





"""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,
)


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

_lowerCamelCase       =	'''
    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 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Tuple ,      lowercase_   :							Any=8		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __SCREAMING_SNAKE_CASE :			List[str]												=       width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							int=512 ,      lowercase_   :							List[Any]=512		):

 '''simple docstring'''






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






class 			snake_case		(   __UpperCAmelCase   ):

      def __init__(			self   :Any						,							_lowerCamelCase   :UNetaDConditionModel						,							_lowerCamelCase   :DDPMScheduler						,							_lowerCamelCase   :VQModel						,							):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase						,							scheduler=_lowerCamelCase						,							movq=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       2 ** (len(self.movq.config.block_out_channels						) - 1)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[str]						):
       # get the original timestep using init_timestep
       __SCREAMING_SNAKE_CASE :			int												=       min(int(num_inference_steps * strength						)						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       max(num_inference_steps - init_timestep						,							0						)
       __SCREAMING_SNAKE_CASE :			str												=       self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Union[str, Any]=None						):
       if not isinstance(_lowerCamelCase						,							(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(_lowerCamelCase						)}'''						)

       __SCREAMING_SNAKE_CASE :			Any												=       image.to(device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       batch_size * num_images_per_prompt

       if image.shape[1] == 4:
        __SCREAMING_SNAKE_CASE :			Any												=       image

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

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

        __SCREAMING_SNAKE_CASE :			str												=       self.movq.config.scaling_factor * init_latents

       __SCREAMING_SNAKE_CASE :			int												=       torch.cat([init_latents]						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Any												=       init_latents.shape
       __SCREAMING_SNAKE_CASE :			Any												=       randn_tensor(_lowerCamelCase						,							generator=_lowerCamelCase						,							device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       # get latents
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.scheduler.add_noise(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       init_latents

       return latents

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Tuple=0						):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

       __SCREAMING_SNAKE_CASE :			str												=       torch.device(f'''cuda:{gpu_id}'''						)

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Dict=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.'''						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.device(f'''cuda:{gpu_id}'''						)

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

       __SCREAMING_SNAKE_CASE :			Dict												=       None
       for cpu_offloaded_model in [self.unet, self.movq]:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       cpu_offload_with_hook(_lowerCamelCase						,							_lowerCamelCase						,							prev_module_hook=_lowerCamelCase						)

       # We'll offload the last model manually.
       __SCREAMING_SNAKE_CASE :			Tuple												=       hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       if not hasattr(self.unet						,							'''_hf_hook'''						):
        return self.device
       for module in self.unet.modules():
        if (
            hasattr(_lowerCamelCase						,							'''_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(_lowerCamelCase						)
      def __call__(			self   :Dict						,							_lowerCamelCase   :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase   :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,							_lowerCamelCase   :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase   :torch.FloatTensor						,							_lowerCamelCase   :int = 5_1_2						,							_lowerCamelCase   :int = 5_1_2						,							_lowerCamelCase   :int = 1_0_0						,							_lowerCamelCase   :float = 4.0						,							_lowerCamelCase   :float = 0.3						,							_lowerCamelCase   :int = 1						,							_lowerCamelCase   :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							_lowerCamelCase   :Optional[str] = "pil"						,							_lowerCamelCase   :bool = True						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       self._execution_device

       __SCREAMING_SNAKE_CASE :			Dict												=       guidance_scale > 1.0

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			int												=       torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			Tuple												=       torch.cat(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       image_embeds.shape[0]

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE :			Tuple												=       image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       negative_image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       hint.repeat_interleave(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE :			Dict												=       torch.cat([negative_image_embeds, image_embeds]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.cat([hint, hint]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)

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

       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.cat([prepare_image(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						) for i in image]						,							dim=0						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       image.to(dtype=image_embeds.dtype						,							device=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       self.movq.encode(_lowerCamelCase						)['''latents''']
       __SCREAMING_SNAKE_CASE :			str												=       latents.repeat_interleave(_lowerCamelCase						,							dim=0						)
       self.scheduler.set_timesteps(_lowerCamelCase						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       self.get_timesteps(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       downscale_height_and_width(_lowerCamelCase						,							_lowerCamelCase						,							self.movq_scale_factor						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.prepare_latents(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							image_embeds.dtype						,							_lowerCamelCase						,							_lowerCamelCase						)
       for i, t in enumerate(self.progress_bar(_lowerCamelCase						)						):
        # expand the latents if we are doing classifier free guidance
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       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 :			Union[str, Any]												=       self.unet(
            sample=_lowerCamelCase						,							timestep=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							added_cond_kwargs=_lowerCamelCase						,							return_dict=_lowerCamelCase						,							)[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 :			List[Any]												=       noise_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       variance_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE :			List[Any]												=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         __SCREAMING_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"]
        ):
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       noise_pred.split(latents.shape[1]						,							dim=1						)

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

        __SCREAMING_SNAKE_CASE :			List[str]												=       self.scheduler.step(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							generator=_lowerCamelCase						,							)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.movq.decode(_lowerCamelCase						,							force_not_quantize=_lowerCamelCase						)['''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 :			int												=       image * 0.5 + 0.5
        __SCREAMING_SNAKE_CASE :			int												=       image.clamp(0						,							1						)
        __SCREAMING_SNAKE_CASE :			List[Any]												=       image.cpu().permute(0						,							2						,							3						,							1						).float().numpy()

       if output_type == "pil":
        __SCREAMING_SNAKE_CASE :			List[str]												=       self.numpy_to_pil(_lowerCamelCase						)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase						)









"""simple docstring"""
class 		a_      :







      '''simple docstring'''

      class 		a_      :







            '''simple docstring'''

            lowerCamelCase__       :				List[Any] =	'_prev', '_data', '_next'




            def __init__(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

             '''simple docstring'''



             lowerCamelCase__     :			Optional[int]     				=						link_p
             lowerCamelCase__     :			Optional[Any]     				=						element
             lowerCamelCase__     :			List[Any]     				=						link_n





            def 			a__							(self					):

             '''simple docstring'''



             return (
                 f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
             )




      def __init__(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			Dict     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			List[str]     				=						self._trailer
       lowerCamelCase__     :			Union[str, Any]     				=						self._header
       lowerCamelCase__     :			Any     				=						0




      def __len__(self					):

       '''simple docstring'''



       return self._size




      def 			a__							(self					):

       '''simple docstring'''



       return self.__len__() == 0




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			Optional[int]     				=						new_node
       lowerCamelCase__     :			List[Any]     				=						new_node
       self._size += 1
       return self





      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						node._prev
       lowerCamelCase__     :			List[str]     				=						node._next

       lowerCamelCase__     :			Tuple     				=						successor
       lowerCamelCase__     :			Tuple     				=						predecessor
       self._size -= 1
       lowerCamelCase__     :			int     				=						node._data
       lowerCamelCase__     :			Dict     				=						None
       del node
       return temp



class 		a_      (	_DoublyLinkedBase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise Exception('List is empty'					)
       return self._header._next._data




      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise Exception('List is empty'					)
       return self._trailer._prev._data




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       return self._insert(self._header,     lowerCamelCase_,     self._header._next					)




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       return self._insert(self._trailer._prev,     lowerCamelCase_,     self._trailer					)




      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise IndexError('remove_first from empty list'					)
       return self._delete(self._header._next					)





      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise IndexError('remove_first from empty list'					)
       return self._delete(self._trailer._prev					)

