



import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput



class       _lowercase				(  nn.Module      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=1.0						):
						"""simple docstring"""

						super().__init__()

						a   	=  cut_size
						a   	=  cut_power




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						a	,							a   	=  pixel_values.shape[2:4]
						a   	=  min(lowerCamelCase_						,			lowerCamelCase_						)
						a   	=  min(lowerCamelCase_						,			lowerCamelCase_						,			self.cut_size						)
						a   	=  []
						for _ in range(lowerCamelCase_						):
											a   	=  int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
											a   	=  torch.randint(0						,			sideX - size + 1						,			()						)
											a   	=  torch.randint(0						,			sideY - size + 1						,			()						)
											a   	=  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
											cutouts.append(F.adaptive_avg_poolad(lowerCamelCase_						,			self.cut_size						)						)
						return torch.cat(lowerCamelCase_						)






def        a( A			:       Tuple					,	A			:       Dict			)						->			List[str]:
					"""simple docstring"""





					a   	=  F.normalize(A					,	dim=-1			)
					a   	=  F.normalize(A					,	dim=-1			)
					return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)






def        a( A			:       int					,	A			:       Union[str, Any]			)						->			Tuple:
					"""simple docstring"""





					for param in model.parameters():
										a   	=  value







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__()
						self.register_modules(
						    vae=lowerCamelCase_						,			text_encoder=lowerCamelCase_						,			clip_model=lowerCamelCase_						,			tokenizer=lowerCamelCase_						,			unet=lowerCamelCase_						,			scheduler=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						,			)

						a   	=  transforms.Normalize(mean=feature_extractor.image_mean						,			std=feature_extractor.image_std						)
						a   	=  (
						    feature_extractor.size
						    if isinstance(feature_extractor.size						,			lowerCamelCase_						)
						    else feature_extractor.size["shortest_edge"]
						)
						a   	=  MakeCutouts(self.cut_out_size						)

						set_requires_grad(self.text_encoder						,			lowerCamelCase_						)
						set_requires_grad(self.clip_model						,			lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_ = "auto"						):
						"""simple docstring"""

						if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											a   	=  self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self.enable_attention_slicing(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						set_requires_grad(self.vae						,			lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						set_requires_grad(self.vae						,			lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						set_requires_grad(self.unet						,			lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						set_requires_grad(self.unet						,			lowerCamelCase_						)




	@torch.enable_grad()
	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_=True						,			):
						"""simple docstring"""

						a   	=  latents.detach().requires_grad_()

						a   	=  self.scheduler.scale_model_input(lowerCamelCase_						,			lowerCamelCase_						)

						# predict the noise residual
						a   	=  self.unet(lowerCamelCase_						,			lowerCamelCase_						,			encoder_hidden_states=lowerCamelCase_						).sample

						if isinstance(self.scheduler						,			(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
											a   	=  self.scheduler.alphas_cumprod[timestep]
											a   	=  1 - alpha_prod_t
											# compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											a   	=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

											a   	=  torch.sqrt(lowerCamelCase_						)
											a   	=  pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler						,			lowerCamelCase_						):
											a   	=  self.scheduler.sigmas[index]
											a   	=  latents - sigma * noise_pred
						else:
											raise ValueError(F'''scheduler type {type(self.scheduler						)} not supported'''						)

						a   	=  1 / self.vae.config.scaling_factor * sample
						a   	=  self.vae.decode(lowerCamelCase_						).sample
						a   	=  (image / 2 + 0.5).clamp(0						,			1						)

						if use_cutouts:
											a   	=  self.make_cutouts(lowerCamelCase_						,			lowerCamelCase_						)
						else:
											a   	=  transforms.Resize(self.cut_out_size						)(lowerCamelCase_						)
						a   	=  self.normalize(lowerCamelCase_						).to(latents.dtype						)

						a   	=  self.clip_model.get_image_features(lowerCamelCase_						)
						a   	=  image_embeddings_clip / image_embeddings_clip.norm(p=2						,			dim=-1						,			keepdim=lowerCamelCase_						)

						if use_cutouts:
											a   	=  spherical_dist_loss(lowerCamelCase_						,			lowerCamelCase_						)
											a   	=  dists.view([num_cutouts, sample.shape[0], -1]						)
											a   	=  dists.sum(2						).mean(0						).sum() * clip_guidance_scale
						else:
											a   	=  spherical_dist_loss(lowerCamelCase_						,			lowerCamelCase_						).mean() * clip_guidance_scale

						a   	=  -torch.autograd.grad(lowerCamelCase_						,			lowerCamelCase_						)[0]

						if isinstance(self.scheduler						,			lowerCamelCase_						):
											a   	=  latents.detach() + grads * (sigma**2)
											a   	=  noise_pred_original
						else:
											a   	=  noise_pred_original - torch.sqrt(lowerCamelCase_						) * grads
						return noise_pred, latents




	@torch.no_grad()
	def __call__(self						,			lowerCamelCase_						,			lowerCamelCase_ = 512						,			lowerCamelCase_ = 512						,			lowerCamelCase_ = 50						,			lowerCamelCase_ = 7.5						,			lowerCamelCase_ = 1						,			lowerCamelCase_ = 0.0						,			lowerCamelCase_ = 100						,			lowerCamelCase_ = None						,			lowerCamelCase_ = 4						,			lowerCamelCase_ = True						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = "pil"						,			lowerCamelCase_ = True						,			):
						"""simple docstring"""

						if isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  1
						elif isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  len(lowerCamelCase_						)
						else:
											raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_						)}'''						)

						if height % 8 != 0 or width % 8 != 0:
											raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''						)

						# get prompt text embeddings
						a   	=  self.tokenizer(
						    lowerCamelCase_						,			padding="max_length"						,			max_length=self.tokenizer.model_max_length						,			truncation=lowerCamelCase_						,			return_tensors="pt"						,			)
						a   	=  self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
						# duplicate text embeddings for each generation per prompt
						a   	=  text_embeddings.repeat_interleave(lowerCamelCase_						,			dim=0						)

						if clip_guidance_scale > 0:
											if clip_prompt is not None:
																a   	=  self.tokenizer(
																    lowerCamelCase_						,			padding="max_length"						,			max_length=self.tokenizer.model_max_length						,			truncation=lowerCamelCase_						,			return_tensors="pt"						,			).input_ids.to(self.device						)
											else:
																a   	=  text_input.input_ids.to(self.device						)
											a   	=  self.clip_model.get_text_features(lowerCamelCase_						)
											a   	=  text_embeddings_clip / text_embeddings_clip.norm(p=2						,			dim=-1						,			keepdim=lowerCamelCase_						)
											# duplicate text embeddings clip for each generation per prompt
											a   	=  text_embeddings_clip.repeat_interleave(lowerCamelCase_						,			dim=0						)

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						a   	=  guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
											a   	=  text_input.input_ids.shape[-1]
											a   	=  self.tokenizer([""]						,			padding="max_length"						,			max_length=lowerCamelCase_						,			return_tensors="pt"						)
											a   	=  self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
											# duplicate unconditional embeddings for each generation per prompt
											a   	=  uncond_embeddings.repeat_interleave(lowerCamelCase_						,			dim=0						)

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											a   	=  torch.cat([uncond_embeddings, text_embeddings]						)

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						a   	=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						a   	=  text_embeddings.dtype
						if latents is None:
											if self.device.type == "mps":
																# randn does not work reproducibly on mps
																a   	=  torch.randn(lowerCamelCase_						,			generator=lowerCamelCase_						,			device="cpu"						,			dtype=lowerCamelCase_						).to(
																    self.device						)
											else:
																a   	=  torch.randn(lowerCamelCase_						,			generator=lowerCamelCase_						,			device=self.device						,			dtype=lowerCamelCase_						)
						else:
											if latents.shape != latents_shape:
																raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''						)
											a   	=  latents.to(self.device						)

						# set timesteps
						a   	=  "offset" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
						a   	=  {}
						if accepts_offset:
											a   	=  1

						self.scheduler.set_timesteps(lowerCamelCase_						,			**lowerCamelCase_						)

						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						a   	=  self.scheduler.timesteps.to(self.device						)

						# scale the initial noise by the standard deviation required by the scheduler
						a   	=  latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						a   	=  "eta" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
						a   	=  {}
						if accepts_eta:
											a   	=  eta

						# check if the scheduler accepts generator
						a   	=  "generator" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
						if accepts_generator:
											a   	=  generator

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

											# predict the noise residual
											a   	=  self.unet(lowerCamelCase_						,			lowerCamelCase_						,			encoder_hidden_states=lowerCamelCase_						).sample

											# perform classifier free guidance
											if do_classifier_free_guidance:
																a	,							a   	=  noise_pred.chunk(2						)
																a   	=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											# perform clip guidance
											if clip_guidance_scale > 0:
																a   	=  (
																    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
																)
																a	,							a   	=  self.cond_fn(
																    lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			)

											# compute the previous noisy sample x_t -> x_t-1
											a   	=  self.scheduler.step(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						).prev_sample

						# scale and decode the image latents with vae
						a   	=  1 / self.vae.config.scaling_factor * latents
						a   	=  self.vae.decode(lowerCamelCase_						).sample

						a   	=  (image / 2 + 0.5).clamp(0						,			1						)
						a   	=  image.cpu().permute(0						,			2						,			3						,			1						).numpy()

						if output_type == "pil":
											a   	=  self.numpy_to_pil(lowerCamelCase_						)

						if not return_dict:
											return (image, None)

						return StableDiffusionPipelineOutput(images=lowerCamelCase_						,			nsfw_content_detected=lowerCamelCase_						)





_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
