




"""simple docstring"""

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,
)


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

a					=			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def 						_snake_case     (					_snake_case							:					Optional[Any]				,  _snake_case							:					Tuple				,  _snake_case							:					int=8							)    ->					Any:






      '''simple docstring'''


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







class 				lowercase_       (     A__							):



 '''simple docstring'''

 def __init__( self       :       int    ,							_UpperCAmelCase       :       UNetaDConditionModel    ,							_UpperCAmelCase       :       DDPMScheduler    ,							_UpperCAmelCase       :       VQModel    ,							):
       super().__init__()

       self.register_modules(
           unet=_UpperCAmelCase    ,							scheduler=_UpperCAmelCase    ,							movq=_UpperCAmelCase    ,							)
       _A					   =   2 ** (len(self.movq.config.block_out_channels		) - 1)

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Tuple		):






       if latents is None:
             _A					   =   randn_tensor(_UpperCAmelCase    ,							generator=_UpperCAmelCase    ,							device=_UpperCAmelCase    ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A					   =   latents.to(_UpperCAmelCase		)

       _A					   =   latents * scheduler.init_noise_sigma
       return latents

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Optional[Any]=0		):






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

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

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

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       Union[str, 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.'		)

       _A					   =   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)

       _A					   =   None
       for cpu_offloaded_model in [self.unet, self.movq]:
             _A					   =   cpu_offload_with_hook(_UpperCAmelCase    ,							_UpperCAmelCase    ,							prev_module_hook=_UpperCAmelCase		)

       # We'll offload the last model manually.
       _A					   =   hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 			lowerCAmelCase_							( self       :       Any		):






       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       :       Any    ,							_UpperCAmelCase       :       Union[torch.FloatTensor, List[torch.FloatTensor]]    ,							_UpperCAmelCase       :       Union[torch.FloatTensor, List[torch.FloatTensor]]    ,							_UpperCAmelCase       :       torch.FloatTensor    ,							_UpperCAmelCase       :       int = 512    ,							_UpperCAmelCase       :       int = 512    ,							_UpperCAmelCase       :       int = 100    ,							_UpperCAmelCase       :       float = 4.0    ,							_UpperCAmelCase       :       int = 1    ,							_UpperCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_UpperCAmelCase       :       Optional[torch.FloatTensor] = None    ,							_UpperCAmelCase       :       Optional[str] = "pil"    ,							_UpperCAmelCase       :       bool = True    ,							):
       _A					   =   self._execution_device

       _A					   =   guidance_scale > 1.0

       if isinstance(_UpperCAmelCase    ,							_UpperCAmelCase		):
             _A					   =   torch.cat(_UpperCAmelCase    ,							dim=0		)
       if isinstance(_UpperCAmelCase    ,							_UpperCAmelCase		):
             _A					   =   torch.cat(_UpperCAmelCase    ,							dim=0		)
       if isinstance(_UpperCAmelCase    ,							_UpperCAmelCase		):
             _A					   =   torch.cat(_UpperCAmelCase    ,							dim=0		)

       _A					   =   image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
             _A					   =   image_embeds.repeat_interleave(_UpperCAmelCase    ,							dim=0		)
             _A					   =   negative_image_embeds.repeat_interleave(_UpperCAmelCase    ,							dim=0		)
             _A					   =   hint.repeat_interleave(_UpperCAmelCase    ,							dim=0		)

       _A					   =   torch.cat([negative_image_embeds, image_embeds]    ,							dim=0		).to(dtype=self.unet.dtype    ,							device=_UpperCAmelCase		)
       _A					   =   torch.cat([hint, hint]    ,							dim=0		).to(dtype=self.unet.dtype    ,							device=_UpperCAmelCase		)
       self.scheduler.set_timesteps(_UpperCAmelCase    ,							device=_UpperCAmelCase		)
       _A					   =   self.scheduler.timesteps

       _A					   =   self.movq.config.latent_channels

       _A					   =   downscale_height_and_width(_UpperCAmelCase    ,							_UpperCAmelCase    ,							self.movq_scale_factor		)

       # create initial latent
       _A					   =   self.prepare_latents(
           (batch_size, num_channels_latents, height, width)    ,							image_embeds.dtype    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							self.scheduler    ,							)

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

             _A					   =   {"image_embeds": image_embeds, "hint": hint}
             _A					   =   self.unet(
                 sample=_UpperCAmelCase    ,							timestep=_UpperCAmelCase    ,							encoder_hidden_states=_UpperCAmelCase    ,							added_cond_kwargs=_UpperCAmelCase    ,							return_dict=_UpperCAmelCase    ,							)[0]

             if do_classifier_free_guidance:
                   _A					   =   noise_pred.split(latents.shape[1]    ,							dim=1		)
                   _A					   =   noise_pred.chunk(2		)
                   _A					   =   variance_pred.chunk(2		)
                   _A					   =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _A					   =   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"]
             ):
                   _A					   =   noise_pred.split(latents.shape[1]    ,							dim=1		)

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

       if output_type == "pil":
             _A					   =   self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)









def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()