






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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    lowercase__   (	lowerCamelCase__		):

	def __init__(							self						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase				)->    Union[str, Any]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(vqvae=lowercase__						,			unet=lowercase__						,			scheduler=lowercase__				)


	@torch.no_grad()
	def __call__(							self						,			__UpperCAmelCase = 1						,			__UpperCAmelCase = None						,			__UpperCAmelCase = 0.0						,			__UpperCAmelCase = 50						,			__UpperCAmelCase = "pil"						,			__UpperCAmelCase = True						,			**__UpperCAmelCase						,			)->    Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       					=  randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,			generator=lowercase__						,			)
							lowerCAmelCase__       					=  latents.to(self.device				)

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

							self.scheduler.set_timesteps(lowercase__				)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							lowerCAmelCase__       					=  "eta" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)

							lowerCAmelCase__       					=  {}
							if accepts_eta:
													lowerCAmelCase__       					=  eta

							for t in self.progress_bar(self.scheduler.timesteps				):
													lowerCAmelCase__       					=  self.scheduler.scale_model_input(lowercase__						,			lowercase__				)
													# predict the noise residual
													lowerCAmelCase__       					=  self.unet(lowercase__						,			lowercase__				).sample
													# compute the previous noisy sample x_t -> x_t-1
													lowerCAmelCase__       					=  self.scheduler.step(lowercase__						,			lowercase__						,			lowercase__						,			**lowercase__				).prev_sample

							# decode the image latents with the VAE
							lowerCAmelCase__       					=  self.vqvae.decode(lowercase__				).sample

							lowerCAmelCase__       					=  (image / 2 + 0.5).clamp(0						,			1				)
							lowerCAmelCase__       					=  image.cpu().permute(0						,			2						,			3						,			1				).numpy()
							if output_type == "pil":
													lowerCAmelCase__       					=  self.numpy_to_pil(lowercase__				)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowercase__				)









'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError('''You need to specify an `image_processor`.'''			)
   if tokenizer is None:
    raise ValueError('''You need to specify a `tokenizer`.'''			)

   super().__init__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     int			):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

   if text is not None:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer.model_input_names
   __lowercase							  = self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

