


from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase__   	=							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=8						)     ->			Union[str, Any]:







 """simple docstring"""


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







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Tuple:
      super().__init__()

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      if latents is None:
       _lowercase							      =randn_tensor(UpperCAmelCase		,     generator=UpperCAmelCase		,     device=UpperCAmelCase		,     dtype=UpperCAmelCase       )
      else:
       if latents.shape != shape:
        raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"       )
       _lowercase							      =latents.to(UpperCAmelCase       )

      _lowercase							      =latents * scheduler.init_noise_sigma
      return latents





     def      __A					(self		,     UpperCAmelCase=0       )     ->   Tuple:
      if is_accelerate_available():
       from accelerate import cpu_offload
      else:
       raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

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

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





     def      __A					(self		,     UpperCAmelCase=0       )     ->   str:
      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.'''       )

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

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

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

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





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def      __A					(self       )     ->   Union[str, Any]:
      if not hasattr(self.unet		,     '''_hf_hook'''       ):
       return self.device
      for module in self.unet.modules():
       if (
           hasattr(UpperCAmelCase		,     '''_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(UpperCAmelCase       )
     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = 5_1_2		,     UpperCAmelCase = 5_1_2		,     UpperCAmelCase = 1_0_0		,     UpperCAmelCase = 4.0		,     UpperCAmelCase = 1		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "pil"		,     UpperCAmelCase = True		,     )     ->   Any:
      _lowercase							      =self._execution_device

      _lowercase							      =guidance_scale > 1.0

      if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
       _lowercase							      =torch.cat(UpperCAmelCase		,     dim=0       )
      if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
       _lowercase							      =torch.cat(UpperCAmelCase		,     dim=0       )
      if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
       _lowercase							      =torch.cat(UpperCAmelCase		,     dim=0       )

      _lowercase							      =image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
       _lowercase							      =image_embeds.repeat_interleave(UpperCAmelCase		,     dim=0       )
       _lowercase							      =negative_image_embeds.repeat_interleave(UpperCAmelCase		,     dim=0       )
       _lowercase							      =hint.repeat_interleave(UpperCAmelCase		,     dim=0       )

      _lowercase							      =torch.cat([negative_image_embeds, image_embeds]		,     dim=0       ).to(dtype=self.unet.dtype		,     device=UpperCAmelCase       )
      _lowercase							      =torch.cat([hint, hint]		,     dim=0       ).to(dtype=self.unet.dtype		,     device=UpperCAmelCase       )
      self.scheduler.set_timesteps(UpperCAmelCase		,     device=UpperCAmelCase       )
      _lowercase							      =self.scheduler.timesteps

      _lowercase							      =self.movq.config.latent_channels

      _lowercase      ,    _lowercase							      =downscale_height_and_width(UpperCAmelCase		,     UpperCAmelCase		,     self.movq_scale_factor       )

      # create initial latent
      _lowercase							      =self.prepare_latents(
          (batch_size, num_channels_latents, height, width)		,     image_embeds.dtype		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     self.scheduler		,     )

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

       _lowercase							      ={'''image_embeds''': image_embeds, '''hint''': hint}
       _lowercase							      =self.unet(
           sample=UpperCAmelCase		,     timestep=UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     added_cond_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]

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

       # compute the previous noisy sample x_t -> x_t-1
       _lowercase							      =self.scheduler.step(
           UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     generator=UpperCAmelCase		,     )[0]
      # post-processing
      _lowercase							      =self.movq.decode(UpperCAmelCase		,     force_not_quantize=UpperCAmelCase       )['''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"]:
       _lowercase							      =image * 0.5 + 0.5
       _lowercase							      =image.clamp(0		,     1       )
       _lowercase							      =image.cpu().permute(0		,     2		,     3		,     1       ).float().numpy()

      if output_type == "pil":
       _lowercase							      =self.numpy_to_pil(UpperCAmelCase       )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase       )





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

