
'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
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

   # setable values
   _snake_case		   =							42
   _snake_case		   =							42
   _snake_case		   =							None







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      return cls(common=__lowerCamelCase ,   init_noise_sigma=__lowerCamelCase ,   timesteps=__lowerCamelCase   )




@dataclass
class 						lowercase__	(  _A   ):






   '''simple docstring'''







   _snake_case		   =							42




class 						lowercase__	(  _A,					_A   ):






   '''simple docstring'''







   _snake_case		   =							[e.name for e in FlaxKarrasDiffusionSchedulers]

   _snake_case		   =							42







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return True







   @register_to_config
   def __init__(			self ,   lowerCamelCase__ = 1_0_0_0 ,   lowerCamelCase__ = 0.0001 ,   lowerCamelCase__ = 0.02 ,   lowerCamelCase__ = "linear" ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "fixed_small" ,   lowerCamelCase__ = True ,   lowerCamelCase__ = "epsilon" ,   lowerCamelCase__ = jnp.floataa ,   ):

      '''simple docstring'''







      UpperCamelCase       =  dtype







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if common is None:
         UpperCamelCase       =  CommonSchedulerState.create(self   )

      # standard deviation of the initial noise distribution
      UpperCamelCase       =  jnp.array(1.0 ,   dtype=self.dtype   )

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

      return DDPMSchedulerState.create(
          common=__lowerCamelCase ,   init_noise_sigma=__lowerCamelCase ,   timesteps=__lowerCamelCase ,   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      return sample







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = ()   ):

      '''simple docstring'''







      UpperCamelCase       =  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
      UpperCamelCase       =  (jnp.arange(0 ,   __lowerCamelCase   ) * step_ratio).round()[::-1]

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      UpperCamelCase       =  state.common.alphas_cumprod[t]
      UpperCamelCase       =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype   )   )

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      UpperCamelCase       =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
         UpperCamelCase       =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
         UpperCamelCase       =  jnp.clip(__lowerCamelCase ,   a_min=1e-2_0   )
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
         UpperCamelCase       =  jnp.log(jnp.clip(__lowerCamelCase ,   a_min=1e-2_0   )   )
      elif variance_type == "fixed_large":
         UpperCamelCase       =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
         # Glide max_log
         UpperCamelCase       =  jnp.log(state.common.betas[t]   )
      elif variance_type == "learned":
         return predicted_variance
      elif variance_type == "learned_range":
         UpperCamelCase       =  variance
         UpperCamelCase       =  state.common.betas[t]
         UpperCamelCase       =  (predicted_variance + 1) / 2
         UpperCamelCase       =  frac * max_log + (1 - frac) * min_log

      return variance







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  timestep

      if key is None:
         UpperCamelCase       =  jax.random.PRNGKey(0   )

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         UpperCamelCase       =  jnp.split(__lowerCamelCase ,   sample.shape[1] ,   axis=1   )
      else:
         UpperCamelCase       =  None

      # 1. compute alphas, betas
      UpperCamelCase       =  state.common.alphas_cumprod[t]
      UpperCamelCase       =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype   )   )
      UpperCamelCase       =  1 - alpha_prod_t
      UpperCamelCase       =  1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
         UpperCamelCase       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
         UpperCamelCase       =  model_output
      elif self.config.prediction_type == "v_prediction":
         UpperCamelCase       =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
         raise ValueError(
             f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
             ''' for the FlaxDDPMScheduler.'''   )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         UpperCamelCase       =  jnp.clip(__lowerCamelCase ,   -1 ,   1   )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase       =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase       =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase       =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
         UpperCamelCase       =  jax.random.split(__lowerCamelCase ,   num=1   )
         UpperCamelCase       =  jax.random.normal(__lowerCamelCase ,   shape=model_output.shape ,   dtype=self.dtype   )
         return (self._get_variance(__lowerCamelCase ,   __lowerCamelCase ,   predicted_variance=__lowerCamelCase   ) ** 0.5) * noise

      UpperCamelCase       =  jnp.where(t > 0 ,   random_variance() ,   jnp.zeros(model_output.shape ,   dtype=self.dtype   )   )

      UpperCamelCase       =  pred_prev_sample + variance

      if not return_dict:
         return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase ,   state=__lowerCamelCase   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







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







   def __len__(			self   ):

      '''simple docstring'''







      return self.config.num_train_timesteps







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_ :  List[Any]     =					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					[
       'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecAudioForAudioFrameClassification',
       'Data2VecAudioForCTC',
       'Data2VecAudioForSequenceClassification',
       'Data2VecAudioForXVector',
       'Data2VecAudioModel',
       'Data2VecAudioPreTrainedModel',
   ]
   snake_case_ :  str     =					[
       'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecTextForCausalLM',
       'Data2VecTextForMaskedLM',
       'Data2VecTextForMultipleChoice',
       'Data2VecTextForQuestionAnswering',
       'Data2VecTextForSequenceClassification',
       'Data2VecTextForTokenClassification',
       'Data2VecTextModel',
       'Data2VecTextPreTrainedModel',
   ]
   snake_case_ :  Optional[int]     =					[
       'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecVisionForImageClassification',
       'Data2VecVisionForMaskedImageModeling',
       'Data2VecVisionForSemanticSegmentation',
       'Data2VecVisionModel',
       'Data2VecVisionPreTrainedModel',
   ]

if is_tf_available():
   snake_case_ :  List[str]     =					[
       'TFData2VecVisionForImageClassification',
       'TFData2VecVisionForSemanticSegmentation',
       'TFData2VecVisionModel',
       'TFData2VecVisionPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





