


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,
)


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

SCREAMING_SNAKE_CASE       :      Optional[Any]												= "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=8 )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _lowercase       :							Any      		=		width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   Dict:




           """simple docstring"""

           super().__init__()

           self.register_modules(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      movq=lowerCamelCase,      )
           _lowercase       :							List[str]      		=		2 ** (len(self.movq.config.block_out_channels) - 1)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           if latents is None:
               _lowercase       :							Optional[Any]      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)
           else:
               if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
               _lowercase       :							int      		=		latents.to(lowerCamelCase)

           _lowercase       :							int      		=		latents * scheduler.init_noise_sigma
           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Optional[int]:




           """simple docstring"""

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

           _lowercase       :							Tuple      		=		torch.device(F'''cuda:{gpu_id}''')

           _lowercase       :							Optional[Any]      		=		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   int:




           """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.')

           _lowercase       :							Union[str, Any]      		=		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)

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

           # We'll offload the last model manually.
           _lowercase       :							Dict      		=		hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           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,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 1_00,      lowerCamelCase = 4.0,      lowerCamelCase = 1,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		self._execution_device

           _lowercase       :							Any      		=		guidance_scale > 1.0

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           _lowercase       :							Optional[int]      		=		image_embeds.shape[0] * num_images_per_prompt
           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Optional[int]      		=		torch.cat(lowerCamelCase,      dim=0)

           if do_classifier_free_guidance:
               _lowercase       :							str      		=		image_embeds.repeat_interleave(lowerCamelCase,      dim=0)
               _lowercase       :							int      		=		negative_image_embeds.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		torch.cat([negative_image_embeds, image_embeds],      dim=0).to(dtype=self.unet.dtype,      device=lowerCamelCase)

           self.scheduler.set_timesteps(lowerCamelCase,      device=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.scheduler.timesteps

           _lowercase       :							List[str]      		=		self.unet.config.in_channels

           _lowercase		,				_lowercase       :							Union[str, Any]      		=		downscale_height_and_width(lowerCamelCase,      lowerCamelCase,      self.movq_scale_factor)

           # create initial latent
           _lowercase       :							str      		=		self.prepare_latents(
               (batch_size, num_channels_latents, height, width),      image_embeds.dtype,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      self.scheduler,      )

           for i, t in enumerate(self.progress_bar(lowerCamelCase)):
               # expand the latents if we are doing classifier free guidance
               _lowercase       :							List[Any]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents

               _lowercase       :							Tuple      		=		{'image_embeds': image_embeds}
               _lowercase       :							Optional[Any]      		=		self.unet(
                   sample=lowerCamelCase,      timestep=lowerCamelCase,      encoder_hidden_states=lowerCamelCase,      added_cond_kwargs=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

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

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

           if output_type == "pil":
               _lowercase       :							Optional[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)



"""simple docstring"""
class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	data
        a__:     List[Any]										=	previous
        a__:     Any										=	next_node



      def __str__(						self)	->			str:





        '''simple docstring'''





        return f'{self.data}'



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.data



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return self.next



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        return self.previous







class    __snake_case       :



      def __init__(						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	head



      def __iter__(						self)	->			List[Any]:





        '''simple docstring'''





        return self



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if not self.current:
          raise StopIteration
        else:
          a__:     Dict										=	self.current.get_data()
          a__:     Optional[Any]										=	self.current.get_next()
          return value







class    __snake_case       :



      def __init__(						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	None  # First node in list
        a__:     Optional[int]										=	None  # Last node in list



      def __str__(						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Dict										=	self.head
        a__:     Optional[Any]										=	[]
        while current is not None:
          nodes.append(current.get_data())
          a__:     str										=	current.get_next()
        return " ".join(str(lowercase) for node in nodes)



      def __contains__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.head
        while current:
          if current.get_data() == value:
            return True
          a__:     Dict										=	current.get_next()
        return False



      def __iter__(						self)	->			int:





        '''simple docstring'''





        return LinkedListIterator(self.head)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if self.head:
          return self.head.get_data()
        return None



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if self.tail:
          return self.tail.get_data()
        return None



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          a__:     Optional[Any]										=	node
          a__:     Optional[Any]										=	node
        else:
          self.insert_before_node(self.head    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          self.set_head(lowercase)
        else:
          self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Tuple										=	Node(lowercase)
        if self.head is None:
          self.set_head(lowercase)
        else:
          self.set_tail(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Union[str, Any]										=	node
        a__:     Optional[Any]										=	node.previous

        if node.get_previous() is None:
          a__:     Tuple										=	node_to_insert
        else:
          a__:     int										=	node_to_insert

        a__:     Optional[int]										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Optional[int]										=	node
        a__:     Tuple										=	node.next

        if node.get_next() is None:
          a__:     Optional[int]										=	node_to_insert
        else:
          a__:     Any										=	node_to_insert

        a__:     str										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Any										=	1
        a__:     Tuple										=	Node(lowercase)
        a__:     Tuple										=	self.head
        while node:
          if current_position == position:
            self.insert_before_node(lowercase    ,			lowercase)
            return
          current_position += 1
          a__:     List[Any]										=	node.next
        self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Node:





        '''simple docstring'''





        a__:     Tuple										=	self.head
        while node:
          if node.get_data() == item:
            return node
          a__:     List[str]										=	node.get_next()
        raise Exception('Node not found')



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        if (node := self.get_node(lowercase)) is not None:
          if node == self.head:
            a__:     Any										=	self.head.get_next()

          if node == self.tail:
            a__:     List[Any]										=	self.tail.get_previous()

          self.remove_node_pointers(lowercase)



      @staticmethod
      def   lowerCamelCase_   (						lowercase)	->			None:





        '''simple docstring'''





        if node.get_next():
          a__:     Any										=	node.previous

        if node.get_previous():
          a__:     List[str]										=	node.next

        a__:     int										=	None
        a__:     Union[str, Any]										=	None



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self.head is None







def 						__a   (	)	->None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()


