'''simple docstring'''





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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left





@flax.struct.dataclass
class 	_lowercase		:
 lowercase         =     None
 lowercase         =     None
 lowercase         =     None

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							List[Any]    )     ->		Dict:

  """simple docstring"""





  return cls()





@dataclass
class 	_lowercase		(      UpperCAmelCase_ ):
 lowercase         =     4_2
 lowercase         =     4_2
 lowercase         =     None





class 	_lowercase		(      UpperCAmelCase_		,   UpperCAmelCase_ ):

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Any:

  """simple docstring"""





  return True

 @register_to_config
 def __init__(				self			:							Tuple     ,   snake_case			:							Dict = 2_0_0_0     ,   snake_case			:							List[str] = 0.15     ,   snake_case			:							Optional[Any] = 0.01     ,   snake_case			:							List[Any] = 1_3_4_8.0     ,   snake_case			:							List[Any] = 1e-5     ,   snake_case			:							List[Any] = 1     ,   )     ->		Optional[Any]:

  """simple docstring"""





  pass

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      ScoreSdeVeSchedulerState.create()
  return self.set_sigmas(
      __UpperCamelCase     ,   self.config.num_train_timesteps     ,   self.config.sigma_min     ,   self.config.sigma_max     ,   self.config.sampling_eps     ,   )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[Any]     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Optional[int] = ()     ,   snake_case			:							Any = None    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		str				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

  UpperCamelCase_      :		Dict				=      jnp.linspace(1     ,   __UpperCamelCase     ,   __UpperCamelCase    )
  return state.replace(timesteps=__UpperCamelCase    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Any     ,   snake_case			:							Any = None     ,   snake_case			:							Union[str, Any] = None     ,   snake_case			:							Dict = None     ,   )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      sigma_min if sigma_min is not None else self.config.sigma_min
  UpperCamelCase_      :		int				=      sigma_max if sigma_max is not None else self.config.sigma_max
  UpperCamelCase_      :		Dict				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  if state.timesteps is None:
   UpperCamelCase_      :		List[Any]				=      self.set_timesteps(__UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase    )

  UpperCamelCase_      :		int				=      jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase    )     ,   jnp.log(__UpperCamelCase    )     ,   __UpperCamelCase    )    )
  UpperCamelCase_      :		Optional[int]				=      jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]    )

  return state.replace(discrete_sigmas=__UpperCamelCase     ,   sigmas=__UpperCamelCase    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							int     ,   snake_case			:							List[Any]     ,   snake_case			:							int    )     ->		List[Any]:

  """simple docstring"""





  return jnp.where(timesteps == 0     ,   jnp.zeros_like(__UpperCamelCase    )     ,   state.discrete_sigmas[timesteps - 1]    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Tuple     ,   snake_case			:							int     ,   snake_case			:							Tuple     ,   snake_case			:							List[str] = True     ,   )     ->		Optional[int]:

  """simple docstring"""





  if state.timesteps is None:
   raise ValueError(
       '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'    )

  UpperCamelCase_      :		str				=      timestep * jnp.ones(
      sample.shape[0]     ,   )
  UpperCamelCase_      :		int				=      (timestep * (len(state.timesteps    ) - 1)).long()

  UpperCamelCase_      :		List[Any]				=      state.discrete_sigmas[timesteps]
  UpperCamelCase_      :		int				=      self.get_adjacent_sigma(__UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase    )
  UpperCamelCase_      :		List[str]				=      jnp.zeros_like(__UpperCamelCase    )
  UpperCamelCase_      :		Any				=      (sigma**2 - adjacent_sigma**2) ** 0.5

  # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  # also equation 47 shows the analog from SDE models to ancestral sampling methods
  UpperCamelCase_      :		Optional[Any]				=      diffusion.flatten()
  UpperCamelCase_      :		List[Any]				=      broadcast_to_shape_from_left(__UpperCamelCase     ,   sample.shape    )
  UpperCamelCase_      :		int				=      drift - diffusion**2 * model_output

  #  equation 6: sample noise for the diffusion term of
  UpperCamelCase_      :		Any				=      random.split(__UpperCamelCase     ,   num=1    )
  UpperCamelCase_      :		int				=      random.normal(key=__UpperCamelCase     ,   shape=sample.shape    )
  UpperCamelCase_      :		Any				=      sample - drift  # subtract because `dt` is a small negative timestep
  # TODO is the variable diffusion the correct scaling term for the noise?
  UpperCamelCase_      :		Union[str, Any]				=      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

  if not return_dict:
   return (prev_sample, prev_sample_mean, state)

  return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,   prev_sample_mean=__UpperCamelCase     ,   state=__UpperCamelCase    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							Optional[Any]     ,   snake_case			:							int     ,   snake_case			:							Optional[int]     ,   snake_case			:							Any     ,   snake_case			:							int = True     ,   )     ->		int:

  """simple docstring"""





  if state.timesteps is None:
   raise ValueError(
       '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'    )

  # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  # sample noise for correction
  UpperCamelCase_      :		List[str]				=      random.split(__UpperCamelCase     ,   num=1    )
  UpperCamelCase_      :		int				=      random.normal(key=__UpperCamelCase     ,   shape=sample.shape    )

  # compute step size from the model_output, the noise, and the snr
  UpperCamelCase_      :		int				=      jnp.linalg.norm(__UpperCamelCase    )
  UpperCamelCase_      :		Optional[int]				=      jnp.linalg.norm(__UpperCamelCase    )
  UpperCamelCase_      :		Optional[int]				=      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  UpperCamelCase_      :		Optional[Any]				=      step_size * jnp.ones(sample.shape[0]    )

  # compute corrected sample: model_output term and noise term
  UpperCamelCase_      :		Union[str, Any]				=      step_size.flatten()
  UpperCamelCase_      :		int				=      broadcast_to_shape_from_left(__UpperCamelCase     ,   sample.shape    )
  UpperCamelCase_      :		List[str]				=      sample + step_size * model_output
  UpperCamelCase_      :		Tuple				=      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

  if not return_dict:
   return (prev_sample, state)

  return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,   state=__UpperCamelCase    )


 def __len__(				self			:							Dict    )     ->		int:

  """simple docstring"""





  return self.config.num_train_timesteps




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

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



