





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->EulerDiscrete
class    snake_case_				(		__lowercase						):
  A_										=    42
  A_										=    None






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Any    ,    SCREAMING_SNAKE_CASE		:List[Any]=0.9_99    ,    SCREAMING_SNAKE_CASE		:str="cosine"    ,    )			->    Union[str, Any]:



 if alpha_transform_type == "cosine":

  def alpha_bar_fn(SCREAMING_SNAKE_CASE		:Optional[Any]    ):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

 elif alpha_transform_type == "exp":

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

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

 __lowerCAmelCase     :       str						=   []
 for i in range(UpperCamelCase_    ):
  __lowerCAmelCase     :       Optional[int]						=   i / num_diffusion_timesteps
  __lowerCAmelCase     :       List[Any]						=   (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    snake_case_				(		__lowercase     ,__lowercase						):
  A_										=    [e.name for e in KarrasDiffusionSchedulers]
  A_										=    1






  @register_to_config
  def __init__(							self :     Tuple   ,  _snake_case :     Optional[Any] = 1000   ,  _snake_case :     Union[str, Any] = 0.0_001   ,  _snake_case :     Optional[Any] = 0.02   ,  _snake_case :     int = "linear"   ,  _snake_case :     Optional[int] = None   ,  _snake_case :     List[Any] = "epsilon"   ,  _snake_case :     int = "linear"   ,  _snake_case :     List[str] = False   ,  _snake_case :     Optional[int] = "linspace"   ,  _snake_case :     Optional[Any] = 0   ,  )->int:






   '''simple docstring'''

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

   __lowerCAmelCase     :       Union[str, Any]						=   1.0 - self.betas
   __lowerCAmelCase     :       Dict						=   torch.cumprod(self.alphas   ,  dim=0			)

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

   # setable values
   __lowerCAmelCase     :       List[str]						=   None
   __lowerCAmelCase     :       Tuple						=   np.linspace(0   ,  num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase			)[::-1].copy()
   __lowerCAmelCase     :       Any						=   torch.from_numpy(_UpperCamelCase			)
   __lowerCAmelCase     :       int						=   False
   __lowerCAmelCase     :       Dict						=   use_karras_sigmas






  @property
  def       UpperCAmelCase__						(							self :     int			)->int:






   '''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 :     Union[str, Any]   ,  _snake_case :     Union[str, Any]   ,  _snake_case :     int			)->Any:






   '''simple docstring'''

   if isinstance(_UpperCamelCase   ,  torch.Tensor			):
    __lowerCAmelCase     :       Tuple						=   timestep.to(self.timesteps.device			)
   __lowerCAmelCase     :       Dict						=   (self.timesteps == timestep).nonzero().item()
   __lowerCAmelCase     :       Optional[int]						=   self.sigmas[step_index]

   __lowerCAmelCase     :       List[Any]						=   sample / ((sigma**2 + 1) ** 0.5)

   __lowerCAmelCase     :       Tuple						=   True
   return sample






  def       UpperCAmelCase__						(							self :     Optional[int]   ,  _snake_case :     List[str]   ,  _snake_case :     Dict = None			)->Any:






   '''simple docstring'''

   __lowerCAmelCase     :       List[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":
    __lowerCAmelCase     :       List[Any]						=   np.linspace(0   ,  self.config.num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase			)[
        ::-1
    ].copy()
   elif self.config.timestep_spacing == "leading":
    __lowerCAmelCase     :       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
    __lowerCAmelCase     :       List[str]						=   (np.arange(0   ,  _UpperCamelCase			) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase			)
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __lowerCAmelCase     :       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
    __lowerCAmelCase     :       Dict						=   (np.arange(self.config.num_train_timesteps   ,  0   ,  -step_ratio			)).round().copy().astype(_UpperCamelCase			)
    timesteps -= 1
   else:
    raise ValueError(
        F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

   __lowerCAmelCase     :       int						=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
   __lowerCAmelCase     :       Union[str, Any]						=   np.log(_UpperCamelCase			)

   if self.config.interpolation_type == "linear":
    __lowerCAmelCase     :       Tuple						=   np.interp(_UpperCamelCase   ,  np.arange(0   ,  len(_UpperCamelCase			)			)   ,  _UpperCamelCase			)
   elif self.config.interpolation_type == "log_linear":
    __lowerCAmelCase     :       int						=   torch.linspace(np.log(sigmas[-1]			)   ,  np.log(sigmas[0]			)   ,  num_inference_steps + 1			).exp()
   else:
    raise ValueError(
        F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
        """ \'linear\' or \'log_linear\'"""			)

   if self.use_karras_sigmas:
    __lowerCAmelCase     :       int						=   self._convert_to_karras(in_sigmas=_UpperCamelCase   ,  num_inference_steps=self.num_inference_steps			)
    __lowerCAmelCase     :       Union[str, Any]						=   np.array([self._sigma_to_t(_UpperCamelCase   ,  _UpperCamelCase			) for sigma in sigmas]			)

   __lowerCAmelCase     :       Union[str, Any]						=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
   __lowerCAmelCase     :       Optional[int]						=   torch.from_numpy(_UpperCamelCase			).to(device=_UpperCamelCase			)
   if str(_UpperCamelCase			).startswith("""mps"""			):
    # mps does not support float64
    __lowerCAmelCase     :       int						=   torch.from_numpy(_UpperCamelCase			).to(_UpperCamelCase   ,  dtype=torch.floataa			)
   else:
    __lowerCAmelCase     :       List[Any]						=   torch.from_numpy(_UpperCamelCase			).to(device=_UpperCamelCase			)






  def       UpperCAmelCase__						(							self :     Optional[int]   ,  _snake_case :     Dict   ,  _snake_case :     Tuple			)->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase     :       Optional[Any]						=   np.log(_UpperCamelCase			)

   # get distribution
   __lowerCAmelCase     :       Optional[int]						=   log_sigma - log_sigmas[:, np.newaxis]

   # get sigmas range
   __lowerCAmelCase     :       Dict						=   np.cumsum((dists >= 0)   ,  axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
   __lowerCAmelCase     :       List[Any]						=   low_idx + 1

   __lowerCAmelCase     :       Optional[Any]						=   log_sigmas[low_idx]
   __lowerCAmelCase     :       Dict						=   log_sigmas[high_idx]

   # interpolate sigmas
   __lowerCAmelCase     :       Tuple						=   (low - log_sigma) / (low - high)
   __lowerCAmelCase     :       Dict						=   np.clip(_UpperCamelCase   ,  0   ,  1			)

   # transform interpolation to time range
   __lowerCAmelCase     :       List[str]						=   (1 - w) * low_idx + w * high_idx
   __lowerCAmelCase     :       List[str]						=   t.reshape(sigma.shape			)
   return t






  def       UpperCAmelCase__						(							self :     str   ,  _snake_case :     Optional[int]   ,  _snake_case :     Tuple			)->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase     :       Optional[int]						=   in_sigmas[-1].item()
   __lowerCAmelCase     :       int						=   in_sigmas[0].item()

   __lowerCAmelCase     :       Optional[Any]						=   7.0  # 7.0 is the value used in the paper
   __lowerCAmelCase     :       Any						=   np.linspace(0   ,  1   ,  _UpperCamelCase			)
   __lowerCAmelCase     :       List[Any]						=   sigma_min ** (1 / rho)
   __lowerCAmelCase     :       Tuple						=   sigma_max ** (1 / rho)
   __lowerCAmelCase     :       Dict						=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas






  def       UpperCAmelCase__						(							self :     Tuple   ,  _snake_case :     List[Any]   ,  _snake_case :     int   ,  _snake_case :     List[str]   ,  _snake_case :     Optional[int] = 0.0   ,  _snake_case :     List[Any] = 0.0   ,  _snake_case :     Tuple = float("""inf"""			)   ,  _snake_case :     Optional[Any] = 1.0   ,  _snake_case :     Tuple = None   ,  _snake_case :     Any = True   ,  )->List[Any]:






   '''simple docstring'''

   if (
       isinstance(_UpperCamelCase   ,  _UpperCamelCase			)
       or isinstance(_UpperCamelCase   ,  torch.IntTensor			)
       or isinstance(_UpperCamelCase   ,  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(_UpperCamelCase   ,  torch.Tensor			):
    __lowerCAmelCase     :       List[Any]						=   timestep.to(self.timesteps.device			)

   __lowerCAmelCase     :       Optional[int]						=   (self.timesteps == timestep).nonzero().item()
   __lowerCAmelCase     :       Tuple						=   self.sigmas[step_index]

   __lowerCAmelCase     :       List[str]						=   min(s_churn / (len(self.sigmas			) - 1)   ,  2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

   __lowerCAmelCase     :       str						=   randn_tensor(
       model_output.shape   ,  dtype=model_output.dtype   ,  device=model_output.device   ,  generator=_UpperCamelCase			)

   __lowerCAmelCase     :       Optional[int]						=   noise * s_noise
   __lowerCAmelCase     :       Tuple						=   sigma * (gamma + 1)

   if gamma > 0:
    __lowerCAmelCase     :       Tuple						=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   # NOTE: "original_sample" should not be an expected prediction_type but is left in for
   # backwards compatibility
   if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
    __lowerCAmelCase     :       Dict						=   model_output
   elif self.config.prediction_type == "epsilon":
    __lowerCAmelCase     :       Union[str, Any]						=   sample - sigma_hat * model_output
   elif self.config.prediction_type == "v_prediction":
    # * c_out + input * c_skip
    __lowerCAmelCase     :       Optional[Any]						=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
   else:
    raise ValueError(
        F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

   # 2. Convert to an ODE derivative
   __lowerCAmelCase     :       List[str]						=   (sample - pred_original_sample) / sigma_hat

   __lowerCAmelCase     :       str						=   self.sigmas[step_index + 1] - sigma_hat

   __lowerCAmelCase     :       Optional[int]						=   sample + derivative * dt

   if not return_dict:
    return (prev_sample,)

   return EulerDiscreteSchedulerOutput(prev_sample=_UpperCamelCase   ,  pred_original_sample=_UpperCamelCase			)






  def       UpperCAmelCase__						(							self :     Optional[int]   ,  _snake_case :     Union[str, Any]   ,  _snake_case :     int   ,  _snake_case :     Union[str, Any]   ,  )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase     :       Tuple						=   self.sigmas.to(device=original_samples.device   ,  dtype=original_samples.dtype			)
   if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase			):
    # mps does not support float64
    __lowerCAmelCase     :       Tuple						=   self.timesteps.to(original_samples.device   ,  dtype=torch.floataa			)
    __lowerCAmelCase     :       Dict						=   timesteps.to(original_samples.device   ,  dtype=torch.floataa			)
   else:
    __lowerCAmelCase     :       int						=   self.timesteps.to(original_samples.device			)
    __lowerCAmelCase     :       List[Any]						=   timesteps.to(original_samples.device			)

   __lowerCAmelCase     :       str						=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

   __lowerCAmelCase     :       Union[str, Any]						=   sigmas[step_indices].flatten()
   while len(sigma.shape			) < len(original_samples.shape			):
    __lowerCAmelCase     :       Any						=   sigma.unsqueeze(-1			)

   __lowerCAmelCase     :       Union[str, Any]						=   original_samples + noise * sigma
   return noisy_samples






  def __len__(							self :     int			)->str:






   '''simple docstring'''

   return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case			:			Optional[int]			   =   {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			List[Any]			   =   [
					    """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SEWDForCTC""",
					    """SEWDForSequenceClassification""",
					    """SEWDModel""",
					    """SEWDPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					__snake_case			:			Dict			   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





