


'''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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Union[str, Any]            =       42
 _lowerCamelCase			:       str            =       None







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					str=0.9_9_9     ,		_UpperCamelCase   :					int="cosine"     ,		)		->       Optional[Any]:





   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_UpperCamelCase   :					Optional[int] ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_UpperCamelCase   :					int ):
         return math.exp(t * -1_2.0 )

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

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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Union[str, Any]            =       [e.name for e in KarrasDiffusionSchedulers]
 _lowerCamelCase			:       str            =       1

 @register_to_config
 def __init__(			self			:						Tuple,       a_			:						int = 1000,       a_			:						float = 0.0_001,       a_			:						float = 0.02,       a_			:						str = "linear",       a_			:						Optional[Union[np.ndarray, List[float]]] = None,       a_			:						str = "epsilon",       a_			:						str = "linspace",       a_			:						int = 0,       ):





    """simple docstring"""



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

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

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

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

 @property
 def 		lowercase_    (			self			:						List[str]       ):





    """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			:						List[str],       a_			:						torch.FloatTensor,       a_			:						Union[float, torch.FloatTensor]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict,       a_			:						int,       a_			:						Union[str, torch.device] = None       ):





    """simple docstring"""



    UpperCamelCase__			    =    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__			    =    np.linspace(0,       self.config.num_train_timesteps - 1,       lowercase__,       dtype=lowercase__       )[
           ::-1
       ].copy()
    elif self.config.timestep_spacing == "leading":
       UpperCamelCase__			    =    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__			    =    (np.arange(0,       lowercase__       ) * step_ratio).round()[::-1].copy().astype(lowercase__       )
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       UpperCamelCase__			    =    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__			    =    (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\'.'       )

    UpperCamelCase__			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    UpperCamelCase__			    =    np.interp(lowercase__,       np.arange(0,       len(lowercase__       )       ),       lowercase__       )
    UpperCamelCase__			    =    np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
    UpperCamelCase__			    =    torch.from_numpy(lowercase__       ).to(device=lowercase__       )
    if str(lowercase__       ).startswith("mps"       ):
       # mps does not support float64
       UpperCamelCase__			    =    torch.from_numpy(lowercase__       ).to(lowercase__,       dtype=torch.floataa       )
    else:
       UpperCamelCase__			    =    torch.from_numpy(lowercase__       ).to(device=lowercase__       )

 def 		lowercase_    (			self			:						Optional[int],       a_			:						torch.FloatTensor,       a_			:						Union[float, torch.FloatTensor],       a_			:						torch.FloatTensor,       a_			:						Optional[torch.Generator] = None,       a_			:						bool = 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       ):
       UpperCamelCase__			    =    timestep.to(self.timesteps.device       )

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

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       UpperCamelCase__			    =    sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       UpperCamelCase__			    =    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__			    =    self.sigmas[step_index]
    UpperCamelCase__			    =    self.sigmas[step_index + 1]
    UpperCamelCase__			    =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
    UpperCamelCase__			    =    (sigma_to**2 - sigma_up**2) ** 0.5

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

    UpperCamelCase__			    =    sigma_down - sigma

    UpperCamelCase__			    =    sample + derivative * dt

    UpperCamelCase__			    =    model_output.device
    UpperCamelCase__			    =    randn_tensor(model_output.shape,       dtype=model_output.dtype,       device=lowercase__,       generator=lowercase__       )

    UpperCamelCase__			    =    prev_sample + noise * sigma_up

    if not return_dict:
       return (prev_sample,)

    return EulerAncestralDiscreteSchedulerOutput(
        prev_sample=lowercase__,       pred_original_sample=lowercase__       )

 def 		lowercase_    (			self			:						int,       a_			:						torch.FloatTensor,       a_			:						torch.FloatTensor,       a_			:						torch.FloatTensor,       ):





    """simple docstring"""



    UpperCamelCase__			    =    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
       UpperCamelCase__			    =    self.timesteps.to(original_samples.device,       dtype=torch.floataa       )
       UpperCamelCase__			    =    timesteps.to(original_samples.device,       dtype=torch.floataa       )
    else:
       UpperCamelCase__			    =    self.timesteps.to(original_samples.device       )
       UpperCamelCase__			    =    timesteps.to(original_samples.device       )

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

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

    UpperCamelCase__			    =    original_samples + noise * sigma
    return noisy_samples

 def __len__(			self			:						Dict       ):





    """simple docstring"""



    return self.config.num_train_timesteps







'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


