






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__magic_name__   :  List[str]        =							logging.get_logger(__name__)

__magic_name__   :  Any        =							'''▁'''

__magic_name__   :  Optional[int]        =							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__magic_name__   :  str        =							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__magic_name__   :  Any        =							{'''vinai/bartpho-syllable''': 1024}





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					VOCAB_FILES_NAMES
     snake_case__					=					PRETRAINED_VOCAB_FILES_MAP
     snake_case__					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__					=					["""input_ids""", """attention_mask"""]







     def __init__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]="<s>"   ,						_SCREAMING_SNAKE_CASE      :    Tuple="</s>"   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]="</s>"   ,						_SCREAMING_SNAKE_CASE      :    int="<s>"   ,						_SCREAMING_SNAKE_CASE      :    str="<unk>"   ,						_SCREAMING_SNAKE_CASE      :    Any="<pad>"   ,						_SCREAMING_SNAKE_CASE      :    Dict="<mask>"   ,						_SCREAMING_SNAKE_CASE      :    Optional[Dict[str, Any]] = None   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else mask_token

           UpperCamelCase					       =  {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=_SCREAMING_SNAKE_CASE   ,						eos_token=_SCREAMING_SNAKE_CASE   ,						unk_token=_SCREAMING_SNAKE_CASE   ,						sep_token=_SCREAMING_SNAKE_CASE   ,						cls_token=_SCREAMING_SNAKE_CASE   ,						pad_token=_SCREAMING_SNAKE_CASE   ,						mask_token=_SCREAMING_SNAKE_CASE   ,						sp_model_kwargs=self.sp_model_kwargs   ,						**_SCREAMING_SNAKE_CASE   ,						)

           UpperCamelCase					       =  vocab_file
           UpperCamelCase					       =  monolingual_vocab_file
           UpperCamelCase					       =  spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )

           # Load the reduced vocab

           # Keep order of special tokens for backward compatibility
           UpperCamelCase					       =  {}
           UpperCamelCase					       =  0
           for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                 if str(_SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
                       UpperCamelCase					       =  cnt
                       cnt += 1
           with open(_SCREAMING_SNAKE_CASE   ,						'r'   ,						encoding='utf-8' ) as f:
                 for line in f.readlines():
                       UpperCamelCase					       =  line.strip().split()[0]
                       UpperCamelCase					       =  len(self.fairseq_tokens_to_ids )
           if str(_SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
                 UpperCamelCase					       =  len(self.fairseq_tokens_to_ids )

           UpperCamelCase					       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







     def __getstate__( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.__dict__.copy()
           UpperCamelCase					       =  None
           UpperCamelCase					       =  self.sp_model.serialized_model_proto()
           return state







     def __setstate__( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  d

           # for backward compatibility
           if not hasattr(self   ,						'sp_model_kwargs' ):
                 UpperCamelCase					       =  {}

           UpperCamelCase					       =  spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None ):






           """simple docstring"""




           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCamelCase					       =  [self.cls_token_id]
           UpperCamelCase					       =  [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False ):






           """simple docstring"""




           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_SCREAMING_SNAKE_CASE   ,						token_ids_a=_SCREAMING_SNAKE_CASE   ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE )

           if token_ids_a is None:
                 return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
           return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None ):






           """simple docstring"""




           UpperCamelCase					       =  [self.sep_token_id]
           UpperCamelCase					       =  [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           return len(self.fairseq_ids_to_tokens )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
           vocab.update(self.added_tokens_encoder )
           return vocab







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           return self.sp_model.encode(_SCREAMING_SNAKE_CASE   ,						out_type=_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           else:
                 return self.unk_token_id







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           return self.fairseq_ids_to_tokens[index]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  ''.join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE   ,						' ' ).strip()
           return out_string







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None ):






           """simple docstring"""




           if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                 return
           UpperCamelCase					       =  os.path.join(
               _SCREAMING_SNAKE_CASE   ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
           UpperCamelCase					       =  os.path.join(
               _SCREAMING_SNAKE_CASE   ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']   ,						)

           if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
                 copyfile(self.vocab_file   ,						_SCREAMING_SNAKE_CASE )
           elif not os.path.isfile(self.vocab_file ):
                 with open(_SCREAMING_SNAKE_CASE   ,						'wb' ) as fi:
                       UpperCamelCase					       =  self.sp_model.serialized_model_proto()
                       fi.write(_SCREAMING_SNAKE_CASE )

           if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
               _SCREAMING_SNAKE_CASE ) and os.path.isfile(self.monolingual_vocab_file ):
                 copyfile(self.monolingual_vocab_file   ,						_SCREAMING_SNAKE_CASE )
           elif not os.path.isfile(self.monolingual_vocab_file ):
                 with open(_SCREAMING_SNAKE_CASE   ,						'w'   ,						encoding='utf-8' ) as fp:
                       for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                   fp.write(f'{str(_SCREAMING_SNAKE_CASE )} \n' )

           return out_vocab_file, out_monolingual_vocab_file











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


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





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    WhisperForConditionalGeneration   ,						_SCREAMING_SNAKE_CASE      :    WhisperProcessor   ,						_SCREAMING_SNAKE_CASE      :    AutoencoderKL   ,						_SCREAMING_SNAKE_CASE      :    CLIPTextModel   ,						_SCREAMING_SNAKE_CASE      :    CLIPTokenizer   ,						_SCREAMING_SNAKE_CASE      :    UNetaDConditionModel   ,						_SCREAMING_SNAKE_CASE      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,						_SCREAMING_SNAKE_CASE      :    StableDiffusionSafetyChecker   ,						_SCREAMING_SNAKE_CASE      :    CLIPImageProcessor   ,						):






           """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=_SCREAMING_SNAKE_CASE   ,						speech_processor=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						feature_extractor=_SCREAMING_SNAKE_CASE   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, int]] = "auto" ):






           """simple docstring"""




           if slice_size == "auto":
                 UpperCamelCase					       =  self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )







     @torch.no_grad()
     def __call__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=1_6000   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 50   ,						_SCREAMING_SNAKE_CASE      :    float = 7.5   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, List[str]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 1   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.Generator] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.FloatTensor] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = "pil"   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.speech_processor.feature_extractor(
               _SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						sampling_rate=_SCREAMING_SNAKE_CASE ).input_features.to(self.device )
           UpperCamelCase					       =  self.speech_model.generate(_SCREAMING_SNAKE_CASE   ,						max_length=48_0000 )

           UpperCamelCase					       =  self.speech_processor.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE   ,						normalize=_SCREAMING_SNAKE_CASE )[
               0
           ]

           if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  1
           elif isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  len(_SCREAMING_SNAKE_CASE )
           else:
                 raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE )}' )

           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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
           ):
                 raise ValueError(
                     f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                     f' {type(_SCREAMING_SNAKE_CASE )}.' )

           # get prompt text embeddings
           UpperCamelCase					       =  self.tokenizer(
               _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=self.tokenizer.model_max_length   ,						return_tensors='pt'   ,						)
           UpperCamelCase					       =  text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 UpperCamelCase					       =  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}' )
                 UpperCamelCase					       =  text_input_ids[:, : self.tokenizer.model_max_length]
           UpperCamelCase					       =  self.text_encoder(text_input_ids.to(self.device ) )[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  text_embeddings.shape
           UpperCamelCase					       =  text_embeddings.repeat(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
           UpperCamelCase					       =  text_embeddings.view(bs_embed * num_images_per_prompt   ,						_SCREAMING_SNAKE_CASE   ,						-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.
           UpperCamelCase					       =  guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                 UpperCamelCase					       =  42
                 if negative_prompt is None:
                       UpperCamelCase					       =  [''] * 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					       =  [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					       =  negative_prompt

                 UpperCamelCase					       =  text_input_ids.shape[-1]
                 UpperCamelCase					       =  self.tokenizer(
                     _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=_SCREAMING_SNAKE_CASE   ,						truncation=_SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						)
                 UpperCamelCase					       =  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 UpperCamelCase					       =  uncond_embeddings.shape[1]
                 UpperCamelCase					       =  uncond_embeddings.repeat(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
                 UpperCamelCase					       =  uncond_embeddings.view(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					       =  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`.
           UpperCamelCase					       =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           UpperCamelCase					       =  text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device='cpu'   ,						dtype=_SCREAMING_SNAKE_CASE ).to(
                           self.device )
                 else:
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device=self.device   ,						dtype=_SCREAMING_SNAKE_CASE )
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
                 UpperCamelCase					       =  latents.to(self.device )

           # set timesteps
           self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           UpperCamelCase					       =  self.scheduler.timesteps.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           UpperCamelCase					       =  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]
           UpperCamelCase					       =  'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
           UpperCamelCase					       =  {}
           if accepts_eta:
                 UpperCamelCase					       =  eta

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

                 # predict the noise residual
                 UpperCamelCase					       =  self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE ).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase					       =  self.scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

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

           UpperCamelCase					       =  1 / 0.1_8_2_1_5 * latents
           UpperCamelCase					       =  self.vae.decode(_SCREAMING_SNAKE_CASE ).sample

           UpperCamelCase					       =  (image / 2 + 0.5).clamp(0   ,						1 )

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

           if output_type == "pil":
                 UpperCamelCase					       =  self.numpy_to_pil(_SCREAMING_SNAKE_CASE )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE   ,						nsfw_content_detected=_SCREAMING_SNAKE_CASE )



