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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							torch.FloatTensor
   A				:							Optional[torch.FloatTensor]			     =     None



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=0.999	,							SCREAMING_SNAKE_CASE_="cosine"	,							):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE_				):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

     elif alpha_transform_type == "exp":

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

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

     lowercase_       :							Optional[int]  					=       []
     for i in range(SCREAMING_SNAKE_CASE_				):
          lowercase_       :							Optional[Any]  					=       i / num_diffusion_timesteps
          lowercase_       :							List[str]  					=       (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    UpperCAmelCase__	(							_snake_case       ,  _snake_case      ):






   """simple docstring"""
   A				:							List[str]			     =     [e.name for e in KarrasDiffusionSchedulers]
   A				:							Union[str, Any]			     =     1
   @register_to_config
   def __init__(self							,					_a = 1_000							,					_a = 0.00_01							,					_a = 0.02							,					_a = "linear"							,					_a = None							,					_a = "epsilon"							,					_a = "linspace"							,					_a = 0							,					)     ->  Tuple:
        if trained_betas is not None:
             lowercase_       :							str  					=       torch.tensor(_a							,					dtype=torch.floataa					)
        elif beta_schedule == "linear":
             lowercase_       :							Dict  					=       torch.linspace(_a							,					_a							,					_a							,					dtype=torch.floataa					)
        elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             lowercase_       :							str  					=       (
                 torch.linspace(beta_start**0.5							,					beta_end**0.5							,					_a							,					dtype=torch.floataa					) ** 2
             )
        elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             lowercase_       :							Any  					=       betas_for_alpha_bar(_a					)
        else:
             raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

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

        # setable values
        lowercase_       :							List[Any]  					=       None
        lowercase_       :							Union[str, Any]  					=       np.linspace(0							,					num_train_timesteps - 1							,					_a							,					dtype=_a					)[::-1].copy()
        lowercase_       :							List[str]  					=       torch.from_numpy(_a					)
        lowercase_       :							Optional[int]  					=       False
   @property
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        # 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							,					_a							,					_a					)     ->  torch.FloatTensor:
        if isinstance(_a							,					torch.Tensor					):
             lowercase_       :							Dict  					=       timestep.to(self.timesteps.device					)
        lowercase_       :							Any  					=       (self.timesteps == timestep).nonzero().item()
        lowercase_       :							Optional[int]  					=       self.sigmas[step_index]
        lowercase_       :							List[Any]  					=       sample / ((sigma**2 + 1) ** 0.5)
        lowercase_       :							List[str]  					=       True
        return sample
   def 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  int:
        lowercase_       :							Tuple  					=       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":
             lowercase_       :							Tuple  					=       np.linspace(0							,					self.config.num_train_timesteps - 1							,					_a							,					dtype=_a					)[
                 ::-1
             ].copy()
        elif self.config.timestep_spacing == "leading":
             lowercase_       :							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
             lowercase_       :							Optional[Any]  					=       (np.arange(0							,					_a					) * step_ratio).round()[::-1].copy().astype(_a					)
             timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
             lowercase_       :							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
             lowercase_       :							List[str]  					=       (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\'.'''					)

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

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

        lowercase_       :							List[Any]  					=       (self.timesteps == timestep).nonzero().item()
        lowercase_       :							Optional[int]  					=       self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
             lowercase_       :							List[Any]  					=       sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             lowercase_       :							Union[str, Any]  					=       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`'''					)

        lowercase_       :							Optional[Any]  					=       self.sigmas[step_index]
        lowercase_       :							Optional[Any]  					=       self.sigmas[step_index + 1]
        lowercase_       :							Tuple  					=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowercase_       :							Tuple  					=       (sigma_to**2 - sigma_up**2) ** 0.5

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

        lowercase_       :							str  					=       sigma_down - sigma

        lowercase_       :							Any  					=       sample + derivative * dt

        lowercase_       :							Dict  					=       model_output.device
        lowercase_       :							Optional[int]  					=       randn_tensor(model_output.shape							,					dtype=model_output.dtype							,					device=_a							,					generator=_a					)

        lowercase_       :							Union[str, Any]  					=       prev_sample + noise * sigma_up

        if not return_dict:
             return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=_a							,					pred_original_sample=_a					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					)     ->  torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase_       :							Optional[Any]  					=       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
             lowercase_       :							int  					=       self.timesteps.to(original_samples.device							,					dtype=torch.floataa					)
             lowercase_       :							Optional[Any]  					=       timesteps.to(original_samples.device							,					dtype=torch.floataa					)
        else:
             lowercase_       :							List[str]  					=       self.timesteps.to(original_samples.device					)
             lowercase_       :							Tuple  					=       timesteps.to(original_samples.device					)

        lowercase_       :							Any  					=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

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





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





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




