

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,
)


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

_a			=       '''
    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 _a			(	SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Tuple=8		)   ->   Optional[int]:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Any=5_12   , SCREAMING_SNAKE_CASE		:      Optional[Any]=5_12		)   ->   List[Any]:

 """simple docstring"""






 __lowerCAmelCase:       Tuple            =				pil_image.resize((w, h)   , resample=Image.BICUBIC   , reducing_gap=1		)
 __lowerCAmelCase:       List[str]            =				np.array(pil_image.convert('RGB'		)		)
 __lowerCAmelCase:       Union[str, Any]            =				arr.astype(np.floataa		) / 1_2_7.5 - 1
 __lowerCAmelCase:       Optional[Any]            =				np.transpose(SCREAMING_SNAKE_CASE   , [2, 0, 1]		)
 __lowerCAmelCase:       Dict            =				torch.from_numpy(SCREAMING_SNAKE_CASE		).unsqueeze(0		)
 return image






class 							A_			(		snake_case__ ):


     def __init__(							self      :		Any      ,  UpperCAmelCase      :		UNetaDConditionModel      ,  UpperCAmelCase      :		DDPMScheduler      ,  UpperCAmelCase      :		VQModel      ,  )							->     Optional[Any]:
      super().__init__()

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


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Union[str, Any] )							->     str:
      # get the original timestep using init_timestep
      __lowerCAmelCase:       Any            =				min(int(num_inference_steps * strength )      ,  UpperCAmelCase )

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

      return timesteps, num_inference_steps - t_start


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Optional[Any]=None )							->     str:
      if not isinstance(UpperCAmelCase      ,  (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(UpperCAmelCase )}''' )

      __lowerCAmelCase:       Any            =				image.to(device=UpperCAmelCase      ,  dtype=UpperCAmelCase )

      __lowerCAmelCase:       List[str]            =				batch_size * num_images_per_prompt

      if image.shape[1] == 4:
       __lowerCAmelCase:       List[Any]            =				image

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

       elif isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
        __lowerCAmelCase:       int            =				[
            self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(UpperCAmelCase )
        ]
        __lowerCAmelCase:       Optional[Any]            =				torch.cat(UpperCAmelCase      ,  dim=0 )
       else:
        __lowerCAmelCase:       Optional[int]            =				self.movq.encode(UpperCAmelCase ).latent_dist.sample(UpperCAmelCase )

       __lowerCAmelCase:       Optional[Any]            =				self.movq.config.scaling_factor * init_latents

      __lowerCAmelCase:       Tuple            =				torch.cat([init_latents]      ,  dim=0 )

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

      # get latents
      __lowerCAmelCase:       Any            =				self.scheduler.add_noise(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )

      __lowerCAmelCase:       Optional[int]            =				init_latents

      return latents


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

      __lowerCAmelCase:       Optional[Any]            =				torch.device(F'''cuda:{gpu_id}''' )

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


     def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		Optional[Any]=0 )							->     List[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:       Dict            =				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)

      __lowerCAmelCase:       List[str]            =				None
      for cpu_offloaded_model in [self.unet, self.movq]:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Tuple            =				cpu_offload_with_hook(UpperCAmelCase      ,  UpperCAmelCase      ,  prev_module_hook=UpperCAmelCase )

      # We'll offload the last model manually.
      __lowerCAmelCase:       List[str]            =				hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
      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      :		Tuple      ,  UpperCAmelCase      :		Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  UpperCAmelCase      :		torch.FloatTensor      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 1_0_0      ,  UpperCAmelCase      :		float = 4.0      ,  UpperCAmelCase      :		float = 0.3      ,  UpperCAmelCase      :		int = 1      ,  UpperCAmelCase      :		Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  )							->     List[str]:
      __lowerCAmelCase:       Union[str, Any]            =				self._execution_device

      __lowerCAmelCase:       List[Any]            =				guidance_scale > 1.0

      if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       __lowerCAmelCase:       Tuple            =				torch.cat(UpperCAmelCase      ,  dim=0 )
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       __lowerCAmelCase:       Tuple            =				torch.cat(UpperCAmelCase      ,  dim=0 )
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       __lowerCAmelCase:       Optional[int]            =				torch.cat(UpperCAmelCase      ,  dim=0 )

      __lowerCAmelCase:       Union[str, Any]            =				image_embeds.shape[0]

      if do_classifier_free_guidance:
       __lowerCAmelCase:       Dict            =				image_embeds.repeat_interleave(UpperCAmelCase      ,  dim=0 )
       __lowerCAmelCase:       Optional[Any]            =				negative_image_embeds.repeat_interleave(UpperCAmelCase      ,  dim=0 )
       __lowerCAmelCase:       Tuple            =				hint.repeat_interleave(UpperCAmelCase      ,  dim=0 )

      __lowerCAmelCase:       Optional[int]            =				torch.cat([negative_image_embeds, image_embeds]      ,  dim=0 ).to(dtype=self.unet.dtype      ,  device=UpperCAmelCase )
      __lowerCAmelCase:       Union[str, Any]            =				torch.cat([hint, hint]      ,  dim=0 ).to(dtype=self.unet.dtype      ,  device=UpperCAmelCase )

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

      __lowerCAmelCase:       str            =				torch.cat([prepare_image(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase ) for i in image]      ,  dim=0 )
      __lowerCAmelCase:       Tuple            =				image.to(dtype=image_embeds.dtype      ,  device=UpperCAmelCase )

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

       __lowerCAmelCase:       List[str]            =				{'image_embeds': image_embeds, 'hint': hint}
       __lowerCAmelCase:       List[str]            =				self.unet(
           sample=UpperCAmelCase      ,  timestep=UpperCAmelCase      ,  encoder_hidden_states=UpperCAmelCase      ,  added_cond_kwargs=UpperCAmelCase      ,  return_dict=UpperCAmelCase      ,  )[0]

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

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

       __lowerCAmelCase:       Any            =				self.scheduler.step(
           UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  generator=UpperCAmelCase      ,  )[0]

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

      if output_type == "pil":
       __lowerCAmelCase:       Tuple            =				self.numpy_to_pil(UpperCAmelCase )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase )








from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any






class 							A_			:
     _lowercase      :					Any       =     ('_front', '_back', '_len')






     @dataclass
     class 							A_			:
          _lowercase      :					Any       =     None
          _lowercase      :					Deque._Node | None       =     None
          _lowercase      :					Deque._Node | None       =     None







     class 							A_			:
          _lowercase      :					Optional[Any]       =     ('_cur',)


          def __init__(							self      :		Any      ,  UpperCAmelCase      :		Deque._Node | None )							->     None:
           __lowerCAmelCase:       int            =				cur


          def __iter__(							self      :		int )							->     Deque._Iterator:
           return self


          def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Any:
           if self._cur is None:
            # finished iterating
            raise StopIteration
           __lowerCAmelCase:       Optional[Any]            =				self._cur.val
           __lowerCAmelCase:       List[Any]            =				self._cur.next_node

           return val







     def __init__(							self      :		Any      ,  UpperCAmelCase      :		Iterable[Any] | None = None )							->     None:
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       int            =				0

      if iterable is not None:
       # append every value to the deque
       for val in iterable:
        self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       Union[str, Any]            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       int            =				node
       __lowerCAmelCase:       List[Any]            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Optional[Any]            =				node
       __lowerCAmelCase:       str            =				self._back
       __lowerCAmelCase:       Optional[int]            =				node  # assign new back to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       int            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       Union[str, Any]            =				node
       __lowerCAmelCase:       str            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Dict            =				self._front
       __lowerCAmelCase:       Optional[int]            =				node
       __lowerCAmelCase:       Any            =				node  # assign new front to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.appendleft(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[int]            =				self._back
      __lowerCAmelCase:       Optional[Any]            =				self._back.prev_node  # set new back
      # drop the last node - python will deallocate memory automatically
      __lowerCAmelCase:       Optional[int]            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[Any]            =				self._front
      __lowerCAmelCase:       Optional[Any]            =				self._front.next_node  # set new front and drop the first node
      __lowerCAmelCase:       int            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     bool:
      return self._front is None


     def __len__(							self      :		List[Any] )							->     int:
      return self._len


     def __eq__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		object )							->     bool:

      if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       return NotImplemented

      __lowerCAmelCase:       List[Any]            =				self._front
      __lowerCAmelCase:       Dict            =				other._front

      # if the length of the dequeues are not the same, they are not equal
      if len(self ) != len(UpperCAmelCase ):
       return False

      while me is not None and oth is not None:
       # compare every value
       if me.val != oth.val:
        return False
       __lowerCAmelCase:       Optional[Any]            =				me.next_node
       __lowerCAmelCase:       Optional[Any]            =				oth.next_node

      return True


     def __iter__(							self      :		Tuple )							->     Deque._Iterator:
      return Deque._Iterator(self._front )


     def __repr__(							self      :		Optional[Any] )							->     str:
      __lowerCAmelCase:       Union[str, Any]            =				[]
      __lowerCAmelCase:       Optional[int]            =				self._front
      while aux is not None:
       # append the values in a list to display
       values_list.append(aux.val )
       __lowerCAmelCase:       Any            =				aux.next_node

      return F'''[{', '.join(repr(UpperCAmelCase ) for val in values_list )}]'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()





