"""simple docstring"""


from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  str   ,				__lowerCAmelCase	:  Distribution   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  int=0					):
      _UpperCAmelCase						 =      1.0 if scale is None else scale
      _UpperCAmelCase						 =      0.0 if loc is None else loc

      super().__init__(__lowerCAmelCase   ,				[AffineTransform(loc=self.loc   ,				scale=self.scale   ,				event_dim=__lowerCAmelCase					)]					)


    @property
    def      lowerCAmelCase_  (					self	:  Any					):
      return self.base_dist.mean * self.scale + self.loc


    @property
    def      lowerCAmelCase_  (					self	:  List[Any]					):
      return self.base_dist.variance * self.scale**2


    @property
    def      lowerCAmelCase_  (					self	:  Tuple					):
      return self.variance.sqrt()



class        a				(		nn.Module   ):


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Dict[str, int]   ,				__lowerCAmelCase	:  Callable[..., Tuple[torch.Tensor]]   ,				**__lowerCAmelCase	:  Optional[int]					):
      super().__init__(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      args_dim
      _UpperCAmelCase						 =      nn.ModuleList([nn.Linear(__lowerCAmelCase   ,				__lowerCAmelCase					) for dim in args_dim.values()]					)
      _UpperCAmelCase						 =      domain_map


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  torch.Tensor					):
      _UpperCAmelCase						 =      [proj(__lowerCAmelCase					) for proj in self.proj]

      return self.domain_map(*__lowerCAmelCase					)



class        a				(		nn.Module   ):


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]					):
      super().__init__()
      _UpperCAmelCase						 =      function


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				*__lowerCAmelCase	:  Tuple					):
      return self.function(__lowerCAmelCase   ,				*__lowerCAmelCase					)



class        a				:
    _snake_case     :  type
    _snake_case     :  int
    _snake_case     :  Dict[str, int]


    def __init__(					self	:  str   ,				__lowerCAmelCase	:  int = 1					):
      _UpperCAmelCase						 =      dim
      _UpperCAmelCase						 =      {k: dim * self.args_dim[k] for k in self.args_dim}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Tuple					):
      if self.dim == 1:
        return self.distribution_class(*__lowerCAmelCase					)
      else:
        return Independent(self.distribution_class(*__lowerCAmelCase					)   ,				1					)


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[torch.Tensor] = None   ,				__lowerCAmelCase	:  Optional[torch.Tensor] = None   ,				):
      _UpperCAmelCase						 =      self._base_distribution(__lowerCAmelCase					)
      if loc is None and scale is None:
        return distr
      else:
        return AffineTransformed(__lowerCAmelCase   ,				loc=__lowerCAmelCase   ,				scale=__lowerCAmelCase   ,				event_dim=self.event_dim					)


    @property
    def      lowerCAmelCase_  (					self	:  List[Any]					):
      return () if self.dim == 1 else (self.dim,)


    @property
    def      lowerCAmelCase_  (					self	:  Dict					):
      return len(self.event_shape					)


    @property
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      return 0.0


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  int					):
      return ParameterProjection(
          in_features=__lowerCAmelCase   ,				args_dim=self.args_dim   ,				domain_map=LambdaLayer(self.domain_map					)   ,				)


    def      lowerCAmelCase_  (					self	:  Dict   ,				*__lowerCAmelCase	:  torch.Tensor					):
      raise NotImplementedError()


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  torch.Tensor					):
      return (x + torch.sqrt(torch.square(__lowerCAmelCase					) + 4.0					)) / 2.0



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Dict[str, int]     							=							{"df": 1, "loc": 1, "scale": 1}
    _snake_case     :  type     							=							StudentT


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[Any]   ,				__lowerCAmelCase	:  torch.Tensor   ,				__lowerCAmelCase	:  torch.Tensor   ,				__lowerCAmelCase	:  torch.Tensor					):
      _UpperCAmelCase						 =      cls.squareplus(__lowerCAmelCase					).clamp_min(torch.finfo(scale.dtype					).eps					)
      _UpperCAmelCase						 =      2.0 + cls.squareplus(__lowerCAmelCase					)
      return df.squeeze(-1					), loc.squeeze(-1					), scale.squeeze(-1					)



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Dict[str, int]     							=							{"loc": 1, "scale": 1}
    _snake_case     :  type     							=							Normal


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Dict   ,				__lowerCAmelCase	:  torch.Tensor   ,				__lowerCAmelCase	:  torch.Tensor					):
      _UpperCAmelCase						 =      cls.squareplus(__lowerCAmelCase					).clamp_min(torch.finfo(scale.dtype					).eps					)
      return loc.squeeze(-1					), scale.squeeze(-1					)



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Dict[str, int]     							=							{"total_count": 1, "logits": 1}
    _snake_case     :  type     							=							NegativeBinomial


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Union[str, Any]   ,				__lowerCAmelCase	:  torch.Tensor   ,				__lowerCAmelCase	:  torch.Tensor					):
      _UpperCAmelCase						 =      cls.squareplus(__lowerCAmelCase					)
      return total_count.squeeze(-1					), logits.squeeze(-1					)


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  List[Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      distr_args
      if self.dim == 1:
        return self.distribution_class(total_count=__lowerCAmelCase   ,				logits=__lowerCAmelCase					)
      else:
        return Independent(self.distribution_class(total_count=__lowerCAmelCase   ,				logits=__lowerCAmelCase					)   ,				1					)


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[torch.Tensor] = None   ,				__lowerCAmelCase	:  Optional[torch.Tensor] = None					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      distr_args

      if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

      return self._base_distribution((total_count, logits)					)

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    UpperCAmelCase__  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
