code: 
  
  
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  @flax.struct.dataclass
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  							snake_case_        =		42
  
  							# setable values
  							snake_case_        =		42
  							snake_case_        =		42
  							snake_case_        =		None
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return cls(
  													    common=_UpperCAmelCase			,    final_alpha_cumprod=_UpperCAmelCase			,    init_noise_sigma=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )
  
  
  
  
  
  @dataclass
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__			,UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  							snake_case_        =		42
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return True
  
  
  							@register_to_config
  							def __init__(   self			,    _UpperCAmelCase = 1_000			,    _UpperCAmelCase = 0.0_0_0_1			,    _UpperCAmelCase = 0.0_2			,    _UpperCAmelCase = "linear"			,    _UpperCAmelCase = None			,    _UpperCAmelCase = True			,    _UpperCAmelCase = 0			,    _UpperCAmelCase = "epsilon"			,    _UpperCAmelCase = jnp.floataa			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dtype
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if common is None:
  																			a_					=		CommonSchedulerState.create(self		)
  
  													# At every step in ddim, we are looking into the previous alphas_cumprod
  													# For the final step, there is no previous alphas_cumprod because we are already at 0
  													# `set_alpha_to_one` decides whether we set this parameter simply to one or
  													# whether we use the final alpha of the "non-previous" one.
  													a_					=		(
  													    jnp.array(1.0			,    dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  													)
  
  													# standard deviation of the initial noise distribution
  													a_					=		jnp.array(1.0			,    dtype=self.dtype		)
  
  													a_					=		jnp.arange(0			,    self.config.num_train_timesteps		).round()[::-1]
  
  													return DDIMSchedulerState.create(
  													    common=_UpperCAmelCase			,    final_alpha_cumprod=_UpperCAmelCase			,    init_noise_sigma=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return sample
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = ()		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.config.num_train_timesteps // num_inference_steps
  													# creates integer timesteps by multiplying by ratio
  													# rounding to avoid issues when num_inference_step is power of 3
  													a_					=		(jnp.arange(0			,    _UpperCAmelCase		) * step_ratio).round()[::-1] + self.config.steps_offset
  
  													return state.replace(
  													    num_inference_steps=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		state.common.alphas_cumprod[timestep]
  													a_					=		jnp.where(
  													    prev_timestep >= 0			,    state.common.alphas_cumprod[prev_timestep]			,    state.final_alpha_cumprod		)
  													a_					=		1 - alpha_prod_t
  													a_					=		1 - alpha_prod_t_prev
  
  													a_					=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  													return variance
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = 0.0			,    _UpperCAmelCase = True			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if state.num_inference_steps is None:
  																			raise ValueError(
  																			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""		)
  
  													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  													# Ideally, read DDIM paper in-detail understanding
  
  													# Notation (<variable name> -> <name in paper>
  													# - pred_noise_t -> e_theta(x_t, t)
  													# - pred_original_sample -> f_theta(x_t, t) or x_0
  													# - std_dev_t -> sigma_t
  													# - eta -> η
  													# - pred_sample_direction -> "direction pointing to x_t"
  													# - pred_prev_sample -> "x_t-1"
  
  													# 1. get previous step value (=t-1)
  													a_					=		timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  													a_					=		state.common.alphas_cumprod
  													a_					=		state.final_alpha_cumprod
  
  													# 2. compute alphas, betas
  													a_					=		alphas_cumprod[timestep]
  													a_					=		jnp.where(prev_timestep >= 0			,    alphas_cumprod[prev_timestep]			,    _UpperCAmelCase		)
  
  													a_					=		1 - alpha_prod_t
  
  													# 3. compute predicted original sample from predicted noise also called
  													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  													if self.config.prediction_type == "epsilon":
  																			a_					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  																			a_					=		model_output
  													elif self.config.prediction_type == "sample":
  																			a_					=		model_output
  																			a_					=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  													elif self.config.prediction_type == "v_prediction":
  																			a_					=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  																			a_					=		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  													else:
  																			raise ValueError(
  																			    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
  																			    """ `v_prediction`"""		)
  
  													# 4. compute variance: "sigma_t(η)" -> see formula (16)
  													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  													a_					=		self._get_variance(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		eta * variance ** 0.5
  
  													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  													a_					=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  													a_					=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  													if not return_dict:
  																			return (prev_sample, state)
  
  													return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase			,    state=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return add_noise_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return get_velocity_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  
  
  
  
  							def __len__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.config.num_train_timesteps
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)