
"""simple docstring"""







# 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 			A__	(     _lowerCamelCase):
		A_						:	torch.FloatTensor
		A_						:	torch.FloatTensor




class 			A__	(     _lowerCamelCase	,					_lowerCamelCase):
		A_						:	Tuple           =     1

		@register_to_config
		def __init__(				self	,		_SCREAMING_SNAKE_CASE = 20_00	,		_SCREAMING_SNAKE_CASE = 0.15	,		_SCREAMING_SNAKE_CASE = 0.01	,		_SCREAMING_SNAKE_CASE = 1348.0	,		_SCREAMING_SNAKE_CASE = 1E-5	,		_SCREAMING_SNAKE_CASE = 1	,		):
			# standard deviation of the initial noise distribution
			__lowerCAmelCase      :						str						      =				sigma_max

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

			self.set_sigmas(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			return sample

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						Union[str, Any]						      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

			__lowerCAmelCase      :						List[Any]						      =				torch.linspace(1	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		device=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						List[str]						      =				sigma_min if sigma_min is not None else self.config.sigma_min
			__lowerCAmelCase      :						List[Any]						      =				sigma_max if sigma_max is not None else self.config.sigma_max
			__lowerCAmelCase      :						int						      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
			if self.timesteps is None:
				self.set_timesteps(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
			__lowerCAmelCase      :						int						      =				torch.exp(torch.linspace(math.log(_SCREAMING_SNAKE_CASE			)	,		math.log(_SCREAMING_SNAKE_CASE			)	,		_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						int						      =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = True	,		):
			if self.timesteps is None:
				raise ValueError(
				    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

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

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

			#  equation 6: sample noise for the diffusion term of
			__lowerCAmelCase      :						Dict						      =				randn_tensor(
			    sample.shape	,		layout=sample.layout	,		generator=_SCREAMING_SNAKE_CASE	,		device=sample.device	,		dtype=sample.dtype			)
			__lowerCAmelCase      :						int						      =				sample - drift  # subtract because `dt` is a small negative timestep
			# TODO is the variable diffusion the correct scaling term for the noise?
			__lowerCAmelCase      :						str						      =				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=_SCREAMING_SNAKE_CASE	,		prev_sample_mean=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 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
			__lowerCAmelCase      :						Optional[int]						      =				randn_tensor(sample.shape	,		layout=sample.layout	,		generator=_SCREAMING_SNAKE_CASE			).to(sample.device			)

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

			if not return_dict:
				return (prev_sample,)

			return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		):
			# Make sure sigmas and timesteps have the same device and dtype as original_samples
			__lowerCAmelCase      :						List[Any]						      =				timesteps.to(original_samples.device			)
			__lowerCAmelCase      :						int						      =				self.discrete_sigmas.to(original_samples.device			)[timesteps]
			__lowerCAmelCase      :						Union[str, Any]						      =				(
			    noise * sigmas[:, None, None, None]
			    if noise is not None
			    else torch.randn_like(_SCREAMING_SNAKE_CASE			) * sigmas[:, None, None, None]
			)
			__lowerCAmelCase      :						Tuple						      =				noise + original_samples
			return noisy_samples







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

"""simple docstring"""







import warnings
from functools import wraps
from typing import Callable
def    __lowerCAmelCase    (_UpperCamelCase		):

	@wraps(_UpperCamelCase		)
	def _inner_fn(*_UpperCamelCase	,  **_UpperCamelCase		):
		warnings.warn(
		    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")	,  _UpperCamelCase	,  )
		return fn(*_UpperCamelCase	,  **_UpperCamelCase		)

	return _inner_fn