






# 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 		_snake_case  (  A__					):
			_lowercase		:    torch.FloatTensor
			_lowercase		:    torch.FloatTensor



class 		_snake_case  (  A__   ,    A__					):
			_lowercase		:    List[str]			     =							1






			@register_to_config
			def __init__(       self		,					a = 2000		,					a = 0.15		,					a = 0.01		,					a = 13_48.0		,					a = 1E-5		,					a = 1		,					)      ->       str:
						# standard deviation of the initial noise distribution
						SCREAMING_SNAKE_CASE						= sigma_max

						# setable values
						SCREAMING_SNAKE_CASE						= None

						self.set_sigmas(a		,					a		,					a		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       torch.FloatTensor:
						return sample






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None)      ->       Dict:
						SCREAMING_SNAKE_CASE						= sampling_eps if sampling_eps is not None else self.config.sampling_eps

						SCREAMING_SNAKE_CASE						= torch.linspace(1		,					a		,					a		,					device=a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None)      ->       Tuple:
						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(a		,					a)

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






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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a = None		,					a = True		,					)      ->       Union[SdeVeOutput, Tuple]:
						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(a		,					a).to(sample.device)
						SCREAMING_SNAKE_CASE						= torch.zeros_like(a)
						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=a		,					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=a		,					prev_sample_mean=a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = None		,					a = True		,					)      ->       Union[SchedulerOutput, Tuple]:
						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=a).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=a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					)      ->       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(a) * sigmas[:, None, None, None]
						)
						SCREAMING_SNAKE_CASE						= noise + original_samples
						return noisy_samples







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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




