



"""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,
)


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

snake_case_    					=	"""
    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 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=8						):
						UpperCAmelCase					=			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCAmelCase					=			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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

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






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:List[str]       ,  lowercase_			:Dict       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:int       ,  lowercase_			:Dict )    ->      Any:
												if latents is None:
																		UpperCAmelCase					=			randn_tensor(lowercase_       ,  generator=lowercase_       ,  device=lowercase_       ,  dtype=lowercase_ )
												else:
																		if latents.shape != shape:
																								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
																		UpperCAmelCase					=			latents.to(lowercase_ )

												UpperCAmelCase					=			latents * scheduler.init_noise_sigma
												return latents






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

												UpperCAmelCase					=			torch.device(f"""cuda:{gpu_id}""" )

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






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:List[Any]=0 )    ->      Tuple:
												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.' )

												UpperCAmelCase					=			torch.device(f"""cuda:{gpu_id}""" )

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

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

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






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      UpperCAmelCase__					( self			:int )    ->      List[Any]:
												if not hasattr(self.unet       ,  '_hf_hook' ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(lowercase_       ,  '_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(lowercase_ )
						def __call__( self			:List[str]       ,  lowercase_			:Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  lowercase_			:Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  lowercase_			:torch.FloatTensor       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 1_00       ,  lowercase_			:float = 4.0       ,  lowercase_			:int = 1       ,  lowercase_			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  )    ->      List[str]:
												UpperCAmelCase					=			self._execution_device

												UpperCAmelCase					=			guidance_scale > 1.0

												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )
												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )
												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )

												UpperCAmelCase					=			image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																		UpperCAmelCase					=			image_embeds.repeat_interleave(lowercase_       ,  dim=0 )
																		UpperCAmelCase					=			negative_image_embeds.repeat_interleave(lowercase_       ,  dim=0 )
																		UpperCAmelCase					=			hint.repeat_interleave(lowercase_       ,  dim=0 )

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

												UpperCAmelCase					=			self.movq.config.latent_channels

												UpperCAmelCase				,   UpperCAmelCase					=			downscale_height_and_width(lowercase_       ,  lowercase_       ,  self.movq_scale_factor )

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

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

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

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

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

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_ )










"""simple docstring"""


def 			_lowerCAmelCase				(       ):
						for n in range(1		,     1000000						):
												yield n * (n + 1) // 2

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			1
						UpperCAmelCase					=			2
						while i * i <= n:
												UpperCAmelCase					=			0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count

def 			_lowerCAmelCase				(       ):
						return next(i for i in triangle_number_generator() if count_divisors(lowercase_						) > 500						)


if __name__ == "__main__":
				print(solution())





