


'''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 		_snake_case					(     a__	):
					lowerCAmelCase      :torch.FloatTensor
					lowerCAmelCase      :torch.FloatTensor


class 		_snake_case					(     a__       ,   a__	):
					lowerCAmelCase      :List[str]   =					1




					@register_to_config
					def __init__(      self							,							_lowerCamelCase = 2000							,							_lowerCamelCase = 0.15							,							_lowerCamelCase = 0.01							,							_lowerCamelCase = 1348.0							,							_lowerCamelCase = 1e-5							,							_lowerCamelCase = 1							,							):
								# standard deviation of the initial noise distribution
								UpperCAmelCase__  :					Any        =     sigma_max

								# setable values
								UpperCAmelCase__  :					Tuple        =     None

								self.set_sigmas(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								return sample




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					List[str]        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase__  :					Optional[int]        =     torch.linspace(1							,							_lowerCamelCase							,							_lowerCamelCase							,							device=_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					Optional[int]        =     sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase__  :					Union[str, Any]        =     sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase__  :					Optional[int]        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
											self.set_timesteps(_lowerCamelCase							,							_lowerCamelCase)

								UpperCAmelCase__  :					Tuple        =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase__  :					Optional[Any]        =     torch.exp(torch.linspace(math.log(_lowerCamelCase)							,							math.log(_lowerCamelCase)							,							_lowerCamelCase))
								UpperCAmelCase__  :					Tuple        =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])




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




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

								UpperCAmelCase__  :					int        =     timestep * torch.ones(
								    sample.shape[0]							,							device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase__  :					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
								UpperCAmelCase__  :					Tuple        =     timesteps.to(self.discrete_sigmas.device)

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

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase__  :					Optional[int]        =     randn_tensor(
								    sample.shape							,							layout=sample.layout							,							generator=_lowerCamelCase							,							device=sample.device							,							dtype=sample.dtype)
								UpperCAmelCase__  :					Union[str, Any]        =     sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase__  :					Tuple        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = 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
								UpperCAmelCase__  :					List[Any]        =     randn_tensor(sample.shape							,							layout=sample.layout							,							generator=_lowerCamelCase).to(sample.device)

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

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowerCamelCase)




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




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



'''simple docstring'''



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

								super().__init__(_lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=_lowerCamelCase)])




					@property
					def 				snake_case__						(      self):
								return self.base_dist.mean * self.scale + self.loc




					@property
					def 				snake_case__						(      self):
								return self.base_dist.variance * self.scale**2




					@property
					def 				snake_case__						(      self):
								return self.variance.sqrt()


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




					def __init__(      self							,							_lowerCamelCase = 1):
								UpperCAmelCase__  :					Optional[Any]        =     dim
								UpperCAmelCase__  :					int        =     {k: dim * self.args_dim[k] for k in self.args_dim}




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




					@property
					def 				snake_case__						(      self):
								return () if self.dim == 1 else (self.dim,)




					@property
					def 				snake_case__						(      self):
								return len(self.event_shape)




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

								if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

								return self._base_distribution((total_count, logits))