






"""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    lowerCamelCase__						(		_UpperCAmelCase    ):
      def __init__(		self	:	List[str]      ,       A_	:	Distribution      ,       A_	:	Optional[int]=None      ,       A_	:	Optional[Any]=None      ,       A_	:	Union[str, Any]=0							):






       '''simple docstring'''


       __lowercase									=	1.0 if scale is None else scale
       __lowercase									=	0.0 if loc is None else loc

       super().__init__(A_      ,       [AffineTransform(loc=self.loc      ,       scale=self.scale      ,       event_dim=A_							)]							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       return self.base_dist.mean * self.scale + self.loc
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return self.base_dist.variance * self.scale**2






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return self.variance.sqrt()






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	Any      ,       A_	:	int      ,       A_	:	Dict[str, int]      ,       A_	:	Callable[..., Tuple[torch.Tensor]]      ,       **A_	:	int							):






       '''simple docstring'''


       super().__init__(**A_							)
       __lowercase									=	args_dim
       __lowercase									=	nn.ModuleList([nn.Linear(A_      ,       A_							) for dim in args_dim.values()]							)
       __lowercase									=	domain_map






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	torch.Tensor							):






       '''simple docstring'''


       __lowercase									=	[proj(A_							) for proj in self.proj]

       return self.domain_map(*A_							)






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	List[Any]      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	function






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Dict      ,       *A_	:	Optional[Any]							):






       '''simple docstring'''


       return self.function(A_      ,       *A_							)






class    lowerCamelCase__						:
      a :     type
      a :     int
      a :     Dict[str, int]
      def __init__(		self	:	Optional[int]      ,       A_	:	int = 1							):






       '''simple docstring'''


       __lowercase									=	dim
       __lowercase									=	{k: dim * self.args_dim[k] for k in self.args_dim}
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       if self.dim == 1:
        return self.distribution_class(*A_							)
       else:
        return Independent(self.distribution_class(*A_							)      ,       1							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	Union[str, Any]      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       ):






       '''simple docstring'''


       __lowercase									=	self._base_distribution(A_							)
       if loc is None and scale is None:
        return distr
       else:
        return AffineTransformed(A_      ,       loc=A_      ,       scale=A_      ,       event_dim=self.event_dim							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return () if self.dim == 1 else (self.dim,)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return len(self.event_shape							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return 0.0
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	int							):






       '''simple docstring'''


       return ParameterProjection(
           in_features=A_      ,       args_dim=self.args_dim      ,       domain_map=LambdaLayer(self.domain_map							)      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       *A_	:	torch.Tensor							):






       '''simple docstring'''


       raise NotImplementedError()






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		A_	:	torch.Tensor							):






       '''simple docstring'''


       return (x + torch.sqrt(torch.square(A_							) + 4.0							)) / 2.0






class    lowerCamelCase__						(		_UpperCAmelCase    ):
      a :     Dict[str, int]											=		{"df": 1, "loc": 1, "scale": 1}
      a :     type											=		StudentT
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Optional[int]      ,       A_	:	torch.Tensor      ,       A_	:	torch.Tensor      ,       A_	:	torch.Tensor							):






       '''simple docstring'''


       __lowercase									=	cls.squareplus(A_							).clamp_min(torch.finfo(scale.dtype							).eps							)
       __lowercase									=	2.0 + cls.squareplus(A_							)
       return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)






class    lowerCamelCase__						(		_UpperCAmelCase    ):
      a :     Dict[str, int]											=		{"loc": 1, "scale": 1}
      a :     type											=		Normal
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Any      ,       A_	:	torch.Tensor      ,       A_	:	torch.Tensor							):






       '''simple docstring'''


       __lowercase									=	cls.squareplus(A_							).clamp_min(torch.finfo(scale.dtype							).eps							)
       return loc.squeeze(-1							), scale.squeeze(-1							)






class    lowerCamelCase__						(		_UpperCAmelCase    ):
      a :     Dict[str, int]											=		{"total_count": 1, "logits": 1}
      a :     type											=		NegativeBinomial
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Union[str, Any]      ,       A_	:	torch.Tensor      ,       A_	:	torch.Tensor							):






       '''simple docstring'''


       __lowercase									=	cls.squareplus(A_							)
       return total_count.squeeze(-1							), logits.squeeze(-1							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	distr_args
       if self.dim == 1:
        return self.distribution_class(total_count=A_      ,       logits=A_							)
       else:
        return Independent(self.distribution_class(total_count=A_      ,       logits=A_							)      ,       1							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Dict      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	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_tf_available, is_torch_available


UpperCAmelCase__  							={
    "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:
       UpperCAmelCase__  							=[
           "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecAudioForAudioFrameClassification",
           "Data2VecAudioForCTC",
           "Data2VecAudioForSequenceClassification",
           "Data2VecAudioForXVector",
           "Data2VecAudioModel",
           "Data2VecAudioPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecTextForCausalLM",
           "Data2VecTextForMaskedLM",
           "Data2VecTextForMultipleChoice",
           "Data2VecTextForQuestionAnswering",
           "Data2VecTextForSequenceClassification",
           "Data2VecTextForTokenClassification",
           "Data2VecTextModel",
           "Data2VecTextPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecVisionForImageClassification",
           "Data2VecVisionForMaskedImageModeling",
           "Data2VecVisionForSemanticSegmentation",
           "Data2VecVisionModel",
           "Data2VecVisionPreTrainedModel",
       ]

if is_tf_available():
       UpperCAmelCase__  							=[
           "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

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