





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

__UpperCAmelCase       :   Optional[int]				       =							"\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__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=8)   ->    List[str]:
  __snake_case:						Tuple   =						height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __snake_case:						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     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    List[Any]							,				A :    Optional[Any]							,				A :    Dict							,				A :    Optional[int]							,				):
         super().__init__()

         self.register_modules(
             unet=A							,				scheduler=A							,				movq=A							,				)
         __snake_case:						Dict   =						2 ** (len(self.movq.config.block_out_channels		) - 1)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Dict							,				A :    Optional[Any]							,				A :    Dict							,				A :    Optional[int]							,				A :    str							,				A :    Optional[Any]		):
         if latents is None:
           __snake_case:						Union[str, 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}'''		)
           __snake_case:						int   =						latents.to(A		)

         __snake_case:						List[Any]   =						latents * scheduler.init_noise_sigma
         return latents




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Any=0		):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

         __snake_case:						List[Any]   =						torch.device(f'''cuda:{gpu_id}'''		)

         __snake_case:						Union[str, Any]   =						[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(A							,				A		)




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

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

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




       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 							UpperCAmelCase__ (  self :    List[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 :    Dict							,				A :    str							,				A :    Optional[Any]							,				A :    Any = 512							,				A :    List[Any] = 512							,				A :    Optional[Any] = 100							,				A :    str = 4.0							,				A :    Optional[Any] = 1							,				A :    List[Any] = None							,				A :    Tuple = None							,				A :    Any = "pil"							,				A :    Optional[int] = True							,				):
         __snake_case:						Dict   =						self._execution_device

         __snake_case:						Union[str, Any]   =						guidance_scale > 1.0

         if isinstance(A							,				A		):
           __snake_case:						List[Any]   =						torch.cat(A							,				dim=0		)
         if isinstance(A							,				A		):
           __snake_case:						List[Any]   =						torch.cat(A							,				dim=0		)
         if isinstance(A							,				A		):
           __snake_case:						Dict   =						torch.cat(A							,				dim=0		)

         __snake_case:						List[Any]   =						image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
           __snake_case:						str   =						image_embeds.repeat_interleave(A							,				dim=0		)
           __snake_case:						int   =						negative_image_embeds.repeat_interleave(A							,				dim=0		)
           __snake_case:						Optional[Any]   =						hint.repeat_interleave(A							,				dim=0		)

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

         __snake_case:						Dict   =						self.movq.config.latent_channels

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

         # create initial latent
         __snake_case:						Union[str, Any]   =						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
           __snake_case:						int   =						torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

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

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

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

         if output_type == "pil":
           __snake_case:						Dict   =						self.numpy_to_pil(A		)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=A		)













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

