





"""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      lowercase_				:
       snake_case__				:							CommonSchedulerState
       snake_case__				:							jnp.ndarray

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

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(			cls   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

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



@dataclass
class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							DDIMSchedulerState



class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):
       snake_case__				:							Any											=     [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case__				:							jnp.dtype

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return True

       @register_to_config
       def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 1000   ,					__SCREAMING_SNAKE_CASE = 0.0001   ,					__SCREAMING_SNAKE_CASE = 0.02   ,					__SCREAMING_SNAKE_CASE = "linear"   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = True   ,					__SCREAMING_SNAKE_CASE = 0   ,					__SCREAMING_SNAKE_CASE = "epsilon"   ,					__SCREAMING_SNAKE_CASE = jnp.floataa   ,					):






        '''simple docstring'''

        snake_case__    							=				dtype

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        if common is None:
         snake_case__    							=				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.
        snake_case__    							=				(
            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
        snake_case__    							=				jnp.array(1.0   ,					dtype=self.dtype     )

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

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        return sample

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = ()     ):






        '''simple docstring'''

        snake_case__    							=				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
        snake_case__    							=				(jnp.arange(0   ,					__SCREAMING_SNAKE_CASE     ) * step_ratio).round()[::-1] + self.config.steps_offset

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				state.common.alphas_cumprod[timestep]
        snake_case__    							=				jnp.where(
            prev_timestep >= 0   ,					state.common.alphas_cumprod[prev_timestep]   ,					state.final_alpha_cumprod     )
        snake_case__    							=				1 - alpha_prod_t
        snake_case__    							=				1 - alpha_prod_t_prev

        snake_case__    							=				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 0.0   ,					__SCREAMING_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)
        snake_case__    							=				timestep - self.config.num_train_timesteps // state.num_inference_steps

        snake_case__    							=				state.common.alphas_cumprod
        snake_case__    							=				state.final_alpha_cumprod

        # 2. compute alphas, betas
        snake_case__    							=				alphas_cumprod[timestep]
        snake_case__    							=				jnp.where(prev_timestep >= 0   ,					alphas_cumprod[prev_timestep]   ,					__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				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":
         snake_case__    							=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         snake_case__    							=				model_output
        elif self.config.prediction_type == "sample":
         snake_case__    							=				model_output
         snake_case__    							=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
         snake_case__    							=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         snake_case__    							=				(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)
        snake_case__    							=				self._get_variance(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				eta * variance ** 0.5

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

        if not return_dict:
         return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					state=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        return get_velocity_common(state.common   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_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_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

