





from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__lowercase													=			{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def 					__SCREAMING_SNAKE_CASE  (  __lowercase:		dict     ,							__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				{start}
  __lowerCAmelCase			=				[start]
  __lowerCAmelCase			=				Queue()
  queue.put(lowercase__)
  while not queue.empty():
    __lowerCAmelCase			=				queue.get()
    for w in graph[v]:
      if w not in explored:
        explored.add(lowercase__)
        result.append(lowercase__)
        queue.put(lowercase__)
  return result


def 					__SCREAMING_SNAKE_CASE  (  __lowercase:		dict     ,							__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				{start}
  __lowerCAmelCase			=				[start]
  __lowerCAmelCase			=				deque([start])
  while queue:
    __lowerCAmelCase			=				queue.popleft()
    for child in graph[v]:
      if child not in visited:
        visited.add(lowercase__)
        result.append(lowercase__)
        queue.append(lowercase__)
  return result


def 					__SCREAMING_SNAKE_CASE  (  __lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				F'''from __main__ import G, {name}'''
  __lowerCAmelCase			=				1_00_00
  __lowerCAmelCase			=				timeit(F'''{name}(G, \'A\')'''     ,							setup=lowercase__     ,							number=lowercase__)
  print(F'''{name:<35} finished {number} runs in {res:.5f} seconds''')


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      benchmark_function("""breadth_first_search""")
      benchmark_function("""breadth_first_search_with_deque""")
      # breadth_first_search                finished 10000 runs in 0.20999 seconds
      # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds







import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	42
    _lowerCAmelCase     =	None


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		Optional[Any]=0.999     ,							__lowercase:		List[str]="cosine"     ,							):



  '''simple docstring'''






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__lowercase:		List[Any]):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

  elif alpha_transform_type == "exp":

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

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

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




class   __snake_case				(			_a       ,				_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	[e.name for e in KarrasDiffusionSchedulers]
    _lowerCAmelCase     =	1





    @register_to_config
    def __init__(   self						:		Optional[int]			,						snake_case						:		int = 1_000			,						snake_case						:		float = 0.0001			,						snake_case						:		float = 0.02			,						snake_case						:		str = "linear"			,						snake_case						:		Optional[Union[np.ndarray, List[float]]] = None			,						snake_case						:		Optional[bool] = False			,						snake_case						:		str = "epsilon"			,						snake_case						:		str = "linspace"			,						snake_case						:		int = 0			,						):

      """simple docstring"""





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

      __lowerCAmelCase			=				1.0 - self.betas
      __lowerCAmelCase			=				torch.cumprod(self.alphas			,						dim=0							)

      __lowerCAmelCase			=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
      __lowerCAmelCase			=				np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
      __lowerCAmelCase			=				torch.from_numpy(snake_case							)

      # setable values
      __lowerCAmelCase			=				None
      __lowerCAmelCase			=				use_karras_sigmas
      self.set_timesteps(snake_case			,						snake_case							)
      __lowerCAmelCase			=				[]
      __lowerCAmelCase			=				False





    @property
    def 	A		(   self						:		Any							):

      """simple docstring"""





      if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		Union[float, torch.FloatTensor]							):

      """simple docstring"""





      if isinstance(snake_case			,						torch.Tensor							):
        __lowerCAmelCase			=				timestep.to(self.timesteps.device							)
      __lowerCAmelCase			=				(self.timesteps == timestep).nonzero().item()
      __lowerCAmelCase			=				self.sigmas[step_index]
      __lowerCAmelCase			=				sample / ((sigma**2 + 1) ** 0.5)
      __lowerCAmelCase			=				True
      return sample





    def 	A		(   self						:		Optional[int]			,						snake_case						:		List[str]			,						snake_case						:		int			,						snake_case						:		Any							):

      """simple docstring"""





      def lms_derivative(snake_case						:		Union[str, Any]							):
        __lowerCAmelCase			=				1.0
        for k in range(snake_case							):
          if current_order == k:
            continue
          prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

      __lowerCAmelCase			=				integrate.quad(snake_case			,						self.sigmas[t]			,						self.sigmas[t + 1]			,						epsrel=1e-4							)[0]

      return integrated_coeff





    def 	A		(   self						:		List[str]			,						snake_case						:		int			,						snake_case						:		Union[str, torch.device] = None							):

      """simple docstring"""





      __lowerCAmelCase			=				num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
        __lowerCAmelCase			=				np.linspace(0			,						self.config.num_train_timesteps - 1			,						snake_case			,						dtype=snake_case							)[
            ::-1
        ].copy()
      elif self.config.timestep_spacing == "leading":
        __lowerCAmelCase			=				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
        __lowerCAmelCase			=				(np.arange(0			,						snake_case							) * step_ratio).round()[::-1].copy().astype(snake_case							)
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        __lowerCAmelCase			=				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
        __lowerCAmelCase			=				(np.arange(self.config.num_train_timesteps			,						0			,						-step_ratio							)).round().copy().astype(snake_case							)
        timesteps -= 1
      else:
        raise ValueError(
            f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''							)

      __lowerCAmelCase			=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
      __lowerCAmelCase			=				np.log(snake_case							)
      __lowerCAmelCase			=				np.interp(snake_case			,						np.arange(0			,						len(snake_case							)							)			,						snake_case							)

      if self.use_karras_sigmas:
        __lowerCAmelCase			=				self._convert_to_karras(in_sigmas=snake_case							)
        __lowerCAmelCase			=				np.array([self._sigma_to_t(snake_case			,						snake_case							) for sigma in sigmas]							)

      __lowerCAmelCase			=				np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

      __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(device=snake_case							)
      if str(snake_case							).startswith('''mps'''							):
        # mps does not support float64
        __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(snake_case			,						dtype=torch.floataa							)
      else:
        __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(device=snake_case							)

      __lowerCAmelCase			=				[]





    def 	A		(   self						:		List[Any]			,						snake_case						:		List[Any]			,						snake_case						:		Dict							):

      """simple docstring"""





      __lowerCAmelCase			=				np.log(snake_case							)

      # get distribution
      __lowerCAmelCase			=				log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      __lowerCAmelCase			=				np.cumsum((dists >= 0)			,						axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
      __lowerCAmelCase			=				low_idx + 1

      __lowerCAmelCase			=				log_sigmas[low_idx]
      __lowerCAmelCase			=				log_sigmas[high_idx]

      # interpolate sigmas
      __lowerCAmelCase			=				(low - log_sigma) / (low - high)
      __lowerCAmelCase			=				np.clip(snake_case			,						0			,						1							)

      # transform interpolation to time range
      __lowerCAmelCase			=				(1 - w) * low_idx + w * high_idx
      __lowerCAmelCase			=				t.reshape(sigma.shape							)
      return t





    def 	A		(   self						:		List[Any]			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				in_sigmas[-1].item()
      __lowerCAmelCase			=				in_sigmas[0].item()

      __lowerCAmelCase			=				7.0  # 7.0 is the value used in the paper
      __lowerCAmelCase			=				np.linspace(0			,						1			,						self.num_inference_steps							)
      __lowerCAmelCase			=				sigma_min ** (1 / rho)
      __lowerCAmelCase			=				sigma_max ** (1 / rho)
      __lowerCAmelCase			=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas





    def 	A		(   self						:		List[str]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		Union[float, torch.FloatTensor]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		int = 4			,						snake_case						:		bool = True			,						):

      """simple docstring"""





      if not self.is_scale_input_called:
        warnings.warn(
            '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
            '''See `StableDiffusionPipeline` for a usage example.'''							)

      if isinstance(snake_case			,						torch.Tensor							):
        __lowerCAmelCase			=				timestep.to(self.timesteps.device							)
      __lowerCAmelCase			=				(self.timesteps == timestep).nonzero().item()
      __lowerCAmelCase			=				self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        __lowerCAmelCase			=				sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        __lowerCAmelCase			=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
        __lowerCAmelCase			=				model_output
      else:
        raise ValueError(
            f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''							)

      # 2. Convert to an ODE derivative
      __lowerCAmelCase			=				(sample - pred_original_sample) / sigma
      self.derivatives.append(snake_case							)
      if len(self.derivatives							) > order:
        self.derivatives.pop(0							)

      # 3. Compute linear multistep coefficients
      __lowerCAmelCase			=				min(step_index + 1			,						snake_case							)
      __lowerCAmelCase			=				[self.get_lms_coefficient(snake_case			,						snake_case			,						snake_case							) for curr_order in range(snake_case							)]

      # 4. Compute previous sample based on the derivatives path
      __lowerCAmelCase			=				sample + sum(
          coeff * derivative for coeff, derivative in zip(snake_case			,						reversed(self.derivatives							)							)							)

      if not return_dict:
        return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=snake_case			,						pred_original_sample=snake_case							)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self.sigmas.to(device=original_samples.device			,						dtype=original_samples.dtype							)
      if original_samples.device.type == "mps" and torch.is_floating_point(snake_case							):
        # mps does not support float64
        __lowerCAmelCase			=				self.timesteps.to(original_samples.device			,						dtype=torch.floataa							)
        __lowerCAmelCase			=				timesteps.to(original_samples.device			,						dtype=torch.floataa							)
      else:
        __lowerCAmelCase			=				self.timesteps.to(original_samples.device							)
        __lowerCAmelCase			=				timesteps.to(original_samples.device							)

      __lowerCAmelCase			=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      __lowerCAmelCase			=				sigmas[step_indices].flatten()
      while len(sigma.shape							) < len(original_samples.shape							):
        __lowerCAmelCase			=				sigma.unsqueeze(-1							)

      __lowerCAmelCase			=				original_samples + noise * sigma
      return noisy_samples





    def __len__(   self						:		Dict							):

      """simple docstring"""





      return self.config.num_train_timesteps
