
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,
)


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

UpperCAmelCase_       :   Dict   =       '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''







def 		A_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Optional[int]=8							):



      """simple docstring"""




      _lowerCamelCase				:   Tuple								=   height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _lowerCamelCase				:   Optional[Any]								=   width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor



class  UpperCAmelCase__						(       UpperCAmelCase__ ):



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

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



    def   lowerCamelCase_  (	self      :       str,__A      :       Optional[Any],__A      :       Optional[Any],__A      :       List[str],__A      :       Optional[Any],__A      :       Tuple,__A      :       Dict     ):






          if latents is None:
                _lowerCamelCase				:   Union[str, 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}'     )
                _lowerCamelCase				:   List[Any]								=   latents.to(__lowerCAmelCase     )

          _lowerCamelCase				:   List[Any]								=   latents * scheduler.init_noise_sigma
          return latents



    def   lowerCamelCase_  (	self      :       Any,__A      :       Any=0     ):






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

          _lowerCamelCase				:   Dict								=   torch.device(f'cuda:{gpu_id}'     )

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



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Any=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."     )

          _lowerCamelCase				:   List[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)

          _lowerCamelCase				:   Optional[int]								=   None
          for cpu_offloaded_model in [self.unet, self.movq]:
                _lowerCamelCase						,		_lowerCamelCase				:   str								=   cpu_offload_with_hook(__lowerCAmelCase,__lowerCAmelCase,prev_module_hook=__lowerCAmelCase     )

          # We'll offload the last model manually.
          _lowerCamelCase				:   Optional[Any]								=   hook



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def   lowerCamelCase_  (	self      :       List[Any]     ):






          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      :       List[str],__A      :       Union[torch.FloatTensor, List[torch.FloatTensor]],__A      :       Union[torch.FloatTensor, List[torch.FloatTensor]],__A      :       int = 5_1_2,__A      :       int = 5_1_2,__A      :       int = 1_0_0,__A      :       float = 4.0,__A      :       int = 1,__A      :       Optional[Union[torch.Generator, List[torch.Generator]]] = None,__A      :       Optional[torch.FloatTensor] = None,__A      :       Optional[str] = "pil",__A      :       bool = True,):
          _lowerCamelCase				:   Dict								=   self._execution_device

          _lowerCamelCase				:   List[Any]								=   guidance_scale > 1.0

          if isinstance(__lowerCAmelCase,__lowerCAmelCase     ):
                _lowerCamelCase				:   Optional[int]								=   torch.cat(__lowerCAmelCase,dim=0     )
          _lowerCamelCase				:   int								=   image_embeds.shape[0] * num_images_per_prompt
          if isinstance(__lowerCAmelCase,__lowerCAmelCase     ):
                _lowerCamelCase				:   List[Any]								=   torch.cat(__lowerCAmelCase,dim=0     )

          if do_classifier_free_guidance:
                _lowerCamelCase				:   List[str]								=   image_embeds.repeat_interleave(__lowerCAmelCase,dim=0     )
                _lowerCamelCase				:   Any								=   negative_image_embeds.repeat_interleave(__lowerCAmelCase,dim=0     )

          _lowerCamelCase				:   Optional[Any]								=   torch.cat([negative_image_embeds, image_embeds],dim=0     ).to(dtype=self.unet.dtype,device=__lowerCAmelCase     )

          self.scheduler.set_timesteps(__lowerCAmelCase,device=__lowerCAmelCase     )
          _lowerCamelCase				:   List[str]								=   self.scheduler.timesteps

          _lowerCamelCase				:   Dict								=   self.unet.config.in_channels

          _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   downscale_height_and_width(__lowerCAmelCase,__lowerCAmelCase,self.movq_scale_factor     )

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

                _lowerCamelCase				:   Optional[int]								=   {"image_embeds": image_embeds}
                _lowerCamelCase				:   int								=   self.unet(
                    sample=__lowerCAmelCase,timestep=__lowerCAmelCase,encoder_hidden_states=__lowerCAmelCase,added_cond_kwargs=__lowerCAmelCase,return_dict=__lowerCAmelCase,)[0]

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

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

          if output_type == "pil":
                _lowerCamelCase				:   Optional[Any]								=   self.numpy_to_pil(__lowerCAmelCase     )

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=__lowerCAmelCase     )

'''simple docstring'''






from __future__ import annotations







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if (direction == 1 and array[indexa] > array[indexa]) or (
          direction == 0 and array[indexa] < array[indexa]
      ):
            _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   array[indexa], array[indexa]







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[int]								=   int(length / 2							)
            for i in range(_lowerCAmelCase				,	low + middle							):
                  comp_and_swap(_lowerCAmelCase				,	_lowerCAmelCase				,	i + middle				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[Any]								=   int(length / 2							)
            bitonic_sort(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	1							)
            bitonic_sort(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	0							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase_       :   Optional[int]   =       [int(item.strip()) for item in user_input.split(',')]

 bitonic_sort(unsorted, 0, len(unsorted), 1)
 print('\nSorted array in ascending order is: ', end='')
 print(*unsorted, sep=', ')

 bitonic_merge(unsorted, 0, len(unsorted), 0)
 print('Sorted array in descending order is: ', end='')
 print(*unsorted, sep=', ')