case_     :       Optional[Any]=True						,snake_case_     :       int=None						,snake_case_     :       Tuple=[]						,snake_case_     :       Any=None						,snake_case_     :       int=None						,**snake_case_     :       Tuple						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      pl.seed_everything(args.seed    )
  
      # init model
      UpperCamelCase				:   List[Any]          =							Path(model.hparams.output_dir    )
      odir.mkdir(exist_ok=lowercase__    )
  
      # add custom checkpoints
      if checkpoint_callback is None:
          UpperCamelCase				:   Optional[int]          =							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(lowercase__    )
      if logging_callback is None:
          UpperCamelCase				:   Optional[int]          =							LoggingCallback()
  
      UpperCamelCase				:   Any          =							{}
  
      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
          UpperCamelCase				:   List[Any]          =							1_6
          # train_params["amp_level"] = args.fp16_opt_level
  
      if args.gpus > 1:
          UpperCamelCase				:   Any          =							"""auto"""  # "ddp"
          UpperCamelCase				:   Dict          =							"""ddp"""
  
      UpperCamelCase				:   List[Any]          =							args.accumulate_grad_batches
      UpperCamelCase				:   List[Any]          =							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      UpperCamelCase				:   int          =							"""auto"""
  
      UpperCamelCase				:   List[Any]          =							pl.Trainer.from_argparse_args(
          lowercase__						,weights_summary=lowercase__						,callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,logger=lowercase__						,**lowercase__						,)
  
      if args.do_train:
          trainer.fit(lowercase__    )
  
      return trainer
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  __A :		Optional[Any]      							=    getLogger(__name__)
  
  
  __A :		Any      							=    4
  __A :		str      							=    7
  
  __A :		Any      							=    {
      '''float16''': torch.floataa,
      '''float32''': torch.floataa,
  }
  def 					A_			(		snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,torch.Tensor    ):
          return image
      elif isinstance(snake_case_						,PIL.Image.Image    ):
          UpperCamelCase				:   List[Any]          =							[image]
  
      if isinstance(image[0]						,PIL.Image.Image    ):
          UpperCamelCase       ,							UpperCamelCase				:   Any          =							image[0].size
          UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32
  
          UpperCamelCase				:   Tuple          =							[np.array(i.resize((w, h)    )    )[None, :] for i in image]
          UpperCamelCase				:   Union[str, Any]          =							np.concatenate(snake_case_						,axis=0    )
          UpperCamelCase				:   Union[str, Any]          =							np.array(snake_case_    ).astype(np.floataa    ) / 255.0
          UpperCamelCase				:   Dict          =							image.transpose(0						,3						,1						,2    )
          UpperCamelCase				:   Tuple          =							2.0 * image - 1.0
          UpperCamelCase				:   Tuple          =							torch.from_numpy(snake_case_    )
      elif isinstance(image[0]						,torch.Tensor    ):
          UpperCamelCase				:   Union[str, Any]          =							torch.cat(snake_case_						,dim=0    )
  
      return image
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 350    ,      ):
         super().__init__(
             vae=SCREAMING_SNAKE_CASE_    ,      text_encoder=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      unet=SCREAMING_SNAKE_CASE_    ,      low_res_scheduler=SCREAMING_SNAKE_CASE_    ,      scheduler=SCREAMING_SNAKE_CASE_    ,      safety_checker=SCREAMING_SNAKE_CASE_    ,      feature_extractor=SCREAMING_SNAKE_CASE_    ,      watermarker=SCREAMING_SNAKE_CASE_    ,      max_noise_level=SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 75    ,      SCREAMING_SNAKE_CASE_ = 9.0    ,      SCREAMING_SNAKE_CASE_ = 20    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "pil"    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      ):
         self.check_inputs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         # 2. Define call parameters
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Tuple          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         UpperCamelCase				:   Optional[int]          =							self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCamelCase				:   str          =							guidance_scale > 1.0
  
         # 3. Encode input prompt
         UpperCamelCase				:   Dict          =							self._encode_prompt(
             SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      prompt_embeds=SCREAMING_SNAKE_CASE_    ,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   Optional[int]          =							ORT_TO_PT_TYPE[str(text_embeddings.dtype )]
  
         # 4. Preprocess image
         UpperCamelCase				:   List[Any]          =							preprocess(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							image.cpu()
  
         # 5. set timesteps
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[str]          =							self.scheduler.timesteps
  
         # 5. Add noise to image
         UpperCamelCase				:   Union[str, Any]          =							torch.tensor([noise_level]    ,      dtype=torch.long    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							torch.randn(image.shape    ,      generator=SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Dict          =							2 if do_classifier_free_guidance else 1
         UpperCamelCase				:   List[Any]          =							np.concatenate([image] * batch_multiplier * num_images_per_prompt )
         UpperCamelCase				:   Any          =							np.concatenate([noise_level] * image.shape[0] )
  
         # 6. Prepare latent variables
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							image.shape[2:]
         UpperCamelCase				:   Any          =							self.prepare_latents(
             batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      )
  
         # 7. Check that sizes of image and latents match
         UpperCamelCase				:   Any          =							image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                 f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
                 f' `num_channels_image`: {num_channels_image} '
                 f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
                 """ `pipeline.unet` or your `image` input.""" )
  
         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         UpperCamelCase				:   Union[str, Any]          =							self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[int]          =							next(
             (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")    ,      """tensor(float)""" )
         UpperCamelCase				:   Tuple          =							ORT_TO_NP_TYPE[timestep_dtype]
  
         # 9. Denoising loop
         UpperCamelCase				:   Optional[Any]          =							len(SCREAMING_SNAKE_CASE_ ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=SCREAMING_SNAKE_CASE_ ) as progress_bar:
             for i, t in enumerate(SCREAMING_SNAKE_CASE_ ):
                 # expand the latents if we are doing classifier free guidance
                 UpperCamelCase				:   int          =							np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents
  
                 # concat latents, mask, masked_image_latents in the channel dimension
                 UpperCamelCase				:   Dict          =							self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   List[str]          =							np.concatenate([latent_model_input, image]    ,      axis=1 )
  
                 # timestep to tensor
                 UpperCamelCase				:   Any          =							np.array([t]    ,      dtype=SCREAMING_SNAKE_CASE_ )
  
                 # predict the noise residual
                 UpperCamelCase				:   List[Any]          =							self.unet(
                     sample=SCREAMING_SNAKE_CASE_    ,      timestep=SCREAMING_SNAKE_CASE_    ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_    ,      class_labels=noise_level.astype(np.intaa )    ,      )[0]
  
                 # perform guidance
                 if do_classifier_free_guidance:
                     UpperCamelCase       ,							UpperCamelCase				:   Dict          =							np.split(SCREAMING_SNAKE_CASE_    ,      2 )
                 UpperCamelCase				:   Tuple          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase				:   Tuple          =							self.scheduler.step(
                     torch.from_numpy(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ).prev_sample
  
                 # call the callback, if provided
                 if i == len(SCREAMING_SNAKE_CASE_ ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
          # 10. Post-processing
         UpperCamelCase				:   List[str]          =							self.decode_latents(latents.float() )
  
         # 11. Convert to PIL
         if output_type == "pil":
             UpperCamelCase				:   str          =							self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
  
         if not return_dict:
             return (image,)
  
         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							1 / 0.08333 * latents
         UpperCamelCase				:   Union[str, Any]          =							self.vae(latent_sample=SCREAMING_SNAKE_CASE_ )[0]
         UpperCamelCase				:   Union[str, Any]          =							np.clip(image / 2 + 0.5    ,      0    ,      1 )
         UpperCamelCase				:   Any          =							image.transpose((0, 2, 3, 1) )
         return image
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   List[Any]          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Dict          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         if prompt_embeds is None:
             UpperCamelCase				:   List[Any]          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
             UpperCamelCase				:   int          =							text_inputs.input_ids
             UpperCamelCase				:   List[Any]          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      padding="""longest"""    ,      return_tensors="""pt""" ).input_ids
  
             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   Dict          =							self.tokenizer.batch_decode(
                     untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
                 logger.warning(
                     """The following part of your input was truncated because CLIP can only handle sequences up to"""
                     f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
  
             # no positional arguments to text_encoder
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   str          =							prompt_embeds[0]
  
         UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase				:   Tuple          =							prompt_embeds.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -1 )
  
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             UpperCamelCase				:   List[str]
             if negative_prompt is None:
                 UpperCamelCase				:   Optional[int]          =							[""""""] * batch_size
             elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
                 raise TypeError(
                     f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='
                     f' {type(SCREAMING_SNAKE_CASE_ )}.' )
             elif isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   List[Any]          =							[negative_prompt]
             elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
                 raise ValueError(
                     f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'
                     f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                     """ the batch size of `prompt`.""" )
             else:
                 UpperCamelCase				:   Optional[Any]          =							negative_prompt
  
             UpperCamelCase				:   Union[str, Any]          =							text_input_ids.shape[-1]
             UpperCamelCase				:   Tuple          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=SCREAMING_SNAKE_CASE_    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
  
             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None
  
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   Tuple          =							uncond_embeddings[0]
  
         if do_classifier_free_guidance:
             UpperCamelCase				:   Dict          =							uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCamelCase				:   Tuple          =							uncond_embeddings.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[str]          =							uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -1 )
  
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCamelCase				:   Any          =							np.concatenate([uncond_embeddings, prompt_embeds] )
  
         return prompt_embeds
  
  
  
  
  