
"""simple docstring"""







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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 		UpperCAmelCase_	(A_						):
	"""simple docstring"""




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




def 			A_			(					lowercase						, lowercase=0.999						, lowercase="cosine"						, )     ->							List[Any]:


					"""simple docstring"""







					if alpha_transform_type == "cosine":

										def alpha_bar_fn(lowercase      ):
															return math.cos((t + 0.008) / 1.008 * 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}'''      )

					UpperCAmelCase_						:    List[Any]    					=  []
					for i in range(lowercase__      ):
										UpperCAmelCase_						:    Optional[Any]    					=  i / num_diffusion_timesteps
										UpperCAmelCase_						:    Any    					=  (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 		UpperCAmelCase_	(A_  , A_						):
	"""simple docstring"""




	UpperCamelCase_						:     Any				   =    [e.name for e in KarrasDiffusionSchedulers]
	UpperCamelCase_						:     Union[str, Any]				   =    1




	@register_to_config
	def __init__( self       : int     ,   a_       : List[Any] = 10_00     ,   a_       : List[str] = 0.0001     ,   a_       : Any = 0.02     ,   a_       : List[Any] = "linear"     ,   a_       : Tuple = None     ,   a_       : Optional[Any] = False     ,   a_       : Optional[int] = "epsilon"     ,   a_       : Tuple = "linspace"     ,   a_       : Union[str, Any] = 0     ,   )->					int:



						"""simple docstring"""

						if trained_betas is not None:
											UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(a_     ,   dtype=torch.floataa )
						elif beta_schedule == "linear":
											UpperCAmelCase_						:    List[Any]    					=  torch.linspace(a_     ,   a_     ,   a_     ,   dtype=torch.floataa )
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											UpperCAmelCase_						:    int    					=  (
											    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_						:    Dict    					=  betas_for_alpha_bar(a_ )
						else:
											raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

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

						# setable values
						UpperCAmelCase_						:    Union[str, Any]    					=  None
						UpperCAmelCase_						:    List[Any]    					=  use_karras_sigmas
						self.set_timesteps(a_     ,   a_ )
						UpperCAmelCase_						:    Any    					=  []
						UpperCAmelCase_						:    Any    					=  False




	@property
	def   a      ( self       : Optional[int] )->					Optional[Any]:



						"""simple docstring"""

						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5




	def   a      ( self       : Optional[Any]     ,   a_       : Union[str, Any]     ,   a_       : Optional[Any] )->					Optional[Any]:



						"""simple docstring"""

						if isinstance(a_     ,   torch.Tensor ):
											UpperCAmelCase_						:    Optional[int]    					=  timestep.to(self.timesteps.device )
						UpperCAmelCase_						:    str    					=  (self.timesteps == timestep).nonzero().item()
						UpperCAmelCase_						:    str    					=  self.sigmas[step_index]
						UpperCAmelCase_						:    Optional[int]    					=  sample / ((sigma**2 + 1) ** 0.5)
						UpperCAmelCase_						:    str    					=  True
						return sample




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



						"""simple docstring"""

						def lms_derivative(a_       : List[Any] ):
											UpperCAmelCase_						:    str    					=  1.0
											for k in range(a_ ):
																if current_order == k:
																					continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
											return prod

						UpperCAmelCase_						:    List[Any]    					=  integrate.quad(a_     ,   self.sigmas[t]     ,   self.sigmas[t + 1]     ,   epsrel=1E-4 )[0]

						return integrated_coeff




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



						"""simple docstring"""

						UpperCAmelCase_						:    Optional[int]    					=  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_						:    int    					=  np.linspace(0     ,   self.config.num_train_timesteps - 1     ,   a_     ,   dtype=a_ )[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											UpperCAmelCase_						:    Optional[int]    					=  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_						:    int    					=  (np.arange(0     ,   a_ ) * step_ratio).round()[::-1].copy().astype(a_ )
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											UpperCAmelCase_						:    Tuple    					=  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_						:    Optional[int]    					=  (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_						:    List[str]    					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
						UpperCAmelCase_						:    Tuple    					=  np.log(a_ )
						UpperCAmelCase_						:    Optional[Any]    					=  np.interp(a_     ,   np.arange(0     ,   len(a_ ) )     ,   a_ )

						if self.use_karras_sigmas:
											UpperCAmelCase_						:    Any    					=  self._convert_to_karras(in_sigmas=a_ )
											UpperCAmelCase_						:    int    					=  np.array([self._sigma_to_t(a_     ,   a_ ) for sigma in sigmas] )

						UpperCAmelCase_						:    Optional[int]    					=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )

						UpperCAmelCase_						:    int    					=  torch.from_numpy(a_ ).to(device=a_ )
						if str(a_ ).startswith("""mps""" ):
											# mps does not support float64
											UpperCAmelCase_						:    Any    					=  torch.from_numpy(a_ ).to(a_     ,   dtype=torch.floataa )
						else:
											UpperCAmelCase_						:    Any    					=  torch.from_numpy(a_ ).to(device=a_ )

						UpperCAmelCase_						:    List[str]    					=  []




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



						"""simple docstring"""

						UpperCAmelCase_						:    List[Any]    					=  np.log(a_ )

						# get distribution
						UpperCAmelCase_						:    List[Any]    					=  log_sigma - log_sigmas[:, np.newaxis]

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

						UpperCAmelCase_						:    List[Any]    					=  log_sigmas[low_idx]
						UpperCAmelCase_						:    List[Any]    					=  log_sigmas[high_idx]

						# interpolate sigmas
						UpperCAmelCase_						:    Tuple    					=  (low - log_sigma) / (low - high)
						UpperCAmelCase_						:    Any    					=  np.clip(a_     ,   0     ,   1 )

						# transform interpolation to time range
						UpperCAmelCase_						:    Dict    					=  (1 - w) * low_idx + w * high_idx
						UpperCAmelCase_						:    Optional[int]    					=  t.reshape(sigma.shape )
						return t




	def   a      ( self       : Optional[Any]     ,   a_       : List[Any] )->					List[str]:



						"""simple docstring"""

						UpperCAmelCase_						:    Optional[Any]    					=  in_sigmas[-1].item()
						UpperCAmelCase_						:    Tuple    					=  in_sigmas[0].item()

						UpperCAmelCase_						:    Union[str, Any]    					=  7.0  # 7.0 is the value used in the paper
						UpperCAmelCase_						:    Any    					=  np.linspace(0     ,   1     ,   self.num_inference_steps )
						UpperCAmelCase_						:    Any    					=  sigma_min ** (1 / rho)
						UpperCAmelCase_						:    List[Any]    					=  sigma_max ** (1 / rho)
						UpperCAmelCase_						:    List[Any]    					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas




	def   a      ( self       : Dict     ,   a_       : Optional[int]     ,   a_       : Tuple     ,   a_       : Union[str, Any]     ,   a_       : Union[str, Any] = 4     ,   a_       : List[Any] = True     ,   )->					List[Any]:



						"""simple docstring"""

						if not self.is_scale_input_called:
											warnings.warn(
											    """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_						:    Union[str, Any]    					=  timestep.to(self.timesteps.device )
						UpperCAmelCase_						:    Tuple    					=  (self.timesteps == timestep).nonzero().item()
						UpperCAmelCase_						:    Optional[Any]    					=  self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											UpperCAmelCase_						:    Tuple    					=  sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											UpperCAmelCase_						:    List[Any]    					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
											UpperCAmelCase_						:    Union[str, Any]    					=  model_output
						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
						UpperCAmelCase_						:    Optional[Any]    					=  (sample - pred_original_sample) / sigma
						self.derivatives.append(a_ )
						if len(self.derivatives ) > order:
											self.derivatives.pop(0 )

						# 3. Compute linear multistep coefficients
						UpperCAmelCase_						:    List[Any]    					=  min(step_index + 1     ,   a_ )
						UpperCAmelCase_						:    str    					=  [self.get_lms_coefficient(a_     ,   a_     ,   a_ ) for curr_order in range(a_ )]

						# 4. Compute previous sample based on the derivatives path
						UpperCAmelCase_						:    Any    					=  sample + sum(
						    coeff * derivative for coeff, derivative in zip(a_     ,   reversed(self.derivatives ) ) )

						if not return_dict:
											return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=a_     ,   pred_original_sample=a_ )




	def   a      ( self       : int     ,   a_       : Any     ,   a_       : str     ,   a_       : Union[str, Any]     ,   )->					int:



						"""simple docstring"""

						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_						:    Optional[int]    					=  self.timesteps.to(original_samples.device     ,   dtype=torch.floataa )
											UpperCAmelCase_						:    str    					=  timesteps.to(original_samples.device     ,   dtype=torch.floataa )
						else:
											UpperCAmelCase_						:    Dict    					=  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_						:    Optional[Any]    					=  sigmas[step_indices].flatten()
						while len(sigma.shape ) < len(original_samples.shape ):
											UpperCAmelCase_						:    int    					=  sigma.unsqueeze(-1 )

						UpperCAmelCase_						:    Dict    					=  original_samples + noise * sigma
						return noisy_samples






	def __len__( self       : int )->					str:



						"""simple docstring"""

						return self.config.num_train_timesteps



"""simple docstring"""







import warnings

from .generation import GenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 warnings.warn(
     """Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will """
     """be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."""  , lowerCamelCase_  , )

