






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	torch.FloatTensor
					_A  :	Optional[torch.FloatTensor]     =					None







def 					snake_case_						(   lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  List[str]=0.999				,				lowerCAmelCase_				:  Union[str, Any]="cosine"				,				):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCAmelCase_				:  Any					):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCAmelCase_				:  Optional[int]					):
																					return math.exp(t * -12.0					)

							else:
														raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

							__lowercase					:							List[str]			=   []
							for i in range(lowerCAmelCase_					):
														__lowercase					:							str			=   i / num_diffusion_timesteps
														__lowercase					:							str			=   (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCAmelCase_					) / alpha_bar_fn(lowerCAmelCase_					)				,				lowerCAmelCase_					)					)
							return torch.tensor(lowerCAmelCase_				,				dtype=torch.floataa					)



class 						lowerCAmelCase   (       __a					,				__a	):

					'''simple docstring'''

					_A  :	Tuple     =					1



					@register_to_config
					def __init__(     self :		Any						,   __a :		int = 1000						,   __a :		float = 0.0001						,   __a :		float = 0.02						,   __a :		str = "linear"						,   __a :		Optional[Union[np.ndarray, List[float]]] = None						,   __a :		bool = True						,   __a :		bool = True						,   __a :		int = 0						,   __a :		str = "epsilon"						,   __a :		float = 1.0						,   **__a :		str						,   )	->     Any:


												"""simple docstring"""

												if kwargs.get("""set_alpha_to_one"""						,   __a							) is not None:
																			__lowercase					:							List[Any]			=   (
																			    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
																			)
																			deprecate("""set_alpha_to_one"""						,   """1.0.0"""						,   __a						,   standard_warn=__a							)
																			__lowercase					:							Any			=   kwargs["""set_alpha_to_one"""]
												if trained_betas is not None:
																			__lowercase					:							Any			=   torch.tensor(__a						,   dtype=torch.floataa							)
												elif beta_schedule == "linear":
																			__lowercase					:							Tuple			=   torch.linspace(__a						,   __a						,   __a						,   dtype=torch.floataa							)
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__lowercase					:							Tuple			=   (
																			    torch.linspace(beta_start**0.5						,   beta_end**0.5						,   __a						,   dtype=torch.floataa							) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__lowercase					:							List[Any]			=   betas_for_alpha_bar(__a							)
												else:
																			raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

												__lowercase					:							Optional[int]			=   1.0 - self.betas
												__lowercase					:							List[str]			=   torch.cumprod(self.alphas						,   dim=0							)

												# At every step in inverted ddim, we are looking into the next alphas_cumprod
												# For the final step, there is no next alphas_cumprod, and the index is out of bounds
												# `set_alpha_to_zero` decides whether we set this parameter simply to zero
												# in this case, self.step() just output the predicted noise
												# or whether we use the final alpha of the "non-previous" one.
												__lowercase					:							List[Any]			=   torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

												# standard deviation of the initial noise distribution
												__lowercase					:							List[Any]			=   1.0

												# setable values
												__lowercase					:							Tuple			=   None
												__lowercase					:							str			=   torch.from_numpy(np.arange(0						,   __a							).copy().astype(np.intaa							)							)



					def 					lowerCAmelCase (     self :		str						,   __a :		torch.FloatTensor						,   __a :		Optional[int] = None							)	->     torch.FloatTensor:


												"""simple docstring"""

												return sample



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		int						,   __a :		Union[str, torch.device] = None							)	->     Optional[int]:


												"""simple docstring"""

												if num_inference_steps > self.config.num_train_timesteps:
																			raise ValueError(
																			    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																			    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																			    F" maximal {self.config.num_train_timesteps} timesteps."							)

												__lowercase					:							Optional[Any]			=   num_inference_steps
												__lowercase					:							Optional[Any]			=   self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												__lowercase					:							Tuple			=   (np.arange(0						,   __a							) * step_ratio).round().copy().astype(np.intaa							)
												__lowercase					:							Union[str, Any]			=   torch.from_numpy(__a							).to(__a							)
												self.timesteps += self.config.steps_offset



					def 					lowerCAmelCase (     self :		int						,   __a :		torch.FloatTensor						,   __a :		int						,   __a :		torch.FloatTensor						,   __a :		float = 0.0						,   __a :		bool = False						,   __a :		Optional[torch.FloatTensor] = None						,   __a :		bool = True						,   )	->     Union[DDIMSchedulerOutput, Tuple]:


												"""simple docstring"""

												__lowercase					:							Any			=   timestep + self.config.num_train_timesteps // self.num_inference_steps

												# 2. compute alphas, betas
												# change original implementation to exactly match noise levels for analogous forward process
												__lowercase					:							int			=   self.alphas_cumprod[timestep]
												__lowercase					:							List[str]			=   (
												    self.alphas_cumprod[prev_timestep]
												    if prev_timestep < self.config.num_train_timesteps
												    else self.final_alpha_cumprod
												)

												__lowercase					:							int			=   1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																			__lowercase					:							List[Any]			=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			__lowercase					:							Any			=   model_output
												elif self.config.prediction_type == "sample":
																			__lowercase					:							Any			=   model_output
																			__lowercase					:							Any			=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																			__lowercase					:							Union[str, Any]			=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			__lowercase					:							str			=   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																			raise ValueError(
																			    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																			    """ `v_prediction`"""							)

												# 4. Clip or threshold "predicted x_0"
												if self.config.clip_sample:
																			__lowercase					:							List[str]			=   pred_original_sample.clamp(
																			    -self.config.clip_sample_range						,   self.config.clip_sample_range							)

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__lowercase					:							Union[str, Any]			=   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__lowercase					:							str			=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																			return (prev_sample, pred_original_sample)
												return DDIMSchedulerOutput(prev_sample=__a						,   pred_original_sample=__a							)



					def __len__(     self :		List[Any]							)	->     Dict:


												"""simple docstring"""

												return self.config.num_train_timesteps







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)