'''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,
)


lowerCAmelCase_	:		Optional[int]			=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_	:		Optional[int]			=     """
    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							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=8					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_UpperCamelCase			:			List[Any]   			=						width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						UNetaDConditionModel       ,		lowercase__					:						DDPMScheduler       ,		lowercase__					:						VQModel       ,		)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__()

										self.register_modules(
										    unet=lowercase__       ,		scheduler=lowercase__       ,		movq=lowercase__       ,		)
										_UpperCamelCase			:			Tuple   			=						2 ** (len(self.movq.config.block_out_channels					) - 1)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						int       ,		lowercase__					:						Dict       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						str       ,		lowercase__					:						str       ,		lowercase__					:						Any					)							->Dict:


										'''simple docstring'''




										if latents is None:
																_UpperCamelCase			:			Dict   			=						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			:			int   			=						latents.to(lowercase__					)

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





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Tuple=0					)							->Tuple:


										'''simple docstring'''




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

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

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





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						int=0					)							->List[str]:


										'''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."					)

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

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





				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def  snake_case__				(						self					:						Union[str, Any]					)							->Any:


										'''simple docstring'''




										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					:						int       ,		lowercase__					:						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		lowercase__					:						Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		lowercase__					:						int = 512       ,		lowercase__					:						int = 512       ,		lowercase__					:						int = 100       ,		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[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self._execution_device

										_UpperCamelCase			:			List[Any]   			=						guidance_scale > 1.0

										if isinstance(lowercase__       ,		lowercase__					):
																_UpperCamelCase			:			Tuple   			=						torch.cat(lowercase__       ,		dim=0					)
										_UpperCamelCase			:			Tuple   			=						image_embeds.shape[0] * num_images_per_prompt
										if isinstance(lowercase__       ,		lowercase__					):
																_UpperCamelCase			:			List[Any]   			=						torch.cat(lowercase__       ,		dim=0					)

										if do_classifier_free_guidance:
																_UpperCamelCase			:			Tuple   			=						image_embeds.repeat_interleave(lowercase__       ,		dim=0					)
																_UpperCamelCase			:			Tuple   			=						negative_image_embeds.repeat_interleave(lowercase__       ,		dim=0					)

										_UpperCamelCase			:			List[Any]   			=						torch.cat([negative_image_embeds, image_embeds]       ,		dim=0					).to(dtype=self.unet.dtype       ,		device=lowercase__					)

										self.scheduler.set_timesteps(lowercase__       ,		device=lowercase__					)
										_UpperCamelCase			:			str   			=						self.scheduler.timesteps

										_UpperCamelCase			:			int   			=						self.unet.config.in_channels

										_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						downscale_height_and_width(lowercase__       ,		lowercase__       ,		self.movq_scale_factor					)

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

																_UpperCamelCase			:			Dict   			=						{"image_embeds": image_embeds}
																_UpperCamelCase			:			str   			=						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			:			Optional[Any]   			=						noise_pred.split(latents.shape[1]       ,		dim=1					)
																						_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						noise_pred.chunk(2					)
																						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						variance_pred.chunk(2					)
																						_UpperCamelCase			:			Dict   			=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						_UpperCamelCase			:			int   			=						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			:			Union[str, Any]   			=						noise_pred.split(latents.shape[1]       ,		dim=1					)

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

										if output_type == "pil":
																_UpperCamelCase			:			List[str]   			=						self.numpy_to_pil(lowercase__					)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowercase__					)



'''simple docstring'''





import requests
from bsa import BeautifulSoup
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						BeautifulSoup(requests.get(UpperCAmelCase     ,params=UpperCAmelCase					).content     ,"html.parser"					)
						_UpperCamelCase			:			Union[str, Any]   			=						soup.find("div"     ,attrs={"class": "gs_ri"}					)
						_UpperCamelCase			:			Optional[Any]   			=						div.find("div"     ,attrs={"class": "gs_fl"}					).find_all("a"					)
						return anchors[2].get_text()


if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[int]			=     {
			    """title""": (
			        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
			        """capacitance, volumetric capacitance, and energy density"""
			    ),
			    """journal""": """Chem. Mater.""",
			    """volume""": 30,
			    """pages""": """3979-3990""",
			    """year""": 2018,
			    """hl""": """en""",
			}
			print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


