






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      :       Union[str, Any]				  =   logging.get_logger(__name__)  # pylint: disable=invalid-name

__a      :       Any				  =   "\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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			Optional[Any]=8    )						->      List[str]:





    """simple docstring"""

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






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def __init__(				self       :			Any ,	UpperCamelCase_       :			UNetaDConditionModel ,	UpperCamelCase_       :			DDPMScheduler ,	UpperCamelCase_       :			VQModel ,	):


         """simple docstring"""



         super().__init__()

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




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         if latents is None:
             __A         =	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}"				)
             __A         =	latents.to(UpperCamelCase_				)

         __A         =	latents * scheduler.init_noise_sigma
         return latents




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Union[str, Any]=0				):


         """simple docstring"""



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

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

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




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Union[str, 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."""				)

         __A         =	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)

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

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




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


         """simple docstring"""



         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       :			Optional[Any] ,	UpperCamelCase_       :			Union[torch.FloatTensor, List[torch.FloatTensor]] ,	UpperCamelCase_       :			Union[torch.FloatTensor, List[torch.FloatTensor]] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			int = 512 ,	UpperCamelCase_       :			int = 512 ,	UpperCamelCase_       :			int = 100 ,	UpperCamelCase_       :			float = 4.0 ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			Optional[Union[torch.Generator, List[torch.Generator]]] = None ,	UpperCamelCase_       :			Optional[torch.FloatTensor] = None ,	UpperCamelCase_       :			Optional[str] = "pil" ,	UpperCamelCase_       :			bool = True ,	):


         """simple docstring"""



         __A         =	self._execution_device

         __A         =	guidance_scale > 1.0

         if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
             __A         =	torch.cat(UpperCamelCase_ ,	dim=0				)
         if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
             __A         =	torch.cat(UpperCamelCase_ ,	dim=0				)
         if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
             __A         =	torch.cat(UpperCamelCase_ ,	dim=0				)

         __A         =	image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
             __A         =	image_embeds.repeat_interleave(UpperCamelCase_ ,	dim=0				)
             __A         =	negative_image_embeds.repeat_interleave(UpperCamelCase_ ,	dim=0				)
             __A         =	hint.repeat_interleave(UpperCamelCase_ ,	dim=0				)

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

         __A         =	self.movq.config.latent_channels

         __A    ,	__A         =	downscale_height_and_width(UpperCamelCase_ ,	UpperCamelCase_ ,	self.movq_scale_factor				)

         # create initial latent
         __A         =	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
             __A         =	torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

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

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

         if output_type == "pil":
             __A         =	self.numpy_to_pil(UpperCamelCase_				)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=UpperCamelCase_				)












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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




     @register_to_config
     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


         """simple docstring"""



         super().__init__()

         # pass init params to Encoder
         __A         =	Encoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	down_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	double_z=UpperCamelCase_ ,	)

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

         # pass init params to Decoder
         __A         =	Decoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	up_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	norm_type=UpperCamelCase_ ,	)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




