

# 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  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42

  # setable values
  a     =      42
  a     =      42
  a     =      None






  @classmethod
  def    lowercase_	(							cls		:	Optional[int]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[str]      )	->							Union[str, Any]:
       return cls(common=A_			,     init_noise_sigma=A_			,     timesteps=A_      )






@dataclass
class  UpperCAmelCase__	( __snake_case ):






  """simple docstring"""


  a     =      42






class  UpperCAmelCase__	( __snake_case		,  __snake_case ):






  """simple docstring"""


  a     =      [e.name for e in FlaxKarrasDiffusionSchedulers]

  a     =      42






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Dict:
       return True






  @register_to_config
  def __init__(							self		:	str			,     __lowerCamelCase		:	Any = 1000			,     __lowerCamelCase		:	str = 0.0001			,     __lowerCamelCase		:	Tuple = 0.02			,     __lowerCamelCase		:	int = "linear"			,     __lowerCamelCase		:	Dict = None			,     __lowerCamelCase		:	Tuple = "fixed_small"			,     __lowerCamelCase		:	Optional[int] = True			,     __lowerCamelCase		:	Any = "epsilon"			,     __lowerCamelCase		:	List[str] = jnp.floataa			,     )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					dtype






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	str = None      )	->							Optional[Any]:




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

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

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

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






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any] = None      )	->							Optional[int]:
       return sample






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int] = ()      )	->							Optional[int]:
       SCREAMING_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
       SCREAMING_SNAKE_CASE__					=					(jnp.arange(0			,     A_      ) * step_ratio).round()[::-1]

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






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Dict=None			,     __lowerCamelCase		:	Optional[int]=None      )	->							int:
       SCREAMING_SNAKE_CASE__					=					state.common.alphas_cumprod[t]
       SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

       return variance






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Any = True			,     )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					timestep

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

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

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

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

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

       SCREAMING_SNAKE_CASE__					=					pred_prev_sample + variance

       if not return_dict:
            return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=A_			,     state=A_      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[Any]			,     )	->							int:
       return add_noise_common(state.common			,     A_			,     A_			,     A_      )






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     )	->							Any:
       return get_velocity_common(state.common			,     A_			,     A_			,     A_      )







  def __len__(							self		:	str      )	->							Optional[int]:
       return self.config.num_train_timesteps





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    ['''RobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RobertaForCausalLM''',
           '''RobertaForMaskedLM''',
           '''RobertaForMultipleChoice''',
           '''RobertaForQuestionAnswering''',
           '''RobertaForSequenceClassification''',
           '''RobertaForTokenClassification''',
           '''RobertaModel''',
           '''RobertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRobertaForCausalLM''',
           '''TFRobertaForMaskedLM''',
           '''TFRobertaForMultipleChoice''',
           '''TFRobertaForQuestionAnswering''',
           '''TFRobertaForSequenceClassification''',
           '''TFRobertaForTokenClassification''',
           '''TFRobertaMainLayer''',
           '''TFRobertaModel''',
           '''TFRobertaPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    [
           '''FlaxRobertaForCausalLM''',
           '''FlaxRobertaForMaskedLM''',
           '''FlaxRobertaForMultipleChoice''',
           '''FlaxRobertaForQuestionAnswering''',
           '''FlaxRobertaForSequenceClassification''',
           '''FlaxRobertaForTokenClassification''',
           '''FlaxRobertaModel''',
           '''FlaxRobertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						str         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


