'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):
     lowerCamelCase  :					Any							=1






     @register_to_config
     def __init__(			self		:		Dict  ,    a		:		List[str]=20_00  ,    a		:		Optional[Any]=0.1  ,    a		:		str=20  ,    a		:		Dict=1e-3   ):





         """simple docstring"""



         __lowerCamelCase          =  None
         __lowerCamelCase          =  None
         __lowerCamelCase          =  None






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





         """simple docstring"""



         __lowerCamelCase          =  torch.linspace(1  ,    self.config.sampling_eps  ,    a  ,    device=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		str  ,    a		:		List[str]  ,    a		:		str  ,    a		:		Any=None   ):





         """simple docstring"""



         if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         __lowerCamelCase          =  (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         __lowerCamelCase          =  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
         __lowerCamelCase          =  std.flatten()
         while len(std.shape   ) < len(score.shape   ):
             __lowerCamelCase          =  std.unsqueeze(-1   )
         __lowerCamelCase          =  -score / std

         # compute
         __lowerCamelCase          =  -1.0 / len(self.timesteps   )

         __lowerCamelCase          =  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         __lowerCamelCase          =  beta_t.flatten()
         while len(beta_t.shape   ) < len(x.shape   ):
             __lowerCamelCase          =  beta_t.unsqueeze(-1   )
         __lowerCamelCase          =  -0.5 * beta_t * x

         __lowerCamelCase          =  torch.sqrt(a   )
         __lowerCamelCase          =  drift - diffusion**2 * score
         __lowerCamelCase          =  x + drift * dt

         # add noise
         __lowerCamelCase          =  randn_tensor(x.shape  ,    layout=x.layout  ,    generator=a  ,    device=x.device  ,    dtype=x.dtype   )
         __lowerCamelCase          =  x_mean + diffusion * math.sqrt(-dt   ) * noise

         return x, x_mean






     def __len__(			self		:		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_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase												={
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=["WhisperTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WhisperForConditionalGeneration",
      "WhisperModel",
      "WhisperPreTrainedModel",
      "WhisperForAudioClassification",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFWhisperForConditionalGeneration",
      "TFWhisperModel",
      "TFWhisperPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "FlaxWhisperForConditionalGeneration",
      "FlaxWhisperModel",
      "FlaxWhisperPreTrainedModel",
      "FlaxWhisperForAudioClassification",
  ]


if TYPE_CHECKING:
  from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
  from .feature_extraction_whisper import WhisperFeatureExtractor
  from .processing_whisper import WhisperProcessor
  from .tokenization_whisper import WhisperTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_whisper_fast import WhisperTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_whisper import (
        WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        WhisperForAudioClassification,
        WhisperForConditionalGeneration,
        WhisperModel,
        WhisperPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_whisper import (
        TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFWhisperForConditionalGeneration,
        TFWhisperModel,
        TFWhisperPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_whisper import (
        FlaxWhisperForAudioClassification,
        FlaxWhisperForConditionalGeneration,
        FlaxWhisperModel,
        FlaxWhisperPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase												=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
