
'''simple docstring'''
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


A__ :		Optional[int]					=logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			__A					(			snake_case_      ):
 lowerCamelCase												=42
 lowerCamelCase												=None







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]=0.999      ,  __SCREAMING_SNAKE_CASE    :	List[Any]="cosine"      ,  )					->							Tuple:





  """simple docstring"""

  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__SCREAMING_SNAKE_CASE    :	List[str]			):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__SCREAMING_SNAKE_CASE    :	List[Any]			):
      return math.exp(t * -12.0			)

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

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







class 			__A					(			snake_case_     ,    snake_case_      ):
 lowerCamelCase												=[e.name for e in KarrasDiffusionSchedulers]
 lowerCamelCase												=1
 @register_to_config
 def __init__( self      :     Dict					, lowerCamelCase      :     Union[str, Any] = 10_00					, lowerCamelCase      :     Tuple = 0.0_001					, lowerCamelCase      :     List[Any] = 0.02					, lowerCamelCase      :     int = "linear"					, lowerCamelCase      :     Optional[Any] = None					, lowerCamelCase      :     str = "epsilon"					, lowerCamelCase      :     Optional[int] = "linspace"					, lowerCamelCase      :     str = 0					, ):



   """simple docstring"""





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

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

   __A		:  int       				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
   __A		:  List[str]       				=		np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
   __A		:  List[Any]       				=		torch.from_numpy(lowercase_							)

   # setable values
   __A		:  Optional[int]       				=		None
   __A		:  Tuple       				=		np.linspace(0					, num_train_timesteps - 1					, lowercase_					, dtype=lowercase_							)[::-1].copy()
   __A		:  str       				=		torch.from_numpy(lowercase_							)
   __A		:  List[str]       				=		False
 @property
 def 		lowercase_( self      :     Dict							):



   """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      :     Optional[int]					, lowerCamelCase      :     Any					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   if isinstance(lowercase_					, torch.Tensor							):
     __A		:  Union[str, Any]       				=		timestep.to(self.timesteps.device							)
   __A		:  Dict       				=		(self.timesteps == timestep).nonzero().item()
   __A		:  List[str]       				=		self.sigmas[step_index]
   __A		:  Optional[Any]       				=		sample / ((sigma**2 + 1) ** 0.5)
   __A		:  Any       				=		True
   return sample
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Optional[int] = None							):



   """simple docstring"""





   __A		:  str       				=		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":
     __A		:  int       				=		np.linspace(0					, self.config.num_train_timesteps - 1					, lowercase_					, dtype=lowercase_							)[
         ::-1
     ].copy()
   elif self.config.timestep_spacing == "leading":
     __A		:  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
     __A		:  Optional[int]       				=		(np.arange(0					, lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
     timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
     __A		:  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
     __A		:  str       				=		(np.arange(self.config.num_train_timesteps					, 0					, -step_ratio							)).round().copy().astype(lowercase_							)
     timesteps -= 1
   else:
     raise ValueError(
         f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

   __A		:  List[Any]       				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
   __A		:  Optional[Any]       				=		np.interp(lowercase_					, np.arange(0					, len(lowercase_							)							)					, lowercase_							)
   __A		:  Any       				=		np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
   __A		:  Any       				=		torch.from_numpy(lowercase_							).to(device=lowercase_							)
   if str(lowercase_							).startswith("""mps"""							):
     # mps does not support float64
     __A		:  int       				=		torch.from_numpy(lowercase_							).to(lowercase_					, dtype=torch.floataa							)
   else:
     __A		:  Union[str, Any]       				=		torch.from_numpy(lowercase_							).to(device=lowercase_							)
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     Any					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     Any					, lowerCamelCase      :     Tuple = None					, lowerCamelCase      :     Optional[int] = True					, ):



   """simple docstring"""





   if (
       isinstance(lowercase_					, lowercase_							)
       or isinstance(lowercase_					, torch.IntTensor							)
       or isinstance(lowercase_					, 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(lowercase_					, torch.Tensor							):
     __A		:  Any       				=		timestep.to(self.timesteps.device							)

   __A		:  Tuple       				=		(self.timesteps == timestep).nonzero().item()
   __A		:  Dict       				=		self.sigmas[step_index]

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
     __A		:  Any       				=		sample - sigma * model_output
   elif self.config.prediction_type == "v_prediction":
     # * c_out + input * c_skip
     __A		:  Optional[int]       				=		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`"							)

   __A		:  Any       				=		self.sigmas[step_index]
   __A		:  Tuple       				=		self.sigmas[step_index + 1]
   __A		:  Tuple       				=		(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
   __A		:  Union[str, Any]       				=		(sigma_to**2 - sigma_up**2) ** 0.5

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

   __A		:  Dict       				=		sigma_down - sigma

   __A		:  List[str]       				=		sample + derivative * dt

   __A		:  str       				=		model_output.device
   __A		:  str       				=		randn_tensor(model_output.shape					, dtype=model_output.dtype					, device=lowercase_					, generator=lowercase_							)

   __A		:  Dict       				=		prev_sample + noise * sigma_up

   if not return_dict:
     return (prev_sample,)

   return EulerAncestralDiscreteSchedulerOutput(
       prev_sample=lowercase_					, pred_original_sample=lowercase_							)
 def 		lowercase_( self      :     Dict					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Optional[Any]					, ):



   """simple docstring"""





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

   __A		:  List[str]       				=		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

   __A		:  Optional[Any]       				=		original_samples + noise * sigma
   return noisy_samples






 def __len__( self      :     List[Any]							):



   """simple docstring"""





   return self.config.num_train_timesteps








def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					str(SCREAMING_SNAKE_CASE__  ).strip()
						if not bin_string:
												raise ValueError("""Empty string was passed to the function"""  )
						lowerCAmelCase	:   Optional[int]					     =					bin_string[0] == """-"""
						if is_negative:
												lowerCAmelCase	:   Tuple					     =					bin_string[1:]
						if not all(char in """01""" for char in bin_string  ):
												raise ValueError("""Non-binary value was passed to the function"""  )
						lowerCAmelCase	:   Union[str, Any]					     =					0
						for char in bin_string:
												lowerCAmelCase	:   int					     =					2 * decimal_number + int(SCREAMING_SNAKE_CASE__  )
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()

