





"""simple docstring"""




# 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 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			CommonSchedulerState
      lowercase_				:			jnp.ndarray

      # setable values
      lowercase_				:			jnp.ndarray
      lowercase_				:			jnp.ndarray
      lowercase_				:			Optional[int]        =       None

      @classmethod
      def  lowerCamelCase_      (					cls ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				):

        """simple docstring"""

        return cls(
            common=snake_case_ ,				final_alpha_cumprod=snake_case_ ,				init_noise_sigma=snake_case_ ,				timesteps=snake_case_ ,				)


@dataclass
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			DDIMSchedulerState


class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Any        =       [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowercase_				:			jnp.dtype

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return True

      @register_to_config
      def __init__(					self ,				snake_case_ = 1_0_0_0 ,				snake_case_ = 0.00_01 ,				snake_case_ = 0.02 ,				snake_case_ = "linear" ,				snake_case_ = None ,				snake_case_ = True ,				snake_case_ = 0 ,				snake_case_ = "epsilon" ,				snake_case_ = jnp.floataa ,				):

        """simple docstring"""

        A_  :	List[Any]    						=  dtype

      def  lowerCamelCase_      (					self ,				snake_case_ = None  ):

        """simple docstring"""

        if common is None:
          A_  :	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.
        A_  :	Union[str, Any]    						=  (
            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_  :	int    						=  jnp.array(1.0 ,				dtype=self.dtype  )

        A_  :	List[Any]    						=  jnp.arange(0 ,				self.config.num_train_timesteps  ).round()[::-1]

        return DDIMSchedulerState.create(
            common=snake_case_ ,				final_alpha_cumprod=snake_case_ ,				init_noise_sigma=snake_case_ ,				timesteps=snake_case_ ,				)

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None  ):

        """simple docstring"""

        return sample

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = ()  ):

        """simple docstring"""

        A_  :	Tuple    						=  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_  :	List[str]    						=  (jnp.arange(0 ,				snake_case_  ) * step_ratio).round()[::-1] + self.config.steps_offset

        return state.replace(
            num_inference_steps=snake_case_ ,				timesteps=snake_case_ ,				)

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  state.common.alphas_cumprod[timestep]
        A_  :	List[Any]    						=  jnp.where(
            prev_timestep >= 0 ,				state.common.alphas_cumprod[prev_timestep] ,				state.final_alpha_cumprod  )
        A_  :	Optional[int]    						=  1 - alpha_prod_t
        A_  :	Any    						=  1 - alpha_prod_t_prev

        A_  :	str    						=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = 0.0 ,				snake_case_ = 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_  :	Tuple    						=  timestep - self.config.num_train_timesteps // state.num_inference_steps

        A_  :	Optional[int]    						=  state.common.alphas_cumprod
        A_  :	Union[str, Any]    						=  state.final_alpha_cumprod

        # 2. compute alphas, betas
        A_  :	Optional[Any]    						=  alphas_cumprod[timestep]
        A_  :	str    						=  jnp.where(prev_timestep >= 0 ,				alphas_cumprod[prev_timestep] ,				snake_case_  )

        A_  :	str    						=  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_  :	List[str]    						=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          A_  :	Any    						=  model_output
        elif self.config.prediction_type == "sample":
          A_  :	List[str]    						=  model_output
          A_  :	str    						=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
          A_  :	Dict    						=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          A_  :	List[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)
        A_  :	Dict    						=  self._get_variance(snake_case_ ,				snake_case_ ,				snake_case_  )
        A_  :	Tuple    						=  eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        A_  :	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
        A_  :	str    						=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
          return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=snake_case_ ,				state=snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				):

        """simple docstring"""

        return add_noise_common(state.common ,				snake_case_ ,				snake_case_ ,				snake_case_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				):

        """simple docstring"""

        return get_velocity_common(state.common ,				snake_case_ ,				snake_case_ ,				snake_case_  )

      def __len__(					self  ):

        """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


lowerCamelCase_					:						Any      = {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						Optional[Any]      = [
       'Constraint',
       'ConstraintListState',
       'DisjunctiveConstraint',
       'PhrasalConstraint',
   ]
   lowerCamelCase_					:						List[str]      = [
       'BeamHypotheses',
       'BeamScorer',
       'BeamSearchScorer',
       'ConstrainedBeamSearchScorer',
   ]
   lowerCamelCase_					:						Dict      = [
       'EpsilonLogitsWarper',
       'EtaLogitsWarper',
       'ForcedBOSTokenLogitsProcessor',
       'ForcedEOSTokenLogitsProcessor',
       'HammingDiversityLogitsProcessor',
       'InfNanRemoveLogitsProcessor',
       'LogitsProcessor',
       'LogitsProcessorList',
       'LogitsWarper',
       'MinLengthLogitsProcessor',
       'MinNewTokensLengthLogitsProcessor',
       'NoBadWordsLogitsProcessor',
       'NoRepeatNGramLogitsProcessor',
       'PrefixConstrainedLogitsProcessor',
       'RepetitionPenaltyLogitsProcessor',
       'SequenceBiasLogitsProcessor',
       'EncoderRepetitionPenaltyLogitsProcessor',
       'TemperatureLogitsWarper',
       'TopKLogitsWarper',
       'TopPLogitsWarper',
       'TypicalLogitsWarper',
       'EncoderNoRepeatNGramLogitsProcessor',
       'ExponentialDecayLengthPenalty',
       'LogitNormalization',
   ]
   lowerCamelCase_					:						str      = [
       'MaxNewTokensCriteria',
       'MaxLengthCriteria',
       'MaxTimeCriteria',
       'StoppingCriteria',
       'StoppingCriteriaList',
       'validate_stopping_criteria',
   ]
   lowerCamelCase_					:						List[str]      = [
       '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:
   lowerCamelCase_					:						Union[str, Any]      = [
       'TFForcedBOSTokenLogitsProcessor',
       'TFForcedEOSTokenLogitsProcessor',
       'TFLogitsProcessor',
       'TFLogitsProcessorList',
       'TFLogitsWarper',
       'TFMinLengthLogitsProcessor',
       'TFNoBadWordsLogitsProcessor',
       'TFNoRepeatNGramLogitsProcessor',
       'TFRepetitionPenaltyLogitsProcessor',
       'TFTemperatureLogitsWarper',
       'TFTopKLogitsWarper',
       'TFTopPLogitsWarper',
       'TFForceTokensLogitsProcessor',
       'TFSuppressTokensAtBeginLogitsProcessor',
       'TFSuppressTokensLogitsProcessor',
   ]
   lowerCamelCase_					:						Union[str, 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:
   lowerCamelCase_					:						int      = [
       'FlaxForcedBOSTokenLogitsProcessor',
       'FlaxForcedEOSTokenLogitsProcessor',
       'FlaxLogitsProcessor',
       'FlaxLogitsProcessorList',
       'FlaxLogitsWarper',
       'FlaxMinLengthLogitsProcessor',
       'FlaxTemperatureLogitsWarper',
       'FlaxTopKLogitsWarper',
       'FlaxTopPLogitsWarper',
   ]
   lowerCamelCase_					:						Tuple      = [
       '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

   lowerCamelCase_					:						Tuple      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)