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'''
      SCREAMING_SNAKE_CASE:							CommonSchedulerState
      SCREAMING_SNAKE_CASE:							jnp.ndarray
  
      # setable values
      SCREAMING_SNAKE_CASE:							jnp.ndarray
      SCREAMING_SNAKE_CASE:							jnp.ndarray
      SCREAMING_SNAKE_CASE:							Optional[int]        =						None
  
  
  
  
      @classmethod
      def     _a						(						cls					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	):
         return cls(
             common=lowerCamelCase__					,	final_alpha_cumprod=lowerCamelCase__					,	init_noise_sigma=lowerCamelCase__					,	timesteps=lowerCamelCase__					,	)
  
  
  
  
  
  @dataclass
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							DDIMSchedulerState
  
  
  
  
  
  class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Any        =						[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      SCREAMING_SNAKE_CASE:							jnp.dtype
  
  
  
  
      @property
      def     _a						(						self   ):
         return True
  
  
  
  
      @register_to_config
      def __init__(						self					,	lowerCamelCase__ = 1_000					,	lowerCamelCase__ = 0.0_0_0_1					,	lowerCamelCase__ = 0.0_2					,	lowerCamelCase__ = "linear"					,	lowerCamelCase__ = None					,	lowerCamelCase__ = True					,	lowerCamelCase__ = 0					,	lowerCamelCase__ = "epsilon"					,	lowerCamelCase__ = jnp.floataa					,	):
         lowerCAmelCase_:       Union[str, Any]         =  dtype
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__ = None   ):
         if common is None:
            lowerCAmelCase_:       Dict         =  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.
         lowerCAmelCase_:       str         =  (
             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
         lowerCAmelCase_:       Union[str, Any]         =  jnp.array(1.0					,	dtype=self.dtype   )
  
         lowerCAmelCase_:       Tuple         =  jnp.arange(0					,	self.config.num_train_timesteps   ).round()[::-1]
  
         return DDIMSchedulerState.create(
             common=lowerCamelCase__					,	final_alpha_cumprod=lowerCamelCase__					,	init_noise_sigma=lowerCamelCase__					,	timesteps=lowerCamelCase__					,	)
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         return sample
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = ()   ):
         lowerCAmelCase_:       Dict         =  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
         lowerCAmelCase_:       Tuple         =  (jnp.arange(0					,	lowerCamelCase__   ) * step_ratio).round()[::-1] + self.config.steps_offset
  
         return state.replace(
             num_inference_steps=lowerCamelCase__					,	timesteps=lowerCamelCase__					,	)
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[Any]         =  state.common.alphas_cumprod[timestep]
         lowerCAmelCase_:       Any         =  jnp.where(
             prev_timestep >= 0					,	state.common.alphas_cumprod[prev_timestep]					,	state.final_alpha_cumprod   )
         lowerCAmelCase_:       str         =  1 - alpha_prod_t
         lowerCAmelCase_:       List[Any]         =  1 - alpha_prod_t_prev
  
         lowerCAmelCase_:       Any         =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
         return variance
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = 0.0					,	lowerCamelCase__ = True					,	):
         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)
         lowerCAmelCase_:       Tuple         =  timestep - self.config.num_train_timesteps // state.num_inference_steps
  
         lowerCAmelCase_:       int         =  state.common.alphas_cumprod
         lowerCAmelCase_:       Union[str, Any]         =  state.final_alpha_cumprod
  
         # 2. compute alphas, betas
         lowerCAmelCase_:       List[str]         =  alphas_cumprod[timestep]
         lowerCAmelCase_:       Dict         =  jnp.where(prev_timestep >= 0					,	alphas_cumprod[prev_timestep]					,	lowerCamelCase__   )
  
         lowerCAmelCase_:       int         =  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":
            lowerCAmelCase_:       int         =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            lowerCAmelCase_:       Tuple         =  model_output
         elif self.config.prediction_type == "sample":
            lowerCAmelCase_:       int         =  model_output
            lowerCAmelCase_:       Tuple         =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
            lowerCAmelCase_:       Dict         =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            lowerCAmelCase_:       Optional[Any]         =  (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)
         lowerCAmelCase_:       str         =  self._get_variance(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
         lowerCAmelCase_:       Optional[int]         =  eta * variance ** 0.5
  
         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         lowerCAmelCase_:       List[Any]         =  (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
         lowerCAmelCase_:       Union[str, Any]         =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
         if not return_dict:
            return (prev_sample, state)
  
         return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase__					,	state=lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	):
         return add_noise_common(state.common					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	):
         return get_velocity_common(state.common					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def __len__(						self   ):
         return self.config.num_train_timesteps
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)