




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput





@dataclass
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    42
 lowercase_				    =    42





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    1



 @register_to_config
 def __init__(							self  :		Any				,				_lowerCAmelCase  :		int = 2_000				,				_lowerCAmelCase  :		float = 0.15				,				_lowerCAmelCase  :		float = 0.01				,				_lowerCAmelCase  :		float = 1348.0				,				_lowerCAmelCase  :		float = 1E-5				,				_lowerCAmelCase  :		int = 1				,				):
       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE_							=			sigma_max

       # setable values
       SCREAMING_SNAKE_CASE_							=			None

       self.set_sigmas(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		Optional[int] = None						):
       return sample



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		float = None				,				_lowerCAmelCase  :		Union[str, torch.device] = None						):
       SCREAMING_SNAKE_CASE_							=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

       SCREAMING_SNAKE_CASE_							=			torch.linspace(1				,				_lowerCAmelCase				,				_lowerCAmelCase				,				device=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		float = None				,				_lowerCAmelCase  :		float = None				,				_lowerCAmelCase  :		float = None						):
       SCREAMING_SNAKE_CASE_							=			sigma_min if sigma_min is not None else self.config.sigma_min
       SCREAMING_SNAKE_CASE_							=			sigma_max if sigma_max is not None else self.config.sigma_max
       SCREAMING_SNAKE_CASE_							=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
             self.set_timesteps(_lowerCAmelCase				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       SCREAMING_SNAKE_CASE_							=			torch.exp(torch.linspace(math.log(_lowerCAmelCase						)				,				math.log(_lowerCAmelCase						)				,				_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_							=			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		str						):
       return torch.where(
           timesteps == 0				,				torch.zeros_like(t.to(timesteps.device						)						)				,				self.discrete_sigmas[timesteps - 1].to(timesteps.device						)				,				)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		Optional[torch.Generator] = None				,				_lowerCAmelCase  :		bool = True				,				):
       if self.timesteps is None:
             raise ValueError(
                 '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'						)

       SCREAMING_SNAKE_CASE_							=			timestep * torch.ones(
           sample.shape[0]				,				device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
       SCREAMING_SNAKE_CASE_							=			(timestep * (len(self.timesteps						) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       SCREAMING_SNAKE_CASE_							=			timesteps.to(self.discrete_sigmas.device						)

       SCREAMING_SNAKE_CASE_							=			self.discrete_sigmas[timesteps].to(sample.device						)
       SCREAMING_SNAKE_CASE_							=			self.get_adjacent_sigma(_lowerCAmelCase				,				_lowerCAmelCase						).to(sample.device						)
       SCREAMING_SNAKE_CASE_							=			torch.zeros_like(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			(sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       SCREAMING_SNAKE_CASE_							=			diffusion.flatten()
       while len(diffusion.shape						) < len(sample.shape						):
             SCREAMING_SNAKE_CASE_							=			diffusion.unsqueeze(-1						)
       SCREAMING_SNAKE_CASE_							=			drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       SCREAMING_SNAKE_CASE_							=			randn_tensor(
           sample.shape				,				layout=sample.layout				,				generator=_lowerCAmelCase				,				device=sample.device				,				dtype=sample.dtype						)
       SCREAMING_SNAKE_CASE_							=			sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       SCREAMING_SNAKE_CASE_							=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
             return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=_lowerCAmelCase				,				prev_sample_mean=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		Optional[torch.Generator] = None				,				_lowerCAmelCase  :		bool = True				,				):
       if self.timesteps is None:
             raise ValueError(
                 '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'						)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       SCREAMING_SNAKE_CASE_							=			randn_tensor(sample.shape				,				layout=sample.layout				,				generator=_lowerCAmelCase						).to(sample.device						)

       # compute step size from the model_output, the noise, and the snr
       SCREAMING_SNAKE_CASE_							=			torch.norm(model_output.reshape(model_output.shape[0]				,				-1						)				,				dim=-1						).mean()
       SCREAMING_SNAKE_CASE_							=			torch.norm(noise.reshape(noise.shape[0]				,				-1						)				,				dim=-1						).mean()
       SCREAMING_SNAKE_CASE_							=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       SCREAMING_SNAKE_CASE_							=			step_size * torch.ones(sample.shape[0]						).to(sample.device						)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       SCREAMING_SNAKE_CASE_							=			step_size.flatten()
       while len(step_size.shape						) < len(sample.shape						):
             SCREAMING_SNAKE_CASE_							=			step_size.unsqueeze(-1						)
       SCREAMING_SNAKE_CASE_							=			sample + step_size * model_output
       SCREAMING_SNAKE_CASE_							=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		torch.FloatTensor				,				_lowerCAmelCase  :		torch.FloatTensor				,				):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       SCREAMING_SNAKE_CASE_							=			timesteps.to(original_samples.device						)
       SCREAMING_SNAKE_CASE_							=			self.discrete_sigmas.to(original_samples.device						)[timesteps]
       SCREAMING_SNAKE_CASE_							=			(
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(_lowerCAmelCase						) * sigmas[:, None, None, None]
       )
       SCREAMING_SNAKE_CASE_							=			noise + original_samples
       return noisy_samples




 def __len__(							self  :		Tuple						):
       return self.config.num_train_timesteps





from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )