





'''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    _A   (				__lowercase		):
  lowercase__:      torch.FloatTensor
  lowercase__:      Optional[torch.FloatTensor]							    =      None







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase=0.9_99						,  _lowerCamelCase="cosine"						,  )   ->							int:



   """simple docstring"""


   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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




class    _A   (				__lowercase	,				__lowercase		):
  lowercase__:      Tuple							    =      [e.name for e in KarrasDiffusionSchedulers]
  lowercase__:      List[Any]							    =      1

  @register_to_config
  def __init__(      self				:      Optional[int]     ,			__magic_name__				:      int = 10_00     ,			__magic_name__				:      float = 0.0001     ,			__magic_name__				:      float = 0.02     ,			__magic_name__				:      str = "linear"     ,			__magic_name__				:      Optional[Union[np.ndarray, List[float]]] = None     ,			__magic_name__				:      Optional[bool] = False     ,			__magic_name__				:      str = "epsilon"     ,			__magic_name__				:      str = "linspace"     ,			__magic_name__				:      int = 0     ,			)				->						List[Any]:


     """simple docstring"""

     if trained_betas is not None:
        __snake_case				:			Union[str, Any]       			=    torch.tensor(__magic_name__     ,			dtype=torch.floataa )
     elif beta_schedule == "linear":
        __snake_case				:			List[str]       			=    torch.linspace(__magic_name__     ,			__magic_name__     ,			__magic_name__     ,			dtype=torch.floataa )
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        __snake_case				:			Any       			=    (
            torch.linspace(beta_start**0.5     ,			beta_end**0.5     ,			__magic_name__     ,			dtype=torch.floataa ) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        __snake_case				:			Optional[Any]       			=    betas_for_alpha_bar(__magic_name__ )
     else:
        raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

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

     # setable values
     __snake_case				:			str       			=    None
     __snake_case				:			Union[str, Any]       			=    use_karras_sigmas
     self.set_timesteps(__magic_name__     ,			__magic_name__ )
     __snake_case				:			str       			=    []
     __snake_case				:			str       			=    False

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


     """simple docstring"""

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

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

  def   lowercase__    (      self				:      str     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      Union[float, torch.FloatTensor] )				->						torch.FloatTensor:


     """simple docstring"""

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

  def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      int )				->						List[str]:


     """simple docstring"""

     def lms_derivative(__magic_name__				:      Optional[int] ):
        __snake_case				:			Any       			=    1.0
        for k in range(__magic_name__ ):
           if current_order == k:
              continue
           prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

     __snake_case				:			str       			=    integrate.quad(__magic_name__     ,			self.sigmas[t]     ,			self.sigmas[t + 1]     ,			epsrel=1E-4 )[0]

     return integrated_coeff

  def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      int     ,			__magic_name__				:      Union[str, torch.device] = None )				->						Tuple:


     """simple docstring"""

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

     if self.use_karras_sigmas:
        __snake_case				:			Optional[Any]       			=    self._convert_to_karras(in_sigmas=__magic_name__ )
        __snake_case				:			Union[str, Any]       			=    np.array([self._sigma_to_t(__magic_name__     ,			__magic_name__ ) for sigma in sigmas] )

     __snake_case				:			Tuple       			=    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )

     __snake_case				:			Optional[int]       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )
     if str(__magic_name__ ).startswith("""mps""" ):
        # mps does not support float64
        __snake_case				:			int       			=    torch.from_numpy(__magic_name__ ).to(__magic_name__     ,			dtype=torch.floataa )
     else:
        __snake_case				:			Dict       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )

     __snake_case				:			Optional[int]       			=    []

  def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      List[Any] )				->						List[Any]:


     """simple docstring"""

     __snake_case				:			List[str]       			=    np.log(__magic_name__ )

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

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

     __snake_case				:			Dict       			=    log_sigmas[low_idx]
     __snake_case				:			Any       			=    log_sigmas[high_idx]

     # interpolate sigmas
     __snake_case				:			List[Any]       			=    (low - log_sigma) / (low - high)
     __snake_case				:			Optional[int]       			=    np.clip(__magic_name__     ,			0     ,			1 )

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

  def   lowercase__    (      self				:      Optional[int]     ,			__magic_name__				:      torch.FloatTensor )				->						torch.FloatTensor:


     """simple docstring"""

     __snake_case				:			float       			=    in_sigmas[-1].item()
     __snake_case				:			float       			=    in_sigmas[0].item()

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

  def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      Union[float, torch.FloatTensor]     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      int = 4     ,			__magic_name__				:      bool = True     ,			)				->						Union[LMSDiscreteSchedulerOutput, Tuple]:


     """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(__magic_name__     ,			torch.Tensor ):
        __snake_case				:			Tuple       			=    timestep.to(self.timesteps.device )
     __snake_case				:			Any       			=    (self.timesteps == timestep).nonzero().item()
     __snake_case				:			Optional[Any]       			=    self.sigmas[step_index]

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

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

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

     if not return_dict:
        return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=__magic_name__     ,			pred_original_sample=__magic_name__ )

  def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			)				->						torch.FloatTensor:


     """simple docstring"""

     __snake_case				:			List[str]       			=    self.sigmas.to(device=original_samples.device     ,			dtype=original_samples.dtype )
     if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__ ):
        # mps does not support float64
        __snake_case				:			str       			=    self.timesteps.to(original_samples.device     ,			dtype=torch.floataa )
        __snake_case				:			List[Any]       			=    timesteps.to(original_samples.device     ,			dtype=torch.floataa )
     else:
        __snake_case				:			Tuple       			=    self.timesteps.to(original_samples.device )
        __snake_case				:			Union[str, Any]       			=    timesteps.to(original_samples.device )

     __snake_case				:			int       			=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

     __snake_case				:			List[str]       			=    original_samples + noise * sigma
     return noisy_samples

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


     """simple docstring"""

     return self.config.num_train_timesteps









'''simple docstring'''






import torch
from torch import autograd




class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[Any]     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float     ,			__magic_name__				:      bool )				->						Optional[int]:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    inputs.numel()
					__snake_case				:			List[Any]       			=    int(0.005 * nb_elems ) + 1
					if sigmoid:
								__snake_case				:			Dict       			=    (torch.sigmoid(__magic_name__ ) > threshold).type(inputs.type() )
					else:
								__snake_case				:			Tuple       			=    (inputs > threshold).type(inputs.type() )
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__snake_case				:			Tuple       			=    inputs.flatten().kthvalue(max(nb_elems - nb_min     ,			1 ) ).values
								__snake_case				:			Union[str, Any]       			=    (inputs > k_threshold).type(inputs.type() )
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      int     ,			__magic_name__				:      Any )				->						Tuple:


					"""simple docstring"""

					return gradOutput, None, None





class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      Tuple     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						int:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Dict       			=    inputs.flatten().sort(descending=__magic_name__ )
					__snake_case				:			List[Any]       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Optional[Any]       			=    mask.flatten()
					__snake_case				:			List[str]       			=    0
					__snake_case				:			Any       			=    1
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[str]     ,			__magic_name__				:      Any )				->						Optional[Any]:


					"""simple docstring"""

					return gradOutput, None





class    _A   (				__lowercase		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Tuple       			=    inputs.abs().flatten().sort(descending=__magic_name__ )
					__snake_case				:			Any       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Union[str, Any]       			=    mask.flatten()
					__snake_case				:			Tuple       			=    0
					__snake_case				:			int       			=    1
					return mask


