


# 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 	snake_case_       (__UpperCamelCase				):
					UpperCAmelCase__ :   torch.FloatTensor
					UpperCAmelCase__ :   Optional[torch.FloatTensor]   				=      None






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    int=0.999 ,   __lowerCAmelCase							:    List[str]="cosine" ,   ):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(__lowerCAmelCase							:    int       ):
																					return math.exp(t * -12.0       )

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

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







class 	snake_case_       (__UpperCamelCase     ,						__UpperCamelCase				):
					UpperCAmelCase__ :   Any   				=      1
					@register_to_config
					def __init__(				self       :Optional[Any]      ,__snake_case       :int = 10_00      ,__snake_case       :float = 0.00_01      ,__snake_case       :float = 0.02      ,__snake_case       :str = "linear"      ,__snake_case       :Optional[Union[np.ndarray, List[float]]] = None      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :int = 0      ,__snake_case       :str = "epsilon"      ,__snake_case       :float = 1.0      ,**__snake_case       :List[Any]      ,) ->   Union[str, Any]:
												if kwargs.get('set_alpha_to_one'      ,_lowerCAmelCase				) is not None:
																			a__					=					(
																			    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
																			)
																			deprecate('set_alpha_to_one'      ,'1.0.0'      ,_lowerCAmelCase      ,standard_warn=_lowerCAmelCase				)
																			a__					=					kwargs["""set_alpha_to_one"""]
												if trained_betas is not None:
																			a__					=					torch.tensor(_lowerCAmelCase      ,dtype=torch.floataa				)
												elif beta_schedule == "linear":
																			a__					=					torch.linspace(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,dtype=torch.floataa				)
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			a__					=					(
																			    torch.linspace(beta_start**0.5      ,beta_end**0.5      ,_lowerCAmelCase      ,dtype=torch.floataa				) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			a__					=					betas_for_alpha_bar(_lowerCAmelCase				)
												else:
																			raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

												a__					=					1.0 - self.betas
												a__					=					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.
												a__					=					torch.tensor(0.0				) if set_alpha_to_zero else self.alphas_cumprod[-1]

												# standard deviation of the initial noise distribution
												a__					=					1.0

												# setable values
												a__					=					None
												a__					=					torch.from_numpy(np.arange(0      ,_lowerCAmelCase				).copy().astype(np.intaa				)				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :torch.FloatTensor      ,__snake_case       :Optional[int] = None				) ->   List[Any]:
												return sample
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :int      ,__snake_case       :Union[str, torch.device] = None				) ->   Union[str, Any]:

												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.'				)

												a__					=					num_inference_steps
												a__					=					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
												a__					=					(np.arange(0      ,_lowerCAmelCase				) * step_ratio).round().copy().astype(np.intaa				)
												a__					=					torch.from_numpy(_lowerCAmelCase				).to(_lowerCAmelCase				)
												self.timesteps += self.config.steps_offset
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :torch.FloatTensor      ,__snake_case       :int      ,__snake_case       :torch.FloatTensor      ,__snake_case       :float = 0.0      ,__snake_case       :bool = False      ,__snake_case       :Optional[torch.FloatTensor] = None      ,__snake_case       :bool = True      ,) ->   List[str]:
												# 1. get previous step value (=t+1)
												a__					=					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
												a__					=					self.alphas_cumprod[timestep]
												a__					=					(
												    self.alphas_cumprod[prev_timestep]
												    if prev_timestep < self.config.num_train_timesteps
												    else self.final_alpha_cumprod
												)

												a__					=					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":
																			a__					=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			a__					=					model_output
												elif self.config.prediction_type == "sample":
																			a__					=					model_output
																			a__					=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																			a__					=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			a__					=					(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:
																			a__					=					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
												a__					=					(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
												a__					=					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=_lowerCAmelCase      ,pred_original_sample=_lowerCAmelCase				)



					def __len__(				self       :int				) ->   Tuple:
												return self.config.num_train_timesteps








def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def 							__lowercase       (			__lowerCAmelCase							:    dict[int, list[int]]       ):
							a__					=					0
							a__					=					len(__lowerCAmelCase       )  # No of vertices in graph
							a__					=					[0] * n
							a__					=					[False] * n

							def dfs(__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
														a__					=					True
														a__					=					id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   id_       )
																												a__					=					min(low[at] ,   low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												a__					=					min(low[at] ,   low[to]       )

							a__					=					[]
							for i in range(__lowerCAmelCase       ):
														if not visited[i]:
																					dfs(__lowerCAmelCase ,   -1 ,   __lowerCAmelCase ,   id_       )
							return bridges


if __name__ == "__main__":
						import doctest

						doctest.testmod()




