

'''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 							_snake_case (       __lowerCamelCase						):
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Any=0							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					1.0 if scale is None else scale
     SCREAMING_SNAKE_CASE:Dict						      =					0.0 if loc is None else loc

     super().__init__(SCREAMING_SNAKE_CASE_     ,[AffineTransform(loc=self.loc     ,scale=self.scale     ,event_dim=SCREAMING_SNAKE_CASE_							)]							)
   @property
   def     __UpperCamelCase						( self			:      Optional[int]							):
     return self.base_dist.mean * self.scale + self.loc
   @property
   def     __UpperCamelCase						( self			:      str							):
     return self.base_dist.variance * self.scale**2







   @property
   def     __UpperCamelCase						( self			:      str							):
     return self.variance.sqrt()





class 							_snake_case (       nn.Module						):
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     super().__init__(**SCREAMING_SNAKE_CASE_							)
     SCREAMING_SNAKE_CASE:Dict						      =					args_dim
     SCREAMING_SNAKE_CASE:List[str]						      =					nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_							) for dim in args_dim.values()]							)
     SCREAMING_SNAKE_CASE:List[str]						      =					domain_map







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:str						      =					[proj(SCREAMING_SNAKE_CASE_							) for proj in self.proj]

     return self.domain_map(*SCREAMING_SNAKE_CASE_							)





class 							_snake_case (       nn.Module						):
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Any							):
     super().__init__()
     SCREAMING_SNAKE_CASE:Optional[int]						      =					function







   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      Any							):
     return self.function(SCREAMING_SNAKE_CASE_     ,*SCREAMING_SNAKE_CASE_							)





class 							_snake_case :
   _A       :     Dict       =					4_2
   _A       :     Any       =					4_2
   _A       :     str       =					4_2
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int = 1							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					dim
     SCREAMING_SNAKE_CASE:int						      =					{k: dim * self.args_dim[k] for k in self.args_dim}
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     if self.dim == 1:
       return self.distribution_class(*SCREAMING_SNAKE_CASE_							)
     else:
       return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE_							)     ,1							)
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[Any] = None     ,SCREAMING_SNAKE_CASE__			:      Tuple = None     ,):
     SCREAMING_SNAKE_CASE:int						      =					self._base_distribution(SCREAMING_SNAKE_CASE_							)
     if loc is None and scale is None:
       return distr
     else:
       return AffineTransformed(SCREAMING_SNAKE_CASE_     ,loc=SCREAMING_SNAKE_CASE_     ,scale=SCREAMING_SNAKE_CASE_     ,event_dim=self.event_dim							)
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return () if self.dim == 1 else (self.dim,)
   @property
   def     __UpperCamelCase						( self			:      int							):
     return len(self.event_shape							)
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return 0.0
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     return ParameterProjection(
         in_features=SCREAMING_SNAKE_CASE_     ,args_dim=self.args_dim     ,domain_map=LambdaLayer(self.domain_map							)     ,)
   def     __UpperCamelCase						( self			:      int     ,*SCREAMING_SNAKE_CASE__			:      str							):
     raise NotImplementedError()







   @staticmethod
   def     __UpperCamelCase						( SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE_							) + 4.0							)) / 2.0





class 							_snake_case (       __lowerCamelCase						):
   _A       :     int       =					{'''df''': 1, '''loc''': 1, '''scale''': 1}
   _A       :     List[Any]       =					StudentT
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     SCREAMING_SNAKE_CASE:int						      =					cls.squareplus(SCREAMING_SNAKE_CASE_							).clamp_min(torch.finfo(scale.dtype							).eps							)
     SCREAMING_SNAKE_CASE:Any						      =					2.0 + cls.squareplus(SCREAMING_SNAKE_CASE_							)
     return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)





class 							_snake_case (       __lowerCamelCase						):
   _A       :     str       =					{'''loc''': 1, '''scale''': 1}
   _A       :     Optional[Any]       =					Normal
   @classmethod
   def     __UpperCamelCase						( cls			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					cls.squareplus(SCREAMING_SNAKE_CASE_							).clamp_min(torch.finfo(scale.dtype							).eps							)
     return loc.squeeze(-1							), scale.squeeze(-1							)





class 							_snake_case (       __lowerCamelCase						):
   _A       :     str       =					{'''total_count''': 1, '''logits''': 1}
   _A       :     Tuple       =					NegativeBinomial
   @classmethod
   def     __UpperCamelCase						( cls			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					cls.squareplus(SCREAMING_SNAKE_CASE_							)
     return total_count.squeeze(-1							), logits.squeeze(-1							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					distr_args
     if self.dim == 1:
       return self.distribution_class(total_count=SCREAMING_SNAKE_CASE_     ,logits=SCREAMING_SNAKE_CASE_							)
     else:
       return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE_     ,logits=SCREAMING_SNAKE_CASE_							)     ,1							)







   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      Any = None							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					distr_args

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

     return self._base_distribution((total_count, logits)							)





'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    A_       						=							{
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    A_       						=							{
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






