
"""simple docstring"""
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    _lowerCAmelCase						(						nn.Module						):



							def __init__(      self ,						a_ ,						a_=1.0   )							->							List[str]:
								super().__init__()

								_UpperCAmelCase =						cut_size
								_UpperCAmelCase =						cut_power



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





def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






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




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






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



class    _lowerCAmelCase						(						lowerCamelCase						):



							def __init__(      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						)							->							Optional[Any]:
								super().__init__()
								self.register_modules(
								    vae=a_ ,						text_encoder=a_ ,						clip_model=a_ ,						tokenizer=a_ ,						unet=a_ ,						scheduler=a_ ,						feature_extractor=a_ ,						)

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

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



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



							def       _a   (      self   )							->							str:
								self.enable_attention_slicing(a_   )



							def       _a   (      self   )							->							Optional[int]:
								set_requires_grad(self.vae ,						a_   )



							def       _a   (      self   )							->							Tuple:
								set_requires_grad(self.vae ,						a_   )



							def       _a   (      self   )							->							int:
								set_requires_grad(self.unet ,						a_   )



							def       _a   (      self   )							->							List[Any]:
								set_requires_grad(self.unet ,						a_   )



							@torch.enable_grad()
							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_=True ,						)							->							Dict:
								_UpperCAmelCase =						latents.detach().requires_grad_()

								_UpperCAmelCase =						self.scheduler.scale_model_input(a_ ,						a_   )

								# predict the noise residual
								_UpperCAmelCase =						self.unet(a_ ,						a_ ,						encoder_hidden_states=a_   ).sample

								if isinstance(self.scheduler ,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
									_UpperCAmelCase =						self.scheduler.alphas_cumprod[timestep]
									_UpperCAmelCase =						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
									_UpperCAmelCase =						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

								_UpperCAmelCase =						-torch.autograd.grad(a_ ,						a_   )[0]

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



							@torch.no_grad()
							def __call__(      self ,						a_ ,						a_ = 512 ,						a_ = 512 ,						a_ = 50 ,						a_ = 7.5 ,						a_ = 1 ,						a_ = 0.0 ,						a_ = 100 ,						a_ = None ,						a_ = 4 ,						a_ = True ,						a_ = None ,						a_ = None ,						a_ = "pil" ,						a_ = True ,						)							->							str:
								if isinstance(a_ ,						a_   ):
									_UpperCAmelCase =						1
								elif isinstance(a_ ,						a_   ):
									_UpperCAmelCase =						len(a_   )
								else:
									raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(a_   )}"   )

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

								if clip_guidance_scale > 0:
									if clip_prompt is not None:
										_UpperCAmelCase =						self.tokenizer(
										    a_ ,						padding="max_length" ,						max_length=self.tokenizer.model_max_length ,						truncation=a_ ,						return_tensors="pt" ,						).input_ids.to(self.device   )
									else:
										_UpperCAmelCase =						text_input.input_ids.to(self.device   )
									_UpperCAmelCase =						self.clip_model.get_text_features(a_   )
									_UpperCAmelCase =						text_embeddings_clip / text_embeddings_clip.norm(p=2 ,						dim=-1 ,						keepdim=a_   )
									# duplicate text embeddings clip for each generation per prompt
									_UpperCAmelCase =						text_embeddings_clip.repeat_interleave(a_ ,						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.
								_UpperCAmelCase =						guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									_UpperCAmelCase =						text_input.input_ids.shape[-1]
									_UpperCAmelCase =						self.tokenizer([""] ,						padding="max_length" ,						max_length=a_ ,						return_tensors="pt"   )
									_UpperCAmelCase =						self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
									# duplicate unconditional embeddings for each generation per prompt
									_UpperCAmelCase =						uncond_embeddings.repeat_interleave(a_ ,						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
									_UpperCAmelCase =						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`.
								_UpperCAmelCase =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								_UpperCAmelCase =						text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not work reproducibly on mps
										_UpperCAmelCase =						torch.randn(a_ ,						generator=a_ ,						device="cpu" ,						dtype=a_   ).to(
										    self.device   )
									else:
										_UpperCAmelCase =						torch.randn(a_ ,						generator=a_ ,						device=self.device ,						dtype=a_   )
								else:
									if latents.shape != latents_shape:
										raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
									_UpperCAmelCase =						latents.to(self.device   )

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

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

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

								# scale the initial noise by the standard deviation required by the scheduler
								_UpperCAmelCase =						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]
								_UpperCAmelCase =						"eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
								_UpperCAmelCase =						{}
								if accepts_eta:
									_UpperCAmelCase =						eta

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

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

									# predict the noise residual
									_UpperCAmelCase =						self.unet(a_ ,						a_ ,						encoder_hidden_states=a_   ).sample

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

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

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

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

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

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

								if not return_dict:
									return (image, None)

								return StableDiffusionPipelineOutput(images=a_ ,						nsfw_content_detected=a_   )





"""simple docstring"""
import heapq




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						[(0, start)]  # cost from start node,end node
	_UpperCAmelCase =						set()
	while heap:
		((_UpperCAmelCase) ,      (_UpperCAmelCase)) =						heapq.heappop(UpperCamelCase__      )
		if u in visited:
			continue
		visited.add(UpperCamelCase__      )
		if u == end:
			return cost
		for v, c in graph[u]:
			if v in visited:
				continue
			_UpperCAmelCase =						cost + c
			heapq.heappush(UpperCamelCase__				,     (next_item, v)      )
	return -1


__magic_name__		       =				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__magic_name__		       =				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__magic_name__		       =				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__magic_name__		       =				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
			import doctest

			doctest.testmod()




