





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,
)


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

_A					:    str   =  '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=8     )				->				Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__   :						List[str]					   =							height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowerCamelCase__   :						Optional[Any]					   =							width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_					):







       def __init__(      self     :	int		,					A     :	List[Any]		,					A     :	int		,					A     :	List[Any]		,					)   ->Tuple:
          super().__init__()

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







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	Any		,					A     :	Any		,					A     :	Dict		,					A     :	Dict		,					A     :	Union[str, Any]		,					A     :	List[str]			)   ->Optional[Any]:
          if latents is None:
             lowerCamelCase__   :						Dict					   =							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}"			)
             lowerCamelCase__   :						Optional[Any]					   =							latents.to(A			)

          lowerCamelCase__   :						Tuple					   =							latents * scheduler.init_noise_sigma
          return latents







       def 						__lowerCamelCase    (      self     :	List[Any]		,					A     :	int=0			)   ->Tuple:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

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

          lowerCamelCase__   :						Optional[int]					   =							[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                cpu_offload(A		,					A			)







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	Optional[Any]=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.'''			)

          lowerCamelCase__   :						Union[str, Any]					   =							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)

          lowerCamelCase__   :						int					   =							None
          for cpu_offloaded_model in [self.unet, self.movq]:
             lowerCamelCase__   ,							lowerCamelCase__   :						Tuple					   =							cpu_offload_with_hook(A		,					A		,					prev_module_hook=A			)

          # We'll offload the last model manually.
          lowerCamelCase__   :						Optional[Any]					   =							hook







       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Union[str, Any]:
          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     :	Dict		,					A     :	Optional[Any]		,					A     :	List[str]		,					A     :	List[str]		,					A     :	Tuple = 5_1_2		,					A     :	Optional[int] = 5_1_2		,					A     :	Tuple = 1_0_0		,					A     :	int = 4.0		,					A     :	List[str] = 1		,					A     :	Dict = None		,					A     :	Tuple = None		,					A     :	Optional[Any] = "pil"		,					A     :	Dict = True		,					)   ->Any:
          lowerCamelCase__   :						List[str]					   =							self._execution_device

          lowerCamelCase__   :						List[Any]					   =							guidance_scale > 1.0

          if isinstance(A		,					A			):
             lowerCamelCase__   :						int					   =							torch.cat(A		,					dim=0			)
          if isinstance(A		,					A			):
             lowerCamelCase__   :						str					   =							torch.cat(A		,					dim=0			)
          if isinstance(A		,					A			):
             lowerCamelCase__   :						int					   =							torch.cat(A		,					dim=0			)

          lowerCamelCase__   :						Union[str, Any]					   =							image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
             lowerCamelCase__   :						Any					   =							image_embeds.repeat_interleave(A		,					dim=0			)
             lowerCamelCase__   :						Union[str, Any]					   =							negative_image_embeds.repeat_interleave(A		,					dim=0			)
             lowerCamelCase__   :						Tuple					   =							hint.repeat_interleave(A		,					dim=0			)

          lowerCamelCase__   :						List[str]					   =							torch.cat([negative_image_embeds, image_embeds]		,					dim=0			).to(dtype=self.unet.dtype		,					device=A			)
          lowerCamelCase__   :						str					   =							torch.cat([hint, hint]		,					dim=0			).to(dtype=self.unet.dtype		,					device=A			)
          self.scheduler.set_timesteps(A		,					device=A			)
          lowerCamelCase__   :						List[Any]					   =							self.scheduler.timesteps

          lowerCamelCase__   :						Union[str, Any]					   =							self.movq.config.latent_channels

          lowerCamelCase__   ,							lowerCamelCase__   :						Tuple					   =							downscale_height_and_width(A		,					A		,					self.movq_scale_factor			)

          # create initial latent
          lowerCamelCase__   :						Tuple					   =							self.prepare_latents(
              (batch_size, num_channels_latents, height, width)		,					image_embeds.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
             lowerCamelCase__   :						Union[str, Any]					   =							torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

             lowerCamelCase__   :						int					   =							{'''image_embeds''': image_embeds, '''hint''': hint}
             lowerCamelCase__   :						Tuple					   =							self.unet(
                 sample=A		,					timestep=A		,					encoder_hidden_states=A		,					added_cond_kwargs=A		,					return_dict=A		,					)[0]

             if do_classifier_free_guidance:
                lowerCamelCase__   ,							lowerCamelCase__   :						str					   =							noise_pred.split(latents.shape[1]		,					dim=1			)
                lowerCamelCase__   ,							lowerCamelCase__   :						Union[str, Any]					   =							noise_pred.chunk(2			)
                lowerCamelCase__   ,							lowerCamelCase__   :						List[Any]					   =							variance_pred.chunk(2			)
                lowerCamelCase__   :						Dict					   =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                lowerCamelCase__   :						Optional[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"]
             ):
                lowerCamelCase__   ,							lowerCamelCase__   :						Optional[Any]					   =							noise_pred.split(latents.shape[1]		,					dim=1			)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__   :						List[str]					   =							self.scheduler.step(
                 A		,					A		,					A		,					generator=A		,					)[0]
          # post-processing
          lowerCamelCase__   :						Union[str, 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"]:
             lowerCamelCase__   :						Tuple					   =							image * 0.5 + 0.5
             lowerCamelCase__   :						Tuple					   =							image.clamp(0		,					1			)
             lowerCamelCase__   :						Dict					   =							image.cpu().permute(0		,					2		,					3		,					1			).float().numpy()

          if output_type == "pil":
             lowerCamelCase__   :						str					   =							self.numpy_to_pil(A			)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A			)







# flake8: noqa
# Lint as: python3
UpperCAmelCase__   	=							[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental

