




'''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,
)


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

lowercase__								=						"""
    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 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=8 ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case						,  snake_case=5_12						,  snake_case=5_12 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			pil_image.resize((w, h)						,  resample=Image.BICUBIC						,  reducing_gap=1 )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			np.array(pil_image.convert('''RGB''' ) )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			arr.astype(np.floataa ) / 127.5 - 1
   SCREAMING_SNAKE_CASE__      :					int							    =			np.transpose(_SCREAMING_SNAKE_CASE						,  [2, 0, 1] )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.from_numpy(_SCREAMING_SNAKE_CASE ).unsqueeze(0 )
   return image







class     lowercase  ( __UpperCAmelCase):






      def __init__(self			,	a__			,	a__			,	a__			,	)							-> List[str]:
         super().__init__()

         self.register_modules(
             unet=a__			,	scheduler=a__			,	movq=a__			,	)
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			2 ** (len(self.movq.config.block_out_channels       ) - 1)






      def  A__(self			,	a__			,	a__			,	a__       )							-> List[Any]:
         # get the original timestep using init_timestep
         SCREAMING_SNAKE_CASE__      :					Dict							    =			min(int(num_inference_steps * strength       )			,	a__       )

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

         return timesteps, num_inference_steps - t_start






      def  A__(self			,	a__			,	a__			,	a__			,	a__			,	a__			,	a__			,	a__=None       )							-> Optional[Any]:
         if not isinstance(a__			,	(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(a__       )}"""       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			image.to(device=a__			,	dtype=a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			batch_size * num_images_per_prompt

         if image.shape[1] == 4:
            SCREAMING_SNAKE_CASE__      :					int							    =			image

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

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

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.movq.config.scaling_factor * init_latents

         SCREAMING_SNAKE_CASE__      :					Any							    =			torch.cat([init_latents]			,	dim=0       )

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			init_latents.shape
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			randn_tensor(a__			,	generator=a__			,	device=a__			,	dtype=a__       )

         # get latents
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.scheduler.add_noise(a__			,	a__			,	a__       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			init_latents

         return latents






      def  A__(self			,	a__=0       )							-> Optional[int]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

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

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
               cpu_offload(a__			,	a__       )






      def  A__(self			,	a__=0       )							-> Union[str, Any]:
         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.'''       )

         SCREAMING_SNAKE_CASE__      :					Optional[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)

         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			None
         for cpu_offloaded_model in [self.unet, self.movq]:
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			cpu_offload_with_hook(a__			,	a__			,	prev_module_hook=a__       )

         # We'll offload the last model manually.
         SCREAMING_SNAKE_CASE__      :					str							    =			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(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			,	a__			,	a__			,	a__			,	a__			,	a__ = 5_1_2			,	a__ = 5_1_2			,	a__ = 1_0_0			,	a__ = 4.0			,	a__ = 0.3			,	a__ = 1			,	a__ = None			,	a__ = "pil"			,	a__ = True			,	)							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self._execution_device

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			guidance_scale > 1.0

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.cat(a__			,	dim=0       )
         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.cat(a__			,	dim=0       )
         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Any							    =			torch.cat(a__			,	dim=0       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_embeds.shape[0]

         if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_embeds.repeat_interleave(a__			,	dim=0       )
            SCREAMING_SNAKE_CASE__      :					int							    =			negative_image_embeds.repeat_interleave(a__			,	dim=0       )
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			hint.repeat_interleave(a__			,	dim=0       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			torch.cat([negative_image_embeds, image_embeds]			,	dim=0       ).to(dtype=self.unet.dtype			,	device=a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			torch.cat([hint, hint]			,	dim=0       ).to(dtype=self.unet.dtype			,	device=a__       )

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

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.cat([prepare_image(a__			,	a__			,	a__       ) for i in image]			,	dim=0       )
         SCREAMING_SNAKE_CASE__      :					str							    =			image.to(dtype=image_embeds.dtype			,	device=a__       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.movq.encode(a__       )['''latents''']
         SCREAMING_SNAKE_CASE__      :					Any							    =			latents.repeat_interleave(a__			,	dim=0       )
         self.scheduler.set_timesteps(a__			,	device=a__       )
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			self.get_timesteps(a__			,	a__			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			downscale_height_and_width(a__			,	a__			,	self.movq_scale_factor       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.prepare_latents(
             a__			,	a__			,	a__			,	a__			,	image_embeds.dtype			,	a__			,	a__       )
         for i, t in enumerate(self.progress_bar(a__       )       ):
            # expand the latents if we are doing classifier free guidance
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{'''image_embeds''': image_embeds, '''hint''': hint}
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.unet(
                sample=a__			,	timestep=a__			,	encoder_hidden_states=a__			,	added_cond_kwargs=a__			,	return_dict=a__			,	)[0]

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

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

            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.scheduler.step(
                a__			,	a__			,	a__			,	generator=a__			,	)[0]

         # post-processing
         SCREAMING_SNAKE_CASE__      :					int							    =			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"]:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			image.clamp(0			,	1       )
            SCREAMING_SNAKE_CASE__      :					str							    =			image.cpu().permute(0			,	2			,	3			,	1       ).float().numpy()

         if output_type == "pil":
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.numpy_to_pil(a__       )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=a__       )



'''simple docstring'''







from ..utils import DummyObject, requires_backends


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Any							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Optional[Any]							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )