





'''simple docstring'''






import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

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


__UpperCamelCase					    =	logging.getLogger(__name__)




@dataclass
class  _A :
		lowercase__:   str				     =   field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
		lowercase__:   Optional[str]				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,					)
		lowercase__:   Optional[bool]				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}		)
		lowercase__:   Optional[bool]				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''Whether to log verbose messages or not.'''}						,					)
		lowercase__:   Optional[float]				     =   field(
		    default=2.0						,					metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}		)
		lowercase__:   Optional[float]				     =   field(
		    default=0.5						,					metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}		)
		lowercase__:   Optional[float]				     =   field(
		    default=0.9_9_9_9_9_5						,					metadata={'''help''': '''Decay of gumbel temperature during training.'''}		)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


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




@dataclass
class  _A :
		lowercase__:   str				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
		lowercase__:   Optional[str]				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
		lowercase__:   Optional[str]				     =   field(
		    default='''train'''						,					metadata={
		        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
		    }						,					)
		lowercase__:   Optional[str]				     =   field(
		    default='''validation'''						,					metadata={
		        '''help''': (
		            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
		        )
		    }						,					)
		lowercase__:   Optional[str]				     =   field(
		    default='''file'''						,					metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}						,					)
		lowercase__:   bool				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}		)
		lowercase__:   Optional[int]				     =   field(
		    default=1						,					metadata={
		        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
		    }						,					)
		lowercase__:   Optional[int]				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''The number of processes to use for the preprocessing.'''}						,					)
		lowercase__:   Optional[float]				     =   field(
		    default=2_0.0						,					metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}		)





@dataclass
class  _A :
		lowercase__:   WavaVecaForPreTraining
		lowercase__:   WavaVecaFeatureExtractor
		lowercase__:   Union[bool, str]				     =   "longest"
		lowercase__:   Optional[int]				     =   None
		lowercase__:   Optional[int]				     =   None

		def __call__(   self					:   Any  ,      __magic_name__					:   List[Dict[str, Union[List[int], torch.Tensor]]]			)					->    Dict[str, torch.Tensor]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.feature_extractor.pad(
					    __magic_name__  ,      max_length=self.max_length  ,      padding=self.padding  ,      pad_to_multiple_of=self.pad_to_multiple_of  ,      return_tensors="""pt"""  ,      )
					__snake_case					:      int							      =     self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]			)

					__snake_case					:      Optional[Any]							      =     batch["""input_values"""].shape[0]

					# make sure that no loss is computed on padded inputs
					if batch["attention_mask"] is not None:
								# compute real output lengths according to convolution formula
								__snake_case					:      Optional[int]							      =     self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1			)			).to(
								    torch.long			)

								__snake_case					:      Any							      =     torch.zeros(
								    (batch_size, mask_indices_seq_length)  ,      dtype=torch.long  ,      device=batch["""input_values"""].device			)

								# these two operations makes sure that all values
								# before the output lengths indices are attended to
								__snake_case					:      Dict							      =     1
								__snake_case					:      Optional[Any]							      =     attention_mask.flip([-1]			).cumsum(-1			).flip([-1]			).bool()

					# sample randomly masked indices
					__snake_case					:      int							      =     _compute_mask_indices(
					    (batch_size, mask_indices_seq_length)  ,      self.model.config.mask_time_prob  ,      self.model.config.mask_time_length  ,      attention_mask=__magic_name__  ,      min_masks=2  ,      )

					return batch





class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      *__magic_name__					:   Any  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   int=0  ,      __magic_name__					:   Tuple=1.0  ,      **__magic_name__					:   Any			)					->    Dict:


					"""simple docstring"""

					super().__init__(*__magic_name__  ,      **__magic_name__			)
					__snake_case					:      List[str]							      =     0
					__snake_case					:      Optional[Any]							      =     max_gumbel_temp
					__snake_case					:      int							      =     min_gumbel_temp
					__snake_case					:      Union[str, Any]							      =     gumbel_temp_decay

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   nn.Module  ,      __magic_name__					:   Dict[str, Union[torch.Tensor, Any]]			)					->    torch.Tensor:


					"""simple docstring"""

					model.train()
					__snake_case					:      int							      =     self._prepare_inputs(__magic_name__			)

					if self.use_amp:
								with autocast():
											__snake_case					:      str							      =     self.compute_loss(__magic_name__  ,      __magic_name__			)
					else:
								__snake_case					:      Tuple							      =     self.compute_loss(__magic_name__  ,      __magic_name__			)

					if self.args.n_gpu > 1 or self.deepspeed:
								if model.module.config.ctc_loss_reduction == "mean":
											__snake_case					:      Union[str, Any]							      =     loss.mean()
								elif model.module.config.ctc_loss_reduction == "sum":
											__snake_case					:      str							      =     loss.sum() / (inputs["""mask_time_indices"""]).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:
								__snake_case					:      List[Any]							      =     loss / self.args.gradient_accumulation_steps

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

					self.num_update_step += 1
					# make sure gumbel softmax temperature is decayed
					if self.args.n_gpu > 1 or self.deepspeed:
								model.module.set_gumbel_temperature(
								    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step  ,      self.min_gumbel_temp			)			)
					else:
								model.set_gumbel_temperature(
								    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step  ,      self.min_gumbel_temp			)			)

					return loss.detach()







def 					_a						(       ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

			__snake_case      ,     __snake_case      ,     __snake_case					:      List[Any]							      =     parser.parse_args_into_dataclasses()
			configure_logger(_lowerCamelCase    ,	_lowerCamelCase )

			# Downloading and loading a dataset from the hub.
			__snake_case					:      Tuple							      =     load_dataset(data_args.dataset_name    ,	data_args.dataset_config_name    ,	cache_dir=model_args.cache_dir )

			if "validation" not in datasets.keys():
						# make sure only "validation" and "train" keys remain"
						__snake_case					:      int							      =     DatasetDict()
						__snake_case					:      Union[str, Any]							      =     load_dataset(
						    data_args.dataset_name    ,	data_args.dataset_config_name    ,	split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''    ,	cache_dir=model_args.cache_dir    ,	)
						__snake_case					:      Optional[int]							      =     load_dataset(
						    data_args.dataset_name    ,	data_args.dataset_config_name    ,	split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''    ,	cache_dir=model_args.cache_dir    ,	)
			else:
						# make sure only "validation" and "train" keys remain"
						__snake_case					:      Optional[Any]							      =     DatasetDict()
						__snake_case					:      Optional[Any]							      =     load_dataset(
						    data_args.dataset_name    ,	data_args.dataset_config_name    ,	split="""validation"""    ,	cache_dir=model_args.cache_dir    ,	)
						__snake_case					:      str							      =     load_dataset(
						    data_args.dataset_name    ,	data_args.dataset_config_name    ,	split=F'''{data_args.train_split_name}'''    ,	cache_dir=model_args.cache_dir    ,	)

			# only normalized-inputs-training is supported
			__snake_case					:      Union[str, Any]							      =     WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path    ,	cache_dir=model_args.cache_dir    ,	do_normalize=_lowerCamelCase )

			def prepare_dataset(_lowerCamelCase ):
						# check that all files have the correct sampling rate
						__snake_case      ,     __snake_case					:      int							      =     librosa.load(batch[data_args.speech_file_column]    ,	sr=feature_extractor.sampling_rate )
						return batch

			# load audio files into numpy arrays
			__snake_case					:      List[str]							      =     datasets.map(
			    _lowerCamelCase    ,	num_proc=data_args.preprocessing_num_workers    ,	remove_columns=datasets["""train"""].column_names )

			# filter audio files that are too long
			__snake_case					:      Optional[Any]							      =     vectorized_datasets.filter(
			    lambda _lowerCamelCase : len(data["""speech"""] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )

			def normalize(_lowerCamelCase ):
						return feature_extractor(batch["""speech"""]    ,	sampling_rate=feature_extractor.sampling_rate )

			# normalize and transform to `BatchFeatures`
			__snake_case					:      str							      =     vectorized_datasets.map(
			    _lowerCamelCase    ,	batched=_lowerCamelCase    ,	num_proc=data_args.preprocessing_num_workers    ,	load_from_cache_file=not data_args.overwrite_cache    ,	remove_columns=vectorized_datasets["""train"""].column_names    ,	)

			# pretraining is only supported for "newer" stable layer norm architecture
			# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
			__snake_case					:      str							      =     WavaVecaConfig.from_pretrained(
			    model_args.model_name_or_path    ,	cache_dir=model_args.cache_dir    ,	gradient_checkpointing=training_args.gradient_checkpointing    ,	)

			if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
						raise ValueError(
						    """PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
						    """ ``config.feat_extract_norm='layer'""" )

			__snake_case					:      Tuple							      =     WavaVecaForPreTraining(_lowerCamelCase )

			__snake_case					:      int							      =     DataCollatorForWavaVecaPretraining(model=_lowerCamelCase    ,	feature_extractor=_lowerCamelCase )

			__snake_case					:      List[str]							      =     WavaVecaPreTrainer(
			    model=_lowerCamelCase    ,	data_collator=_lowerCamelCase    ,	args=_lowerCamelCase    ,	train_dataset=vectorized_datasets["""train"""]    ,	eval_dataset=vectorized_datasets["""validation"""]    ,	tokenizer=_lowerCamelCase    ,	max_gumbel_temp=model_args.max_gumbel_temperature    ,	min_gumbel_temp=model_args.min_gumbel_temperature    ,	gumbel_temp_decay=model_args.gumbel_temperature_decay    ,	)
			trainer.train()


if __name__ == "__main__":
	main()









'''simple docstring'''






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   WhisperForConditionalGeneration  ,      __magic_name__					:   WhisperProcessor  ,      __magic_name__					:   AutoencoderKL  ,      __magic_name__					:   CLIPTextModel  ,      __magic_name__					:   CLIPTokenizer  ,      __magic_name__					:   UNetaDConditionModel  ,      __magic_name__					:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,      __magic_name__					:   StableDiffusionSafetyChecker  ,      __magic_name__					:   CLIPImageProcessor  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					super().__init__()

					if safety_checker is None:
								logger.warning(
								    f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
								    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
								    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
								    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
								    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
								    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""			)

					self.register_modules(
					    speech_model=__magic_name__  ,      speech_processor=__magic_name__  ,      vae=__magic_name__  ,      text_encoder=__magic_name__  ,      tokenizer=__magic_name__  ,      unet=__magic_name__  ,      scheduler=__magic_name__  ,      feature_extractor=__magic_name__  ,      )

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Optional[Union[str, int]] = "auto"			)					->    Union[str, Any]:


					"""simple docstring"""

					if slice_size == "auto":
								__snake_case					:      str							      =     self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.enable_attention_slicing(__magic_name__			)

		@torch.no_grad()
		def __call__(   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict=1_60_00  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 50  ,      __magic_name__					:   float = 7.5  ,      __magic_name__					:   Optional[Union[str, List[str]]] = None  ,      __magic_name__					:   Optional[int] = 1  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   Optional[torch.FloatTensor] = None  ,      __magic_name__					:   Optional[str] = "pil"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      __magic_name__					:   int = 1  ,      **__magic_name__					:   List[str]  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.speech_processor.feature_extractor(
					    __magic_name__  ,      return_tensors="""pt"""  ,      sampling_rate=__magic_name__			).input_features.to(self.device			)
					__snake_case					:      List[str]							      =     self.speech_model.generate(__magic_name__  ,      max_length=48_00_00			)

					__snake_case					:      List[Any]							      =     self.speech_processor.tokenizer.batch_decode(__magic_name__  ,      skip_special_tokens=__magic_name__  ,      normalize=__magic_name__			)[
					    0
					]

					if isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Tuple							      =     1
					elif isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Optional[int]							      =     len(__magic_name__			)
					else:
								raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__magic_name__			)}'''			)

					if height % 8 != 0 or width % 8 != 0:
								raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''			)

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

					# get prompt text embeddings
					__snake_case					:      Dict							      =     self.tokenizer(
					    __magic_name__  ,      padding="""max_length"""  ,      max_length=self.tokenizer.model_max_length  ,      return_tensors="""pt"""  ,      )
					__snake_case					:      Optional[Any]							      =     text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
								__snake_case					:      Tuple							      =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
								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}'''			)
								__snake_case					:      Any							      =     text_input_ids[:, : self.tokenizer.model_max_length]
					__snake_case					:      int							      =     self.text_encoder(text_input_ids.to(self.device			)			)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					__snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     text_embeddings.shape
					__snake_case					:      List[Any]							      =     text_embeddings.repeat(1  ,      __magic_name__  ,      1			)
					__snake_case					:      Dict							      =     text_embeddings.view(bs_embed * num_images_per_prompt  ,      __magic_name__  ,      -1			)

					# 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.
					__snake_case					:      Optional[int]							      =     guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
								__snake_case					:      List[str]
								if negative_prompt is None:
											__snake_case					:      Optional[Any]							      =     [""""""] * batch_size
								elif type(__magic_name__			) is not type(__magic_name__			):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__			)} !='''
											    f''' {type(__magic_name__			)}.'''			)
								elif isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     [negative_prompt]
								elif batch_size != len(__magic_name__			):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__			)}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    """ the batch size of `prompt`."""			)
								else:
											__snake_case					:      int							      =     negative_prompt

								__snake_case					:      List[str]							      =     text_input_ids.shape[-1]
								__snake_case					:      Any							      =     self.tokenizer(
								    __magic_name__  ,      padding="""max_length"""  ,      max_length=__magic_name__  ,      truncation=__magic_name__  ,      return_tensors="""pt"""  ,      )
								__snake_case					:      Dict							      =     self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

								# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
								__snake_case					:      Optional[int]							      =     uncond_embeddings.shape[1]
								__snake_case					:      Union[str, Any]							      =     uncond_embeddings.repeat(1  ,      __magic_name__  ,      1			)
								__snake_case					:      Tuple							      =     uncond_embeddings.view(batch_size * num_images_per_prompt  ,      __magic_name__  ,      -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
								__snake_case					:      Dict							      =     torch.cat([uncond_embeddings, text_embeddings]			)

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					__snake_case					:      List[Any]							      =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__snake_case					:      List[Any]							      =     text_embeddings.dtype
					if latents is None:
								if self.device.type == "mps":
											# randn does not exist on mps
											__snake_case					:      Optional[int]							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device="""cpu"""  ,      dtype=__magic_name__			).to(
											    self.device			)
								else:
											__snake_case					:      int							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device=self.device  ,      dtype=__magic_name__			)
					else:
								if latents.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
								__snake_case					:      List[str]							      =     latents.to(self.device			)

					# set timesteps
					self.scheduler.set_timesteps(__magic_name__			)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__snake_case					:      Optional[int]							      =     self.scheduler.timesteps.to(self.device			)

					# scale the initial noise by the standard deviation required by the scheduler
					__snake_case					:      str							      =     latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__snake_case					:      Tuple							      =     """eta""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
					__snake_case					:      List[str]							      =     {}
					if accepts_eta:
								__snake_case					:      str							      =     eta

					for i, t in enumerate(self.progress_bar(__magic_name__			)			):
								# expand the latents if we are doing classifier free guidance
								__snake_case					:      Union[str, Any]							      =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
								__snake_case					:      Dict							      =     self.scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								# predict the noise residual
								__snake_case					:      Tuple							      =     self.unet(__magic_name__  ,      __magic_name__  ,      encoder_hidden_states=__magic_name__			).sample

								# perform guidance
								if do_classifier_free_guidance:
											__snake_case      ,     __snake_case					:      str							      =     noise_pred.chunk(2			)
											__snake_case					:      Any							      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								__snake_case					:      Optional[Any]							      =     self.scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      **__magic_name__			).prev_sample

								# call the callback, if provided
								if callback is not None and i % callback_steps == 0:
											callback(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					__snake_case					:      int							      =     1 / 0.18215 * latents
					__snake_case					:      Optional[Any]							      =     self.vae.decode(__magic_name__			).sample

					__snake_case					:      Any							      =     (image / 2 + 0.5).clamp(0  ,      1			)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					__snake_case					:      Any							      =     image.cpu().permute(0  ,      2  ,      3  ,      1			).float().numpy()

					if output_type == "pil":
								__snake_case					:      Tuple							      =     self.numpy_to_pil(__magic_name__			)

					if not return_dict:
								return image

					return StableDiffusionPipelineOutput(images=__magic_name__  ,      nsfw_content_detected=__magic_name__			)


