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       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Distribution					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       str=None					,	UpperCamelCase__			:       Optional[Any]=0		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							1.0 if scale is None else scale
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0.0 if loc is None else loc

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


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return self.base_dist.mean * self.scale + self.loc


						@property
						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return self.base_dist.variance * self.scale**2



						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return self.variance.sqrt()






class       lowercase__		(     nn.Module):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       Callable[..., Tuple[torch.Tensor]]					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							args_dim
												SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.ModuleList([nn.Linear(UpperCamelCase__					,	UpperCamelCase__		) for dim in args_dim.values()]		)
												SCREAMING_SNAKE_CASE							:					Dict           =							domain_map



						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							[proj(UpperCamelCase__		) for proj in self.proj]

												return self.domain_map(*UpperCamelCase__		)






class       lowercase__		(     nn.Module):


						def __init__( self			:       str					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												super().__init__()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							function



						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]					,	*UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return self.function(UpperCamelCase__					,	*UpperCamelCase__		)






class       lowercase__		:
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42
						UpperCamelCase_  =	42


						def __init__( self			:       str					,	UpperCamelCase__			:       int = 1		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							dim
												SCREAMING_SNAKE_CASE							:					List[str]           =							{k: dim * self.args_dim[k] for k in self.args_dim}


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												if self.dim == 1:
																		return self.distribution_class(*UpperCamelCase__		)
												else:
																		return Independent(self.distribution_class(*UpperCamelCase__		)					,	1		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self._base_distribution(UpperCamelCase__		)
												if loc is None and scale is None:
																		return distr
												else:
																		return AffineTransformed(UpperCamelCase__					,	loc=UpperCamelCase__					,	scale=UpperCamelCase__					,	event_dim=self.event_dim		)


						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												return () if self.dim == 1 else (self.dim,)


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												return len(self.event_shape		)


						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return 0.0


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												return ParameterProjection(
												    in_features=UpperCamelCase__					,	args_dim=self.args_dim					,	domain_map=LambdaLayer(self.domain_map		)					,	)


						def  __A ( self			:       List[str]					,	*UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												raise NotImplementedError()



						@staticmethod
						def  __A ( UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												return (x + torch.sqrt(torch.square(UpperCamelCase__		) + 4.0		)) / 2.0






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	{"df": 1, "loc": 1, "scale": 1}
						UpperCamelCase_  =	StudentT


						@classmethod
						def  __A ( cls			:       Optional[Any]					,	UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							cls.squareplus(UpperCamelCase__		).clamp_min(torch.finfo(scale.dtype		).eps		)
												SCREAMING_SNAKE_CASE							:					Any           =							2.0 + cls.squareplus(UpperCamelCase__		)
												return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	{"loc": 1, "scale": 1}
						UpperCamelCase_  =	Normal


						@classmethod
						def  __A ( cls			:       str					,	UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							cls.squareplus(UpperCamelCase__		).clamp_min(torch.finfo(scale.dtype		).eps		)
												return loc.squeeze(-1		), scale.squeeze(-1		)






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	{"total_count": 1, "logits": 1}
						UpperCamelCase_  =	NegativeBinomial


						@classmethod
						def  __A ( cls			:       Optional[int]					,	UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       torch.Tensor		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							cls.squareplus(UpperCamelCase__		)
												return total_count.squeeze(-1		), logits.squeeze(-1		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							distr_args
												if self.dim == 1:
																		return self.distribution_class(total_count=UpperCamelCase__					,	logits=UpperCamelCase__		)
												else:
																		return Independent(self.distribution_class(total_count=UpperCamelCase__					,	logits=UpperCamelCase__		)					,	1		)



						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Optional[torch.Tensor] = None					,	UpperCamelCase__			:       Optional[torch.Tensor] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							distr_args

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

												return self._base_distribution((total_count, logits)		)






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





