


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        _lowercase      (			A__       ):






				'''simple docstring'''



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

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


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


				@property
				def 		__magic_name__(       self			:List[str]	)		->			List[Any]:
										return self.base_dist.variance * self.scale**2



				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Optional[int]:
										return self.variance.sqrt()




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



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



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:torch.Tensor	)		->			Tuple[torch.Tensor]:
										__SCREAMING_SNAKE_CASE						:     str					=		[proj(lowerCAmelCase__	) for proj in self.proj]

										return self.domain_map(*lowerCAmelCase__	)




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Dict	)		->			Dict:
										super().__init__()
										__SCREAMING_SNAKE_CASE						:     int					=		function



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Optional[int]			,			*lowerCAmelCase__			:Optional[int]	)		->			Optional[Any]:
										return self.function(lowerCAmelCase__			,			*lowerCAmelCase__	)




class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			type
				SCREAMING_SNAKE_CASE__				:			int
				SCREAMING_SNAKE_CASE__				:			Dict[str, int]


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


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:int	)		->			Dict:
										if self.dim == 1:
																return self.distribution_class(*lowerCAmelCase__	)
										else:
																return Independent(self.distribution_class(*lowerCAmelCase__	)			,			1	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			)		->			Distribution:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		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 		__magic_name__(       self			:int	)		->			Tuple:
										return () if self.dim == 1 else (self.dim,)


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


				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			float:
										return 0.0


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:int	)		->			nn.Module:
										return ParameterProjection(
										    in_features=lowerCAmelCase__			,			args_dim=self.args_dim			,			domain_map=LambdaLayer(self.domain_map	)			,			)


				def 		__magic_name__(       self			:int			,			*lowerCAmelCase__			:torch.Tensor	)		->			List[str]:
										raise NotImplementedError()



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




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"df": 1, "loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		StudentT


				@classmethod
				def 		__magic_name__(       cls			:List[Any]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		cls.squareplus(lowerCAmelCase__	).clamp_min(torch.finfo(scale.dtype	).eps	)
										__SCREAMING_SNAKE_CASE						:     str					=		2.0 + cls.squareplus(lowerCAmelCase__	)
										return df.squeeze(-1	), loc.squeeze(-1	), scale.squeeze(-1	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		Normal


				@classmethod
				def 		__magic_name__(       cls			:Union[str, Any]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Dict					=		cls.squareplus(lowerCAmelCase__	).clamp_min(torch.finfo(scale.dtype	).eps	)
										return loc.squeeze(-1	), scale.squeeze(-1	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"total_count": 1, "logits": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		NegativeBinomial


				@classmethod
				def 		__magic_name__(       cls			:List[str]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		cls.squareplus(lowerCAmelCase__	)
										return total_count.squeeze(-1	), logits.squeeze(-1	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Optional[int]	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		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 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		distr_args

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

										return self._base_distribution((total_count, logits)	)




import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def        _UpperCamelCase							(      *lowercase__    ,       lowercase__ = None    ,       lowercase__=True    ,       lowercase__=2      ):
						from .. import __version__

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		take_from
						__SCREAMING_SNAKE_CASE						:     List[str]					=		()
						if not isinstance(args[0]    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(args,)

						for attribute, version_name, message in args:
												if version.parse(version.parse(lowercase__      ).base_version      ) >= version.parse(lowercase__      ):
																		raise ValueError(
																		    F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
																		    F''' version {__version__} is >= {version_name}'''      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
												if isinstance(lowercase__    ,       lowercase__      ) and attribute in deprecated_kwargs:
																		values += (deprecated_kwargs.pop(lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
												elif hasattr(lowercase__    ,       lowercase__      ):
																		values += (getattr(lowercase__    ,       lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
												elif deprecated_kwargs is None:
																		__SCREAMING_SNAKE_CASE						:     str					=		F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

												if warning is not None:
																		__SCREAMING_SNAKE_CASE						:     Any					=		warning + ''' ''' if standard_warn else ''''''
																		warnings.warn(warning + message    ,       lowercase__    ,       stacklevel=lowercase__      )

						if isinstance(lowercase__    ,       lowercase__      ) and len(lowercase__      ) > 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inspect.getouterframes(inspect.currentframe()      )[1]
												__SCREAMING_SNAKE_CASE						:     Dict					=		call_frame.filename
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		call_frame.lineno
												__SCREAMING_SNAKE_CASE						:     int					=		call_frame.function
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		next(iter(deprecated_kwargs.items()      )      )
												raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''      )

						if len(lowercase__      ) == 0:
												return
						elif len(lowercase__      ) == 1:
												return values[0]
						return values
