





# 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'''


   a		:							CommonSchedulerState

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




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       return cls(common=__magic_name__,	init_noise_sigma=__magic_name__,	timesteps=__magic_name__							)






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							DDPMSchedulerState






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							str     	=	[e.name for e in FlaxKarrasDiffusionSchedulers]

   a		:							jnp.dtype




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return True




   @register_to_config
   def __init__(					self,	__magic_name__ = 1000,	__magic_name__ = 0.0001,	__magic_name__ = 0.02,	__magic_name__ = "linear",	__magic_name__ = None,	__magic_name__ = "fixed_small",	__magic_name__ = True,	__magic_name__ = "epsilon",	__magic_name__ = jnp.floataa,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   dtype




   def    UpperCamelCase__						(					self,	__magic_name__ = None							)				->   DDPMSchedulerState:






       """simple docstring"""
       if common is None:
           UpperCamelCase__							:       List[str]      =   CommonSchedulerState.create(self							)

       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       Union[str, Any]      =   jnp.array(1.0,	dtype=self.dtype							)

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

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None							)				->   jnp.ndarray:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = ()							)				->   DDPMSchedulerState:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   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__							:       str      =   (jnp.arange(0,	__magic_name__							) * step_ratio).round()[::-1]

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__=None,	__magic_name__=None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   state.common.alphas_cumprod[t]
       UpperCamelCase__							:       int      =   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__							:       Dict      =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
           UpperCamelCase__							:       Dict      =   self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
           UpperCamelCase__							:       Dict      =   jnp.clip(__magic_name__,	a_min=1E-20							)
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
           UpperCamelCase__							:       List[Any]      =   jnp.log(jnp.clip(__magic_name__,	a_min=1E-20							)							)
       elif variance_type == "fixed_large":
           UpperCamelCase__							:       Optional[int]      =   state.common.betas[t]
       elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__							:       List[Any]      =   jnp.log(state.common.betas[t]							)
       elif variance_type == "learned":
           return predicted_variance
       elif variance_type == "learned_range":
           UpperCamelCase__							:       Union[str, Any]      =   variance
           UpperCamelCase__							:       int      =   state.common.betas[t]
           UpperCamelCase__							:       Optional[Any]      =   (predicted_variance + 1) / 2
           UpperCamelCase__							:       List[str]      =   frac * max_log + (1 - frac) * min_log

       return variance




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[FlaxDDPMSchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   timestep

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

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

       # 1. compute alphas, betas
       UpperCamelCase__							:       Optional[Any]      =   state.common.alphas_cumprod[t]
       UpperCamelCase__							:       Optional[int]      =   jnp.where(t > 0,	state.common.alphas_cumprod[t - 1],	jnp.array(1.0,	dtype=self.dtype							)							)
       UpperCamelCase__							:       Optional[int]      =   1 - alpha_prod_t
       UpperCamelCase__							:       str      =   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__							:       List[Any]      =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
           UpperCamelCase__							:       Optional[int]      =   model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__							:       Optional[Any]      =   (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__							:       str      =   jnp.clip(__magic_name__,	-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__							:       Any      =   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       UpperCamelCase__							:       Dict      =   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__							:       Optional[int]      =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
           UpperCamelCase__							:       Optional[int]      =   jax.random.split(__magic_name__,	num=1							)
           UpperCamelCase__							:       List[Any]      =   jax.random.normal(__magic_name__,	shape=model_output.shape,	dtype=self.dtype							)
           return (self._get_variance(__magic_name__,	__magic_name__,	predicted_variance=__magic_name__							) ** 0.5) * noise

       UpperCamelCase__							:       Optional[Any]      =   jnp.where(t > 0,	random_variance(),	jnp.zeros(model_output.shape,	dtype=self.dtype							)							)

       UpperCamelCase__							:       List[str]      =   pred_prev_sample + variance

       if not return_dict:
           return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__,	state=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   jnp.ndarray:






       """simple docstring"""
       return add_noise_common(state.common,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   jnp.ndarray:






       """simple docstring"""
       return get_velocity_common(state.common,	__magic_name__,	__magic_name__,	__magic_name__							)




   def __len__(					self							)				->   str:






       """simple docstring"""
       return self.config.num_train_timesteps








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

