






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      __SCREAMING_SNAKE_CASE(		lowercase__		):
							_UpperCAmelCase       			=				42
							_UpperCAmelCase       			=				None


def    a_       (					_A					,      _A=0.999					,      _A="cosine"					,      )	->	Optional[int]:







				"""simple docstring"""

				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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


class      __SCREAMING_SNAKE_CASE(		lowercase__			,     lowercase__		):
							_UpperCAmelCase       			=				1
							@register_to_config
							def __init__(   self:					int				,		UpperCamelCase:					int = 10_00				,		UpperCamelCase:					float = 0.0_001				,		UpperCamelCase:					float = 0.02				,		UpperCamelCase:					str = "linear"				,		UpperCamelCase:					Optional[Union[np.ndarray, List[float]]] = None				,		UpperCamelCase:					bool = True				,		UpperCamelCase:					bool = True				,		UpperCamelCase:					int = 0				,		UpperCamelCase:					str = "epsilon"				,		UpperCamelCase:					float = 1.0				,		**UpperCamelCase:					Union[str, Any]				,		)						->						Optional[Any]:






											if kwargs.get('set_alpha_to_one'				,		_a  ) is not None:
															snake_case__													=   (
															    '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  )
															snake_case__													=   kwargs['set_alpha_to_one']
											if trained_betas is not None:
															snake_case__													=   torch.tensor(_a				,		dtype=torch.floataa  )
											elif beta_schedule == "linear":
															snake_case__													=   torch.linspace(_a				,		_a				,		_a				,		dtype=torch.floataa  )
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case__													=   (
															    torch.linspace(beta_start**0.5				,		beta_end**0.5				,		_a				,		dtype=torch.floataa  ) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case__													=   betas_for_alpha_bar(_a  )
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

											# standard deviation of the initial noise distribution
											snake_case__													=   1.0

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






											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.'''  )

											snake_case__													=   num_inference_steps
											snake_case__													=   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
											snake_case__													=   (np.arange(0				,		_a  ) * step_ratio).round().copy().astype(np.intaa  )
											snake_case__													=   torch.from_numpy(_a  ).to(_a  )
											self.timesteps += self.config.steps_offset
							def lowerCAmelCase_       (   self:					List[str]				,		UpperCamelCase:					torch.FloatTensor				,		UpperCamelCase:					int				,		UpperCamelCase:					torch.FloatTensor				,		UpperCamelCase:					float = 0.0				,		UpperCamelCase:					bool = False				,		UpperCamelCase:					Optional[torch.FloatTensor] = None				,		UpperCamelCase:					bool = True				,		)						->						List[str]:
											snake_case__													=   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
											snake_case__													=   self.alphas_cumprod[timestep]
											snake_case__													=   (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											snake_case__													=   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":
															snake_case__													=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															snake_case__													=   model_output
											elif self.config.prediction_type == "sample":
															snake_case__													=   model_output
															snake_case__													=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
															snake_case__													=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															snake_case__													=   (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:
															snake_case__													=   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
											snake_case__													=   (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
											snake_case__													=   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[str]  )						->						Tuple:
											return self.config.num_train_timesteps






'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



