"""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


__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      42
    snake_case_											=      None


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       str=0.9_9_9,				_snake_case		:       Any="cosine",				):
   if alpha_transform_type == "cosine":

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

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

   _lowercase							     =       []
   for i in range(_snake_case       ):
      _lowercase							     =       i / num_diffusion_timesteps
      _lowercase							     =       (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_	(				lowercase__,    lowercase__   ):
    snake_case_											=      [e.name for e in KarrasDiffusionSchedulers]
    snake_case_											=      1
    @register_to_config
    def __init__(	self			:       List[Any]   ,						_lowercase			:       int = 1_0_0_0   ,						_lowercase			:       float = 0.00_01   ,						_lowercase			:       float = 0.02   ,						_lowercase			:       str = "linear"   ,						_lowercase			:       Optional[Union[np.ndarray, List[float]]] = None   ,						_lowercase			:       str = "epsilon"   ,						_lowercase			:       str = "linspace"   ,						_lowercase			:       int = 0   ,						)						->    Union[str, Any]:
       if trained_betas is not None:
          _lowercase							     =       torch.tensor(_lowercase   ,						dtype=torch.floataa		)
       elif beta_schedule == "linear":
          _lowercase							     =       torch.linspace(_lowercase   ,						_lowercase   ,						_lowercase   ,						dtype=torch.floataa		)
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          _lowercase							     =       (
              torch.linspace(beta_start**0.5   ,						beta_end**0.5   ,						_lowercase   ,						dtype=torch.floataa		) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          _lowercase							     =       betas_for_alpha_bar(_lowercase		)
       else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

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

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

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

       _lowercase							     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
       _lowercase							     =       np.interp(_lowercase   ,						np.arange(0   ,						len(_lowercase		)		)   ,						_lowercase		)
       _lowercase							     =       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
       _lowercase							     =       torch.from_numpy(_lowercase		).to(device=_lowercase		)
       if str(_lowercase		).startswith("mps"		):
          # mps does not support float64
          _lowercase							     =       torch.from_numpy(_lowercase		).to(_lowercase   ,						dtype=torch.floataa		)
       else:
          _lowercase							     =       torch.from_numpy(_lowercase		).to(device=_lowercase		)
    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       Union[float, torch.FloatTensor]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       Optional[torch.Generator] = None   ,						_lowercase			:       bool = True   ,						)						->    Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

       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		):
          _lowercase							     =       timestep.to(self.timesteps.device		)

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

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

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

       _lowercase							     =       sigma_down - sigma

       _lowercase							     =       sample + derivative * dt

       _lowercase							     =       model_output.device
       _lowercase							     =       randn_tensor(model_output.shape   ,						dtype=model_output.dtype   ,						device=_lowercase   ,						generator=_lowercase		)

       _lowercase							     =       prev_sample + noise * sigma_up

       if not return_dict:
          return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=_lowercase   ,						pred_original_sample=_lowercase		)
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       torch.FloatTensor   ,						)						->    torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       _lowercase							     =       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
          _lowercase							     =       self.timesteps.to(original_samples.device   ,						dtype=torch.floataa		)
          _lowercase							     =       timesteps.to(original_samples.device   ,						dtype=torch.floataa		)
       else:
          _lowercase							     =       self.timesteps.to(original_samples.device		)
          _lowercase							     =       timesteps.to(original_samples.device		)

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

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

       _lowercase							     =       original_samples + noise * sigma
       return noisy_samples




    def __len__(	self			:       Optional[int]		)						->    List[str]:
       return self.config.num_train_timesteps
"""simple docstring"""


from ..utils import DummyObject, requires_backends
class   UpperCAmelCase_	(				metaclass=lowercase__   ):
    snake_case_											=      ["""torch""", """transformers""", """k_diffusion"""]
    def __init__(	self			:       Optional[Any]   ,						*_lowercase			:       Optional[int]   ,						**_lowercase			:       List[str]		)						->    Dict:
       requires_backends(self   ,						["torch", "transformers", "k_diffusion"]		)
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Union[str, Any]   ,						*_lowercase			:       Any   ,						**_lowercase			:       List[Any]		)						->    str:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)




    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						*_lowercase			:       List[Any]   ,						**_lowercase			:       Any		)						->    Any:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)