





"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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


a__    :   List[str]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name

a__    :   Optional[Any]    						= """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=8		)			->     Optional[int]:

 '''simple docstring'''






 snake_case__										= height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 snake_case__										= width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=5_1_2		,   _UpperCAmelCase=5_1_2		)			->     Dict:

 '''simple docstring'''






 snake_case__										= pil_image.resize((w, h)		,   resample=Image.BICUBIC		,   reducing_gap=1		)
 snake_case__										= np.array(pil_image.convert("RGB"		)		)
 snake_case__										= arr.astype(np.floataa		) / 127.5 - 1
 snake_case__										= np.transpose(_UpperCAmelCase		,   [2, 0, 1]		)
 snake_case__										= torch.from_numpy(_UpperCAmelCase		).unsqueeze(0		)
 return image



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(
            unet=__SCREAMING_SNAKE_CASE      ,    scheduler=__SCREAMING_SNAKE_CASE      ,    movq=__SCREAMING_SNAKE_CASE      ,    )
        snake_case__										= 2 ** (len(self.movq.config.block_out_channels   ) - 1)

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= min(int(num_inference_steps * strength   )      ,    __SCREAMING_SNAKE_CASE   )

        snake_case__										= max(num_inference_steps - init_timestep      ,    0   )
        snake_case__										= self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        if not isinstance(__SCREAMING_SNAKE_CASE      ,    (torch.Tensor, PIL.Image.Image, list)   ):
         raise ValueError(
             F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE   )}"   )

        snake_case__										= image.to(device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )

        snake_case__										= batch_size * num_images_per_prompt

        if image.shape[1] == 4:
         snake_case__										= image

        else:
         if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != batch_size:
          raise ValueError(
              F"You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE   )}, but requested an effective batch"
              F" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

         elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= [
              self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__SCREAMING_SNAKE_CASE   )
          ]
          snake_case__										= torch.cat(__SCREAMING_SNAKE_CASE      ,    dim=0   )
         else:
          snake_case__										= self.movq.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

         snake_case__										= self.movq.config.scaling_factor * init_latents

        snake_case__										= torch.cat([init_latents]      ,    dim=0   )

        snake_case__										= init_latents.shape
        snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )

        # get latents
        snake_case__										= self.scheduler.add_noise(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        snake_case__										= init_latents

        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

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

        snake_case__										= torch.device(F"cuda:{gpu_id}"   )

        snake_case__										= [
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
          cpu_offload(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE=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."   )

        snake_case__										= torch.device(F"cuda:{gpu_id}"   )

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

        snake_case__										= None
        for cpu_offloaded_model in [self.unet, self.movq]:
         snake_case__							,      snake_case__										= cpu_offload_with_hook(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    prev_module_hook=__SCREAMING_SNAKE_CASE   )

        # We'll offload the last model manually.
        snake_case__										= hook

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        if not hasattr(self.unet      ,    "_hf_hook"   ):
         return self.device
        for module in self.unet.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE      ,    "_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(__SCREAMING_SNAKE_CASE   )
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 512      ,    __SCREAMING_SNAKE_CASE = 512      ,    __SCREAMING_SNAKE_CASE = 100      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 0.3      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= self._execution_device

        snake_case__										= guidance_scale > 1.0

        if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= torch.cat(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= torch.cat(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= torch.cat(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        snake_case__										= image_embeds.shape[0]

        if do_classifier_free_guidance:
         snake_case__										= image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
         snake_case__										= negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
         snake_case__										= hint.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        snake_case__										= torch.cat([negative_image_embeds, image_embeds]      ,    dim=0   ).to(dtype=self.unet.dtype      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__										= torch.cat([hint, hint]      ,    dim=0   ).to(dtype=self.unet.dtype      ,    device=__SCREAMING_SNAKE_CASE   )

        if not isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= [image]
        if not all(isinstance(__SCREAMING_SNAKE_CASE      ,    (PIL.Image.Image, torch.Tensor)   ) for i in image   ):
         raise ValueError(
             F"Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"   )

        snake_case__										= torch.cat([prepare_image(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) for i in image]      ,    dim=0   )
        snake_case__										= image.to(dtype=image_embeds.dtype      ,    device=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.movq.encode(__SCREAMING_SNAKE_CASE   )["latents"]
        snake_case__										= latents.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__							,      snake_case__										= self.get_timesteps(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= timesteps[:1].repeat(batch_size * num_images_per_prompt   )
        snake_case__							,      snake_case__										= downscale_height_and_width(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.movq_scale_factor   )
        snake_case__										= self.prepare_latents(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    image_embeds.dtype      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE   )   ):
         # expand the latents if we are doing classifier free guidance
         snake_case__										= torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

         snake_case__										= {"image_embeds": image_embeds, "hint": hint}
         snake_case__										= self.unet(
             sample=__SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    added_cond_kwargs=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    )[0]

         if do_classifier_free_guidance:
          snake_case__							,      snake_case__										= noise_pred.split(latents.shape[1]      ,    dim=1   )
          snake_case__							,      snake_case__										= noise_pred.chunk(2   )
          snake_case__							,      snake_case__										= variance_pred.chunk(2   )
          snake_case__										= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
          snake_case__										= 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"]
         ):
          snake_case__							,      snake_case__										= noise_pred.split(latents.shape[1]      ,    dim=1   )

         # compute the previous noisy sample x_t -> x_t-1

         snake_case__										= self.scheduler.step(
             __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    )[0]

        # post-processing
        snake_case__										= self.movq.decode(__SCREAMING_SNAKE_CASE      ,    force_not_quantize=__SCREAMING_SNAKE_CASE   )["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"]:
         snake_case__										= image * 0.5 + 0.5
         snake_case__										= image.clamp(0      ,    1   )
         snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   ).float().numpy()

        if output_type == "pil":
         snake_case__										= self.numpy_to_pil(__SCREAMING_SNAKE_CASE   )

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE   )








"""simple docstring"""


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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a__    :   Optional[int]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a__	(     self		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= image.to(device=_UpperCAmelCase		)
 snake_case__										= image  # take image as image_embeddings
 snake_case__										= image_embeddings.unsqueeze(1		)

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 snake_case__							,      snake_case__							,      snake_case__										= image_embeddings.shape
 snake_case__										= image_embeddings.repeat(1		,   _UpperCAmelCase		,   1		)
 snake_case__										= image_embeddings.view(bs_embed * num_images_per_prompt		,   _UpperCAmelCase		,   -1		)

 if do_classifier_free_guidance:
  snake_case__										= torch.zeros_like(_UpperCAmelCase		)

  # 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__										= torch.cat([uncond_embeddings, image_embeddings]		)

 return image_embeddings



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        snake_case__										= decoder_pipe_kwargs.get("torch_dtype"      ,    __SCREAMING_SNAKE_CASE   ) or prior.dtype

        snake_case__										= StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"      ,    **__SCREAMING_SNAKE_CASE   )

        # replace `_encode_image` method
        snake_case__										= types.MethodType(_encode_image      ,    self.decoder_pipe   )

        self.register_modules(
            prior=__SCREAMING_SNAKE_CASE      ,    tokenizer=__SCREAMING_SNAKE_CASE      ,    text_encoder=__SCREAMING_SNAKE_CASE      ,    prior_scheduler=__SCREAMING_SNAKE_CASE      ,    )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        if text_model_output is None:
         snake_case__										= len(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else 1
         # get prompt text embeddings
         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="pt"      ,    )
         snake_case__										= text_inputs.input_ids
         snake_case__										= text_inputs.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case__										= 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__										= text_input_ids[:, : self.tokenizer.model_max_length]

         snake_case__										= self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= text_encoder_output.text_embeds
         snake_case__										= text_encoder_output.last_hidden_state

        else:
         snake_case__										= text_model_output[0].shape[0]
         snake_case__							,      snake_case__										= text_model_output[0], text_model_output[1]
         snake_case__										= text_attention_mask

        snake_case__										= text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        if do_classifier_free_guidance:
         snake_case__										= [""] * batch_size

         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    truncation=__SCREAMING_SNAKE_CASE      ,    return_tensors="pt"      ,    )
         snake_case__										= uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )
         snake_case__										= self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= uncond_embeddings_text_encoder_output.text_embeds
         snake_case__										= uncond_embeddings_text_encoder_output.last_hidden_state

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

         snake_case__										= uncond_embeddings.shape[1]
         snake_case__										= uncond_embeddings.repeat(1      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= uncond_embeddings.view(batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE   )

         snake_case__										= uncond_text_encoder_hidden_states.shape[1]
         snake_case__										= uncond_text_encoder_hidden_states.repeat(1      ,    __SCREAMING_SNAKE_CASE      ,    1   )
         snake_case__										= uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE      ,    -1   )
         snake_case__										= uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    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
         snake_case__										= torch.cat([uncond_embeddings, text_embeddings]   )
         snake_case__										= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

         snake_case__										= torch.cat([uncond_text_mask, text_mask]   )

        return text_embeddings, text_encoder_hidden_states, text_mask

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        if self.device != torch.device("meta"   ) or not hasattr(self.prior      ,    "_hf_hook"   ):
         return self.device
        for module in self.prior.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE      ,    "_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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if latents is None:
         snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )
        else:
         if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
         snake_case__										= latents.to(__SCREAMING_SNAKE_CASE   )

        snake_case__										= latents * scheduler.init_noise_sigma
        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        self.decoder_pipe.to(__SCREAMING_SNAKE_CASE   )
        super().to(__SCREAMING_SNAKE_CASE   )

       @torch.no_grad()
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 25      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 8.0      ,    __SCREAMING_SNAKE_CASE = 50      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if prompt is not None:
         if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= 1
         elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= len(__SCREAMING_SNAKE_CASE   )
         else:
          raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE   )}"   )
        else:
         snake_case__										= text_model_output[0].shape[0]

        snake_case__										= self._execution_device

        snake_case__										= batch_size * num_images_per_prompt

        snake_case__										= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case__							,      snake_case__							,      snake_case__										= self._encode_prompt(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        # prior

        self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.prior_scheduler.timesteps

        snake_case__										= self.prior.config.embedding_dim

        snake_case__										= self.prepare_latents(
            (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.prior_scheduler      ,    )

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

         snake_case__										= self.prior(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    proj_embedding=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    ).predicted_image_embedding

         if do_classifier_free_guidance:
          snake_case__							,      snake_case__										= predicted_image_embedding.chunk(2   )
          snake_case__										= predicted_image_embedding_uncond + prior_guidance_scale * (
              predicted_image_embedding_text - predicted_image_embedding_uncond
          )

         if i + 1 == prior_timesteps_tensor.shape[0]:
          snake_case__										= None
         else:
          snake_case__										= prior_timesteps_tensor[i + 1]

         snake_case__										= self.prior_scheduler.step(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    sample=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    prev_timestep=__SCREAMING_SNAKE_CASE      ,    ).prev_sample

        snake_case__										= self.prior.post_process_latents(__SCREAMING_SNAKE_CASE   )

        snake_case__										= prior_latents

        snake_case__										= self.decoder_pipe(
            image=__SCREAMING_SNAKE_CASE      ,    height=__SCREAMING_SNAKE_CASE      ,    width=__SCREAMING_SNAKE_CASE      ,    num_inference_steps=__SCREAMING_SNAKE_CASE      ,    guidance_scale=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    output_type=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,    eta=__SCREAMING_SNAKE_CASE      ,    )
        return output

