


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


_snake_case   = logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: torch.FloatTensor
 _UpperCamelCase				: Optional[torch.FloatTensor]      =					None







def   A   (		A__		,		A__=0.999		,		A__="cosine"		,		):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(A__ ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(A__ ):
               return math.exp(t * -12.0 )

     else:
          raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )

     UpperCAmelCase						:				Any		  =			[]
     for i in range(A__ ):
          UpperCAmelCase						:				Optional[int]		  =			i / num_diffusion_timesteps
          UpperCAmelCase						:				Union[str, Any]		  =			(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ )		,		A__ ) )
     return torch.tensor(A__		,		dtype=torch.floataa )




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 _UpperCamelCase				: Dict      =					[e.name for e in KarrasDiffusionSchedulers]
 _UpperCamelCase				: List[str]      =					1
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 1_0_0_0       ,    _a					: float = 0.0_0_0_1       ,    _a					: float = 0.0_2       ,    _a					: str = "linear"       ,    _a					: Optional[Union[np.ndarray, List[float]]] = None       ,    _a					: str = "epsilon"       ,    _a					: str = "linspace"       ,    _a					: int = 0       ,    )    ->			Optional[int]:
      if trained_betas is not None:
           UpperCAmelCase						:				List[str]		  =			torch.tensor(_a       ,    dtype=torch.floataa				)
      elif beta_schedule == "linear":
           UpperCAmelCase						:				Dict		  =			torch.linspace(_a       ,    _a       ,    _a       ,    dtype=torch.floataa				)
      elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           UpperCAmelCase						:				Any		  =			(
               torch.linspace(beta_start**0.5       ,    beta_end**0.5       ,    _a       ,    dtype=torch.floataa				) ** 2
           )
      elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           UpperCAmelCase						:				str		  =			betas_for_alpha_bar(_a				)
      else:
           raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'				)

      UpperCAmelCase						:				Dict		  =			1.0 - self.betas
      UpperCAmelCase						:				Optional[Any]		  =			torch.cumprod(self.alphas       ,    dim=0				)

      UpperCAmelCase						:				Dict		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
      UpperCAmelCase						:				Union[str, Any]		  =			np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
      UpperCAmelCase						:				Optional[Any]		  =			torch.from_numpy(_a				)

      # setable values
      UpperCAmelCase						:				Optional[int]		  =			None
      UpperCAmelCase						:				Tuple		  =			np.linspace(0       ,    num_train_timesteps - 1       ,    _a       ,    dtype=_a				)[::-1].copy()
      UpperCAmelCase						:				str		  =			torch.from_numpy(_a				)
      UpperCAmelCase						:				Any		  =			False
 @property
 def   _UpperCamelCase    (			self					: Any				)    ->			Optional[Any]:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: torch.FloatTensor       ,    _a					: Union[float, torch.FloatTensor]				)    ->			torch.FloatTensor:
      if isinstance(_a       ,    torch.Tensor				):
           UpperCAmelCase						:				Dict		  =			timestep.to(self.timesteps.device				)
      UpperCAmelCase						:				int		  =			(self.timesteps == timestep).nonzero().item()
      UpperCAmelCase						:				Dict		  =			self.sigmas[step_index]
      UpperCAmelCase						:				int		  =			sample / ((sigma**2 + 1) ** 0.5)
      UpperCAmelCase						:				List[str]		  =			True
      return sample
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: int       ,    _a					: Union[str, torch.device] = None				)    ->			Union[str, Any]:
      UpperCAmelCase						:				Optional[Any]		  =			num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
           UpperCAmelCase						:				List[Any]		  =			np.linspace(0       ,    self.config.num_train_timesteps - 1       ,    _a       ,    dtype=_a				)[
               ::-1
           ].copy()
      elif self.config.timestep_spacing == "leading":
           UpperCAmelCase						:				Dict		  =			self.config.num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCAmelCase						:				Tuple		  =			(np.arange(0       ,    _a				) * step_ratio).round()[::-1].copy().astype(_a				)
           timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
           UpperCAmelCase						:				Optional[Any]		  =			self.config.num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCAmelCase						:				Any		  =			(np.arange(self.config.num_train_timesteps       ,    0       ,    -step_ratio				)).round().copy().astype(_a				)
           timesteps -= 1
      else:
           raise ValueError(
               f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

      UpperCAmelCase						:				Dict		  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
      UpperCAmelCase						:				List[Any]		  =			np.interp(_a       ,    np.arange(0       ,    len(_a				)				)       ,    _a				)
      UpperCAmelCase						:				Optional[Any]		  =			np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
      UpperCAmelCase						:				Optional[Any]		  =			torch.from_numpy(_a				).to(device=_a				)
      if str(_a				).startswith("mps"				):
           # mps does not support float64
           UpperCAmelCase						:				str		  =			torch.from_numpy(_a				).to(_a       ,    dtype=torch.floataa				)
      else:
           UpperCAmelCase						:				Union[str, Any]		  =			torch.from_numpy(_a				).to(device=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: torch.FloatTensor       ,    _a					: Union[float, torch.FloatTensor]       ,    _a					: torch.FloatTensor       ,    _a					: Optional[torch.Generator] = None       ,    _a					: bool = True       ,    )    ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      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"
                   " `EulerDiscreteScheduler.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				):
           UpperCAmelCase						:				List[Any]		  =			timestep.to(self.timesteps.device				)

      UpperCAmelCase						:				Optional[Any]		  =			(self.timesteps == timestep).nonzero().item()
      UpperCAmelCase						:				Any		  =			self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
           UpperCAmelCase						:				Optional[int]		  =			sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           UpperCAmelCase						:				str		  =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
           raise NotImplementedError("prediction_type not implemented yet: sample"				)
      else:
           raise ValueError(
               f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

      UpperCAmelCase						:				Optional[Any]		  =			self.sigmas[step_index]
      UpperCAmelCase						:				Optional[int]		  =			self.sigmas[step_index + 1]
      UpperCAmelCase						:				List[str]		  =			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      UpperCAmelCase						:				Dict		  =			(sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      UpperCAmelCase						:				Any		  =			(sample - pred_original_sample) / sigma

      UpperCAmelCase						:				str		  =			sigma_down - sigma

      UpperCAmelCase						:				List[Any]		  =			sample + derivative * dt

      UpperCAmelCase						:				Any		  =			model_output.device
      UpperCAmelCase						:				int		  =			randn_tensor(model_output.shape       ,    dtype=model_output.dtype       ,    device=_a       ,    generator=_a				)

      UpperCAmelCase						:				List[Any]		  =			prev_sample + noise * sigma_up

      if not return_dict:
           return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=_a       ,    pred_original_sample=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: torch.FloatTensor       ,    _a					: torch.FloatTensor       ,    _a					: torch.FloatTensor       ,    )    ->			torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      UpperCAmelCase						:				Dict		  =			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
           UpperCAmelCase						:				List[str]		  =			self.timesteps.to(original_samples.device       ,    dtype=torch.floataa				)
           UpperCAmelCase						:				Optional[Any]		  =			timesteps.to(original_samples.device       ,    dtype=torch.floataa				)
      else:
           UpperCAmelCase						:				Optional[int]		  =			self.timesteps.to(original_samples.device				)
           UpperCAmelCase						:				int		  =			timesteps.to(original_samples.device				)

      UpperCAmelCase						:				Optional[Any]		  =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      UpperCAmelCase						:				Dict		  =			sigmas[step_indices].flatten()
      while len(sigma.shape				) < len(original_samples.shape				):
           UpperCAmelCase						:				List[Any]		  =			sigma.unsqueeze(-1				)

      UpperCAmelCase						:				Union[str, Any]		  =			original_samples + noise * sigma
      return noisy_samples

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

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

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      UpperCAmelCase						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

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

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			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=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      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
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

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

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

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps



