






'''simple docstring'''
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 SchedulerMixin


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


@dataclass
class     _UpperCAmelCase  (						A__     ):
    __lowerCamelCase:				Dict						    =      42


class     _UpperCAmelCase  (						A__			,   A__     ):
    __lowerCamelCase:				Optional[int]						    =      1




    @register_to_config
    def __init__(     self					:    str					,       a					:    str = 4_0					,       a					:    Dict = 0.002					,       a					:    Dict = 80.0					,       a					:    List[Any] = 0.5					,       a					:    str = 1.0					,       a					:    Tuple = 7.0					,       a					:    List[Any] = True					,       ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       sigma_max

       lowercase_		:      Optional[int]          =       np.linspace(0					,       1					,       snake_case_     )
       lowercase_		:      Dict          =       self._convert_to_karras(snake_case_     )
       lowercase_		:      List[Any]          =       self.sigma_to_t(snake_case_     )

       # setable values
       lowercase_		:      Optional[Any]          =       None
       lowercase_		:      List[Any]          =       torch.from_numpy(snake_case_     )
       lowercase_		:      Dict          =       torch.from_numpy(snake_case_     )
       lowercase_		:      Tuple          =       False
       lowercase_		:      int          =       False




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    Dict					,       a					:    List[str]=None     ):






       '''simple docstring'''






       if schedule_timesteps is None:
          lowercase_		:      List[Any]          =       self.timesteps

       lowercase_		:      List[str]          =       (schedule_timesteps == timestep).nonzero()
       return indices.item()




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Dict					,       a					:    Union[str, Any]     ):






       '''simple docstring'''






       if isinstance(snake_case_					,       torch.Tensor     ):
          lowercase_		:      int          =       timestep.to(self.timesteps.device     )
       lowercase_		:      Any          =       self.index_for_timestep(snake_case_     )
       lowercase_		:      Any          =       self.sigmas[step_idx]

       lowercase_		:      List[Any]          =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       lowercase_		:      Tuple          =       True
       return sample




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    Optional[Any]     ):






       '''simple docstring'''






       if not isinstance(snake_case_					,       np.ndarray     ):
          lowercase_		:      str          =       np.array(snake_case_					,       dtype=np.floataa     )

       lowercase_		:      Any          =       1_0_0_0 * 0.25 * np.log(sigmas + 1e-44     )

       return timesteps




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Union[str, Any] = None					,       a					:    List[Any] = None					,       a					:    Any = None					,       ):






       '''simple docstring'''






       if num_inference_steps is None and timesteps is None:
          raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."     )

       if num_inference_steps is not None and timesteps is not None:
          raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."     )

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
          for i in range(1					,       len(snake_case_     )     ):
             if timesteps[i] >= timesteps[i - 1]:
                raise ValueError("`timesteps` must be in descending order."     )

          if timesteps[0] >= self.config.num_train_timesteps:
             raise ValueError(
                 f"""`timesteps` must start before `self.config.train_timesteps`:"""
                 f""" {self.config.num_train_timesteps}."""     )

          lowercase_		:      Any          =       np.array(snake_case_					,       dtype=np.intaa     )
          lowercase_		:      int          =       True
       else:
          if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                 f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                 f""" maximal {self.config.num_train_timesteps} timesteps."""     )

          lowercase_		:      Any          =       num_inference_steps

          lowercase_		:      Any          =       self.config.num_train_timesteps // self.num_inference_steps
          lowercase_		:      str          =       (np.arange(0					,       snake_case_     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
          lowercase_		:      Optional[int]          =       False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       lowercase_		:      int          =       self.config.num_train_timesteps
       lowercase_		:      List[str]          =       timesteps[::-1].copy()
       lowercase_		:      Dict          =       ramp / (num_train_timesteps - 1)
       lowercase_		:      Optional[int]          =       self._convert_to_karras(snake_case_     )
       lowercase_		:      Tuple          =       self.sigma_to_t(snake_case_     )

       lowercase_		:      Optional[Any]          =       np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
       lowercase_		:      str          =       torch.from_numpy(snake_case_     ).to(device=snake_case_     )

       if str(snake_case_     ).startswith("mps"     ):
          # mps does not support float64
          lowercase_		:      Tuple          =       torch.from_numpy(snake_case_     ).to(snake_case_					,       dtype=torch.floataa     )
       else:
          lowercase_		:      List[Any]          =       torch.from_numpy(snake_case_     ).to(device=snake_case_     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      str          =       self.config.sigma_min
       lowercase_		:      Optional[Any]          =       self.config.sigma_max

       lowercase_		:      str          =       self.config.rho
       lowercase_		:      Tuple          =       sigma_min ** (1 / rho)
       lowercase_		:      Optional[int]          =       sigma_max ** (1 / rho)
       lowercase_		:      Dict          =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Any     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.config.sigma_data

       lowercase_		:      Dict          =       sigma_data**2 / (sigma**2 + sigma_data**2)
       lowercase_		:      str          =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.config.sigma_min
       lowercase_		:      Tuple          =       self.config.sigma_data

       lowercase_		:      Dict          =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       lowercase_		:      Any          =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    Optional[int]					,       a					:    Dict					,       a					:    Optional[int]					,       a					:    int = None					,       a					:    Optional[int] = 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"
                  f""" `{self.__class__}.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     ):
          lowercase_		:      Any          =       timestep.to(self.timesteps.device     )

       lowercase_		:      str          =       self.config.sigma_min
       lowercase_		:      List[Any]          =       self.config.sigma_max

       lowercase_		:      Union[str, Any]          =       self.index_for_timestep(snake_case_     )

       # sigma_next corresponds to next_t in original implementation
       lowercase_		:      Union[str, Any]          =       self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
          lowercase_		:      List[str]          =       self.sigmas[step_index + 1]
       else:
          # Set sigma_next to sigma_min
          lowercase_		:      List[Any]          =       self.sigmas[-1]

       # Get scalings for boundary conditions
       lowercase_      ,						lowercase_		:      List[Any]          =       self.get_scalings_for_boundary_condition(snake_case_     )

       # 1. Denoise model output using boundary conditions
       lowercase_		:      List[str]          =       c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
          lowercase_		:      Tuple          =       denoised.clamp(-1					,       1     )

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps     ) > 1:
          lowercase_		:      int          =       randn_tensor(
              model_output.shape					,       dtype=model_output.dtype					,       device=model_output.device					,       generator=snake_case_     )
       else:
          lowercase_		:      Dict          =       torch.zeros_like(snake_case_     )
       lowercase_		:      Dict          =       noise * self.config.s_noise

       lowercase_		:      int          =       sigma_next.clamp(min=snake_case_					,       max=snake_case_     )

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       lowercase_		:      Union[str, Any]          =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
          return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case_     )




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    int					,       a					:    Optional[Any]					,       a					:    Dict					,       ):






       '''simple docstring'''






       lowercase_		:      Dict          =       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
          lowercase_		:      Dict          =       self.timesteps.to(original_samples.device					,       dtype=torch.floataa     )
          lowercase_		:      str          =       timesteps.to(original_samples.device					,       dtype=torch.floataa     )
       else:
          lowercase_		:      str          =       self.timesteps.to(original_samples.device     )
          lowercase_		:      List[str]          =       timesteps.to(original_samples.device     )

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

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

       lowercase_		:      Dict          =       original_samples + noise * sigma
       return noisy_samples





    def __len__(     self					:    str     ):






       '''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__)




