





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__			:    Dict			      =		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      :	torch.FloatTensor
  __lowerCAmelCase      :	Optional[torch.FloatTensor]  	=      None




def     UpperCAmelCase_						(						_UpperCAmelCase       :Any     ,	_UpperCAmelCase       :int=0.999     ,	_UpperCAmelCase       :str="cosine"     ,	)  ->							List[Any]:
     '''simple docstring'''


     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_UpperCAmelCase       :Optional[int]							):
               return math.exp(t * -12.0							)

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

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





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	Dict  	=      [e.name for e in KarrasDiffusionSchedulers]
  __lowerCAmelCase      :	Optional[Any]  	=      1


  @register_to_config
  def __init__(       self   ,__snake_case = 1_0_0_0   ,__snake_case = 0.0001   ,__snake_case = 0.02   ,__snake_case = "linear"   ,__snake_case = None   ,__snake_case = "epsilon"   ,__snake_case = "linspace"   ,__snake_case = 0   ,):



       """simple docstring"""





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

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

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

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


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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

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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case = None   ):



       """simple docstring"""





       A_					      =					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_					      =					np.linspace(0   ,self.config.num_train_timesteps - 1   ,__snake_case   ,dtype=__snake_case   )[
                ::-1
            ].copy()
       elif self.config.timestep_spacing == "leading":
            A_					      =					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_					      =					(np.arange(0   ,__snake_case   ) * step_ratio).round()[::-1].copy().astype(__snake_case   )
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            A_					      =					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_					      =					(np.arange(self.config.num_train_timesteps   ,0   ,-step_ratio   )).round().copy().astype(__snake_case   )
            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_					      =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       A_					      =					np.interp(__snake_case   ,np.arange(0   ,len(__snake_case   )   )   ,__snake_case   )
       A_					      =					np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       A_					      =					torch.from_numpy(__snake_case   ).to(device=__snake_case   )
       if str(__snake_case   ).startswith('''mps'''   ):
            # mps does not support float64
            A_					      =					torch.from_numpy(__snake_case   ).to(__snake_case   ,dtype=torch.floataa   )
       else:
            A_					      =					torch.from_numpy(__snake_case   ).to(device=__snake_case   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case = None   ,__snake_case = True   ,):



       """simple docstring"""





       if (
           isinstance(__snake_case   ,__snake_case   )
           or isinstance(__snake_case   ,torch.IntTensor   )
           or isinstance(__snake_case   ,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(__snake_case   ,torch.Tensor   ):
            A_					      =					timestep.to(self.timesteps.device   )

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

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

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

       A_					      =					sigma_down - sigma

       A_					      =					sample + derivative * dt

       A_					      =					model_output.device
       A_					      =					randn_tensor(model_output.shape   ,dtype=model_output.dtype   ,device=__snake_case   ,generator=__snake_case   )

       A_					      =					prev_sample + noise * sigma_up

       if not return_dict:
            return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=__snake_case   ,pred_original_sample=__snake_case   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case   ,):



       """simple docstring"""





       A_					      =					self.sigmas.to(device=original_samples.device   ,dtype=original_samples.dtype   )
       if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case   ):
            # mps does not support float64
            A_					      =					self.timesteps.to(original_samples.device   ,dtype=torch.floataa   )
            A_					      =					timesteps.to(original_samples.device   ,dtype=torch.floataa   )
       else:
            A_					      =					self.timesteps.to(original_samples.device   )
            A_					      =					timesteps.to(original_samples.device   )

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

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

       A_					      =					original_samples + noise * sigma
       return noisy_samples




  def __len__(       self   ):



       """simple docstring"""





       return self.config.num_train_timesteps










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__			:    Tuple			      =		{'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__			:    Optional[int]			      =		[
          'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'YosoForMaskedLM',
          'YosoForMultipleChoice',
          'YosoForQuestionAnswering',
          'YosoForSequenceClassification',
          'YosoForTokenClassification',
          'YosoLayer',
          'YosoModel',
          'YosoPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      a__			:    Union[str, Any]			      =		_LazyModule(__name__, globals()['__file__'], _import_structure)



