"""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 	UpperCamelCase    (					lowercase		):



				def __init__(self       :   Any			,						_A       :   Distribution			,						_A       :   str=None			,						_A       :   List[str]=None			,						_A       :   List[Any]=0)					->			List[str]:
					__snake_case    :							Tuple   	=				1.0 if scale is None else scale
					__snake_case    :							Optional[Any]   	=				0.0 if loc is None else loc

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



				@property
				def       _lowercase  (self       :   List[str])					->			List[Any]:
					return self.base_dist.mean * self.scale + self.loc



				@property
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					return self.base_dist.variance * self.scale**2






				@property
				def       _lowercase  (self       :   Any)					->			Dict:
					return self.variance.sqrt()







class 	UpperCamelCase    (					nn.Module		):



				def __init__(self       :   Union[str, Any]			,						_A       :   int			,						_A       :   Dict[str, int]			,						_A       :   Callable[..., Tuple[torch.Tensor]]			,						**_A       :   Optional[Any])					->			None:
					super().__init__(**_A)
					__snake_case    :							List[str]   	=				args_dim
					__snake_case    :							Dict   	=				nn.ModuleList([nn.Linear(_A			,						_A) for dim in args_dim.values()])
					__snake_case    :							Tuple   	=				domain_map






				def       _lowercase  (self       :   Any			,						_A       :   torch.Tensor)					->			Tuple[torch.Tensor]:
					__snake_case    :							Any   	=				[proj(_A) for proj in self.proj]

					return self.domain_map(*_A)







class 	UpperCamelCase    (					nn.Module		):



				def __init__(self       :   List[str]			,						_A       :   Union[str, Any])					->			Dict:
					super().__init__()
					__snake_case    :							Optional[Any]   	=				function






				def       _lowercase  (self       :   List[Any]			,						_A       :   Any			,						*_A       :   Optional[Any])					->			str:
					return self.function(_A			,						*_A)







class 	UpperCamelCase    :
				UpperCAmelCase       :   type
				UpperCAmelCase       :   int
				UpperCAmelCase       :   Dict[str, int]



				def __init__(self       :   List[Any]			,						_A       :   int = 1)					->			None:
					__snake_case    :							str   	=				dim
					__snake_case    :							List[str]   	=				{k: dim * self.args_dim[k] for k in self.args_dim}



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   List[str])					->			Optional[Any]:
					if self.dim == 1:
						return self.distribution_class(*_A)
					else:
						return Independent(self.distribution_class(*_A)			,						1)



				def       _lowercase  (self       :   int			,						_A       :   Tuple			,						_A       :   Optional[torch.Tensor] = None			,						_A       :   Optional[torch.Tensor] = None			,						)					->			Distribution:
					__snake_case    :							Optional[int]   	=				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       _lowercase  (self       :   Dict)					->			Tuple:
					return () if self.dim == 1 else (self.dim,)



				@property
				def       _lowercase  (self       :   Tuple)					->			int:
					return len(self.event_shape)



				@property
				def       _lowercase  (self       :   Optional[int])					->			float:
					return 0.0



				def       _lowercase  (self       :   Optional[int]			,						_A       :   int)					->			nn.Module:
					return ParameterProjection(
					    in_features=_A			,						args_dim=self.args_dim			,						domain_map=LambdaLayer(self.domain_map)			,						)



				def       _lowercase  (self       :   Dict			,						*_A       :   torch.Tensor)					->			Tuple:
					raise NotImplementedError()






				@staticmethod
				def       _lowercase  (_A       :   torch.Tensor)					->			torch.Tensor:
					return (x + torch.sqrt(torch.square(_A) + 4.0)) / 2.0







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict[str, int]					=	{"df": 1, "loc": 1, "scale": 1}
				UpperCAmelCase       :   type					=	StudentT



				@classmethod
				def       _lowercase  (cls       :   List[str]			,						_A       :   torch.Tensor			,						_A       :   torch.Tensor			,						_A       :   torch.Tensor)					->			Union[str, Any]:
					__snake_case    :							int   	=				cls.squareplus(_A).clamp_min(torch.finfo(scale.dtype).eps)
					__snake_case    :							Dict   	=				2.0 + cls.squareplus(_A)
					return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict[str, int]					=	{"loc": 1, "scale": 1}
				UpperCAmelCase       :   type					=	Normal



				@classmethod
				def       _lowercase  (cls       :   Optional[int]			,						_A       :   torch.Tensor			,						_A       :   torch.Tensor)					->			str:
					__snake_case    :							Dict   	=				cls.squareplus(_A).clamp_min(torch.finfo(scale.dtype).eps)
					return loc.squeeze(-1), scale.squeeze(-1)







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict[str, int]					=	{"total_count": 1, "logits": 1}
				UpperCAmelCase       :   type					=	NegativeBinomial



				@classmethod
				def       _lowercase  (cls       :   Dict			,						_A       :   torch.Tensor			,						_A       :   torch.Tensor)					->			Any:
					__snake_case    :							int   	=				cls.squareplus(_A)
					return total_count.squeeze(-1), logits.squeeze(-1)



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			Distribution:
					__snake_case    :							Union[str, Any]   	=				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       _lowercase  (self       :   List[Any]			,						_A       :   Any			,						_A       :   Optional[torch.Tensor] = None			,						_A       :   Optional[torch.Tensor] = None)					->			Distribution:
					__snake_case    :							Dict   	=				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,
)


_a  :  Optional[Any]=				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Optional[Any]=				[
	    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Wav2Vec2ForAudioFrameClassification",
	    "Wav2Vec2ForCTC",
	    "Wav2Vec2ForMaskedLM",
	    "Wav2Vec2ForPreTraining",
	    "Wav2Vec2ForSequenceClassification",
	    "Wav2Vec2ForXVector",
	    "Wav2Vec2Model",
	    "Wav2Vec2PreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[str]=				[
	    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFWav2Vec2ForCTC",
	    "TFWav2Vec2Model",
	    "TFWav2Vec2PreTrainedModel",
	    "TFWav2Vec2ForSequenceClassification",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[Any]=				[
	    "FlaxWav2Vec2ForCTC",
	    "FlaxWav2Vec2ForPreTraining",
	    "FlaxWav2Vec2Model",
	    "FlaxWav2Vec2PreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
	from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
	from .processing_wavaveca import WavaVecaProcessor
	from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca import (
		    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaForAudioFrameClassification,
		    WavaVecaForCTC,
		    WavaVecaForMaskedLM,
		    WavaVecaForPreTraining,
		    WavaVecaForSequenceClassification,
		    WavaVecaForXVector,
		    WavaVecaModel,
		    WavaVecaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWavaVecaForCTC,
		    TFWavaVecaForSequenceClassification,
		    TFWavaVecaModel,
		    TFWavaVecaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    FlaxWavaVecaForCTC,
		    FlaxWavaVecaForPreTraining,
		    FlaxWavaVecaModel,
		    FlaxWavaVecaPreTrainedModel,
		)


else:
	import sys

	_a  :  Tuple=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





