






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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				torch.FloatTensor
    __lowerCamelCase:				Optional[torch.FloatTensor]						    =      None




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase=0.999  ,						_UpperCamelCase="cosine"  ,						):


   """simple docstring"""




   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   lowercase_		:      Optional[int]          =       []
   for i in range(_UpperCamelCase							):
      lowercase_		:      Any          =       i / num_diffusion_timesteps
      lowercase_		:      Optional[int]          =       (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  (						snake_case			,   snake_case     ):
    __lowerCamelCase:				str						    =      [e.name for e in KarrasDiffusionSchedulers]
    __lowerCamelCase:				Tuple						    =      1




    @register_to_config
    def __init__(     self					:    List[Any]					,       a					:    int = 1_0_0_0					,       a					:    float = 0.0001					,       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:
          lowercase_		:      Optional[int]          =       torch.tensor(a					,       dtype=torch.floataa     )
       elif beta_schedule == "linear":
          lowercase_		:      Union[str, Any]          =       torch.linspace(a					,       a					,       a					,       dtype=torch.floataa     )
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_		:      Union[str, Any]          =       (
              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_		:      Optional[int]          =       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_		:      int          =       torch.cumprod(self.alphas					,       dim=0     )

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

       # setable values
       lowercase_		:      Tuple          =       None
       lowercase_		:      str          =       np.linspace(0					,       num_train_timesteps - 1					,       a					,       dtype=a     )[::-1].copy()
       lowercase_		:      str          =       torch.from_numpy(a     )
       lowercase_		:      List[Any]          =       False




    @property
    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






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

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




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    torch.FloatTensor					,       a					:    Union[float, torch.FloatTensor]     ):






       '''simple docstring'''






       if isinstance(a					,       torch.Tensor     ):
          lowercase_		:      Any          =       timestep.to(self.timesteps.device     )
       lowercase_		:      Any          =       (self.timesteps == timestep).nonzero().item()
       lowercase_		:      Dict          =       self.sigmas[step_index]
       lowercase_		:      str          =       sample / ((sigma**2 + 1) ** 0.5)
       lowercase_		:      Union[str, Any]          =       True
       return sample




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






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    torch.FloatTensor					,       a					:    Union[float, torch.FloatTensor]					,       a					:    torch.FloatTensor					,       a					:    Optional[torch.Generator] = None					,       a					:    bool = True					,       ):






       '''simple docstring'''






       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_		:      Tuple          =       timestep.to(self.timesteps.device     )

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

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

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

       lowercase_		:      Optional[int]          =       sigma_down - sigma

       lowercase_		:      Dict          =       sample + derivative * dt

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

       lowercase_		:      List[str]          =       prev_sample + noise * sigma_up

       if not return_dict:
          return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=a					,       pred_original_sample=a     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    torch.FloatTensor					,       a					:    torch.FloatTensor					,       a					:    torch.FloatTensor					,       ):






       '''simple docstring'''






       lowercase_		:      Union[str, 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_		:      Tuple          =       self.timesteps.to(original_samples.device					,       dtype=torch.floataa     )
          lowercase_		:      List[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_		:      Union[str, Any]          =       sigmas[step_indices].flatten()
       while len(sigma.shape     ) < len(original_samples.shape     ):
          lowercase_		:      str          =       sigma.unsqueeze(-1     )

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





    def __len__(     self					:    Optional[int]     ):






       '''simple docstring'''






       return self.config.num_train_timesteps











'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




