# 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 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		42
						UpperCamelCase_     =		42






class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_):
						UpperCamelCase_     =		1


						@register_to_config
						def __init__(       self	:				List[Any]    ,		UpperCamelCase__	:				int = 2000    ,		UpperCamelCase__	:				float = 0.15    ,		UpperCamelCase__	:				float = 0.01    ,		UpperCamelCase__	:				float = 1348.0    ,		UpperCamelCase__	:				float = 1E-5    ,		UpperCamelCase__	:				int = 1    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							sigma_max

												# setable values
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							None

												self.set_sigmas(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				Optional[int] = None						):





												'''simple docstring'''





												return sample


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				float = None    ,		UpperCamelCase__	:				Union[str, torch.device] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

												SCREAMING_SNAKE_CASE							:    Dict							=							torch.linspace(1    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		device=UpperCamelCase__						)


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				float = None    ,		UpperCamelCase__	:				float = None    ,		UpperCamelCase__	:				float = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sigma_min if sigma_min is not None else self.config.sigma_min
												SCREAMING_SNAKE_CASE							:    Any							=							sigma_max if sigma_max is not None else self.config.sigma_max
												SCREAMING_SNAKE_CASE							:    int							=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																		self.set_timesteps(UpperCamelCase__    ,		UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.exp(torch.linspace(math.log(UpperCamelCase__						)    ,		math.log(UpperCamelCase__						)    ,		UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    int							=							torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												return torch.where(
												    timesteps == 0    ,		torch.zeros_like(t.to(timesteps.device						)						)    ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device						)    ,		)


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				Optional[torch.Generator] = None    ,		UpperCamelCase__	:				bool = True    ,		):





												'''simple docstring'''





												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							:    Union[str, Any]							=							timestep * torch.ones(
												    sample.shape[0]    ,		device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
												SCREAMING_SNAKE_CASE							:    List[Any]							=							(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							:    int							=							timesteps.to(self.discrete_sigmas.device						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.discrete_sigmas[timesteps].to(sample.device						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_adjacent_sigma(UpperCamelCase__    ,		UpperCamelCase__						).to(sample.device						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.zeros_like(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							(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							:    int							=							diffusion.flatten()
												while len(diffusion.shape						) < len(sample.shape						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							diffusion.unsqueeze(-1						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							randn_tensor(
												    sample.shape    ,		layout=sample.layout    ,		generator=UpperCamelCase__    ,		device=sample.device    ,		dtype=sample.dtype						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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							:    Dict							=							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=UpperCamelCase__    ,		prev_sample_mean=UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				Optional[torch.Generator] = None    ,		UpperCamelCase__	:				bool = True    ,		):





												'''simple docstring'''





												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							:    Union[str, Any]							=							randn_tensor(sample.shape    ,		layout=sample.layout    ,		generator=UpperCamelCase__						).to(sample.device						)

												# compute step size from the model_output, the noise, and the snr
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.norm(model_output.reshape(model_output.shape[0]    ,		-1						)    ,		dim=-1						).mean()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.norm(noise.reshape(noise.shape[0]    ,		-1						)    ,		dim=-1						).mean()
												SCREAMING_SNAKE_CASE							:    List[str]							=							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												SCREAMING_SNAKE_CASE							:    Dict							=							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							:    List[str]							=							step_size.flatten()
												while len(step_size.shape						) < len(sample.shape						):
																		SCREAMING_SNAKE_CASE							:    List[str]							=							step_size.unsqueeze(-1						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							sample + step_size * model_output
												SCREAMING_SNAKE_CASE							:    List[str]							=							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__						)


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				torch.FloatTensor    ,		UpperCamelCase__	:				torch.FloatTensor    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							timesteps.to(original_samples.device						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.discrete_sigmas.to(original_samples.device						)[timesteps]
												SCREAMING_SNAKE_CASE							:    List[str]							=							(
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(UpperCamelCase__						) * sigmas[:, None, None, None]
												)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							noise + original_samples
												return noisy_samples



						def __len__(       self	:				List[str]						):





												'''simple docstring'''





												return self.config.num_train_timesteps






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





