# 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
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__)