
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class 			__A					:
 # setable values
 lowerCamelCase												=None
 lowerCamelCase												=None
 lowerCamelCase												=None
 @classmethod
 def 		lowercase_( cls      :     Any							):



   """simple docstring"""





   return cls()







@dataclass
class 			__A					(			__snake_case      ):
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=None







class 			__A					(			__snake_case     ,    __snake_case      ):
 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   return True
 @register_to_config
 def __init__( self      :     str					, lowerCamelCase      :     int = 20_00					, lowerCamelCase      :     float = 0.15					, lowerCamelCase      :     float = 0.01					, lowerCamelCase      :     float = 13_48.0					, lowerCamelCase      :     float = 1e-5					, lowerCamelCase      :     int = 1					, ):



   """simple docstring"""





   pass
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  str       				=		ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       lowerCamelCase					, self.config.num_train_timesteps					, self.config.sigma_min					, self.config.sigma_max					, self.config.sampling_eps					, )
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     ScoreSdeVeSchedulerState					, lowerCamelCase      :     int					, lowerCamelCase      :     Tuple = ()					, lowerCamelCase      :     float = None							):



   """simple docstring"""





   __A		:  int       				=		sampling_eps if sampling_eps is not None else self.config.sampling_eps

   __A		:  Any       				=		jnp.linspace(1					, lowerCamelCase					, lowerCamelCase							)
   return state.replace(timesteps=lowerCamelCase							)
 def 		lowercase_( self      :     str					, lowerCamelCase      :     ScoreSdeVeSchedulerState					, lowerCamelCase      :     int					, lowerCamelCase      :     float = None					, lowerCamelCase      :     float = None					, lowerCamelCase      :     float = None					, ):



   """simple docstring"""





   __A		:  Any       				=		sigma_min if sigma_min is not None else self.config.sigma_min
   __A		:  Any       				=		sigma_max if sigma_max is not None else self.config.sigma_max
   __A		:  List[Any]       				=		sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if state.timesteps is None:
     __A		:  Any       				=		self.set_timesteps(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)

   __A		:  Union[str, Any]       				=		jnp.exp(jnp.linspace(jnp.log(lowerCamelCase							)					, jnp.log(lowerCamelCase							)					, lowerCamelCase							)							)
   __A		:  Any       				=		jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]							)

   return state.replace(discrete_sigmas=lowerCamelCase					, sigmas=lowerCamelCase							)
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     str					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return jnp.where(timesteps == 0					, jnp.zeros_like(lowerCamelCase							)					, state.discrete_sigmas[timesteps - 1]							)
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     ScoreSdeVeSchedulerState					, lowerCamelCase      :     jnp.ndarray					, lowerCamelCase      :     int					, lowerCamelCase      :     jnp.ndarray					, lowerCamelCase      :     random.KeyArray					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





   if state.timesteps is None:
     raise ValueError(
         """`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""							)

   __A		:  Tuple       				=		timestep * jnp.ones(
       sample.shape[0]					, )
   __A		:  Tuple       				=		(timestep * (len(state.timesteps							) - 1)).long()

   __A		:  List[Any]       				=		state.discrete_sigmas[timesteps]
   __A		:  Optional[int]       				=		self.get_adjacent_sigma(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)
   __A		:  Optional[int]       				=		jnp.zeros_like(lowerCamelCase							)
   __A		:  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
   __A		:  str       				=		diffusion.flatten()
   __A		:  Tuple       				=		broadcast_to_shape_from_left(lowerCamelCase					, sample.shape							)
   __A		:  List[Any]       				=		drift - diffusion**2 * model_output

   #  equation 6: sample noise for the diffusion term of
   __A		:  Tuple       				=		random.split(lowerCamelCase					, num=1							)
   __A		:  Optional[int]       				=		random.normal(key=lowerCamelCase					, shape=sample.shape							)
   __A		:  Tuple       				=		sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   __A		:  Optional[Any]       				=		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

   if not return_dict:
     return (prev_sample, prev_sample_mean, state)

   return FlaxSdeVeOutput(prev_sample=lowerCamelCase					, prev_sample_mean=lowerCamelCase					, state=lowerCamelCase							)
 def 		lowercase_( self      :     Tuple					, lowerCamelCase      :     ScoreSdeVeSchedulerState					, lowerCamelCase      :     jnp.ndarray					, lowerCamelCase      :     jnp.ndarray					, lowerCamelCase      :     random.KeyArray					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





   if state.timesteps is None:
     raise ValueError(
         """`state.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
   __A		:  int       				=		random.split(lowerCamelCase					, num=1							)
   __A		:  List[Any]       				=		random.normal(key=lowerCamelCase					, shape=sample.shape							)

   # compute step size from the model_output, the noise, and the snr
   __A		:  int       				=		jnp.linalg.norm(lowerCamelCase							)
   __A		:  List[Any]       				=		jnp.linalg.norm(lowerCamelCase							)
   __A		:  Optional[Any]       				=		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
   __A		:  List[str]       				=		step_size * jnp.ones(sample.shape[0]							)

   # compute corrected sample: model_output term and noise term
   __A		:  Tuple       				=		step_size.flatten()
   __A		:  List[Any]       				=		broadcast_to_shape_from_left(lowerCamelCase					, sample.shape							)
   __A		:  str       				=		sample + step_size * model_output
   __A		:  Optional[int]       				=		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=lowerCamelCase					, state=lowerCamelCase							)






 def __len__( self      :     Tuple							):



   """simple docstring"""





   return self.config.num_train_timesteps






"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__lowerCAmelCase				      =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    torch.FloatTensor




class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):
							__lowerCAmelCase					:    int     =  1

							@register_to_config
							def __init__( self       :    Union[str, Any] ,		a       :    int = 40 ,		a       :    float = 0.002 ,		a       :    float = 80.0 ,		a       :    float = 0.5 ,		a       :    float = 1.0 ,		a       :    float = 7.0 ,		a       :    bool = True ,		)   ->    List[Any]:

									"""simple docstring"""
									# standard deviation of the initial noise distribution
									lowercase										=							sigma_max

									lowercase										=							np.linspace(0 ,		1 ,		a    )
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									# setable values
									lowercase										=							None
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							False
									lowercase										=							False

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Optional[int] ,		a       :    str=None    )   ->    Any:

									"""simple docstring"""
									if schedule_timesteps is None:
											lowercase										=							self.timesteps

									lowercase										=							(schedule_timesteps == timestep).nonzero()
									return indices.item()

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor]    )   ->    torch.FloatTensor:

									"""simple docstring"""
									# Get sigma corresponding to timestep
									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )
									lowercase										=							self.index_for_timestep(a    )
									lowercase										=							self.sigmas[step_idx]

									lowercase										=							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									lowercase										=							True
									return sample

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Union[float, np.ndarray]    )   ->    str:

									"""simple docstring"""
									if not isinstance(a ,		np.ndarray    ):
											lowercase										=							np.array(a ,		dtype=np.floataa    )

									lowercase										=							1000 * 0.25 * np.log(sigmas + 1E-44    )

									return timesteps

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Optional[int] = None ,		a       :    Union[str, torch.device] = None ,		a       :    Optional[List[int]] = None ,		)   ->    Dict:

									"""simple docstring"""
									if num_inference_steps is None and timesteps is None:
											raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''    )

									if num_inference_steps is not None and timesteps is not None:
											raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''    )

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
											for i in range(1 ,		len(a    )    ):
													if timesteps[i] >= timesteps[i - 1]:
															raise ValueError('''`timesteps` must be in descending order.'''    )

											if timesteps[0] >= self.config.num_train_timesteps:
													raise ValueError(
													    f"""`timesteps` must start before `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps}."""    )

											lowercase										=							np.array(a ,		dtype=np.intaa    )
											lowercase										=							True
									else:
											if num_inference_steps > self.config.num_train_timesteps:
													raise ValueError(
													    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
													    f""" maximal {self.config.num_train_timesteps} timesteps."""    )

											lowercase										=							num_inference_steps

											lowercase										=							self.config.num_train_timesteps // self.num_inference_steps
											lowercase										=							(np.arange(0 ,		a    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
											lowercase										=							False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									lowercase										=							self.config.num_train_timesteps
									lowercase										=							timesteps[::-1].copy()
									lowercase										=							ramp / (num_train_timesteps - 1)
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									lowercase										=							np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
									lowercase										=							torch.from_numpy(a    ).to(device=a    )

									if str(a    ).startswith('''mps'''    ):
											# mps does not support float64
											lowercase										=							torch.from_numpy(a    ).to(a ,		dtype=torch.floataa    )
									else:
											lowercase										=							torch.from_numpy(a    ).to(device=a    )

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    int    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.config.rho
									lowercase										=							sigma_min ** (1 / rho)
									lowercase										=							sigma_max ** (1 / rho)
									lowercase										=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    str    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / (sigma**2 + sigma_data**2)
									lowercase										=							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									lowercase										=							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor] ,		a       :    torch.FloatTensor ,		a       :    Optional[torch.Generator] = None ,		a       :    bool = True ,		)   ->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									"""simple docstring"""

									if (
									    isinstance(a ,		a    )
									    or isinstance(a ,		torch.IntTensor    )
									    or isinstance(a ,		torch.LongTensor    )
									):
											raise ValueError(
											    (
											        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
											        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
											        ''' one of the `scheduler.timesteps` as a timestep.'''
											    ) ,		)

									if not self.is_scale_input_called:
											logger.warning(
											    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
											    '''See `StableDiffusionPipeline` for a usage example.'''    )

									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )

									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.index_for_timestep(a    )

									# sigma_next corresponds to next_t in original implementation
									lowercase										=							self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
											lowercase										=							self.sigmas[step_index + 1]
									else:
											# Set sigma_next to sigma_min
											lowercase										=							self.sigmas[-1]

									# Get scalings for boundary conditions
									lowercase					,  lowercase										=							self.get_scalings_for_boundary_condition(a    )

									# 1. Denoise model output using boundary conditions
									lowercase										=							c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
											lowercase										=							denoised.clamp(-1 ,		1    )

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps    ) > 1:
											lowercase										=							randn_tensor(
											    model_output.shape ,		dtype=model_output.dtype ,		device=model_output.device ,		generator=a    )
									else:
											lowercase										=							torch.zeros_like(a    )
									lowercase										=							noise * self.config.s_noise

									lowercase										=							sigma_next.clamp(min=a ,		max=a    )

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									lowercase										=							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
											return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=a    )

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		)   ->    torch.FloatTensor:

									"""simple docstring"""
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowercase										=							self.sigmas.to(device=original_samples.device ,		dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(a    ):
											# mps does not support float64
											lowercase										=							self.timesteps.to(original_samples.device ,		dtype=torch.floataa    )
											lowercase										=							timesteps.to(original_samples.device ,		dtype=torch.floataa    )
									else:
											lowercase										=							self.timesteps.to(original_samples.device    )
											lowercase										=							timesteps.to(original_samples.device    )

									lowercase										=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									lowercase										=							sigmas[step_indices].flatten()
									while len(sigma.shape    ) < len(original_samples.shape    ):
											lowercase										=							sigma.unsqueeze(-1    )

									lowercase										=							original_samples + noise * sigma
									return noisy_samples

							def __len__( self       :    Union[str, Any]    )   ->    Any:

									"""simple docstring"""
									return self.config.num_train_timesteps