



import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
    __lowerCamelCase								=   True
    from torch.cuda.amp import autocast


__lowerCamelCase								=   logging.getLogger(__name__)



@dataclass
class 						_snake_case :





 """simple docstring"""



 lowerCamelCase_       =     field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}			)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Whether to log verbose messages or not.'''}   ,)







def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	List[Any]:
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,			datefmt='''%m/%d/%Y %H:%M:%S'''  ,			handlers=[logging.StreamHandler(sys.stdout							)]  ,			)
  _A          =      logging.WARNING
  if model_args.verbose_logging:
    _A          =      logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank							):
    _A          =      logging.INFO
  logger.setLevel(__snake_case							)



@dataclass
class 						_snake_case :





 """simple docstring"""



 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}			)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
 lowerCamelCase_       =     field(
     default='''train'''   ,metadata={
         '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
     }   ,)
 lowerCamelCase_       =     field(
     default='''validation'''   ,metadata={
         '''help''': (
             '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
         )
     }   ,)
 lowerCamelCase_       =     field(
     default='''text'''   ,metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}   ,)
 lowerCamelCase_       =     field(
     default='''file'''   ,metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}   ,)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}   ,)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}   ,)
 lowerCamelCase_       =     field(
     default='''librispeech'''   ,metadata={
         '''help''': (
             '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
             ''' \'buckwalter\'.'''
         )
     }   ,)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}			)
 lowerCamelCase_       =     field(
     default=lowerCamelCase   ,metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,)







@dataclass
class 						_snake_case :





 """simple docstring"""



 lowerCamelCase_       =     False
 lowerCamelCase_       =     None
 lowerCamelCase_       =     "|"
 lowerCamelCase_       =     field(default_factory=lowerCamelCase			)
 lowerCamelCase_       =     field(default_factory=lowerCamelCase			)
 lowerCamelCase_       =     None
 @classmethod
 def lowercase_						(  cls		,      a   )		->							Dict:




   """simple docstring"""







   if name == "librispeech":
     return cls()
   if name == "timit":
     return cls(
         do_lower_case=a		,      translation_table=str.maketrans({'''-''': ''' '''}   )		,      )
   if name == "buckwalter":
     _A          =      {
         '''-''': ''' ''',  # sometimes used to represent pauses
         '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
     }
     return cls(
         vocab_file=pathlib.Path(a   ).parent.joinpath('''vocab/buckwalter.json'''   )		,      word_delimiter_token='''/'''		,      translation_table=str.maketrans(a   )		,      words_to_remove={'''sil'''}		,      untransliterator=arabic.buckwalter.untransliterate		,      )
   raise ValueError(f'''Unsupported orthography: \'{name}\'.'''   )
 def lowercase_						(  self		,      a   )		->							str:




   """simple docstring"""







   if len(self.translation_table   ) > 0:
     _A          =      text.translate(self.translation_table   )
   if len(self.words_to_remove   ) == 0:
     _A          =      ''' '''.join(text.split()   )  # clean up whitespaces
   else:
     _A          =      ''' '''.join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
   return text



 def lowercase_						(  self		,      a   )		->							WavaVecaProcessor:




   """simple docstring"""







   _A          =      WavaVecaFeatureExtractor.from_pretrained(
       model_args.model_name_or_path		,      cache_dir=model_args.cache_dir   )
   if self.vocab_file:
     _A          =      WavaVecaCTCTokenizer(
         self.vocab_file		,      cache_dir=model_args.cache_dir		,      do_lower_case=self.do_lower_case		,      word_delimiter_token=self.word_delimiter_token		,      )
   else:
     _A          =      WavaVecaCTCTokenizer.from_pretrained(
         model_args.model_name_or_path		,      cache_dir=model_args.cache_dir		,      do_lower_case=self.do_lower_case		,      word_delimiter_token=self.word_delimiter_token		,      )
   return WavaVecaProcessor(a		,      a   )







@dataclass
class 						_snake_case :





 """simple docstring"""



 lowerCamelCase_       =     42
 lowerCamelCase_       =     True
 lowerCamelCase_       =     None
 lowerCamelCase_       =     None
 lowerCamelCase_       =     None
 lowerCamelCase_       =     None
 def __call__(  self		,      a   )		->							Dict[str, torch.Tensor]:




   """simple docstring"""







   _A          =      [{'''input_values''': feature['''input_values''']} for feature in features]
   _A          =      [{'''input_ids''': feature['''labels''']} for feature in features]

   _A          =      self.processor.pad(
       a		,      padding=self.padding		,      max_length=self.max_length		,      pad_to_multiple_of=self.pad_to_multiple_of		,      return_tensors='''pt'''		,      )
   _A          =      self.processor.pad(
       labels=a		,      padding=self.padding		,      max_length=self.max_length_labels		,      pad_to_multiple_of=self.pad_to_multiple_of_labels		,      return_tensors='''pt'''		,      )

   # replace padding with -100 to ignore loss correctly
   _A          =      labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1   )		,      -1_0_0   )

   _A          =      labels

   return batch







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self		,      a		,      a   )		->							torch.Tensor:




   """simple docstring"""







   model.train()
   _A          =      self._prepare_inputs(a   )

   if self.use_amp:
     with autocast():
       _A          =      self.compute_loss(a		,      a   )
   else:
     _A          =      self.compute_loss(a		,      a   )

   if self.args.n_gpu > 1:
     if model.module.config.ctc_loss_reduction == "mean":
       _A          =      loss.mean()
     elif model.module.config.ctc_loss_reduction == "sum":
       _A          =      loss.sum() / (inputs['''labels'''] >= 0).sum()
     else:
       raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''   )

   if self.args.gradient_accumulation_steps > 1:
     _A          =      loss / self.args.gradient_accumulation_steps

   if self.use_amp:
     self.scaler.scale(a   ).backward()
   elif self.use_apex:
     with amp.scale_loss(a		,      self.optimizer   ) as scaled_loss:
       scaled_loss.backward()
   elif self.deepspeed:
     self.deepspeed.backward(a   )
   else:
     loss.backward()

   return loss.detach()







def       UpperCAmelCase__	(							)				->	Optional[Any]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  _A          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)

  _A      ,				_A      ,				_A          =      parser.parse_args_into_dataclasses()
  configure_logger(__snake_case  ,			__snake_case							)

  _A          =      Orthography.from_name(data_args.orthography.lower()							)
  _A          =      orthography.create_processor(__snake_case							)
  _A          =      WavaVecaForCTC.from_pretrained(
      model_args.model_name_or_path  ,			cache_dir=model_args.cache_dir  ,			gradient_checkpointing=training_args.gradient_checkpointing  ,			vocab_size=len(processor.tokenizer							)  ,			)

  _A          =      datasets.load_dataset(
      data_args.dataset_name  ,			data_args.dataset_config_name  ,			split=data_args.train_split_name							)
  _A          =      datasets.load_dataset(
      data_args.dataset_name  ,			data_args.dataset_config_name  ,			split=data_args.validation_split_name							)

  _A          =      datasets.load_metric('''wer'''							)
  _A          =      processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  _A          =      ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(__snake_case							) == 1							)
  _A          =      re.compile(  # remove characters not in vocabulary
      RF'''[^\s{re.escape(__snake_case							)}]'''  ,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0  ,			)
  _A          =      []

  def prepare_example(__snake_case							):  # TODO(elgeish) make use of multiprocessing?
    _A      ,				_A          =      librosa.load(example[data_args.speech_file_column]  ,			sr=__snake_case							)
    if data_args.max_duration_in_seconds is not None:
      _A          =      len(example['''speech''']							) / example['''sampling_rate''']
    # Normalize and clean up text; order matters!
    _A          =      orthography.preprocess_for_training(example[data_args.target_text_column]							)
    _A          =      vocabulary_text_cleaner.sub(''''''  ,			__snake_case							)
    if updated_text != example[data_args.target_text_column]:
      text_updates.append((example[data_args.target_text_column], updated_text)							)
      _A          =      updated_text
    return example

  _A          =      train_dataset.map(__snake_case  ,			remove_columns=[data_args.speech_file_column]							)
  _A          =      val_dataset.map(__snake_case  ,			remove_columns=[data_args.speech_file_column]							)

  if data_args.max_duration_in_seconds is not None:

    def filter_by_max_duration(__snake_case							):
      return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

    _A          =      len(__snake_case							)
    _A          =      len(__snake_case							)
    _A          =      train_dataset.filter(__snake_case  ,			remove_columns=['''duration_in_seconds''']							)
    _A          =      val_dataset.filter(__snake_case  ,			remove_columns=['''duration_in_seconds''']							)
    if len(__snake_case							) > old_train_size:
      logger.warning(
          F'''Filtered out {len(__snake_case							) - old_train_size} train example(s) longer than'''
          F''' {data_args.max_duration_in_seconds} second(s).'''							)
    if len(__snake_case							) > old_val_size:
      logger.warning(
          F'''Filtered out {len(__snake_case							) - old_val_size} validation example(s) longer than'''
          F''' {data_args.max_duration_in_seconds} second(s).'''							)
  logger.info(F'''Split sizes: {len(__snake_case							)} train and {len(__snake_case							)} validation.'''							)

  logger.warning(F'''Updated {len(__snake_case							)} transcript(s) using \'{data_args.orthography}\' orthography rules.'''							)
  if logger.isEnabledFor(logging.DEBUG							):
    for original_text, updated_text in text_updates:
      logger.debug(F'''Updated text: "{original_text}" -> "{updated_text}"'''							)
  _A          =      None

  def prepare_dataset(__snake_case							):
    # check that all files have the correct sampling rate
    assert (
        len(set(batch['''sampling_rate''']							)							) == 1
    ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

    _A          =      processor(
        audio=batch['''speech''']  ,			text=batch[data_args.target_text_column]  ,			sampling_rate=batch['''sampling_rate'''][0]							)
    batch.update(__snake_case							)
    return batch

  _A          =      train_dataset.map(
      __snake_case  ,			batch_size=training_args.per_device_train_batch_size  ,			batched=__snake_case  ,			num_proc=data_args.preprocessing_num_workers  ,			)
  _A          =      val_dataset.map(
      __snake_case  ,			batch_size=training_args.per_device_train_batch_size  ,			batched=__snake_case  ,			num_proc=data_args.preprocessing_num_workers  ,			)

  _A          =      DataCollatorCTCWithPadding(processor=__snake_case  ,			padding=__snake_case							)

  def compute_metrics(__snake_case							):
    _A          =      pred.predictions
    _A          =      np.argmax(__snake_case  ,			axis=-1							)

    _A          =      processor.tokenizer.pad_token_id

    _A          =      processor.batch_decode(__snake_case							)
    # we do not want to group tokens when computing the metrics
    _A          =      processor.batch_decode(pred.label_ids  ,			group_tokens=__snake_case							)
    if logger.isEnabledFor(logging.DEBUG							):
      for reference, predicted in zip(__snake_case  ,			__snake_case							):
        logger.debug(F'''reference: "{reference}"'''							)
        logger.debug(F'''predicted: "{predicted}"'''							)
        if orthography.untransliterator is not None:
          logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(__snake_case							)}"'''							)
          logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(__snake_case							)}"'''							)

    _A          =      wer_metric.compute(predictions=__snake_case  ,			references=__snake_case							)

    return {"wer": wer}

  if model_args.freeze_feature_extractor:
    model.freeze_feature_extractor()

  _A          =      CTCTrainer(
      model=__snake_case  ,			data_collator=__snake_case  ,			args=__snake_case  ,			compute_metrics=__snake_case  ,			train_dataset=__snake_case  ,			eval_dataset=__snake_case  ,			tokenizer=processor.feature_extractor  ,			)

  trainer.train()


if __name__ == "__main__":
    main()




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__lowerCamelCase								=   """
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = \"cuda\"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")
        ```
"""



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def __init__(  self		,      a		,      a   )		->							None:




   """simple docstring"""







   super().__init__()

   self.register_modules(
       unet=a		,      scheduler=a		,      )

   _A          =      None
 def lowercase_						(  self		,      a=0   )		->							Any:




   """simple docstring"""







   if is_accelerate_available() and is_accelerate_version('''>='''		,      '''0.14.0'''   ):
     from accelerate import cpu_offload
   else:
     raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''   )

   _A          =      torch.device(f'''cuda:{gpu_id}'''   )

   if self.device.type != "cpu":
     self.to('''cpu'''		,      silence_dtype_warnings=a   )
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   for cpu_offloaded_model in [self.unet]:
     cpu_offload(a		,      a   )

   if self.safety_checker is not None:
     cpu_offload(self.safety_checker		,      execution_device=a		,      offload_buffers=a   )
 def lowercase_						(  self		,      a=0   )		->							Union[str, Any]:




   """simple docstring"""







   if is_accelerate_available() and is_accelerate_version('''>='''		,      '''0.17.0.dev0'''   ):
     from accelerate import cpu_offload_with_hook
   else:
     raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

   _A          =      torch.device(f'''cuda:{gpu_id}'''   )

   if self.device.type != "cpu":
     self.to('''cpu'''		,      silence_dtype_warnings=a   )
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   _A          =      None
   for cpu_offloaded_model in [self.unet]:
     _A      ,				_A          =      cpu_offload_with_hook(a		,      a		,      prev_module_hook=a   )

   if self.safety_checker is not None:
     _A      ,				_A          =      cpu_offload_with_hook(self.safety_checker		,      a		,      prev_module_hook=a   )

   # We'll offload the last model manually.
   _A          =      hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   if not hasattr(self.unet		,      '''_hf_hook'''   ):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(a		,      '''_hf_hook'''   )
         and hasattr(module._hf_hook		,      '''execution_device'''   )
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device   )
   return self.device
 def lowercase_						(  self		,      a		,      a		,      a		,      a		,      a		,      a		,      a		,      a=None   )		->							Dict:




   """simple docstring"""







   _A          =      (batch_size, num_channels, height, width)
   if isinstance(a		,      a   ) and len(a   ) != batch_size:
     raise ValueError(
         f'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
         f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

   if latents is None:
     _A          =      randn_tensor(a		,      generator=a		,      device=a		,      dtype=a   )
   else:
     _A          =      latents.to(device=a		,      dtype=a   )

   # scale the initial noise by the standard deviation required by the scheduler
   _A          =      latents * self.scheduler.init_noise_sigma
   return latents
 def lowercase_						(  self		,      a		,      a = "pil"   )		->							Tuple:




   """simple docstring"""







   if output_type not in ["pt", "np", "pil"]:
     raise ValueError(
         f'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''   )

   # Equivalent to diffusers.VaeImageProcessor.denormalize
   _A          =      (sample / 2 + 0.5).clamp(0		,      1   )
   if output_type == "pt":
     return sample

   # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
   _A          =      sample.cpu().permute(0		,      2		,      3		,      1   ).numpy()
   if output_type == "np":
     return sample

   # Output_type must be 'pil'
   _A          =      self.numpy_to_pil(a   )
   return sample
 def lowercase_						(  self		,      a		,      a		,      a=None   )		->							Optional[int]:




   """simple docstring"""







   if self.unet.config.num_class_embeds is not None:
     if isinstance(a		,      a   ):
       _A          =      torch.tensor(a		,      dtype=torch.int   )
     elif isinstance(a		,      a   ):
       assert batch_size == 1, "Batch size must be 1 if classes is an int"
       _A          =      torch.tensor([class_labels]		,      dtype=torch.int   )
     elif class_labels is None:
       # Randomly generate batch_size class labels
       # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
       _A          =      torch.randint(0		,      self.unet.config.num_class_embeds		,      size=(batch_size,)   )
     _A          =      class_labels.to(a   )
   else:
     _A          =      None
   return class_labels
 def lowercase_						(  self		,      a		,      a		,      a		,      a		,      a		,      a   )		->							Optional[Any]:




   """simple docstring"""







   if num_inference_steps is None and timesteps is None:
     raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''   )

   if num_inference_steps is not None and timesteps is not None:
     logger.warning(
         f'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
         ''' `timesteps` will be used over `num_inference_steps`.'''   )

   if latents is not None:
     _A          =      (batch_size, 3, img_size, img_size)
     if latents.shape != expected_shape:
       raise ValueError(f'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''   )

   if (callback_steps is None) or (
       callback_steps is not None and (not isinstance(a		,      a   ) or callback_steps <= 0)
   ):
     raise ValueError(
         f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
         f''' {type(a   )}.'''   )



 @torch.no_grad()
 @replace_example_docstring(a   )
 def __call__(  self		,      a = 1		,      a = None		,      a = 1		,      a = None		,      a = None		,      a = None		,      a = "pil"		,      a = True		,      a = None		,      a = 1		,      )		->							Any:




   """simple docstring"""







   _A          =      self.unet.config.sample_size
   _A          =      self._execution_device

   # 1. Check inputs
   self.check_inputs(a		,      a		,      a		,      a		,      a		,      a   )

   # 2. Prepare image latents
   # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
   _A          =      self.prepare_latents(
       batch_size=a		,      num_channels=self.unet.config.in_channels		,      height=a		,      width=a		,      dtype=self.unet.dtype		,      device=a		,      generator=a		,      latents=a		,      )

   # 3. Handle class_labels for class-conditional models
   _A          =      self.prepare_class_labels(a		,      a		,      class_labels=a   )

   # 4. Prepare timesteps
   if timesteps is not None:
     self.scheduler.set_timesteps(timesteps=a		,      device=a   )
     _A          =      self.scheduler.timesteps
     _A          =      len(a   )
   else:
     self.scheduler.set_timesteps(a   )
     _A          =      self.scheduler.timesteps

   # 5. Denoising loop
   # Multistep sampling: implements Algorithm 1 in the paper
   with self.progress_bar(total=a   ) as progress_bar:
     for i, t in enumerate(a   ):
       _A          =      self.scheduler.scale_model_input(a		,      a   )
       _A          =      self.unet(a		,      a		,      class_labels=a		,      return_dict=a   )[0]

       _A          =      self.scheduler.step(a		,      a		,      a		,      generator=a   )[0]

       # call the callback, if provided
       progress_bar.update()
       if callback is not None and i % callback_steps == 0:
         callback(a		,      a		,      a   )

        # 6. Post-process image sample
   _A          =      self.postprocess_image(a		,      output_type=a   )

   # Offload last model to CPU
   if hasattr(self		,      '''final_offload_hook'''   ) and self.final_offload_hook is not None:
     self.final_offload_hook.offload()

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=a   )