


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__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=None   ,    snake_case=None   ,    snake_case=0  ):

						'''simple docstring'''


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

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




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.base_dist.mean * self.scale + self.loc




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


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







			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.variance.sqrt()







class 							lowerCamelCase__						(				nn.Module   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    **snake_case  ):

						'''simple docstring'''


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







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							[proj(snake_case  ) for proj in self.proj]

						return self.domain_map(*snake_case  )







class 							lowerCamelCase__						(				nn.Module   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						UpperCamelCase__       =							function







			def 					snake_case__					(				self   ,    snake_case   ,    *snake_case  ):

						'''simple docstring'''


						return self.function(snake_case   ,    *snake_case  )







class 							lowerCamelCase__						:






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		42
			_UpperCamelCase		:       List[Any] 			=		42
			_UpperCamelCase		:       Optional[Any] 			=		42




			def __init__(				self   ,    snake_case = 1  ):

						'''simple docstring'''


						UpperCamelCase__       =							dim
						UpperCamelCase__       =							{k: dim * self.args_dim[k] for k in self.args_dim}




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if self.dim == 1:
									return self.distribution_class(*snake_case  )
						else:
									return Independent(self.distribution_class(*snake_case  )   ,    1  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							self._base_distribution(snake_case  )
						if loc is None and scale is None:
									return distr
						else:
									return AffineTransformed(snake_case   ,    loc=snake_case   ,    scale=snake_case   ,    event_dim=self.event_dim  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return () if self.dim == 1 else (self.dim,)




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return len(self.event_shape  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return 0.0




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return ParameterProjection(
						    in_features=snake_case   ,    args_dim=self.args_dim   ,    domain_map=LambdaLayer(self.domain_map  )   ,    )




			def 					snake_case__					(				self   ,    *snake_case  ):

						'''simple docstring'''


						raise NotImplementedError()







			@staticmethod
			def 					snake_case__					(				snake_case  ):

						'''simple docstring'''


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







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			_UpperCamelCase		:       Any 			=		{'df': 1, 'loc': 1, 'scale': 1}
			_UpperCamelCase		:       Dict 			=		StudentT




			@classmethod
			def 					snake_case__					(				cls   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


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







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		{'loc': 1, 'scale': 1}
			_UpperCamelCase		:       Tuple 			=		Normal




			@classmethod
			def 					snake_case__					(				cls   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


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







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		{'total_count': 1, 'logits': 1}
			_UpperCamelCase		:       List[str] 			=		NegativeBinomial




			@classmethod
			def 					snake_case__					(				cls   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							cls.squareplus(snake_case  )
						return total_count.squeeze(-1  ), logits.squeeze(-1  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


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







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None  ):

						'''simple docstring'''


						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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



