

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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : torch.FloatTensor
      lowerCamelCase_      : Optional[torch.FloatTensor]  			=   None



def   UpperCamelCase(   lowercase_     ,   lowercase_=0.9_99     ,   lowercase_="cosine"     ,   )      ->							Union[str, Any]:





  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase_				):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

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

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

  snake_case_					= []
  for i in range(lowercase_				):
    snake_case_					= i / num_diffusion_timesteps
    snake_case_					= (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase_				) / alpha_bar_fn(lowercase_				)     ,   lowercase_				)				)
  return torch.tensor(lowercase_     ,   dtype=torch.floataa				)







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : List[str]  			=   [e.name for e in KarrasDiffusionSchedulers]
      lowerCamelCase_      : Any  			=   1







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = "linear"				,    lowerCamelCase = False				,    lowerCamelCase = "linspace"				,    lowerCamelCase = 0				,    )		->			Optional[Any]:
        if trained_betas is not None:
          snake_case_					= torch.tensor(lowerCamelCase				,    dtype=torch.floataa  )
        elif beta_schedule == "linear":
          snake_case_					= torch.linspace(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          snake_case_					= (
              torch.linspace(beta_start**0.5				,    beta_end**0.5				,    lowerCamelCase				,    dtype=torch.floataa  ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          snake_case_					= betas_for_alpha_bar(lowerCamelCase  )
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

        snake_case_					= 1.0 - self.betas
        snake_case_					= torch.cumprod(self.alphas				,    dim=0  )

        snake_case_					= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case_					= np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
        snake_case_					= torch.from_numpy(lowerCamelCase  )

        # setable values
        snake_case_					= None
        snake_case_					= np.linspace(0				,    num_train_timesteps - 1				,    lowerCamelCase				,    dtype=lowerCamelCase  )[::-1].copy()
        snake_case_					= torch.from_numpy(lowerCamelCase  )
        snake_case_					= False
        snake_case_					= use_karras_sigmas







      @property
      def       lowerCAmelCase_       (	self  )		->			List[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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			torch.FloatTensor:
        if isinstance(lowerCamelCase				,    torch.Tensor  ):
          snake_case_					= timestep.to(self.timesteps.device  )
        snake_case_					= (self.timesteps == timestep).nonzero().item()
        snake_case_					= self.sigmas[step_index]

        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)

        snake_case_					= True
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None  )		->			Tuple:
        snake_case_					= 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":
          snake_case_					= np.linspace(0				,    self.config.num_train_timesteps - 1				,    lowerCamelCase				,    dtype=lowerCamelCase  )[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          snake_case_					= 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
          snake_case_					= (np.arange(0				,    lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase  )
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          snake_case_					= 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
          snake_case_					= (np.arange(self.config.num_train_timesteps				,    0				,    -step_ratio  )).round().copy().astype(lowerCamelCase  )
          timesteps -= 1
        else:
          raise ValueError(
              f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

        snake_case_					= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case_					= np.log(lowerCamelCase  )

        if self.config.interpolation_type == "linear":
          snake_case_					= np.interp(lowerCamelCase				,    np.arange(0				,    len(lowerCamelCase  )  )				,    lowerCamelCase  )
        elif self.config.interpolation_type == "log_linear":
          snake_case_					= torch.linspace(np.log(sigmas[-1]  )				,    np.log(sigmas[0]  )				,    num_inference_steps + 1  ).exp()
        else:
          raise ValueError(
              f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
              """ 'linear' or 'log_linear'"""  )

        if self.use_karras_sigmas:
          snake_case_					= self._convert_to_karras(in_sigmas=lowerCamelCase				,    num_inference_steps=self.num_inference_steps  )
          snake_case_					= np.array([self._sigma_to_t(lowerCamelCase				,    lowerCamelCase  ) for sigma in sigmas]  )

        snake_case_					= np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        snake_case_					= torch.from_numpy(lowerCamelCase  ).to(device=lowerCamelCase  )
        if str(lowerCamelCase  ).startswith("""mps"""  ):
          # mps does not support float64
          snake_case_					= torch.from_numpy(lowerCamelCase  ).to(lowerCamelCase				,    dtype=torch.floataa  )
        else:
          snake_case_					= torch.from_numpy(lowerCamelCase  ).to(device=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			Dict:
        # get log sigma
        snake_case_					= np.log(lowerCamelCase  )

        # get distribution
        snake_case_					= log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        snake_case_					= np.cumsum((dists >= 0)				,    axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
        snake_case_					= low_idx + 1

        snake_case_					= log_sigmas[low_idx]
        snake_case_					= log_sigmas[high_idx]

        # interpolate sigmas
        snake_case_					= (low - log_sigma) / (low - high)
        snake_case_					= np.clip(lowerCamelCase				,    0				,    1  )

        # transform interpolation to time range
        snake_case_					= (1 - w) * low_idx + w * high_idx
        snake_case_					= t.reshape(sigma.shape  )
        return t







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			torch.FloatTensor:
        snake_case_					= in_sigmas[-1].item()
        snake_case_					= in_sigmas[0].item()

        snake_case_					= 7.0  # 7.0 is the value used in the paper
        snake_case_					= np.linspace(0				,    1				,    lowerCamelCase  )
        snake_case_					= sigma_min ** (1 / rho)
        snake_case_					= sigma_max ** (1 / rho)
        snake_case_					= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 0.0				,    lowerCamelCase = 0.0				,    lowerCamelCase = float("""inf"""  )				,    lowerCamelCase = 1.0				,    lowerCamelCase = None				,    lowerCamelCase = True				,    )		->			Union[EulerDiscreteSchedulerOutput, Tuple]:

        if (
            isinstance(lowerCamelCase				,    lowerCamelCase  )
            or isinstance(lowerCamelCase				,    torch.IntTensor  )
            or isinstance(lowerCamelCase				,    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(lowerCamelCase				,    torch.Tensor  ):
          snake_case_					= timestep.to(self.timesteps.device  )

        snake_case_					= (self.timesteps == timestep).nonzero().item()
        snake_case_					= self.sigmas[step_index]

        snake_case_					= min(s_churn / (len(self.sigmas  ) - 1)				,    2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

        snake_case_					= randn_tensor(
            model_output.shape				,    dtype=model_output.dtype				,    device=model_output.device				,    generator=lowerCamelCase  )

        snake_case_					= noise * s_noise
        snake_case_					= sigma * (gamma + 1)

        if gamma > 0:
          snake_case_					= sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
        # backwards compatibility
        if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
          snake_case_					= model_output
        elif self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma_hat * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma_hat

        snake_case_					= self.sigmas[step_index + 1] - sigma_hat

        snake_case_					= sample + derivative * dt

        if not return_dict:
          return (prev_sample,)

        return EulerDiscreteSchedulerOutput(prev_sample=lowerCamelCase				,    pred_original_sample=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case_					= self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype  )
        if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase  ):
          # mps does not support float64
          snake_case_					= self.timesteps.to(original_samples.device				,    dtype=torch.floataa  )
          snake_case_					= timesteps.to(original_samples.device				,    dtype=torch.floataa  )
        else:
          snake_case_					= self.timesteps.to(original_samples.device  )
          snake_case_					= timesteps.to(original_samples.device  )

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

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

        snake_case_					= original_samples + noise * sigma
        return noisy_samples







      def __len__(	self  )		->			str:
        return self.config.num_train_timesteps


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : CommonSchedulerState

      # setable values
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : Optional[int]  			=   None

      # running values
      lowerCamelCase_      : Optional[jnp.ndarray]  			=   None







      @classmethod
      def       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

        snake_case_					= jnp.arange(0				,    self.config.num_train_timesteps  ).round()[::-1]
        snake_case_					= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_					= sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

        snake_case_					= state.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    state.sigmas[t]				,    state.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

        snake_case_					= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_					= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_					= jnp.concatenate([sigmas, jnp.array([0.0]				,    dtype=self.dtype  )]  )

        snake_case_					= timesteps.astype(jnp.intaa  )

        # initial running values
        snake_case_					= jnp.zeros((0,) + shape				,    dtype=self.dtype  )

        return state.replace(
            timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    num_inference_steps=lowerCamelCase				,    derivatives=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

        snake_case_					= state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        snake_case_					= state.replace(derivatives=jnp.append(state.derivatives				,    lowerCamelCase  )  )
        if len(state.derivatives  ) > order:
          snake_case_					= state.replace(derivatives=jnp.delete(state.derivatives				,    0  )  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(timestep + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(state.derivatives  )  )  )

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,    state=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







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