

"""simple docstring"""




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       42
  snake_case_				       =       None







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=0.999							,    __UpperCamelCase="cosine"							,    ):
 '''simple docstring'''





 if alpha_transform_type == "cosine":

  def alpha_bar_fn(__UpperCamelCase      ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(__UpperCamelCase      ):
   return math.exp(t * -12.0      )

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

 UpperCAmelCase__					:  Any					   =					[]
 for i in range(__UpperCamelCase      ):
  UpperCAmelCase__					:  List[Any]					   =					i / num_diffusion_timesteps
  UpperCAmelCase__					:  Optional[Any]					   =					(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(__UpperCamelCase      ) / alpha_bar_fn(__UpperCamelCase      )							,    __UpperCamelCase      )      )
 return torch.tensor(__UpperCamelCase							,    dtype=torch.floataa      )







class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase					):

  @register_to_config
  def __init__(  self		:			List[Any]					,A		:			int = 1_000					,A		:			str = "fixed_small_log"					,A		:			bool = True					,A		:			Optional[float] = 1.0					,A		:			str = "epsilon"					,A		:			str = "squaredcos_cap_v2"					,):






   '''simple docstring'''
   if beta_schedule != "squaredcos_cap_v2":
    raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

   UpperCAmelCase__					:  Dict					   =					betas_for_alpha_bar(A    )

   UpperCAmelCase__					:  Dict					   =					1.0 - self.betas
   UpperCAmelCase__					:  Optional[Any]					   =					torch.cumprod(self.alphas					,dim=0    )
   UpperCAmelCase__					:  Any					   =					torch.tensor(1.0    )

   # standard deviation of the initial noise distribution
   UpperCAmelCase__					:  Any					   =					1.0

   # setable values
   UpperCAmelCase__					:  int					   =					None
   UpperCAmelCase__					:  str					   =					torch.from_numpy(np.arange(0					,A    )[::-1].copy()    )

   UpperCAmelCase__					:  Union[str, Any]					   =					variance_type

  def    __lowercase       (  self		:			Tuple					,A		:			torch.FloatTensor					,A		:			Optional[int] = None    ):






   '''simple docstring'''
   return sample

  def    __lowercase       (  self		:			Optional[int]					,A		:			int					,A		:			Union[str, torch.device] = None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					num_inference_steps
   UpperCAmelCase__					:  str					   =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
   UpperCAmelCase__					:  Any					   =					(np.arange(0					,A    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
   UpperCAmelCase__					:  List[Any]					   =					torch.from_numpy(A    ).to(A    )

  def    __lowercase       (  self		:			Dict					,A		:			int					,A		:			Any=None					,A		:			Optional[int]=None					,A		:			Union[str, Any]=None    ):






   '''simple docstring'''
   if prev_timestep is None:
    UpperCAmelCase__					:  str					   =					t - 1

   UpperCAmelCase__					:  Optional[Any]					   =					self.alphas_cumprod[t]
   UpperCAmelCase__					:  Optional[Any]					   =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   UpperCAmelCase__					:  Any					   =					1 - alpha_prod_t
   UpperCAmelCase__					:  List[str]					   =					1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
    UpperCAmelCase__					:  Optional[int]					   =					self.betas[t]
   else:
    UpperCAmelCase__					:  Optional[int]					   =					1 - alpha_prod_t / alpha_prod_t_prev

   # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
   # and sample from it to get previous sample
   # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
   UpperCAmelCase__					:  List[Any]					   =					beta_prod_t_prev / beta_prod_t * beta

   if variance_type is None:
    UpperCAmelCase__					:  int					   =					self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small_log":
    UpperCAmelCase__					:  List[str]					   =					torch.log(torch.clamp(A					,min=1e-20    )    )
    UpperCAmelCase__					:  List[str]					   =					torch.exp(0.5 * variance    )
   elif variance_type == "learned_range":
    # NOTE difference with DDPM scheduler
    UpperCAmelCase__					:  List[Any]					   =					variance.log()
    UpperCAmelCase__					:  List[Any]					   =					beta.log()

    UpperCAmelCase__					:  str					   =					(predicted_variance + 1) / 2
    UpperCAmelCase__					:  str					   =					frac * max_log + (1 - frac) * min_log

   return variance

  def    __lowercase       (  self		:			Dict					,A		:			torch.FloatTensor					,A		:			int					,A		:			torch.FloatTensor					,A		:			Optional[int] = None					,A		:			Any=None					,A		:			bool = True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					torch.split(A					,sample.shape[1]					,dim=1    )
   else:
    UpperCAmelCase__					:  int					   =					None

   # 1. compute alphas, betas
   if prev_timestep is None:
    UpperCAmelCase__					:  Optional[int]					   =					t - 1

   UpperCAmelCase__					:  Union[str, Any]					   =					self.alphas_cumprod[t]
   UpperCAmelCase__					:  Dict					   =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   UpperCAmelCase__					:  Optional[Any]					   =					1 - alpha_prod_t
   UpperCAmelCase__					:  Any					   =					1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
    UpperCAmelCase__					:  List[Any]					   =					self.betas[t]
    UpperCAmelCase__					:  List[str]					   =					self.alphas[t]
   else:
    UpperCAmelCase__					:  List[Any]					   =					1 - alpha_prod_t / alpha_prod_t_prev
    UpperCAmelCase__					:  Dict					   =					1 - beta

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if self.config.prediction_type == "epsilon":
    UpperCAmelCase__					:  Optional[Any]					   =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
    UpperCAmelCase__					:  Union[str, Any]					   =					model_output
   else:
    raise ValueError(
        f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
        """ for the UnCLIPScheduler."""    )

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
    UpperCAmelCase__					:  int					   =					torch.clamp(
        A					,-self.config.clip_sample_range					,self.config.clip_sample_range    )

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase__					:  str					   =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
   UpperCAmelCase__					:  Any					   =					alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase__					:  Optional[Any]					   =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   UpperCAmelCase__					:  Optional[Any]					   =					0
   if t > 0:
    UpperCAmelCase__					:  Any					   =					randn_tensor(
        model_output.shape					,dtype=model_output.dtype					,generator=A					,device=model_output.device    )

    UpperCAmelCase__					:  List[str]					   =					self._get_variance(
        A					,predicted_variance=A					,prev_timestep=A					,)

    if self.variance_type == "fixed_small_log":
     UpperCAmelCase__					:  Union[str, Any]					   =					variance
    elif self.variance_type == "learned_range":
     UpperCAmelCase__					:  Dict					   =					(0.5 * variance).exp()
    else:
     raise ValueError(
         f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
         """ for the UnCLIPScheduler."""    )

    UpperCAmelCase__					:  Dict					   =					variance * variance_noise

   UpperCAmelCase__					:  Any					   =					pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample,)

   return UnCLIPSchedulerOutput(prev_sample=A					,pred_original_sample=A    )




  def    __lowercase       (  self		:			str					,A		:			torch.FloatTensor					,A		:			torch.FloatTensor					,A		:			torch.IntTensor					,):






   '''simple docstring'''
   # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
   UpperCAmelCase__					:  str					   =					self.alphas_cumprod.to(device=original_samples.device					,dtype=original_samples.dtype    )
   UpperCAmelCase__					:  List[str]					   =					timesteps.to(original_samples.device    )

   UpperCAmelCase__					:  int					   =					alphas_cumprod[timesteps] ** 0.5
   UpperCAmelCase__					:  Optional[int]					   =					sqrt_alpha_prod.flatten()
   while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
    UpperCAmelCase__					:  Any					   =					sqrt_alpha_prod.unsqueeze(-1    )

   UpperCAmelCase__					:  List[str]					   =					(1 - alphas_cumprod[timesteps]) ** 0.5
   UpperCAmelCase__					:  int					   =					sqrt_one_minus_alpha_prod.flatten()
   while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
    UpperCAmelCase__					:  int					   =					sqrt_one_minus_alpha_prod.unsqueeze(-1    )

   UpperCAmelCase__					:  Dict					   =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples








"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 UpperCAmelCase__					:  Any					   =					w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			str					,A		:			MultilingualCLIP					,A		:			XLMRobertaTokenizer					,A		:			UNetaDConditionModel					,A		:			Union[DDIMScheduler, DDPMScheduler]					,A		:			VQModel					,):






   '''simple docstring'''
   super().__init__()

   self.register_modules(
       text_encoder=A					,tokenizer=A					,unet=A					,scheduler=A					,movq=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Tuple					,A		:			Dict					,A		:			int					,A		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   if latents is None:
    UpperCAmelCase__					:  Any					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )
   else:
    if latents.shape != shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
    UpperCAmelCase__					:  int					   =					latents.to(A    )

   UpperCAmelCase__					:  Any					   =					latents * scheduler.init_noise_sigma
   return latents

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Optional[Any]					,A		:			str=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					len(A    ) if isinstance(A					,A    ) else 1
   # get prompt text embeddings
   UpperCAmelCase__					:  List[Any]					   =					self.tokenizer(
       A					,padding="""max_length"""					,truncation=A					,max_length=77					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)

   UpperCAmelCase__					:  List[str]					   =					text_inputs.input_ids
   UpperCAmelCase__					:  Any					   =					self.tokenizer(A					,padding="""longest"""					,return_tensors="""pt"""    ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,A    ):
    UpperCAmelCase__					:  List[str]					   =					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}"    )

   UpperCAmelCase__					:  str					   =					text_input_ids.to(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					text_inputs.attention_mask.to(A    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.text_encoder(
       input_ids=A					,attention_mask=A    )

   UpperCAmelCase__					:  Optional[int]					   =					prompt_embeds.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder_hidden_states.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  List[str]					   =					text_mask.repeat_interleave(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  List[str]
    if negative_prompt is None:
     UpperCAmelCase__					:  List[Any]					   =					[""""""] * batch_size
    elif type(A    ) is not type(A    ):
     raise TypeError(
         f"`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="
         f" {type(A    )}."    )
    elif isinstance(A					,A    ):
     UpperCAmelCase__					:  Any					   =					[negative_prompt]
    elif batch_size != len(A    ):
     raise ValueError(
         f"`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"
         f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
         """ the batch size of `prompt`."""    )
    else:
     UpperCAmelCase__					:  List[Any]					   =					negative_prompt

    UpperCAmelCase__					:  Any					   =					self.tokenizer(
        A					,padding="""max_length"""					,max_length=77					,truncation=A					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)
    UpperCAmelCase__					:  Optional[int]					   =					uncond_input.input_ids.to(A    )
    UpperCAmelCase__					:  str					   =					uncond_input.attention_mask.to(A    )

    UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.text_encoder(
        input_ids=A					,attention_mask=A    )

    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.shape[1]
    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.repeat(1					,A    )
    UpperCAmelCase__					:  str					   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					,A    )

    UpperCAmelCase__					:  Dict					   =					uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.repeat(1					,A					,1    )
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt					,A					,-1    )
    UpperCAmelCase__					:  List[Any]					   =					uncond_text_mask.repeat_interleave(A					,dim=0    )

    # done duplicates

    # 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					   =					torch.cat([negative_prompt_embeds, prompt_embeds]    )
    UpperCAmelCase__					:  int					   =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

    UpperCAmelCase__					:  str					   =					torch.cat([uncond_text_mask, text_mask]    )

   return prompt_embeds, text_encoder_hidden_states, text_mask

  def    __lowercase       (  self		:			Tuple					,A		:			Dict=0    ):






   '''simple docstring'''
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

   UpperCAmelCase__					:  str					   =					torch.device(f"cuda:{gpu_id}"    )

   UpperCAmelCase__					:  Tuple					   =					[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(A					,A    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=0    ):






   '''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."""    )

   UpperCAmelCase__					:  List[str]					   =					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)

   UpperCAmelCase__					:  List[str]					   =					None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

   if self.safety_checker is not None:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					cpu_offload_with_hook(self.safety_checker					,A					,prev_module_hook=A    )

   # We'll offload the last model manually.
   UpperCAmelCase__					:  Any					   =					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




  @torch.no_grad()
  @replace_example_docstring(A    )
  def __call__(  self		:			Union[str, Any]					,A		:			Union[str, List[str]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Optional[Union[str, List[str]]] = None					,A		:			int = 512					,A		:			int = 512					,A		:			int = 100					,A		:			float = 4.0					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			Optional[torch.FloatTensor] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

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

   UpperCAmelCase__					:  Optional[int]					   =					self._execution_device

   UpperCAmelCase__					:  Dict					   =					batch_size * num_images_per_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					guidance_scale > 1.0

   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self._encode_prompt(
       A					,A					,A					,A					,A    )

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  Optional[int]					   =					image_embeds.repeat_interleave(A					,dim=0    )
    UpperCAmelCase__					:  Tuple					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

   UpperCAmelCase__					:  List[str]					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(
       dtype=prompt_embeds.dtype					,device=A    )

   self.scheduler.set_timesteps(A					,device=A    )
   UpperCAmelCase__					:  int					   =					self.scheduler.timesteps

   UpperCAmelCase__					:  Union[str, Any]					   =					self.unet.config.in_channels

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					get_new_h_w(A					,A					,self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase__					:  Dict					   =					self.prepare_latents(
       (batch_size, num_channels_latents, height, width)					,text_encoder_hidden_states.dtype					,A					,A					,A					,self.scheduler					,)

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

    UpperCAmelCase__					:  Optional[Any]					   =					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
    UpperCAmelCase__					:  Any					   =					self.unet(
        sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

    if do_classifier_free_guidance:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					noise_pred.split(latents.shape[1]					,dim=1    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					noise_pred.chunk(2    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					variance_pred.chunk(2    )
     UpperCAmelCase__					:  int					   =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     UpperCAmelCase__					:  List[Any]					   =					torch.cat([noise_pred, variance_pred_text]					,dim=1    )

    if not (
        hasattr(self.scheduler.config					,"""variance_type"""    )
        and self.scheduler.config.variance_type in ["learned", "learned_range"]
    ):
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					noise_pred.split(latents.shape[1]					,dim=1    )

    # compute the previous noisy sample x_t -> x_t-1
    UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.step(
        A					,A					,A					,generator=A					,).prev_sample
   # post-processing
   UpperCAmelCase__					:  List[Any]					   =					self.movq.decode(A					,force_not_quantize=A    )["""sample"""]

   if output_type not in ["pt", "np", "pil"]:
    raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

   if output_type in ["np", "pil"]:
    UpperCAmelCase__					:  Union[str, Any]					   =					image * 0.5 + 0.5
    UpperCAmelCase__					:  Optional[Any]					   =					image.clamp(0					,1    )
    UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

   if output_type == "pil":
    UpperCAmelCase__					:  List[str]					   =					self.numpy_to_pil(A    )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A    )





